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Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

Intelligent Automation & Soft Computing 2023年第7期37卷 1-16页

作者： Jixun Gao Bingyi Hu Jialei Liu Huaichen Wang Quanzhen Huang Yuanyuan Zhao School of Computer Henan University of EngineeringZhengzhou451191China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Computer Hubei University of Arts and ScienceXiangyang441053China School of Software Engineering Anyang Normal UniversityAnyang455000China School of Computer and Information Engineering Henan Normal UniversityXinxiang453003China The Publicity Department Zhengzhou University of TechnologyZhengzhou450044China

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service ***,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste *** the backup virtual machine under the above circumstances can effectively improve resource ***,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for *** deployment locations have different resource utilization and average service response *** want toﬁnd a balanced solution that minimizes the average service response time of the IoT application while maximizing resource *** this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment ***,the simulation results show that the TDA can signiﬁcantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical *** experimental results conﬁrm that the perfor-mance of TDA is better than that of other two methods.

关键词： Mobile edge computing overbooking resource utilization service response time task deployment algorithm

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Research on Multi-Model Fusion Method for Similar Multi-domain 5

Research on Multi-Model Fusion Method for Similar Multi-doma...

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5th International Conference on Robotics and computer Vision, ICRCV 2023

作者： Zhao, Junhua Song, Qingzeng Xue, Yongjiang Yu, Jing School of Computer Science and Technology Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Haihe Lab of ITAI Tianjin China School of Telecommunication Engineering Beijing Polytechnic Beijing China

ISBN: (纸本)9798350326369

In order to improve the classification accuracy of test samples from different datasets in multi-domain image classification, this paper proposes a multi-domain sample classification method based on Baidu AI open platform image recognition and multi-model fusion. The classification task is divided into two steps: first, the domain to which the sample belongs is determined based on the predicted label and its probability output by the Baidu API, and then the deep learning model trained on that domain is used to predict the label of the sample. The effectiveness of the proposed method is verified through extensive experiments on three public datasets: Cifar-10, Cifar-100, and Mini-ImageNet. In addition, compared with existing algorithms, the proposed method shows significant improvements in classification performance. © 2023 IEEE.

关键词： Image classification

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science Guangdong China Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. © 2024 IEEE.

关键词： Zero-shot learning

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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computer Modeling in engineering & sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition 6

Dynamic Language Refinement for Graceful Degradation in Scen...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tian, Shaocong Jin, Rize Paik, Joon-Young Fu, Chen Tiangong University School of Software Tianjin China Tiangong University School of Computer Science &technology Tianjin China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350325485

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-To-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-To-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-The-Art results. © 2023 IEEE.

关键词： Iterative methods

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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

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computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

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mRS-DBFT: An improved consensus mechanism suitable for end-edge-cloud hybrid trust models 5

mRS-DBFT: An improved consensus mechanism suitable for end-e...

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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Zhai, Xue Pang, Shanchen Qiao, Sibo Yu, Shihang Qingdao Institute of Software College Computer Science and Technology Qingdao China Tiangong University School of Mechanical Engineering Tianjin China

ISBN: (纸本)9798350309034

As a three-terminal collaborative architecture of terminal, edge and cloud, end-edge-cloud is jointly participated by multiple institutions or organizations, and has the characteristics of a hybrid trust architecture of 'partially trustworthy, globally untrustworthy'. Traditional consensus algorithm designs are mostly used in relatively unified trust model environments and do not provide a mechanism for cross-organizational cooperation. Therefore, they cannot effectively solve the trust and consensus issues between organizations. We propose an improved DPoS-PBFT consensus mechanism based on reputation scoring (mRS-DBFT). The core idea of the mRS-DBFT is to add a reputation scoring mechanism based on the DPOS-PBFT consensus algorithm. The reputation score is calculated based on factors such as the node's historical behavior, contribution, and credibility. Nodes with higher reputation score have greater influence and decision-making power in the consensus process. This mechanism can solve the problem of mutual distrust among nodes of different organizations in the end-edge-cloud architecture, and each node participates in the consensus process based on its reputation score, thus forming a credible and consistent system, achieving cross-organizational consensus, and ensuring node trust and data consistency in an open end-edge-cloud environment. The experimental results demonstrate that the mRS-DBFT consensus algorithm helps select nodes with higher stability to participate in the consensus by evaluating the reputation of computing devices, it can reduce fluctuations in delay and improve the stability of the consensus process. © 2023 IEEE.

关键词： consensus mechanism delegated proof of stake end-edge-cloud computing practical byzantine fault tolerance reputation scoring

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A Vision-Based Algorithm of Slope Angle Estimation by Using Cooperation of Snake Robots 20

A Vision-Based Algorithm of Slope Angle Estimation by Using ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Li, Shuai Liu, Jian Liu, Wenjun Liu, Ziming Tian, Jingrun Wang, Ruonan Xiao, Xuan Wang, Zengzeng Tiangong University School of Software Tianjin China Tiangong University Center of Engineering Practice Training Tianjin China Tiangong University School of Economics&Management Tianjin China Tiangong University School of Computer Science&Technology Tianjin China

ISBN: (纸本)9798350320831

When snake robots perform tasks, their ability of sensing the environment is very important. This paper proposes an algorithm for estimating the slope angle based on the cooperation of two snake robots by using the visual system. Through the movement of two snake robots, the corresponding geometric models will be created. After that, the angle of the slope is calculated by substituting the coordinates that are recorded by the visual system. Finally, the experiments are conducted to verify the performance of the algorithm. The results show the errors are within 2° when the slope range is between 15° and 75°. © 2023 IEEE.

关键词： Robot vision

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An efficient and balanced feature pyramid network: shared-FPN

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Optoelectronics Letters 2025年

作者： Tian Sulong Qin Longfei Pang Wenchao Gao Ying Zhao Dexin School of Computer Science and Engineering， Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology， Tianjin University of Technology

In the field of object detection， it is challenging to achieve a balance between neck complexity and accuracy. To address this issue， we propose an efficient and decoupled neck module called Shared-FPN(Shared Feature Pyramid Network). Our design aims to effectively address the trade-off between these two factors. Neck modules are crucial for effectively fusing multi-scale information in neural network models. However， traditional neck designs， with their single structure， have struggled to significantly improve detection performance. In recent years， the incorporation of neural architecture search techniques in neck design has led to an increase in model complexity. This trade-off between model complexity and detection performance highlights the need for more efficient neck design approaches. Not only does Shared-FPN not increase the number of model parameters and FLOPs， but it also can be easily ported to any of the detection models. It improves on PAFPN by using transposed convolution with a large convolution kernel as the upsampling module， designing spatial pyramidal pooling-fast downsampling(SPPFD) based on shared pooling， and designing shared convolution as the right part module. To evaluate the performance of Shared-FPN in object detection tasks， we conducted experiments on object detection datasets. The results show that Shared-FPN achieved excellent performance across all sizes. In particular， on the VOC 2012 dataset， which contains mostly large objects， Shared-FPN's mAP was improved by 11.2% compared to FPN with YOLOX-s as the detector. On the COCO dataset， which contains mostly small objects， Shared-FPN's mAP was improved by 7.8% compared to FPN and 7.1% compared to PAFPN with Faster RCNN as the detector. Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios， such as small， medium or large objects.

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