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International Conference on Computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

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SAR Image Scene Classification Based on Metric Learning and CNNs

SAR Image Scene Classification Based on Metric Learning and ...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Yaozhong Wang Zhiqiang Zhao Lei Wang School of Computer Science and Engineering and the Shaanxi Key Laboratory of Network Computing and Security Technology Xi‘an University of Technology Xi'an China School of Mathematics and Computer Science Shaanxi University of Technology Hanzhong China

ISBN: (纸本)9781665430302

With the development of synthetic aperture radar (SAR) imaging techniques, many high-quality images are acquired for exactly earth observation. As a result of heavy speckle, it is difficult to extract effective representation of different scene images to distinguish them accurately. In this paper, a metric-learning based convolutional neural network (CNN) is proposed to characterize SAR images. Specifically, a regularization term is proposed, which is constrained by the Mahalanobis distance. The regularization term learns a new metric space based on CNN, where the similar images should be close and those of dissimilar images should be separated. Comparing with the other approaches, experimental results of the proposed approach illustrate a superior classification for different scenes.

关键词： Earth Image analysis Conferences Speckle Extraterrestrial measurements Radar polarimetry Convolutional neural networks

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Algorithm Application of Speech Recognition technology Based on Multimodal Information in Human-Computer Interaction

Algorithm Application of Speech Recognition Technology Based...

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Artificial Intelligence, Internet and Digital Economy (ICAID), International Conference on

作者： Lin Shi Yan Lu Yi Zhou Weiliu Chenxi Chuan Zhou School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Jinan China Inspur software Co. Ltd. Jinan China School of Microelectronics Tianjin University Tianjin China Beijing Minzu University of China Beijing China

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

Multimodal human-computer interaction (MMHCI) has been considered a key direction during the evolution of the human-computer interaction (HCI) domain. To speed up the technical advancement of HCI systems, this investigation initially combines attention mechanism (AM) with the Connectionist Temporal Classification (CTC) algorithm for developing a speech recognition (SR) framework. The framework is further enhanced by applying an optimized artificial swarming algorithm to the end-to-end SR framework, resulting in an SR model appropriate for the MMHCI system. The experimental outcomes indicate that the proposed improved swarm algorithm achieves better convergence, solving the solution set with less difference from the real solution set, the hypervolume metric converges at 0.861, and the inverse generation distance converges at 0.194. The SR model surpasses others in terms of evaluation parameters containing accuracy, precision, and recall, while achieving the lowest error rates in various aspects as the sentence level, word, and character, which are correspondingly $0.035,0.037$, and 0.036. This ensures better accuracy of recognition while maintaining a balanced on-access processing rate. As to a system of multimodal interaction, the relatively higher scores of mean opinion and objective grading for voice quality were received in the experimental group (EG), instead of the control group (CG). The speech processing technology developed in such investigation is quite essential for elevating interaction efficiency together with user experience, offering valuable insights and guidance for research in the areas of HCI and SR.

关键词： Human computer interaction Measurement Accuracy Speech recognition User experience Classification algorithms Internet Speech processing Optimization Convergence

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A Deep Learning-Based Framework for the Normalization of WOS Institutional Names

A Deep Learning-Based Framework for the Normalization of WOS...

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Artificial Intelligence, Internet and Digital Economy (ICAID), International Conference on

作者： Lin Shi Yan Lu Chuan Zhou Weiliu Chenxi Yi Zhou School of Cyber Science and Engineering Southeast University Nanjing China School of Microelectronics Tianjin University Tianjin China Minzu University of China Beijing China Inspur software Co. Ltd. Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Jinan China

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

Academic evaluation assesses the contributions and influence of researchers, institutions, or disciplines, determining their reputation and standing within a specific field. As a leading citation database, Web of Science (WOS) is a crucial source for academic evaluation data. However, ambiguities in institutional names due to name changes, multilingual translations, transcription errors, and author preferences significantly impact the accuracy of evaluations. To make data more reliable in academic assessment, this paper addresses deficiencies in deep learning-based techniques for normalizing institutional names in literature. It proposes a framework that combines multiple granular embeddings and multiple context information for synonym detection of WOS institutional names.

关键词： Accuracy Databases Neural networks Organizations Multilingual Internet Reliability Artificial intelligence

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

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network security Situational Awareness Model Based on Threat Intelligence 14th

Network Security Situational Awareness Model Based on Threat...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Gao, Hongbin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang05000 China

ISBN: (纸本)9783030898137

In order to deal with the problems that the increasing scale of the network in the real environment leads to the continuous high incidence of network attacks, the threat intelligence was applied to situational awareness, and the situational awareness model based on random game was constructed. Threat perception of the target system was performed by comparing the similarity between the exogenous threat intelligence and the internal security events of the system. At the same time, internal threat intelligence was generated based on the threat information inside the system. In this process, game theory was used to quantify the current network security situation of the system, evaluate the security status of the network. Finally, the prediction of the network security situation was realized. The experimental results show that the network security situation awareness method based on threat intelligence can reflect the changes in the network security situation and predict attack behaviors accurately. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Game theory

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Social-enhanced recommendation using graph-based contrastive learning

Social-enhanced recommendation using graph-based contrastive...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Peng Xue Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models.

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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On the ADEP and DOR Analysis of RIS-aided URLLC Systems with Partial CSI in Smart Factory

On the ADEP and DOR Analysis of RIS-aided URLLC Systems with...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Bao, Tingnan Wang, Haiming Yang, Hong-Chuan Wang, Wen-Jing Hasna, Mazen O. Lenovo Research + Institute of Computing Technology Chinese Academy of Sciences Beijing China Lenovo Reseach Lenovo Group Limited Beijing China University of Victoria Department of Electrical and Computer Engineering Victoria Canada Shaanxi Key Laboratory of Information Communication Network and Security XUPT Xi'an China Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9781665423908

Factory automation applications impose stringent quality of service (QoS) over ultra-reliable and low-latency communication (URLLC) system. However, the existing wireless systems cannot meet the requirement for these mission-critical industrial internet-of-things (IIoT) applications. In this paper, we consider the reconfigurable intelligent surface (RIS)-aided URLLC systems in the smart factory. To facilitate the performance analysis of the practical situation, the presence of phase error at RIS is discussed. We analyze and derive approximate closed-form expression of average decoding error probability (ADEP) and the closed-form expression of performance limits de-lay outage rate (DOR) under our proposed scenario, respectively. Through analytical results and associated numerical examples, we demonstrate the effect of the number of reflecting elements at the RIS, channel blocklength and packet size in achieving high reliability and low latency. Therefore, the deployment of RIS can offer an attractive solution to enhance the reliability over URLLC systems in the smart factory. © 2021 IEEE.

关键词： Quality of service

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