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arXiv 2022年

作者： Li, Xinrao Zhang, Tong Wang, Shuai Zhu, Guangxu Wang, Rui Chang, Tsung-Hui The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Hong Kong

Edge intelligence autonomous driving (EIAD) offers computing resources in autonomous vehicles for training deep neural networks. However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles. Moreover, the required number of training samples for different data modalities, e.g., images, point-clouds, is diverse. Consequently, when collecting these datasets from vehicles to the edge server, the associated bandwidth and power allocation across all data frames is a large-scale multimodal optimization problem. This article proposes a highly computationally efficient algorithm that directly maximizes the quality of training (QoT). The key ingredients include a data-driven model for quantifying the priority of data modality and two first-order methods termed accelerated gradient projection and dual decomposition for low-complexity resource allocation. Finally, high-fidelity simulations in Car Learning to Act (CARLA) show that the proposed algorithm reduces the perception error by 3% and the computation time by 98%. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Named entity recognition for social media texts with semantic augmentation

Named entity recognition for social media texts with semanti...

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2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020

作者： Nie, Yuyang Tian, Yuanhe Wan, Xiang Song, Yan Dai, Bo University of Electronic Science and Technology of China China University of Washington United States Shenzhen Research Institute of Big Data China The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781952148606

Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation. In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account. In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively. Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets. © 2020 Association for Computational Linguistics

关键词： Semantics

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Grnmopt: Inference of Gene Regulatory Networks Based on a Multi-Objective Optimization Approach

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SSRN 2024年

作者： Dong, Heng Ma, Baoshan Meng, Yangyang Wu, Yiming Liu, Yongjing Zeng, Tao Huang, Jinyan School of Information Science and Technology Dalian Maritime University Dalian116026 China Biomedical Big Data Center The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou310003 China Zhejiang Provincial Key Laboratory of Pancreatic Disease Zhejiang University School of Medicine First Affiliated Hospital Hangzhou310003 China Zhejiang University Cancer Center Zhejiang University Hangzhou310058 China

Background and ObjectiveThe reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN *** study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene *** experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of *** propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition

arXiv

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arXiv 2023年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Computer Vision Institute Shenzhen University China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China Alan Turing Institute United Kingdom SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace. © 2023, CC BY.

关键词： Face recognition

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HGeoHashBase: an optimized storage model of spatial objects for location-based services

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Frontiers of Computer science 2020年第1期14卷 208-218页

作者： Jingwei ZHANG Chao YANG Qing YANG Yuming LIN Yanchun ZHANG Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic TechnologyGuilin 541004China Centre for Applied Informatics College of Engineering and ScienceVictoria UniversityMelbourne 8001Australia

Many location-based services need to query objects existing in a specific space,such as location-based tourism resource *** a large number of spatial objects and the real-time object access requirements of location-based services pose a big challenge for spatial object storage and query *** this paper,we propose HGeoHashBase,an improved storage model by integrating GeoHash with key-value structure,to organize spatial objects for efficient range *** is responsible for spatial encoding and key-value structure as underlying data *** the similarity of the encodings for objects in the close geographical locations and the multi-version data mechanism are blended into the proposed model *** the tradeoff between encoding precision and query performance,a theoretical proof is *** experiments are designed and conducted,whose results show that the proposed model can gain significant performance improvement.

关键词： location-based services distributed storage model storage and access optimization

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Toward Failure-Aware Energy-Efficient Service Provisioning in Vehicular Fog Computing

Toward Failure-Aware Energy-Efficient Service Provisioning i...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Chaogang Tang Chunsheng Zhu Huaming Wu Lei Ning Joel J. P. C. Rodrigues School of Computer Science and Technology China University of Mining and Technology Xuzhou China College of Big Data and Internet Shenzhen Technology University Shenzhen Guangdong China The Center for Applied Mathematics Tianjin University Tianjin China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Instituto de Telecomunicações Covilha Portugal

ISBN: (纸本)9781665435413

The fast-growing Internet of Things (IoT) have generated a vast number of IoT tasks, and these tasks are usually featured by strict response latency requirements. To cater for the time-sensitive IoT application scenarios, vehicular fog computing (VFC) can be adopted to serve the offloading requests from the IoT devices. However, current works in VFC seldom consider the task execution failures that are actually inevitable owing to limited computing resources in VFC compared to cloud computing. Hence, we strive to enhance the VFC system by incorporating the failures for task execution into our system model, which makes task offloading more general and practical. We formulate our energy consumption optimization as a mixed integer nonlinear programming problem and further put forward an iterative algorithm to solve it. We validate our approach by extensive simulation and the experimental results have proven its advantages in terms of the optimal values.

关键词： Energy consumption Cloud computing Computational modeling Quality of service Programming Internet of Things Iterative methods

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Ggt-Snn: Graph Learning and Gaussian Prior Integrated Spiking Graph Neural Network for Event-Driven Tactile Object Recognition

SSRN

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SSRN 2024年

作者： Yu, Zukun Yang, Jing Li, Shaobo Cao, Yang Hu, JianJun Xu, Ji Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Ministry of Education Guizhou University Guiyang550025 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai201100 China School of Mechanical Engineering Guizhou University Guiyang550025 China Department of Computer Science and Engineering University of South Carolina Columbia29208 United States

Spiking neural networks possess asynchronous, discrete, and sparse characteristics that enable direct processing of event-based tactile sensor data and more efficient transmission of information. These networks have been widely applied in the tactile perception field. However, due to the sparsity of tactile data, these models are prone to generalization issues. Additionally, existing methods for tactile graph construction are constrained when spatially connecting high-dimensional tactile data, making it challenging for the model to fuse and process such information effectively. To address these challenges, we propose the GGT-SNN, which introduces a parameter estimation method with Gaussian priors to alleviate generalization problems caused by sparse tactile data. Furthermore, we design M-tree and Z-tree tactile graph construction methods to compensate for deficiencies of other approaches when handling high-dimensional tactile spatial connections. This approach enhances the model's ability to efficiently process high-dimensional event-driven tactile information. We introduce an approximate LIF neuron activation function to enable backpropagation within the model and comprehensively compare and evaluate its performance against different approximate functions. The experimental results demonstrate that our proposed method performs significantly better than SOTA methods, exhibiting a 10.83% improvement over TactileSGNet on the EvTouch-Containers dataset and a 2.92% improvement over TactileSGNet on the EvTouch-Objects dataset. © 2024, The Authors. All rights reserved.

关键词： Object recognition

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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BED: A Block-Level Deduplication-Based Container Deployment Framework 15th

BED: A Block-Level Deduplication-Based Container Deployment ...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Zhang, Shiqiang Wu, Song Fan, Hao Zou, Deqing Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Scinece and Technology Wuhan430074 China

ISBN: (纸本)9783030642426

Container technology has gained great popularity in cloud environment since containers provide near-native performance and are lighter and less expensive than traditional virtual machines. However, starting up a non-local container, whose image is unavailable locally, is time-consuming. The reason is that pulling an image from a remote registry requires a long time. In this paper, we experimentally find that a lot of redundant data exists among image layers when we pull images from the registry. This redundant data causes additional pull time and makes the startup of a non-local container slower. To minimize the amount of pulled data while providing intact images, we propose a container deployment framework, BED, based on block-level deduplication. To be specific, BED stores an image layer as numerous data blocks and a fingerprint list which is generated based on these data blocks. When BED needs to pull an image, it pulls the fingerprint lists of the image, deduplicates these fingerprint lists, and pulls the data blocks non-existing locally from the registry. Based on the local and pulled blocks, BED reconstructs the image layers for a container. Experiments show that compared with original Docker, BED reduces the time of pulling images by 35% on average and saves about 48% data transmission in the network. © 2020, Springer Nature Switzerland AG.

关键词： Containers

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Rational Delegation Learning Outsourcing Scheme Based on Game Theory

Rational Delegation Learning Outsourcing Scheme Based on Gam...

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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Xiang, Kang Tian, Youliang Gao, Sheng Peng, Changgen Tan, Weijie Institute of Cryptography and Data Security Guizhou University Guiyang550025 China School of Information Central University of Finance and Economics Beijing100081 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9781728189543

At present, many enterprises or individuals with a lot of data are limited by their own computing power, so can't mine useful information from the data. To address this problem, this paper proposes a rational delegation machine learning pattern. In the proposed pattern, we firstly construct a reliable game model, and then derive a formal model of rational delegation machine learning according to delegation computation thought. Finally, we design an outsourcing scheme for decision tree model. We analyze the security and performance of the proposed scheme, the analysis results show that the proposed scheme can not disclose any useful information. Last but not least, the experimental result demonstrates that the proposed scheme can obtain a decision tree model with high accuracy. © 2020 IEEE.

关键词： Game theory

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