检索结果-内蒙古大学图书馆

A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

Journal of Physics: Conference Series 2020年第1期1453卷

作者： Yuxi Chen Xiaotong Zhang Qing Zhao Faheem Akhtar School of Statistics and Mathematics Country Central University of Finance and Economics Beijing 100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York 13902 America Faculty of Information Technology Beijing University of Technology Beijing 100124 China. Department of Computer Science Vrije Universiteit Amsterdam 1081 HV The Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing 100124 China Department of Computer Science Sukkur IBA University Sukkur 65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Preface

EAI/Springer Innovations in Communication and Computing

引用

EAI/Springer Innovations in Communication and Computing 2022年 v-vii页

作者： Sur, Samarendra Nath Balas, Valentina Emilia Bhoi, Akash Kumar Nayyar, Anand Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar India Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania Faculty of Information Technology Duy Tan University Da Nang Viet Nam

来源：评论

学校读者我要写书评

暂无评论

Quantum majorization and a complete set of entropic conditions for quantum thermodynamics

arXiv

引用

arXiv 2017年

作者： Gour, Gilad Jennings, David Buscemi, Francesco Duan, Runyao Marvian, Iman Department of Mathematics and Statistics University of Calgary ABT2N 1N4 Canada Institute for Quantum Science and Technology University of Calgary ABT2N 1N4 Canada Department of Physics University of Oxford OxfordOX1 3PU United Kingdom Department of Physics Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Mathematical Informatics Nagoya University Chikusa-ku Nagoya464-8601 Japan Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney NSW2007 Australia MIT Research Laboratory of Electronics United States

What does it mean for one quantum process to be more disordered than another? Here we provide a precise answer to this question in terms of a quantum-mechanical generalization of majorization. The framework admits a complete description in terms of single-shot entropies, and provides a range of significant applications. These include applications to the comparison of quantum statistical models and quantum channels, to the resource theory of asymmetry, and to quantum thermodynamics. In particular, within quantum thermodynamics, we apply our results to provide the first complete set of necessary and sufficient conditions for arbitrary quantum state transformations under thermodynamic processes, and which rigorously accounts for quantum-mechanical properties, such as coherence. Our framework of generalized thermal processes extends thermal operations, and is based on natural physical principles, namely, energy conservation, the existence of equilibrium states, and the requirement that quantum coherence be accounted for thermodynamically. In the zero coherence case we recover thermo-majorization while in the asymptotic coherence regime we obtain a constraint that takes the form of a Page-Wootters clock condition. Copyright © 2017, The Authors. All rights reserved.

关键词： Thermodynamics

来源：评论

学校读者我要写书评

暂无评论

Evaluating the impact of requirements analysis tools using simulation

引用

software Process: Improvement and Practice 2008年第1期13卷

作者： David Raffo Robert Ferguson Siri-on Setamanit Bhuricha Sethanandha School of Business Administration Portland State University Portland Oregon 97201 USA Maseeh College of Engineering and Computer Science Portland State University Portland Oregon 97201 USA Software Engineering Institute Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Faculty of Commerce and Accountancy Chulalongkorn University Bangkok 10330 Thailand

Adopting new tools and technologies on a development process can be a risky endeavor. Will the project accept the new technology? What will be the impact? Far too often the project is forced to adopt the new technology without planning how it will be applied on the project or evaluating the technology's potential impact. In this article, we provide a case study evaluating a new technology. Specifically we assess the merits of an automated requirements analysis tool. First, we provide a background on automated requirements analysis tool technology. Then, using Process Simulation (P-SIM), we find situations where the use of this new technology is useful and situations where the use of this new technology is useless for large-scale NASA projects that utilize a process similar to the IEEE 12207 systems development life cycle. The method can be applied in assessing the impact (including Return on Investment), breakeven point, and the overall value of applying any tool on a project. Copyright © 2008 John Wiley & Sons, Ltd.

关键词： process improvement economic impact analysis of new technologies evaluation of new software tools process simulation

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: Automatic detection technology for sports players based on image recognition technology: the significance of big data technology in China’s sports field

引用

Annals of Operations Research 2022年第1期326卷 97-97页

作者： Li, Hongge Manickam, Adhiyaman Samuel, R. Dinesh Jackson Physical Education Department Changchun University of Technology Changchun China Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

引用

arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Workshop on Numerical Computation in 2D & 3D Data Analysis, WNCDA 2017 Symposium 18, ICNAAM 2017

引用

AIP Conference Proceedings 2018年第1期1978卷

作者： Ryszard Kozera Agnieszka Szczęsna Artur Wiliński 1Warsaw University of Life Sciences - SGGW Faculty of Applied Informatics and Mathematics Nowoursynowska 159 02-776 Warsaw Poland 2University of Western Australia School of Computer Science and Software Engineering 35 Stirling Highway Crawley W.A. 6009 Perth Australia 3Vistula University Faculty of Computer Science Stokłosy 3 02-787 Warsaw Poland 4Institute of Informatics Silesian University of Technology Akademicka 16 44-100 Gliwice Poland

Ryszard Kozera, Agnieszka Szczęsna, Artur Wiliński; Workshop on Numerical Computation in 2D & 3D Data Analysis, WNCDA 2017 Symposium 18, ICNAAM 2017, AIP Co

关键词：

来源：评论

学校读者我要写书评

暂无评论

Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

引用

arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaotong Wu Yuwen Liu Xiaoxiao Chi Rong Jiang Xiaokang Zhou Wajid Rafique Maqbool Khan Alibaba Business School Hangzhou Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computing Macquarie University Australia Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan Department of Electrical and Software Engineering University of Calgary Canada Software Competence Center Hagenberg Austria

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

关键词： collaborative learning optimal adaption vehicle application data security self-adaptive system

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: Deep learning assisted convolutional auto-encoders framework for glaucoma detection and anterior visual pathway recognition from retinal fundus images

引用

Journal of Ambient Intelligence and Humanized Computing 2022年第1期15卷 211-211页

作者： Saravanan, Vijayalakshmi Samuel, R. Dinesh Jackson Krishnamoorthy, Sujatha Manickam, Adhiyaman Ryerson University Toronto Canada Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK Wenzhou Kean University Wenzhou China Post Doctoral Researcher Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen China

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：