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42nd Chinese Control Conference, CCC 2023

作者： Chen, Tianrui Tuo, Shouheng Feng, Zengyu School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9789887581543

The identification of drug-protein interactions (DTIs) is a critical step in drug development and repositioning. However, detecting these interactions using scientific methods presents a formidable challenge. Existing models often struggle to extract relevant information from drug-protein sequences, and they fail to consider pre-existing interactions between pairs. Here, we introduce a deep learning model, DPAG, based on attention mechanisms and graph neural networks, which overcomes these limitations. DPAG leverages information from both the drug and protein sequences, as well as the association between drug-protein pairs, to predict DTIs. We conducted multiple experiments to evaluate the performance of the model in benchmark datasets, and the results showed that DPAG achieved excellent performance in terms of AUC, AUPR, precision, and recall. Our findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and repositioning. Code is available at https://***/veresse/DPAG. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Graph neural networks

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Optimal algorithm for profiling dynamic arrays with finite values 22

Optimal algorithm for profiling dynamic arrays with finite v...

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22nd International Conference on Extending database technology, EDBT 2019

作者： Yang, Dingcheng Yu, Wenjian Deng, Junhui Liu, Shenghua BNRist Dept. Computer Science and Tech. Tsinghua Univ. Beijing China CAS Key Lab. Network Data Science and Tech. Inst. Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783893180813

How can one quickly answer the most and top popular objects at any time, given a large log stream in a system of billions of users? It is equivalent to find the mode and top-frequent elements in a dynamic array corresponding to the log stream. However, most existing work either restrain the dynamic array within a sliding window, or do not take advantages of only one element can be added or removed in a log stream. Therefore, we propose a profiling algorithm, named S-Profile, which is of O(1) time complexity for every updating of the dynamic array, and optimal in terms of computational complexity. With the profiling results, answering the queries on the statistics of dynamic array becomes trivial and fast. With the experiments of various settings of dynamic arrays, our accurate S-Profile algorithm outperforms the existing methods, showing at least 2X speedup to the heap based approach and 13X speedup to the balanced tree based approach. © 2019 Copyright held by the owner/author(s).

关键词： Trees (mathematics)

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CLSDRL:A routing optimization method for traffic feature extraction

CLSDRL:A routing optimization method for traffic feature ext...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Xia, Hong Li, Jianguo Chen, Yanping Lv, Ning Wang, ZhongMin Dong, Qingyi Xi'an University of Posts and Telecommunications School of Computer Science and Technology Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665441582

In order to solve the impact of the temporal and spatial characteristics of traffic on network routing optimization, this paper proposes convolution long-short memory neural network deep reinforcement learning (CLSDRL) model for routing optimization. The CLSDRL model consists of deep deterministic policy gradients (DDPG) deep couple with convolution neural network (CNN) and long-short memory neural network (LSTM). After extracting the spatial and temporal characteristics of network traffic with CNN and LSTM, routing decisions are made with DDPG algorithm. Experiments are conducted under different load intensities, and the network performance is evaluated by the average network delay and packet loss rate, experimental results show that this method can improve significantly network performance. © 2021 IEEE.

关键词： Reinforcement learning

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LCS-based Visual Recognition for AGV Guide Paths under Complex Illumination Conditions 15

LCS-based Visual Recognition for AGV Guide Paths under Compl...

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15th International Conference on Measurement technology and Mechanical Automation, ICMTMA 2023

作者： Wang, Z.M. Li, M. Gao, C. Jin, X.M. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

Complex lighting is one of the most challenging problems in automatic guided vehicle (AGV) vision recognition system. In order to overcome the influence of uneven illumination on the accuracy and robustness of path recognition, this paper proposes the LCS based visual recognition for AGV guide paths under complex illumination conditions method, which converts the collected image into an invariant image through logarithmic chromaticity space (LCS) to eliminate the influence of illumination, and the minimum average entropy angle is used as the projection angle to generate the invariant image to improve the speed of image conversion to the invariant image. Experimental results show that the proposed method can effectively improve the robustness of AGV vision recognition system under complex lighting conditions. © Published under licence by IOP Publishing Ltd.

关键词： Lighting

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EagleMine: Vision-guided mining in large graphs

arXiv

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arXiv 2017年

作者： Feng, Wenjie Liu, Shenghua Faloutsos, Christos Hooi, Bryan Shen, Huawei Cheng, Xueqi Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Carnegie Mellon University

Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics, and how can we detect them and separate anomalous nodes in a way similar to human vision? In this paper, we propose a vision-guided algorithm, EagleMine, to summarize micro-cluster patterns in two-dimensional histogram plots constructed from node features in a large graph. EagleMine utilizes a water-level tree to capture cluster structures according to vision-based intuition at multi-resolutions. EagleMine traverses the waterlevel tree from the root and adopts statistical hypothesis tests to determine the optimal clusters that should be fitted along the path, and summarizes each cluster with a truncated Gaussian distribution. Experiments on real data show that our method can find truncated and overlapped elliptical clusters, even when some baseline methods split one visual cluster into pieces with Gaussian spheres. To identify potentially anomalous micro-clusters, EagleMine also a designates score to measure the suspiciousness of outlier groups (i.e. node clusters) and outlier nodes, detecting bots and anomalous users with high accuracy in the real Microblog data. Copyright © 2017, The Authors. All rights reserved.

关键词： Statistics

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User Information Enhanced Knowledge Graph Convolutional networks for Recommender Systems 14

User Information Enhanced Knowledge Graph Convolutional Netw...

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14th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Hao, Junyu Li, Xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an71012 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves' influence diffusion of information of user and items in a unified framework. The model combines user information and user-item interaction information into a user knowledge graph, which advances KGCN by injecting high-order user potential information reflected in user graph and high-order influence of items reflected in item knowledge graph. In addition, we have designed the influence factors of different items on users, which are used to characterize the weight of items on the user's information dissemination, so as to distinguish users with different information. Through extensive experiments on real-world datasets, we demonstrate that KGCN++ achieves substantial gains in a variety of ecenarios, including movie and book recommendation, better performance compared to the other methods. © 2022 IEEE

关键词： Recommender systems

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One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment 28

One Comment from One Perspective: An Effective Strategy for ...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Huo, Tengfei Liu, Zhiqiang Zhang, Jinchao Zhou, Jie CAS Key Lab of Network Data Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

ISBN: (纸本)9781952148279

The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Music

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eGPS 1.0: comprehensive software for multi-omic and evolutionary analyses

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National science Review 2019年第5期6卷 867-869页

作者： Dalang Yu Lili Dong Fangqi Yan Hailong Mu Bixia Tang Xiao Yang Tao Zeng Qing Zhou Feng Gao Zhonghuang Wang Ziqian Hao Hongen Kang Yi Zheng Hongwei Huang Yuzhang Wei Wei Pan Yaochen Xu Junwei Zhu Shilei Zhao Ciran Wang Pengyu Wang Long Dai Mushan Li Li Lan Yiwei Wang Hua Chen Yi-Xue Li Yun-Xin Fu Zhen Shao Yiming Bao Fangqing Zhao Luo-Nan Chen Guo-Qing Zhang Wenming Zhao Haipeng Li CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesUniversity of Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of Sciences BIG Data Center Beijing Institute of GenomicsChinese Academy of Sciences Shanghai Center for Bioinformation Technology Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences CAS Laboratory of Genomic and Precision Medicine Beijing Institute of Genomics Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Department of Biostatistics and Data Science School of Public Health University of Texas Health Science Center at Houston Computational Genomics Lab Beijing Institutes of Life Science Chinese Academy of Sciences Shanghai Research Center for Brain Science and Brain-Inspired Intelligence

It has become increasingly challenging for researchers to analyze the exponentially expanding amount of multi-omic ***, we describe multi-functional software named evolutionary GenotypePhenotype Systems (eGPS) that en... 详细信息

关键词： GPS eGPS 1.0 comprehensive software for multi-omic and evolutionary analyses

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Microbiologically Influenced Corrosion Behavior of Fe_(40)(CoCrMnNi)_(60) and Fe_(60)(CoCrMnNi)_(40) Medium Entropy Alloys in the Presence of Pseudomonas Aeruginosa

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Acta Metallurgica Sinica(English Letters) 2023年第3期36卷 379-390页

作者： Weiwei Chang Yangyang Li Huaibei Zheng Hongchang Qian Dawei Guo Shuyuan Zhang Yuntian Lou Chi Tat Kwok Lap Mou Tam Dawei Zhang National Materials Corrosion and Protection Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528399China State Key Laboratory of Metal Material for Marine Equipment and Application Anshan 114002China Department of Electromechanical Engineering University of MacaoMacao 999078China Institute for the Development and Quality Macao 999078China

In this work,the microbiologically influenced corrosion(MIC)of Fe_(40)(CoCrMnNi)_(60) and Fe_(60)(CoCrMnNi)_(40) medium entropy alloys(MEAs)induced by Pseudomonas aeruginosa(***)was *** behaviors during 14 days of immersion in sterile and ***-inoculated culture media are *** sterile conditions,both MEAs exhibited good corrosion resistance against the culture medium *** the presence of ***,the pitting corrosion of MEAs was *** results of inductively coupled plasma‒mass spectrometry(ICP‒MS)and potentiodynamic polarization tests showed that the presence of *** promoted the selective dissolution of passive film and accelerated the corrosion of *** results of X-ray photoelectron spectroscopy(XPS)and Mott-Schottky measurements further demonstrated the degradation effect of *** on the passive *** with Fe_(60)(CoCrMnNi)_(40),Fe_(40)(CoCrMnNi)_(60) manifested better resistance to the MIC caused by ***,which may be attributed to more Cr oxides and fewer Fe oxides of the passive film.

关键词： Microbiologically influenced corrosion Medium entropy alloys Pseudomonas aeruginosa

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OpenKN: An open knowledge computational engine for network big data

OpenKN: An open knowledge computational engine for network b...

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International Conference on Advances in Social network Analysis and Mining, ASONAM

作者： Yantao Jia Yuanzhuo Wang Xueqi Cheng Xiaolong Jin Jiafeng Guo CAS Key Laboratory of Network Data Science and Technology Chinese Academy of Sciences Beijing P. R. China

With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open knowledge network (OpenKN), which is a self-adaptive and evolutionable knowledge computational engine for network big data. To the best of our knowledge, this is the first work of designing the end-to-end and holistic knowledge processing pipeline in regard with the network big data. Moreover, to capture the evolutionable computing capability of OpenKN, we present the evolutionable knowledge network for knowledge representation. A case study demonstrates the effectiveness of the evolutionable computing of OpenKN.

关键词： Knowledge based systems Knowledge engineering Big data Engines Syntactics Semantics data mining

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