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HDTCNet: A hybrid-dimensional convolutional network for multivariate time series classification

Pattern Recognition 2025年 168卷

作者： Gu, Yongli Yan, Xiang Qin, Hanlin Akhtar, Naveed Yuan, Shuai Fu, Honghao Yang, Shuowen Mian, Ajmal School of Optoelectronic Engineering Xidian University Xi'an710071 China Xi'an Key Laboratory of Infrared Technology and System Xi'an710071 China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3052 Australia School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Software Engineering The University of Western Australia CrawleyWA6009 Australia

Currently, transformer-based models dominate time series classification due to their ability to capture global data dependencies. However, their large memory footprint and high computational demands make them less desirable for long multivariate time series modeling. At the same time, convolution-based models promise a desirable performance versus efficiency trade-off for the task. Nevertheless, the popularity of transformer models has impeded exploration of sophisticated convolution-based modeling for time series classification. In this work, we introduce a novel convolution-based model, called HDTCNet, that alternates between 1D and 2D convolutional modes to capture temporal pattern correlations between intra- and inter-sensor variations. We develop a Wavelet Time Convolution block to expand the effective receptive field of the underlying neural model with a small convolutional kernel that embeds a set of 1D convolution operators between a cascade of wavelet decomposition and reconstruction of the input time series. To efficiently represent the local features of multivariate time series, we devise a hierarchical difference convolution network to capture intra- and inter-sensor gradient variations. To refine the feature representation of sensor-level time series and achieve multi-view self-enhancement, we design a self-enhancing module. These innovations enable HDTCNet to achieve superior performance while maintaining efficiency as a purely convolutional model. Experimental results demonstrate that HDTCNet achieves 2.1% accuracy and 1% F1-score performance improvements over state-of-the-art methods on 25 UEA classification datasets and 3 anomaly detection datasets, respectively, while reducing parameter counts by two orders of magnitude. Our code will be available at https://***/Guuuli/HDTCNet. © 2025

关键词： Wavelet decomposition

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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IEEE International Conference on Artificial Intelligence and computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence Distributed processing software engineering

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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Distributed Stochastic Alternating Direction Method of Multipliers for Big Data Classification 9th

Distributed Stochastic Alternating Direction Method of Multi...

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Wang, Huihui Li, Xinwen Chen, Xingguo Qi, Lianyong Xu, Xiaolong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China School of Computer Science and Technology School of Software Nanjing University of Posts and Telecommunications Nanjing China School of Information Science and Engineering Qufu Normal University Jining China

ISBN: (纸本)9783030485122

In recent years, classification with big data sets has become one of the latest research topic in machine learning. Distributed classification have received much attention from industry and academia. Recently, the Alternating Direction Method of Multipliers (ADMM) is a widely-used method to solve learning problems in a distributed manner due to its simplicity and scalability. However, distributed ADMM usually converges slowly and thus suffers from expensive time cost in practice. To overcome this limitation, we propose a novel distributed stochastic ADMM (DS-ADMM) algorithm for big data classification based on the MPI framework. By formulating the original problem as a series of sub-problems through a cluster of multiple computers (nodes). In particular, we exploit a stochastic method for sub-problem optimization in parallel to further improve time efficiency. The experimental results show that our proposed distributed algorithm is suitable to enhance the performance of ADMM, and can be effectively applied for big data classification. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Big data

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

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Procedia computer Science 2021年 191卷 328-336页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications’ quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.

关键词： Cloud computing Computation offloading Edge computing Energy-efficiency Machine learning Queuing theory Support Vector Machine (SVM) Vehicular network

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Haste Makes Waste: An Empirical Study of Fast Answers in Stack Overflow

Haste Makes Waste: An Empirical Study of Fast Answers in Sta...

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International Conference on software Maintenance (ICSM)

作者： Yao Lu Xinjun Mao Minghui Zhou Yang Zhang Tao Wang Zude Li College of Computer Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China School of Electronics Engineering and Computer Science Peking University Beijing China School of Information Science and Engineering Central South University Changsha China

ISBN: (数字)9781728156194

ISBN: (纸本)9781728156200

Modern programming question & answer (Q&A) sites such as Stack Overflow (SO) employ gamified mechanisms to stimulate volunteers' contributions. To maximize the chances of winning gamification rewards such as reputation and badges, a portion of users race to post answers as quickly as possible (i.e., fast answers or FAs), which makes SO the fastest Q&A site; however, this behavior may affect the contribution quality as well. In this paper, we report on a large-scale, mixed-methods empirical study of the gamification-influenced FA phenomenon in SO. We first quantitatively investigate the popularity of the phenomenon and user behaviors regarding FAs. Then, we study the quality of FAs by using regression modeling and qualitatively analyzing 300 instances of FAs. Our main findings reveal that more than 70% and 90% of FAs are not edited by the answerers and other users, respectively, and that later incoming answers have lower chances of being voted on and accepted. Notably, we find that the answer length, code snippets length, and readability of FAs are significantly lower than those of non-fast answers. Although FAs have higher crowd assessment scores, they have no relationship with acceptance from the perspective of asker assessment, and a considerable portion of FAs solve the problem by interacting with the asker in the comments. These results help us better understand the effects of reward-based gamification on crowdsourced software engineering communitites and provide implications for designers of gamified systems.

关键词： software maintenance Analytical models Conferences Programming software engineering

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A proof-of-majority consensus protocol for blockchain-enabled collaboration infrastructure of 5G network slice brokers 2

A proof-of-majority consensus protocol for blockchain-enable...

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2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2020, Co-located with AsiaCCS 2020

作者： Lin, Wenmin Xu, Xiaolong Qi, Lianyong Zhang, Xuyun Dou, Wanchun Khosravi, Mohammad R. Institute of VR and Intelligent Systems Alibaba Business School Hangzhou Normal University Hangzhou China Nanjing University of Information and Technology Nanjing China Qufu Normal University Rizhao China Department of Computing Macquarie University Sydney Australia State Key Laboratory of Novel Software Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Department of Computer Engineering Persian Gulf University Bushehr Iran

ISBN: (纸本)9781450376105

With network slicing technology, 5G changes the classical mobile network business model from a network-operator-oriented business to a more open system with multiple stakeholders. In this context, a network slice broker plays the role of agency between network resource providers and vertical service providers, to lease and provide on-demand network slice services. In practice, a single network slice broker normally owns limited types of network slices. And a collaboration infrastructure could be built up to enable network slice sharing among multiple network slice brokers, so as to improve user experience, and enhance the resource utilization rate. However, a major challenge with such an infrastructure is how to support fair and secure slice trading process among multiple un-trusted network slice providers. Blockchain technology, on the other side, brings in high opportunities to implement a fair and secure trading system without a trusted third-party. In view of this observation, this paper proposes a design of blockchain-enabled collaboration infrastructure for network slice brokers, where a group of registered brokers can trade their network slices in a fair and secure manner. To enhance the accountability of slice trading records, transactions are verified and sorted by each broker node, which are further packed into blocks and appended to a shared ledger maintained by each member in the collaboration network. More specifically, we design a Proof-of-Majority (PoM) consensus protocol to sort slice trading transactions before packing transaction blocks of the shared ledger. Furthermore, an optimization for the basic PoM protocol is discussed, with dependency resolving strategy to improve system performance. Experiments are conducted to verify the feasibility of our proposal. © 2020 ACM.

关键词： 5G mobile communication systems

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Methods for determination of psychophysiological condition of user within smart environment based on complex analysis of heterogeneous data 14th

Methods for determination of psychophysiological condition o...

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14th International Conference on Electromechanics and Robotics - Zavalishin's Readings, ER(ZR) 2019

作者： Levonevskii, Dmitrii Shumskaya, Olga Velichko, Alena Uzdiaev, Mikhael Malov, Dmitrii Laboratory of Big Data of Socio-Cyberphysical Systems SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia Laboratory of Autonomous Robotic Systems SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia Laboratory of Speech and Multimodal Interfaces SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia Laboratory of Computer-Information Systems and Software Engineering SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia

ISBN: (纸本)9789811392665

Smart Environments (SE)’ implementation provides great possibilities and services for users. But also such systems can be potentially harmful in terms of destructive behavior that can be met in this kind of informational space. In this case, it is important to prevent such information from users, especially from children. To solve this problem, it is necessary to analyze all available information in such systems: video (images), text, and audio. In this paper, the review of state-of-the-art methods of data analysis from different modalities is presented. Also the way of transfer learning technique implementation of heterogeneous data analysis is proposed. © Springer Nature Singapore Pte Ltd 2020.

关键词： Neural networks

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Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”

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IEEE Access 2025年 13卷 31659-31659页

作者： Khaoula Taji Ali Sohail Tariq Shahzad Bilal Shoaib Khan Muhammad Adnan Khan Khmaies Ouahada Electronic Systems Information Processing Mechanics and Energy Laboratory Kenitra Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science Minhaj University Lahore Lahore Pakistan Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Computer Science Green International University Lahore Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si Republic of Korea

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.

关键词： Plant diseases Metaheuristics

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