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arXiv 2021年

作者： Huang, Junjie Shen, Huawei Hou, Liang Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies on real-world datasets to analyze the social mechanism in signed directed networks. Guided by related sociological theories, we propose a novel Signed Directed Graph Neural networks model named SDGNN to learn node embeddings for signed directed networks. The proposed model simultaneously reconstructs link signs, link directions, and signed directed triangles. We validate our model’s effectiveness on five real-world datasets, which are commonly used as the benchmark for signed network embeddings. Experiments demonstrate the proposed model outperforms existing models, including feature-based methods, network embedding methods, and several GNN methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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On the Robustness of Generative Information Retrieval Models

arXiv

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arXiv 2024年

作者： Liu, Yu-An Zhang, Ruqing Guo, Jiafeng Zhou, Changjiang de Rijke, Maarten Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China University of Amsterdam Netherlands

Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative information retrieval (IR) models. Less attention has been paid to the robustness of these models. It is critical to assess the out-of-distribution (OOD) generalization of generative IR models, i.e., how would such models generalize to new distributions? To answer this question, we focus on OOD scenarios from four perspectives in retrieval problems: (i) query variations;(ii) unseen query types;(iii) unseen tasks;and (iv) corpus expansion. Based on this taxonomy, we conduct empirical studies to analyze the OOD robustness of representative generative IR models against dense retrieval models. Our empirical results indicate that the OOD robustness of generative IR models is in need of improvement By inspecting the OOD robustness of generative IR models we aim to contribute to the development of more reliable IR models. The code is available at https://***/Davion-Liu/GR_OOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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The latest breakthrough on NLRP6 inflammasome

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Precision Clinical Medicine 2022年第3期5卷 187-191页

作者： Runzhi Li Yang Zan Kaiwen Sui Shu Zhu Institute of Immunology CAS Key Laboratory of Innate Immunity and Chronic DiseaseSchool of Basic Medical SciencesDivision of Life Science and MedicineUniversity of Science and Technology of ChinaHefei 230027China School of Data Science University of Science and Technology of ChinaHefei 230026China Institute of Health and Medicine Hefei Comprehensive National Science CenterHefei 230051China

NLRP6,a Nod-like receptor family member,has been shown to affect intestinal homeostasis and microbial colonization through organizing a huge protein complex called ***6 inflammasome promotes the cleavage and secretion of inflammatory cytokines or the cleavage of pore-forming Gasdermin D to initiate the inflammatory cell death called pyroptosis,which plays important roles in responding to pathogen ***,questions about the ligand(s)that trigger NLRP6 inflammasome activation,or the mechanisms that how a ligand triggers NLRP6 inflammasome assembly,are *** this mini-review,we summarize the current understandings of ligand recognition of NLRP6,the role of liquid-liquid phase separation in NLRP6 inflammasome assembly,and potential links with human health and diseases.

关键词： NLRP6 inflammasome phase separation human disease

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Plagiarism detection of multi-threaded programs using frequent behavioral pattern mining 32

Plagiarism detection of multi-threaded programs using freque...

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32nd International Conference on Software Engineering and Knowledge Engineering, SEKE 2020

作者： Wang, Qing Tian, Zhenzhou Gao, Cong Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China College of Information Sciences and Technology Pennsylvania State University PA United States

ISBN: (纸本)1891706500

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program's behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multithreaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth cope better with multi-threaded plagiarism detection than alternative approaches. © 2020 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Recent advances in ethanol gas sensors based on metal oxide semiconductor heterojunctions

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Rare Metals 2022年第6期41卷 1818-1842页

作者： Ling-Yun Gai Run-Ping Lai Xian-Hui Dong Xing Wu Qiao-Tong Luan Jue Wang Hao-Feng Lin Wen-Hao Ding Guang-Lei Wu Wan-Feng Xie Big Data and Network Management Center Qingdao Agricultural UniversityQingdao 266109China School of Mechanical Electronic and Information Engineering China University of Mining and Technology-BeijingBeijing 10083China School of Material Science and Engineering State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China School of Electronics and Information State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic EngineeringEast China Normal UniversityShanghai 200241China

Metal oxide semiconductor heterojunctions(MOSHs)can enhance the performance of ethanol gas sen-sors *** gas sensors based on MOSHs are cost-effective and have excellent sensing response,good selectivity,fast response and recovery,long-term stability or repeatability,a low operating temperature,a facile fabrica-tion process,and versatile *** paper reviews the recent advances in gas sensors that are based on MOSHs and the advantages of using them to detect ethanol *** to the literature,compared with ethanol gas sen-sors that use single-component sensing materials,the MOSHs exhibit superior performance due to the synergy between the different components,which can amplify the reception and transduction components of the sensor *** the best of our knowledge,heterojunctions can be grouped into four main categories as metal oxide/metal oxide,metal oxide/metal sulfide,metal oxide/noble metal,and metal oxide/other materials,including rare-earth metals,g-C_(3)N_(4),and graphene,*** future trends and challenges that would be faced in the development of ethanol gas sensors based on MOSHs are discussed in ***,critical ideas and thinking regarding the future progress of MOSH-based gas sensors are presented.

关键词： Metal oxides Heterojunction Chemiresistori Ethanol sensor Synergistic behavior

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Towards fine-grained compiler identification with neural modeling 32

Towards fine-grained compiler identification with neural mod...

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32nd International Conference on Software Engineering and Knowledge Engineering, SEKE 2020

作者： Xie, Borun Tian, Zhenzhou Gao, Cong Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China College of Information Sciences and Technology Pennsylvania State University PA United States

ISBN: (纸本)1891706500

Different compilers and optimization levels can be used to compile the source code. Revealed in reverse from the produced binaries, these compiler details facilitate essential binary analysis tasks, such as malware forensics and binary code similarity analysis. Most existing approaches adopt a signature matching based or machine learning based strategy to identify the compiler details, showing limits in either the detection accuracy or granularity. In this work, we propose NeuralCI (Neural modeling-based Compiler Identification) to perform the identification of compiler family and optimization level on individual functions. The basic idea is to formulate sophisticated neural networks to process abstracted instruction sequences generated using a lightweight function abstraction strategy. To evaluate the performance of NeuralCI, a large dataset consisting of 413,119 unique functions collected from real-world projects is constructed. The experiments show that NeuralCI achieves over 99% and 90% accuracy in identifying the compiler family and optimization level respectively, which outperforms most state-of-the-art function level compiler identification methods. Also, we explore for the first time the possibility of conducting compiler identification on binary code snippets rather than complete functions, where NeuralCI still achieves 96% accuracy, indicating its ability of capturing subtle yet significant features. © 2020 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Binary codes

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Efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0

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science China(Information sciences) 2022年第1期65卷 192-206页

作者： Chenyu WANG Ding WANG Guoai XU Debiao HE School of Cyber Security Beijing University of Posts and Telecommunications State Key Laboratory of Cryptology College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Nankai University School of Cyber Science and Engineering Wuhan University

Industry 4.0, which combines information technology, network and industrial production, is expected to have a tremendous impact on our daily lives. In such a complex and security-critical system with resource-constrained sensor nodes, the design of a secure user authentication scheme for preventing real-time data from unauthorized access is full of challenges, and the main crux lies in how to realize the important property of forward secrecy. Existing schemes either fail to achieve forward secrecy or achieve forward secrecy with high computation cost on sensor nodes. Besides, they often fail to conform to the development trend of industry 4.0 systems where a cloud center is necessary to help intelligent decision-making and alleviate computation and storage pressure. Therefore, in this paper, we propose an efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0, and formally prove its security in the random oracle model. Compared with previous schemes, it has three advantages:(1) all eleven state-of-the-art criteria are achieved;(2) its computation cost on sensor nodes is comparable to those insecure schemes that employ only symmetric cryptographic algorithms, and is superior to those that also use asymmetric cryptographic algorithms;(3) it takes the advantage of the computation and storage capabilities of the cloud center to achieve user anonymity and the resistance to offline dictionary attack without performing any asymmetric cryptographic algorithms on gateways. Our computation cost on gateways is the smallest among all state-of-the-art relevant schemes for comparison.

关键词： industry 4.0 wireless sensor networks password authentication forward secrecy offline dictionary attack

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Hepatocellular carcinoma redirects to ketolysis for progression under nutrition deprivation stress

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细胞研究（英文版） 2016年第10期26卷 1112-1130页

作者： De Huang Tingting Li Lin Wang Long Zhang Ronghui Yan Kui Li Songge Xing CAS Key laboratory of Innate Immunity and Chronic Disease Innovation Center for Cell Signaling Network School of Life Science Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of China Hefei Anhui 230027 China CAS Key laboratory of Innate Immunity and Chronic Disease Innovation Center for Cell Signaling Network School of Life Science Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of China Hefei Anhui 230027 China Anhui Key Laboratory of Hepatopancreatobiliary Surgery Department of General Surgery Anhui Provincial Hospital Hefei Anhui 230001 China

Cancer cells are known for their capacity to rewire metabolic pathways to support survival and proliferation under various stress conditions. Ketone bodies, though produced in the liver, are not consumed in normal adult liver cells. We find here that ketone catabolism or ketolysis is re-activated in hepatocellular carcinoma (HCC) cells under nutrition deprivation conditions. Mechanistically, 3-oxoacid CoA-transferase 1 (OXCT1), a rate-limiting ketolytic enzyme whose expression is suppressed in normal adult liver tissues, is re-induced by serum starvation-triggered mTORC2-AKT-SP1 signaling in HCC cells. Moreover, we observe that enhanced ketolysis in HCC is critical for repression of AMPK activation and protects HCC cells from excessive autophagy, thereby enhancing tumor growth. Importantly, analysis of clinical HCC samples reveals that increased OXCT1 expression predicts higher patient mortality. Taken together, we uncover here a novel metabolic adaptation by which nutrition-deprived HCC cells employ ketone bodies for energy supply and cancer progression.

关键词： ketone bodies HCC OXCT1 AKT AMPK autophagy

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Information and Sensing Beamforming Optimization for Multi-User Multi-Target MIMO ISAC Systems 48

Information and Sensing Beamforming Optimization for Multi-U...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, IcasSP 2023

作者： Zhu, Minghe Li, Lei Xia, Shuqiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science & Engineering Shenzhen China Shenzhen Research Institute of Big Data China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9781728163277

In this paper, we consider the joint beamforming design for simultaneous sensing and communication in a wireless multi-user system. Different from the existing works that mostly are for single target, we consider sensing the channel parameters of multiple targets while communicating with multiple users. The design goal is to minimize a weighted sum of the Cramer-Rao bounds (CRB) of target parameters subject to the communication sum rate and transmission power constraints. While the classical weighted minimum mean square error (WMMSE) and semidefinite relaxation (SDR) can be used to handle the problem, we propose to reformulate the problem into a max-min form, by leveraging the tightness of SDR, and solve it by a low-complex first-order method. Numerical results not only demonstrate the computation efficiency of the proposed algorithm but also its effectiveness in enhancing the sensing performance in practice. © 2023 IEEE.

关键词： Mean square error

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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