检索结果-内蒙古大学图书馆

25th International Multi Topic Conference, INMIC 2023

作者： Ramzan, Hafiz Arslan Sohaib, Ahmed Ramzan, Sadia Kalsum, Tehmina Aqeel, Muhammad Majeed, Mohsin Khwaja Fareed University of Engineering and Information Technology Institute of Computer and Software Engineering Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering & Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350317701

Facial Emotion Recognition is one of the in-demand and rapidly growing research topics in the domain of computer Vision (CV) and artificial intelligence (AI). The ability to identify or detect human emotions from real-time facial expressions (FEs) has vast conceivable applications in different domains, such as sentiment analysis, human-computer interaction, human resource management, security, and human psychology. In this paper, a Convolutional Neural Network (CNN) based deep learning model is trained with haar-cascade classifier to recognize the real-time FEs. The suggested model is specially trained to categorize the FEs into one of the seven emotion categories, namely six basic emotions (sad, happy, angry, surprised, disgusted, fear) and a neutral emotion. It includes several convolutional layers, as well as fully connected neurons followed by max-pooling layers, and soft-max activation function with the corresponding seven classes. ReLU activation functions along with various kernels to enhance filtering depth, and extraction of facial features. FER-2013 dataset is used for experimentation purpose. To improve the classification performance and model accuracy, a data augmentation technique is used for rescaling and horizontal flipping. The proposed model outperforms the previous related works by achieving a validation accuracy of 71.96% and training accuracy above 90%, with fewer epochs. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms 15

Machine Learning Techniques for Identifying Child Abusive Te...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Uddin, Mohammad Kamal Aziz, Mohammad Tarek Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention. © 2024 IEEE.

关键词： Deep Learning Machine Learning Natural Language Processing Sentiment Analysis Supervised Model

来源：评论

学校读者我要写书评

暂无评论

A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

引用

International Journal of Information Technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of Technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

来源：评论

学校读者我要写书评

暂无评论

Pre-Trained Language Models Represent Some Geographic Populations Better Than Others 30

Pre-Trained Language Models Represent Some Geographic Popula...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Dunn, Jonathan Adams, Benjamin Madabushi, Harish Tayyar University of Illinois Urbana-Champaign Department of Linguistics United States University of Canterbury Department of Computer Science and Software Engineering New Zealand University of Bath Department of Computer Science United Kingdom

ISBN: (纸本)9782493814104

This paper measures the skew in how well two families of LLMs represent diverse geographic populations. A spatial probing task is used with geo-referenced corpora to measure the degree to which pre-trained language models from the opt and bloom series represent diverse populations around the world. Results show that these models perform much better for some populations than others. In particular, populations across the US and the UK are represented quite well while those in South and Southeast Asia are poorly represented. Analysis shows that both families of models largely share the same skew across populations. At the same time, this skew cannot be fully explained by sociolinguistic factors, economic factors, or geographic factors. The basic conclusion from this analysis is that pre-trained models do not equally represent the world's population: there is a strong skew towards specific geographic populations. This finding challenges the idea that a single model can be used for all populations. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： geographic corpora population skew spatial probing under-represented populations

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graph Driven Inference Testing for Question Answering software 24

Knowledge Graph Driven Inference Testing for Question Answer...

引用

44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Wang, Jun Li, Yanhui Chen, Zhifei Chen, Lin Zhang, Xiaofang Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology China School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Technology Soochow University China

ISBN: (纸本)9798400702174

In the wake of developments in the field of Natural Language Processing, Question Answering (QA) software has penetrated our daily lives. Due to the data-driven programming paradigm, QA software inevitably contains bugs, i.e., misbehaving in real-world applications. Current testing techniques for testing QA software include two folds, reference-based testing and metamorphic testing. This paper adopts a different angle to achieve testing for QA software: we notice that answers to questions would have inference relations, i.e., the answers to some questions could be logically inferred from the answers to other questions. If these answers on QA software do not satisfy the inference relations, an inference bug is detected. To generate the questions with the inference relations automatically, we propose a novel testing method Knowledge Graph driven Inference Testing (KGIT), which employs facts in the Knowledge Graph (KG) as the seeds to logically construct test cases containing questions and contexts with inference relations. To evaluate the effectiveness of KGIT, we conduct an extensive empirical study with more than 2.8 million test cases generated from the large-scale KG YAGO4 and three QA models based on the state-of-the-art QA model structure. The experimental results show that our method (a) could detect a considerable number of inference bugs in all three studied QA models and (b) is helpful in retraining QA models to improve their inference ability. © 2024 ACM.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Efficient Detection of Selfish Mining Attacks on Large-Scale Blockchain Networks 24

Efficient Detection of Selfish Mining Attacks on Large-Scale...

引用

24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Erfan, Fatemeh Bellaiche, Martine Halabi, Talal Polytechnique Montréal Department of Computer and Software Engineering MontréalQC Canada Université Laval Department of Computer Science QuébecQC Canada

ISBN: (纸本)9798350365658

Selfish mining attacks pose a significant and ongoing security threat to blockchain networks, including major platforms like Bitcoin and Ethereum. Understanding and effectively countering these attacks is crucial for maintaining the stability and integrity of these networks. This attack strategy involves a miner or a group of miners seeking to gain an advantage by delaying the immediate broadcasting of their mined blocks to the network. The selfish miners potentially mine more blocks in secret, increasing their rewards at the expense of other miners and the stability of the blockchain. Research has mainly focused on detecting this attack by analyzing data related to blocks and forks. This paper presents a new approach to efficiently detect selfish mining attacks in large-scale networks by analyzing various network indicators. To achieve this, we simulate a Bitcoin network and generate a dataset consisting of several features of individual miners and the overall network. We select the most reliable indicators by analyzing network features and reducing feature dimensions. Then, we employ random forest classification (RFC) to classify benign and selfish network behaviors. This approach not only achieves enhanced accuracy (99.96%), surpassing state-of-the-art methods utilizing deep learning, but also significantly reduces computational, storage, and temporal complexities. In doing so, it fortifies blockchain security more efficiently and accurately. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Performance Based Scheduling in Distributed Mixed Criticality Systems

引用

IEEE Access 2025年 13卷 95321-95340页

作者： Ali, Amjad Wasly, Saud Khattak, Asad Masood Ali, Ihsan Iqbal, Shahid Hayat, Bashir University of Swat Department of Computer and Software Technology Khyber Pakhtunkhwa Swat Pakistan King Abdul Aziz University Electrical and Computer Engineering Department Jaddah Saudi Arabia Zayed University College of Technological Innovation United Arab Emirates Institute of Management Sciences School of Computer Science and IT Peshawar Pakistan

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores while considering interference, ensuring better performance and adherence to critical deadlines. This method reduces task waiting times, which minimizes the scheduling duration and improving the schedulability of the task set. The novel proposed approach is compared with Mixed-Criticality Multicore Compositional Earliest Deadline First (MMC-EDF), Global and Mixed Criticality Partitioning (MC-Partitioning) approaches to show the efficiency. The proposed approach schedules 75% tasks while the MMC-EDF, Global and MC-Partitioning approaches schedules 65%, 0% and 18% tasks respectively for target utilization U=0.8. As the utilization of mixed criticality (MC) workload increases, the schedulability of MC task sets decreases, but still the proposed approach performs better than the MMC-EDF, Global and MC-partitioning approaches. © 2013 IEEE.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

SSA: a novel method for Single-cell and Spatial transcriptomics Alignment 16th

SSA: a novel method for Single-cell and Spatial transcriptom...

引用

16th International Conference on Bioinformatics and Computational Biology, BICOB 2024

作者： Tran, Bang Tran, Dao Nguyen, Tin Department of Engineering & Computer Science California State University 6000 J St SacramentoCA95819 United States Department of Computer Science and Software Engineering 345 W Magnolia Ave AuburnAL36849 United States

Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcriptomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spatial locations in actual tissue based on their expression profiles. SSA has demonstrated superior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression. © 2024, EasyChair. All rights reserved.

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Using Blockchain to Overcome the Issues in Land Registry Management: A Systematic Review 1

Using Blockchain to Overcome the Issues in Land Registry Man...

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Riaz, Ayesha Ikram, Muhammad Jawad Asadullah, Najmussaba University of Engineering and Technology Department of Computer Software Engineering Mardan Pakistan Jeddah International College Department of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

A Blockchain network contains a distributed ledger that is used to store a secure and permanent record of transactions among multiple parties. As the registries of land records are historically stored in the form of paper documentation where, there might arise the issue of losing, destroying, and modifying needed documents. This land registration process is complicated and time-consuming and required a lot of effort to transfer ownership of the land registry or change any information. For this purpose, various contributions have been made by different researchers to overcome the problem of the classical land registration process by using blockchain technology. In this survey, we shed light on some advantages (reduce time and effort, security, permanency, etc.) of blockchain to identify some early problems in the paper-based land registration process. © 2023 IEEE.

关键词： Blockchain

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：