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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Chen, Wei Wang, Zhiheng School of Computer and Information Engineering Heilongjiang University of Science and Technology Heilongjiang China School of Computer and Information Engineering Heilongjiang University of Science and Technology Anhui China

ISBN: (纸本)9798400718199

In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing. © 2024 ACM.

关键词： Image segmentation

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A computing offloading strategy for UAV based on improved bat algorithm

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Cognitive Robotics 2023年第1期3卷 265-283页

作者： Xu, Fei Zi, Shun Wang, Jianguo Ma, Jiajun School of Computer Science and Engineering Xi'an Technological University Xi'an China School of Ordnance Science and Technology Xi'an Technological University Xi'an China

In the process of multi-UAVs cooperative reconnaissance operations, due to the limited battery capacity and computing resources of the unmanned aerial vehicle (UAV), processing tasks can not only lead to excessive delay, but also increase the energy consumption of the UAV, which reduces the endurance time of the UAV. Therefore, we have proposed a mobile edge computing (MEC) system architecture composed of single unmanned helicopter (UH) and multiple reconnaissance UAVs. Among them, the UH as a MEC server to provide computing services for reconnaissance UAVs. By solving the computing offloading strategy problem of multi-UAVs, the objective is to minimize the weighted sum of energy consumption and delay for the multi-UAVs' task execution. In solving the problem, previous heuristic algorithms such as the Particle Swarm Optimization (PSO) are often used as basic algorithms for research, but they tend to converge early, fall into local optimum easily, and have low solution accuracy, making it difficult to obtain the optimal offloading strategy. Therefore, this paper proposes an improved bat algorithm (IBA) with fast convergence ability and global search ability. Through the simulation experiments and comparative analysis of PSO, BA, IPSO and IBA, it is proved that the IBA is more accurate, stable, and efficient in solving this problem based on the system architecture proposed in this paper, and effectively reduces the weighted sum of energy consumption and delay for the multi-UAVs' task execution. © 2023

关键词： Energy utilization

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A hybrid personality-aware recommendation system based on personality traits and types models

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12775-12788页

作者： Dhelim, Sahraoui Chen, Liming Aung, Nyothiri Zhang, Wenyin Ning, Huansheng School of Computer Science University College Dublin Dublin Ireland School of Computing Ulster University Belfast United Kingdom School of Information Science and Engineering Linyi University Linyi China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Personality-aware recommendation systems have been proven to achieve high accuracy compared to conventional recommendation systems. In addition to that, personality-aware recommendation systems could help alleviate cold start and data sparsity problems by adding the user’s personality traits in the recommendation process. The majority of the literature works used Big-Five personality model to represent the user’s personality, this is due to the popularity of Big-Five model in the literature of psychology. However, from personality computing perspective, the choice of the most suitable personality model that satisfy the requirements of the recommendation application and the recommended content type still needs further investigation. In this paper, we study and compare four personality-aware recommendation systems based on different personality models, namely Big-Five traits model, Eysenck model and HEXACO model from the personality traits theory, and Myers–Briggs Type Indicator (MPTI) from the personality types theory. Furthermore, we propose a hybrid personality model for recommendation that takes advantage of the personality traits models, as well as the personality types models. Through extensive experiments on recommendation dataset, we prove the efficiency of the proposed model, especially in cold start settings. Our proposed hybrid personality-aware recommendation model improves the precision and recall in cold start settings by 21% and 18% respectively compared to the widely used Big-Five traits model. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Recommender systems

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Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network

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Chinese Journal of Electronics 2025年第2期34卷 698-711页

作者： Xie Wenwu Xiong Ming Ren Ziqing Wang Ji Yang Zhihe School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang 414006 China School of Computer Science Hefei Normal University Hefei 230001 China College of Physical Science and Technology Central China Normal University Wuhan 430079 China

With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight \begin{document}$\alpha$\end{document} term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight \begin{document}$\alpha$\end{document} term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.

关键词： Semantic communication Residual neural networks (ResNets) Joint control mechanisms Edge reasoning

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A Novel Secure Scan Design Based on Delayed Physical Unclonable Function

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computers, Materials & Continua 2023年第3期74卷 6605-6622页

作者： Weizheng Wang Xingxing Gong Xiangqi Wang Gwang-jun Kim Fayez Alqahtani Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Information Science and Engineering Hunan Women’s UniversityChangsha410138China College of Mathematics and Computational Science Hunan First Normal UniversityChangsha410138China Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.

关键词： Cryptographic chips scan testing scan-based attacks hardware security PUF

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of computer science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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Improved Harris Hawks Algorithm and Its Application in Feature Selection

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computers, Materials & Continua 2024年第10期81卷 1251-1273页

作者： Qianqian Zhang Yingmei Li Jianjun Zhan Shan Chen School of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China College of Systems Engineering National University of Defense TechnologyChangsha410073China

This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration ***,and a lack of thorough exploitation *** tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation *** effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test *** findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving ***,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional *** analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight *** results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature ***,IHHO-FS shows strong competitiveness relative to numerous algorithms.

关键词： HHO IHHO population diversity energy factor update strategy deep exploitation strategy feature selection

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica university of Bucharest. All rights reserved.

关键词： Radiation effects

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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

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Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation 24

Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Co...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Sun, Jin Shi, Xiaoshuang Wang, Zhiyuan Xu, Kaidi Shen, Heng Tao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China SiChuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Tsinghua University SiChuan Chengdu China Department of Computer Science Drexel University Philadelphia United States School of Computer Science and Engineering University of Electronic Science and Technology of China Tongji University SiChuan Chengdu China

ISBN: (纸本)9798400706868

Modeling in computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convolution, famous for its sliding window scheme, also suffers from this scheme of redundancy and lower parallel computation. In this paper, we seek to dispense with the windowing scheme and introduce a more elaborate and parallelizable method to exploit locality. To this end, we propose a new MLP module, namely Shifted-Pillars-Concatenation (SPC), that consists of two steps of processes: (1) Pillars-Shift, which generates four neighboring maps by shifting the input image along four directions, and (2) Pillars-Concatenation, which applies linear transformations and concatenation on the maps to aggregate local features. SPC module offers superior local modeling power and performance gains, making it a promising alternative to the convolutional layer. Then, we build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet. Extensive experiments show Caterpillar's excellent performance on both small-scale and ImageNet-1k classification benchmarks, with remarkable scalability and transfer capability possessed as well. The code is available at https://***/sunjin19126/Caterpillar. © 2024 ACM.

关键词： Convolution

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