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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and 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 and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Cross Modal Sentiment Classification of Social Media Based on Meta Heuristic Algorithm

Cross Modal Sentiment Classification of Social Media Based o...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Wantong Du Wanli Min Yushui Geng Hu Liang Hehu Zhou Ministry of Education Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security 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 Synthesis Electronic Technology Co. Ltd

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The evolution of online social media has reshaped public behaviors, and social data infused with emotions provides crucial decision support for sentiment analysis tasks. Conventional multimodal approaches, influenced by redundant information in feature extraction during sentiment analysis, often focus solely on modality interactions without considering the unique content of each modality. This paper introduces the HGB-HFN model, addressing feature redundancy by optimizing extracted features using metaheuristic algorithm techniques, achieving a higher accuracy optimal feature dataset at lower computational costs. To counteract the neglect of modal-independent information, a hybrid fusion approach is adopted. It prioritizes visual features to extract precise semantic and emotional information from textual content, training multiple base classifiers to learn independent and diverse discriminative information. Comparative experiments on streaming datasets MOSI and MOSEI demonstrate the computational approach’s superiority in both accuracy and efficiency over existing methods.

关键词： Training Sentiment analysis Visualization Accuracy Social networking (online) Metaheuristics Semantics

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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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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province 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 Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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A Heterogeneous Graph Attention network-Based Web Service Link Prediction 3

A Heterogeneous Graph Attention Network-Based Web Service Li...

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3rd International Conference on computer Communication and the Internet, ICCCI 2021

作者： He, Wenhui Xia, Chunhe Li, Zhong Liu, Xiaochen Wang, Tianbo Beijing Key Laboratory of Network Technology Beijing China School of Computer Science and Engineering Beihang University Beijing China Guangxi Key Laboratory of Multi-Source Information Mining and Security School of Computer Science and Information Technology Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (纸本)9781728176185

With the rise of service computing, the increasing number and diversity of web services make it an intractable task to search for suitable services. Service composition, service selection and recommendation have become the focus of service computing. As the fundamental research of service network, service link prediction is used to explore the composition mode between services, which can facilitate the development of service composition, service selection and recommendation. However, the existing link prediction methods are mainly based on manual modeling and derivation, which cannot make full use of the global structure information and perform poorly in complex networks. The challenging problem in service link prediction is the heterogeneity and sparseness of the service network. Therefore, we propose a novel web service link prediction method based on a heterogeneous graph attention network. By analyzing the interaction between services, five types of neighbors that are associated with service links are chosen, and two levels of attention are applied to learn the importance of neighbors and calculate the embedding of services. In addition, in order to improve accuracy, we design a Service-TextRank algorithm to extract the key information of the service description. Extensive experimental results on real-world data-ProgrammableWeb validate the effectiveness of our approach. © 2021 IEEE.

关键词： Web services

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MSONoC: A Metasurface-assisted Hybrid Waveguide/FSO network-on-Chip Architecture

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2024年

作者： Sun, Wei Li, Jingsi Guo, Pengxing He, Xiangyu Wang, Zimo Hu, Cong Liu, Weichen Hou, Weigang Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communications and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering Nanyang Technological University Singapore

Optical networks-on-chips (ONoCs) have emerged as the most promising solution for on-chip interconnection networks, offering distinct advantages such as high bandwidth and low latency. However, traditional wired-based ONoCs often suffer from a high average number of hops, leading to challenges such as increased insertion loss, crosstalk noise, and energy consumption. Therefore, this paper presents a novel ONoC structure known as the metasurface ONoC (MSONoC), which integrates wired waveguide and free-space optical (FSO) transmission with the assistance of metasurfaces. Specifically, this work encompasses the design of a passive metasurface structure tailored for ONoCs, a virtual network cluster partitioning method, an optical metasurface access points select strategy, and a shortest-path routing algorithm. Performance verification is conducted using a modified Noxim simulator. The simulation results demonstrate that the proposed MSONoC offers advantages over traditional NoCs, radio frequency (RF)-based hybrid wired/wireless NoCs, and wired-based ONoCs in terms of throughput, delay, energy consumption, and signal-to-noise ratio under both synthetic traffic and real traffic models. These improvements proves the effectiveness of the MSONoC architecture in optimizing network performance, making it a promising solution for high-performance, energy-efficient communication in on-chip networks. © 2017 IEEE.

关键词： network-on-chip

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FusionTC: Encrypted App Traffic Classification Using Decision-Level Multimodal Fusion Learning of Flow Sequence

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Li, Shengbao Huang, Yuchuan Gao, Tianye Yang, Lanqi Chen, Yige Pan, Quanbo Zang, Tianning Chen, Fei Institute of Information Engineering Chinese Academy of Sciences Beijing100045 China School of Cyber Security University of Chinese Academy of Sciences Beijing101408 China National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch Jinan250004 China Ministry of Emergency Management Big Data Center Beijing100010 China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal University Anhui Wuhu240002 China College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325000 China National Computer Network Emergency Response Technical Team/Coordination Center of China Xinjiang Branch Urumqi830000 China

In recent years, an increasing number of mobile platforms and applications have adopted traffic encryption protocol technology to ensure privacy and security. Existing researches on encrypted traffic identification approaches often rely on a single-modal feature pattern (such as packet sequence and statistical features), which cannot fully represent the detail information of complex traffic features, and so their predictions are susceptible to anomalies. In order to improve the effect of classification on encrypted app traffic, we propose FusionTC, a novel app traffic classification framework based on feature fusion of flow sequence. FusionTC consists of two-level subclassifiers, which are used to perform decision-level fusion of multimodal features by an upgraded stacking method. The comprehensive capture and fusion of multimodal traffic details, coupled with the refined processing and segmentation of traffic, enables FusionTC to significantly promote classification accuracy and enhance robustness in challenging situations. Based on our self-built app traffic dataset, FusionTC improves the accuracy by at least 3.2% over the state-of-the-art approaches. © 2023 Shengbao Li et al.

关键词： Cryptography

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Several new classes of optimal ternary cyclic codes with two or three zeros

arXiv

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arXiv 2024年

作者： Wu, Gaofei You, Zhuohui Zha, Zhengbang Zhang, Yuqing The State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xidian University Xi’an710071 China The Advanced Cryptography System Security Key Laboratory of Sichuan Province China The College of Mathematics and System Sciences Xinjiang University Urumqi830046 China The National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing101408 China

Cyclic codes are a subclass of linear codes and have wide applications in data storage systems, communication systems and consumer electronics due to their efficient encoding and decoding algorithms. Let α be a generator of F∗3m, where m is a positive integer. Denote by C(i1,i2,···,it) the cyclic code with generator polynomial mαi1 (x)mαi2 (x) · · · mαit (x), where mαi(x) is the minimal polynomial of αi over F3. In this paper, by analyzing the solutions of certain equations over finite fields, we present four classes of optimal ternary cyclic codes C(0,1,e) and C(1,e,s) with parameters [3m − 1, 3m − 3m/2 − 2, 4], where s = 3m−1/2 . In addition, by determining the solutions of certain equations and analyzing the irreducible factors of certain polynomials over F3m, we present four classes of optimal ternary cyclic codes C(2,e) and C(1,e) with parameters [3m−1, 3m−2m−1, 4]. We show that our new optimal cyclic codes are inequivalent to the known ones. Copyright © 2024, The Authors. All rights reserved.

关键词： Polynomials

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Detecting Unknown HTTP-based Malicious Communication Behavior via Generated Adversarial Flows and Hierarchical Traffic Features

arXiv

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arXiv 2023年

作者： Yun, Xiaochun Xie, Jiang Li, Shuhao Zhang, Yongzheng Sun, Peishuai National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Malicious communication behavior is the network communication behavior generated by malware (botnet, spyware, etc.) after victim devices are infected. Experienced adversaries often hide malicious information in HTTP traffic to evade detection. However, related detection methods have inadequate generalization ability because they are usually based on artificial feature engineering and outmoded datasets. In this paper, we propose an HTTP-based Malicious Communication traffic Detection Model (HMCD-Model) based on generated adversarial flows and hierarchical traffic features. HMCD-Model consists of two parts. The first is a generation algorithm based on WGAN-GP to generate HTTP-based malicious communication traffic for data enhancement. The second is a hybrid neural network based on CNN and LSTM to extract hierarchical spatial-temporal features of HTTP-based traffic. In addition, we collect and publish a dataset, HMCT-2020, which consists of large-scale malicious and benign traffic during three years (2018-2020). Taking the data in HMCT-2020(18) as the training set and the data in other datasets as the test set, the experimental results show that the HMCD-Model can effectively detect unknown HTTP-based malicious communication traffic. It can reach F1 ≈ 98.66% in the dataset HMCT-2020(19-20), F1 ≈ 90.69% in the public dataset CIC-IDS-2017 and F1 ≈ 83.66% in the real traffic, which is 20+% higher than other representative methods on average. This validates that HMCD-Model has the ability to discover unknown HTTP-based malicious communication behavior. © 2023, CC0.

关键词： HTTP

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HSTF-Model: an HTTP-based Trojan Detection Model via the Hierarchical Spatio-Temporal Features of Traffics

arXiv

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arXiv 2023年

作者： Xie, Jiang Li, Shuhao Yun, Xiaochun Zhang, Yongzheng Chang, Peng Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

HTTP-based Trojan is extremely threatening, and it is difficult to be effectively detected because of its concealment and confusion. Previous detection methods usually are with poor generalization ability due to outdated datasets and reliance on manual feature extraction, which makes these methods always perform well under their private dataset, but poorly or even fail to work in real network environment. In this paper, we propose an HTTP-based Trojan detection model via the Hierarchical Spatio-Temporal Features of traffics (HSTF-Model) based on the formalized description of traffic spatio-temporal behavior from both packet level and flow level. In this model, we employ Convolutional Neural network (CNN) to extract spatial information and Long Short-Term Memory (LSTM) to extract temporal information. In addition, we present a dataset consisting of Benign and Trojan HTTP Traffic (BTHT-2018). Experimental results show that our model can guarantee high accuracy (the F1 of 98.62%∼99.81% and the FPR of 0.34%∼0.02% in BTHT-2018). More importantly, our model has a huge advantage over other related methods in generalization ability. HSTF-Model trained with BTHT-2018 can reach the F1 of 93.51% on the public dataset ISCX-2012, which is 20+% better than the best of related machine learning methods. © 2023, CC BY.

关键词： HTTP

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