检索结果-内蒙古大学图书馆

Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

SN computer science 2024年第6期5卷 712页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

来源：评论

学校读者我要写书评

暂无评论

A review of photovoltaic/thermal (PV/T) technology applied in building environment control

引用

Energy and Built Environment 2025年第3期6卷 402-431页

作者： Cao, Xuhui Li, Niansi Li, Yulin Che, Lei Yu, Bendong Liu, Huifang College of Urban Construction Nanjing Tech University Jiangsu Nanjing210009 China Department of Thermal Science and Energy Engineering University of Science and Technology of China Hefei230026 China

Solar energy is widely used to replace traditional energy because of its clean and efficient characteristics. Solar energy has great application potential and prospects in building energy conservation. Combining solar energy with buildings can achieve higher efficiency while reducing costs, and the life of the system is longer. PV/T technology combines solar photovoltaic and photothermal technology and obtains sustainable electrical energy and thermal energy at the same time. PV/T systems can be easily integrated to buildings and the electricity and heat generated by PV/T can drive a variety of physical and chemical processes such as heat collection process, solid/liquid dehumidification process and photo/thermal catalytic purification process. Therefore, building-integrated PV/T system has good application potential in building environment control such as indoor air temperature, air humidity and gaseous pollutants control. In the review of the past decade at home and abroad, most of the research on PV/T focuses on the development process, equipment and materials, accounting for 28 %, 21 % and 19 %, respectively, while only 15 % is related to the building-integrated PV/T system. The present work mainly investigates the application of PV/T in building environment in the past ten years, and reports a variety of application cases of PV/T technology combined with building systems to regulate indoor comfort and improve indoor environment. In addition, this paper reviews the simulation and experimental works of different building solar integrated PV/T systems and puts forward the prospect and suggestion for future research. © 2023

关键词： Solar energy

来源：评论

学校读者我要写书评

暂无评论

Semantic and secure search over encrypted outsourcing cloud based on BERT

引用

Frontiers of computer science 2022年第2期16卷 152-159页

作者： Zhangjie FU Yan WANG Xingming SUN Xiaosong ZHANG Engineering Research Center of Digital Forensice Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Searchable encryption provides an effective way for data security and privacy in cloud *** can retrieve encrypted data in the cloud under the premise of protecting their own data security and ***,most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the *** this paper,we propose two secure and semantic retrieval schemes based on BERT(bidirectional encoder representations from transformers)named SSRB-1,*** training the documents with BERT,the keyword vector is generated to contain more semantic information of the documents,which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s ***,through testing on real data sets,it is shown that both of our solutions are feasible and effective.

关键词： cloud computing semantic search BERT model searchable encryption

来源：评论

学校读者我要写书评

暂无评论

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

引用

computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

来源：评论

学校读者我要写书评

暂无评论

Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

引用

computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

来源：评论

学校读者我要写书评

暂无评论

A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

引用

Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi’an University of TechnologyXi’an 710048ShaanxiChina School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjing 211167JiangsuChina Department of Energy Aalborg UniversityAalborg 9220Denmark

To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Battery degradation trajectory Early prediction Autoencoder Synthetic data generation

来源：评论

学校读者我要写书评

暂无评论

Learning continuous network emerging dynamics from scarce observations via data-adaptive stochastic processes

引用

science China(Information sciences) 2024年第12期67卷 240-255页

作者： Jiaxu CUI Qipeng WANG Bingyi SUN Jiming LIU Bo YANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Public Computer Education and Research Center Jilin University Department of Computer Science Hong Kong Baptist University

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.

关键词： complex networks network dynamics emerging spatio-temporal dynamics neural processes

来源：评论

学校读者我要写书评

暂无评论

Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

引用

computers, Materials & Continua 2024年第9期80卷 3573-3589页

作者： Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik Institute of Systems Engineering Macao University of Science and TechnologyMacao99078China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin75-453Poland

The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关键词： Distributed scheduling hybrid flow shop meta-heuristics local search Q-learning

来源：评论

学校读者我要写书评

暂无评论

Cluster Based Concurrent Data Collection with Fault Tolerance for Shared IoT Applications 16

Cluster Based Concurrent Data Collection with Fault Toleranc...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kumar, Arvind Matam, Rakesh Swain, Srinibas Mukherjee, Mithun Indian Institute of Information Technology Guwahati Department of Computer Science and Engineering India Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350383119

Sharing IoT device infrastructure by multiple entities or applications for their respective sensing and monitoring needs is an effective way to reduce the upfront setup cost, ownership costs, and also to prevent over provisioning. Concurrent data collection in such a shared environment is a major challenge, given the number of devices and multiple processes relying on that data. Most of the existing works in this direction typically consider network scenarios where devices can reach the base station(s) directly. As this assumption is not always valid, few other works focused on concurrent data collection over multiple-hops. In such a shared multi-hop environment, the chances of device failures are high, especially considering the power constraints of devices. To address the issue of reliability in data collection, this paper presents a cluster-based fault tolerant network structure for concurrent data collection. The proposed work aims to minimize the delay and handles the topology changes in case of faults, and also lowers the transmission count. The validity of the proposed work is shown via analytical and simulation results. © 2024 IEEE.

关键词： Concurrent Data collection in IoT Network topology Shared IoT device infrastructure

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Fusion Approach for Weakly Supervised Segmentation of MRI Medical Images 17

A Multimodal Fusion Approach for Weakly Supervised Segmentat...

引用

17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Lu, Yingzhu Tang, Chengyu Wang, Chaoyuan Fan, Yawen Nanjing University of Posts and Telecommunications Department of Computer Science and Technology Nanjing China Nanjing Forestry University Department of Computer Science and Technology Nanjing China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798331507398

Due to the scarcity and high cost of acquiring labeled data in the field of image segmentation, weakly supervised semantic segmentation (WSSS) methods based on Class Activation Mapping (CAM) have been proposed. In the medical image segmentation domain, current research on weakly supervised learning primarily focuses on single-modal methods. However, multimodal imaging provides richer information. Magnetic Resonance Imaging (MRI) is commonly used for brain tumor segmentation. We propose a novel optimized CAM method tailored for MRI brain tumor segmentation, with the core idea being multimodal fusion. Initially, we generate CAM for different MRI modalities (in our experiments, we selected T2 and FIAIR modalities), employing weakly supervised methods. Subsequently, we fuse these CAM to obtain optimized CAM for segmentation. Our method is evaluated on the BraTS dataset, and experimental results demonstrate its superiority over existing techniques at the same level of supervision. © 2024 IEEE.

关键词： Semantic Segmentation

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：