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arXiv 2022年

作者： Jiang, Yangbangyan Li, Xiaodan Chen, Yuefeng He, Yuan Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Security Department of Alibaba Group Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this paper. Specifically, we first present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants. We then provide a simple but effective algorithm to solve the proposed minimax problem, and theoretically prove that it converges to a stationary point. Experiments on five popular benchmark datasets validate the effectiveness of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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Cross-modal consensus network for weakly supervised temporal action localization

arXiv

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arXiv 2021年

作者： Hong, Fa-Ting Feng, Jia-Chang Xu, Dan Shan, Ying Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Tencent PCG Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science and Engineering HKUST Hong Kong Pazhou Lab Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Previous works use the appearance and motion features extracted from pre-trained feature encoder directly, e.g., feature concatenation or score-level fusion. In this work, we argue that the features extracted from the pre-trained extractors, e.g., I3D, which are trained for trimmed video action classification, but not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization. Therefore, the feature re-calibration is needed for reducing the task-irrelevant information redundancy. Here, we propose a cross-modal consensus network (CO2-Net) to tackle this problem. In CO2-Net, we mainly introduce two identical proposed cross-modal consensus modules (CCM) that design a cross-modal attention mechanism to filter out the task-irrelevant information redundancy using the global information from the main modality and the cross-modal local information from the auxiliary modality. Moreover, we further explore inter-modality consistency, where we treat the attention weights derived from each CCM as the pseudo targets of the attention weights derived from another CCM to maintain the consistency between the predictions derived from two CCMs, forming a mutual learning manner. Finally, we conduct extensive experiments on two commonly used temporal action localization datasets, THUMOS14 and ActivityNet1.2, to verify our method, which we achieve the state-of-the-art results. The experimental results show that our proposed cross-modal consensus module can produce more representative features for temporal action localization. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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A bi-level formulation for label noise learning with spectral cluster discovery 29

A bi-level formulation for label noise learning with spectra...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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Univariate Forecasting of time series data Based on Seq2Seq Model 4

Univariate Forecasting of time series data Based on Seq2Seq ...

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4th International Conference on Electronic Information Technology and computer engineering, EITCE 2020

作者： Guo, Yanwen Wu, Tao Liu, Yanbing Jiang, Yixin Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Internet and Information Sccurity Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781450387811

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction. © 2020 ACM.

关键词： Time series

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Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud computing: A Review and Future Directions

arXiv

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arXiv 2021年

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar Xue, Ruini Song, Liang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Laboratory Department of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

With the acceleration of the internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning (DL) and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling. © 2021, CC BY.

关键词： Deep reinforcement learning

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On the ADEP and DOR Analysis of RIS-aided URLLC Systems with Partial CSI in Smart Factory

On the ADEP and DOR Analysis of RIS-aided URLLC Systems with...

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IEEE Globecom Workshops

作者： Tingnan Bao Haiming Wang Hong-Chuan Yang Wen-Jing Wang Mazen O. Hasna Lenovo Research + Institute Of Computing Technology Chinese Academy of Sciences Beijing China Lenovo Reseach Lenovo Group Limited Bejiing China Department of Electrical and Computer Engineering University of Victoria Victoria Canada Shaanxi Key Laboratory of Information Communication Network and Security XUPT Xi'an China Department of Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9781665423908

ISBN: (纸本)9781665423915

Factory automation applications impose stringent quality of service (QoS) over ultra-reliable and low-latency communication (URLLC) system. However, the existing wireless systems cannot meet the requirement for these mission-critical industrial internet-of-things (IIoT) applications. In this paper, we consider the reconfigurable intelligent surface (RIS)-aided URLLC systems in the smart factory. To facilitate the performance analysis of the practical situation, the presence of phase error at RIS is discussed. We analyze and derive approximate closed-form expression of average decoding error probability (ADEP) and the closed-form expression of performance limits de-lay outage rate (DOR) under our proposed scenario, respectively. Through analytical results and associated numerical examples, we demonstrate the effect of the number of reflecting elements at the RIS, channel blocklength and packet size in achieving high reliability and low latency. Therefore, the deployment of RIS can offer an attractive solution to enhance the reliability over URLLC systems in the smart factory.

关键词： Wireless communication Closed-form solutions Error probability Rician channels Quality of service Ultra reliable low latency communication Reconfigurable intelligent surfaces

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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2020 International Conference on computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Terahertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade

arXiv

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arXiv 2021年

作者： Akyildiz, Ian F. Han, Chong Hu, Zhifeng Nie, Shuai Jornet, Josep M. Truva Inc. AlpharettaGA30022 United States Laboratory Shanghai Jiao Tong University Shanghai200240 China The School of Computing University of Nebraska-Lincoln LincolnNE68588 United States The Ultrabroadband Nanonetworking Laboratory Institute for the Wireless Internet of Things Department of Electrical and Computer Engineering Northeastern University BostonMA02215 United States

Terahertz (THz) band communications are envisioned as a key technology for 6G and Beyond. As a fundamental wireless infrastructure, THz communication can boost abundant promising applications. In 2014, our team published two comprehensive roadmaps for the development and progress of THz communication networks [1], [2], which helped the research community to start research on this subject afterwards. The topic of THz communications became very important and appealing to the research community due to 6G wireless systems design and development in recent years. Many papers are getting published covering different aspects of wireless systems using the THz band. With this paper, our aim is looking back to the last decade and revisiting the old problems and pointing out what has been achieved in the research community so far. Furthermore, in this paper, open challenges and new research directions still to be investigated for the THz band communication systems are presented, by covering diverse topics ranging from devices, channel behavior, communication and networking, to physical testbeds and demonstration systems. The key aspects presented in this paper will enable THz communications as a pillar of 6G and Beyond wireless systems in the next decade. Copyright © 2021, The Authors. All rights reserved.

关键词： Testbeds

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