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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Takanashi, Yuya Kimura, Shigetomo University of Tsukuba Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and System Tsukuba Japan

ISBN: (纸本)9798350306941

DNS over HTTPS (DoH) enhances user privacy by encrypting DNS communications over HTTPS instead of plaintext. When all DNS messages are sent in plaintext, DNS queries can be examined and domain filtering applied if the queried domain name is identified as a phishing site or other such undesirable site. However, if DNS messages are encrypted over HTTPS, it can create many problems for network administrators. This paper proposes a method for detecting DoH communications from only non-encrypted information on a middlebox between user and resolvers by exploiting the fact that users always send a DNS query before they access a new domain. The middlebox can also identify the destination of the detected DoH traffic so that network administrators can recommend users to send DNS messages to a local DoH resolver with domain filtering instead of sending them to an open DoH resolver. In experiments to detect DoH communications during real communication from a web browser we achieved detection accuracy rates reaching 100% under certain parameters when the number of access IP addresses exceeded 350. To confirm the accuracy and generalizability of our experiments, the proposed method was also applied to captured HTTPS traffic data involving different web browsers and different DoH resolvers with an almost identical level of detection accuracy. © 2023 IEEE.

关键词： Web browsers

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Creating Collaborative Data Representations Using Matrix Manifold Optimal Computation and Automated Hyperparameter Tuning 3

Creating Collaborative Data Representations Using Matrix Man...

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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Nosaka, Keiyu Yoshise, Akiko Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Institute of System and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350333862

Balancing performance and privacy in centralized machine learning, especially in sensitive domains such as medicine and finance, remains a significant issue. Concerns about data confidentiality result in a reluctance to share confidential data even among related entities, hindering integrated analysis despite its acknowledged benefits. Recent advancements in distributed data analysis have provided a solution to the concern, particularly through the framework of federated learning. This framework enables multiple clients to train models collaboratively while preserving the confidentiality of their data. Fed-DR-Filter extends federated learning by introducing the Privacy-Preserving Data Representation (PPDR) transformation algorithm that converts private datasets into a Global Data Representation (GDR) across multiple clients [4]. The GDR retains the correlation of the original data while ensuring its confidentiality. The PPDR is obtained by adding noise to the GDR, which is then used to filter noisy labels in federated learning environments based on the preserved correlation. Another recent development in distributed data analysis is Data Collaboration Analysis (DCA) [5]. It employs Collaborative Data Representation (CDR), a data representation almost identical to GDR, to directly generate high-performance models without the need for model sharing or iterative communication among clients. The present study builds the crucial role of CDRs and GDRs in DCA and Fed-DR-Filter by proposing a novel method for CDR creation with a solid theoretical foundation. The purpose is to enhance the performance of these techniques by providing an alternative and more effective algorithm for CDR and GDR creation. The primary contributions of this study include a novel optimization for CDR creation utilizing matrix manifold optimization and automated hyperparameter tuning. An algorithm for solving this problem is proposed with its empirical evaluation of artificial and real-world dataset

关键词： Metadata

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Video Conference system in Mixed Reality Using a Hololens

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Computer Modeling in engineering & sciences 2023年第1期134卷 383-403页

作者： Baolin Sun Xuesong Gao Weiqiang Chen Qihao Sun Xiaoxiao Cui HaoGuo Cishahayo Remesha Kevin Shuaishuai Liu Zhi Liu School of Information Science and Engineering Shandong UniversityQingdao266000China State Key Laboratory of Digital Multi-Media Technology Hisense Co.Ltd.Qingdao266000China

The mixed reality conference system proposed in this paper is a robust,real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference,which realizes the entire process of holographic video conference from client to cloud to the *** paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed *** mixed reality conference system components are discussed,including data collection,data transmission,processing,and mixed reality *** data layer is mainly used for data collection,integration,and video and audio *** network layer uses Web-RTC to realize peer-to-peer data *** data processing layer is the core part of the system,mainly for human video matting and human-computer interaction,which is the key to realizing mixed reality conferences and improving the interactive *** presentation layer explicitly includes the login interface of the mixed reality conference system,the presentation of real-time matting of human subjects,and the presentation *** the mixed reality conference system,conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience.

关键词： Mixed reality AI segmentation hologram video conference Web-RTC

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Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

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Journal of Modern Power systems and Clean Energy 2024年第3期12卷 874-885页

作者： Wenlong Liao Dechang Yang Qi Liu Yixiong Jia Chenxi Wang Zhe Yang Wind Engineering and Renewable Energy Laboratory Ecole Polytechnique Federale de Lausanne(EPFL)Lausanne 1015Switzerland the College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Electrical and Electronic Engineering(Energy Digitalization Laboratory) The University of Hong KongHong KongChina the Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time *** improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven *** graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling ***,a rulebased strategy is proposed to adjust infeasible solutions that violate *** results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load ***,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.

关键词： Reactive power optimization graph neural network distribution network machine learning data-driven

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Incremental Learning of Radio Modulation Classification Based on Sample Recall

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China Communications 2023年第7期20卷 258-272页

作者： Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou 310023China

Radio modulation classification has always been an important technology in the field of *** difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old *** this paper,we propose a sample memory and recall framework for incremental learning of radio modulation *** data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for *** compare the performance of our proposed method with three baselines through a large number of simulation *** show that our method achieves far higher classification accuracy than finetuning method and feature extraction ***,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

关键词： radio modulation classification incremen-tal learning deep learning convolutional neural net-work.

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Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

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Journal of Ocean University of China 2024年第2期23卷 392-404页

作者： ZHOU Zhiyu LIU Mingxuan JI Haodong WANG Yaming ZHU Zefei School of Information Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China Zhejiang Key Laboratory of DDIMCCP Lishui UniversityLishui 323000China School of Mechanical Engineering Hangzhou Dianzi UniversityHangzhou 310018China

The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of *** obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning *** learning ensures the initial performance of the network and helps in the development of a high-precision classification ***,a TRVFL was proposed to improve the classification property of the *** construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were *** parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of ***,the TRVFL classifier was used to classify features and obtain classification *** proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,*** best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,*** increases imply improved systematization properties in underwater image classification *** image classification model offers important performance advantages and better stability compared with existing methods.

关键词： underwater image classification EfficientnetB0 random vector functional link convolutional neural network

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Leveraging spatial prior and grid attention for few-shot medical segmentation 3

Leveraging spatial prior and grid attention for few-shot med...

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3rd International Conference on Electronic information engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Wei, Lin Yang, Yafeng Gao, Yufei Cheng, Guozhen School of Cyber Science and Engineering Zhengzhou University Zhengzhou450001 China SongShan Laboratory Zhengzhou450001 China Institute of Information Technology Information Engineering University Henan Zhengzhou450002 China

ISBN: (纸本)9781510680449

In trained few-shot segmentation, an inevitable bias towards the base classes may occur. By leveraging the spatial consistency of organ distribution in medical image segmentation, spatial priors can be utilized to roughly predict the location of new categories. This helps to avoid bias towards the base classes. This paper introduces a novel prototype network that integrates prior spatial knowledge and grid attention to enhance the accuracy of few-shot medical image segmentation by predicting target locations and offering guidance for segmentation algorithms. We introduce dedicated spatial prior module to extract information about the location of targets. This module utilizes the spatial and shape consistency of supported and query targets in medical images to better guide the segmentation process. By incorporating a spatial prior module, the segmentation network is guided to follow the desired spatial structure of the target. Additionally, to emphasize essential local semantic information in medical images, we have devised a Grid Attention (GA) module. The GA module adaptively adjusts the importance of different positions in the feature map, allowing more attention to be placed on regions relevant to the segmentation task. By combining spatial priors with grid attention, our method can better capture target information in medical images and achieve more accurate segmentation results. This claim was substantiated through our experiments focusing on segmenting abdominal organs in MRI scans. Our method was objectively compared against several established models, demonstrating that the inclusion of spatial priors and grid attention greatly improved the segmentation results. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Magnetic resonance imaging

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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Artificial intelligence for sustainable internet research

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International Journal of Computers and Applications 2022年第6期44卷 501-502页

作者： Haldorai, Anandakumar Sharif, Muhammad Ravana, Sri Devi Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering COMSATS University Islamabad Wah Cantt Pakistan Department of Information System Faculty of Computer Science & Information Technology University of Malaya Malaysia

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Application of Fish Swarm Algorithm in Space Based Dynamic Multicast Routing

Application of Fish Swarm Algorithm in Space Based Dynamic M...

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2022 International Conference on Electronic information engineering and Computer science, EIECS 2022

作者： Duan, Qi-Feng Liu, Li-Xiang Institute of Software Chinese Academy of Sciences Science & Technology on Integrated Information System Laboratory University of Chinese Academy of Sciences Beijing China

By applying the fish swarm algorithm to the generation of the multicast tree under the dynamic satellite network, a method for generating the multicast tree with good adaptability and inheritance was found, and the ge... 详细信息

ISBN: (纸本)9781510663312

关键词： Trees (mathematics)

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