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On RNN-Based k-WTA Models With Time-Dependent Inputs

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2034-2036页

作者： Mei Liu Mingsheng Shang the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China

Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeas... 详细信息

关键词： letter convergence finite

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Research on Enhanced Hybrid Whale Optimization Algorithm with Inverse Learning Strategy and Lévy Flight Mechanism 4

Research on Enhanced Hybrid Whale Optimization Algorithm wit...

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2023 4th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Guo, Danyang Zheng, Yinghao Lu, Jiaxin Liang, Sihua Chen, Tuyin Yang, Jialin Zhou, Chao Guangzhou City University of Technology School of Robotics Engineering 510800 China Guilin University of Electronic Technology School of Materials Science and Engineering 541000 China Guangdong Baiyun University School of Big Data and Computing 510450 China

ISBN: (纸本)9798350308877

In recent years, bio-inspired optimization algorithms have attained significant success in addressing complex global optimization issues. Nonetheless, a single bio-inspired search strategy may struggle to handle diverse and intricate problems. To surmount this constraint, this paper introduces an enhanced hybrid whale algorithm (MEHWOA) based on reverse learning strategy and Lévy flight mechanism improvement. This approach amalgamates the global search capabilities of the gray wolf algorithm with the local search prowess of the whale algorithm, further augmenting the convergence speed and optimization accuracy of MEHWOA by incorporating reverse learning strategy and Lévy flight mechanism. To assess the performance of MEHWOA, we conducted experiments on 23 general benchmark test functions and compared it with original gray wolf (GWO), whale (WOA), particle swarm (PSO), and sparrow search (SSA) optimization algorithms. The experimental outcomes reveal that MEHWOA exhibits faster convergence speed and superior accuracy across various test functions, including unimodal, multimodal, and composite benchmark test functions. These findings corroborate that MEHWOA possesses considerable potential for solving complex global optimization problems. © 2023 IEEE.

关键词： Global optimization

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ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction 37

ES-Mask: Evolutionary Strip Mask for Explaining Time Series ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Sun, Yifei Song, Cheng Lu, Feng Li, Wei Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia

ISBN: (纸本)9781577358800

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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A Hybrid Multi-attribute Group Decision-Making Method Using Normal Cloud Model and Multi-granularity Information 11th

A Hybrid Multi-attribute Group Decision-Making Method Using ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Jiang, Zhihui Zhang, Qinghua Wang, Yutai School of Science Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783031927430

In multi-attribute group decision making (MAGDM), it is important to deal with heterogeneous data and integrate the evaluation information from different decision makers (DMs). To more accurately capture the uncertain information in the real decision-making environment, a hybrid MAGDM method using normal cloud model (NCM) and multi-granularity information is proposed. First, utilizing the theta scaling method, multi-granularity linguistic term sets are established. Considering the exact numbers, interval numbers, multi-granularity linguistic terms, and multi-granularity linguistic expressions, the concept of multi-granularity information is further expanded. Second, these data with different granularity are uniformly converted into NCMs, which can simultaneously preserve both magnitude information and uncertainty information. Third, based on the internal consensus index (ICI) and the external consensus index (ECI), a novel group analytic hierarchy process (GAHP) method is proposed for calculating attribute weights. Finally, NCM-extended VIKOR method is proposed for ranking the alternatives. The flexibility and superiority of the proposed method are confirmed through a case study and comparisons with previous methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linguistics

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A New Model Based on Differential Evolutionary Algorithm and Markov Clustering for Identifying Protein Complexes 42

A New Model Based on Differential Evolutionary Algorithm and...

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42nd Chinese Control Conference, CCC 2023

作者： Feng, Zengyu Tuo, Shouheng Chen, Tianrui School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9789887581543

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology, ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Proteins

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used bigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Auxiliary Attributes-Guided Face Age Estimation 11th

Auxiliary Attributes-Guided Face Age Estimation

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： You, Fan Zhong, Fujin Yu, Hong Hu, Jun Yang, Yan Liu, Mengqi School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China

ISBN: (纸本)9783031927409

Facial age estimation is a critical task within computer vision, with wide-ranging applications in demographic analysis, video retrieval, and access control. Prior research has highlighted that facial aging is significantly influenced by attributes such as gender and ethnicity, which present substantial challenges to achieving accurate and robust age prediction. To tackle this challenge, we propose MSAN, an innovative end-to-end age estimation framework that integrates auxiliary information, including gender and ethnicity, during the prediction phase and dynamically models their interactions with age. Extensive evaluations on multiple public datasets demonstrate that our model outperforms several state-of-the-art approaches. This study provides an effective solution for enhancing the accuracy of facial age estimation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Face recognition

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Research on PM2.5 Pollution Prediction Method in Hefei City Based on CNN-LSTM Hybrid Model 4

Research on PM2.5 Pollution Prediction Method in Hefei City ...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Wang, Qiang Zhang, Yiwen School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China School of Computing and Information Technologies National University Manila Philippines

The air quality data has the characteristics of large scale and high time sequence. In the deep learning model, CNN has good feature extraction and expression ability through convolution operation. LSTM has strong time series data processing ability, which effectively solves the long-term dependence problem in the time series data that CNN cannot process. However, a single feature extraction or time series processing method will affect the accuracy of the model. This paper proposes a PM2.5 pollution prediction method based on the CNN-LSTM hybrid model, which can fuse the time series and nonlinear features of the data. By selecting the air quality and meteorological data of Hefei City in Anhui Province from 2018 to 2021 for prediction research, the experimental results show that the CNN-LSTM hybrid model is better than the LSTM model in terms of RMSE, MAE, and R2 evaluation indexes, which reflects higher prediction accuracy. It can not only be used for real-time prediction of PM2.5 pollution, but also for the prediction of other pollutants in the atmosphere. It also provides a reference for the application of CNN-LSTM model in solving other time series big data. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Demystifying Ethereum account diversity:observations,models and analysis

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Frontiers of Computer Science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

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