检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Faklaris, Cori Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte CharlotteNC United States

U.S. Federal Trade Commission data for 2020-2022 shows that 23% of all fraud reports include Text as the contact method, with reported losses in the same period rising 279%, to $326 million. These numbers, and estimates of other unreported losses, have spurred our lab and others in industry and academia to direct resources to understanding and mitigating smishing. In this workshop paper, we describe three principal challenges in smishing mitigation that are distinct from traditional phishing: (1) limitations of mobile device affordances, (2) the complexity of cellular network infrastructure, and (3) cognitive and contextual factors in mobile device usage. We describe ideas for mitigating smishing, including a high-level overview of our lab's work to create and test interface designs for text-message recipients. © 2024, CC BY-SA.

关键词： Mobile security

来源：评论

学校读者我要写书评

暂无评论

Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition 21

Texture and Orientation-based Feature Extraction for Robust ...

引用

21st IEEE/ACIS International Conference on software Engineering Research, Management and Applications, SERA 2023

作者： Khan, Sajid Ali Bin Abdulrahman Alawairdhi, Mohammed Al-Akhras, Mousa Foundation University Islamabad Department of Software Engineering Islamabad Pakistan Saudi Electronic University College of Computing and Informatics Saudi Arabia The University of Jordan Amman King Abdullah II School for Information Technology Computer Information Systems Department Jordan

ISBN: (纸本)9798350345889

Facial expressions are the most effective way to characterize people's motives, emotions, and feelings. Several new methods are proposed each year;however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods. © 2023 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

引用

4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Wijeratne, Ashansa Nirubikaa, Ravikumar Kumara, Samantha Prasanth, Senthan Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9798350369786

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models - Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM) - were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics - accuracy, precision, recall, and F1 score - for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included 'student,' 'education,' 'research,' and 'teacher,' indicating a strong association of ChatGPT with educational and research contexts. Similarly, 'AI,' 'student,' 'research,' 'course,' and 'education' dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public's perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools. © 2023 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Approach to Studying Information Dissemination in Social Networks with Simulation Tools

Approach to Studying Information Dissemination in Social Net...

引用

International Conference on Application of Information and Communication Technologies (AICT)

作者： Alexander Kolesnikov Elena Zamyatina Lyudmila Lyadova Viacheslav Lanin Department of Computing Systems Software Perm State University Perm Russia Department of Information Technologies in Business HSE University Perm Russia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

To understand information dissemination researchers must combine different methods. The lack of social network research tools combining different analysis methods is a problem for users. An approach to studying social networks based on using simulation system TriadNS is proposed in paper. The important characteristics of social networks and the metrics that allow you to evaluate them are discussed. Information dissemination models are described. The paper provides a description of simulation tools to explore both structural characteristics and dynamic behavior of social networks, to study not only virtual networks, but also real ones. The results of calculating and visualizing different metrics and executing simulation experiments are demonstrated.

关键词： Measurement Analytical models Visualization Social networking (online) Simulation Data visualization Static analysis Programming Ontologies Data models

来源：评论

学校读者我要写书评

暂无评论

Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

引用

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

AI-based Programming Assistants for Privacy-related Code Generation: The Developers’ Experience

arXiv

引用

arXiv 2025年

作者： Madampe, Kashumi Grundy, John Arachchilage, Nalin HumaniSE Lab Department of Software Systems and Cybersecurity Monash University Melbourne Australia School of Computing Technologies RMIT University Melbourne Australia

With the popularising of generative AI, the existence of AI-based programming assistants for developers is no surprise. Developers increasingly use them for their work, including generating code to fulfil the data protection requirements (privacy) of the apps they build. We wanted to know if the reality is the same as expectations of AI-based programming assistants when trying to fulfil software privacy requirements, and the challenges developers face when using AI-based programming assistants and how these can be improved. To this end, we conducted a survey with 51 developers worldwide. We found that AI-based programming assistants need to be improved in order for developers to better trust them with generating code that ensures privacy. In this paper, we provide some practical recommendations for developers to consider following when using AI-based programming assistants for privacy-related code development, and some key further research directions. © 2025, CC BY-NC-ND.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Aircraft Identification System Using Convolution Neural Networks 12

An Aircraft Identification System Using Convolution Neural N...

引用

12th IEEE International Conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Turchenko, Viktor Turchenko, Iryna Vasylkiv, Nadiia West Ukrainian National University Department for Information Computer Systems and Control Ternopil46000 Ukraine McMaster University Department of Computing and Software HamiltonL8S 4L8 Canada Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350358056

The task of aircraft identification using their registration number is important as visual identification remains the only method of their identification in case of air traffic control systems or radar failure. In this paper, an analysis of known solutions was carried out and an aircraft identification system was developed using a pre-trained YOLO (You Only Look Once) deep convolutional neural network and Optical Character Recognition (OCR) technology. The system consists of three modules: the M1 module is designed for the detection of region of interest-tail, the M2 module is designed for the detection of region of interest-registration number, the M3 module is designed for the classification of the registration number. 2309 aircraft images were used for training, 426 for validation and 408 for testing from the Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) dataset. Our results show that the mean average precision for the M1 and M2 YOLOv7 models on the test set were 0.92 and 0.85 respectively, and for the module M3 OCR technology the classification error was 0.2. © 2023 IEEE.

关键词： aircraft convolutional neural networks deep learning detection transfer learning YOLOv7

来源：评论

学校读者我要写书评

暂无评论

VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

引用

Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

来源：评论

学校读者我要写书评

暂无评论

Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

引用

Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Sumayya Babangida-Sabo Samuel C. Avemaria Utulu Abel Ajibesin Department of Informaton Systems School of Informtaon Technology and Computing American University of Nigeria Yola Nigeria Department of Software Engineering School of Information Technology and Computing Amerucan University of Nigeria Yola Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria’s attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR.

关键词： Ethics Data security Standards organizations Data protection Collaboration Organizations Production

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：