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Automatic Image Annotation for Human Machine Interaction in Industrial IoT Flexible Manufacturing

IEEE Internet of Things Journal 2024年

作者： Wang, Xiaojie Zheng, Guifeng Wu, Yu Xiong, Xuanrui Zhou, Guanghai Tolba, Amr Ning, Zhaolong Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Dalian University of Technology School of Software Dalian116024 China King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia

With the explosive growth in Industrial Internet-of-things (IIoT) devices, the volume of multimedia data in the field of flexible manufacturing has also increased significantly in recent years, especially the vast amount of unlabelled image data. Image annotation provides machines with a more natural way to interact with users, enhancing the level of intelligence in IIoT flexible manufacturing. This paper proposes a multi-feature fusion multi-kernel learning image annotation method to tackle imbalanced label distribution, image weak labeling, and varying representational abilities of features. Initially, oversampling techniques with synthetic minority class samples address the influence of minority classes, while a label enhancer extends label vectors to overcome the influence of weak labeling. Subsequently, the integration of traditional visual features with deep features based on multi-kernel learning is investigated to enhance feature representation capability. This approach combines complementary information from multiple features, establishing intrinsic connections between images and annotated keywords. Experimental evaluations are conducted on three benchmark datasets, comparing our method with several classical methods. Evaluation results demonstrate that our proposed method captures semantic information more accurately and comprehensively. By effectively accomplishing automatic image annotation, our method can enhance human-machine-interaction to improve the level of intelligence in IIoT flexible manufacturing. © 2014 IEEE.

关键词： Smart manufacturing

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A Two-stage Anomaly Detection System for Fog Computing Services 5

A Two-stage Anomaly Detection System for Fog Computing Servi...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ogundoyin, Sunday Oyinlola Adeniyi Kamil, Ismaila Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Bowen University Electrical and Electronic Engineering Programme Iwo Nigeria University of Ibadan Department of Electrical and Electronic Engineering Ibadan Nigeria Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bando40601 Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai602105 India

ISBN: (纸本)9798331542559

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10-5. The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively. © 2024 IEEE.

关键词： Fog computing

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Learning from Crowds with Mutual Correction-Based Co-Training

Learning from Crowds with Mutual Correction-Based Co-Trainin...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Nianshang Tian Ming Wu Jiqiong Jiang Jing Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Zhejiang Lab Research Center of Intelligent Computing Software Hangzhou China

Collecting labels from crowds is a feasible solution for large knowledge graph creation. However, errors in crowd-sourced labels damage the predictive model training. Although various methods have been developed to correct noisy labels, these traditional methods on the one hand do not take full advantage of the information from crowdsourced labels, and on the other hand do not perform well facing complicated annotation conditions, especially when some crowd workers make highly correlated mistakes. To address this issue, this paper proposes a novel learning from crowds method, which can simultaneously correct label noises and learn predictive models. The proposed method trains two data classifiers and a crowd aggregator. Each data classifier divides crowdsourced labeled data into a clean set and a noisy set and predicts the labels of the instances with a large loss in the noisy set. These instances are removed from the noisy set and put into the clean set. Then, the loss of the classifier is updated using the new clean and noisy sets. The data classifier with smaller loss is selected and trained with the crowd aggregator by maximizing their mutual information. Simultaneously, the smaller loss is back propagated to the other data classifier for training it. Experimental results on both synthetic and real-world datasets consistently show that the proposed method significantly outperforms the state-of-the-art methods in noise correction and predictive model learning.

关键词： Training Crowdsourcing Annotations Predictive models Propagation losses Data models Noise measurement

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TransOff: Towards Fast Transferable Computation Offloading in MEC via Embedded Reinforcement Learning

TransOff: Towards Fast Transferable Computation Offloading i...

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International Conference on Distributed Computing Systems

作者： Zheyuan Hu Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China

Mobile edge computing (MEC) has been proposed as a promising paradigm to provide mobile devices with both satisfactory computing capacity and task latency. One key issue in MEC is computation offloading (CompOff), which has attracted numerous research interests. Most existing CompOff approaches are developed based on iterative programming (IterProg), that calculates a CompOff action based on system dynamics each time mobile tasks arrive. Due to the heavy dependency of IterProg on reliable system dynamics, as well as the online computational burden, recent years have seen a popular trend to develop CompOff approaches based on deep reinforcement learning (DRL), which could generate real-time model-free CompOff actions. However, due to the intrinsic poor generalization of DRL, it is hard to directly apply DRL-based policies in new MEC environments, and long-time fine-tuning is often required. To address the challenge, this paper proposes a fast transferable CompOff framework (named TransOff), based on the idea of embedded reinforcement learning. Specifically, TransOff is composed of multiple primitive CompOff policies (pCOPs) and a multiplicative composition function (MCF). The pCOPs and MCF are pre-trained in a diverse variety of MEC environments. When encountering new MEC environments, pCOPs are kept fixed to prevent catastrophic forgetting of pre-trained CompOff skills, while only MCF is fine-tuned to produce new compositions of pCOPs to achieve fast transfer. We conduct extensive experiments via both numerical simulation and real testbed, indicating the fast transfer ability of TransOff compared to the state-of-the-art DRL-based and meta learning-based CompOff approaches.

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On the adversarial robustness of out-of-distribution generalization models 23

On the adversarial robustness of out-of-distribution general...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xin Zou Weiwei Liu School of Computer Science Wuhan University and National Engineering Research Center for Multimedia Software Wuhan University and Institute of Artificial Intelligence Wuhan University and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

Out-of-distribution (OOD) generalization has attracted increasing research attention in recent years, due to its promising experimental results in real-world applications. Interestingly, we find that existing OOD generalization methods are vulnerable to adversarial attacks. This motivates us to study OOD adversarial robustness. We first present theoretical analyses of OOD adversarial robustness in two different complementary settings. Motivated by the theoretical results, we design two algorithms to improve the OOD adversarial robustness. Finally, we conduct experiments to validate the effectiveness of our proposed algorithms. Our code is available at https://***/ZouXinn/OOD-Adv.

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Joint Consolidated Middleboxes and Virtual Switch Deployment and Routing for Load Balancing in software-Defined Network 5

Joint Consolidated Middleboxes and Virtual Switch Deployment...

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5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

作者： Ge, Juncheng Zhao, Gongming Yang, Xuwei School of Software Engineering China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9781728160245

Network Function Virtualization (NFV) is a rapidly developing network architecture concept, in which virtualization technologies transform network functions in hardware devices to software middleboxes. For reasons such as improving security and performance, flows in the network need to traverse specific service function chains (SFCs). Due to the rapid increases in traffic volume, traffic variety, and service requirements, it becomes much complex for SFC routing. The existing solutions for joint middlebox placement and routing still consume a lot of forwarding rules and result in overloaded controllers, which decrease the scalability of middlebox networks. To solve these problems, this paper proposes a joint deployment scheme, which uses the consolidated middlebox to simplify the processing of SFC and uses the virtual switch (e.g., OVS) to reduce TCAM usage. Specifically, We formulate the joint optimization of consolidated middleboxes and the virtual switch placement, as well as routing (JPR) problem into an integer linear programming model, and prove its NP-hardness. An efficient algorithm is proposed to solve the JPR problem. Extensive simulation results show that the proposed solution can achieve better load balancing while using fewer forwarding rules than other existing algorithms. For example, our algorithm can reduce the maximum flow entries by 69%, and reduce the maximum link load by 45% compared with the state-of-the-art approaches. © 2020 IEEE.

关键词： Network function virtualization

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GCMNet: Gated Cascade Multi-scale Network for Crowd Counting 18th

GCMNet: Gated Cascade Multi-scale Network for Crowd Counting

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zheng, Jinfang Zhao, Panpan Xie, Jinyang Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030893620

With the rapid development of convolutional neural networks, many CNN-based methods have emerged and made promising progress in the field of crowd counting. However, dealing with extremely scale variation remains a challenging but attractive issue. In this paper, we propose an innovative Gated Cascade Multi-scale Network (GCMNet) to tackle with the issue by taking full advantage of the representation of multi-scale features in a multi-level network. First of all, we implement such an idea by obtaining rich contextual information with a multi-scale contextual information enhancement module. Then, considering the pixel-level image detail information that is lost during the successive feature extraction process, we propose a hopping cascade module to refine this detail information. However, naively refining all the detail information is sub-optimal. Therefore, a gated information selection delivery module is designed to adaptively control the delivery of information between multi-level features. Combined with our proposed module, our method can effectively generate high-quality crowd density maps. The superiority of our method over current methods is demonstrated through extensive experiments on four challenging datasets. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Ensemble Distillation for Out-of-distribution Detection

Ensemble Distillation for Out-of-distribution Detection

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Dongdong Wang Jingyao Xu Siyang Lu Xiang Wei Liqiang Wang Department of Civil Environmental and Construction Engineering University of Central Florida Orlando FL US School of Computer and Information Technolog Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Department of Computer Science University of Central Florida Orlando FL US

Out-of-distribution detection is critical to a reliable application of deep neural networks. To reduce model uncertainty, we propose a simple yet effective approach of ensemble knowledge distillation. We blend ensemble model and knowledge distillation to improve model generalization on indomain recognition, thereby yielding accurate and robust out-of-distribution detection. The former effectively expands data recognition feature space, while the latter further regularizes the model through knowledge distillation, enhancing in-domain feature recognition. This effective integration successfully yields lower model uncertainty on in-domain feature recognition and improves anomaly detection in a more scalable manner. We justify our approach through extensive experiments on various benchmarks, demonstrating its significant improvement in out- of-distribution detection. We validate our approach with a variety of up-to-date DNNs, like Vision Transformer.

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Toward Trustworthy Identity Tracing via Multi-attribute Synergistic Identification

arXiv

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arXiv 2023年

作者： Liu, Decheng Yu, Jiahao Hu, Ruimin Feng, Wenbin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China School of Cyber Engineering Xidian University Xi'An710071 China

Identity tracing is a technology that uses the selection and collection of identity attributes of the object to be tested to discover its true identity, and it is one of the most important foundational issues in the field of social security prevention. However, traditional identity recognition technologies based on single attributes have difficulty achieving ultimate recognition accuracy, where deep learning-based model always lacks interpretability. Multivariate attribute collaborative identification is a possible key way to overcome mentioned recognition errors and low data quality problems. In this paper, we propose the "Trustworthy Identity Tracing" (TIT) task and a Multi-attribute Synergistic Identification based TIT framework. We first established a novel identity model based on identity entropy theoretically. The individual conditional identity entropy and core identification set are defined to reveal the intrinsic mechanism of multivariate attribute collaborative identification. Based on the proposed identity model, we propose a trustworthy identity tracing framework (TITF) with multi-attribute synergistic identification to determine the identity of unknown objects, which can optimize the core identification set and provide an interpretable identity tracing process. Actually, the essence of identity tracing is revealed to be the process of the identity entropy value converges to zero. To cope with the lack of test data, we construct a dataset of 1000 objects to simulate real-world scenarios, where 20 identity attributes are labeled to trace unknown object identities. The experiment results conducted on the mentioned dataset show the proposed TITF algorithm can achieve satisfactory identification performance. Furthermore, the proposed TIT task explores the interpretable quantitative mathematical relationship between attributes and identity, which can help expand the identity representation from a single-attribute feature domain to a multi-attribute collaborat

关键词： Entropy

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Delay-Aware Service Caching in Edge Cloud: An Adversarial Semi-Bandits Learning-Based Approach

Delay-Aware Service Caching in Edge Cloud: An Adversarial Se...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Jinpeng Li Yunni Xia Xiaoning Sun Peng Chen Xiaobo Li Jiafeng Feng Collage of Computer Science Chongqing University Chongqing China School of Computer and Information Science Chongqing Normal University Chongqing China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China School of Computer and Software Engineering Xihua University Chengdu China Chongqing Animal Husbandry Techniques Extension Center Chongqing China China Huadian Corporation Guangdong Branch Guangdong China

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Mobile Edge Computing (MEC) is an emerging computing paradigm that offloads cloud center functions to the edge server. In a MEC environment, edge servers' limited storage and processing capacity require selective service caching, where only a part of required content can be placed directly upon the destination edge server and the remaining at remote cloud end. A primary challenge in this context is the creation of an effective and responsive service caching algorithm that improves the Quality of Service (QoS) perceived by users while reducing operational costs. This study applies an $M$ / G /1 queuing model as the foundational framework and transforms the service caching problem as an adversarial semi-bandit problem. We propose a delay-aware Genetic-Follow-the-Regularized-Leader (GFRL) algorithm, which is capable of guiding decentralized caching decisions. Experimental results indicate that GFRL outperforms traditional methods across various performance metrics.

关键词： Measurement Cloud computing Upper bound Multi-access edge computing Costs Quality of service Transforms

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