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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： Sun, Xiao Xu, Xiaolong Xia, Fei Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Information and communication Branch State Grid Jiangsu Electric Power Company Nanjing China

ISBN: (纸本)9781728120584

Information explosion is a typical feature of the big data. Learners can easily find a wide variety of knowledge information online. However, the expansion of information also makes it difficult for learners to retrieve the information they want and fall into the problem of knowledge overload. In order to solve this problem effectively, we propose a content-based recommendation optimization algorithm for personalized knowledge services (CROA). First, the feature vector model is established, where each item information is represented by specific words and key information is highlighted by weighting. Second, a dynamic user vector model is then established to capture the latest preference information of the user. Finally, the feature vector model is combined with the multi-user comparison results thereby obtaining a list of recommendations. Experimental results indicate that CROA outstands in knowledge recommendation mechanism and can also alleviate knowledge overload. © 2019 IEEE.

关键词： Optimization

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An optimal profit route planning scheme based on ACO-OPP 21

An optimal profit route planning scheme based on ACO-OPP

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作者： Qiu, Yuhua Xu, Xiaolong Xia, Fei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Information and communication Branch State Grid Jiangsu Electric Power Company Nanjing China

ISBN: (纸本)9781728120584

Taxi plays an important role of urban public transportation system. However, without appropriate route planning scheme, taxi drivers can only choose to wait or seek passengers in the absence of orders, leading to wasting a lot of time and fuel. In this paper, an optimal profit route planning scheme for taxi drivers with Ant Colony Optimization Algorithm based on Optimal Profit Points (ACO-OPP) is proposed. First, the taxi trajectory data are preprocessed and the pick-up and drop-off points are extracted to obtain high-quality data;second, the data spatiotemporal distribution is analyzed to plan a more appropriate and rational route;the clustering algorithm is used to obtain the optimal profit points to increase the occupancy rate of taxis;then, ACO-OPP is utilized to plan the profit routes for each taxi. A series of experiments are implemented based on the real taxi trajectory dataset to test and compare the performance of the simulated annealing algorithm, the genetic algorithm, the ant colony algorithm and ACO-OPP on the optimal route length and the profitability with the unit time profit function. The experimental results show that ACO-OPP has the shortest optimal route length and the best profit per unit time. © 2019 IEEE.

关键词： Profitability

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Community-based Message Forwarding in Mobile Social Networks

Community-based Message Forwarding in Mobile Social Networks

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作者： Zhiming Chen Yang Xiang School of Computer Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing

With the popularity of various smart devices and the application of sensor network technology, message transmission using mobile devices is becoming *** paper focuses on the forwarding in mobile social network(MSN).The MSN is a special Delay Tolerant Network(DTN) consisting of mobile *** MSN, nodes move and share information with each other through carried short-range wireless communication *** nodes in the MSN typically access some building areas more frequently, such as schools, companies, or apartments, while visiting other areas, such as the roads between buildings, less *** building areas that nodes frequently visit are called *** increase delivery ratio and reduce transmission time in MSN, this paper proposes a novel zero-knowledge multi-copy routing algorithm, Mixed Message Forwarding(MMF) which exploits and improves the metric, namely *** reflects the importance of a node in the *** improves copy diffusion by using different directions of node movement as *** facilities called boundary boxes are added to the network *** boxes are special throw *** boxes are relays with large storage space and fixed *** is designed and evaluated, which utilizes the aforementioned boundary boxes to reduce transmission *** simulation results show that the MMF can improve the delivery ratio and reduce the transmission delay, compared with other algorithms.

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A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

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Disease Markers 2022年第1期2022卷 7593750页

作者： Xu, He Zheng, Qunli Zhu, Jingshu Xie, Zuoling Cheng, Haitao Li, Peng Ji, Yimu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing 210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing 210023 China Nanjing Center of HPC China Nanjing 210023 China Jiangsu Research Engineering of HPC and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Endocrinology Zhongda Hospital Southeast University Nanjing 210009 China

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis. © 2022 He Xu et al.

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Throughput Maximization for UAV-Enabled data Collection

Throughput Maximization for UAV-Enabled Data Collection

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Junchao Gong Xiaojun Zhu Lijie Xu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of nodes placed on a two-dimensional (2-D) plane. The UAV flies along a given line to collect data. To prevent transmission collisions, only one node is allowed to transmit data to the UAV at any time, and the number of nodes that can transmit data to the UAV cannot exceed a specified number to avoid frequent switches. The problem is to select a subset of nodes and schedule their transmission time to maximize the UAV's throughput. After formulating the problem, we find that it is difficult to solve due to mixture of real variables and integer variables. We then propose to decompose the problem into subproblems, and give a polynomial time algorithm to solve each subproblem to optimality. We then get an exact algorithm by solving all subproblems. Unfortunately, due to an exponential number of subproblems, the overall running time is exponential with respect to the input size. We then propose two polynomial-time suboptimal algorithms to solve the problem, both of which explore a polynomial number of subproblems. Simulations show that the suboptimal algorithms perform comparably with the exponential-time exact algorithm, while the running time is much smaller.

关键词： Schedules Heuristic algorithms Two dimensional displays data collection Throughput Unmanned aerial vehicles Sensors

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Consistent and Complementary Graph Regularized Multi-view Subspace Clustering

arXiv

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arXiv 2020年

作者： Zheng, Qinghai Zhu, Jihua Li, Zhongyu Pang, Shanmin Wang, Jun Chen, Lei School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views for clustering and are unable to fully exploit the valuable information. Therefore, we propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC), which simultaneously integrates a consistent graph regularizer with a complementary graph regularizer into the objective function. In particular, the consistent graph regularizer learns the intrinsic affinity relationship of data points shared by all views. The complementary graph regularizer investigates the specific information of multiple views. It is noteworthy that the consistent and complementary regularizers are formulated by two different graphs constructed from the first-order proximity and second-order proximity of multiple views, respectively. The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering. Extensive experiments on six benchmark datasets serve to validate the effectiveness of the proposed method over other state-of-the-art multi-view clustering methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Research on KDT-DBSCAN-based Personal Semantic Location Acquisition

Research on KDT-DBSCAN-based Personal Semantic Location Acqu...

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第32届中国控制与决策会议

作者： Rong Nie Zixuan Yao Dong Wei Kaizhang Hou Jishuai Lin Beijing Engineering Research Center of Security Alarm and Security Check School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Haidian Branch of Beijing Public Security Bureau

With the development of science and technology in recent years,the planning and construction of smart cities have entered a new *** them,smart city safety management is the foundation that supports the stable development of the entire city,and location-based services are one of its important technical *** popularity of GPS-equipped devices provides a large amount of data for trajectory *** personal semantic locations by mining these data is an application based on location *** semantic locations are frequently visited by individual users and have significant semantic meaning to users（such as home,work place,etc.）.The discovery of the user’s personal semantic location involves obtaining the physical location and *** present,related research mostly uses clustering algorithms to obtain the physical location,of which the DBSCAN algorithm is the most commonly *** the traditional DBSCAN algorithm determines whether a sample belongs to a certain cluster,it will generate a large number of repeated calculations,which will reduce the program operation *** order to solve this problem,this paper proposes an improved DBSCAN algorithm *** algorithm uses a k-d tree to screen samples that need to be *** excluding samples that do not need to be calculated,the purpose of improving the calculation efficiency can be *** the problem of semantic location recognition,this paper pre-defines the daily behavior patterns of people,and identifies the location semantics based on the temporal characteristics of the samples in each *** calculating the distance between samples,the algorithm proposed here is 8.8 times more efficient than the traditional algorithm.

关键词： Personal Semantic Location DBSCAN K-D Tree

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Transfer learning with active queries for relational data modeling across multiple information networks 25th

Transfer learning with active queries for relational data mo...

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25th International Conference on Neural Information processing, ICONIP 2018

作者： Chen, Ke-Jia Zhang, Kai Jiang, Xi-Lin Wang, Yunyun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9783030041816

This paper studies the relationship prediction problem in multi-network scenarios, aiming to overcome the network sparsity challenge where the labeled data (connected node pairs) are much less than the unlabeled data (unconnected node pairs). The TAQIL framework is proposed by using transfer learning to get knowledge from the related source networks and then use active learning to query the labels of the most informative instances from the oracle in the target network. A new query function is also proposed in order to better use the parameters output by the transfer learning method. The alternate use of transfer learning and active learning allows adaptive transfer of knowledge across multiple networks to mitigate cold start and meantime improve the prediction accuracy with active queries in the target network. The experimental results on both non-network datasets and network datasets demonstrate the significant improvement in prediction accuracy compared with several benchmark methods and related state-of-art methods. © Springer Nature Switzerland AG 2018.

关键词： Forecasting

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

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arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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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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