检索结果-内蒙古大学图书馆

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Yanchun Deng, Senlin Shen, Dongsu Tian, Shujuan Long, Saiqin School of Computer Science School of Cyberspace Science Xiangtan University China Hunan International Scientific and Technological Cooperation Base of Intelligent Network China Key Laboratory of Hunan Province for Internet of Things and Information Security China College of Information Science and Technology Jinan University China

ISBN: (纸本)9782493814104

Fine-tuning Pre-trained Language Models (PLMs) is a popular Natural Language Processing (NLP) paradigm for addressing Named Entity Recognition (NER) tasks. However, neural network models often demonstrate poor generalization capabilities due to significant disparities between the knowledge learned by PLMs and the distribution of the target dataset, as well as data scarcity issues. In addition, token omission in predictions due to insufficient learning remains a challenge in NER. In this paper, we propose a kNN retrieval enhancement algorithm (WkNER) that incorporates word segmentation information to enhance the model's generalization ability and alleviate the problem of missing entity tokens in prediction. The introduction of word segmentation information is used to preliminarily determine the boundaries of entities and alleviate the common prediction errors of missing tokens within entities made by the fine-tuned model. Secondly, we find that non-entities in the retrieval table contain a large amount of redundant information, and explore the effects of introducing non-entity information of different scales on the model. Experimental results show that our proposed method significantly improves the performance of baseline models, and achieves better or compared recognition accuracy than previous state-of-the-art models in multiple public Chinese and English datasets. Especially in low-resource scenarios, our method achieves higher accuracy on 20% of the dataset than the original method on the full dataset. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Cyber-Physical Systems Design in An Uncertain Environment with Time Uncertainty Concern

Cyber-Physical Systems Design in An Uncertain Environment wi...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Si Chen Lida Huang Xiongren Xiao Yan Liu Guoqi Xie Renfa Li College of Computer Science and Electronic Engineering Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing China

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. The uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. The state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RTISM: Real-Time Inter-VM Communication Based on Shared Memory for Mixed-Criticality Flows

RTISM: Real-Time Inter-VM Communication Based on Shared Memo...

引用

Real-Time Systems Symposium (RTSS)

作者： Zonghong Li Guoqi Xie Wenhong Ma Xiongren Xiao Yong Xie Wei Ren Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University China Research Institute of Hunan University in Chongqing China Nanjing University of Posts and Telecommunications China Huawei Technologies China

Virtual machines (VMs) have been increasingly deployed in real-time systems to enhance heterogeneous resource sharing. Despite being isolated for security and prevention of failure propagation, VMs need to communicate with each other to complete certain tasks jointly. A real-time inter-VM communication framework has been proposed based on VirtIO, which is in essence a mechanism of message passing with high latency and low scalability on data amount. In contrary, IVSHMEM implements inter-VM communication with shared memory, which is generally fast and large in throughput. Unfortunately, IVSHMEM does not resolve resource contention between flows and hence cannot be applied in real-time scenarios. In this work, we propose a real-time inter-VM communication framework RTISM built upon shared memory and IVSHMEM. RTISM provides priority-based scheduling and supports mixed-criticality flows. Worst-Case Response Time (WCRT) analysis is reported to bound the end-to-end communication delay and a Limited Priority Assignment (LPA) algorithm is developed to enhance schedulability. Experimental evaluation shows that (i) RTISM has about 4 times higher throughput than the VirtIO-based inter-VM communication framework; (ii) LPA improves schedulability by over 25% compared to the state-of-the-art; (iii) WCRT produces a tight bound.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Method for Evaluating Core Developers In Open Source Software 13

A New Method for Evaluating Core Developers In Open Source S...

引用

13th IEEE International Conference on Software Engineering and Service Science, ICSESS 2022

作者： Song, Yulin Wang, Tao Shen, Yang Chang, Junsheng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Province Changsha China National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Province Changsha China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The high-quality development of Open Source Software(OSS) benefits from the contributions of developers. An important research question is how to truly assess the extent of contribution of all developers in open source software and effectively identify the core developers to avoid their loss. In this paper, we use the undirected weighted network model to build a Developer Collaboration network(DCN) based on developers and their contributions to the GitHub community's open source software Rails, analyse the characteristics of developer partnerships through the Developer Collaboration network, and propose a Developer Personal Impact Factor(DPIF) to evaluate the importance of developer, select the top 10 core developers in Rails, focus on their participation in the open source software process, and predicting the changes of their contributions. The experimental results show that core developers with extensive partnerships are more likely to associate with developers with fewer partnerships. The average degree of developer contribution does not increase with more developers. Developer partnerships are characterized by close localization and loose overall relationships. The Developer Personal Impact Factor identifies the core developers in open source software well. The Developer Personal Impact Factor gives a ranking in which the core team members rank in the top 1.17% on average. © 2022 IEEE.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Personalized News Recommendation Based on the Text and Image Integration

引用

Computers, Materials & Continua 2020年第7期64卷 557-570页

作者： Kehua Yang Shaosong Long Wei Zhang Jiqing Yao Jing Liu College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company New York10007USA

The personalized news recommendation has been very popular in the news recommendation *** most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual ***,in this paper,a model that combines images and texts in the news is *** this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is *** AT algorithm selects the tags the user is interested in based on the user *** particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user *** proposed AT algorithm is verified by *** experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.

关键词： News recommendation F1 AUC MRR feedback correlation graph

来源：评论

学校读者我要写书评

暂无评论

AutoToolVCC: Variable consistency check tool for automotive software projects

AutoToolVCC: Variable consistency check tool for automotive ...

引用

作者： Xie, Pingfu Wang, Yunlong He, Bo Xie, Guoqi College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China United Automotive Electronic Systems Co. Ltd. Shanghai China

Automotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s. © 2020 John Wiley & Sons, Ltd.

关键词： Accidents

来源：评论

学校读者我要写书评

暂无评论

STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking

arXiv

引用

arXiv 2022年

作者： Wang, Haidong Li, Zhiyong Li, Yaping Nai, Ke Wen, Ming College of Computer Science Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Hunan Province Changsha410082 China State Grid Hunan Electric Power Company Limited Economical Technical Research Institute Hunan Key Laboratory of Energy Internet Supply-demand and Operation Hunan Province Changsha410000 China

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an object sequence, while each candidate detection is just a flat image, which lacks temporal features of the object sequence. The feature difference between current candidate detections and historical tracklets makes the object association much harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning (STURE) approach which learns spatial-temporal representations between current candidate detections and historical sequences in a mutual representation space. For historical trackelets, the detection learning network is forced to match the representations of sequence learning network in a mutual representation space. The proposed approach is capable of extracting more distinguishing detection and sequence representations by using various designed losses in object association. As a result, spatial-temporal feature is learned mutually to reinforce the current detection features, and the feature difference can be relieved. To prove the robustness of the STURE, it is applied to the public MOT challenge benchmarks and performs well compared with various state-of-the-art online MOT trackers based on identity-preserving metrics. Copyright © 2022, The Authors. All rights reserved.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Energy allocation for activity recognition in wearable devic...

引用

作者： Xiao, Ling Meng, Yu Tian, Xiaobing Luo, Haibo College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Science Guangdong University of Science And Technology Dongguan China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China College of Computer and Control Engineering Minjiang University Fujian China

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices. © 2021 John Wiley & Sons, Ltd.

关键词： Kinetic energy

来源：评论

学校读者我要写书评

暂无评论

Stacking-Based Attention Temporal Convolutional network for Action Segmentation 48

Stacking-Based Attention Temporal Convolutional Network for ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Liu Jiang, Yu Hong, Junkun Wu, Zhenjie Yang, Zhan Long, Jun Central South University School of Computer Science and Engineering China Central South University Big Data Institute Central South University China Central South University Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha China

ISBN: (纸本)9781728163277

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Monocular Camera-Based Visual Truck Type Recognition

SSRN

引用

SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：