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International Conference on computer Supported Cooperative Work in Design

作者： Jian Song Xiaolong Xu Xinheng Wang Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computing and Engineering University of West London London UK

ISBN: (纸本)9781728165974;9781728165981

Community Question Answering (CQA) provides a platform to share knowledge for users. With the increasing number of users and questions, askers have to wait a long time for an answer with high quality while responders may not be interested in assigned questions. Current methods usually try to address this issue based on text or link analysis. However, most of them suffer from delayed answers or low coverage of best answer. In this paper, we design a novel expert recommendation mechanism by incorporating the deep structured semantic model (DSSM) [20] with our proposed graph-based algorithm, a topic sensitive answerer rank algorithm (TSAR). In the process of constructing transition probability matrix, we not only take into account both the number of questions answered by the user and question difficulty, but also consider the user's average response time for providing the answer. The experiments carried out on Yahoo! Answers and Stack Overflow datasets demonstrate that the proposed mechanism outperforms the current typical algorithms [9] on multiple metrics and achieves the best answer coverages, which are 61.5% and 53.8%, respectively.

关键词： Measurement Conferences Semantics Knowledge discovery Collaborative work Time factors

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REPDL: Research-oriented e-learning platform based on digital library

REPDL: Research-oriented e-learning platform based on digita...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Xiaolong Xu Kun Wang Yan Deng Tao Li School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Graduate School Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

In accordance with the researchers, students, and faculty' need to use the digital library for efficient literature investigation and knowledge learning, this paper proposes REPDL, a research-oriented e-learning platform based on digital library. REPDL includes the structural literature organization and reading route planning mechanism based on knowledge map, the personalized research progress management, the key information marker & sharing mechanism, and the Fine-grained multi-dimensional cooperative research network and multi-level communication mechanism. The design of REPDL is in accordance with the constructivism-based learning theory. The main target is to further integrate the massive literature resources and the communication channels of digital library, comprehensively use various advanced technologies such as data mining, artificial intelligence, machine learning, visualization technology and social networking, in order to provide the researcher-oriented professional knowledge architecture and the hierarchical literature navigation services to high-level learners, reduce the learners' cognitive load, and effectively prevent disorientation during learning. With REPDL, especially with the automatically personalized manage research progress and the key information marker interface, the literature reading can be more efficient and more satisfy personalized requirements of learners. The researcher cooperative communication network and positioning function of REPDL ensures that learners at different research phases can conduct clearer knowledge communications on multiple levels.

关键词： Conferences Education Knowledge engineering Libraries

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Improving the Efficiency of Blockchain Applications with Smart Contract based Cyber-insurance

Improving the Efficiency of Blockchain Applications with Sma...

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IEEE International Conference on Communications (ICC)

作者： Jia Xu Yongqi Wu Xiapu Luo Dejun Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hongkong China Department of Computer Science Colorado School of Mines Golden USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

关键词： Contracts Crowdsourcing Task analysis Insurance Bitcoin

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Collaborative Bi-Aggregation for Directed Graph Embedding

SSRN

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SSRN 2023年

作者： Liu, Linsong Chen, Ke-Jia Liu, Zheng Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China State Key Laboratory of Novel Software Technology Nanjing University Jiangsu Nanjing210093 China

Directed graph is able to model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning source and target embeddings of nodes separately to preserve edge asymmetry has become the dominant approach, but also poses challenge for learning representations of low or even zero in/out degree nodes that are ubiquitous in sparse graphs. In this paper, a collaborative bi-directional aggregation method (COBA) for directed graph embedding is proposed. Firstly, the source and target embeddings of the central node are learned by aggregating from the counterparts of the source and target neighbors, respectively;Secondly, the source/target embeddings of the zero in/out degree central nodes are enhanced by aggregating the counterparts of opposite-directional neighbors (i.e. target/source neighbors);Finally, source and target embeddings of the same node are correlated to achieve collaborative aggregation. Both the feasibility and rationality of the model are theoretically analyzed. Extensive experiments on real-world datasets demonstrate that COBA comprehensively outperforms state-of-the-art methods on multiple tasks and meanwhile validates the effectiveness of proposed aggregation strategies. © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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DUE Distribution and Pairing in D2D Communication

DUE Distribution and Pairing in D2D Communication

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International Conference on computer Communications and Networks (ICCCN)

作者： Weifeng Lu Xiaoqiang Ren Jia Xu Siguang Chen Lijun Yang Jian Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China College of IoT Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

The D2D (Device-to-Device) communication has been very popular as it is a promising and low-cost solution to reduce the burden on the cellular network. However, there are rare concerns about the distribution and pairing of DUEs(D2D user equipments), which have a significant impact on QoS (Quality of Service) of D2D communication. In this paper, we propose a novel algorithm based on the coalitional game to optimally adjust the distribution of DUEs. The proposed algorithm aims to form the optimal coalition structure, which achieves a balance between the throughput and power consumption of each coalition, obtaining the enhanced QoS of D2D. We show that our algorithm is superior to the benchmark models in terms of the throughput and energy efficiency of the DUE coalition. To further improve the QoS, we also propose a method to predict and maximize the pairing probability of DUEs. The proposed prediction method adopts the Logistic Regression to model the global pairing probability according to the communication parameters of DUEs. Experimental results show that the proposed prediction method is significantly superior to the benchmark methods in terms of prediction accuracy. In addition, the pairing probability maximization algorithm proposed also significantly improves the pairing probability.

关键词： Device-to-device communication Throughput Power demand Fading channels Games Quality of service Time division multiple access

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Analysis on Attention Mechanism Application Schemes for Automatic Question Answering Systems

Analysis on Attention Mechanism Application Schemes for Auto...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Xinyuan Lu Xiaolong Xu Yanfei Sun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Office of Scientific R&D Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (纸本)9781538676738;9781538676721

An automatic question answering system is generally composed of the question analysis module, the information retrieval module, the answer selection module, etc. The core component of automatic question answering system is the answer selection module, which focuses on extracting adequate information from the questions and answers and representing them effectively. The performance of answer selection directly determines the quality of the answers submitted to users. In this paper, we studied the answer selection schemes, especially the Attentive LSTM scheme, focusing on the application of the attention mechanism commonly utilized in deep learning models. Meanwhile, a self-attentive LSTM is proposed by combining the Attentive LSTM with self-attention which is capable of extracting the local features and the global features of texts at the same time. In addition, a multi-attentive LSTM is proposed so that multiple parts of information in the question are available for the answer. We performed a series of experiments based on the datasets InsuranceQA and TrecQA, compared and analyzed on the performances of the above schemes.

关键词： Feature extraction Task analysis Neural networks Logic gates data mining Knowledge discovery Deep learning

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Network-coding-based video transmission via WiFi direct in local mobile networks

Network-coding-based video transmission via WiFi direct in l...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Lei Wang Qing Wang Zhongwei Sun Jun Yin Lijie Xu Yuwang Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing P.R. China School of Computer Nanjing University of Posts and Telecommunications Nanjing P.R. China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing P.R. China

It will incur high expense by sharing large-scale files (such as video) with others through commercial 4G networks. When the users are located at the same area, the traffic cost could be saved via the technology of WiFi direct. In this paper, a network-coding-based video transmission scheme is proposed for such scenario. In the scheme, when a source user needs to transmit a video to others, it equally splits the original file into multiple segments. When a neighbour connects to it and requests a segment, the user will linearly generate a new segment with all the segments it buffers, and then send the re-encoded segment to other neighbours. After the neighbours receive all or part of the segments, they could become a new source and send data to their own neighbours through the technology of WiFi direct. The results show that compared with the traditional scheme, the transmission scheme based on network coding could be used to complete transmission at a higher rate.

关键词： Switches Analytical models Network coding Streaming media Wireless fidelity Mobile handsets Mobile computing

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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VFL: A Verifiable Federated Learning with Privacy-Preserving for big data in Industrial IoT

arXiv

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arXiv 2020年

作者： Fu, Anmin Zhang, Xianglong Xiong, Naixue Gao, Yansong Wang, Huaqun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK United States Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Due to the strong analytical ability of big data, deep learning has been widely applied to model the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Identifying outliers in astronomical images with unsupervised machine learning

arXiv

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arXiv 2022年

作者： Han, Yang Zou, Zhiqiang Li, Nan Chen, Yanli School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data. However, it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload. Supervised learning is also unsuitable for this purpose since designing proper training sets for unanticipated signals is unworkable. Motivated by these challenges, we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers. For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE) + KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods. Results show that attCAE KNN achieves the best recall (78%), which is 53% higher than the classical KNN method and 22% higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior to KNN (4 hours) and equal to CAE+KNN (10 minutes) for accomplishing the same task. Thus, we believe it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN to available survey datasets to assess its applicability and reliability. Copyright © 2022, The Authors. All rights reserved.

关键词： k-nearest neighbors

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