New submissions for Mon, 29 Apr 24

[qiaoyuet]

Keyword: differential privacy

State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

• Authors: Chaoyu Zhang
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16847
• Pdf link: https://arxiv.org/pdf/2404.16847
• Abstract This paper examines the evolving landscape of machine learning (ML) and its profound impact across various sectors, with a special focus on the emerging field of Privacy-preserving Machine Learning (PPML). As ML applications become increasingly integral to industries like telecommunications, financial technology, and surveillance, they raise significant privacy concerns, necessitating the development of PPML strategies. The paper highlights the unique challenges in safeguarding privacy within ML frameworks, which stem from the diverse capabilities of potential adversaries, including their ability to infer sensitive information from model outputs or training data. We delve into the spectrum of threat models that characterize adversarial intentions, ranging from membership and attribute inference to data reconstruction. The paper emphasizes the importance of maintaining the confidentiality and integrity of training data, outlining current research efforts that focus on refining training data to minimize privacy-sensitive information and enhancing data processing techniques to uphold privacy. Through a comprehensive analysis of privacy leakage risks and countermeasures in both centralized and collaborative learning settings, this paper aims to provide a thorough understanding of effective strategies for protecting ML training data against privacy intrusions. It explores the balance between data privacy and model utility, shedding light on privacy-preserving techniques that leverage cryptographic methods, Differential Privacy, and Trusted Execution Environments. The discussion extends to the application of these techniques in sensitive domains, underscoring the critical role of PPML in ensuring the privacy and security of ML systems.

  Evaluations of Machine Learning Privacy Defenses are Misleading

• Authors: Michael Aerni, Jie Zhang, Florian Tramèr
• Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17399
• Pdf link: https://arxiv.org/pdf/2404.17399
• Abstract Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries. We identify severe pitfalls in existing empirical privacy evaluations (based on membership inference attacks) that result in misleading conclusions. In particular, we show that prior evaluations fail to characterize the privacy leakage of the most vulnerable samples, use weak attacks, and avoid comparisons with practical differential privacy baselines. In 5 case studies of empirical privacy defenses, we find that prior evaluations underestimate privacy leakage by an order of magnitude. Under our stronger evaluation, none of the empirical defenses we study are competitive with a properly tuned, high-utility DP-SGD baseline (with vacuous provable guarantees).

  Keyword: privacy

  Machine Unlearning in Large Language Models

• Authors: Kongyang Chen, Zixin Wang, Bing Mi, Waixi Liu, Shaowei Wang, Xiaojun Ren, Jiaxing Shen
• Subjects: Cryptography and Security (cs.CR)
• Arxiv link: https://arxiv.org/abs/2404.16841
• Pdf link: https://arxiv.org/pdf/2404.16841
• Abstract Recently, large language models (LLMs) have emerged as a notable field, attracting significant attention for its ability to automatically generate intelligent contents for various application domains. However, LLMs still suffer from significant security and privacy issues. For example, LLMs might expose user privacy from hacking attacks or targeted prompts. To address this problem, this paper introduces a novel machine unlearning framework into LLMs. Our objectives are to make LLMs not produce harmful, hallucinatory, or privacy-compromising responses, while retaining their standard output capabilities. To accomplish this, we use an evaluative model to pinpoint dialogues needing unlearning. We also establish a distance loss to function as the model's negative loss, diverting it from previous undesirable outputs. Furthermore, we determine the expected output's cluster mean to formulate a positive loss, directing the model's outputs toward preferable outcomes without compromising its reasoning abilities and performance. Experimental results show that our approach effectively meets unlearning objectives without substantially compromising model performance.

  State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

• Authors: Chaoyu Zhang
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16847
• Pdf link: https://arxiv.org/pdf/2404.16847
• Abstract This paper examines the evolving landscape of machine learning (ML) and its profound impact across various sectors, with a special focus on the emerging field of Privacy-preserving Machine Learning (PPML). As ML applications become increasingly integral to industries like telecommunications, financial technology, and surveillance, they raise significant privacy concerns, necessitating the development of PPML strategies. The paper highlights the unique challenges in safeguarding privacy within ML frameworks, which stem from the diverse capabilities of potential adversaries, including their ability to infer sensitive information from model outputs or training data. We delve into the spectrum of threat models that characterize adversarial intentions, ranging from membership and attribute inference to data reconstruction. The paper emphasizes the importance of maintaining the confidentiality and integrity of training data, outlining current research efforts that focus on refining training data to minimize privacy-sensitive information and enhancing data processing techniques to uphold privacy. Through a comprehensive analysis of privacy leakage risks and countermeasures in both centralized and collaborative learning settings, this paper aims to provide a thorough understanding of effective strategies for protecting ML training data against privacy intrusions. It explores the balance between data privacy and model utility, shedding light on privacy-preserving techniques that leverage cryptographic methods, Differential Privacy, and Trusted Execution Environments. The discussion extends to the application of these techniques in sensitive domains, underscoring the critical role of PPML in ensuring the privacy and security of ML systems.

  Membership Information Leakage in Federated Contrastive Learning

• Authors: Kongyang Chen, Wenfeng Wang, Zixin Wang, Wangjun Zhang, Zhipeng Li, Yao Huang
• Subjects: Cryptography and Security (cs.CR)
• Arxiv link: https://arxiv.org/abs/2404.16850
• Pdf link: https://arxiv.org/pdf/2404.16850
• Abstract Federated Contrastive Learning (FCL) represents a burgeoning approach for learning from decentralized unlabeled data while upholding data privacy. In FCL, participant clients collaborate in learning a global encoder using unlabeled data, which can serve as a versatile feature extractor for diverse downstream tasks. Nonetheless, FCL is susceptible to privacy risks, such as membership information leakage, stemming from its distributed nature, an aspect often overlooked in current solutions. This study delves into the feasibility of executing a membership inference attack on FCL and proposes a robust attack methodology. The attacker's objective is to determine if the data signifies training member data by accessing the model's inference output. Specifically, we concentrate on attackers situated within a client framework, lacking the capability to manipulate server-side aggregation methods or discern the training status of other clients. We introduce two membership inference attacks tailored for FCL: the \textit{passive membership inference attack} and the \textit{active membership inference attack}, contingent on the attacker's involvement in local model training. Experimental findings across diverse datasets validate the effectiveness of our attacks and underscore the inherent privacy risks associated with the federated contrastive learning paradigm.

  EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

• Authors: Kongyang Chen, Yi Lin, Hui Luo, Bing Mi, Yatie Xiao, Chao Ma, Jorge Sá Silva
• Subjects: Cryptography and Security (cs.CR)
• Arxiv link: https://arxiv.org/abs/2404.16851
• Pdf link: https://arxiv.org/pdf/2404.16851
• Abstract In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data processing capabilities. Recently, these edge nodes have evolved to facilitate the implementation of distributed machine learning models, utilizing their computational resources to enable intelligent decision-making, thereby giving rise to an emerging domain referred to as edge intelligence. However, within the realm of edge intelligence, susceptibility to numerous security and privacy threats against machine learning models becomes evident. This paper addresses the issue of membership inference leakage in distributed edge intelligence systems. Specifically, our focus is on an autonomous scenario wherein edge nodes collaboratively generate a global model. The utilization of membership inference attacks serves to elucidate the potential data leakage in this particular context. Furthermore, we delve into the examination of several defense mechanisms aimed at mitigating the aforementioned data leakage problem. Experimental results affirm that our approach is effective in detecting data leakage within edge intelligence systems, and the implementation of our defense methods proves instrumental in alleviating this security threat. Consequently, our findings contribute to safeguarding data privacy in the context of edge intelligence systems.

  Improving Privacy-Preserving Techniques for Smart Grid using Lattice-based Cryptography

• Authors: Saleh Darzi, Bahareh Akhbari, Hassan Khodaiemehr
• Subjects: Cryptography and Security (cs.CR)
• Arxiv link: https://arxiv.org/abs/2404.16865
• Pdf link: https://arxiv.org/pdf/2404.16865
• Abstract Advancements in communication and information tech birthed the Smart Grid, optimizing energy and data transmission. Yet, user privacy is at risk due to frequent data collection. Existing privacy schemes face vulnerability with quantum machines. To tackle this, the LPM2DA scheme is introduced, utilizing lattice-based encryption and signatures for secure data aggregation. It ensures privacy, integrity, and authentication, enabling statistical analysis while preserving user privacy. Traditional aggregation schemes suffer from weak network models and centralization issues. Enter SPDBlock, a blockchain-based solution ensuring privacy, integrity, and resistance to attacks. It detects and prosecutes malicious entities while efficiently handling multi-dimensional data transmission. Through distributed decryption and secret sharing, only valid data can be decrypted with minimal involvement from smart meters. Performance tests reveal SPDBlock's superiority in communication and computational efficiency over traditional schemes.

  Adapting an Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool for Mpox Detection: The HeHealth Experience

• Authors: Rayner Kay Jin Tan, Dilruk Perera, Salomi Arasaratnam, Yudara Kularathne
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16885
• Pdf link: https://arxiv.org/pdf/2404.16885
• Abstract Artificial Intelligence applications have shown promise in the management of pandemics and have been widely used to assist the identification, classification, and diagnosis of medical images. In response to the global outbreak of Monkeypox (Mpox), the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases to develop a digital screening test for symptomatic Mpox through AI approaches. Prior to the global outbreak of Mpox, the team developed a smartphone app, where app users can use their own smartphone cameras to take pictures of their own penises to screen for symptomatic STD. The AI model was initially developed using 5000 cases and use a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including Syphilis, Herpes Simplex Virus, and Human Papilloma Virus. From June 2022 to October 2022, a total of about 22,000 users downloaded the HeHealth app, and about 21,000 images have been analyzed using HeHealth AI technology. We then engaged in formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool from July 2022. From July 2022 to October 2022, a total of 1000 Mpox related images had been used to train the Mpox symptom checker tool. Our digital symptom checker tool showed accuracy of 87% to rule in Mpox and 90% to rule out symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalizability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritizing pragmatism, as well as the concept that big data in fact is made up of small data.

  On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

• Authors: Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16894
• Pdf link: https://arxiv.org/pdf/2404.16894
• Abstract As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demands limit their applications, leading to the emergence of Tiny Machine Learning (TinyML) as a more suitable solution for resource-constrained environments. TinyML is widely applied in areas such as smart homes, healthcare, and industrial automation. TinyML focuses on optimizing ML algorithms for small, low-power devices, enabling intelligent data processing directly on edge devices. This paper provides a comprehensive review of common challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints; it also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. On the other hand, this paper discusses TinyML's applications in advancing cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a representative use case. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage, with a slight trade-off in accuracy. Additionally, the study includes a practical setup using the ESP32 microcontroller in the PlatformIO environment, which provides a hands-on assessment of TinyML's application in cybersecurity for EVCI.

  Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models

• Authors: Eren Dogan, M. Egemen Uzun, Atahan Uz, H. Emre Seyrek, Ahmed Zeer, Ezgi Sevi, H. Toprak Kesgin, M. Kaan Yuce, M. Fatih Amasyali
• Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17010
• Pdf link: https://arxiv.org/pdf/2404.17010
• Abstract The developments that language models have provided in fulfilling almost all kinds of tasks have attracted the attention of not only researchers but also the society and have enabled them to become products. There are commercially successful language models available. However, users may prefer open-source language models due to cost, data privacy, or regulations. Yet, despite the increasing number of these models, there is no comprehensive comparison of their performance for Turkish. This study aims to fill this gap in the literature. A comparison is made among seven selected language models based on their contextual learning and question-answering abilities. Turkish datasets for contextual learning and question-answering were prepared, and both automatic and human evaluations were conducted. The results show that for question-answering, continuing pretraining before fine-tuning with instructional datasets is more successful in adapting multilingual models to Turkish and that in-context learning performances do not much related to question-answering performances.

  Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis

• Authors: Shivangi Yadav, Arun Ross
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.17105
• Pdf link: https://arxiv.org/pdf/2404.17105
• Abstract Biometric systems based on iris recognition are currently being used in border control applications and mobile devices. However, research in iris recognition is stymied by various factors such as limited datasets of bonafide irides and presentation attack instruments; restricted intra-class variations; and privacy concerns. Some of these issues can be mitigated by the use of synthetic iris data. In this paper, we present a comprehensive review of state-of-the-art GAN-based synthetic iris image generation techniques, evaluating their strengths and limitations in producing realistic and useful iris images that can be used for both training and testing iris recognition systems and presentation attack detectors. In this regard, we first survey the various methods that have been used for synthetic iris generation and specifically consider generators based on StyleGAN, RaSGAN, CIT-GAN, iWarpGAN, StarGAN, etc. We then analyze the images generated by these models for realism, uniqueness, and biometric utility. This comprehensive analysis highlights the pros and cons of various GANs in the context of developing robust iris matchers and presentation attack detectors.

  On the Federated Learning Framework for Cooperative Perception

• Authors: Zhenrong Zhang, Jianan Liu, Xi Zhou, Tao Huang, Qing-Long Han, Jingxin Liu, Hongbin Liu
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17147
• Pdf link: https://arxiv.org/pdf/2404.17147
• Abstract Cooperative perception is essential to enhance the efficiency and safety of future transportation systems, requiring extensive data sharing among vehicles on the road, which raises significant privacy concerns. Federated learning offers a promising solution by enabling data privacy-preserving collaborative enhancements in perception, decision-making, and planning among connected and autonomous vehicles (CAVs). However, federated learning is impeded by significant challenges arising from data heterogeneity across diverse clients, potentially diminishing model accuracy and prolonging convergence periods. This study introduces a specialized federated learning framework for CP, termed the federated dynamic weighted aggregation (FedDWA) algorithm, facilitated by dynamic adjusting loss (DALoss) function. This framework employs dynamic client weighting to direct model convergence and integrates a novel loss function that utilizes Kullback-Leibler divergence (KLD) to counteract the detrimental effects of non-independently and identically distributed (Non-IID) and unbalanced data. Utilizing the BEV transformer as the primary model, our rigorous testing on the OpenV2V dataset, augmented with FedBEVT data, demonstrates significant improvements in the average intersection over union (IoU). These results highlight the substantial potential of our federated learning framework to address data heterogeneity challenges in CP, thereby enhancing the accuracy of environmental perception models and facilitating more robust and efficient collaborative learning solutions in the transportation sector.

  Enhancing Privacy and Security of Autonomous UAV Navigation

• Authors: Vatsal Aggarwal, Arjun Ramesh Kaushik, Charanjit Jutla, Nalini Ratha
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.17225
• Pdf link: https://arxiv.org/pdf/2404.17225
• Abstract Autonomous Unmanned Aerial Vehicles (UAVs) have become essential tools in defense, law enforcement, disaster response, and product delivery. These autonomous navigation systems require a wireless communication network, and of late are deep learning based. In critical scenarios such as border protection or disaster response, ensuring the secure navigation of autonomous UAVs is paramount. But, these autonomous UAVs are susceptible to adversarial attacks through the communication network or the deep learning models - eavesdropping / man-in-the-middle / membership inference / reconstruction. To address this susceptibility, we propose an innovative approach that combines Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) for secure autonomous UAV navigation. This end-to-end secure framework is designed for real-time video feeds captured by UAV cameras and utilizes FHE to perform inference on encrypted input images. While FHE allows computations on encrypted data, certain computational operators are yet to be implemented. Convolutional neural networks, fully connected neural networks, activation functions and OpenAI Gym Library are meticulously adapted to the FHE domain to enable encrypted data processing. We demonstrate the efficacy of our proposed approach through extensive experimentation. Our proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance.

  Lazy Data Practices Harm Fairness Research

• Authors: Jan Simson, Alessandro Fabris, Christoph Kern
• Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY); Applications (stat.AP); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.17293
• Pdf link: https://arxiv.org/pdf/2404.17293
• Abstract Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications. Our analyses identify three main areas of concern: (1) a \textbf{lack of representation for certain protected attributes} in both data and evaluations; (2) the widespread \textbf{exclusion of minorities} during data preprocessing; and (3) \textbf{opaque data processing} threatening the generalization of fairness research. By conducting exemplary analyses on the utilization of prominent datasets, we demonstrate how unreflective data decisions disproportionately affect minority groups, fairness metrics, and resultant model comparisons. Additionally, we identify supplementary factors such as limitations in publicly available data, privacy considerations, and a general lack of awareness, which exacerbate these challenges. To address these issues, we propose a set of recommendations for data usage in fairness research centered on transparency and responsible inclusion. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets.

  Evaluations of Machine Learning Privacy Defenses are Misleading

• Authors: Michael Aerni, Jie Zhang, Florian Tramèr
• Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17399
• Pdf link: https://arxiv.org/pdf/2404.17399
• Abstract Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries. We identify severe pitfalls in existing empirical privacy evaluations (based on membership inference attacks) that result in misleading conclusions. In particular, we show that prior evaluations fail to characterize the privacy leakage of the most vulnerable samples, use weak attacks, and avoid comparisons with practical differential privacy baselines. In 5 case studies of empirical privacy defenses, we find that prior evaluations underestimate privacy leakage by an order of magnitude. Under our stronger evaluation, none of the empirical defenses we study are competitive with a properly tuned, high-utility DP-SGD baseline (with vacuous provable guarantees).

  Enhancing Legal Compliance and Regulation Analysis with Large Language Models

• Authors: Shabnam Hassani
• Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17522
• Pdf link: https://arxiv.org/pdf/2404.17522
• Abstract This research explores the application of Large Language Models (LLMs) for automating the extraction of requirement-related legal content in the food safety domain and checking legal compliance of regulatory artifacts. With Industry 4.0 revolutionizing the food industry and with the General Data Protection Regulation (GDPR) reshaping privacy policies and data processing agreements, there is a growing gap between regulatory analysis and recent technological advancements. This study aims to bridge this gap by leveraging LLMs, namely BERT and GPT models, to accurately classify legal provisions and automate compliance checks. Our findings demonstrate promising results, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency, notably by reducing manual workload and improving accuracy within reasonable time and financial constraints.

  Federated Transfer Component Analysis Towards Effective VNF Profiling

• Authors: Xunzheng ZhangB, Shadi Moazzeni, Juan Marcelo Parra-Ullauri, Reza Nejabati, Dimitra Simeonidou
• Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
• Arxiv link: https://arxiv.org/abs/2404.17553
• Pdf link: https://arxiv.org/pdf/2404.17553
• Abstract The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a Federated Transfer Component Analysis (FTCA) method between the source and target VNFs. FTCA first trains Generative Adversarial Networks (GANs) based on the source VNF profiling data, and the trained GANs model is sent to the target VNF domain. Then, FTCA realizes federated domain adaptation by using the generated source VNF data and less target VNF profiling data, while keeping the raw data locally. Experiments show that the proposed FTCA can effectively predict the required resources for the target VNF. Specifically, the RMSE index of the regression model decreases by 38.5% and the R-squared metric advances up to 68.6%.

  Keyword: machine learning

  Leaf-Based Plant Disease Detection and Explainable AI

• Authors: Saurav Sagar, Mohammed Javed, David S Doermann
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16833
• Pdf link: https://arxiv.org/pdf/2404.16833
• Abstract The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synthetic fertilizers, archaic practices, environmental conditions, etc., which impact the farm yield and subsequently hinder the economy. To address this issue, researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users. By consolidating this knowledge, the survey offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.

  Predicting SSH keys in Open SSH Memory dumps

• Authors: Florian Rascoussier
• Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16838
• Pdf link: https://arxiv.org/pdf/2404.16838
• Abstract As the digital landscape evolves, cybersecurity has become an indispensable focus of IT systems. Its ever-escalating challenges have amplified the importance of digital forensics, particularly in the analysis of heap dumps from main memory. In this context, the Secure Shell protocol (SSH) designed for encrypted communications, serves as both a safeguard and a potential veil for malicious activities. This research project focuses on predicting SSH keys in OpenSSH memory dumps, aiming to enhance protective measures against illicit access and enable the development of advanced security frameworks or tools like honeypots. This Masterarbeit is situated within the broader SmartVMI project, and seeks to build upon existing research on key prediction in OpenSSH heap dumps. Utilizing machine learning (ML) and deep learning models, the study aims to refine features for embedding techniques and explore innovative methods for effective key detection based on recent advancements in Knowledge Graph and ML. The objective is to accurately predict the presence and location of SSH keys within memory dumps. This work builds upon, and aims to enhance, the foundations laid by SSHkex and SmartKex, enriching both the methodology and the results of the original research while exploring the untapped potential of newly proposed approaches. The current thesis dives into memory graph modelization from raw binary heap dump files. Each memory graph can support a range of embeddings that can be used directly for model training, through the use of classic ML models and graph neural network. It offers an in-depth discussion on the current state-of-the-art in key prediction for OpenSSH memory dumps, research questions, experimental setups, programs development, results as well as discussing potential future directions.

  Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review

• Authors: Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Signal Processing (eess.SP)
• Arxiv link: https://arxiv.org/abs/2404.16844
• Pdf link: https://arxiv.org/pdf/2404.16844
• Abstract Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. We address these gaps in this review. We discuss that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology.

  State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

• Authors: Chaoyu Zhang
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16847
• Pdf link: https://arxiv.org/pdf/2404.16847
• Abstract This paper examines the evolving landscape of machine learning (ML) and its profound impact across various sectors, with a special focus on the emerging field of Privacy-preserving Machine Learning (PPML). As ML applications become increasingly integral to industries like telecommunications, financial technology, and surveillance, they raise significant privacy concerns, necessitating the development of PPML strategies. The paper highlights the unique challenges in safeguarding privacy within ML frameworks, which stem from the diverse capabilities of potential adversaries, including their ability to infer sensitive information from model outputs or training data. We delve into the spectrum of threat models that characterize adversarial intentions, ranging from membership and attribute inference to data reconstruction. The paper emphasizes the importance of maintaining the confidentiality and integrity of training data, outlining current research efforts that focus on refining training data to minimize privacy-sensitive information and enhancing data processing techniques to uphold privacy. Through a comprehensive analysis of privacy leakage risks and countermeasures in both centralized and collaborative learning settings, this paper aims to provide a thorough understanding of effective strategies for protecting ML training data against privacy intrusions. It explores the balance between data privacy and model utility, shedding light on privacy-preserving techniques that leverage cryptographic methods, Differential Privacy, and Trusted Execution Environments. The discussion extends to the application of these techniques in sensitive domains, underscoring the critical role of PPML in ensuring the privacy and security of ML systems.

  EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

• Authors: Kongyang Chen, Yi Lin, Hui Luo, Bing Mi, Yatie Xiao, Chao Ma, Jorge Sá Silva
• Subjects: Cryptography and Security (cs.CR)
• Arxiv link: https://arxiv.org/abs/2404.16851
• Pdf link: https://arxiv.org/pdf/2404.16851
• Abstract In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data processing capabilities. Recently, these edge nodes have evolved to facilitate the implementation of distributed machine learning models, utilizing their computational resources to enable intelligent decision-making, thereby giving rise to an emerging domain referred to as edge intelligence. However, within the realm of edge intelligence, susceptibility to numerous security and privacy threats against machine learning models becomes evident. This paper addresses the issue of membership inference leakage in distributed edge intelligence systems. Specifically, our focus is on an autonomous scenario wherein edge nodes collaboratively generate a global model. The utilization of membership inference attacks serves to elucidate the potential data leakage in this particular context. Furthermore, we delve into the examination of several defense mechanisms aimed at mitigating the aforementioned data leakage problem. Experimental results affirm that our approach is effective in detecting data leakage within edge intelligence systems, and the implementation of our defense methods proves instrumental in alleviating this security threat. Consequently, our findings contribute to safeguarding data privacy in the context of edge intelligence systems.

  Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization

• Authors: Bailey J. Eccles, Leon Wong, Blesson Varghese
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16877
• Pdf link: https://arxiv.org/pdf/2404.16877
• Abstract Edge machine learning (ML) enables localized processing of data on devices and is underpinned by deep neural networks (DNNs). However, DNNs cannot be easily run on devices due to their substantial computing, memory and energy requirements for delivering performance that is comparable to cloud-based ML. Therefore, model compression techniques, such as pruning, have been considered. Existing pruning methods are problematic for edge ML since they: (1) Create compressed models that have limited runtime performance benefits (using unstructured pruning) or compromise the final model accuracy (using structured pruning), and (2) Require substantial compute resources and time for identifying a suitable compressed DNN model (using neural architecture search). In this paper, we explore a new avenue, referred to as Pruning-at-Initialization (PaI), using structured pruning to mitigate the above problems. We develop Reconvene, a system for rapidly generating pruned models suited for edge deployments using structured PaI. Reconvene systematically identifies and prunes DNN convolution layers that are least sensitive to structured pruning. Reconvene rapidly creates pruned DNNs within seconds that are up to 16.21x smaller and 2x faster while maintaining the same accuracy as an unstructured PaI counterpart.

  ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning

• Authors: Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky, Anthony Garland, Dan S. Bolintineanu, Dan R. Moser, Michael J. Heiden, Amir Barati Farimani
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16882
• Pdf link: https://arxiv.org/pdf/2404.16882
• Abstract We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a $R^2$ score of 0.57 and our model for porosity localization produced an average IoU score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity "Digital Twins" based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

  Review of Data-centric Time Series Analysis from Sample, Feature, and Period

• Authors: Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16886
• Pdf link: https://arxiv.org/pdf/2404.16886
• Abstract Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models. A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence, as well as task outcomes and costs. The emergence of data-centric AI represents a shift in the landscape from model refinement to prioritizing data quality. Even though time-series data processing methods frequently come up in a wide range of research fields, it hasn't been well investigated as a specific topic. To fill the gap, in this paper, we systematically review different data-centric methods in time series analysis, covering a wide range of research topics. Based on the time-series data characteristics at sample, feature, and period, we propose a taxonomy for the reviewed data selection methods. In addition to discussing and summarizing their characteristics, benefits, and drawbacks targeting time-series data, we also introduce the challenges and opportunities by proposing recommendations, open problems, and possible research topics.

  Anomaly Detection for Incident Response at Scale

• Authors: Hanzhang Wang, Gowtham Kumar Tangirala, Gilkara Pranav Naidu, Charles Mayville, Arighna Roy, Joanne Sun, Ramesh Babu Mandava
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16887
• Pdf link: https://arxiv.org/pdf/2404.16887
• Abstract We present a machine learning-based anomaly detection product, AI Detect and Respond (AIDR), that monitors Walmart's business and system health in real-time. During the validation over 3 months, the product served predictions from over 3000 models to more than 25 application, platform, and operation teams, covering 63\% of major incidents and reducing the mean-time-to-detect (MTTD) by more than 7 minutes. Unlike previous anomaly detection methods, our solution leverages statistical, ML and deep learning models while continuing to incorporate rule-based static thresholds to incorporate domain-specific knowledge. Both univariate and multivariate ML models are deployed and maintained through distributed services for scalability and high availability. AIDR has a feedback loop that assesses model quality with a combination of drift detection algorithms and customer feedback. It also offers self-onboarding capabilities and customizability. AIDR has achieved success with various internal teams with lower time to detection and fewer false positives than previous methods. As we move forward, we aim to expand incident coverage and prevention, reduce noise, and integrate further with root cause recommendation (RCR) to enable an end-to-end AIDR experience.

  Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge

• Authors: Neha Kumari, Sumit Kumar. Sneha Priya, Ayush Kumar, Akash Fogla
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.16893
• Pdf link: https://arxiv.org/pdf/2404.16893
• Abstract In safety-critical systems that interface with the real world, the role of uncertainty in decision-making is pivotal, particularly in the context of machine learning models. For the secure functioning of Cyber-Physical Systems (CPS), it is imperative to manage such uncertainty adeptly. In this research, we focus on the development of a vehicle's lateral control system using a machine learning framework. Specifically, we employ a Bayesian Neural Network (BNN), a probabilistic learning model, to address uncertainty quantification. This capability allows us to gauge the level of confidence or uncertainty in the model's predictions. The BNN based controller is trained using simulated data gathered from the vehicle traversing a single track and subsequently tested on various other tracks. We want to share two significant results: firstly, the trained model demonstrates the ability to adapt and effectively control the vehicle on multiple similar tracks. Secondly, the quantification of prediction confidence integrated into the controller serves as an early-warning system, signaling when the algorithm lacks confidence in its predictions and is therefore susceptible to failure. By establishing a confidence threshold, we can trigger manual intervention, ensuring that control is relinquished from the algorithm when it operates outside of safe parameters.

  On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

• Authors: Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16894
• Pdf link: https://arxiv.org/pdf/2404.16894
• Abstract As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demands limit their applications, leading to the emergence of Tiny Machine Learning (TinyML) as a more suitable solution for resource-constrained environments. TinyML is widely applied in areas such as smart homes, healthcare, and industrial automation. TinyML focuses on optimizing ML algorithms for small, low-power devices, enabling intelligent data processing directly on edge devices. This paper provides a comprehensive review of common challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints; it also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. On the other hand, this paper discusses TinyML's applications in advancing cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a representative use case. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage, with a slight trade-off in accuracy. Additionally, the study includes a practical setup using the ESP32 microcontroller in the PlatformIO environment, which provides a hands-on assessment of TinyML's application in cybersecurity for EVCI.

  mlr3summary: Concise and interpretable summaries for machine learning models

• Authors: Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16899
• Pdf link: https://arxiv.org/pdf/2404.16899
• Abstract This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions: First, our summary function is model-agnostic and provides a unified summary output also for non-parametric machine learning models; Second, the summary output is more extensive and customizable -- it comprises information on the dataset, model performance, model complexity, model's estimated feature importances, feature effects, and fairness metrics; Third, models are evaluated based on resampling strategies for unbiased estimates of model performances, feature importances, etc. Overall, the clear, structured output should help to enhance and expedite the model selection process, making it a helpful tool for practitioners and researchers alike.

  A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs

• Authors: Christian N. Mayemba, D'Jeff K. Nkashama, Jean Marie Tshimula, Maximilien V. Dialufuma, Jean Tshibangu Muabila, Mbuyi Mukendi Didier, Hugues Kanda, René Manassé Galekwa, Heber Dibwe Fita, Serge Mundele, Kalonji Kalala, Aristarque Ilunga, Lambert Mukendi Ntobo, Dominique Muteba, Aaron Aruna Abedi
• Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.16921
• Pdf link: https://arxiv.org/pdf/2404.16921
• Abstract This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies. Forecasting population movement is crucial for informing epidemiological models and facilitating effective response planning in public health emergencies. Predicting mobility patterns can enable authorities to better anticipate the geographical and temporal spread of diseases, allocate resources more efficiently, and implement targeted interventions. We review a range of approaches utilizing both pretrained language models like BERT and Large Language Models (LLMs) tailored specifically for mobility prediction tasks. These models have demonstrated significant potential in capturing complex spatio-temporal dependencies and contextual patterns in textual data.

  A Survey of Generative Search and Recommendation in the Era of Large Language Models

• Authors: Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, Tat-Seng Chua
• Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.16924
• Pdf link: https://arxiv.org/pdf/2404.16924
• Abstract With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items. In the recent few decades, search and recommendation have experienced synchronous technological paradigm shifts, including machine learning-based and deep learning-based paradigms. Recently, the superintelligent generative large language models have sparked a new paradigm in search and recommendation, i.e., generative search (retrieval) and recommendation, which aims to address the matching problem in a generative manner. In this paper, we provide a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective. Rather than simply categorizing existing works, we abstract a unified framework for the generative paradigm and break down the existing works into different stages within this framework to highlight the strengths and weaknesses. And then, we distinguish generative search and recommendation with their unique challenges, identify open problems and future directions, and envision the next information-seeking paradigm.

  Taming False Positives in Out-of-Distribution Detection with Human Feedback

• Authors: Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.16954
• Pdf link: https://arxiv.org/pdf/2404.16954
• Abstract Robustness to out-of-distribution (OOD) samples is crucial for safely deploying machine learning models in the open world. Recent works have focused on designing scoring functions to quantify OOD uncertainty. Setting appropriate thresholds for these scoring functions for OOD detection is challenging as OOD samples are often unavailable up front. Typically, thresholds are set to achieve a desired true positive rate (TPR), e.g., $95\%$ TPR. However, this can lead to very high false positive rates (FPR), ranging from 60 to 96\%, as observed in the Open-OOD benchmark. In safety-critical real-life applications, e.g., medical diagnosis, controlling the FPR is essential when dealing with various OOD samples dynamically. To address these challenges, we propose a mathematically grounded OOD detection framework that leverages expert feedback to \emph{safely} update the threshold on the fly. We provide theoretical results showing that it is guaranteed to meet the FPR constraint at all times while minimizing the use of human feedback. Another key feature of our framework is that it can work with any scoring function for OOD uncertainty quantification. Empirical evaluation of our system on synthetic and benchmark OOD datasets shows that our method can maintain FPR at most $5\%$ while maximizing TPR.

  ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain

• Authors: Zhikai Li, Steve Vott, Bhaskar Krishnamachar
• Subjects: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
• Arxiv link: https://arxiv.org/abs/2404.16967
• Pdf link: https://arxiv.org/pdf/2404.16967
• Abstract With the growing concern of AI safety, there is a need to trust the computations done by machine learning (ML) models. Blockchain technology, known for recording data and running computations transparently and in a tamper-proof manner, can offer this trust. One significant challenge in deploying ML Classifiers on-chain is that while ML models are typically written in Python using an ML library such as Pytorch, smart contracts deployed on EVM-compatible blockchains are written in Solidity. We introduce Machine Learning to Smart Contract (ML2SC), a PyTorch to Solidity translator that can automatically translate multi-layer perceptron (MLP) models written in Pytorch to Solidity smart contract versions. ML2SC uses a fixed-point math library to approximate floating-point computation. After deploying the generated smart contract, we can train our models off-chain using PyTorch and then further transfer the acquired weights and biases to the smart contract using a function call. Finally, the model inference can also be done with a function call providing the input. We mathematically model the gas costs associated with deploying, updating model parameters, and running inference on these models on-chain, showing that the gas costs increase linearly in various parameters associated with an MLP. We present empirical results matching our modeling. We also evaluate the classification accuracy showing that the outputs obtained by our transparent on-chain implementation are identical to the original off-chain implementation with Pytorch.

  Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics

• Authors: Ulrich Römer, Stefan Hartmann, Jendrik-Alexander Tröger, David Anton, Henning Wessels, Moritz Flaschel, Laura De Lorenzis
• Subjects: Computational Engineering, Finance, and Science (cs.CE)
• Arxiv link: https://arxiv.org/abs/2404.16980
• Pdf link: https://arxiv.org/pdf/2404.16980
• Abstract In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are starting to emerge and can also be addressed in a parameter estimation framework. These developments call for a new unified perspective, which is able to cover both traditional parameter estimation methods and novel approaches in which the state variables or the model structure itself are inferred as well. Adopting concepts discussed in the inverse problems community, we distinguish between all-at-once and reduced approaches. With this general framework, we are able to structure a large portion of the literature on parameter estimation in computational mechanics - and we can identify combinations that have not yet been addressed, two of which are proposed in this paper. We also discuss statistical approaches to quantify the uncertainty related to the estimated parameters, and we propose a novel two-step procedure for identification of complex material models based on both frequentist and Bayesian principles. Finally, we illustrate and compare several of the aforementioned methods with mechanical benchmarks based on synthetic and real data.

  Defending Spiking Neural Networks against Adversarial Attacks through Image Purification

• Authors: Weiran Chen, Qi Sun, Qi Xu
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.17092
• Pdf link: https://arxiv.org/pdf/2404.17092
• Abstract Spiking Neural Networks (SNNs) aim to bridge the gap between neuroscience and machine learning by emulating the structure of the human nervous system. However, like convolutional neural networks, SNNs are vulnerable to adversarial attacks. To tackle the challenge, we propose a biologically inspired methodology to enhance the robustness of SNNs, drawing insights from the visual masking effect and filtering theory. First, an end-to-end SNN-based image purification model is proposed to defend against adversarial attacks, including a noise extraction network and a non-blind denoising network. The former network extracts noise features from noisy images, while the latter component employs a residual U-Net structure to reconstruct high-quality noisy images and generate clean images. Simultaneously, a multi-level firing SNN based on Squeeze-and-Excitation Network is introduced to improve the robustness of the classifier. Crucially, the proposed image purification network serves as a pre-processing module, avoiding modifications to classifiers. Unlike adversarial training, our method is highly flexible and can be seamlessly integrated with other defense strategies. Experimental results on various datasets demonstrate that the proposed methodology outperforms state-of-the-art baselines in terms of defense effectiveness, training time, and resource consumption.

  CLARE: Cognitive Load Assessment in REaltime with Multimodal Data

• Authors: Anubhav Bhatti, Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Dirk Rodenburg, Heather Braund, P.James Mclellan, Aaron Ruberto, Geoffery Harrison, Daryl Wilson, Adam Szulewski, Dan Howes, Ali Etemad, Paul Hungler
• Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17098
• Pdf link: https://arxiv.org/pdf/2404.17098
• Abstract We present a novel multimodal dataset for Cognitive Load Assessment in REaltime (CLARE). The dataset contains physiological and gaze data from 24 participants with self-reported cognitive load scores as ground-truth labels. The dataset consists of four modalities, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Electroencephalogram (EEG), and Gaze tracking. To map diverse levels of mental load on participants during experiments, each participant completed four nine-minutes sessions on a computer-based operator performance and mental workload task (the MATB-II software) with varying levels of complexity in one minute segments. During the experiment, participants reported their cognitive load every 10 seconds. For the dataset, we also provide benchmark binary classification results with machine learning and deep learning models on two different evaluation schemes, namely, 10-fold and leave-one-subject-out (LOSO) cross-validation. Benchmark results show that for 10-fold evaluation, the convolutional neural network (CNN) based deep learning model achieves the best classification performance with ECG, EDA, and Gaze. In contrast, for LOSO, the best performance is achieved by the deep learning model with ECG, EDA, and EEG.

  Cycling into the workshop: predictive maintenance for Barcelona's bike-sharing system

• Authors: Jordi Grau-Escolano, Aleix Bassolas, Julian Vicens
• Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)
• Arxiv link: https://arxiv.org/abs/2404.17217
• Pdf link: https://arxiv.org/pdf/2404.17217
• Abstract Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

  Scene-Extrapolation: Generating Interactive Traffic Scenarios

• Authors: Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.17224
• Pdf link: https://arxiv.org/pdf/2404.17224
• Abstract Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seed-scene) to address the individuality of various highly automated driving functions and to avoid the problems associated with a predefined test traffic scenario. Different highly autonomous driving functions, or their distinct iterations, may display different behaviors under the same operating conditions. To make a generalizable statement about a seed-scene, we simulate possible outcomes based on various behavior profiles. We utilize our lightweight simulation environment and populate it with rule-based and machine learning behavior models for individual actors in the scenario. We analyze resulting scenarios using a variety of criticality metrics. The density distributions of the resulting criticality values enable us to make a profound statement about the significance of a particular scene, considering various eventualities.

  Adversarial Reweighting with $α$-Power Maximization for Domain Adaptation

• Authors: Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17275
• Pdf link: https://arxiv.org/pdf/2404.17275
• Abstract The practical Domain Adaptation (DA) tasks, e.g., Partial DA (PDA), open-set DA, universal DA, and test-time adaptation, have gained increasing attention in the machine learning community. In this paper, we propose a novel approach, dubbed Adversarial Reweighting with $\alpha$-Power Maximization (ARPM), for PDA where the source domain contains private classes absent in target domain. In ARPM, we propose a novel adversarial reweighting model that adversarially learns to reweight source domain data to identify source-private class samples by assigning smaller weights to them, for mitigating potential negative transfer. Based on the adversarial reweighting, we train the transferable recognition model on the reweighted source distribution to be able to classify common class data. To reduce the prediction uncertainty of the recognition model on the target domain for PDA, we present an $\alpha$-power maximization mechanism in ARPM, which enriches the family of losses for reducing the prediction uncertainty for PDA. Extensive experimental results on five PDA benchmarks, i.e., Office-31, Office-Home, VisDA-2017, ImageNet-Caltech, and DomainNet, show that our method is superior to recent PDA methods. Ablation studies also confirm the effectiveness of components in our approach. To theoretically analyze our method, we deduce an upper bound of target domain expected error for PDA, which is approximately minimized in our approach. We further extend ARPM to open-set DA, universal DA, and test time adaptation, and verify the usefulness through experiments.

  Machine Learning based prediction of Vanadium Redox Flow Battery temperature rise under different charge-discharge conditions

• Authors: Anirudh Narayan D, Akshat Johar, Divye Kalra, Bhavya Ardeshna, Ankur Bhattacharjee
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17284
• Pdf link: https://arxiv.org/pdf/2404.17284
• Abstract Accurate prediction of battery temperature rise is very essential for designing an efficient thermal management scheme. In this paper, machine learning (ML) based prediction of Vanadium Redox Flow Battery (VRFB) thermal behavior during charge-discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; Linear Regression (LR), Support Vector Regression (SVR) and Extreme Gradient Boost (XGBoost) have been used for the prediction work. The training and validation of ML algorithms have been done by the practical dataset of a 1kW 6kWh VRFB storage under 40A, 45A, 50A and 60A charge-discharge currents and 10 L min-1 of flow rate. A comparative analysis among the ML algorithms is done in terms of performance metrics such as correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE). It is observed that XGBoost shows the highest accuracy in prediction of around 99%. The ML based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as indicator for further development of an optimized thermal management system.

  Lazy Data Practices Harm Fairness Research

• Authors: Jan Simson, Alessandro Fabris, Christoph Kern
• Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY); Applications (stat.AP); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.17293
• Pdf link: https://arxiv.org/pdf/2404.17293
• Abstract Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications. Our analyses identify three main areas of concern: (1) a \textbf{lack of representation for certain protected attributes} in both data and evaluations; (2) the widespread \textbf{exclusion of minorities} during data preprocessing; and (3) \textbf{opaque data processing} threatening the generalization of fairness research. By conducting exemplary analyses on the utilization of prominent datasets, we demonstrate how unreflective data decisions disproportionately affect minority groups, fairness metrics, and resultant model comparisons. Additionally, we identify supplementary factors such as limitations in publicly available data, privacy considerations, and a general lack of awareness, which exacerbate these challenges. To address these issues, we propose a set of recommendations for data usage in fairness research centered on transparency and responsible inclusion. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets.

  Colosseum: The Open RAN Digital Twin

• Authors: Michele Polese, Leonardo Bonati, Salvatore D'Oro, Pedram Johari, Davide Villa, Sakthivel Velumani, Rajeev Gangula, Maria Tsampazi, Clifton Paul Robinson, Gabriele Gemmi, Andrea Lacava, Stefano Maxenti, Hai Cheng, Tommaso Melodia
• Subjects: Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
• Arxiv link: https://arxiv.org/abs/2404.17317
• Pdf link: https://arxiv.org/pdf/2404.17317
• Abstract Recent years have witnessed the Open Radio Access Network (RAN) paradigm transforming the fundamental ways cellular systems are deployed, managed, and optimized. This shift is led by concepts such as openness, softwarization, programmability, interoperability, and intelligence of the network, all of which had never been applied to the cellular ecosystem before. The realization of the Open RAN vision into practical architectures, intelligent data-driven control loops, and efficient software implementations, however, is a multifaceted challenge, which requires (i) datasets to train Artificial Intelligence (AI) and Machine Learning (ML) models; (ii) facilities to test models without disrupting production networks; (iii) continuous and automated validation of the RAN software; and (iv) significant testing and integration efforts. This paper poses itself as a tutorial on how Colosseum - the world's largest wireless network emulator with hardware in the loop - can provide the research infrastructure and tools to fill the gap between the Open RAN vision, and the deployment and commercialization of open and programmable networks. We describe how Colosseum implements an Open RAN digital twin through a high-fidelity Radio Frequency (RF) channel emulator and end-to-end softwarized O-RAN and 5G-compliant protocol stacks, thus allowing users to reproduce and experiment upon topologies representative of real-world cellular deployments. Then, we detail the twinning infrastructure of Colosseum, as well as the automation pipelines for RF and protocol stack twinning. Finally, we showcase a broad range of Open RAN use cases implemented on Colosseum, including the real-time connection between the digital twin and real-world networks, and the development, prototyping, and testing of AI/ML solutions for Open RAN.

  Evaluations of Machine Learning Privacy Defenses are Misleading

• Authors: Michael Aerni, Jie Zhang, Florian Tramèr
• Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17399
• Pdf link: https://arxiv.org/pdf/2404.17399
• Abstract Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries. We identify severe pitfalls in existing empirical privacy evaluations (based on membership inference attacks) that result in misleading conclusions. In particular, we show that prior evaluations fail to characterize the privacy leakage of the most vulnerable samples, use weak attacks, and avoid comparisons with practical differential privacy baselines. In 5 case studies of empirical privacy defenses, we find that prior evaluations underestimate privacy leakage by an order of magnitude. Under our stronger evaluation, none of the empirical defenses we study are competitive with a properly tuned, high-utility DP-SGD baseline (with vacuous provable guarantees).

  Conformal Prediction with Learned Features

• Authors: Shayan Kiyani, George Pappas, Hamed Hassani
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.17487
• Pdf link: https://arxiv.org/pdf/2404.17487
• Abstract In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with full conditional coverage guarantees. A wealth of research has considered relaxations of full conditional guarantees, relying on some predefined uncertainty structures. Departing from this line of thinking, we propose Partition Learning Conformal Prediction (PLCP), a framework to improve conditional validity of prediction sets through learning uncertainty-guided features from the calibration data. We implement PLCP efficiently with alternating gradient descent, utilizing off-the-shelf machine learning models. We further analyze PLCP theoretically and provide conditional guarantees for infinite and finite sample sizes. Finally, our experimental results over four real-world and synthetic datasets show the superior performance of PLCP compared to state-of-the-art methods in terms of coverage and length in both classification and regression scenarios.

  Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems

• Authors: Dorian Florescu, Matthew England
• Subjects: Symbolic Computation (cs.SC); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17508
• Pdf link: https://arxiv.org/pdf/2404.17508
• Abstract We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development.

  Large Language Model Agent as a Mechanical Designer

• Authors: Yayati Jadhav, Amir Barati Farimani
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.17525
• Pdf link: https://arxiv.org/pdf/2404.17525
• Abstract Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously.

  Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems

• Authors: Imran Nasim, Joaõ Lucas de Sousa Almeida
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17535
• Pdf link: https://arxiv.org/pdf/2404.17535
• Abstract The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In this study, we investigate the capabilities of Neural Implicit Flow (NIF), a recently developed mesh-agnostic neural operator, for representing the latent dynamics of canonical systems such as the Kuramoto-Sivashinsky (KS), forced Korteweg-de Vries (fKdV), and Sine-Gordon (SG) equations, as well as for extracting dynamically relevant information from them. Finally we assess the applicability of NIF as a dimensionality reduction algorithm and conduct a comparative analysis with another widely recognized family of neural operators, known as Deep Operator Networks (DeepONets).

  Keyword: optimization

  AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs

• Authors: Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, Yuandong Tian
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16873
• Pdf link: https://arxiv.org/pdf/2404.16873
• Abstract While recently Large Language Models (LLMs) have achieved remarkable successes, they are vulnerable to certain jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires finding adversarial prompts that cause such jailbreaking, e.g. by appending a suffix to a given instruction, which is inefficient and time-consuming. On the other hand, automatic adversarial prompt generation often leads to semantically meaningless attacks that can easily be detected by perplexity-based filters, may require gradient information from the TargetLLM, or do not scale well due to time-consuming discrete optimization processes over the token space. In this paper, we present a novel method that uses another LLM, called the AdvPrompter, to generate human-readable adversarial prompts in seconds, $\sim800\times$ faster than existing optimization-based approaches. We train the AdvPrompter using a novel algorithm that does not require access to the gradients of the TargetLLM. This process alternates between two steps: (1) generating high-quality target adversarial suffixes by optimizing the AdvPrompter predictions, and (2) low-rank fine-tuning of the AdvPrompter with the generated adversarial suffixes. The trained AdvPrompter generates suffixes that veil the input instruction without changing its meaning, such that the TargetLLM is lured to give a harmful response. Experimental results on popular open source TargetLLMs show state-of-the-art results on the AdvBench dataset, that also transfer to closed-source black-box LLM APIs. Further, we demonstrate that by fine-tuning on a synthetic dataset generated by AdvPrompter, LLMs can be made more robust against jailbreaking attacks while maintaining performance, i.e. high MMLU scores.

  On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

• Authors: Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16894
• Pdf link: https://arxiv.org/pdf/2404.16894
• Abstract As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demands limit their applications, leading to the emergence of Tiny Machine Learning (TinyML) as a more suitable solution for resource-constrained environments. TinyML is widely applied in areas such as smart homes, healthcare, and industrial automation. TinyML focuses on optimizing ML algorithms for small, low-power devices, enabling intelligent data processing directly on edge devices. This paper provides a comprehensive review of common challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints; it also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. On the other hand, this paper discusses TinyML's applications in advancing cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a representative use case. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage, with a slight trade-off in accuracy. Additionally, the study includes a practical setup using the ESP32 microcontroller in the PlatformIO environment, which provides a hands-on assessment of TinyML's application in cybersecurity for EVCI.

  QuERLoc: Towards Next-Generation Localization with Quantum-Enhanced Ranging

• Authors: Entong He, Yuxiang Yang, Chenshu Wu
• Subjects: Emerging Technologies (cs.ET)
• Arxiv link: https://arxiv.org/abs/2404.16895
• Pdf link: https://arxiv.org/pdf/2404.16895
• Abstract Remarkable advances have been achieved in localization techniques in past decades, rendering it one of the most important technologies indispensable to our daily lives. In this paper, we investigate a novel localization approach for future computing by presenting QuERLoc, the first study on localization using quantum-enhanced ranging. By fine-tuning the evolution of an entangled quantum probe, quantum ranging can output the information integrated in the probe as a specific mapping of distance-related parameters. QuERLoc is inspired by this unique property to measure a special combination of distances between a target sensor and multiple anchors within one single physical measurement. Leveraging this capability, QuERLoc settles two drawbacks of classical localization approaches: (i) the target-anchor distances must be measured individually and sequentially, and (ii) the resulting optimization problems are non-convex and are sensitive to noise. We first present the theoretical formulation of preparing the probing quantum state and controlling its dynamic to induce a convexified localization problem, and then solve it efficiently via optimization. We conduct extensive numerical analysis of QuERLoc under various settings. The results show that QuERLoc consistently outperforms classical approaches in accuracy and closely follows the theoretical lowerbound, while maintaining low time complexity. It achieves a minimum reduction of 73% in RMSE and 97.6% in time consumption compared to baselines. By introducing range-based quantum localization to the mobile computing community and showing its superior performance, QuERLoc sheds light on next-generation localization technologies and opens up new directions for future research.

  Evolve Cost-aware Acquisition Functions Using Large Language Models

• Authors: Yiming Yao, Fei Liu, Ji Cheng, Qingfu Zhang
• Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16906
• Pdf link: https://arxiv.org/pdf/2404.16906
• Abstract Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in addressing these challenges. To approach the global optimum within a limited budget in a cost-efficient manner, the design of cost-aware acquisition functions (AFs) becomes a crucial step. However, traditional manual design paradigm typically requires extensive domain knowledge and involves a labor-intensive trial-and-error process. This paper introduces EvolCAF, a novel framework that integrates large language models (LLMs) with evolutionary computation (EC) to automatically design cost-aware AFs. Leveraging the crossover and mutation in the algorithm space, EvolCAF offers a novel design paradigm, significantly reduces the reliance on domain expertise and model training. The designed cost-aware AF maximizes the utilization of available information from historical data, surrogate models and budget details. It introduces novel ideas not previously explored in the existing literature on acquisition function design, allowing for clear interpretations to provide insights into its behavior and decision-making process. In comparison to the well-known EIpu and EI-cool methods designed by human experts, our approach showcases remarkable efficiency and generalization across various tasks, including 12 synthetic problems and 3 real-world hyperparameter tuning test sets.

  Piecewise Stochastic Barrier Functions

• Authors: Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti, Morteza Lahijanian
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.16986
• Pdf link: https://arxiv.org/pdf/2404.16986
• Abstract This paper presents a novel stochastic barrier function (SBF) framework for safety analysis of stochastic systems based on piecewise (PW) functions. We first outline a general formulation of PW-SBFs. Then, we focus on PW-Constant (PWC) SBFs and show how their simplicity yields computational advantages for general stochastic systems. Specifically, we prove that synthesis of PWC-SBFs reduces to a minimax optimization problem. Then, we introduce three efficient algorithms to solve this problem, each offering distinct advantages and disadvantages. The first algorithm is based on dual linear programming (LP), which provides an exact solution to the minimax optimization problem. The second is a more scalable algorithm based on iterative counter-example guided synthesis, which involves solving two smaller LPs. The third algorithm solves the minimax problem using gradient descent, which admits even better scalability. We provide an extensive evaluation of these methods on various case studies, including neural network dynamic models, nonlinear switched systems, and high-dimensional linear systems. Our benchmarks demonstrate that PWC-SBFs outperform state-of-the-art methods, namely sum-of-squares and neural barrier functions, and can scale to eight dimensional systems.

  Learning Actionable Counterfactual Explanations in Large State Spaces

• Authors: Keziah Naggita, Matthew R. Walter, Avrim Blum
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17034
• Pdf link: https://arxiv.org/pdf/2404.17034
• Abstract Counterfactual explanations (CFEs) are sets of actions that an agent with a negative classification could take to achieve a (desired) positive classification, for consequential decisions such as loan applications, hiring, admissions, etc. In this work, we consider settings where optimal CFEs correspond to solutions of weighted set cover problems. In particular, there is a collection of actions that agents can perform that each have their own cost and each provide the agent with different sets of capabilities. The agent wants to perform the cheapest subset of actions that together provide all the needed capabilities to achieve a positive classification. Since this is an NP-hard optimization problem, we are interested in the question: can we, from training data (instances of agents and their optimal CFEs) learn a CFE generator that will quickly provide optimal sets of actions for new agents? In this work, we provide a deep-network learning procedure that we show experimentally is able to achieve strong performance at this task. We consider several problem formulations, including formulations in which the underlying "capabilities" and effects of actions are not explicitly provided, and so there is an informational challenge in addition to the computational challenge. Our problem can also be viewed as one of learning an optimal policy in a family of large but deterministic Markov Decision Processes (MDPs).

  Evaluating Collaborative Autonomy in Opposed Environments using Maritime Capture-the-Flag Competitions

• Authors: Jordan Beason, Michael Novitzky, John Kliem, Tyler Errico, Zachary Serlin, Kevin Becker, Tyler Paine, Michael Benjamin, Prithviraj Dasgupta, Peter Crowley, Charles O'Donnell, John James
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.17038
• Pdf link: https://arxiv.org/pdf/2404.17038
• Abstract The objective of this work is to evaluate multi-agent artificial intelligence methods when deployed on teams of unmanned surface vehicles (USV) in an adversarial environment. Autonomous agents were evaluated in real-world scenarios using the Aquaticus test-bed, which is a Capture-the-Flag (CTF) style competition involving teams of USV systems. Cooperative teaming algorithms of various foundations in behavior-based optimization and deep reinforcement learning (RL) were deployed on these USV systems in two versus two teams and tested against each other during a competition period in the fall of 2023. Deep reinforcement learning applied to USV agents was achieved via the Pyquaticus test bed, a lightweight gymnasium environment that allows simulated CTF training in a low-level environment. The results of the experiment demonstrate that rule-based cooperation for behavior-based agents outperformed those trained in Deep-reinforcement learning paradigms as implemented in these competitions. Further integration of the Pyquaticus gymnasium environment for RL with MOOS-IvP in terms of configuration and control schema will allow for more competitive CTF games in future studies. As the development of experimental deep RL methods continues, the authors expect that the competitive gap between behavior-based autonomy and deep RL will be reduced. As such, this report outlines the overall competition, methods, and results with an emphasis on future works such as reward shaping and sim-to-real methodologies and extending rule-based cooperation among agents to react to safety and security events in accordance with human experts intent/rules for executing safety and security processes.

  Transductive Spiking Graph Neural Networks for Loihi

• Authors: Shay Snyder (1), Victoria Clerico (1, 2), Guojing Cong (3), Shruti Kulkarni (3), Catherine Schuman (4), Sumedh R. Risbud (5), Maryam Parsa (1) ((1) George Mason University, (2) Universidad Politecnica de Madrid, (3) Oak Ridge National Laboratory, (4) University of Tennessee - Knoxville, (5) Intel Labs)
• Subjects: Emerging Technologies (cs.ET); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
• Arxiv link: https://arxiv.org/abs/2404.17048
• Pdf link: https://arxiv.org/pdf/2404.17048
• Abstract Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, these approaches are limited to traditional Von Neumann-based computing systems, which still face hardware inefficiencies. In this study, we present a fully neuromorphic implementation of spiking graph neural networks designed for Loihi 2. We optimize network parameters using Lava Bayesian Optimization, a novel hyperparameter optimization system compatible with neuromorphic computing architectures. We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons. Our results demonstrate the capability of integer-precision, Loihi 2 compatible spiking neural networks in performing citation graph classification with comparable accuracy to existing floating point implementations.

  Asynchronous Neuromorphic Optimization with Lava

• Authors: Shay Snyder (1), Sumedh R. Risbud (2), Maryam Parsa (1) ((1) George Mason University, (2) Intel Labs)
• Subjects: Emerging Technologies (cs.ET)
• Arxiv link: https://arxiv.org/abs/2404.17052
• Pdf link: https://arxiv.org/pdf/2404.17052
• Abstract Performing optimization with event-based asynchronous neuromorphic systems presents significant challenges. Intel's neuromorphic computing framework, Lava, offers an abstract application programming interface designed for constructing event-based computational graphs. In this study, we introduce a novel framework tailored for asynchronous Bayesian optimization that is also compatible with Loihi 2. We showcase the capability of our asynchronous optimization framework by connecting it with a graph-based satellite scheduling problem running on physical Loihi 2 hardware.

  Solving the Graph Burning Problem for Large Graphs

• Authors: Felipe de Carvalho Pereira, Pedro Jussieu de Rezende, Tallys Yunes, Luiz Fernando Batista Morato
• Subjects: Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS); Combinatorics (math.CO)
• Arxiv link: https://arxiv.org/abs/2404.17080
• Pdf link: https://arxiv.org/pdf/2404.17080
• Abstract We propose an exact algorithm for the Graph Burning Problem ($\texttt{GBP}$), an NP-hard optimization problem that models the spread of influence on social networks. Given a graph $G$ with vertex set $V$, the objective is to find a sequence of $k$ vertices in $V$, namely, $v_1, v_2, \dots, vk$, such that $k$ is minimum and $\bigcup{i = 1}^{k} {u! \in! V! : d(u, v_i) \leq k - i} = V$, where $d(u,v)$ denotes the distance between $u$ and $v$. We formulate the problem as a set covering integer programming model and design a row generation algorithm for the $\texttt{GBP}$. Our method exploits the fact that a very small number of covering constraints is often sufficient for solving the integer model, allowing the corresponding rows to be generated on demand. To date, the most efficient exact algorithm for the $\texttt{GBP}$, denoted here by $\texttt{GDCA}$, is able to obtain optimal solutions for graphs with up to 14,000 vertices within two hours of execution. In comparison, our algorithm finds provably optimal solutions approximately 236 times faster, on average, than $\texttt{GDCA}$. For larger graphs, memory space becomes a limiting factor for $\texttt{GDCA}$. Our algorithm, however, solves real-world instances with almost 200,000 vertices in less than 35 seconds, increasing the size of graphs for which optimal solutions are known by a factor of 14.

  Rank-Preference Consistency as the Appropriate Metric for Recommender Systems

• Authors: Tung Nguyen, Jeffrey Uhlmann
• Subjects: Information Retrieval (cs.IR)
• Arxiv link: https://arxiv.org/abs/2404.17097
• Pdf link: https://arxiv.org/pdf/2404.17097
• Abstract In this paper we argue that conventional unitary-invariant measures of recommender system (RS) performance based on measuring differences between predicted ratings and actual user ratings fail to assess fundamental RS properties. More specifically, posing the optimization problem as one of predicting exact user ratings provides only an indirect suboptimal approximation for what RS applications typically need, which is an ability to accurately predict user preferences. We argue that scalar measures such as RMSE and MAE with respect to differences between actual and predicted ratings are only proxies for measuring RS ability to accurately estimate user preferences. We propose what we consider to be a measure that is more fundamentally appropriate for assessing RS performance, rank-preference consistency, which simply counts the number of prediction pairs that are inconsistent with the user's expressed product preferences. For example, if an RS predicts the user will prefer product A over product B, but the user's withheld ratings indicate s/he prefers product B over A, then rank-preference consistency has been violated. Our test results conclusively demonstrate that methods tailored to optimize arbitrary measures such as RMSE are not generally effective at accurately predicting user preferences. Thus, we conclude that conventional methods used for assessing RS performance are arbitrary and misleading.

  Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting

• Authors: Simon J. Ward, Muhamed Baljevic, Sharon M. Weiss
• Subjects: Machine Learning (cs.LG); Signal Processing (eess.SP)
• Arxiv link: https://arxiv.org/abs/2404.17144
• Pdf link: https://arxiv.org/pdf/2404.17144
• Abstract The response time of a biosensor is a crucial metric in safety-critical applications such as medical diagnostics where an earlier diagnosis can markedly improve patient outcomes. However, the speed at which a biosensor reaches a final equilibrium state can be limited by poor mass transport and long molecular diffusion times that increase the time it takes target molecules to reach the active sensing region of a biosensor. While optimization of system and sensor design can promote molecules reaching the sensing element faster, a simpler and complementary approach for response time reduction that is widely applicable across all sensor platforms is to use time-series forecasting to predict the ultimate steady-state sensor response. In this work, we show that ensembles of long short-term memory (LSTM) networks can accurately predict equilibrium biosensor response from a small quantity of initial time-dependent biosensor measurements, allowing for significant reduction in response time by a mean and median factor of improvement of 18.6 and 5.1, respectively. The ensemble of models also provides simultaneous estimation of uncertainty, which is vital to provide confidence in the predictions and subsequent safety-related decisions that are made. This approach is demonstrated on real-time experimental data collected by exposing porous silicon biosensors to buffered protein solutions using a multi-channel fluidic cell that enables the automated measurement of 100 porous silicon biosensors in parallel. The dramatic improvement in sensor response time achieved using LSTM network ensembles and associated uncertainty quantification opens the door to trustworthy and faster responding biosensors, enabling more rapid medical diagnostics for improved patient outcomes and healthcare access, as well as quicker identification of toxins in food and the environment.

  An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging

• Authors: Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.17187
• Pdf link: https://arxiv.org/pdf/2404.17187
• Abstract Deep Reinforcement Learning is an effective tool for drug dosing for chronic condition management. However, the final protocol is generally a black box without any justification for its prescribed doses. This paper addresses this issue by proposing an explainable dosing protocol for warfarin using a Proximal Policy Optimization method combined with Policy Distillation. We introduce Action Forging as an effective tool to achieve explainability. Our focus is on the maintenance dosing protocol. Results show that the final model is as easy to understand and deploy as the current dosing protocols and outperforms the baseline dosing algorithms.

  Camera Motion Estimation from RGB-D-Inertial Scene Flow

• Authors: Samuel Cerezo, Javier Civera
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.17251
• Pdf link: https://arxiv.org/pdf/2404.17251
• Abstract In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate the camera motion in a rigid 3D environment, along with the state of the inertial measurement unit (IMU). Our proposed method offers the flexibility to operate as a multi-frame optimization or to marginalize older data, thus effectively utilizing past measurements. To assess the performance of our method, we conducted evaluations using both synthetic data from the ICL-NUIM dataset and real data sequences from the OpenLORIS-Scene dataset. Our results show that the fusion of these two sensors enhances the accuracy of camera motion estimation when compared to using only visual data.

  Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences

• Authors: Kürsat Petek, Niclas Vödisch, Johannes Meyer, Daniele Cattaneo, Abhinav Valada, Wolfram Burgard
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.17298
• Pdf link: https://arxiv.org/pdf/2404.17298
• Abstract Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCalib which is a novel method for camera-LiDAR calibration that requires neither human supervision nor any specific target objects. Instead, we utilize sensor motion estimates from visual and LiDAR odometry as well as deep learning-based 2D-pixel-to-3D-point correspondences that are obtained without in-domain retraining. We represent the camera-LiDAR calibration as a graph optimization problem and minimize the costs induced by constraints from sensor motion and point correspondences. In extensive experiments, we demonstrate that our approach yields highly accurate extrinsic calibration parameters and is robust to random initialization. Additionally, our approach generalizes to a wide range of sensor setups, which we demonstrate by employing it on various robotic platforms including a self-driving perception car, a quadruped robot, and a UAV. To make our calibration method publicly accessible, we release the code on our project website at this http URL

  Certified MaxSAT Preprocessing

• Authors: Hannes Ihalainen, Andy Oertel, Yong Kiam Tan, Jeremias Berg, Matti Järvisalo, Jakob Nordström
• Subjects: Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17316
• Pdf link: https://arxiv.org/pdf/2404.17316
• Abstract Building on the progress in Boolean satisfiability (SAT) solving over the last decades, maximum satisfiability (MaxSAT) has become a viable approach for solving NP-hard optimization problems, but ensuring correctness of MaxSAT solvers has remained an important concern. For SAT, this is largely a solved problem thanks to the use of proof logging, meaning that solvers emit machine-verifiable proofs of (un)satisfiability to certify correctness. However, for MaxSAT, proof logging solvers have started being developed only very recently. Moreover, these nascent efforts have only targeted the core solving process, ignoring the preprocessing phase where input problem instances can be substantially reformulated before being passed on to the solver proper. In this work, we demonstrate how pseudo-Boolean proof logging can be used to certify the correctness of a wide range of modern MaxSAT preprocessing techniques. By combining and extending the VeriPB and CakePB tools, we provide formally verified, end-to-end proof checking that the input and preprocessed output MaxSAT problem instances have the same optimal value. An extensive evaluation on applied MaxSAT benchmarks shows that our approach is feasible in practice.

  Exploring Wireless Channels in Rural Areas: A Comprehensive Measurement Study

• Authors: Tianyi Zhang, Guoying Zu, Taimoor Ul Islam, Evan Gossling, Sarath Babu, Daji Qiao, Hongwei Zhang
• Subjects: Networking and Internet Architecture (cs.NI)
• Arxiv link: https://arxiv.org/abs/2404.17434
• Pdf link: https://arxiv.org/pdf/2404.17434
• Abstract The study of wireless channel behavior has been an active research topic for many years. However, there exists a noticeable scarcity of studies focusing on wireless channel characteristics in rural areas. With the advancement of smart agriculture practices in rural regions, there has been an increasing demand for affordable, high-capacity, and low-latency wireless networks to support various precision agriculture applications such as plant phenotyping, livestock health monitoring, and agriculture automation. To address this research gap, we conducted a channel measurement study on multiple wireless frequency bands at various crop and livestock farms near Ames, Iowa, based on Iowa State University~(ISU)'s ARA Wireless Living lab - one of the NSF PAWR platforms. We specifically investigate the impact of weather conditions, humidity, temperature, and farm buildings on wireless channel behavior. The resulting measurement dataset, which will soon be made publicly accessible, represents a valuable resource for researchers interested in wireless channel prediction and optimization.

  A Continuous Relaxation for Discrete Bayesian Optimization

• Authors: Richard Michael, Simon Bartels, Miguel González-Duque, Yevgen Zainchkovskyy, Jes Frellsen, Søren Hauberg, Wouter Boomsma
• Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.17452
• Pdf link: https://arxiv.org/pdf/2404.17452
• Abstract To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can be computationally tractable. We consider in particular the optimization domain where very few observations and strict budgets exist; motivated by optimizing protein sequences for expensive to evaluate bio-chemical properties. The advantages of our approach are two-fold: the problem is treated in the continuous setting, and available prior knowledge over sequences can be incorporated directly. More specifically, we utilize available and learned distributions over the problem domain for a weighting of the Hellinger distance which yields a covariance function. We show that the resulting acquisition function can be optimized with both continuous or discrete optimization algorithms and empirically assess our method on two bio-chemical sequence optimization tasks.

  Inhomogeneous illuminated image enhancement under extremely low visibility condition

• Authors: Libang Chen, Yikun Liu, Jianying Zhou
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics)
• Arxiv link: https://arxiv.org/abs/2404.17503
• Pdf link: https://arxiv.org/pdf/2404.17503
• Abstract Imaging through fog significantly impacts fields such as object detection and recognition. In conditions of extremely low visibility, essential image information can be obscured, rendering standard extraction methods ineffective. Traditional digital processing techniques, such as histogram stretching, aim to mitigate fog effects by enhancing object light contrast diminished by atmospheric scattering. However, these methods often experience reduce effectiveness under inhomogeneous illumination. This paper introduces a novel approach that adaptively filters background illumination under extremely low visibility and preserve only the essential signal information. Additionally, we employ a visual optimization strategy based on image gradients to eliminate grayscale banding. Finally, the image is transformed to achieve high contrast and maintain fidelity to the original information through maximum histogram equalization. Our proposed method significantly enhances signal clarity in conditions of extremely low visibility and outperforms existing algorithms.

  Understanding the Cluster LP for Correlation Clustering

• Authors: Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman, Lukas Vogl
• Subjects: Data Structures and Algorithms (cs.DS)
• Arxiv link: https://arxiv.org/abs/2404.17509
• Pdf link: https://arxiv.org/pdf/2404.17509
• Abstract In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla~(FOCS 2002), the input is a complete graph where edges are labeled either $+$ or $-$, and the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the -edges within parts. In recent years, Chawla, Makarychev, Schramm and Yaroslavtsev~(STOC 2015) gave a 2.06-approximation by providing a near-optimal rounding of the standard LP, and Cohen-Addad, Lee, Li, and Newman~(FOCS 2022, 2023) finally bypassed the integrality gap of 2 for this LP giving a $1.73$-approximation for the problem. In order to create a simple and unified framework for Correlation Clustering similar to those for {\em typical} approximate optimization tasks, we propose the {\em cluster LP} as a strong linear program that might tightly capture the approximability of Correlation Clustering. It unifies all the previous relaxations for the problem. We demonstrate the power of the cluster LP by presenting a simple rounding algorithm, and providing two analyses, one analytically proving a 1.49-approximation and the other solving a factor-revealing SDP to show a 1.437-approximation. Both proofs introduce principled methods by which to analyze the performance of the algorithm, resulting in a significantly improved approximation guarantee. Finally, we prove an integrality gap of $4/3$ for the cluster LP, showing our 1.437-upper bound cannot be drastically improved. Our gap instance directly inspires an improved NP-hardness of approximation with a ratio $24/23 \approx 1.042$; no explicit hardness ratio was known before.

  Large Language Model Agent as a Mechanical Designer

• Authors: Yayati Jadhav, Amir Barati Farimani
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.17525
• Pdf link: https://arxiv.org/pdf/2404.17525
• Abstract Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously.

  Keyword: deep learning

  Leaf-Based Plant Disease Detection and Explainable AI

• Authors: Saurav Sagar, Mohammed Javed, David S Doermann
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16833
• Pdf link: https://arxiv.org/pdf/2404.16833
• Abstract The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synthetic fertilizers, archaic practices, environmental conditions, etc., which impact the farm yield and subsequently hinder the economy. To address this issue, researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users. By consolidating this knowledge, the survey offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.

  Predicting SSH keys in Open SSH Memory dumps

• Authors: Florian Rascoussier
• Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16838
• Pdf link: https://arxiv.org/pdf/2404.16838
• Abstract As the digital landscape evolves, cybersecurity has become an indispensable focus of IT systems. Its ever-escalating challenges have amplified the importance of digital forensics, particularly in the analysis of heap dumps from main memory. In this context, the Secure Shell protocol (SSH) designed for encrypted communications, serves as both a safeguard and a potential veil for malicious activities. This research project focuses on predicting SSH keys in OpenSSH memory dumps, aiming to enhance protective measures against illicit access and enable the development of advanced security frameworks or tools like honeypots. This Masterarbeit is situated within the broader SmartVMI project, and seeks to build upon existing research on key prediction in OpenSSH heap dumps. Utilizing machine learning (ML) and deep learning models, the study aims to refine features for embedding techniques and explore innovative methods for effective key detection based on recent advancements in Knowledge Graph and ML. The objective is to accurately predict the presence and location of SSH keys within memory dumps. This work builds upon, and aims to enhance, the foundations laid by SSHkex and SmartKex, enriching both the methodology and the results of the original research while exploring the untapped potential of newly proposed approaches. The current thesis dives into memory graph modelization from raw binary heap dump files. Each memory graph can support a range of embeddings that can be used directly for model training, through the use of classic ML models and graph neural network. It offers an in-depth discussion on the current state-of-the-art in key prediction for OpenSSH memory dumps, research questions, experimental setups, programs development, results as well as discussing potential future directions.

  ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning

• Authors: Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky, Anthony Garland, Dan S. Bolintineanu, Dan R. Moser, Michael J. Heiden, Amir Barati Farimani
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16882
• Pdf link: https://arxiv.org/pdf/2404.16882
• Abstract We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a $R^2$ score of 0.57 and our model for porosity localization produced an average IoU score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity "Digital Twins" based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

  Anomaly Detection for Incident Response at Scale

• Authors: Hanzhang Wang, Gowtham Kumar Tangirala, Gilkara Pranav Naidu, Charles Mayville, Arighna Roy, Joanne Sun, Ramesh Babu Mandava
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.16887
• Pdf link: https://arxiv.org/pdf/2404.16887
• Abstract We present a machine learning-based anomaly detection product, AI Detect and Respond (AIDR), that monitors Walmart's business and system health in real-time. During the validation over 3 months, the product served predictions from over 3000 models to more than 25 application, platform, and operation teams, covering 63\% of major incidents and reducing the mean-time-to-detect (MTTD) by more than 7 minutes. Unlike previous anomaly detection methods, our solution leverages statistical, ML and deep learning models while continuing to incorporate rule-based static thresholds to incorporate domain-specific knowledge. Both univariate and multivariate ML models are deployed and maintained through distributed services for scalability and high availability. AIDR has a feedback loop that assesses model quality with a combination of drift detection algorithms and customer feedback. It also offers self-onboarding capabilities and customizability. AIDR has achieved success with various internal teams with lower time to detection and fewer false positives than previous methods. As we move forward, we aim to expand incident coverage and prevention, reduce noise, and integrate further with root cause recommendation (RCR) to enable an end-to-end AIDR experience.

  On-the-fly Data Augmentation for Forecasting with Deep Learning

• Authors: Vitor Cerqueira, Moisés Santos, Yassine Baghoussi, Carlos Soares
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2404.16918
• Pdf link: https://arxiv.org/pdf/2404.16918
• Abstract Deep learning approaches are increasingly used to tackle forecasting tasks. A key factor in the successful application of these methods is a large enough training sample size, which is not always available. In these scenarios, synthetic data generation techniques are usually applied to augment the dataset. Data augmentation is typically applied before fitting a model. However, these approaches create a single augmented dataset, potentially limiting their effectiveness. This work introduces OnDAT (On-the-fly Data Augmentation for Time series) to address this issue by applying data augmentation during training and validation. Contrary to traditional methods that create a single, static augmented dataset beforehand, OnDAT performs augmentation on-the-fly. By generating a new augmented dataset on each iteration, the model is exposed to a constantly changing augmented data variations. We hypothesize this process enables a better exploration of the data space, which reduces the potential for overfitting and improves forecasting performance. We validated the proposed approach using a state-of-the-art deep learning forecasting method and 8 benchmark datasets containing a total of 75797 time series. The experiments suggest that OnDAT leads to better forecasting performance than a strategy that applies data augmentation before training as well as a strategy that does not involve data augmentation. The method and experiments are publicly available.

  A Survey of Generative Search and Recommendation in the Era of Large Language Models

• Authors: Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, Tat-Seng Chua
• Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.16924
• Pdf link: https://arxiv.org/pdf/2404.16924
• Abstract With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items. In the recent few decades, search and recommendation have experienced synchronous technological paradigm shifts, including machine learning-based and deep learning-based paradigms. Recently, the superintelligent generative large language models have sparked a new paradigm in search and recommendation, i.e., generative search (retrieval) and recommendation, which aims to address the matching problem in a generative manner. In this paper, we provide a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective. Rather than simply categorizing existing works, we abstract a unified framework for the generative paradigm and break down the existing works into different stages within this framework to highlight the strengths and weaknesses. And then, we distinguish generative search and recommendation with their unique challenges, identify open problems and future directions, and envision the next information-seeking paradigm.

  CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching

• Authors: Samia Shafique, Shu Kong, Charless Fowlkes
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.16972
• Pdf link: https://arxiv.org/pdf/2404.16972
• Abstract Shoeprints are a common type of evidence found at crime scenes and are used regularly in forensic investigations. However, existing methods cannot effectively employ deep learning techniques to match noisy and occluded crime-scene shoeprints to a shoe database due to a lack of training data. Moreover, all existing methods match crime-scene shoeprints to clean reference prints, yet our analysis shows matching to more informative tread depth maps yields better retrieval results. The matching task is further complicated by the necessity to identify similarities only in corresponding regions (heels, toes, etc) of prints and shoe treads. To overcome these challenges, we leverage shoe tread images from online retailers and utilize an off-the-shelf predictor to estimate depth maps and clean prints. Our method, named CriSp, matches crime-scene shoeprints to tread depth maps by training on this data. CriSp incorporates data augmentation to simulate crime-scene shoeprints, an encoder to learn spatially-aware features, and a masking module to ensure only visible regions of crime-scene prints affect retrieval results. To validate our approach, we introduce two validation sets by reprocessing existing datasets of crime-scene shoeprints and establish a benchmarking protocol for comparison. On this benchmark, CriSp significantly outperforms state-of-the-art methods in both automated shoeprint matching and image retrieval tailored to this task.

  Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach

• Authors: Cristopher McIntyre-Garcia, Adrien Heymans, Beril Borali, Won-Sook Lee, Shiva Nejati
• Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17020
• Pdf link: https://arxiv.org/pdf/2404.17020
• Abstract Deep Learning (DL) models excel in computer vision tasks but can be susceptible to adversarial examples. This paper introduces Triple-Metric EvoAttack (TM-EVO), an efficient algorithm for evaluating the robustness of object-detection DL models against adversarial attacks. TM-EVO utilizes a multi-metric fitness function to guide an evolutionary search efficiently in creating effective adversarial test inputs with minimal perturbations. We evaluate TM-EVO on widely-used object-detection DL models, DETR and Faster R-CNN, and open-source datasets, COCO and KITTI. Our findings reveal that TM-EVO outperforms the state-of-the-art EvoAttack baseline, leading to adversarial tests with less noise while maintaining efficiency.

  Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

• Authors: Tanvi Deshpande, Eva Prakash, Elsie Gyang Ross, Curtis Langlotz, Andrew Ng, Jeya Maria Jose Valanarasu
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.17033
• Pdf link: https://arxiv.org/pdf/2404.17033
• Abstract The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to train deep learning models for medical imaging tasks. In this work, we present a new approach to overcome the hurdle of costly medical image labeling by leveraging foundation models like Segment Anything Model (SAM) and its medical alternate MedSAM. Our pipeline has the ability to generate weak labels for any unlabeled medical image and subsequently use it to augment label-scarce datasets. We perform this by leveraging a model trained on a few gold-standard labels and using it to intelligently prompt MedSAM for weak label generation. This automation eliminates the manual prompting step in MedSAM, creating a streamlined process for generating labels for both real and synthetic images, regardless of quantity. We conduct experiments on label-scarce settings for multiple tasks pertaining to modalities ranging from ultrasound, dermatology, and X-rays to demonstrate the usefulness of our pipeline. The code is available at https://github.com/stanfordmlgroup/Auto-Generate-WLs/.

  Unraveling Code Clone Dynamics in Deep Learning Frameworks

• Authors: Maram Assi, Safwat Hassan, Ying Zou
• Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17046
• Pdf link: https://arxiv.org/pdf/2404.17046
• Abstract Deep Learning (DL) frameworks play a critical role in advancing artificial intelligence, and their rapid growth underscores the need for a comprehensive understanding of software quality and maintainability. DL frameworks, like other systems, are prone to code clones. Code clones refer to identical or highly similar source code fragments within the same project or even across different projects. Code cloning can have positive and negative implications for software development, influencing maintenance, readability, and bug propagation. In this paper, we aim to address the knowledge gap concerning the evolutionary dimension of code clones in DL frameworks and the extent of code reuse across these frameworks. We empirically analyze code clones in nine popular DL frameworks, i.e., TensorFlow, Paddle, PyTorch, Aesara, Ray, MXNet, Keras, Jax and BentoML, to investigate (1) the characteristics of the long-term code cloning evolution over releases in each framework, (2) the short-term, i.e., within-release, code cloning patterns and their influence on the long-term trends, and (3) the file-level code clones within the DL frameworks. Our findings reveal that DL frameworks adopt four distinct cloning trends and that these trends present some common and distinct characteristics. For instance, bug-fixing activities persistently happen in clones irrespective of the clone evolutionary trend but occur more in the "Serpentine" trend. Moreover, the within release level investigation demonstrates that short-term code cloning practices impact long-term cloning trends. The cross-framework code clone investigation reveals the presence of functional and architectural adaptation file-level cross-framework code clones across the nine studied frameworks. We provide insights that foster robust clone practices and collaborative maintenance in the development of DL frameworks.

  Transductive Spiking Graph Neural Networks for Loihi

• Authors: Shay Snyder (1), Victoria Clerico (1, 2), Guojing Cong (3), Shruti Kulkarni (3), Catherine Schuman (4), Sumedh R. Risbud (5), Maryam Parsa (1) ((1) George Mason University, (2) Universidad Politecnica de Madrid, (3) Oak Ridge National Laboratory, (4) University of Tennessee - Knoxville, (5) Intel Labs)
• Subjects: Emerging Technologies (cs.ET); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
• Arxiv link: https://arxiv.org/abs/2404.17048
• Pdf link: https://arxiv.org/pdf/2404.17048
• Abstract Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, these approaches are limited to traditional Von Neumann-based computing systems, which still face hardware inefficiencies. In this study, we present a fully neuromorphic implementation of spiking graph neural networks designed for Loihi 2. We optimize network parameters using Lava Bayesian Optimization, a novel hyperparameter optimization system compatible with neuromorphic computing architectures. We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons. Our results demonstrate the capability of integer-precision, Loihi 2 compatible spiking neural networks in performing citation graph classification with comparable accuracy to existing floating point implementations.

  CLARE: Cognitive Load Assessment in REaltime with Multimodal Data

• Authors: Anubhav Bhatti, Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Dirk Rodenburg, Heather Braund, P.James Mclellan, Aaron Ruberto, Geoffery Harrison, Daryl Wilson, Adam Szulewski, Dan Howes, Ali Etemad, Paul Hungler
• Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2404.17098
• Pdf link: https://arxiv.org/pdf/2404.17098
• Abstract We present a novel multimodal dataset for Cognitive Load Assessment in REaltime (CLARE). The dataset contains physiological and gaze data from 24 participants with self-reported cognitive load scores as ground-truth labels. The dataset consists of four modalities, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Electroencephalogram (EEG), and Gaze tracking. To map diverse levels of mental load on participants during experiments, each participant completed four nine-minutes sessions on a computer-based operator performance and mental workload task (the MATB-II software) with varying levels of complexity in one minute segments. During the experiment, participants reported their cognitive load every 10 seconds. For the dataset, we also provide benchmark binary classification results with machine learning and deep learning models on two different evaluation schemes, namely, 10-fold and leave-one-subject-out (LOSO) cross-validation. Benchmark results show that for 10-fold evaluation, the convolutional neural network (CNN) based deep learning model achieves the best classification performance with ECG, EDA, and Gaze. In contrast, for LOSO, the best performance is achieved by the deep learning model with ECG, EDA, and EEG.

  Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study

• Authors: Yang Wu, Yao Wan, Hongyu Zhang, Yulei Sui, Wucai Wei, Wei Zhao, Guandong Xu, Hai Jin
• Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.17136
• Pdf link: https://arxiv.org/pdf/2404.17136
• Abstract The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study.

  Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration

• Authors: Xiongjun Guan, Jianjiang Feng, Jie Zhou
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.17159
• Pdf link: https://arxiv.org/pdf/2404.17159
• Abstract Fingerprint dense registration aims to finely align fingerprint pairs at the pixel level, thereby reducing intra-class differences caused by distortion. Unfortunately, traditional methods exhibited subpar performance when dealing with low-quality fingerprints while suffering from slow inference speed. Although deep learning based approaches shows significant improvement in these aspects, their registration accuracy is still unsatisfactory. In this paper, we propose a Phase-aggregated Dual-branch Registration Network (PDRNet) to aggregate the advantages of both types of methods. A dual-branch structure with multi-stage interactions is introduced between correlation information at high resolution and texture feature at low resolution, to perceive local fine differences while ensuring global stability. Extensive experiments are conducted on more comprehensive databases compared to previous works. Experimental results demonstrate that our method reaches the state-of-the-art registration performance in terms of accuracy and robustness, while maintaining considerable competitiveness in efficiency.

  Cycling into the workshop: predictive maintenance for Barcelona's bike-sharing system

• Authors: Jordi Grau-Escolano, Aleix Bassolas, Julian Vicens
• Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)
• Arxiv link: https://arxiv.org/abs/2404.17217
• Pdf link: https://arxiv.org/pdf/2404.17217
• Abstract Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

  Enhancing Privacy and Security of Autonomous UAV Navigation

• Authors: Vatsal Aggarwal, Arjun Ramesh Kaushik, Charanjit Jutla, Nalini Ratha
• Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.17225
• Pdf link: https://arxiv.org/pdf/2404.17225
• Abstract Autonomous Unmanned Aerial Vehicles (UAVs) have become essential tools in defense, law enforcement, disaster response, and product delivery. These autonomous navigation systems require a wireless communication network, and of late are deep learning based. In critical scenarios such as border protection or disaster response, ensuring the secure navigation of autonomous UAVs is paramount. But, these autonomous UAVs are susceptible to adversarial attacks through the communication network or the deep learning models - eavesdropping / man-in-the-middle / membership inference / reconstruction. To address this susceptibility, we propose an innovative approach that combines Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) for secure autonomous UAV navigation. This end-to-end secure framework is designed for real-time video feeds captured by UAV cameras and utilizes FHE to perform inference on encrypted input images. While FHE allows computations on encrypted data, certain computational operators are yet to be implemented. Convolutional neural networks, fully connected neural networks, activation functions and OpenAI Gym Library are meticulously adapted to the FHE domain to enable encrypted data processing. We demonstrate the efficacy of our proposed approach through extensive experimentation. Our proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance.

  Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition

• Authors: Houtan Ghaffari, Paul Devos
• Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
• Arxiv link: https://arxiv.org/abs/2404.17252
• Pdf link: https://arxiv.org/pdf/2404.17252
• Abstract Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning, particularly in data-scarce scenarios. Pre-training refers to the initial step of training models outside the current task of interest, typically on another dataset. It can be done via supervised models using human-annotated datasets or self-supervised models trained on unlabeled datasets. In both cases, many pre-trained models are available to fine-tune for the task of interest. Interestingly, research has shown that pre-trained models from ImageNet can be helpful for audio tasks despite being trained on image datasets. Hence, it's unclear whether in-domain models would be advantageous compared to competent out-domain models, such as convolutional neural networks from ImageNet. Our experiments will demonstrate the usefulness of in-domain models and datasets for bird species recognition by leveraging VICReg, a recent and powerful self-supervised method.

  Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences

• Authors: Kürsat Petek, Niclas Vödisch, Johannes Meyer, Daniele Cattaneo, Abhinav Valada, Wolfram Burgard
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2404.17298
• Pdf link: https://arxiv.org/pdf/2404.17298
• Abstract Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCalib which is a novel method for camera-LiDAR calibration that requires neither human supervision nor any specific target objects. Instead, we utilize sensor motion estimates from visual and LiDAR odometry as well as deep learning-based 2D-pixel-to-3D-point correspondences that are obtained without in-domain retraining. We represent the camera-LiDAR calibration as a graph optimization problem and minimize the costs induced by constraints from sensor motion and point correspondences. In extensive experiments, we demonstrate that our approach yields highly accurate extrinsic calibration parameters and is robust to random initialization. Additionally, our approach generalizes to a wide range of sensor setups, which we demonstrate by employing it on various robotic platforms including a self-driving perception car, a quadruped robot, and a UAV. To make our calibration method publicly accessible, we release the code on our project website at this http URL

  Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

• Authors: Yuanman Li, Yingjie He, Changsheng Chen, Li Dong, Bin Li, Jiantao Zhou, Xia Li
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2404.17310
• Pdf link: https://arxiv.org/pdf/2404.17310
• Abstract Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods.

  Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand

• Authors: Slawek Smyl, Boris N. Oreshkin, Paweł Pełka, Grzegorz Dudek
• Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.17451
• Pdf link: https://arxiv.org/pdf/2404.17451
• Abstract Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Distributional forecasting is used to control and mitigate risks associated with this uncertainty. Recent progress in deep learning has helped to significantly improve the accuracy of point forecasts, while accurate distributional forecasting still presents a significant challenge. In this paper, we propose a novel general approach for distributional forecasting capable of predicting arbitrary quantiles. We show that our general approach can be seamlessly applied to two distinct neural architectures leading to the state-of-the-art distributional forecasting results in the context of short-term electricity demand forecasting task. We empirically validate our method on 35 hourly electricity demand time-series for European countries. Our code is available here: https://github.com/boreshkinai/any-quantile.

  Interpreting Deepcode, a learned feedback code

• Authors: Yingyao Zhou, Natasha Devroye, Gyorgy Turan, Milos Zefran
• Subjects: Information Theory (cs.IT); Signal Processing (eess.SP)
• Arxiv link: https://arxiv.org/abs/2404.17519
• Pdf link: https://arxiv.org/pdf/2404.17519
• Abstract Deep learning methods have recently been used to construct non-linear codes for the additive white Gaussian noise (AWGN) channel with feedback. However, there is limited understanding of how these black-box-like codes with many learned parameters use feedback. This study aims to uncover the fundamental principles underlying the first deep-learned feedback code, known as Deepcode, which is based on an RNN architecture. Our interpretable model based on Deepcode is built by analyzing the influence length of inputs and approximating the non-linear dynamics of the original black-box RNN encoder. Numerical experiments demonstrate that our interpretable model -- which includes both an encoder and a decoder -- achieves comparable performance to Deepcode while offering an interpretation of how it employs feedback for error correction.

  Large Language Model Agent as a Mechanical Designer

• Authors: Yayati Jadhav, Amir Barati Farimani
• Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2404.17525
• Pdf link: https://arxiv.org/pdf/2404.17525
• Abstract Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously.

  An exactly solvable model for emergence and scaling laws

• Authors: Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Ard Louis
• Subjects: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (stat.ML)
• Arxiv link: https://arxiv.org/abs/2404.17563
• Pdf link: https://arxiv.org/pdf/2404.17563
• Abstract Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time ($T$), training data ($D$), or model size ($N$) increases, a phenomenon known as emergence. In this paper, we present a framework where each new ability (a skill) is represented as a basis function. We solve a simple multi-linear model in this skill-basis, finding analytic expressions for the emergence of new skills, as well as for scaling laws of the loss with training time, data size, model size, and optimal compute ($C$). We compare our detailed calculations to direct simulations of a two-layer neural network trained on multitask sparse parity, where the tasks in the dataset are distributed according to a power-law. Our simple model captures, using a single fit parameter, the sigmoidal emergence of multiple new skills as training time, data size or model size increases in the neural network.

[RajKumar088]

Can anyone help me making a "mobility website for plant maintenance activities" It's a project that I got from a steel industry and I literally don't know much about web development