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International Conference on Computer Supported Cooperative Work in Design

作者： Wanshan Liu Yubing Han Guijuan Wang Yao Huang Anming Dong Jiguo Yu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The Internet of Things (IoT) connects numerous de-vices and sensors, generating data with significant informational and economic value. However, data silos hinder effective data utilization and trading, leading to the dispersion of data across various devices and systems. Additionally, traditional third-party trading models face challenges related to data security and trust. To address these issues, this paper proposes a secure data trading framework based on a consortium blockchain and designs a corresponding solution. Specifically, it introduces the integration of zero-knowledge proofs into the smart contract scheme for authenticity and integrity verification of transaction data. From the perspective of IoT device users, this paper aims to enable secure data trading through a decentralized platform, using off-chain storage methods to reduce the blockchain's data burden while ensuring security and privacy. Off-chain storage encrypts and securely stores sensitive data, recording only necessary information on the blockchain, effectively protecting user privacy. To validate the practicality of the proposed solution, experiments were conducted using Hyperledger Fabric, demonstrating its feasibility in facilitating secure storage and trustworthy trading of IoT data. Finally, this study analyzes the experimental results and offers valuable insights for future research.

关键词： data privacy Limiting Scalability Smart contracts Reliability engineering Blockchains Sensors Recording Internet of Things Secure storage

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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A k-Nearest-Neighbor Machine Translation Method Combining Certainty Factor and Region Density 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Qi, Rui Shi, Xiangyu Man, Zhibo Xu, Jinan Chen, Yufeng Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Lab of Traffic Data Analysis and Mining Beijing100044 China

ISBN: (纸本)9780000000002

k-Nearest-Neighbor Machine Translation (kNN-MT) is an important research direction in the field of neural machine translation in recent years. It can improve the translation quality without updating the model, but there are still many problems to be solved: the number of each label in training data is not balanced, and the fixed k value can not produce good translation results for the data in different density regions. In this paper, we propose an innovative KNN-MT method to reduce the influence of imbalanced dataset by introducing a certainty factor (CF), and to optimize the search effect by adjusting the k value dynamically according to the data density around the test point. On the multi-domain German-English translation dataset, compared with the baseline experiment, our method has improved the translation performance in all four domains, three of which have improved by more than 1 BLEU. © 2024 China National Conference on Computational Linguistics.

关键词： Nearest neighbor search

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

arXiv

引用

arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China

Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing methods consider molecular generation for protein pockets independently while neglecting the underlying connections such as subpocket-level similarities. Subpockets are the local protein environments of lig- and fragments and pockets with similar subpockets may bind the same molecular fragment (motif) even though their overall structures are different. Therefore, the trained models can hardly generalize to unseen protein pockets in real-world applications. In this paper, we propose a novel method DrugGPS for generalizable structure-based drug design. With the biochemical priors, we propose to learn subpocket prototypes and construct a global interaction graph to model the interactions between subpocket prototypes and molecular motifs. Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule generation scheme are used to improve the model’s performance. The experimental results show that our model consistently outperforms baselines in generating realistic drug candidates with high affinities in challenging out-of-distribution settings. © 2023, CC BY-NC-SA.

关键词： Proteins

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MDEC: Mamba-based Debiased Extended Contrast Learning in Sequential Recommendation

MDEC: Mamba-based Debiased Extended Contrast Learning in Seq...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yan Huang Zhenyu Yang Baojie Xu Wenyue Hu Zhibo Zhang 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 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Recommender systems are critical for mitigating information overload, assisting users in uncovering their latent interests, and enhancing their overall experience. Sequential recommendation leverages users' historical interaction sequences to predict dynamic interests more effectively than traditional rec-ommendation approaches. However, existing models-including RNN-based and Transformer-based methods-face significant limitations. RNNs struggle with vanishing gradients and long-term dependency capture, while Transformers, though effective for long-range relationships, suffer from computational inefficiency due to their quadratic attention complexity. Recent advancements have employed contrastive learning for sequential recommendation, aiming to enhance the consistency between augmented views and improve self-supervised learning signals. Despite their promise, these methods often lack diversity in data augmentation strategies, which restricts their capacity for bias mitigation, resulting in augmented data that still retains inherent biases. To address these challenges, we propose MDEC, a novel sequential modeling framework that leverages State Space Models (SSM) combined with unbiased contrastive learning. MDEC utilizes Mamba to efficiently model user preferences as an alternative to Transformer-based models. Additionally, it integrates graph-based information, including item transition and co-interaction data, to improve data augmentation comprehensively. Finally, we introduce adaptive anchor-enhanced contrastive learning, which adaptively utilizes augmented samples to improve representation quality and bias mitigation. Extensive experiments on multiple datasets demonstrate that MDEC significantly out-performs existing models, showcasing improved efficiency, better mitigation of biases, and enhanced recommendation quality. Code is available at https://***/Echohuangyan/CSLP.

关键词： Adaptation models Federated learning Prevention and mitigation Computational modeling data visualization Contrastive learning Diversity methods Transformers data augmentation Recommender systems

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TSEN: TRANSFORMER AND SNOWBALL GRAPH CONVOLUTION LEARNING FOR BRAIN FUNCTIONAL network CLASSIFICATION

arXiv

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arXiv 2023年

作者： Hu, Jinlong Huang, Yangmin Dong, Shoubin Guangdong Key Lab of Communication and Computer Network School of Computer Science and Engineering South China University of Technology Guangzhou China

Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and predict brain disorders. In this paper, we proposed a novel Transformer and snowball encoding networks (TSEN) for brain functional network classification, which introduced Transformer architecture with graph snowball connection into GNNs for learning whole-graph representation. TSEN combined graph snowball connection with graph Transformer by snowball encoding layers, which enhanced the power to capture multi-scale information and global patterns of brain functional networks. TSEN also introduced snowball graph convolution as position embedding in Transformer structure, which was a simple yet effective method for capturing local patterns naturally. We evaluated the proposed model by two large-scale brain functional network datasets from autism spectrum disorder and major depressive disorder respectively, and the results demonstrated that TSEN outperformed the state-of-the-art GNN models and the graph-transformer based GNN models. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Transformer and Snowball Graph Convolution Learning for Brain Functional network Analysis

Transformer and Snowball Graph Convolution Learning for Brai...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinlong Hu Yangmin Huang Shoubin Dong School of Computer Science and Engineering Guangdong Key Lab of Communication and Computer Network South China University of Technology Guangzhou China

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