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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Runhao Zeng Haoming Xu Wenbing Huang Peihao Chen Mingkui Tan Chuang Gan School of Software Engineering South China University of Technology China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University MIT-IBM Watson AI Lab

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy. Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query. We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS).

关键词： Grounding Training Task analysis Proposals Semantics Magnetic heads Feature extraction

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Simplified Deep Forest Model based Just-In-Time Defect Prediction for Android Mobile Apps

Simplified Deep Forest Model based Just-In-Time Defect Predi...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Kunsong Zhao Zhou Xu Tao Zhang Yutian Tang School of Computer Science Wuhan University Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China Faculty of Information Technology Macau University of Science and Technology Macao China The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728189130

ISBN: (纸本)9781728189147

The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-In-Time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we modify a state-of-the-art model, called Simplified Deep Forest (SDF) to conduct JIT defect prediction for Android mobile apps. This method uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of three performance indicators.

关键词： Forestry software quality Predictive models Data models Mobile applications software reliability Security

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Learning for Exception: Dynamic Service Caching in 5G-Enabled MECs with Bursty User Demands

Learning for Exception: Dynamic Service Caching in 5G-Enable...

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International Conference on Distributed Computing Systems

作者： Zichuan Xu Shengnan Wang Shipei Liu Haipeng Dai Qiufen Xia Weifa Liang Guowei Wu School of Software Dalian University of Technology Dalian China Nanjing University Nanjing Jiangsu China International School of Information Science & Engineering Dalian University of Technology Dalian China Research School of Computer Science Australian National University Canberra ACT Australia

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Mobile edge computing (MEC) is envisioned as an enabling technology for extreme low-latency services in the next generation 5G access networks. In a 5G-enabled MEC, computing resources are attached to base stations. In this way, network service providers can cache their services from remote data centers to base stations in the MEC to serve user tasks in their close proximity, thereby reducing the service latency. However, mobile users usually have various dynamic hidden features, such as their locations, user group tags, and mobility patterns. Such hidden features normally lead to uncertainties of the 5G-enabled MEC, such as user demand and processing delay. This poses significant challenges for the service caching and task offloading in a 5G-enabled MEC. In this paper, we investigate the problem of dynamic service caching and task offloading in a 5G-enabled MEC with user demand and processing delay uncertainties. We first propose an online learning algorithm for the problem with given user demands by utilizing the technique of Multi-Armed Bandits (MAB), and theoretically analyze the regret bound of the algorithm. We also propose a novel architecture of Generative Adversarial Networks (GAN) to accurately predict the user demands based on small samples of hidden features of mobile users. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the uncertainties of both user demand and processing delay. We finally evaluate the performance of the proposed algorithms by simulations based on a realistic dataset of user data. Experiment results show that the performance of the proposed algorithms outperform existing algorithms by around 15%.

关键词： Uncertainty Heuristic algorithms Predictive models Prediction algorithms Generative adversarial networks Delays Task analysis

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Integration of smart grid with Industry 5.0: Applications, challenges and solutions

Measurement: Energy

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Measurement: Energy 2025年 5卷

作者： Sunawar khan Tehseen Mazhar Tariq Shahzad Muhammad Amir khan Ateeq Ur Rehman Habib Hamam Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar 62300 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia School of Computing Gachon University Seongnam 13120 Republic of Korea Faculty of Engineering Uni de Moncton Moncton NB E1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Introduction In this study, an investigation of the nexus between state-of-the-art technology and green industrial processes with a view to how smart grid systems can be incorporated into industry 5.0 is done. Industry 5.0 stresses human-machine collaboration together with Artificial Intelligence, the Internet of Things, and Big Data while the recent electrical networks enriched by digital communication technologies are defined as the contemporary smart grids. Notwithstanding advances in both domains, there is a major research gap at the intersection of the two. Objectives In this study, the essential elements, advantages, and potential impacts of coupling smart grids with Industry 5.0 will be examined. These aims are aimed at sustaining and improving the reliability and efficiency of industrial processes maximizing resource consumption and minimizing ecological damage. Method ology: The use and benefits of this integration are analyzed using case studies from industrialized countries. It assesses technological developments, challenges and the emerging trends dealing with the combination of smart grid technologies with Industry 5.0. Findings and discussion In addition, smart grid technology can make industrial processes more dependable and efficient; resulting in more appropriate resource utilization and lower emissions. It promises to revolutionize the energy management systems and production procedures. Conclusion Drawing from this research, this integration offers the capabilities of developing a technologically advanced and environment-friendly industrial ecosystem that enables a truly sustainable future.

关键词： Smart grid Energy efficiency Industry 5.0 Haman-machine collaboration Advanced metering infrastructure (AMI) Real-time data analytics

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ServeNet: A Deep Neural Network for Web Services Classification

ServeNet: A Deep Neural Network for Web Services Classificat...

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IEEE International Conference on Web Services (ICWS)

作者： Yilong Yang Nafees Qamar Peng Liu Katarina Grolinger Weiru Wang Zhi Li Zhifang Liao School of Software Beihang University Beijing China Governors State University Chicago United States Faculty of Science and Technology University of Macau Macau University of Western Ontario Canada College of Computer Science and Information Technology Guangxi Normal University Guilin China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.

关键词： Web services Conferences Neural networks Machine learning Manuals

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Interval Markov decision processes with multiple objectives: From Robust strategies to Pareto curves

Interval Markov decision processes with multiple objectives:...

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作者： Hahn, Ernst Moritz Hashemi, Vahid Hermanns, Holger Lahijanian, Morteza Turrini, Andrea School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom State Key Laboratory of Computer Science Institute of Software CAS Beijing China Department of Information Technology Audi AG Ingolstadt Germany Saarland University Saarland Informatics Campus Saarbrücken Germany Institute of Intelligent Software Guangzhou Guangzhou China Department of Smead Aerospace Engineering and Sciences University of Colorado BoulderCO United States

Accurate Modelling of a real-world system with probabilistic behaviour is a difficult task. Sensor noise and statistical estimations, among other imprecisions, make the exact probability values impossible to obtain. In this article, we consider Interval Markov decision processes (IMDPs), which generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. We investigate the problem of robust multi-objective synthesis for IMDPs and Pareto curve analysis of multi-objective queries on IMDPs. We study how to find a robust (randomised) strategy that satisfies multiple objectives involving rewards, reachability, and more general ω-regular properties against all possible resolutions of the transition probability uncertainties, as well as to generate an approximate Pareto curve providing an explicit view of the trade-offs between multiple objectives. We show that the multi-objective synthesis problem is PSPACE-hard and provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototype tool. © 2019 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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NERank+: a graph-based approach for entity ranking in document collections

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计算机科学前沿 2018年第3期12卷 504-517页

作者： Chengyu WANG Guomin ZHOU Xiaofeng HE Aoying ZHOU Shanghai Key Laboratory of Trustworthy Computing School of Computer Science and Software EngineeringEast China Normal University Shanghai 200062 China Department of Computer and Information Technology Zhejiang Police College Hangzhou 310053 China School of Data Science and Engineering East China Normal University Shanghai 200062 China

Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user *** rank order of entities is determined by the relevance between the query and contexts of ***,entities can be ranked directly based on their relative importance in a document collection,independent of any *** this paper,we introduce an entity ranking algorithm named NERank+.Given a document collection,NERank+ first constructs a graph model called Topical Tripartite Graph,consisting of document,topic and entity *** design separate ranking functions to compute the prior ranks of entities and topics,respectively.A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph *** evaluate NERank+ over real-life datasets and compare it with *** results illustrate the effectiveness of our approach.

关键词： entity ranking Topical Tripartite Graph prior rank estimation meta-path constrained random walk

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Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

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Journal of computer science & Technology 2018年第4期33卷 807-822页

作者： Xin Xu Jiaheng Lu Wei Wang Laboratory of Science and Technology on Information System Engineering Nanjing Research Institute of Electronics Engineering Nanjing 21000~ China Department of Computer Science University of Helsinki Helsinki 00014 Finland State Key Laboratory for Novel Software and Technology Nanjing University Nanjing 210046 China

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.

关键词： symbolic data analysis stochastic pattern fuzzy interval hierarchical clustering emitter identification

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Smart-edge-CoCaCo: AI-enabled smart edge with joint computation, caching, and communication in heterogeneous IoT

arXiv

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arXiv 2019年

作者： Hao, Yixue Miao, Yiming Tian, Yuanwen Hu, Long Shamim Hossain, M. Muhammad, Ghulam Amin, Syed Umar Huazhong University of Science and Technology Department of Software Engineering College of Computer and Information Sciences King Saud University Department of Computer Engineering College of Computer and Information Sciences King Saud University

The development of mobile communication technology, hardware, distributed computing, and artificial intelligence (AI) technology has promoted the application of edge computing in the field of heterogeneous Internet of Things (IoT). In order to overcome the defects of the traditional cloud computing model in the era of big data. In this article, we first propose a new AI-enabled smart edge with heterogeneous IoT architecture which combines edge computing, caching, and communication. Then, we propose the Smart-Edge-CoCaCo algorithm. To minimize total delay and confirm the computation offloading decision, Smart-Edge-CoCaCo uses joint optimization of the wireless communication model, the collaborative filter caching model in edge cloud, and the computation offloading model. Finally, we built an emotion interaction testbed to perform computational delay experiments in real environments. The experiment results showed that the computation delay of the Smart-Edge-CoCaCo algorithm is lower than that of traditional cloud computing model with the increase of computing task data and the number of concurrent users. Copyright © 2019, The Authors. All rights reserved.

关键词： Edge computing

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Privacy-preserving image retrieval based on additive secret sharing

arXiv

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Xiong, Lizhi Zhou, Wenhao Weng, Jian Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Jinan University Guangzhou510632 China

The rapid growth of digital images motivates individuals and organizations to upload their images to the cloud server. To preserve the privacy, image owners would prefer to encrypt the images before uploading, but it would strongly limit the efficient usage of images. Plenty of existing schemes on privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the balance between security and retrieval ability. However, compared to the advanced technologies in CBIR like Convolutional Neural Network (CNN), the existing PPCBIR schemes are far deficient in both accuracy and efficiency. With more cloud service providers, the collaborative secure image retrieval service provided by multiple cloud servers becomes possible. In this paper, inspired by additive secret sharing technology, we propose a series of additive secure computing protocols on numbers and matrices with better efficiency, and then show their application in PPCBIR. Specifically, we extract CNN features, decrease the dimension of features and build the index securely with the help of our protocols, which include the full process of image retrieval in plaintext domain. The experiments and security analysis demonstrate the efficiency, accuracy and security of our scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

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