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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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AR Intelligent Real-time Method for Cultural Heritage Object Recognition

AR Intelligent Real-time Method for Cultural Heritage Object...

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International Conference on Advanced information and Communication Technologies (AICT)

作者： Oleh Pisnyi Ivan Kit Khrystyna Lipianina-Honcharenko Jürgen Sieck Anatoliy Sachenko Maciej Dobrowolski Grygoriy Sapozhnyk Master's degree in Computer Science West Ukrainian National University Ternopil Ukraine Department for Information-Computing Systems and Control West Ukrainian National University Ternopil Ukraine Hochschule für Technik und Wirtschaft Berlin Berlin Germany Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland

In a world of rapid technological changes and growing interest in cultural heritage, the development of innovative tools for travelers and history enthusiasts becomes incredibly important. Even in the digital age, interactive ways of exploring historical landmarks remain relevant and attractive. In this research, an AR Intelligent Real-time Method for Cultural Heritage Object Recognition has been developed. The method is implemented through a chatbot, which, when using a smartphone's camera, recognizes objects of cultural heritage and provides descriptions of these objects. The chatbot opens up new possibilities for travelers, allowing them to enjoy history and cultural heritage using intelligent tools and augmented reality.

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Behavioural Approach to Network Anomaly Detection for Resource-Constrained System – Presentation of the Novel Solution – Preliminary Study

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IFAC-PapersOnLine 2019年第27期52卷 121-126页

作者： Mariusz Pelc Dawid Galus Magda Zolubak Stepan Ozana Wojciech Chlewicki Katarzyna Cichon Michal Podpora Aleksandra Kawala-Sterniuk Opole University of Technology – Faculty of Electrical Engineering Automatic Control and Informatics ul. Proszkowska 76 45-758 Opole Poland University of Greenwich – Department of Computing and Information Systems – Faculty of Architecture Computing and Humanities Old Royal Naval College Park Row SE10 9LS London UK VSB – Technical University Ostrava – FEECS Department of Cybernetics and Biomedical Engineering Ostrava Czech Republic West Pomeranian University of Technology in Szczecin – Faculty of Electrical Engineering ul. Sikorskiego 37 70-313 Szczecin Poland

In this paper some preliminary results of network anomaly detection for resource-constrained systems have been presented. All described algorithms are embedded into a three-tier system application. The presented solution is a response to nowadays trends in computer/network security domain that indicate gradual shifting from signature-based anomaly detection typically based on some selected statistical measures toward systems that are based on behavioural analysis of computer systems and/or networks. The presented system requirements, explain relevant terminology as well as we include some selected results and their analysis. The solution is potentially destined for very resource-constrained systems (IoT specifically), all tests were carried out in an example network to additionally analyse their effect of the presented approach on the computer resources.

关键词： Network Anomaly Detection Behavioural Analysis Internet of Things

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Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Pei, Yingjie Ni, Wanli Xu, Jin Yue, Xinwei Tao, Xiaofeng Niyato, Dusit School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS;ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity;and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Semantic Core Building of a Site Based on Clustering Algorithms

Semantic Core Building of a Site Based on Clustering Algorit...

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International Conference on Advanced Computer information Technologies (ACIT)

作者： Oleh Adamiv Svitlana Adamiv Vasyl Koval Ivanna Andriychuk Viktor Ostroverkhov Department of Economic Cybernetics and Informatics Ternopil National Economic University Ternopil Ukraine Department of International Economic Relations Ternopil National Economic University Ternopil Ukraine Department for Information Computing Systems and Control Ternopil National Economic University Ternopil Ukraine Department of Practical Psychology Ternopil Volodymyr Hnatyuk National Pedagogical University Ternopil Ukraine Department of Management Public Administration and Personnel Ternopil National Economic University Ternopil Ukraine

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

Websites are the main sources of information in the Internet. The ability to increase the ranking of the site for the search engine is particularly relevant in these circumstances. Engaging users with a demonstrating of a landing page on a search engine is the goal of search engine optimization. One of the steps of such optimization is a semantic core building. Semantic core is the most complete list of keywords that describes the theme and orientation of the site and matches its content. This paper presents semantic core building techniques for a site based on clustering algorithms.

关键词： Semantics Optimization Search engines Clustering algorithms Task analysis Buildings Software

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Cluster Synchronization of Kuramoto Oscillators via Pacemakers and Mean-phase Feedback control

Cluster Synchronization of Kuramoto Oscillators via Pacemake...

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IEEE Conference on Decision and control

作者： Ryota Kokubo Mitsuaki Matsubara Rui Kato Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Systems and Control Engineering Tokyo Institute of Technology Yokohama Japan Laboratoire d’Automatique de Génie des Procédés et de Génie Pharmaceutique (LAGEPP) Université Claude Bernard Lyon 1 Villeurbanne France Department of Information Physics and Computing The University of Tokyo Tokyo Japan

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods.

关键词： Neurological diseases Hands Pathology Design methodology Neurons Pacemakers Convex functions Feedback control Synchronization Oscillators

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A Survey on Big Data for Human Body Shape

A Survey on Big Data for Human Body Shape

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Hongli Peng Xiuqin Shang Chao Guo Gang Xiong Timo R.Nyberg Dong Fan Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Cloud Computing Center Chinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of Io T, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.

关键词： big data human body shape 3D scanning 3D modeling clothing footwear mass customization

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The Application of the Depth Camera in the Social Manufacturing: A review

The Application of the Depth Camera in the Social Manufactur...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Zhen Liang Ni Zhen Shen Chao Guo Gang Xiong Timo R.Nyberg Xiuqin Shang Shuangshuang Li Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Cloud Computing Center Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the information Technology develops rapidly. With the development of the information Technology, the social manufacturing, a new mode of manufacturing, has been proposed. The social manufacturing combines the Internet technology, the three-dimensional scanning technology and the virtual reality technology, and is used in the apparel industry and other fashion industries for the mass customization. The three-dimensional scanning technology and the virtual fitting mirror technology are inseparable from the depth camera. Depth cameras play an important role in the social manufacturing. Manufacturers use depth cameras to obtain three-dimensional models of the body of customers to design suitable clothes. Customers can visually see the effect of clothes by using a virtual fitting mirror which is based on the depth camera. The social manufacturing makes mass customization possible, and the depth camera help develop the social manufacturing. This paper introduces the principles of depth cameras, and the application of depth cameras in the social manufacturing.

关键词： social manufacturing depth camera PMD Kinect time of flight structured light

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Difference in Uptake Pattern Heterogeneity and Reproducibility of Radiomics Features Between FAPI and FDG Pet/Ct Imaging of Tumors

Difference in Uptake Pattern Heterogeneity and Reproducibili...

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IEEE International Symposium on Biomedical Imaging

作者： Tianmeng Xu Yanchao Huang Tingting Ang Shuilin Chu Peiyin Luo Shunfang Wang Yufeng Zhang Lijun Lu Ganghua Tang Wenbing Lv School of Information and Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Department of Nuclear Medicine Laboratory for Quality Control and Evaluation of Radiopharmaceuticals Nanfang Hospital Southern Medical University Guangzhou China School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

To investigate the difference in uptake pattern heterogeneity between fluorodeoxyglucose (FDG) and fibroblast activation protein inhibitor (FAPI) PET/CT and the reproducibility of radiomics features with respect to different calculation methods of standardized uptake value (SUV). 210 patients with various tumors were involved. Among the extracted 497 radiomics features, there are only 20 features that exhibited an ICC difference of more than 0.25 between the two tracers when using different SUV calculation methods, revealing the similar influence of SUV calculation methods. In contrast, the ICC values for most texture features were generally lower (0.22-0.39) between FDG and FAPI PET images, indicating a large difference in uptake pattern heterogeneity between the two tracers. Our results demonstrated that the influence of SUV calculation methods on the reproducibility of FDG and FAPI PET features is similar. The uptake pattern heterogeneity characterized by radiomics features between FDG and FAPI PET images is significantly different.

关键词： Proteins Protein engineering Inhibitors Fibroblasts Feature extraction Reproducibility of results Radiomics

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