检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

来源：评论

学校读者我要写书评

暂无评论

Non-Uniform 3D Massive MIMO Arrays Topology Optimization for Near-Field Communications 35

Non-Uniform 3D Massive MIMO Arrays Topology Optimization for...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Guo, Yunhui Zhang, Yang Pang, Lihua Liu, Yuanwei Ding, Zhiguo Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Xian University of Science and Technology School of Information Engineering Xian710054 China Khalifa University 6G Center Department of Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362244

In this paper, the design of non-uniform antenna topology for 3D massive MIMO arrays in near-field communications is investigated. Specifically, the near-field spherical wavefront radiation characteristics are considered to accurately model the variations of signal phase across array elements. Subsequently, the closed-form expressions of the per-user signal-to-interference noise ratio (SINR) and achievable sum rate for a multi-user MIMO system with maximum-ratio transmission (MRT) precoding are derived. The focus is on the maximization of the achievable sum rate by optimizing the non-uniform 3D antenna array topology. Since the optimization problem exhibits highly nonlinear and nonconvex characteristics, an enhanced particle swarm optimization (EPSO) algorithm is proposed to effectively solve it. Numerical results demonstrate the superiority of the proposed non-uniform 3D array topology in near-field communications for enhancing the achievable sum rate. © 2024 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Reducing Formal Context Extraction: A Newly Proposed Framework from Big Corpora

arXiv

引用

arXiv 2025年

作者： Hassan, Bryar A. Qader, Shko M. Hassan, Alla A. Lu, Joan Ahmed, Aram M. Majidpour, Jafar Rashid, Tarik A. Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal KRI Sulaimani46023 Iraq Information Technology Department Kalar Technical Institute Garmian Polytechnic University Sulaimani Iraq Department of Database Technology Computer Science Institute Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Computer Science University of Raparin Rania Iraq

Automating the extraction of concept hierarchies from free text is advantageous because manual generation is frequently labor- and resource-intensive. Free result, the whole procedure for concept hierarchy learning from free text entails several phases, including sentence-level text processing, sentence splitting, and tokenization. Lemmatization is after formal context analysis (FCA) to derive the pairings. Nevertheless, there could be a few uninteresting and incorrect pairings in the formal context. It may take a while to generate formal context;thus, size reduction formal context is necessary to weed out irrelevant and incorrect pairings to extract the concept lattice and hierarchies more quickly. This study aims to propose a framework for reducing formal context in extracting concept hierarchies from free text to reduce the ambiguity of the formal context. We achieve this by reducing the size of the formal context using a hybrid of a WordNet-based method and a frequency-based technique. Using 385 samples from the Wikipedia corpus and the suggested framework, tests are carried out to examine the reduced size of formal context, leading to concept lattice and concept hierarchy. With the help of concept lattice-invariants, the generated formal context lattice is compared to the normal one. In contrast to basic ones, the homomorphic between the resultant lattices retains up to 98% of the quality of the generating concept hierarchies, and the reduced concept lattice receives the structural connection of the standard one. Additionally, the new framework is compared to five baseline techniques to calculate the running time on random datasets with various densities. The findings demonstrate that, in various fill ratios, hybrid approaches of the proposed method outperform other indicated competing strategies in concept lattice performance. © 2025, CC BY.

关键词： Text mining

来源：评论

学校读者我要写书评

暂无评论

Blockchain Technology and Artificial Intelligence's Future Applications in Cyber Security 3

Blockchain Technology and Artificial Intelligence's Future A...

引用

3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, AI can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and AI are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Terahertz Communications and Sensing Overview 3

Terahertz Communications and Sensing Overview

引用

3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Gupta, Richa Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India

ISBN: (纸本)9798350330724

Terahertz (THz) (0.1-10 THz) wireless communication is one of the cornerstones of the next 6G wireless networks. THz frequencies have the ability to dramatically increase wireless capacity performance and enable high-resolution environment sensing if applied properly due to the enormous quantity of accessible bandwidth. However, the usage of wireless devices in high-frequency bands like THz is constrained by a very unpredictable and dynamic channel. The ultimate result is essentially unreliable intermittent connections since these channel constraints have a naturally restricted communication range and a high susceptibility to blocking and chemical absorption. Because of this, the THz band's potential for high-rate communications and high-resolution sensing may be hindered. This study thoroughly examines the steps necessary to build up and operate next-generation THz wireless networks that will work together to deliver a variety of communication and sensing services in this environment. We first lay the groundwork for this by defining the THz frequency range's fundamentals. Using these fundamentals as a foundation, we outline and carefully investigate seven specific qualities that characterize THz wireless systems: Some of the subjects discussed include the quasi-opticality of the band, wireless architectures suited for THz, synergy with lower frequency bands, cooperative sensing and communication systems, PHY-layer protocols, spectrum access techniques, and real-time network optimization. These seven distinctive features enable our understanding of how to re-engineer wireless systems as we know them today to fit THz bands and their specific settings. On the one hand, THz systems make use of its quasi-optimality and may turn any sensing opportunity into a communication problem, aiding in the development of a new breed of flexible wireless systems that can do many jobs beyond straightforward communications. THz systems can alternatively use intelligent surfaces, lower

关键词： Internet of Everything

来源：评论

学校读者我要写书评

暂无评论

Fuzzy cluster-scaled principal component analysis for mixed data and its application of educational effect based on device selection 27

Fuzzy cluster-scaled principal component analysis for mixed ...

引用

27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Sato-Ilic, Mika Ilic, Peter Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan Center for Language Research Department of Computer Science and Engineering University of Aizu 90 Kamiiawase Tsuruga Itsukimachi Fukushima Aizuwakamatsu965-8580 Japan

This paper proposes a fuzzy cluster-scaled principal component analysis (fuzzy cluster-scaled PCA) for mixed data, which consists of both numerical and categorical data with respect to quantitative and qualitative variables. The fuzzy cluster-scaled PCA has been proposed for high-dimension, low-sample size (HDLSS) data in which the number of variables (or dimensions) is much larger than the number of objects (or samples). In this case, the target data is only for the numerical data. The proposed fuzzy cluster-scaled PCA in this paper can be applied to the mixed type of HDLSS data by utilizing the feature of the fuzzy cluster-scaled correlation, which is decomposed into two parts: the first part is the correlation of classification structures between variables and the second part is the correlation between variables. Then, the first part can be adapted to the categorical data, and the second part can be used for the numerical data through the same objects. Several numerical examples used data from a survey concerned with the relationship between students' choice of devices and scores of examination/class marks to measure the educational effect show a better performance of this proposed method, and insightful, important information on educational effectiveness is clarified. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Fuzzy clustering HDLSS data Mixed data Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

引用

npj Computational Materials 2024年第1期10卷 2171-2181页

作者： Andre K.Y.Low Flore Mekki-Berrada Abhishek Gupta Aleksandr Ostudin Jiaxun Xie Eleonore Vissol-Gaudin Yee-Fun Lim Qianxiao Li Yew Soon Ong Saif A.Khan Kedar Hippalgaonkar School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WayInnovis#08-03Singapore 138634Republic of Singapore Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore School of Mechanical Sciences Indian Institute of Technology GoaGoa 403401India Institute of Sustainability for Chemicals Energy and Environment(ISCE2)Agency for ScienceTechnology and Research(A*STAR)1 Pesek RoadSingapore 627833Republic of Singapore Department of Mathematics National University of SingaporeSingapore 119077Singapore Institute for Functional Intelligent Materials National University of SingaporeSingapore 117544Singapore School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision *** reach target properties efficiently,these platforms are increasingly paired with intelligent experimental ***,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex ***,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.

关键词： optimization driving constrained

来源：评论

学校读者我要写书评

暂无评论

SMDF: Spatial Mass Distribution Features and Deep Learning-Based Technique for Human Activity Recognition

引用

SN computer science 2024年第1期5卷 129页

作者： Raychaudhuri, Amlan Maity, Satyabrata Chakrabarti, Amlan Bhattacharjee, Debotosh Department of Computer Science and Engineering B. P. Poddar Institute of Management and Technology 137 VIP Road Kolkata-52 India Department of Information Technology Techno International New Town 1/1 Service Rd. DG Block New Town Kolkata-156 India A. K. Choudhury School of Information Technology University of Calcutta JD-2 JD Block Sector-III Kolkata-106 India Department of Computer Science and Engineering Jadavpur University 188 Raja S. C. Mallick Road Kolkata-32 India

Automatic detection of human activity is one of the growing research areas due to the wide range of applications like elderly and patient monitoring for ambient assistive living, visual surveillance, etc. This paper presents a novel bi-channel deep learning model to recognize several human daily living activities. One channel includes activity classification using key poses, based on the spatial mass distribution feature (SMDF). The key poses of all the training classes are estimated using the K-means algorithm on the SMDF of the corresponding silhouette images. Similarly, the key poses of a testing video are extracted, and the k-nearest neighbor (KNN) technique is used thereafter to determine the activity classes. The other channel utilizes VGG-16 to classify human activity on preprocessed frame sequence of a video. Ultimately, the results of the two channels are combined to determine the final activity classification. The bi-channel procedure provides better accuracy over any of the individual channels. The proposed technique is verified using the single-view Weizmann and multi-view MuHAVi datasets. For the Weizmann dataset, the accuracy is 98.8%, and for MuHAVi-14 and MuHAVi-8 both, the accuracy is 98.5%. The accuracy rate for the proposed technique is better than the other state-of-the-art methods concerning different parameters that establish the effectiveness of our proposed method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Human activity recognition Human silhouette Key pose Spatial mass distribution features (SMDF) VGG-16

来源：评论

学校读者我要写书评

暂无评论

A Knowledge Flow Empowered Cognitive Framework for Decision Making With Task-Agnostic Data Regulation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2304-2318页

作者： Huang, Liming Wu, Yulei Tempini, Niccolo Department of Computer Science Faculty of Environment University of Exeter Science and Economy ExeterEX4 4RN United Kingdom the Faculty of Science and Engineering and Bristol Digital Futures Institute University of Bristol BristolBS8 1UB United Kingdom Department of Social and Political Sciences Philosophy and Anthropology Faculty of Humanities Arts and Social Sciences University of Exeter ExeterEX4 4RN United Kingdom

The extreme complexity of many real-world tasks poses considerable challenges to agents' decision making. Most existing models only rely on task-related data for knowledge learning, while ignoring the important influence of potential task-agnostic factors. Effective learning coupled with both task-related and task-agnostic data can strongly enrich the agent's knowledge and improve its decision making performance. Furthermore, many existing learning models simply leverage data to learn knowledge but fail to express the thought process of decision making as humans do, which significantly limits their explanatory capability. To this end, we propose a novel knowledge flow empowered cognitive framework for real-world tasks. To obtain more reliable and trustworthy knowledge, a bottom-up knowledge learning model is developed, which incorporates both task-related data and task-agnostic data for comprehensive knowledge accumulation and value assessment of influencing factors. To demonstrate the thought process of decision making, a top-down knowledge expression model is proposed to coordinate different influencing factors by a knowledge flow structure. Two real-world case studies, including traffic anomaly detection and vehicle following anomaly detection, are introduced, where task-agnostic data are presented for the first time in both tasks. Experimental evaluation demonstrates the strong necessity of incorporating task-agnostic data in knowledge accumulation for real-world tasks and the effectiveness of our cognitive framework. © 2020 IEEE.

关键词： Knowledge representation

来源：评论

学校读者我要写书评

暂无评论

SereniSens: a Multimodal AI Framework with LLMs for Stress Prediction through Sleep Biometrics 15

SereniSens: a Multimodal AI Framework with LLMs for Stress P...

引用

15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Wahab, Othman Adda, Mehdi Zrira, Nabila Department of Mathematics Computer Science and Engineering University of Quebec at Rimouski RimouskiQCG5L 3A1 Canada ADOS Team LISTD Laboratory National Superior School of Mines Rabat Morocco

Stress is a physiological and psychological response to threatening, challenging, or demanding situations. It can have profound effects on both mental and physical health such as anxiety and depression. Thus, managing stress levels in mental healthcare is crucial for maintaining overall well-being. In this work, we introduce SereniSens, a multimodal AI framework with conversational agent integration to predict stress levels during sleep using physiological data. We evaluated the performance of four machine learning models - Decision Tree, Support Vector Machines, Multilayer Perceptron, and XGBoost - on the SaYoPillow dataset containing physiological signals recorded during sleep. Our models achieve high accuracy in predicting five stress levels, with SVM obtaining 99.37% accuracy, outperforming previous implementations. We also propose an architecture integrating these predictive models with a Large Language Model to create a context-aware chatbot for stress monitoring and management. This framework demonstrates the potential of AI in mental healthcare, particularly for personalized stress assessment and intervention. © 2024 The Authors.

关键词： Physiological models

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：