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TabID: Automatic Identification and Tabulation of Subproblems in Constraint Models

Journal of Artificial Intelligence Research 2025年 82卷 1999-2056页

作者： Akgün, Özgür Gent, Ian P. Jefferson, Christopher Kiziltan, Zeynep Miguel, Ian Nightingale, Peter Salamon, András Z. Ulrich-Oltean, Felix School of Computer Science University of St Andrews St Andrews FifeKY16 9SX United Kingdom School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming to improve solver performance. These heuristics are intended to encapsulate various factors that contribute to useful tabulation. We also present additional checks to limit the potential drawbacks of suboptimal tabulation. ©2025 The Authors.

关键词： Automatic identification

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Multitask-Oriented Efficient Computational Offloading Orchestrator for IoT Applications in Mobile-Edge Computing

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IEEE Internet of Things Journal 2025年第10期12卷 14096-14108页

作者： Wang, Leilei Deng, Xiaoheng Zhang, Honggang Wan, Shaohua Min, Geyong Xi’an University of Technology School of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Central South University School of Computer Science and Engineering Changsha410075 China Central South University Shenzhen Research Institute Shenzhen518057 China University of Massachusetts at Boston Engineering Department BostonMA02125 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile-edge computing (MEC) can accelerate computation-intensive applications and emerge as a promising technology for enabling Internet of Things (IoT). MEC improves the processing performance of tasks by assigning them to the edge nodes. However, with massive terminals contending for computation and communication resources simultaneously, how to develop a flexible computational offloading mechanism becomes the fundamental issue of MEC-enabled IoT systems. This article aims to develop an effective computational offloading decision scheme by jointly considering the computational resource and diverse user demands with two goals, i.e., minimizing both the latency and the energy consumption. Specifically, we develop a two-stage computational offloading mechanism, where the computational resources and offloading decisions can be allocated and coordinated with the variation of computation requirements. To achieve the two goals, this work introduces an edge node recommendation model within the cloud-edge–end architecture to reduce the offloading optimization search space. Furthermore, we propose a new computational offloading (CROCA) algorithm based on chemical reaction optimization (CRO) for optimizing offloading utility, which thoroughly considers the competition between mobile device requests and computational resources. Extensive evaluation results demonstrate that the proposed CROCA scheme can effectively improve the computational offloading performance. © 2014 IEEE.

关键词： Computation offloading

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Revisiting LARS for Large Batch Training Generalization of Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1321-1333页

作者： Do, Khoi Nguyen, Minh-Duong Hoa, Nguyen Tien Tran-Thanh, Long Tran, Nguyen H. Pham, Quoc-Viet Trinity College Dublin School of Computer Science and Statistics Dublin 2 D02PN40 Ireland Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam University of Warwick School of Computer Science CoventryCV4 7AL United Kingdom The University of Sydney School of Computer Science DarlingtonNSW2006 Australia Kyung Hee University Department of Computer Science and Engineering Yongin446-701 Korea Republic of

This article investigates large batch training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings. In particular, we first show that a state-of-the-art technique, called LARS with the warm-up, tends to be trapped in sharp minimizers early on due to redundant ratio scaling. Additionally, a fixed steep decline in the latter phase restricts deep neural networks from effectively navigating early-phase sharp minimizers. To address these issues, we propose time varying LARS (TVLARS), a novel algorithm that replaces warm-up with a configurable sigmoid-like function for robust training in the initial phase. TVLARS promotes gradient exploration early on, surpassing sharp optimizers and gradually transitioning to LARS for robustness in later stages. Extensive experiments demonstrate that TVLARS consistently outperforms LARS and LAMB in most cases, with up to 2% improvement in classification scenarios. In all self-supervised learning cases, TVLARS achieves up to 10% performance improvement. © 2020 IEEE.

关键词： Self-supervised learning

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Virtual Reduced-Order Model-Based Back EMF Estimation and Speed Sensorless Control for LC-Filtered PMSM Drives

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IEEE Transactions on Power Electronics 2025年第7期40卷 9749-9763页

作者： Xue, Cheng Wu, Xuesong Li, Yuzhuo Li, Yunwei Southwest Jiaotong University School of Integrated Circuits Science and Engineering Chengdu611756 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2V4 Canada

The installment of the LC filter at the inverter output side reshapes the sinusoidal input voltage for the motor terminal, thus, extending a longer motor lifetime. Despite this, achieving speed sensorless control remains essential for enhanced reliability and saved costs. Currently, there is limited research on the development of a speed sensorless control tailored to high-order drive scenarios due to the increased complexity and strong coupling of the system modeling, and the unaltered adoption of the prevailing general observer methodology demands a considerably large dimensional gain matrix to guarantee observability. To fill this important research gap, this article proposes a novel back-electromotive force (EMF) modeling for the permanent magnet synchronous machine drives based on the weighted current between the filter inductor current and motor stator current. This new approach converts the third-order LCL model to the first-order L case (virtual reduced-order modeling), which enables the estimation of back EMF without relying on voltage sensors. In addition, the observer designed based on the proposed virtual model not only removes the dependency on capacitor parameters but also reduces the size of the gain matrix. This further enhances the robustness of the back EMF estimation and simultaneously simplifies the design and computation of the observation algorithm. A Kalman filter observer for back EMF estimation is implemented as a case study to verify the proposed modeling. The efficacy of the proposed speed sensorless control is also evaluated under scenarios involving variations in filter and motor inductance. © 1986-2012 IEEE.

关键词： Permanent magnets

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Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment

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ACM Computing Surveys 2025年第8期57卷 1-36页

作者： Santos, Paulo Roberto Da Paz Ferraz Resende, Paulo Angelo Alves Gondim, João José Costa Drummond, André Costa Department of Computer Science University of Brasilia Brasilia Brazil C4I and Cyber Center George Mason University Fairfax United States GigaCandanga Association Brasilia Brazil Department of Electrical Engineering University of Brasilia Brasilia Brazil Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig Germany

Cyber attacks have become a growing threat in today's interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This article aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the article poses some open questions and challenges, along with suggestions for future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cyber attacks

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Synchronization and implementation of real-time traffic signal optimization regulator

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Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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A Novel Efficient Crash Consistency Solution Enabling Rollback Recovery for Secure NVM in Low-Power Energy Harvesting Systems

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2179-2196页

作者： Han, Youngkwang Hu, Zhenyu Choi, Jongouk Zubair, Kazi Abu Awad, Amro Jung, Changhee Kang, Brent Byunghoon CySecuLab Daejeon34141 Korea Republic of University of Central Florida Computer Science OrlandoFL32816 United States P-Core Architecture Group Intel Corporation San JoseCA95052 United States University of Oxford Department of Engineering Science OxfordshireOX1 4BH United Kingdom Purdue University Department of Computer Science West LafayetteIN47907 United States

Energy Harvesting Systems (EHSs) frequently suffer power failures and are particularly deployed in remote and open environments where physical access attacks on Non-volatile Memories (NVMs) are practical. However, prior crash consistency solutions for secure NVM were designed only for conventional power-rich systems with the assumption that enough power is steadily supplied. Moreover, the prior solutions rely on roll-forward recovery and cause a significant performance overhead in low-power EHSs. To achieve a low-cost and high-performance crash-consistent secure NVM working on low-power EHSs, this paper presents Milestone, the first efficient crash consistency solution that introduces a novel hybrid checkpoint mechanism to enable a rollback recovery for secure NVM working in frequent power *** hybrid checkpointing atomically (1) undo-logs data updates from program writes and (2) redo-logs the updates of security metadata associated with the data updates when an adaptive hardware timer expires. In particular, Milestone discovers an optimized eager update method for the security metadata that can be performed in parallel with the program writes to NVM by leveraging the rollback recovery. Our experimental results demonstrate that Milestone significantly outperforms the state-of-the-art roll-forward recovery-based solution for secure NVM running on low-power EHSs, achieving up to a 1.87x speedup, on average. © 2024 IEEE.

关键词： Nonvolatile Memory Security computer Crashes Metadata Capacitors Registers Energy Harvesting Checkpointing Hybrid Power Systems Random Access Memory Energy Harvesting Systems EH Ss Crash Consistency Secure Architectures Secure Non Volatile Memories NV Ms Energy Harvesting Rollback Non Volatile Memory Energy Harvesting System Crash Consistency Outdoor Environments Updated Data Power Outages Physical Attacks Update Method Checkpoint Mechanisms Energy Consumption Logistics Registry Data Root Node Tree Nodes Hash Function Leaf Node Voltage Levels Total Energy Consumption Encrypted Data Merkle Tree Counter Value Node Update Execution Stage Memory Control Secret Key Data Cache Side Channel Program Execution

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Do Experts Agree About Smelly Infrastructure?

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IEEE Transactions on Software engineering 2025年第5期51卷 1472-1486页

作者： Masoumzadeh, Sogol Saavedra, Nuno Maipradit, Rungroj Wei, Lili Ferreira, Joao F. Varro, Daniel McIntosh, Shane McGill University Department of Electrical and Computer Engineering MontrealH3A 0G4 Canada University of Lisbon INESC-ID IST Lisbon1049-001 Portugal University of Waterloo David R. Cheriton School of Computer Science WaterlooN2L 3G1 Canada Linköping University Department of Computer and Information Science LinköpingSE-581 83 Sweden

Code smells are anti-patterns that violate code understandability, re-usability, changeability, and maintainability. It is important to identify code smells and locate them in the code. For this purpose, automated detection of code smells is a sought-after feature for development tools;however, the design and evaluation of such tools depends on the quality of oracle datasets. The typical approach for creating an oracle dataset involves multiple developers independently inspecting and annotating code examples for their existing code smells. Since multiple inspectors cast votes about each code example, it is possible for the inspectors to disagree about the presence of smells. Such disagreements introduce ambiguity into how smells should be interpreted. Prior work has studied developer perceptions of code smells in traditional source code;however, smells in Infrastructure-as-Code (IaC) have not been investigated. To understand the real-world impact of disagreements among developers and their perceptions of IaC code smells, we conduct an empirical study on the oracle dataset of GLITCH—a state-of-the-art detection tool for security code smells in IaC. We analyze GLITCH's oracle dataset for code smell issues, their types, and individual annotations of the inspectors. Furthermore, we investigate possible confounding factors associated with the incidences of developer misaligned perceptions of IaC code smells. Finally, we triangulate developer perceptions of code smells in traditional source code with our results on IaC. Our study reveals that unlike developer perceptions of smells in traditional source code, their perceptions of smells in IaC are more substantially impacted by subjective interpretation of smell types and their co-occurrence relationships. For instance, the interpretation of admins by default, empty passwords, and hard-coded secrets varies considerably among raters and are more susceptible to misidentification than other IaC code smells. Consequently, the

关键词： Codes Source Coding Labeling Security Passwords Annotations Training Software Servers Imaging Code Smells Infrastructure As Code Developer Perceptions Oracle Datasets Triangulation Tool For Detection Majority Voting Confounding Factors Object Object Code Examples First Author Object Object Cohen X 2019 S Kappa Object Object Level Of Agreement False Alarm Level Of Expertise Entropy Values Technical Performance Ground Truth Labels Unique Code Performance Of Tools Object Object Degree Of Disagreement Perception Of Smell Java Code Programming Paradigm Vote In Favour Object Object

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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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