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arXiv 2024年

作者： Valdeira, Pedro Wang, Shiqiang Chi, Yuejie CMU IST United States IBM Research United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal IBM T. J. Watson Research Center United States

Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples during training harms generalization, and not supporting them during inference limits the utility of the model. Moreover, if any client leaves the federation after training, its partition becomes unavailable, rendering the learned model unusable. Missing feature blocks are therefore a key challenge limiting the applicability of vertical federated learning in real-world scenarios. To address this, we propose LASER-VFL, a vertical federated learning method for efficient training and inference of split neural network-based models that is capable of handling arbitrary sets of partitions. Our approach is simple yet effective, relying on the strategic sharing of model parameters and on task-sampling to train a family of predictors. We show that LASER-VFL achieves a O(1/√T) convergence rate for nonconvex objectives in general, O(1/T) for sufficiently large batch sizes, and linear convergence under the Polyak-Lojasiewicz inequality. Numerical experiments show improved performance of LASER-VFL over the baselines. Remarkably, this is the case even in the absence of missing features. For example, for CIFAR-100, we see an improvement in accuracy of 21.4% when each of four feature blocks is observed with a probability of 0.5 and of 12.2% when all features are observed. © 2024, CC BY.

关键词： Federated learning

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Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology

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Neutrosophic Sets and systems 2025年 83卷 50-64页

作者： Al-Khazraji, Hussein S. Alkhamees, Ahmed M. Al-Doori, Humam M. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Salama, Ahmed S. Nagm, Ahmad M. Department of Electrical Power Engineering Technologies Al-Hussein University College Karbala Iraq College of Health and Medical Technologies Department of Anesthesia Technologies Ahl Al Bayt University Karbala Iraq Department of Computer Engineering Techniques Al-Yarmok University College Diyala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

In the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical ML approaches, such as ensemble approaches and supervised learning, identify anomalies and lessen risks. The examination of ML-based systems integration into comprehensive security frameworks places a strong emphasis on real-time monitoring and adaptive responses. Examples from real-world situations highlight how crucial ML is to improving network security. After, we apply different ML models to the real-world dataset. Then we use the single-valued Neutrosophic numbers (SVNNs) methodology to evaluate these ML models and select the best one. We use the multi-criteria decision-making (MCDM) approach to obtain the criteria weights and rank the ML models using the EDAS method. The results show that the random forest model is the best ML model under different evaluation matrices. © 2025, University of New Mexico. All rights reserved.

关键词： Risk assessment

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Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

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computers, Materials & Continua 2021年第12期69卷 2863-2882页

作者： Jagannath Paramguru Subrat Kumar Barik Ajit Kumar Barisal Gaurav Dhiman Rutvij HJhaveri Mohammed Alkahtani Mustufa Haider Abidi KIIT University BhubaneswarOdishaIndia Department of Electrical Engineering CETBhubaneswarOdishaIndia Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Computer Science&Engineering Pandit Deendayal Petroleum UniversityGandhinagarIndia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in *** non-linearity are added to make the fossil fuel-based power systems more *** order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic *** this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum *** conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in *** adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

关键词： Economic load dispatch valve point loading industry 4.0 prohibited operating zones ramp rate limit oscillatory particle swarm optimization

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Hardware-aware Compilation for Chip-to-Chip Coupler-Connected Modular Quantum systems

arXiv

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arXiv 2025年

作者： Du, Zefan Kan, Shuwen Stein, Samuel A. Liang, Zhiding Li, Ang Mao, Ying Department of Computer and Information Sciences Fordham University NY United States High Performance Computing Group Pacific Northwest National Laboratory WA United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY United States

As quantum processors scale, monolithic architectures face growing challenges due to limited qubit density, heterogeneous error profiles, and restricted connectivity. Modular quantum systems, enabled by chip-to-chip coupler-connected modular architectures, provide a scalable alternative. However, existing quantum compilers fail to accommodate this new architecture. We introduce CCMap, a circuit-compiler co-design framework that enhances existing quantum compilers with system-level coordination across modular chips. It leverages calibration data and introduces a coupler-aligned and noise-aware cost metric to evaluate circuit compilation. CCMap integrates with existing compilers by partitioning circuits into subcircuits compiled on individual chips, followed by a global mapping step to minimize the total cost. We evaluated CCMap on IBM-Q noisy emulators using real hardware calibrations across various coupler-connected topologies. Results show that CCMap improves circuit fidelity by up to 21.9%, representing a 30% increase, and reduces compilation cost by up to 58.6% over state-of-the-art baselines. These findings highlight CCMap’s potential to enable scalable, high-fidelity execution in coupler-connected modular quantum systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Program compilers

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Modeling of Induction Motors and Variable Speed Drives for Multi-Domain System Simulations Using Modelica and the Openipsl Library

SSRN

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SSRN 2023年

作者： Fachini, Fernando de Castro, Marcelo Bogodorova, Tetiana Vanfretti, Luigi Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering 110 8th St TroyNY12180 United States

This paper presents characterization, implementation, and validation of three-phase and single-phase induction motor models in different levels of fidelity, together with a variable-speed drive model, that are meant for studying the interactions between the electrical power grid domain and other domains dynamics (e.g. thermofluidic) that influence the behavior of the motor/motor drive system. This novel approach consists in providing a physically meaningful equation-based mechanical interface of the motor's torque/speed coupled to the electrical model of induction machine-based motor and drive in the phasor domain, thereby allowing the user to integrate different domain subsystems in one single multi-domain model utilizing the Modelica language and the OpenIPSL library. The resulting multi-domain model only needs one Modelica-compliant tool to be simulated, which avoids several limitations of co-simulation. The additional dynamics reflected in the mechanical interface in the induction motor models allows to simulate the dynamic effects of disturbances of the load explicitly by following its own constitutive physics, thereby enabling better understanding of the dynamic interaction between the electrical power domain and the other subsystems/domains coupled through the motor. The modeled components are described through mathematical equations and textual description, with emphasis on how they are modeled in Modelica. Simulation examples are provided to show the validity of the implementation and the multi-domain modeling capabilities offered by the newly developed components. © 2023, The Authors. All rights reserved.

关键词： Variable speed drives

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As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making

arXiv

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arXiv 2024年

作者： Jain, Shomik Calacci, D. Wilson, Ashia Institute for Data Systems and Society Massachusetts Institute of Technology United States Center for Information Technology Policy Princeton University United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether to call the police in Amazon Ring home surveillance videos. We evaluate the decisions of three state-of-the-art LLMs – GPT-4, Gemini 1.0, and Claude 3 Sonnet – in relation to the activities portrayed in the videos, the subjects’ skin-tone and gender, and the characteristics of the neighborhoods where the videos were recorded. Our analysis reveals significant norm inconsistencies: (1) a discordance between the recommendation to call the police and the actual presence of criminal activity, and (2) biases influenced by the racial demographics of the neighborhoods. These results highlight the arbitrariness of model decisions in the surveillance context and the limitations of current bias detection and mitigation strategies in normative decision-making. © 2024, CC BY.

关键词： Decision making

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The Impact of the Digital Transformation of Education on Literacy

The Impact of the Digital Transformation of Education on Lit...

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International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills.

关键词： Power engineering Epidemics Navigation Knowledge acquisition Digital transformation Education Information age Digital intelligence Cultural differences Problem-solving

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Revealing Non-Hermitian Band Structure of Photonic Floquet Media

Revealing Non-Hermitian Band Structure of Photonic Floquet M...

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Frontiers in Optics, FiO 2022

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Center for Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally reveal the unique non-Hermitian band structures of a photonic Floquet medium emulated in the microwave regime. We observe the presence of the momentum gaps and the subharmonic parametric oscillations...

ISBN: (纸本)9781557528209

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Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity

arXiv

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arXiv 2024年

作者： Jia, Zihang Zhang, Zhen Pedrycz, Witold The Institute of Systems Engineering School of Economics and Management Dalian University of Technology Dalian116024 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada The Systems Research Institute Polish Academy of Sciences Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey

Efficient and robust data clustering remains a challenging task in the field of data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clustering algorithms to address this challenge, yielding promising results. However, existing methods for generating GBs often rely on single indicators to measure GB quality and employ threshold-based or greedy strategies, potentially leading to GBs that do not accurately capture the underlying data distribution. To address these limitations, this article introduces a novel GB generation method. The originality of this method lies in leveraging the principle of justifiable granularity to measure the quality of a GB for clustering tasks. To be precise, we define the coverage and specificity of a GB and introduce a comprehensive measure for assessing GB quality. Utilizing this quality measure, the method incorporates a binary tree pruning-based strategy and an anomaly detection method to determine the best combination of sub-GBs for each GB and identify abnormal GBs, respectively. Compared to previous GB generation methods, the new method maximizes the overall quality of generated GBs while ensuring alignment with the data distribution, thereby enhancing the rationality of the generated GBs. Experimental results obtained from both synthetic and publicly available datasets underscore the effectiveness of the proposed GB generation method, showcasing improvements in clustering accuracy and normalized mutual information. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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ViT-Cat: Parallel Vision Transformers With Cross Attention Fusion for Popularity Prediction in MEC Networks

ViT-Cat: Parallel Vision Transformers With Cross Attention F...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zohreh HajiAkhondi-Meybodi Arash Mohammadi Ming Hou Jamshid Abouei Konstantinos N. Plataniotis Electrical and Computer Engineering Concordia University Montreal Canada Concordia Institute of Information Systems Engineering (CIISE) Montreal Canada Defence Research and Development Canada (DRDC) Toronto Canada Department of Electrical Engineering Yazd University Yazd Iran Electrical and Computer Engineering University of Toronto Toronto Canada

Mobile Edge Caching (MEC) is a revolutionary technology for the Sixth Generation (6G) of wireless networks with the promise to significantly reduce users’ latency via offering storage capacities at the edge of the network. The efficiency of the MEC network, however, critically depends on its ability to dynamically predict/update the storage of caching nodes with the top-K popular contents. Conventional statistical caching schemes are not robust to the time-variant nature of the underlying pattern of content requests, resulting in a surge of interest in using Deep Neural Networks (DNNs) for time-series popularity prediction in MEC networks. However, existing DNN models within the context of MEC fail to simultaneously capture both temporal correlations of historical request patterns and the dependencies between multiple contents. This necessitates an urgent quest to develop and design a new and innovative popularity prediction architecture to tackle this critical challenge. The paper addresses this gap by proposing a novel hybrid caching framework based on the attention mechanism. Referred to as the parallel Vision Transformers with Cross Attention (ViT-CAT) Fusion, the proposed architecture consists of two parallel ViT networks, one for collecting temporal correlation, and the other for capturing dependencies between different contents. Followed by a Cross Attention (CA) module as the Fusion Center (FC), the proposed ViT-CAT is capable of learning the mutual information between temporal and spatial correlations, as well, resulting in improving the classification accuracy, and decreasing the model’s complexity about 8 times. Based on the simulation results, the proposed ViT-CAT architecture outperforms its counterparts across the classification accuracy, complexity, and cache-hit ratio.

关键词： Correlation Simulation Wireless networks Neural networks Signal processing Transformers Complexity theory

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