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作者： Gu, Yiren Shen, Hang Bai, Guangwei Wang, Tianjing Liu, Xuejun College of Computer Science and Technology Nanjing Tech University Nanjing211816 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize social welfare while meeting the requirements in quality, timeliness, correlation, and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Machine learning

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Energy Consumption of IT system in Cloud data Center: Architecture, Factors and Prediction 16th

Energy Consumption of IT System in Cloud Data Center: Archit...

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16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Lin, Haowei Xu, Xiaolong Wang, Xinheng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China University of West London LondonW55RF United Kingdom

ISBN: (纸本)9783030307080

In recent years, as cloud data center has grown constantly in size and quantity, the energy consumption of cloud data center has increased dramatically. Therefore, it is of great significance to study the energy-saving issues of cloud data centers in depth. Therefore, this paper analyzes the architecture of energy consumption of IT system in cloud data centers and proposes a new framework for collecting energy consumption. Based on this framework, the factors affecting energy consumption are studied, and various parameters closely related to energy consumption are selected. Finally, the RBF neural network is used to model and predict the energy consumption of the cloud data centers, which is aim to prove the accuracy of the framework for collecting energy consumption and influencing factors. The experimental results show that these parameters under the framework for collecting energy consumption have better accuracy and adaptability to the prediction of energy consumption in cloud data centers than the previous model of energy consumption prediction. © 2019, IFIP International Federation for Information Processing.

关键词： Forecasting

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ConvTrans-TPS: A Convolutional Transformer Model for Disk Failure Prediction in Large-Scale Network Storage systems 26

ConvTrans-TPS: A Convolutional Transformer Model for Disk Fa...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

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

ISBN: (纸本)9798350331684

Disk failure is one of the most important reliability problems in large-scale network storage systems. Disk failure may lead to serious data loss and even disastrous consequences if the missing data cannot be recovered. Therefore, predicting disk failures is an important means of ensuring storage security in network storage systems. However, since the fault data in the fast degradation stage is smaller than the healthy data in the normal state, the mixture of healthy data and faulty data leads to extremely unbalanced data, which brings great challenges to finding hidden fault information, thus making fault prediction more accurate. Aiming at the above problems, a convolutional transformer model ConvTrans-TPS model for disk failure prediction in large-scale network storage systems is proposed. The ConvTrans-TPS model acquires dependencies between long-term sequence data through transformers and uses convolutional projections for attention computation to enhance attention to local contextual information. data augmentation to predict failures in the next 7 days. Validated by the analysis on the Backblaze dataset, the F1 is 0.96 and the Matthews correlation coefficient (MCC) is 0.92. Compared with the popular CNN-LSTM model in recent years, our proposed method improves F1 and MCC by 4% and 5%, respectively, improving the prediction accuracy. © 2023 IEEE.

关键词： Convolution

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An SGX and Blockchain-Based system for Federated Learning in Classifying COVID-19 Images 21

An SGX and Blockchain-Based System for Federated Learning in...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Qin, Sihang Gu, Xianjun Jun, Xing Li, Haitao Huang, Mengqi Dai, Weiqi Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China State Grid Wuhan Power Supply Company China

ISBN: (纸本)9798350329223

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes. © 2023 IEEE.

关键词： Blockchain

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Application and visualization of typical clustering algorithms in seismic data analysis

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Procedia Computer Science 2019年 151卷 171-178页

作者： Z. Fan X. Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

Earthquake brings enormous loss of lives and properties to human beings due to its suddenness, destructiveness and inscrutability. The new techniques for analyzing seismic data can reveal the distribution of earthquakes, which helps us master the laws of earthquake disasters and reduce the risks brought by them. In this paper, we applied K-means and DBSCAN clustering algorithms to the analysis of seismic data. Their performances in fitting seismic belts with seismic datasets are compared. First, we map the positional parameters in the seismic data to coordinate points on a two-dimensional plane and then cluster them with the DBSCAN algorithm. In addition, we combine the magnitude and depth properties of seismic data, use the Elbow method to find the best K value, and then classifies the dataset by K-means algorithm. We visualize the results, and the distinction of each classification is clear. The experimental results show that the DBSCAN algorithm has a better effect on fitting the seismic belt, and the classification results of K-means algorithm for earthquakes are also in line with expectations.

关键词： Seismic Distribution Clustering K-means DBSCAN Visualization

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Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

arXiv

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arXiv 2024年

作者： Huang, Tianhao Pan, Xuan Cai, Xiangrui Zhang, Ying Yuan, Xiaojie College of Computer Science Nankai University China Tianjin Key Laboratory Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education China Science and Technology on Communication Networks Laboratory Shijiazhuang China

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users’ current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users’ personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users’ check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the "Mobility Tree", tailored for hierarchically describing users’ check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over ten state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree. Copyright © 2024, The Authors. All rights reserved.

关键词： Trees (mathematics)

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A security Analysis of Honey Vaults

A Security Analysis of Honey Vaults

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IEEE Symposium on security and Privacy

作者： Fei Duan Ding Wang Chunfu Jia College of Cyber Science Nankai University Tianjin China Key Laboratory of Data and Intelligent System Security (NKU) Ministry of Education Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Honey encryption (HE) protected password vaults (called honey vaults) are promising tools that allow a user to store multiple passwords (called a password vault) and encrypt them with a master password using HE. In case password vaults are somehow leaked and the attackers launch offline password guessing, honey vaults can yield decoy password vaults for incorrect guesses, forcing an offline guessing attacker to interact with the authentication server to identify whether passwords in decrypted vaults are correct or not. Therefore, honey vaults transform the offline guessing attacker into an online guessing attacker, i.e., honey vault distinguishing *** online guessing, attackers can adopt various attacks to perform multiple guesses against multiple vaults, but the existing theoretical message recovery (MR) security for HE only focuses on the advantage of one-time guess against a single vault, which cannot accurately model realistic attackers and thus can not provide practical advice for users’ vault security. To address this issue, we propose a theoretically-grounded optimal strategy for distinguishing attackers, and manage to derive a much tighter upper bound on the advantage against MR security. Particularly, we provide much tighter upper/lower bounds for advantage against HE-related cryptographic security games, i.e., the security of distribution transforming encoder (DTE), known message attack, and known side information attack. This provides a better understanding of the actual security of honey *** better understand the security of honey vault systems, we instantiate our optimal strategy into three practical attacks and propose an encoding attack. Extensive experiments against two major honey vault systems demonstrate that our four attacks can improve the attack success rate by 1.15-4.35 times compared with their counterparts. For the intersection attack, we propose a feature attack against Cheng et al.’s incremental update mechanism (at USENI

关键词： Privacy Upper bound Authentication Passwords Games Transforms Encoding

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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Reinforcement Learning Methods for Fixed-Wing Aircraft Control 9

Reinforcement Learning Methods for Fixed-Wing Aircraft Contr...

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9th International Conference on systems and Informatics, ICSAI 2023

作者： Li, Lun Zhang, Xuebo Wang, Yang Qian, Chenxu Zhao, Minghui Nankai University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin China Tianjin Municipal Data Management Center Tianjin China

ISBN: (纸本)9798350383706

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future. © 2023 IEEE.

关键词： Reinforcement learning

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A multi-source signals separation algorithm for identifying the threatening signals applied in Fiber-Optic Distributed Acoustic Sensor

A multi-source signals separation algorithm for identifying ...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： He, Tao Zhang, Shixiong Li, Hao Yan, Zhijun Liu, Deming Sun, Qizhen School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan430074 China Optics Valley Laboratory Huazhong University of Science and Technology School of Optical and Electronic Information National Laboratory for Optoelectronics National Engineering Laboratory for Next Generation Internet Access System Wuhan430074 China

A multi-source signals separation algorithm is proposed to identify the potential threatening signals submerged in the strong background noises. Finally, the recognition rate of the mixed signals is improved from 62.8... 详细信息

ISBN: (纸本)9781957171180

关键词： Source separation

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