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A Critical Scenario Search Method for Intelligent Vehicle Testing Based on the Social Cognitive Optimization Algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 7974-7986页

作者： Zhu, Bing Sun, Yuhang Zhao, Jian Han, Jiayi Zhang, Peixing Fan, Tianxin Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China

Intelligent vehicle testing has been a hotspot in the field of intelligent vehicles. Due to the multi-dimensional parameters and the continuity of testing scenarios, a complete test of all scenarios requires a large amount of manpower and several material resources. In critical scenarios, deficiencies in an intelligent vehicle's performance and defects of an algorithm can be exposed. Therefore, increasing the search efficiency and coverage of critical scenarios is key in improving scenario-based intelligent vehicle testing technology. In this study, a critical scenario search method for intelligent vehicle testing based on the social cognitive optimization (SCO) algorithm is proposed. This method has two main parts: global search and local search. The global search, based on the modified SCO algorithm, integrates the density peak clustering (DPC) algorithm and the cooling scheduling function, and aims to find all local aggregation areas of critical scenarios in a logical scenario space. The local search applies a multi-dimensional convolution algorithm to the global search results to find critical scenarios near the local aggregation areas. Finally, a longitudinal automatic driving algorithm is tested using the proposed method under a specified logical scenario in a simulation environment. The test results show that the proposed method can improve both the search efficiency and coverage of critical scenarios.

关键词： Testing Intelligent vehicles Optimization Search methods Clustering algorithms Concrete software algorithms Accelerated test critical scenario search intelligent vehicle social cognitive optimization

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A Reinforcement Learning Based Grammatical Inference Algorithm Using Block-Based Delta Inverse Strategy

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IEEE ACCESS 2023年 11卷 12525-12535页

作者： Haneef, Farah Sindhu, Muddassar Azam Quaid i Azam Univ Dept Comp Sci Islamabad 45320 Pakistan

A resurgent interest for grammatical inference aka automaton learning has emerged in several intriguing areas of computer sciences such as machine learning, software engineering, robotics and internet of things. An automaton learning algorithm commonly uses queries to learn the regular grammar of a Deterministic Finite Automaton (DFA). These queries are posed to a Minimum Adequate Teacher (MAT) by the learner (Learning Algorithm). The membership and equivalence queries which the learning algorithm may pose, are often capable of having their answers provided by the MAT. The three main categories of learning algorithms are incremental, sequential, and complete learning algorithms. In the presence of a MAT, the time complexity of existing DFA learning algorithms is polynomial. Therefore, in some applications these algorithms may fail to learn the system. In this study, we have reduced the time complexity of DFA learning from polynomial to logarithmic form. For this, we propose an efficient complete DFA learning algorithm;the Block based DFA Learning through Inverse Query (BDLIQ) using block based delta inverse strategy, which is based on the idea of inverse queries that John Hopcroft introduced for state minimization of a DFA. The BDLIQ algorithm possess O(|sigma|***) complexity when a MAT is available. The MAT is also made capable of responding to inverse queries. We provide theoretical and empirical analysis of the proposed algorithm. Results show that our suggested approach for complete learning;BDLIQ algorithm, is more efficient than the ID algorithm in terms of time complexity.

关键词： Learning automata Machine learning algorithms Inference algorithms Time complexity Symbols software algorithms Reinforcement learning Automaton learning algorithm complete learning inverse query machine learning reinforcement learning

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A Verifiable and Efficient Secure Sharing Scheme in Multiowner Multiuser Settings

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IEEE SYSTEMS JOURNAL 2023年第4期17卷 5798-5809页

作者： Liu, Xingchen Zhang, Shaohui Huang, Haiping Wang, Wenming Malekian, Reza Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor N Nanjing 210023 Peoples R China Anqing Normal Univ Sch Comp & Informat Anqing 246133 Peoples R China Nanjing Univ State Key Lab oratory Novel Software Technol Nanjing 210023 Peoples R China Malmo Univ Dept Comp Sci & Media Technol S-20506 Malmo Sweden

Data security has remained a challenging problem in cloud storage, especially in multiowner data sharing scenarios. As one of the most effective solutions for secure data sharing, multikeyword ranked searchable encryption (MRSE) has been widely used. However, most of the existing MRSE schemes have some shortcomings in multiowner data sharing, such as index trees generated by data owners, relevance scores in plaintext form, and lack of verification function. In this article, we propose a verifiable and efficient secure sharing scheme in multiowner multiuser settings, where the index tree is generated by the trusted authority. To achieve verifiable functionality, the blockchain-based smart contract is adopted to execute the search algorithm. Based on a distributed two-trapdoor public-key cryptosystem, the data uploaded and used are in ciphertext form, and the proposed algorithms are secure in our scheme. For improving efficiency, the encrypted data are aggregated according to the category and the Category ID-based index tree is generated. Extensive experiments are conducted to demonstrate that it can reduce the time cost of index construction by 75% and the time cost of search by 53%, approximately. Moreover, multithreaded optimization is introduced in our scheme, which can reduce the time cost of index construction by 76% and the time cost of search by 67%, approximately (with 16 threads).

关键词： Cryptography Indexes Encryption Blockchains software algorithms Servers Threat modeling Blockchain multiple data owners (DOs) searchable encryption (SE) smart contract verifiable

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An Efficient Method for the Experimental Characterization of Periodic Multilayer Mirrors: A Global Optimization Approach

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2023年第4期70卷 650-658页

作者： Li, Mingfeng Mikki, Said Uzoma, Paul C. Penkov, Oleksiy V. Zhejiang Univ ZJU UIUC Inst Int Campus Haining 314400 Peoples R China Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA

This study proposes a new approach to periodic multilayer mirrors (PMMs) characterizations based on measured X-ray reflectivity (XRR) data. Here, XRR data are used to reconstruct the internal structure of PMMs using grazing incidence XRR (GIXR). A mathematical model of electromagnetic wave reflection by PMMs is employed to implement forward prediction, which will then be used iteratively in a global optimization framework in order to reconstruct the PMM unknown structure parameters. A typical simulation of PMM often includes tens to thousands of unknown structure parameters, rendering standard curve fitting methods cumbersome and impractical. To make the PMM characterization method computationally feasible, this study combines implementation of the Levy flight particle swarm optimization (LFPSO) algorithm with a parallelized version of the electromagnetic solver X-Ray Calc in order to simplify the model parameter reconstruction process. Levy flight, a random walk wherein the Levy distribution is used to determine step size, is a more efficient search strategy for global optimization because of the long jumps made by the particles. It is demonstrated that a PMM model with up to thousands of structure parameters can be reconstructed within several seconds on a regular workstation. The algorithm is tested with both measured and theoretical XRR data using in-house fabricated PMMs with known structures. Excellent agreement with the actual structures is observed, which is attained in short computation time. The new approach avoids manual curve fitting and simplified GIXR analysis and is observed to scale linearly with the size of the PMM structure, making it attractive for X-ray optics systems involving large-and-complex reflecting mirrors.

关键词： Reflectivity Computational modeling Mirrors Iterative methods Fitting Electromagnetics software algorithms Grazing incidence X-ray reflectivity (GIXR) Levy flight particle swarm optimization (LFPSO) periodical multilayer mirrors

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A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第6期34卷 1699-1715页

作者： Srivastava, Ankit Chockalingam, Sriram P. Aluru, Srinivas Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA Georgia Inst Technol Inst Data Engn & Sci Atlanta GA 30332 USA

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this article, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: Grow-Shrink (GS), Incremental Association MB (IAMB), and Interleaved IAMB (Inter-IAMB). Our implementations are available as part of an open-source software called ramBLe, and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC'21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.

关键词： Random variables Scalability Probability distribution Markov processes Machine learning algorithms Bayes methods software algorithms Bayesian networks constraint-based learning parallel machine learning gene networks reproducibility

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PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

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IEEE ACCESS 2023年 11卷 28343-28354页

作者： Razdan, Rahul Akbas, Mustafa Ilhan Sell, Raivo Bellone, Mauro Menase, Mahesh Malayjerdi, Mohsen Florida Polytech Univ Lakeland FL 33805 USA Embry Riddle Aeronaut Univ Dept Elect Engn & Comp Sci Daytona Beach FL 32114 USA Tallinn Univ Technol Dept Mech & Ind Engn EE-19086 Tallinn Estonia Tallinn Univ Technol FinEst Smart City Ctr Excellence EE-19086 Tallinn Estonia Acclivis Technol Pvt Ltd Pune 411038 Maharashtra India

Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem. AVs integrate subsystems in areas such as detection, sensor fusion, localization, perception, and path planning. Each of these subsystems contains significant AI content integrated with traditional hardware and software components. The complexity for validating even a subsystem is daunting and the task of validating the whole system is nearly impossible. Fundamental research in advancing the state-of-the-art for AV V&V is required. However, for V&V researchers, it is exceedingly difficult to make progress because of the massive infrastructure requirements to demonstrate the viability of any solution. This paper presents PolyVerif, the world's first open-source solution focused on V&V researchers with the objective of accelerating the state-of-the-art for AV V&V research. PolyVerif provides an AI design and verification framework consisting of a digital twin creation process, an open-source AV engine, access to several open-source physics based simulators, and open-source symbolic test generation engines. PolyVerif's objective is to arm V&V researchers with a framework which extends the state-of-the-art on any one of the many major axes of interest and use the remainder of the infrastructure to quickly demonstrate the viability of their solution. Given its open-source nature, researchers can also contribute their innovations to the project. Using this critical property of open-source environments, the innovation rate of the whole research community to solve these vexing issues can be greatly accelerated. Finally, the paper also presents results from several projects which have used PolyVerif.

关键词： software engineering Artificial intelligence software algorithms Inference algorithms Autonomous vehicles Hardware System analysis and design validation and verification modeling and simulation artificial intelligence

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What Do AI/ML Practitioners Think About AI/ML Bias?

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IEEE software 2025年第1期42卷 114-118页

作者： Pant, Aastha Hoda, Rashina Turhan, Burak Tantithamthavorn, Chakkrit Monash Univ Clayton Vic 3800 Australia Monash Univ Fac Informat Technol Software Engn Clayton Vic 3800 Australia Univ Oulu Fac Informat Technol & Elect Engn Res Unit M3S Software Engn Oulu 90014 Finland

Presents a panel discussion on the topic of AI/ML Practitioners.

关键词： Artificial intelligence Ethics Companies Australia Training software engineering Collaboration software algorithms Interviews Information technology

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Implementing Power System Protection algorithms in a Digital Hardware-in-the-Loop Substation

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2023年 10卷 270-282页

作者： Hagan, Travis Senaratne, Dilan Meier, Rich Cotilla-Sanchez, Eduardo Kim, Jinsub Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Elect Power Syst Inc Anchorage AK 99503 USA

Real-time power system algorithms are necessary for grid advancement, but few practical applications have been demonstrated in a research usability context. The work in this paper consists of implementing a data correction algorithm, its deployment within realistic substation equipment, and the design of a testbed to demonstrate the overall framework and its usability. A digital simulator is used to generate Phasor Measurement Unit (PMU) data for the algorithm. The algorithm corrects data that has been perturbed by GPS spoofing attacks. Finally, the entire system is visualized on power-utility software, SEL Synchrowave Operations. Considerations and potential issues are discussed and are applicable to digital Hardware-in-the-Loop (HIL) systems as well as to field-deployed systems. The system is demonstrated with 11 simultaneously GPS-spoofing attacked PMUs in a 21 PMU system. The HIL testbed developed in this paper provides a valuable tool for easily testing a variety of real-time power system algorithms and the communications and control necessary for them operate successfully.

关键词： Phasor measurement units Global Positioning System Substations Real-time systems Power systems Clocks software algorithms Synchrophasor phasor measurement unit GPS spoofing hardware-in-the-loop real-time power system simulation

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Gutek: Intelligent Revision algorithms

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IEEE ACCESS 2025年 13卷 86732-86745页

作者： Galka, Lukasz Lublin Univ Technol Dept Computat Intelligence PL-20618 Lublin Poland

This paper introduces Gutek, a novel open-source framework designed to optimize the learning process through intelligent revision algorithms. Gutek is built on a low-code architecture that facilitates rapid customization and extension with minimal alterations to the core codebase. By leveraging the Spring Framework, the system automates essential processes such as dependency injection, component management, and database operations via Spring Data JPA, while employing reflection to dynamically integrate new revision algorithms, charts, and custom card types. The framework supports bidirectional revision interfaces, offering both conventional and reverse revision modes to enhance memory retention and accommodate diverse learning strategies. Comparative evaluations with established open-source systems, including Anki, Mnemosyne, and OpenCards, reveal that Gutek maintains lower code complexity and superior maintainability, as demonstrated by comprehensive software quality metrics and user-based extensibility assessments. Specifically, Gutek exhibits up to 80% fewer lines of code compared to Anki and Mnemosyne and achieves the lowest number of critical issues reported in SonarQube analysis. These results indicate that Gutek provides extensive customization capabilities with minimal coding effort, making it especially suitable for educators, developers, and practitioners seeking a flexible yet robust learning tool.

关键词： Java software Codes software algorithms Complexity theory Graphical user interfaces software development management Optimization Databases Computer architecture Customizable algorithms intelligent revision learning optimization spaced repetition two-way memory training educational software

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Vector Memory-Access Shuffle Fused Instructions for FFT-Like algorithms

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Chinese Journal of Electronics 2023年第5期32卷 1077-1088页

作者： LIU Sheng YUAN Bo GUO Yang SUN Haiyan JIANG Zekun College of Computer National University of Defense Technology

The shuffle operations are the bottleneck when mapping the FFT-like algorithms to the vector single instruction multiple data(SIMD) *** propose six(three pairs) innovative vector memoryaccess shuffle fused instructions, which have been proved mathematically. Combined with the proposed modified binary-exchange method, the innovative instructions can efficiently address the bottleneck problem for decimationin-frequency or decimation-in-time(DIF/DIT) radix-2/4FFT-like algorithms, reach a performance improvement by 17.9%–111.2% and reduce the code size by 5.4%–39.8%.In addition, the proposed instructions fit some hybridradix FFTs and are suitable for the terms of the initial or result data placement for general algorithms. The software and hardware costs of the proposed instructions are moderate.

关键词： Costs Codes software algorithms Computer architecture software Hardware

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