检索结果-内蒙古大学图书馆

2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Chen, Zhaowen Zhang, Mingze Mostyn, Caleb Jiang, Honglu Feng, Xianglong Miami University Department of Computer Science and Software Engineering Oxford45056 United Kingdom

ISBN: (纸本)9798350367942

Federated Learning (FL) offers significant advancements in user/data privacy, learning quality, model efficiency, scalability, and network communication latency. However, it faces notable security challenges, particularly with the emergence of backdoor attacks. The distributed nature of FL complicates the development of backdoor-resistant systems compared to traditional machine learning environments. In this paper, we propose a novel approach to turn the perceived curse of model inversion (MI) attacks into a blessing, using them as a tool for detecting backdoor attacks in FL environments. Leveraging MI outputs, we propose a K-means-based feature extraction and Isolation-Forest-based anomaly detection algorithm to analyze behavior and detect abnormal learning performance, thereby identifying backdoor attacks. Experimental results demonstrate the effectiveness and superior performance of our method in detecting backdoor attacks within FL systems. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study on Small-Sized Datasets Based on Eubank’s Optimal Spacing Theorem

引用

SN computer Science 2025年第1期6卷 1-19页

作者： Abedu, Samuel Mensah, Solomon Boafo, Frederick Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Science University of Ghana Accra Ghana Department of Computer Science Lancaster University Ghana Accra Ghana

Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in SEE. This can be attributed to the relatively small sizes of SEE datasets. The goal of the study is to establish a threshold for small-sized datasets in SEE. Additionally, it looks into how well certain deep learning and traditional machine learning models perform on small-sized datasets. From the body of existing literature, plausible SEE datasets are extracted and ranked along with their attributes and number of project cases. The ranking of the project instances is discretized into three classes (small, medium, and large) using Eubank’s optimal spacing theory. Using the leave-one-out cross-validation, each small-sized dataset is used to train two deep learning models and five conventional machine learning models. Each model’s ability to make predictions is evaluated using its mean absolute error. Results show that, on small-scale datasets, deep learning models outperform traditional machine learning models in terms of prediction accuracy, which contradicts what is previously known. Regularisation techniques can be used in conjunction with deep learning to address SEE. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Eubank’s optimal spacing theory Small-sized software effort estimation Traditional Machine learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

引用

Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

来源：评论

学校读者我要写书评

暂无评论

TagDebias: Entity and Concept Tagging for Social Bias Mitigation in Pretrained Language Models

TagDebias: Entity and Concept Tagging for Social Bias Mitiga...

引用

2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Moslemi, Mehrnaz Zouaq, Amal Department of Computer and Software Engineering LAMA-WeST Lab Polytechnique Montréal Canada

ISBN: (纸本)9798891761193

Pre-trained language models (PLMs) play a crucial role in various applications, including sensitive domains such as the hiring process. However, extensive research has unveiled that these models tend to replicate social biases present in their pre-training data, raising ethical concerns. In this study, we propose the TagDebias method, which proposes debiasing a dataset using type tags. It then proceeds to fine-tune PLMs on this debiased dataset. Experiments show that our proposed TagDebias model, when applied to a ranking task, exhibits significant improvements in bias scores. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

引用

Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Advancing Formal Verification: Fine-Tuning LLMs for Translating Natural Language Requirements to CTL Specifications 35

Advancing Formal Verification: Fine-Tuning LLMs for Translat...

引用

35th International Workshop on Rapid System Prototyping, RSP 2024

作者： Zrelli, Rim Misson, Henrique Amaral Ben Attia, Maroua De Magalhaes, Felipe Gohring Shabah, Abdo Nicolescu, Gabriela Polytechnique Montreal Computer Engineering and Software Engineering Department Montreal Canada Research and Development Humanitas Solutions Montreal Canada

ISBN: (纸本)9798331519537

In the domain of formal verification, translating natural language (NL) requirements into Computation Tree Logic (CTL) specifications presents a notable challenge due to the disparity between human-readable documents and formal specifications. This paper introduces a novel approach that leverages Large Language Models (LLMs) to automate this translation process, thereby enhancing the accuracy and efficiency of formal verification practices. We fine-tune three state-of-the-art LLMs - LLAMA3, Mistral, and Qwen2 - with a particular focus on optimizing the Mistral model due to its superior performance. Our methodology is supported by the Natural2CTL dataset, consisting of 2,095 NL requirements and their corresponding CTL specifications. We employ evaluation metrics such as validation loss, accuracy, semantic similarity, and Structural Operator Jaccard Similarity (SOJS) for a comprehensive assessment of model performance. Additionally, a comparative analysis with human translators, trained in CTL logic, underscores the LLMs' potential to match or even surpass human accuracy in translating NL requirements into formal specifications. Our findings reveal that the fine-tuned Mistral model significantly outperforms the other LLMs and human participants, demonstrating superior accuracy in generating CTL specifications. This study advances the field of formal verification by proposing a scalable solution to the NL-to-CTL translation challenge, setting a new benchmark for the integration of AI tools in complex specification tasks. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew 8

From Linguistics to Practice: a Case Study of Offensive Lang...

引用

8th Workshop on Online Abuse and Harms, WOAH 2024 - co-located with NAACL 2024

作者： Liebeskind, Chaya Vanetik, Natalia Litvak, Marina Department of Computer Science Jerusalem College of Technology Jerusalem Israel Department of Software Engineering Shamoon College of Engineering Beer-Sheva Israel

ISBN: (纸本)9798891761056

The perception of offensive language varies based on cultural, social, and individual perspectives. With the spread of social media, there has been an increase in offensive content online, necessitating advanced solutions for its identification and moderation. This paper addresses the practical application of an offensive language taxonomy, specifically targeting Hebrew social media texts. By introducing a newly annotated dataset, modeled after the taxonomy of explicit offensive language of (Lewandowska-Tomaszczyk et al., 2023), we provide a comprehensive examination of various degrees and aspects of offensive language. Our findings indicate the complexities involved in the classification of such content. We also outline the implications of relying on fixed taxonomies for Hebrew. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Waste Management Sustainability Through Smart and Incentivized Sorting 15

Enhancing Waste Management Sustainability Through Smart and ...

引用

15th Annual Undergraduate Research Conference on Applied Computing, URC 2024

作者： Woga, Muluberhan Negash, Rediet Bekera, Ahmedin Alemu, Nathanael Zaki, Nazar Department of Computer Science and Software Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9798331527341

This paper presents an intelligent waste sorting system that utilizes computer vision and deep learning to accurately categorize waste items. Moreover, the system incentivizes proper waste disposal through a rewards scheme. Testing indicated over 90% accuracy in classifying waste into multiple categories. This sustainable solution has strong potential to address critical waste management challenges. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

MobileNet-Eye: An Efficient Transfer Learning for Eye Disease Classification

MobileNet-Eye: An Efficient Transfer Learning for Eye Diseas...

引用

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Niloy, Golam Mohiuddin Bitto, Abu Kowshir Badruzzaman Biplob, Khalid Been Md. Sammak, Musabbir Hasan Das, Aka Hridoy, Golam Gouse Daffodil International University Department of Software Engineering Dhaka1207 Bangladesh Premier University Department of Computer Engineering Chattogram4203 Bangladesh

ISBN: (纸本)9798350350289

Precisely and promptly diagnosing eye illnesses is crucial for preventing and managing them. Transfer learning shows potential for automatically identifying different eye diseases. This is beneficial for avoiding and addressing eye issues. Enhanced computer vision has significantly benefited eye doctors by allowing computers to assist them extensively. We researched transfer learning strategies to address three eye issues: uveitis, Eyelid (Lid), and Healthy eyes. We examined the performance, precision, and efficacy of 3 popular pre-trained computer algorithms (MobileNetV2, ResNet50, EfficientNetB7) in detecting eye disorders. We utilized 3,000 images of eyes for this task: 1000 images of healthy eyes, 1,000 images of eyes affected by uveitis, and 1,000 images of eyes with Lid problems. MobileNetV2 was the most precise model, achieving a 96% accuracy in detecting eye disorders. EfficientNet-B7 achieved a 95% accuracy, whereas ResNet-50 had a 94% accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors 19

Green Computing Adoption: Understanding the Role of Individu...

引用

19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Anni, Fahima Akter Islam, Muhammad Rezaul Sadia, Farzana Hasan, Mahady Rokonuzzaman, M. Department of Software Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9789897586965

This study investigates the factors influencing the attitudes of software developers and IT professionals towards Green Information Technology (GIT) in Bangladeshi IT/software firms and examines their impact on engagement in green computing practices. Data was collected from 130 participants. A thorough literature review was conducted. The findings highlight key individual factors that influence employees’ attitudes, including awareness, knowledge, and perception of environmental issues. The study demonstrates the connection with GIT attitudes and behavior modification, especially through stated usage of green computing methods. Data analysis confirms 5 out of 8 hypotheses and reveals the complexity of the relationships between the constructs. The report promotes the adoption of Green IT technology by software companies in Bangladesh and highlights the significance of an organized office system integrating Green IT. Limitations include a relatively small sample size and the multidimensional nature of the relationships between the constructs. The findings can assist software companies in addressing customer concerns about the performance and functionality of green computing practices, ultimately promoting sustainable computing practices in the IT sector of Bangladesh. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Green computing

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：