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arXiv 2019年

作者： Jiang, Lingyun Niu, Xiaofu Xu, Jia Yang, Dejun Xu, Lijie Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Department of Computer Science Colorado School of Mines GoldenCO80401 United States

Crowdsourcing has become an efficient paradigm for performing large scale tasks. Truth discovery and incentive mechanism are fundamentally important for the crowdsourcing system. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot be applied to deal with the crowdsourcing with copiers. To address the issue, we formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery. We design an incentive mechanism consisting truth discovery stage and reverse auction stage. In truth discovery stage, we estimate the truth for each task based on both the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery method shows prominent advantage in terms of precision when there are copiers in the crowdsourcing systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Crowdsourcing

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Incentivizing the Biased Requesters: Truthful Task Assignment Mechanisms in Crowdsourcing

Incentivizing the Biased Requesters: Truthful Task Assignmen...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Jia Xu Hui Li Yanxu Li Dejun Yang Tao Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Department of Computer Science Colorado School of Mines Golden CO USA

ISBN: (纸本)9781509066001

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non- monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market. In this paper, we explore truthful task assignment mechanisms to maximize the total value of accomplished tasks for this new scenario. We present three models of crowdsourcing, which take the preferences of the requesters and the workload constraints of the workers into consideration. We design a task assignment mechanism, which follows the matching approach to solve the Valuation Maximizing Assignment (VMA) problem for each of the three models. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed assignment mechanisms achieve computational efficiency, workload feasibility, preference (universal) truthfulness and constant approximation.

关键词： Crowdsourcing Computational modeling Cost accounting Mobile communication Aggregates Mechanical factors Knowledge engineering

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Model-based Offline Policy Optimization with Adversarial Network

arXiv

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arXiv 2023年

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Using NSGA-Ⅲ for optimising biomedical ontology alignment

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CAAI Transactions on Intelligence Technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

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A Privacy Leak Detection Mechanism based on Service Binding

A Privacy Leak Detection Mechanism based on Service Binding

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International Symposium on System and Software Reliability (ISSSR)

作者： Boyang Wang Jinling He Yuanhan Du Ming Tang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid.

关键词： Privacy data privacy Machine learning algorithms Accuracy Operating systems Static analysis Vectors

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Darkfight: A Detection Method of Violent Behavior in Dark Place for intelligent Monitoring System

SSRN

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SSRN 2022年

作者： Wang, Jun Xu, Xiaolong Nanjing University of Posts & Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts & Telecommunications School of Computer Science Nanjing210023 China

With the continuous development of deep learning, the use of deep learning methods for intelligent violent behavior recognition has become an active research field in computer vision. However, there are relatively few studies on violent behavior recognition in dark light environment. In this paper, we propose the DarkFight network model for violent behavior recognition in dark light environment. In the proposed DarkFight model, Resnet50 is used as the basic network model to extract features. In order to obtain multi-scale spatiotemporal features, DarkFight adds a multi-scale attention module PSA to the network. The DarkFight model uses Zero-DCE for low-light image enhancement. In order not to lose the spatiotemporal characteristics of the low-light environment, DarkFight uses the initial dark-light data and the enhanced data as the input to the two paths of the model. In addition, this paper researches the replacement of the traditional temporal global average pooling layer of the network with the BERT module to better utilize the temporal information when fusing the two path features. We construct a violent behavior dataset VDID (Violence Detection in Dark) in a dark-light environment, and conduct experimental verification on this dataset. The verification results show that the recognition precision of the DarkFight model increases by 2.51%, the accuracy increases by 3.4%, and the F1-score improves by 0.032. © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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FBOD: An Outlier Detection Algorithm based on data Features Suitable for processing Large-scale datasets on Distributed Platforms

FBOD: An Outlier Detection Algorithm based on Data Features ...

引用

International Symposium on System and Software Reliability (ISSSR)

作者： Jinbo Zhao Jinling He Hu Song Xinyun Cheng Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

The primary objective of outlier detection is to identify values that are significantly different from other data in the dataset. However, most of the current algorithms are effective for small-scale data and their performance is highly dependent on the choice of parameters. To this, the paper proposes an innovative outlier detection algorithm, named FBOD, which is based on data features. Specifically, FBOD utilizes the number of combinations of attribute values in each tuple across the dataset as a key feature of the tuple. This feature reflects the degree of differentiation of each data tuple from the overall dataset. By identifying data features that are significantly different from objects in the entire dataset, FBOD can accurately determine outliers. Based on the knowledge of attribute importance in Rough Set Theory, FBOD can efficiently reduce the data dimensionality, so high-dimensional datasets can be handled by FBOD. Furthermore, this paper employs a distributed parallelism approach in the algorithm. The performance of FBOD is evaluated on several datasets, including standard datasets and artificial datasets, and is compared with common outlier detection algorithms. Experimental results indicate that, compared to existing typical algorithms, FBOD achieves higher outlier detection accuracy without relying on a prior knowledge of datasets or parameter selection. It also proves the suitability of FBOD for large-scale dataset processing on distributed platforms.

关键词： Accuracy Distributed databases Rough sets Feature extraction Safety Object recognition security

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Anomaly detection and traceback scheme for cloud-edge networks

Anomaly detection and traceback scheme for cloud-edge networ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xuanyan Liu Jinling He Hu Song Xinyun Cheng Luyun Hu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Cloud-edge networks (CEN) has revolutionized the traditional network architecture. However, there are many security issues in CEN needing to be solved. In this case, CEN administrator should be able to detect network anomalies and find out the sources of anomalies rapidly, and respond in time to block anomalies. In order to protect network systems with CEN, we propose a mechanism consisting of two modules, including anomaly detection and anomaly traceback. The self-similarity anomaly detection is introduced for the first time to detect anomalies. In addition, we also propose an anomaly traceback method which utilizes the characteristics of CEN. An CEN testbed is established to evaluate the mechanism we proposed. Results of experiments show that the proposed mechanism can discover anomalies quickly and accurately, and trace anomaly sources and find anomaly paths accurately.

关键词： Federated learning Refining Process control Network security Network architecture Control systems Indexes

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Area Charging for Wireless Rechargeable Sensors

Area Charging for Wireless Rechargeable Sensors

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International Conference on Computer Communications and Networks (ICCCN)

作者： Haipeng Dai Xiaoyu Wang Lijie Xu Chao Dong Qian Liu Lei Meng Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing CHINA Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing CHINA Nanjing University of Aeronautics and Astronautics Nanjing CHINA

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

In this paper, we consider the problem of area charging, that is, assuming that there is a mobile charger (MC) equipped with a directional wireless charger whose charging area is in the shape of a sector, and the MC can only recharge sensors on the boundary of an Area of Interest (AOI), how to design an efficient charging scheme for the MC to recharge any sensor inside the AOI with efficient energy while its overall charging time is minimized. To address this problem, we first partition the AOI into two types of subareas, i.e., rectangle-like subareas and sector-like subareas, based on the Medial Axis of the AOI. Second, we propose rectangle-based moving strategy for the rectangle-like subareas, and sector-based rotating strategy for the sector-like subareas, respectively, for the MC. Finally, we calculate charging time of all subareas based on the above two moving strategies, and prove that our algorithm achieves a constant approximation ratio. We evaluate our algorithm by conducting extensive simulation. The results show that on average, other comparison algorithms require at least 18:70 times of charging time of that of our algorithm.

关键词： Sensors Wireless sensor networks Wireless communication Communication system security Approximation algorithms Shape security

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