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32nd ACM International Conference on Multimedia, MM 2024

作者： Bi, Xiuli Hu, Yang Liu, Bo Li, Weisheng Cosman, Pamela Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering University of California San Diego San Diego United States

ISBN: (纸本)9798400706868

As machine learning advances, machine learning as a service (MLaaS) in the cloud brings convenience to human lives but also privacy risks, as powerful neural networks used for generation, classification or other tasks can also become privacy snoopers. This motivates privacy preservation in the inference phase. Many approaches for preserving privacy in the inference phase introduce multi-objective functions, training models to remove specific private information from users' uploaded data. Although effective, these adversarial learning-based approaches suffer not only from convergence difficulties, but also from limited generalization beyond the specific privacy for which they are trained. To address these issues, we propose a method for privacy preservation in the inference phase by removing task-irrelevant information, which requires no knowledge of the privacy attacks nor introduction of adversarial learning. Specifically, we introduce a metric to distinguish task-irrelevant information from task-relevant information, and achieve more efficient metric estimation to remove task-irrelevant features. The experiments demonstrate the potential of our method in several tasks. Our code will be available at: https://***/iwhoyoung/PriFU. © 2024 ACM.

关键词： Adversarial machine learning

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In-depth analysis of core-shell filaments in nonvolatile NbO_(x) memristive device as an artificial synapse for multifunctional bionic applications

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Science China(Technological Sciences) 2023年第12期66卷 3596-3603页

作者： JIANG Bei KE ShanWu TAO ZiPei JIN YaoYao MENG JiaHao SONG XingJuan XIAO YongYue JIANG Li WANG JinZhao CHEN Yong LI YueBin WEN Xin YE Cong School of Microelectronics Key Laboratory of Intelligent Sensing System and SecurityMinistry of EducationHubei UniversityWuhan 430062China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and TechnologyWuhan 430074China School of Science Hubei University of TechnologyWuhan 430068China Faculty of Chemical Technology and Engineering West Pomeranian University of Technology in SzczecinSzczecin 71-065Poland

As a key building block of the biological cortex,synapses are powerful information processing units that enable highly complex nonlinear *** realization of artificial synapses with similar capabilities has important implications for building intelligent,neuromorphic ***,we demonstrate an artificial synapse based on NbO_(x) nonvolatile memristor to mimic multifunctional bionic applications such as nociceptor and associative *** experimental characterization with COMSOL simulation,the traditional resistance switching characteristics,which are the decisive factor for the synapse properties are in-depth *** can be proposed that the I-V characteristics of Pt/NbO_(x)/TiN memristor are governed by core-shell filaments consisting of the shell region of sub-stoichiometric Nb_(2)O_(5-δ)and the core of NbO_(2).On the basis of the core-shell filament model,it can be reasonably explained that Ohmic conduction and Poole-Frenkel conduction take turns to dominate the current flowing in the memristive device,leading to the zigzag evolution of current during the operation process of NbO_(x)-based *** simulations of synaptic plasticity,including long-term potentiation/depression(LTP/LTD),paired-pulse facilitation(PPF),and spike-timing-dependent plasticity(STDP),exhibiting that the NbO_(x) can be utilized for an artificial ***,bionic functions such as hyperalgesia and allodynia of a nociceptor and a series of associative learning behaviors in Pavlovian dog experiment are mimicked,illustrating that the Pt/NbO_(x)/TiN have great potential for highly simplified artificial neural network applications.

关键词： memristor core-shell filament synaptic plasticity nociceptor associative learning

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Model-Based Offline Policy Optimization with Adversarial Network 26

Model-Based Offline Policy Optimization with Adversarial Net...

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26th European Conference on Artificial Intelligence, ECAI 2023

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

ISBN: (纸本)9781643684369

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. © 2023 The Authors.

关键词： Reinforcement learning

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New Discoveries on Integer Factorization by Valuated Binary Tree 2020

New Discoveries on Integer Factorization by Valuated Binary ...

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5th International Conference on intelligent Information Technology, ICIIT 2020

作者： Wang, Xingbo Zheng, Cong Department of Mechatronic Engineering Foshan University Foshan City Guangdong China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Guangdong Engineering Center of Information Security for Intelligent Manufacturing System China

ISBN: (纸本)9781450376594

The paper proves that an odd composite integer N can be factorized in O(logN) time if N's big divisor is in the form 2α u + 1 or 2α u-1 and its small divisor does not exceed 2α + 1 Theorems and corollaries around the conclusion are proved with detail mathematical reasoning. The results in the paper again demonstrate the significance of applying the valuated binary tree on analyzing the odd integers. © 2020 ACM.

关键词： Binary trees

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Indoor Navigation Mechanism based on 5G network for Large Parking Garage 26

Indoor Navigation Mechanism based on 5G network for Large Pa...

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

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

ISBN: (纸本)9798350331684

With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people;especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments. © 2023 IEEE.

关键词： 5G mobile communication systems

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Customized scheduling for shared bus with deadlines

Customized scheduling for shared bus with deadlines

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作者： Jin, Yong Xu, Jia Xu, Lijie Liu, Linfeng Xiao, Fu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science & Engineering Changshu Institute of Technology Jiangsu Changshu China

Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S-MBSA for the shared bus with strong endurance and E-MBSA for the large-scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S-MBSA and E-MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue. © 2022 John Wiley & Sons Ltd.

关键词： Scheduling

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Leaky Autofill: An Empirical Study on the Privacy Threat of Password Managers’ Autofill Functionality

Leaky Autofill: An Empirical Study on the Privacy Threat of ...

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Annual Computer security Applications Conference

作者： Yanduo Fu Ding Wang 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)9798331520885

ISBN: (纸本)9798331520892

Password managers (PMs) provide users with convenient and robust functionalities to manage their credentials, highly recommended by security experts and major standard bodies. One of the most popular features is the autofill functionality, with which users need a single click or a few clicks to fill in every field in web forms, facilitating the process of completing web forms. However, such indiscriminate autofill brings severe privacy threats. PMs may inadvertently fill data into wrong fields in web forms, even hidden fields, potentially leading to privacy leaks and credential *** this paper, we conduct an empirical study evaluating the effectiveness of 30 popular PMs in identifying and handling hidden fields. We focus on the privacy threats posed by the autofill functionality, which fills data into hidden fields. We develop a semi-automated autofill testing tool and explore whether PMs autofill sensitive data into hidden fields across 15 concealment techniques and three web forms, including personal information, credit card, and login forms. Experimental results reveal that every PM autofills data into hidden fields in at least one web form, with an overall filled probability of 58.7% in 1032 scenarios. Further analysis reveals that login forms are the most vulnerable, with a 65.7% probability of hidden fields autofill. Hidden fields concealed by clip-path and content-visibility are filled with passwords by all PMs. Besides, built-in-browser PMs exhibit a 4.07 times higher likelihood of filling data into hidden fields than separately-installed PMs. Even more concerning, built-in-browser PMs, except Safari, autofill passwords into hidden fields under any concealment technique. 37.7% of autofill scenarios with insufficient user interaction pose heightened privacy threats, as users are unaware of autofill content. These privacy threats have been confirmed by popular PMs like *** mitigate the threats brought by the autofill functionality, we present two a

关键词： Privacy data privacy Visualization Standards organizations Passwords Credit cards Filling data models Usability Testing

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QoI-aware incentive for multimedia crowdsensing enabled learning system

QoI-aware incentive for multimedia crowdsensing enabled lear...

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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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