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Advancing Graph Convolution Network with Revised Laplacian Matrix

Chinese Journal of Electronics 2020年第6期29卷 1134-1140页

作者： WANG Jiahui GUO Yi WANG Zhihong TANG Qifeng WEN Xinxiu East China University of Science and Technology National Engineering Laboratory for Big Data Distribution and Exchange Technologies

Graph convolution networks are extremely efficient on the graph-structure data,which both consider the graph and feature *** existing models mainly focus on redefining the complicated network structure,while ignoring the negative impact of lowquality input data during the aggregation *** paper utilizes the revised Laplacian matrix to improve the performance of the original model in the preprocessing *** comprehensive experimental results testify that our proposed model performs significantly better than other off-the-shelf models with a lower computational complexity,which gains relatively higher accuracy and stability.

关键词： computational complexity convolutional neural nets data structures graph theory matrix algebra graph-structure data feature information graph convolution network network structure computational complexity Laplacian matrix Graph convolution network Clustering Label propagation Laplacian matrix Graph structure Fraud detection

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SELF-ATTENTION RECURRENT SUMMARIZATION NETWORK WITH REINFORCEMENT LEARNING FOR VIDEO SUMMARIZATION TASK

SELF-ATTENTION RECURRENT SUMMARIZATION NETWORK WITH REINFORC...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Phaphuangwittayakul, Aniwat Guo, Yi Ying, Fangli Xu, Wentian Zheng, Zheng Department of Computer Science and Engineering East China University of Science and Technology China National Engineering Laboratory for Big Data Distribution and Exchange Technologies China Shanghai Engineering Research Center of Big Data and Internet Audience China

ISBN: (纸本)9781665438643

With the exponential growth of video data, video summarization techniques are urgently needed for reducing people's efforts in the videos' content exploration by generating succinct but informative summaries from original lengthy videos. Though supervised video summarization approaches have demonstrated the state-of-the-art performance, unsupervised methods are still highly demanded due to resourcefully expensive human annotations and the subjectiveness of video summarization tasks. In this paper, a novel unsupervised-based Deep Self-attention Recurrent summarization network with Reinforcement Learning (DSR-RL) for video summarization is proposed. The model can learn the input video sequence and suggest the key-shot summary without additional human annotations by integrating self-attention, BRNN, and reinforcement learning mechanisms. The DSR-RL improves not only importance score through the attention map vector of self-attention network but also the diversity of summaries via the reward function of reinforcement learning. Our method outperforms the state-of-the-art unsupervised video summarization methods on both SumMe and TVSum datasets. The source code is available at https://***/phaphuang/DSR-RL. © 2021 IEEE

关键词： Reinforcement learning

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Research of distributed computing framework for foreign exchange market monitoring 21

Research of distributed computing framework for foreign exch...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Cheng, Wenliang Wang, Zhihong Zhou, Yu Guo, Yi Zhao, Junfeng Co. Ltd Shanghai China East China University of Science and Technology Shanghai China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China Shihezi University Xinjiang China

ISBN: (纸本)9781728120584

Foreign exchange market monitoring is an important part of financial regulation, which amends the failures of market mechanism, protect the legal rights of market participants and maintain the equality, efficiency, transparency and stability of the foreign exchange market. This paper analyzes and summarizes the business characteristics and existing technology framework of foreign exchange market monitoring, and proposes a novel distributed computing framework for this task based on Spark. Considering the business features of single-market multi-indicator and multi-market multi-indicator, this framework involves market-level directed acyclic graph model (M-DAG) and Market-level resource allocation strategy (M-YARN) based on Spark's job scheduling mechanism and YARN's resource scheduling pool isolation mechanism, respectively. The experimental results prove the performance of our proposed framework in foreign exchange market monitoring, which effectively guarantee the completeness, accuracy and timeliness of foreign exchange market monitoring indicators under the background of big data. © 2019 IEEE.

关键词： Yarn

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NiDts: A New Generation Intelligent data Trading System 7

NiDts: A New Generation Intelligent Data Trading System

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： Tang, Qifeng Shao, Zhiqing Huang, Lihua Cho, Hsunfang Zhang, Yiguang Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200237 China Shanghai Data Exchange Corporation Shanghai201203 China Fudan University School of Computer Science Shanghai China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

As an innovative economic form, the digital economy takes data as a new production factor and becomes a new driving force to promote the high-quality development of economy and society. Building a fully digital trading platform plays a very important role in improving the development level and core competitiveness of China's digital economy. At this stage, there are many problems in data trading, such as insufficient ecosystem, unclear data ownership, imperfect system and mechanism, imperfect supervision system, and shortage of industry professionals. This paper proposes a new generation of Intelligent data Trading System of niDts, which realizes the liberalization of data trading, providing efficient, convenient, transparent, and safe data product trading services for data circulation transactions. The system has stimulated the multiplier effect of data elements and led to the digital transformation in the fields of economy, life, and governance. © 2022 IEEE.

关键词： Blockchain

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Harmonic attentive multimodal neural network for movie recommendation 4

Harmonic attentive multimodal neural network for movie recom...

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2020 4th International Workshop on Advanced Algorithms and Control engineering, IWAACE 2020

作者： Wang, J.X. Guo, Y. Qi, T.M. Tang, Q.F. Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data and Internet Audience Shanghai200072 China

A multimodal neural network based on harmonic self-attention was proposed for movie recommendation. This method can deal with the multi-source data and learn representations of users and items well. The multimodal neural network mainly consists of three sub-networks, ResNet, Bert, and LSTM. In addition, the harmonic self-attention mechanism can explore the user's preference from the perspective of time. Experimental results show that compared with other three latest recommendation methods, HSMNN shows better performance in terms of HR and NDCG. © Published under licence by IOP Publishing Ltd.

关键词： Long short-term memory

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Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery

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Cyborg and Bionic Systems 2023年第1期4卷 121-135页

作者： Ling Li Xiaojian Li Bo Ouyang Hangjie Mo Hongliang Ren Shanlin Yang School of Management Hefei University of TechnologyHefeiChina Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) Hefei University of TechnologyHefeiChina Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance(Ministry of Education) Hefei University of TechnologyHefeiChina Department of Biomedical Engineering Faculty of EngineeringNational University of SingaporeSingaporeSingapore Department of Electronic Engineering Shun Hing Institute of Advanced EngineeringThe Chinese University of Hong KongHong KongChina National Engineering Laboratory for Big Data Distribution and Exchange Technologies ShanghaiChina

In the robot-assisted minimally invasive surgery,if a collision occurs,the robot system program could be damaged,and normal tissues could be *** avoid collisions during surgery,a 3-dimensional collision avoidance method is proposed in this *** proposed method is predicated on the design of 3 strategic vectors:the collision-with-instrument-avoidance(CI)vector,the collision-with-tissues-avoidance(CT)vector,and the constrained-control(CC)*** CI vector demarcates 3 specific directions to forestall collision among the surgical *** CT vector,on the other hand,comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget ***,the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position,ensuring precision in its movements,in alignment with the surgical *** results verify the proposed collision avoidance method for robot-assisted minimally invasive *** code and data are available at https://***/cynerelee/collision-avoidance.

关键词： collision desired verify

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POSTER: Will Sentiment of Forex News Effect Forecast of the RMB exchange Rate? 16

POSTER: Will Sentiment of Forex News Effect Forecast of the ...

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16th ACM International Conference on Computing Frontiers, CF 2019

作者： Cheng, Zhou Wang, Jixiang Qi, Tianmei Zhao, Junfeng Wang, Zhihong Guo, Yi Zhou, Yu Co. Ltd Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

ISBN: (纸本)9781450366854

The forecast and analysis of the trend of the RMB exchange rate have been deeply explored by many researchers in the financial field, but the combination of public opinion sentiment data and historical market data to forecast the RMB exchange rate in the short term has been studied little. With the rapid development of the Internet, the influence of public opinion sentiment on the economy and society is increasing. Online public opinion sentiment data not only have an impact on stock prices [1] and commodity prices, but also have a significant impact on foreign exchange (Forex) rates. However, the public opinion data are not applied for the RMB exchange rate forecast, because the impact of public events on the exchange rate is ignored. Besides, the lack of exact temporal sliding window of public opinion ignores its timeliness and sensibility. © 2019 Association for Computing Machinery.

关键词： Forecasting

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Anomaly detection mechanism based on hierarchical weights through large-scale log data

Anomaly detection mechanism based on hierarchical weights th...

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2021 International Conference on Computer Communication and Artificial Intelligence, CCAI 2021

作者： Xi, Lanlan Xin, Yang Luo, Shoushan Shang, Yanlei Tang, Qifeng University of Posts and Telecommunications School of Beijing Institute of Cyberspace Security Beijing China University of Postsand Telecommunications School of Beijing Institute of Cyberspace Security Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Beijing China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China China-Europe Digital Economy Expert Group Shanghai China

ISBN: (纸本)9781728194011

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery. © 2021 IEEE.

关键词： Anomaly detection

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A robust internet abuse detection method

A robust internet abuse detection method

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IEEE International Conference on big data

作者： Zhou Fa Guang-Gang Geng Zhi-Wei Yan Xiao-Dong Lee China Internet Network Information Center University of Chinese academy of Sciences National Engineering Laboratory for Big Data Distribution and Exchange Technologies Beijing China

JavaScript can modify HyperText Markup Language(HTML) code. tag of HTML can load pages from other websites. These technologies are widely used nowadays for constructing flexible and robust web services. However, illegal websites also use these technologies to hide illegal contents. For traditional methods using web text to detect these illegal webpages (just getting initial source codes that aren't parsed by a browser), they can't give a precise judgment. In this paper, we solved the challenge and proposed a robust Internet abuse detection method. We get HTML codes that had been parsed by simulating the progress how browsers work in order to gather the necessary materials for text detection methods. Besides, we not only extracted the text features of HTML code, but also extracted structure features of HTML codes. With the experiments under detecting Internet abuse scenarios, we demonstrated that the proposal is efficient.

关键词： Internet HTML Web pages Feature extraction Browsers Robustness

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Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

Dynamic Topic Model for Tracking Topic Evolution and Measuri...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Yezheng Liu Jicheng Wang Yang Qian Yuanchun Jiang Jianshan Sun Yidong Chai School of Management Hefei University of Technology National Engineering Laboratory for Big Data Distribution and Exchange Technologies Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education

ISBN: (纸本)9781665418164

With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature.

关键词： Current measurement Conferences Cyberspace data science Market research data models Time factors

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