检索结果-内蒙古大学图书馆

A Deep Learning Based Energy-Efficient Computational Offloading Method in Internet of Vehicles

China Communications 2019年第3期16卷 81-91页

作者： Xiaojie Wang Xiang Wei Lei Wang School of Software Dalian University of Technology School of Computer Science and Engineering Beihang University

With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency.

关键词： computational offloading fog computing deep learning internet of vehicles

来源：评论

学校读者我要写书评

暂无评论

Toward a Unified Framework for Unsupervised Complex Tabular Reasoning

Toward a Unified Framework for Unsupervised Complex Tabular ...

引用

International Conference on Data Engineering

作者： Zhenyu Li Xiuxing Li Zhichao Duan Bowen Dong Ning Liu Jianyong Wang Department of Computer Science and Technology Tsinghua University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (ICT/CAS) University of Chinese Academy of Sciences School of Software Shandong University Jinan China

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. Specifically, we first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. The impressive performance demonstrates that UCTR can significantly reduce the dependence on manual annotation. Moreover, we also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Two-goal local search and inference rules for minimum dominating set 29

Two-goal local search and inference rules for minimum domina...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Cai, Shaowei Hou, Wenying Wang, Yiyuan Luo, Chuan Lin, Qingwei State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Software and Microelectronics Peking University China School of Computer Science and Information Technology Northeast Normal University China Microsoft Research China

ISBN: (纸本)9780999241165

Minimum dominating set (MinDS) is a canonical NP-hard combinatorial optimization problem with applications. For large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure with inference rules. Our algorithm named FastDS is evaluated on 4 standard benchmarks and 3 massive graphs benchmarks. FastDS obtains the best performance for almost all benchmarks, and obtains better solutions than state-of-the-art algorithms on massive graphs. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

Research on Key Word Information Retrieval Based on Inverted Index 8th

Research on Key Word Information Retrieval Based on Inverted...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qi, Meihan Fang, Wei Zhao, Yongming Sha, Yu Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China China Meteorological Administration Training Center Beijing China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067600

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

State prediction and servitisation of manufacturing processing equipment resources in smart cloud manufacturing

State prediction and servitisation of manufacturing processi...

引用

International Journal of Internet Manufacturing and Services

作者： Liu, Shenghui Hao, Xin Zhang, Shuli Ma, Chao School of Computer Science and Technology Harbin University of Science and Technology 150080 China Software and Microelectronics School Harbin University of Science and Technology 150080 China

For enabling the manufacturing processing equipment resources to intelligently perceive their own operating state, this paper proposes an integrated prediction method, in which by combining the clustering ability of SOM and the classification ability of BP together, an integrated intelligent prediction model with the ability of both qualitative and quantitative analysis is defined and used to realise the accurate prediction of operating state. Next, the service encapsulation specification for the various algorithms and model in this method are given. They are encapsulated as a set of cloud services and then published to the smart cloud manufacturing service platform, so as to enable the virtualised resources combine their own processing ability and the intelligent perception ability of these cloud services together by carrying out service composition. The above process realised the networking, servitisation, and intellectualisation of manufacturing processing equipment resources. Finally, the experimental results demonstrate the effectiveness of the proposed method. © 2020 Inderscience Enterprises Ltd.

关键词： Manufacture computer aided manufacturing Forecasting Predictive analytics Self organizing maps Web services Classification ability Integrated prediction Intelligent perception Intelligent prediction model Processing equipment Qualitative and quantitative analysis Service compositions Service encapsulations

来源：评论

学校读者我要写书评

暂无评论

Performance Benchmarking of NewSQL Databases with Yahoo Cloud Serving Benchmark

Performance Benchmarking of NewSQL Databases with Yahoo Clou...

引用

Future Technologies Conference, FTC 2020

作者： Astrova, Irina Koschel, Arne Wellermann, Nils Klostermeyer, Philip Department of Software Science School of IT Tallinn University of Technology Akadeemia Tee 21 Tallinn12618 Estonia Faculty IV Department of Computer Science University of Applied Sciences and Arts Hannover Ricklinger Stadtweg 120 Hannover30459 Germany

ISBN: (纸本)9783030630881

Selecting a NewSQL database product is an important process. Like any other successful database management system technology, the product selected today begins to define the legacy of the future. There are many different parameters that can be used to evaluate the NewSQL database alternatives, and there is no single most-correct process for conducting such an evaluation. Not only are there many possible pertinent evaluation criteria, but there also is typically a degree of uncertainty about the requirements and characteristics of the intended application environment. This paper focuses on three NewSQL databases (viz., VoltDB, MemSQL and NuoDB), with emphasis on evaluating their performance. The evaluation is done based on Yahoo Cloud Serving Benchmark. © 2021, Springer Nature Switzerland AG.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Unified Question Generation with Continual Lifelong Learning

arXiv

引用

arXiv 2022年

作者： Yuan, Wei Yin, Hongzhi He, Tieke Chen, Tong Wang, Qiufeng Cui, Lizhen School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of School of Computer Science and Engineering Southeast University Nanjing China School of Software Shandong University Jinan China

Question Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG methods mainly focus on building or training models for specific QG datasets. These works are subject to two major limitations: (1) They are dedicated to specific QG formats (e.g., answer-extraction or multi-choice QG), therefore, if we want to address a new format of QG, a re-design of the QG model is required. (2) Optimal performance is only achieved on the dataset they were just trained on. As a result, we have to train and keep various QG models for different QG datasets, which is resource-intensive and ungeneralizable. To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats. Specifically, we first build a format-convert encoding to transform different kinds of QG formats into a unified representation. Then, a method named STRIDER (SimilariTy RegularIzed Difficult Example Replay) is built to alleviate catastrophic forgetting in continual QG learning. Extensive experiments were conducted on 8 QG datasets across 4 QG formats (answer-extraction, answer-abstraction, multi-choice, and boolean QG) to demonstrate the effectiveness of our approach. Experimental results demonstrate that our Unified-QG can effectively and continually adapt to QG tasks when datasets and formats vary. In addition, we verify the ability of a single trained Unified-QG model in improving 8 Question Answering (QA) systems' performance through generating synthetic QA data. © 2022, CC BY-NC-SA.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Chu Li Xinyue Peng Haotian Guan Zefan Zhang Guishen Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China School of Nursing Jilin University Changchun China School of Life Sciences Jilin University Changchun China College of Computer Science and Engineering Changchun University of Technology Changchun China

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment

arXiv

引用

arXiv 2022年

作者： Liu, Yuqing Jia, Qi Wang, Shanshe Ma, Siwei Gao, Wen School of Software Dalian University of Technology Dalian116620 China International School of Information Science and Engineering Dalian University of Technology Dalian116620 China School of Electronics Engineering and Computer Science Institute of Digital Media Peking University Beijing100871 China

Image super-resolution (SR) has been widely investigated in recent years. However, it is challenging to fairly estimate the performance of various SR methods, as the lack of reliable and accurate criteria for the perceptual quality. Existing metrics concentrate on the specific kind of degradation without distinguishing the visual sensitive areas, which have no ability to describe the diverse SR degeneration situations in both low-level textural and high-level perceptual information. In this paper, we focus on the textural and perceptual degradation of SR images, and design a dual stream network to jointly explore the textural and perceptual information for quality assessment, dubbed TPNet. By mimicking the human vision system (HVS) that pays more attention to the significant image areas, we develop the spatial attention to make the visual sensitive information more distinguishable and utilize feature normalization (F-Norm) to boost the network representation. Experimental results show the TPNet predicts the visual quality score more accurate than other methods and demonstrates better consistency with the human’s perspective. The source code will be available at http://***/yuqing-liu-dut/NRIQA_SR Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

TDID: Transparent and Efficient Decentralized Identity Management with Blockchain

TDID: Transparent and Efficient Decentralized Identity Manag...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jiakun Hao Jianbo Gao Peng Xiang Jiashuo Zhang Ziming Chen Hao Hu Zhong Chen School of Computer Science Peking University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China State Key Lab for Novel Software Technology Nanjing University Nanjing Jiangsu China

Decentralized identity (DID) is an identity management framework aiming to return the ownership of an identity to its corresponding user. Recent studies propose to store the identifiers of DID issuers and implement identity management systems based on blockchain. However, existing systems cannot avoid identity tampering and verifiable credential abuse of decentralized identities, which makes the identity management opaque. In this paper, we propose TDID, a Transparent and efficient Decentralized IDentity management system with blockchain. The key insight behind TDID is to manage the registration and authentication of DIDs via smart contracts, and design Structured Merkle Patricia Tree (SMPT) as an underlying data structure to store identity data on blockchain. The smart contract based processes can improve transparency of decentralized identity management, while the SMPT data structure can realize efficient storage of DID data. We implement and evaluate TDID on different identity management operations, and the experimental results show that TDID can achieve about 3.1 times for write operation and 6.3 times for read operation while improving the transparency of DID management.

关键词：

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：