检索结果-内蒙古大学图书馆

International Conference on data Engineering

作者： Weixin Zeng Xiang Zhao Zhen Tan Jiuyang Tang Xueqi Cheng Laboratory for Big Data and Decision National University of Defense Technology China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China Institute of Computing Technology CAS China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches $KGs$ in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development.

关键词： Surveys Representation learning Knowledge graphs Benchmark testing data engineering Mirrors

来源：评论

学校读者我要写书评

暂无评论

Learning Self-Supervised Vision Transformers from Scratch for Aerial Person Re- Identification

Learning Self-Supervised Vision Transformers from Scratch fo...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Houfu Peng Lili Xu Xing Lu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

In recent years, person re-identification (Re-ID) as a widely studied computer vision task, has reached a saturation state under closed-world setting, which encourages researchers to further explore more realistic scenarios. Among them, person Re- Idin aerial imagery is proposed and improved due to its unique practical importance in public security. However, since the aerial person images are taken by unmanned aerial vehicles (UAV s), influenced by camera height and angle of view, there are more serious problems such as weak appearance feature and occlusion than ground person images. Most of the current state-of-the-art person Re-ID methods on closed-world datasets are based on local convolution neural network, and hardly works well when applying them to aerial person Re- Idtasks directly. In this paper, we improve the emerging vision transformer (ViT) and apply it to the person Re- Idin aerial imagery. It should be noted that a large amount of data is required to be pretrained for ViTs to achieve competitive performance. Considering the limitations of data, computing power and flexibility in practical scenarios, we improve the pre-training process based on self-supervised learning, and achieve training ViTs from scratch with limited data. Specifically, in pre-training stage, the self-supervised paradigm based on parameter instance discrimination is applied to capture feature alignment and instance similarity, which alleviates the data-hungry of ViTs caused by the lack of inductive bias. Extensive comparative evaluation experiments are conducted on the aerial Re- Iddataset. Our method achieves a Rank-1 accuracy of 65.29% and a mean average precision (mAP) of 57.31%, which proves its effectiveness in aerial person Re-ID tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sentiment of EmojiSets: How Emoji Sequences Improve Sentiment Cognition 21

Sentiment of EmojiSets: How Emoji Sequences Improve Sentimen...

引用

21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Othman, Salem Alshalwi, Sami Caushi, Endi School of Computing and Data Science Wentworth Institute of Technology Boston United States Omar Almukhtar University Computer Science Department Al-Bayda Libya

ISBN: (纸本)9781665490849

The capability to infer emotional insights from emojis found in social media has projected emoji analysis into the spotlight of current emoji-based research. Previous studies mainly used text-surrounding emojis to estimate sentimentality scores. However, trying to conclude the same score based solely on emojis is challenging. In this paper this challenge was welcomed, and with it we created a new concept. This revolutionary scoring method, named the EmojiSets Sentiment Score Rank, proposes using sets of emojis taken from tweets along with information from previous studies [1] to find a sentiment score. This bottom-up scoring approach gives each emoji a sentiment score. It then calculates the context-level sentiment score of a tweet solely dependent on the emojis found within it. To the best of the authors' knowledge, no such approach has been researched in the Emojis Sentiment Analysis area. We tested our model against over 1.2 million tweets concerning Covid-19 and compared it to the VADER model [7] to validate our assumption. Our model corrected around 72% of the tweets that the other model scored as neutral. To succor these findings, 32 human annotators were given the task of annotating 8040 randomly chosen tweets. When calculating similarity using the Jaccard Index, their results were consistent with our approach in over 70% of cases © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Quantum Key Distribution and Blockchain Based Secure Authentication in Medical Cyber-Physical Systems 8th

Quantum Key Distribution and Blockchain Based Secure Authent...

引用

8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Gawali, Piyush P. Mahalle, Parikshit N. Shinde, Gitanjali R. Sable, Nilesh P. Takale, Dattatray G. Barot, Janki Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Information Technology Vishwakarma Institute of Information Technology SPPU Pune Pune India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819965670

Medical cyber-physical systems (MCPSs) enable flexible patient–medical system interaction as the foundation of application in the field of smart health care, realizing all-encompassing three-dimensional medical service. The security barrier must be built using a secure and trustworthy device identity authentication mechanism in order to meet the controlled and believable requirements of MCPS. This work, therefore, develops an efficient authentication and encryption system (EAES) to improve the security and authentication of MCPSs. This research suggests a secure authentication method to safeguard sensitive healthcare data. The suggested modified "elliptic-curve Diffie–Hellman" (ECDH) encryption is first applied to the medical data that is acquired from the patients and stored in the cloud in encrypted form. This is done in order to guarantee secure access to the stored data for use in medical data analysis. The key in the ECFH is controlled via a quantum key distribution system. Additionally, blockchain is considered for the mutual authentication of users in the MCPSs. The proposed methodology is implemented and evaluated by using performance measures. Additionally, it is compared with the conventional techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Network Topology Inference with Sparsity and Laplacian Constraints

Network Topology Inference with Sparsity and Laplacian Const...

引用

International Conference on Information, Communication and Networks (ICICN)

作者： Jiaxi Ying Xi Han Rui Zhou Xiwen Wang Hing Cheung So Hong Kong University of Science and Technology North China University of Technology Shenzhen Research Institute of Big Data City University of Hong Kong

We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian. Recent research [1] has uncovered the limitations of the widely used ℓ 1 -norm in learning sparse graphs under this model: empirically, the number of nonzero entries in the solution grows with the regularization parameter of the ℓ 1 -norm; theoretically, a large regularization parameter leads to a fully connected (densest) graph. To overcome these challenges, we propose a graph Laplacian estimation method incorporating the ℓ 0 -norm constraint. An efficient gradient projection algorithm is developed to solve the resulting optimization problem, characterized by sparsity and Laplacian constraints. Through numerical experiments with synthetic and financial time-series datasets, we demonstrate the effectiveness of the proposed method in network topology inference.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced EHR Lifecycle: Enhancing the EHR Lifecycle with Process Mining

TechRxiv

引用

TechRxiv 2022年

作者： Asare, Esther Wang, Lili Fang, Xianwen Mathematics and Big Data Anhui University of Science and Technology Huainan-Anhui China

Hospitals have increased the adoption of Hospital Information Systems to optimize processes for the efficient and effective delivery of services to customers (i.e., patients). However, there are still challenges in adopting information systems in healthcare, despite the advancements in technology and the enormous business benefits. These challenges have led to resistance of some healthcare providers in using these systems. Moreover, investigative and diagnostic measures are not exhaustively carried out to improve the processes of healthcare centers and the implemented information systems used in executing the processes. Fortunately, process mining techniques help optimize business processes, but they are primarily utilized post-implementation/post-go-live. This paper demonstrates the role process mining can play in adopting an EMR/HER by improving the existing EHR Implementation Lifecycle by proposing an Enhanced Model. We suggest using process mining at suitable phases of the EHR Implementation lifecycle, not post-implementation/post-go-live only. Therefore, we propose an Enhanced EHR Implementation Lifecycle that supports process mining as part of a testing protocol adopted in testing for EMRs/EHRs usability in conformance to the organization’s workflow in other implementation phases. The many application of process mining in existing research proves the proposed Enhanced EHR/EMR implementation model’s feasibility. © 2022, CC BY.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Intelligent Refrigerator using Machine Learning and IoT

Intelligent Refrigerator using Machine Learning and IoT

引用

2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Rajeswari, D. Srinivasan, R. Ramamoorthy, S. Pushpalatha, M. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9781665495295

Machine Learning and Internet of Things (IoT) play a vital role in the current electronics world. Electronic appliances operated by humans are automated and accessed remotely via IoT and 5G networks. Smart Cars, Drone Deliveries, and Intelligent Refrigerators are some of IoT and machine learningmajor applications. Potentially, with the aid of computing devices Intelligent Refrigerators can be accessed from anywhere and anytime. In Intelligent Refrigerators,a single tap on the computing devices predicts the quality of food items kept in the refrigerator. Besides, in order to work smartly, intelligent IoT devices deployed in intelligent refrigerators predict food quality anonymously using machine learningtechniques. This paper uses ensemble machine learning technique with naive bayes, support vector machine and decision tree to classify the considered fruits are spoiled or not. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Predicting Oil and Gas Pipe Wall Thickness Using Machine Learning

Predicting Oil and Gas Pipe Wall Thickness Using Machine Lea...

引用

2021 International Conference on data Analytics for Business and Industry, ICDABI 2021

作者： Mohammed, Abdulrahim Hewahi, Nabil College of Science University of Bahrain Big Data Science and Analytics Program Sakhir Bahrain College of Information Technology University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Pipeline networks are crucial for process industries transportation and business operation. These vital elements, however, are highly exposed to material degradation due to corrosion that seriously impedes operational profitability, reliability, and safety. Industries usually monitor pipeline corrosion rate by gauging pipe wall thickness. Predicting pipe wall thickness helps industries to identify locations with high corrosion rate at an early stage and take necessary actions. The aim of this research is to predict pipe wall thickness at different locations using different machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), fully connected Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). The data processed and analyzed in this study included six different process variables as well as pipe wall thickness at three different locations in a downstream oil and gas industry. The study findings demonstrated that LSTM with test Root Mean Squared Errors (RMSEs) of 0.019 mm, 0.015 mm, and 0.017 mm for the three selected locations, outperformed the other methods applied and successfully identified the pipe wall thickness pattern. © 2021 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Regularized Tucker Decomposition Approach for Traffic data Imputation

arXiv

引用

arXiv 2023年

作者： Gong, Wenwu Huang, Zhejun Yang, Lili Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

In intelligent transportation systems, traffic data imputation, estimating the missing value from partially observed data is an inevitable and challenging task. Previous studies have not fully considered traffic data’s multidimensionality and spatiotemporal correlations, but they are vital to traffic data recovery, especially for high-level missing scenarios. To address this problem, we propose a novel spatiotemporal regularized Tucker decomposition method. First, the traffic matrix is converted into a third-order tensor. Then, based on Tucker decomposition, the tensor is approximated by multiplying non-negative factor matrices with a sparse core tensor. Notably, we do not need to set the tensor rank or determine it through matrix nuclear-norm minimization or tensor rank minimization. The low rankness is characterized by the l1-norm of the core tensor, while the manifold regularization and temporal constraint are employed to capture spatiotemporal correlations and further improve imputation performance. We use an alternating proximal gradient method with guaranteed convergence to address the proposed model. Numerical experiments show that our proposal outperforms matrix-based and tensor-based baselines on real-world spatiotemporal traffic datasets in various missing scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

引用

International Journal of Information technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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：