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2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022

作者： Wu, Lin Xie, Y.L. Wu, Yafeng Liang, Jinyuan Li, Xuan School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security Wuhan China Nsfocus Inc. A9-3/F Optical Valley Software Park Wuhan China

ISBN: (数字)9781665496919

ISBN: (纸本)9781665496919

Malicious intrusion presents normalization and diversification in the current environment. Attackers often evade detection by changing the execution order of programs or adding many confusing operations. Aiming at the problem that the existing methods cannot accurately detect abnormal variant sequences, we propose PSSID, an intrusion detection method based on provenance sequence similarity. Firstly, the system data is transformed into a provenance graph, and each path in the provenance graph is regarded as a provenance sequence. Then the provenance sequence and the rule database sequence are compared to find the maximum common sequence length. Finally, according to the distribution of sequence lengths, obtain the two sequences' similarity value to judge whether it is abnormal. © 2022 IEEE.

关键词： Intrusion detection

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Dual Space Multiple Instance Representative Learning for Medical Image Classification 33

Dual Space Multiple Instance Representative Learning for Med...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Zhang, Xiaoxian Huang, Sheng Zhang, Yi Gao, Mingchen Liu, Chen Zhang, Xiaohong School of Big Data & Software Engineering Chongqing University Chongqing China Department of Computer Science and Engineering University at Buffalo SUNY New York United States Department of Radiology First Affiliated Hospital of Army Medical University Chongqing China

Medical image classification plays a vital role in AI-aided medical diagnosis and is often addressed as a Multiple Instance Learning (MIL) issue (i.e., each sample is a bag of instances). For medical images, the disease area or the discriminative area is usually smaller than the whole tissue. In other words, most of the instances in a bag are irrelevant and could interfere with the bag label inference. To address this issue, we add an instance representative selection process before MIL and propose a novel MIL approach named Dual Space Multiple Instance Representative Learning (DSMIRL). DSMIRL consists of two core steps, Adaptive Instance Representative Selection (AIRS) and Multiple Instance Representative Learning (MIRL). In AIRS, the instances in the same bag are grouped into different sub-bags via clustering, and only one sub-bag is selected as the final collection of instance representatives by ranking the maximum instance predictions of sub-bags, thus adaptively filtering out the irrelevant instances. In MIRL, we perform aggregations on the selected instance representatives in label and feature spaces to further exploit the complementary information of the two spaces. Finally, these two steps are iteratively conducted in each iteration to optimize all modules of DSMIRL progressively. Extensive experiments on five standard MIL benchmarks and two medical image datasets demonstrate the promising performance of DSMIRL over the state-of-the-art MIL approaches. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Benchmarking

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ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect

arXiv

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arXiv 2024年

作者： Li, Dachong Li, Li Chen, Zhuangzhuang Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Mathematical Sciences Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology China

Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that 3 × 3 convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at https://***/lidc54/shift-wiseConv. © 2024, CC BY-NC-SA.

关键词： Convolution

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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Spatial-Temporal Motion Compensation for Learned Video Compression

Spatial-Temporal Motion Compensation for Learned Video Compr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qian Huang Wenting Liu Hao Lu Yiming Wang College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the past few years, learned video compression has received increasing attention. However, most current learned methods rely on the bilinear warping operations in motion compensation, which is equivalent to a low-pass filtering operation and brings the distortion of reconstruction. To address this problem, we propose a spatial-temporal motion compensation for learned video compression (STMC-LVC). STMC-LVC uses a spatial-temporal motion compensation network (STMC-Net) to fully consider the spatial-temporal correlation between successive frames, performing accurate motion compensation. Specifically, STMC-Net mainly consists of the initial feature prediction module and the fusion module. The initial feature prediction module predicts reference features based on DCN to provide sufficient information for motion compensation. The fusion module obtains temporal attention information by calculating the similarity between features, performs spatial attention operations, and finally performs spatial-temporal fusion, further improving the robustness of motion compensation. In addition, STMC-LVC uses a conditional coding framework. We use a concatenation of the current feature and predicted feature as context to explore spatial-temporal correlations in feature space. Experimental results show that our method effectively improves video compression. Specifically, our model achieves an average of 33.86% and 58.70% bitrate savings than x265 (veryslow) on PSNR and MS-SSIM, respectively.

关键词： Correlation Codecs Filtering Video compression Distortion Motion compensation Robustness Encoding Complexity theory Cybernetics

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Leveraging Interactive Paths for Sequential Recommendation 28th

Leveraging Interactive Paths for Sequential Recommendation

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Li, Aoran Zang, Yalei Wang, Yani Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China

ISBN: (纸本)9783031306716

Sequential recommendation systems dynamically predict the users’ next behaviors from chronological historical records to provide more accurate recommendations. However, in many real-world scenarios, recommenders find it difficult to make recommendations for cold-start users who have limited interactions with items. To address this issue, existing methods utilize auxiliary information to capture user preferences, which have achieved performance improvement to some extent. But with the gradual awareness of personal privacy, external information becomes difficult to obtain. Therefore, we develop a novel model called Path-based sequential Recommender for cold-start users (PathRec). PathRec exploits the rich sequential dependencies within the interactive paths to give reasonable recommendations for cold-start users without additional external information. Specifically, (i) PathRec constructs the global graph to learn the representations of the items. (ii) Then, PathRec elaborately selects the sampled paths to derive the underlying dependencies of the cold-start sequence. (iii) Finally, PathRec provides rich and complementary information to user-item interactions by strongly coupling the paths to give suitable recommendations. Extensive experiments on three datasets demonstrate the effectiveness of PathRec. In particular, our model can adapt to the cold-start users and has potential interpretability for recommendations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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Predictive Scheme for Mixed Transmission in Time-Sensitive Networking

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ZTE Communications 2022年第4期20卷 78-88页

作者： LI Zonghui YANG Siqi YU Jinghai HE Fei SHI Qingjiang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China ZTE Corporation Shenzhen 518057China School of Software Tsinghua UniversityBeijing 100084China School of Software Engineering Tongji UniversityShanghai 201804China Shenzhen Research Institute of Big Data Shenzhen 518172China

Time-sensitive networking(TSN)is an important research area for updating the infrastructure of industrial Internet of *** a product of the integration of the operation technology(OT)and the information technology(IT),it meets the real-time and deterministic nature of industrial control and is compatible with Ethernet to support the mixed transmission of industrial control data and Ethernet *** paper systematically summarizes and analyzes the shortcomings of the current mixed transmission technologies of the bursty flows and the periodic *** conquer these shortages,we propose a predictive mixed-transmission scheme of the bursty flows and the periodic *** core idea is to use the predictability of timetriggered transmission of TSN to further reduce bandwidth loss of the previous mixed-transmission *** paper formalizes the probabilistic model of the predictive mixed transmission mechanism and proves that the proposed mecha⁃nism can effectively reduce the loss of ***,based on the formalized probabilistic model,we simulate the bandwidth loss of the proposed *** results demonstrate that compared with the previous mixed-transmission method,the bandwidth loss of the pro⁃posed mechanism achieves a 79.48%reduction on average.

关键词： time-sensitive networking 802.1Qbv 802.1Qbu guard band strategy preemption strategy

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Integrating Branching and Pruning for Efficient Hyperdimensional Computing

Integrating Branching and Pruning for Efficient Hyperdimensi...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Jing Liu Zhiqian Guan Di Liu Shengfa Miao Fei Dai School of Software Yunnan University China Department of Computer Science Norwegian University of Science and Technology Norway School of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

关键词： Energy consumption Accuracy Computational modeling Memory management Predictive models Brain modeling Encoding

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