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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 794-804页

作者： Movin, Maria Siciliano, Federico Ferreira, Rui Silvestri, Fabrizio Tolomei, Gabriele Spotify StockholmSE-111 Sweden Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Spotify LondonWC2N 6AT United Kingdom Sapienza University of Rome Department of Computer Science Rome00161 Italy

Algorithmic recourses are popular methods to provide individuals impacted by machine learning models with recommendations on feasible actions for a more favorable prediction. Most of the previous algorithmic recourse methods work under the assumption that the predictive model does not change over time. However, in reality, models in deployment may both be periodically retrained and have their architecture changed. Therefore, it is desirable that the recourse should remain valid when such a model update occurs, unless new evidence arises. We call this feature consistency. This article presents anomaly control and data coherence (ACDC), a novel model-agnostic recourse method that generates counterfactual explanations, i.e., instance-level recourses. ACDC is inspired by anomaly detection methods and uses a one-class classifier to aid the search for valid, consistent, and feasible counterfactual explanations. The one-class classifier asserts that the generated counterfactual explanations lie on the data manifold and are not outliers of the target class. We compare ACDC against several state-of-the-art recourse methods across four datasets. Our experiments show that ACDC outperforms baselines both in generating consistent counterfactual explanations, and in generating feasible and plausible counterfactual explanations, while still having proximity measures similar to the baseline methods targeting the data manifold. © 2024 IEEE.

关键词： Machine learning

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Quantum Optimization of Microwave Filters

Quantum Optimization of Microwave Filters

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2024 IEEE Joint International Symposium on Electromagnetic Compatibility, Signal and Power Integrity: EMC Japan/Asia Pacific International Symposium on Electromagnetic Compatibility, EMC Japan/APEMC Okinawa 2024

作者： Martinez E, Gabriel F. Niccolai, Alessandro Zich, Eleonora L. Zich, Riccardo E. Politecnico Di Milano Department Of Energy Milano Italy Politecnico Di Torino Department Of Control And Computer Engineering Torino Italy

ISBN: (纸本)9784885523472

Electromagnetic compatibility problems may be extremely computationally expensive and the introduction of evolutionary optimization may need tens of thousands of fitness evaluations. The recent introduction of quantum computing may be extremely effective also in this research field to speed up the EMC compliant design process. In this paper, the problems and the potential of the introduction of quantum computing in evolutionary optimized microwave design has been investigated. Copyright © 2024 IEICE.

关键词： Optimization

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Smart Diagnosis of Active Systems 28th

Smart Diagnosis of Active Systems

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Lamperti, Gianfranco Zhao, Xiangfu Department of Information Engineering University of Brescia Brescia25123 Italy School of Computer and Control Engineering Yantai University Yantai264005 China

Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, where a diagnosis is a set of faulty components or, more generally, a set of faults ascribed to components. An active system (AS) is an asynchronous, distributed discrete-event system, whose model consists of a topology (how components are connected to one another), and a communicating automaton for each component (the mode in which a component reacts to events). A problem afflicting all model-based approaches to diagnosis is a possibly large number of diagnoses explaining the observations, which may jeopardize the task of a diagnostician in charge of monitoring the system, owing to the cognitive overload raised by an overwhelming number of faulty scenarios to examine. This is exacerbated in critical application domains, where, under uncertain conditions, an artificial agent is supposed to perform recovery actions in real-time, even in the order of milliseconds, to possibly restore the system. To make diagnosis of ASs viable in critical, real-time application domains, a Smart Diagnosis Engine is presented, which is grounded on two heuristics: (1) if a diagnosis δ is a superset of a diagnosis δ′, then δ is ignored (minimality);(2) if the cardinality (number of faults) of a diagnosis δ is lower than the cardinality of a diagnosis δ′, then δ is generated before δ′ (sorting). Consequently, the diagnosis output consists in a sequence of minimal diagnoses that are generated in ascending order by cardinality. As indicated by the experimental results, the overall improvement is twofold: most likely diagnoses are generated upfront, thereby supporting real-time recovery actions;also, the abductive search in the behavior space of the AS is reduced considerably, owing to the pruning of the trajectories that will not generate minimal diagnoses, thereby resulting in an

关键词： Finite automata

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Static Deadlock Detection for Interrupt-driven Embedded Software

Static Deadlock Detection for Interrupt-driven Embedded Soft...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Zhang, Yu Zhang, Kaiwen Li, Xiaofeng Xie, Yi Guan, Jian Gu, Bin Liu, Guanjun Tongji University Department of Computer Science and Technology Shanghai China Beijing Control Engineering Institute Beijing China

ISBN: (纸本)9798350376739

Interrupt-driven embedded software is widely used in safety-critical systems, where any occurrence of errors can lead to serious consequences. Deadlock is a common concurrency error, and deadlock detection methods are mainly designed for multi-threaded programs. However, due to the differences in concurrency control and preemption mechanisms between interrupt-driven programs and multi-threaded programs, the existing deadlock detection methods for multi-threaded programs cannot be directly applied to interrupt-driven programs. A static deadlock detection method is proposed for interrupt-driven programs to enhance the reliability and safety of interrupt-driven embedded software. Building upon the traditional context-sensitive lockset analysis, this method takes into account the concurrent semantics of interrupts and extends the lock graph model. Our method effectively reduces false positives by analyzing the concurrent relationships of cycles in a lock graph. Based on these methods, we develop a deadlock detection tool, and experiments are conducted with it. The results demonstrate that the proposed method can effectively detect deadlocks related to interrupts. © 2024 IEEE.

关键词： Embedded software

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An Advanced Rivest Cipher 4 Algorithm to Transfer Fast and Secure Data Using Li-Fi Technology 13

An Advanced Rivest Cipher 4 Algorithm to Transfer Fast and S...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Msallam, Mohammed Majid Samet, Refik Ankara University Computer Engineering Department Ankara Turkey University of Technology Control and Systems Engineering Department Baghdad Iraq

ISBN: (纸本)9798350340891

The Light-Fidelity (Li-Fi) is a wireless communication technology that is light-based and can complete wireless fidelity (Wi-Fi) technologies for many applications. Li-Fi technology which uses light spectrum is a technology for solving the lack of radio spectrum at Wi-Fi. One of the main benefits of Li-Fi technology is the ability to communicate by light with a high level of safety. However, the signal of Li-Fi technology is the same as other signals that are susceptible to eavesdropping because no security system is infallible. The objective of this paper is to decrease the computational cost of encryption and decryption and keep the security of transformed data when transmitted via Li-Fi technology. This study proposes the advanced Rivest Cipher 4 (ARC4) algorithm that will be used in the physical layer of Li-Fi applications. The novelty will be in the pseudo-random generation algorithm (PRGA) which is part of Rivest Cipher 4 (RC4). The data will become an index of the s-array and get the value of that index in the s-array instead of using the XOR operation. The proposed ARC4 was evaluated by the National Institute of Science and Technology (NIST) test. The obtained results illustrate that ARC4 is more robust and effective than RC4 when the data is transformed by Li-Fi communication. © 2023 IEEE.

关键词： Wi-Fi

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Parallel Programming in the Hybrid Model on the HPC Clusters 3rd

Parallel Programming in the Hybrid Model on the HPC Clusters

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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Rak, Tomasz Department of Computer and Control Engineering Rzeszow University of Technology Powstancow Warszawy 12 Rzeszow35-959 Poland

ISBN: (纸本)9789819966899

Support for an ever-expanding range of distributed and parallel strategies and support for performance analysis are constantly evolving. We propose message passing interface (MPI) and open multi-processing (OpenMP) standards that provide various software development tools covering a wide range of techniques not strictly limited to shared memory. The theoretical part focuses on a general overview of the current capabilities of MPI and OpenMP as application programming interfaces. The authors researched the high-performance computing (HPC) cluster environment, and this work provides results and representative examples. Also, in this study, it has been proven that to obtain favorable processing efficiency and write the code correctly— regardless of the programming paradigm used—it is necessary to know the basic concepts related to a given distributed and parallel architecture. Additionally, in this study, we investigated the need to adapt the tools used to solve a specific problem regarding improving processing efficiency. Finally, we presented that failure to apply a critical section may result in unpredictable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Message passing

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A 6-bit Low-Area Hybrid ADC Design For System-on-Chip Measurements

A 6-bit Low-Area Hybrid ADC Design For System-on-Chip Measur...

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2024 IEEE International Conference on Design, Test and Technology of Integrated Systems, DTTIS 2024

作者： Kolahimahmoudi, Nima Insinga, Giorgio Roggi, Stefano Niederl, Josef Bernardi, Paolo Politecnico di Torino Department of Computer and Control Engineering Turin Italy Infineon Technologies AG Villach Austria

ISBN: (纸本)9798350363128

In recent years, with the declining dimensions of transistors, the system-on-chips (SoCs) have had more physical defects. These physical defects ultimately result in failures that cannot be tolerated in functional safety applications such as electric cars, aerospace, etc. For the digital peripherals of the SoCs, there are well-known methods such as scan chains, whereas there are methods for analog circuits such as analog scan chains or Analog Test Bus (ATB). This paper presents a 6-bit, low-area Analog-to-Digital Converter (ADC) for SoC analog voltage measurements. The advantage of the proposed ADC design is the low additional area cost to the design of the SoC and increasing the testability of the analog peripherals. This ADC design converts the analog signals, which are difficult to observe, to the digital domain, which is easy to route and observe. This architecture comprises two small ADCs for doing coarse and fine conversions. The ADC for the coarse conversion is a 3-bit SAR ADC, and the ADC for the fine conversion is a 3-bit flash ADC. The suggested ADC is implemented using the 130 nm technology of the Infineon, and it has a total area of 0.007 mm2. The fine ADC of the proposed ADC can be shared between the peripherals nearby inside the SoC, and the additional area per peripheral would be only 0.0015 mm2. The Signal-to-Noise Distortion Ratio (SNDR) of the design is 37dB, and the Figure of Merit (FoM) is 2.15 pJ/conv. © 2024 IEEE.

关键词： System-on-chip

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Monte Carlo tree search with state merging for reinforcement learning in Regular Decision Processes

Monte Carlo tree search with state merging for reinforcement...

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2023 International Conference of the Italian Association for Artificial Intelligence Doctoral Consortium, AIxIA-DC 2023

作者： Licks, Gabriel Paludo Sapienza University of Rome Department of Computer Control and Management Engineering Via Ariosto 25 Rome00185 Italy

This research focuses on a novel algorithm for Reinforcement Learning (RL) in Regular Decision Processes (RDPs), a model of non-Markovian decision processes where dynamics and rewards depend on regular properties of the history. Our algorithm is inspired by Monte Carlo tree search (MCTS), yet it is improved with state merging capabilities. Performing merges allows us to evolve the tree model into a graph over time, as we periodically perform similarity tests borrowed from automata learning theory to learn states that are equivalent to one another. This results in improved efficiency and scalability over standard MCTS. Empirical results demonstrate orders of magnitude performance improvement over the state-of-the-art RL algorithms for RDPs. We discuss limitations both in concerning the MCTS implementation and similarity tests from probabilistic automata. © 2023 Copyright for this paper by its authors.

关键词： Reinforcement learning

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