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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

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Generative Adversarial Networks for Imputing Sparse Learnin...

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

Allocation Multiplicity: Evaluating the Promises of the Rashomon Set

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arXiv 2025年

作者： Jain, Shomik Wang, Margaret Creel, Kathleen Wilson, Ashia Institute for Data Systems and Society MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Philosophy and Religion Khoury College of Computer Sciences Northeastern University United States

The Rashomon set of equally-good models promises less discriminatory algorithms, reduced outcome homogenization, and fairer decisions through model ensembles or reconciliation. However, we argue from the perspective of allocation multiplicity that these promises may remain unfulfilled. When there are more qualified candidates than resources available, many different allocations of scarce resources can achieve the same utility. This space of equal-utility allocations may not be faithfully reflected by the Rashomon set, as we show in a case study of healthcare allocations. We attribute these unfulfilled promises to several factors: limitations in empirical methods for sampling from the Rashomon set, the standard practice of deterministically selecting individuals with the lowest risk, and structural biases that cause all equally-good models to view some qualified individuals as inherently risky. © 2025, CC BY.

关键词： Consensus algorithm

Detection of Malicious Interpreted Code using ML to Optimize Parameters for Minimizing Prediction Errors

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Detection of Malicious Interpreted Code using ML to Optimize...

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dhawan, Manoj Kuril, Devendra Panadiwal, Himanshu Rawat, Hitesh Rawat, Anjali Rawat, Romil School of Engineering Avantika University Ujjain India Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Information Technology Indore India University of Extremadura Department of Business Management and Economics Badajoz Spain University of Extremadura Department of Computer and Communication Technology Badajoz Spain LabGeoInf-Research LABoratory in GEOmatics and INFormation systems Rome Italy

ISBN: (纸本)9798331538538

Mobile devices face growing cybersecurity threats, including malware, data breaches, malicious applications, and social engineering attacks. Cybercriminals exploit these vulnerabilities to gain unauthorized access, disrupt communication, and compromise sensitive information. Conventional malware detection methods, such as signature-based and heuristic techniques, often struggle to identify evolving threats due to their reliance on predefined patterns. This study presents an advanced malware detection framework that integrates a Reservoir Network with a Deep Learning Approach (DLA) to enhance detection accuracy and minimize prediction errors. The proposed method utilizes two static features-API call permissions and alert signals- to improve detection efficiency. Experimental evaluation, conducted using the Drebin malware dataset, demonstrates that the proposed model achieves an accuracy of 99.7 %, reduces the false positive rate to below 1 %, and ensures optimal processing speed with a batch size of 64 and a learning rate of 0.001. Compared to traditional approaches, this framework exhibits superior adaptability to obfuscated malware while enabling real-time threat detection. The findings emphasize the effectiveness of machine learning (ML)-driven security mechanisms in addressing emerging cybersecurity threats. © 2025 IEEE.

关键词： computer viruses

Dimensionality and dynamics for next-generation artificial neural networks

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Patterns

Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

PathGPT: Leveraging Large Language Models for Personalized Route Generation

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arXiv 2025年

作者： Marcelyn, Steeve Cuthbert Gao, Yucen Zhang, Yuzhe Gao, Xiaofeng Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

The proliferation of GPS-enabled devices has led to the accumulation of a substantial corpus of historical trajectory data. By leveraging these data for training machine learning models, researchers have devised novel data-driven methodologies that address the personalized route recommendation (PRR) problem. In contrast to conventional algorithms such as Dijkstra’s shortest path algorithm, these novel algorithms possess the capacity to discern and learn patterns within the data, thereby facilitating the generation of more personalized paths. However, once these models have been trained, their application is constrained to the generation of routes that align with their training patterns. This limitation renders them less adaptable to novel scenarios and the deployment of multiple machine learning models might be necessary to address new possible scenarios, which can be costly as each model must be trained separately. Inspired by recent advances in the field of Large Language Models (LLMs), we leveraged their natural language understanding capabilities to develop a unified model to solve the PRR problem while being seamlessly adaptable to new scenarios without additional training. To accomplish this, we combined the extensive knowledge LLMs acquired during training with further access to external hand-crafted context information, similar to RAG (Retrieved Augmented Generation) systems, to enhance their ability to generate paths according to user-defined requirements. Extensive experiments on different datasets show a considerable uplift in LLM performance on the PRR problem. © 2025, CC BY.

关键词： Natural language processing systems

electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning

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arXiv 2025年

作者： Rahman, Ratun Moriano, Pablo Khan, Samee U. Nguyen, Dinh C. Department of Electrical and Computer Engineering University of Alabama in Huntsville HuntsvilleAL35899 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37930 United States Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS39762 United States

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting but require data sharing which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. This paper presents a novel personalized federated learning (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches in terms of better load forecasting and reduced operational latency costs. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

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A distributed algorithm with network-independent step-size a...

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

Learn to Bid as a Price-Maker Wind Power Producer

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arXiv 2025年

作者： Singhal, Shobhit Fochesato, Marta Aolaritei, Liviu Dörfler, Florian Automatic Control Laboratory ETH Zürich Switzerland Department of Electrical Engineering and Computer Sciences UC Berkeley United States

Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model bidding as a bilevel optimization problem, but these methods require complex market models, estimating other participants’ actions, and are computationally demanding. To address these challenges, we propose an online learning algorithm that leverages contextual information to optimize WPP bids in the price-maker setting. We formulate the strategic bidding problem as a contextual multi-armed bandit, ensuring provable regret minimization. The algorithm’s performance is evaluated against various benchmark strategies using a numerical simulation of the German day-ahead and real-time markets. Copyright © 2025, The Authors. All rights reserved.

关键词： Wind power

Truncated Phase Reversal Fresnel Lens Antenna for mm-Wave and IoT Applications

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IEEE Transactions on Antennas and Propagation 2025年

作者： Al-Alem, Yazan Sifat, Syed M. Antar, Yahia M.M Kishk, Ahmed A. King Fahd University of Petroleum & Minerals Electrical Engineering Department Interdisciplinary Research Center for Communication Systems and Sensing Dhahran 31261 Saudi Arabia Concordia University Department of Electrical and Computer Engineering QC Canada Queen’s University Department of Electrical and Computer Engineering ON Canada

A novel design approach for truncating a phase-reversal Fresnel lens is presented. This method relies on focusing the feed antenna’s radiation in a single plane through the use of cascaded dielectric slabs, effectively removing the need for a conventional feeding network. The proposed feed is realized using printed circuit board (PCB) technology, offering a highly economical solution. Its microstrip line feed allows direct integration with transceiver ICs, avoiding the need for complex transitions commonly associated with horn or waveguide feeds. Furthermore, the design is compact and maintains a low-profile structure. The proposed method reduces the lens size by 56%. The resulting lens antenna achieves a peak realized gain of 29 dBi at 30 GHz and has an aperture efficiency of 12%. Its impedance bandwidth is 13.3%, while the 3-dB gain bandwidth is 6.7%. The side-lobes are effectively suppressed, ranging from -10 to -15 dB across the entire operating band. This antenna design is well suited for high-gain millimeter-wave (mm-Wave) and Internet of Things (IoT) applications. © 1963-2012 IEEE.

关键词： High gain antennas internet of things low-cost antennas lowprofile antennas