检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Yu, Shuo Alqahtani, Fayez Tolba, Amr Lee, Ivan Jia, Tao Xia, Feng School of Computer Science and Technology Dalian University of Technology Dalian116024 China Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia STEM University of South Australia AdelaideSA5001 Australia College of Computer and Information Science Southwest University Chongqing400715 China Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC3353 Australia

Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Multi-Aspect Incremental Tensor Decomposition Based on Distributed In-Memory Big Data Systems

引用

Journal of Data and Information science 2020年第2期5卷 13-32页

作者： Hye-Kyung Yang Hwan-Seung Yong Department of Computer Software Korean Bible UniversitySeoul01757Republic of Korea Department of Computer Science and Engineering Ewha Womans UniversitySeoul03760Republic of Korea

Purpose:We propose In Par Ten2,a multi-aspect parallel factor analysis three-dimensional tensor decomposition algorithm based on the Apache Spark *** proposed method reduces re-decomposition cost and can handle large ***/methodology/approach:Considering that tensor addition increases the size of a given tensor along all axes,the proposed method decomposes incoming tensors using existing decomposition results without generating ***,In Par Ten2 avoids the calculation of Khari–Rao products and minimizes shuffling by using the Apache Spark ***:The performance of In Par Ten2 is evaluated by comparing its execution time and accuracy with those of existing distributed tensor decomposition methods on various *** results confirm that In Par Ten2 can process large tensors and reduce the re-calculation cost of tensor ***,the proposed method is faster than existing tensor decomposition algorithms and can significantly reduce re-decomposition *** limitations:There are several Hadoop-based distributed tensor decomposition algorithms as well as MATLAB-based decomposition ***,the former require longer iteration time,and therefore their execution time cannot be compared with that of Spark-based algorithms,whereas the latter run on a single machine,thus limiting their ability to handle large *** implications:The proposed algorithm can reduce re-decomposition cost when tensors are added to a given tensor by decomposing them based on existing decomposition results without re-decomposing the entire ***/value:The proposed method can handle large tensors and is fast within the limited-memory framework of Apache ***,In Par Ten2 can handle static as well as incremental tensor decomposition.

关键词： PARAFAC Tensor decomposition Incremental tensor decomposition Apache Spark Big data

来源：评论

学校读者我要写书评

暂无评论

Carousel Storage and Picking Scheduling Issues: A Review

Carousel Storage and Picking Scheduling Issues: A Review

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Shujin Qin Xinyi Yang Jiacun Wang Shixin Liu Xiwang Guo Liang Qi Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal University Shangqiu China Department of Computer Science and Software Engineering Monmouth University West Long Branch NJ USA College of Information Science and Engineering Northeast University Shenyang China College of Information and Control Engineering Liaoning Petrochemical University Fushun China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

引用

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Cross-Scale Transformer Architecture for Robust Medical Image Segmentation

A Hybrid Cross-Scale Transformer Architecture for Robust Med...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ning, Yang Zhang, Shouyi Zhong, Wei Liu, Peide Zhang, Caiming Shandong Jianzhu University School of Computer Science and Technology Jinan China The University of Edinburgh School of Informatics Edinburgh United Kingdom Guangdong Police College Department of Network Information Security Guangzhou China Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shandong University School of Software Jinan China

ISBN: (纸本)9781665468190

Transformer architecture has emerged to be successful in many natural language processing tasks. However, its applications to clinical practice remain largely unexplored. In this study, we propose a Robust Cross-Scale Hybrid Transformer (RCSHT) architecture for medical image segmentation, which can effectively enhance the multi-scale feature representations while integrating local features with global dependencies. Specifically, we propose two new modules based on the self-attention mechanism: PSCM and PCCM, which perform matrix low-rank transformation on cross-scale images in spatial and channel space, respectively, to effectively enhance the discriminant ability of long-range dependencies of multi-scale images and effectively reduce the computational complexity. Meanwhile, we use spatial positional encoding to perform spatial regularization on the two attention modules, so that the model has more opportunities to obtain valuable spatial details and further enhance the spatial retention ability. Besides, by fusing outputs of the two modules, the robustness and discriminant ability of the feature representations are further improved. Note that our hybrid architecture allows transformers to be initialized as convolutional networks without pre-training. Extensive experiments conducted on our newly proposed dataset have demonstrated our RCSHT notably outperforms the state-of-the-art methods by a large margin, holding the promise to generalize well on other medical image segmentation. © 2022 IEEE.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

SCALABLE LINK PREDICTION IN TWITTER USING SELF-CONFIGURED FRAMEWORK

arXiv

引用

arXiv 2022年

作者： Daud, Nur Nasuha Hamid, Siti Hafizah Ab Seri, Chempaka Saadoon, Muntadher Anuar, Nor Badrul Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Link prediction analysis becomes vital to acquire a deeper understanding of events underlying social networks interactions and connections especially in current evolving and large-scale social networks. Traditional link prediction approaches underperformed for most large-scale social networks in terms of its scalability and efficiency. Spark is a distributed open-source framework that facilitate scalable link prediction efficiency in large-scale social networks. The framework provides numerous tunable properties for users to manually configure the parameters for the applications. However, manual configurations open to performance issue when the applications start scaling tremendously, which is hard to set up and expose to human errors. This paper introduced a novel Self-Configured Framework (SCF) to provide an autonomous feature in Spark that predicts and sets the best configuration instantly before the application execution using XGBoost classifier. SCF is evaluated on the Twitter social network using three link prediction applications: Graph Clustering (GC), Overlapping Community Detection (OCD), and Redundant Graph Clustering (RGD) to assess the impact of shifting data sizes on different applications in Twitter. The result demonstrates a 40% reduction in prediction time as well as a balanced resource consumption that makes full use of resources, especially for limited number and size of clusters. © 2022, CC BY.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Weight Expansion: A New Perspective on Dropout and Generalization

arXiv

引用

arXiv 2022年

作者： Jin, Gaojie Yi, Xinping Yang, Pengfei Zhang, Lijun Schewe, Sven Huang, Xiaowei Department of Computer Science University of Liverpool United Kingdom Department of Electrical Engineering and Electronics University of Liverpool United Kingdom State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking. We introduce the concept of weight expansion, an increase in the signed volume of a parallelotope spanned by the column or row vectors of the weight covariance matrix, and show that weight expansion is an effective means of increasing the generalization in a PAC-Bayesian setting. We provide a theoretical argument that dropout leads to weight expansion and extensive empirical support for the correlation between dropout and weight expansion. To support our hypothesis that weight expansion can be regarded as an indicator of the enhanced generalization capability endowed by dropout, and not just as a mere by-product, we have studied other methods that achieve weight expansion (resp. contraction), and found that they generally lead to an increased (resp. decreased) generalization ability. This suggests that dropout is an attractive regularizer, because it is a computationally cheap method for obtaining weight expansion. This insight justifies the role of dropout as a regularizer, while paving the way for identifying regularizers that promise improved generalization through weight expansion. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Emerging Role of Industry 5.0 Digital Twins in Demand Response Electricity Market and Applications

Emerging Role of Industry 5.0 Digital Twins in Demand Respon...

引用

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

Traditional power system is facing challenges demanding new operational requirements to meet targets of Net Zero Emissions by 2050. Aggregators are playing progressively important role in the demand response (DR) electricity market but are often riddled with deep level of market monopoly and lack of transparency/secrecy. Emerging real-time information technology (IT) applications and novel modelling of digital twins (DT) of individual electricity assets are challenging this position by promising improvements and openness that allows TSO direct access to available demand side flexibility. Additionally, DT technologies are facilitating how demand response services are delivered to end-users by allowing individual assets participation at the atomic level. In this study, the application potentials of assets’ DT in participating in the demand response electricity market was examined. Again, an overview of DT applications for DR was conducted. The novelty of this study is highlighted in development of new approach that facilitates individual end-user assets’ contribution to demand response efforts. The research identifies key useful questions that might serve as inspiration for stakeholders and policy-makers to further close existing gaps in the field of DT, smart-grid and demand response.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cost and Availability-Aware VNF Selection and Placement for Network Services in NFV

Cost and Availability-Aware VNF Selection and Placement for ...

引用

2020 International Symposium on Networks, computers and Communications, ISNCC 2020

作者： Alahmad, Yanal Agarwal, Anjali Daradkeh, Tariq Concordia University Department of Computer Science and Software Engineering Montreal Canada Concordia University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9781728156286

Network Function Virtualization (NFV) is a growing computing paradigm for rapid and economical provisioning of networking services (NSs). In NFV, NS is provided through a set of Virtual Network Functions (VNFs) that are hosted on the underlying infrastructure offered by the service provider. Quality of the NS such as availability, as well as the overall cost of providing the NS in NFV domain are raised as concern issues especially their objectives go against each other. This paper tackles two main problems that are directly related to the cost and availability of the NS, VNFs configuration types selection problem and VNF placement problem. Therefore, we propose two Mixed Integer Linear Programming (MILP) optimization models to address both problems and find solutions. We build a proof of concept to evaluate our proposed solutions and compare them with existing solutions from the literature. The results show that our proposed solutions can reduce the overall cost of requested NSs without violating their availability requirements. © 2020 IEEE.

关键词： Network function virtualization

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：