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作者： Dong, Yunquan Chen, Zhengchuan Liu, Shanyun Fan, Pingyi Letaief, Khaled Ben School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Dependable Service Computing in Cyber-Physical Society Ministry of Education Chongqing China Department of Electronic Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In this article, we consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as age upon decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, and controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision-making process. The theoretical results show that: 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and will be minimized if the arrival process is periodic (i.e., deterministic);2) when both the decision process and the arrival process are periodic, the average AuD is larger, but decreases with decision rate to, the average AuD of the corresponding system with the Poisson decisions (i.e., random);and 3) when both the decision process and the arrival process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making the periodical updates and decisions with properly controlled offset is also a promising solution, if the timing information of the two processes can be accessed by the monitor. © 2014 IEEE.

关键词： Scheduling

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Efficient Multi-Grained Wear Leveling for Inodes of Persistent Memory File Systems

Efficient Multi-Grained Wear Leveling for Inodes of Persiste...

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Design Automation Conference

作者： Chaoshu Yang Duo Liu Runyu Zhang Xianzhang Chen Shun Nie Fengshun Wang Qingfeng Zhuge Edwin H.-M. Sha Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China College of Computer Science Chongqing University Chongqing China School of Computer Science and Software Engineering East China Normal University Shanghai China

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Existing persistent memory file systems usually store inodes in fixed locations, which ignores the external and internal imbalanced wears of inodes on the persistent memory (PM). Therefore, the PM for storing inodes can be easily damaged. Existing solutions achieve low accuracy of wear-leveling with high-overhead data migrations. In this paper, we propose a Lightweight and Multi-grained Wear-leveling Mechanism, called LMWM, to solve these problems. We implement the proposed LMWM in Linux kernel based on NOVA, a typical persistent memory file system. Compared with MARCH, the state-of-theart wear-leveling mechanism for inode table, experimental results show that LMWM can improve 2.5× lifetime of PM and 1.12× performance, respectively.

关键词： Silicon Metadata Indexes Linux Kernel File systems

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Robust Bidirectional Generative Network For Generalized Zero-Shot Learning

Robust Bidirectional Generative Network For Generalized Zero...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yun Xing Sheng Huang Luwen Huangfu Feiyu Chen Yongxin Ge Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society School of Big Data and Software Engineering at Chongqing University Chongqing P.R.China Fowler College of Business at San Diego State University San Diego USA

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

In this work, we propose a novel generative approach named Robust Bidirectional Generative Network (RBGN) based on Conditional Generative Adversarial Network (CGAN) for Generalized Zero-shot Learning (GZSL). RBGN employs the adversarial attack to train a more rigorous discriminator, thus enhancing the generalizability and robustness of the feature generator under minimax strategy. Moreover, RBGN decodes the generated visual features back to their semantic representations to further improve the representational ability of generated visual features and alleviate the hubness problem. The experimental results of GZSL on four datasets, i.e. CUB, SUN, AWA1, AWA2, demonstrate that our model achieves competitive performance compared to state-of-the-art approaches and owns better generalizability to the unseen classes over conventional generative GZSL models. Further robustness analysis also validates the strong robustness of our model to the different types of semantic disturbance.

关键词： Semantics Visualization Robustness Generators Training Decoding Gallium nitride

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Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition

arXiv

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arXiv 2022年

作者： Qin, Rong Huangfu, Luwen Hood, Devon Ma, James Huang, Sheng School of Big Data & Software Engineering Chongqing University Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States College of Science San Diego State University San DiegoCA92182 United States Department of Business Analysis University of Colorado Colorado SpringsCO80918 United States Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing400044 China

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNetB3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency. © 2022, CC BY-NC-SA.

关键词： Convolution

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Optimal Power Allocation of Cooperative NOMA with Finite Blocklength Codes

Optimal Power Allocation of Cooperative NOMA with Finite Blo...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Siling Liu Zhengchuan Chen Jinfeng Xie Liang Liang Min Wang Yunjian Jia The Key Laboratory of Dependable Service Computing in Cyber-Physical Society Ministry of Education and School of Microelectronics and Communication Engineering Chongqing University Chongqing China Chongqing Academy of Informatioon and Communications Technology Chongqing China School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

Compared with orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) has enormous advantages in enhancing fairness and spectral efficiency for downlink transmission. To further improve the performance of NOMA, cooperative NOMA (C-NOMA) has attracted attention while power allocation of C-NOMA is of great importance to ameliorate the system performance. To guarantee the requirements of ultra-reliable and low-latency communication (URLLC) nowadays, the code length is supposed to be short enough where finite blocklength (FBL) codes are adopted. This paper presents a two-user half-duplex downlink C-NOMA scheme with low latency constraint. To minimize the error probability, power allocation problems are formulated. For this purpose, the properties of power allocation to objective functions are investigated. First, the error probability is analysed in terms of signal-to-noise ratio. Afterwards, the convexity of the power allocation problems under certain conditions are shown and optimal algorithms that solve the problems are proposed. Numerical results demonstrate that the proposed C-NOMA with finite blocklength codes successfully achieve lower error probability with regard to the conventional NOMA.

关键词： Wireless communication Signal processing Conferences

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Forward engineering completeness for software by using requirements validation framework 31

Forward engineering completeness for software by using requi...

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31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019

作者： Iqbal, Nayyar Sang, Jun Gao, Min Hu, Haibo Xiang, Hong Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Computer Science University of Agriculture Faisalabad Faisalabad38000 Pakistan

ISBN: (纸本)1891706489

In software development environment, software companies usually ignore the user requirements validation process in requirement gathering phase, which results in large number of modifications being required in the software maintenance phase to fulfill the customer requirements. Identification of accurate requirements from user stories and determining the effectiveness of work deliverable of software industry has always been a challenging task. In this paper, a new measurement approach for forward engineering completeness for software was introduced by using requirements validation framework. The forward engineering completeness for software was measured in two steps. In the first step, software component structure was developed in order to find the functional and nonfunctional requirements rejected by the customers in the requirement validation framework. In the second step, completeness of software from component-based development was determined in which the following parameters, such as functional, non-functional completeness attributes, were considered in the measurement process, and the unadopted attributes of the reuse code were also considered. Quality level for the attributes were assigned based upon the valuation of interior quality of the source code. Therefore, it resulted in the reduction of development time required for the software and the cost required for the software development was also reduced. A case study was incorporated in this research to explain the measurement process of forward engineering completeness. If the forward engineering code is satisfying the quality standards, then the code is in the completeness form. The attributes of code that negates to be used were considered as unadopted attributes. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Computer software reusability

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Deep Domain Adaptation for Pavement Crack Detection

arXiv

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arXiv 2021年

作者： Liu, Huijun Yang, Chunhua Li, Ao Huang, Sheng Feng, Xin Ruan, Zhimin Ge, Yongxin College of Computer Science Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Chongqing City Management College Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China College of Computer Science And Engineering Chongqing University of Technology Chongqing400054 China Co. Ltd Chongqing400060 China

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease Dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain. Copyright © 2021, The Authors. All rights reserved.

关键词： Crack detection

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Deep semantic dictionary learning for multi-label image classification

arXiv

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arXiv 2020年

作者： Zhou, Fengtao Huang, Sheng Xing, Yun School of Big data & Software Engineering Chongqing University Shazheng street NO.174 Shapingba District Chongqing400044 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Shazheng street NO.174 Shapingba District Chongqing400044 China

Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. However, these semantic-based methods only take semantic information as type of complements for visual representation without further exploitation. In this paper, we present an innovative path towards the solution of the multi-label image classification which considers it as a dictionary learning task. A novel end-to-end model named Deep Semantic Dictionary Learning (DSDL) is designed. In DSDL, an auto-encoder is applied to generate the semantic dictionary from class-level semantics and then such dictionary is utilized for representing the visual features extracted by Convolutional Neural Network (CNN) with label embeddings. The DSDL provides a simple but elegant way to exploit and reconcile the label, semantic and visual spaces simultaneously via conducting the dictionary learning among them. Moreover, inspired by iterative optimization of traditional dictionary learning, we further devise a novel training strategy named Alternately Parameters Update Strategy (APUS) for optimizing DSDL, which alternately optimizes the representation coefficients and the semantic dictionary in forward and backward propagation. Extensive experimental results on three popular benchmarks demonstrate that our method achieves promising performances in comparison with the state-of-the-arts. Our codes and models have been released1 © 2020, CC BY-NC-SA.

关键词： Image classification

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Consensus of fractional-order multi-agent systems with impulsive disturbance

引用

IFAC-PapersOnLine 2019年第24期52卷 174-178页

作者： Wei Luo Yuqi Liu Tiedong Ma School of Automation Chongqing University Chongqing 400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education Chongqing 400044 China

The consensus problem of fractional-order multi-agent systems (MASs) with impulsive disturbance is investigated in this paper. Based on the theory of fractional order system and Lyapunov stability, some sufficient conditions are derived to ensure the consensus of MASs with impulsive disturbance. Comparing with the existing results, the method proposed in this paper is more applicable. Finally, simulation results verify the correctness and effectiveness of the main results.

关键词： Fractional-order systems consensus multi-agent systems impulsive perturbation

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Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack

arXiv

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arXiv 2021年

作者： Wu, Fan Gao, Min Yu, Junliang Wang, Zongwei Liu, Kecheng Wang, Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing401331 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China School of Information Technology and Electrical Engineering The University of Queensland Queensland4072 Australia Informatics Research Centre Henley Business School University of Reading Berkshire ReadingRG6 6UD United Kingdom School of Mechanical Engineering Chongqing University Chongqing400044 China

To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects. Primitive attacks are highly feasible but less effective due to simplistic handcrafted rules, while upgraded attacks are more powerful but costly and difficult to deploy because they require more knowledge from recommendations. In this paper, we explore a novel shilling attack called Graph cOnvolution-based generative shilling ATtack (GOAT) to balance the attacks' feasibility and effectiveness. GOAT adopts the primitive attacks' paradigm that assigns items for fake users by sampling and the upgraded attacks' paradigm that generates fake ratings by a deep learning-based model. It deploys a generative adversarial network (GAN) that learns the real rating distribution to generate fake ratings. Additionally, the generator combines a tailored graph convolution structure that leverages the correlations between co-rated items to smoothen the fake ratings and enhance their authenticity. The extensive experiments on two public datasets evaluate GOAT's performance from multiple perspectives. Our study of the GOAT demonstrates technical feasibility for building a more powerful and intelligent attack model with a much-reduced cost, enables analysis the threat of such an attack and guides for investigating necessary prevention measures. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

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