检索结果-内蒙古大学图书馆

Moratuwa Engineering Research Conference (MERCon)

作者： Mikael Lye Nethmi Wijesinghe School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Individuals and brands with large fan bases face difficulty in understanding fan sentiment and its potential impact on fan engagement and performance. This is particularly pertinent within fast-paced sports such as Formula 1, where fan opinions can significantly influence driver and team morale. To address the above-mentioned problem, this study proposes the use of deep learning-based sentiment analysis techniques to enhance fan base engagement. The system would act as a tool to automate the process of marketing and public relations teams by analysing textual data provided by the fan base and providing meaningful insight/reports of the fan base’s emotion of the posted content. This is achieved by a novel multi-class sentiment analysis system that utilizes a fine-tuned Distil-BERT model for classification, self-supervised techniques for self-improvement, and an explainable AI (XAI) approach for interpretability. The proposed system demonstrated strong performance during testing and evaluation, achieving an overall accuracy of 82% and an F1-score of 80%. Overall, the systems components and structure focuses on utilizing the least amount of resources while also maintaining a high prediction accuracy and speed. This ultimately results in a budget friendly and robust tool that can be integrated into bigger analytics systems.

关键词： Fans Sentiment analysis Analytical models Adaptation models Accuracy Social networking (online) Data models Tokenization Testing Sports

来源：评论

学校读者我要写书评

暂无评论

Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation

Parameter Efficient Diverse Paraphrase Generation Using Sequ...

引用

International Conference on Advancements in Computational Sciences (ICACS)

作者： Lasal Jayawardena Prasan Yapa School of Computing Informatics Institute of Technology Colombo Sri Lanka

Over the past year, the field of Natural Language Generation (NLG) has experienced an exponential surge, largely due to the introduction of Large Language Models (LLMs). These models have exhibited the most effective performance in a range of domains within the Natural Language Processing and Generation domains. However, their application in domain-specific tasks, such as paraphrasing, presents significant challenges. The extensive number of parameters makes them difficult to operate on commercial hardware, and they require substantial time for inference, leading to high costs in a production setting. In this study, we tackle these obstacles by employing LLMs to develop three distinct models for the paraphrasing field, applying a method referred to as sequence-level knowledge distillation. These distilled models are capable of maintaining the quality of paraphrases generated by the LLM. They demonstrate faster inference times and the ability to generate diverse paraphrases of comparable quality. A notable characteristic of these models is their ability to exhibit syntactic diversity while also preserving lexical diversity, features previously uncommon due to existing data quality issues in datasets and not typically observed in neural-based approaches. Human evaluation of our models shows that there is only a 4% drop in performance compared to the LLM teacher model used in the distillation process, despite being 1000 times smaller. This research provides a significant contribution to the NLG field, offering a more efficient and cost-effective solution for paraphrasing tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LSTM and BERT based transformers models for cyber threat intelligence for intent identification of social media platforms exploitation from darknet forums

引用

International Journal of Information technology (Singapore) 2024年第8期16卷 5277-5292页

作者： Sangher, Kanti Singh Singh, Archana Pandey, Hari Mohan Amity School of Engineering and Technology (ASET) Amity University Noida India Department of Computing and Informatics Bournemouth University Fern Barrow Poole BH12 5BB United Kingdom

Cybercriminals, terrorists, political activists, whistleblowers, and others are drawn to the darknet market and its use for illicit purposes. Various methods are employed to identify the people who are behind these identities and websites. Since DNMs are more recent than other platforms, there are more unexplored research possibilities in this field. Research has been done to identify the buying and selling of products connected to hacking from Darknet Marketplaces, the promotion of cyber threats in hacker’s forums and DNMs, and the supply chain elements of content related to cyber threats. The proposed research covers one of the most promising research areas: darknet markets and social media platforms exploitation tools and strategies. The research uses 6 DNMs publicly available data and then identified the most popular social media platform and intent of discussion based on the interaction available in form of the user remarks and comments. The research caters the social media platform and cybercrimes or threats associated to them, by help of the machine learning algorithms Logistic Regression, RandomForestClassifier, GradientBoostingClassifier, KNeighborsClassifier, XGBClassifier, Voting Classifier and Deep Learning based model LSTM and Transformer based Model used. In existing research, natural language processing techniques were employed to identify the kinds of commodities exchanged in these markets, while machine learning approaches were utilized to classify product *** proposed research work advanced and lighter version of BERT and LSTM model used yielding accuracy of 90.12% and 91.35% respectively. LSTM performed best to extract multiclass classification of actual intension of social media usage by intelligent analysis on hackers’ discussions. Strategies on social media platforms such as Facebook, twitter, Instagram, Snapchat to exploit them using darknet platforms also explored. This paper contributes on cyber threat intelligence that leverages

关键词： BERT CTI LSTM Machine learning Social media crimes Transformer based model

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Approach for Rainfall Class Amount Prediction Using Uganda's Lake Victoria Basin Weather Dataset

Transfer Learning Approach for Rainfall Class Amount Predict...

引用

Big Data Analytics and Practices (IBDAP), International Conference on

作者： Tumusiime Andrew Gahwera Odongo Steven Eyobu Mugume Isaac School of Computing and Informatics Technology Makerere University Kampala Uganda

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

Predicting short-term precipitation amounts is challenging especially due to meteorological data scarcity. Although deep Learning-based models have proven to be more effective in predicting precipitation their performance heavily depends on the size of the training dataset. This paper presents a Multi-station-based Transfer Learning approach with the aim of mitigating the data scarcity problem by transferring knowledge learned from multiple meteorological stations to a target station. In order to achieve this, a Multi-layer Perceptron, Convolutional Neural Networks, and Long-Short Term Memory systems were trained to predict rainfall class amounts on individual weather stations. From the experiments LSTM model outperformed the other-state-of-the-art models with an F1-score of 93% for sample stations of Jinja and Mwanza, and 95% for Musoma respectively. Consequently, the pre-trained LSTM model on each station were used as base models for Transfer Learning on the target station of Kisumu with limited data. The results show that the performance of the resulting transfer learning model improved by 3% for Jinja, Mwanza, and Musoma after model fine-tuning.

关键词： Training Rain Transfer learning Memory management Weather forecasting Predictive models Lakes

来源：评论

学校读者我要写书评

暂无评论

Advancements in Natural Language Processing for Automatic Text Summarization

Advancements in Natural Language Processing for Automatic Te...

引用

Computer Systems (ICCS), IEEE International Conference on

作者： Nevidu Jayatilleke Ruvan Weerasinghe Nipuna Senanayake School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350366341

ISBN: (纸本)9798350366358

The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of text summarization models has been significantly enhanced in a variety of technical domains because of advancements in Natural Language Processing (NLP) and Deep Learning (DL). Despite this, the process of summarizing textual information continues to be significantly constrained by the intricate writing styles of a variety of texts, which involve a range of technical complexities. Text summarization techniques can be broadly categorized into two main types: abstractive summarization and extractive summarization. Extractive summarization involves directly extracting sentences, phrases, or segments of text from the content without making any changes. On the other hand, abstractive summarization is achieved by reconstructing the sentences, phrases, or segments from the original text using linguistic analysis. Through this study, a linguistically diverse categorizations of text summarization approaches have been addressed in a constructive manner. In this paper, the authors explored existing hybrid techniques that have employed both extractive and abstractive methodologies. In addition, the pros and cons of various approaches discussed in the literature are also investigated. Furthermore, the authors conducted a comparative analysis on different techniques and matrices to evaluate the generated summaries using language generation models. This survey endeavors to provide a comprehensive overview of ATS by presenting the progression of language processing regarding this task through a breakdown of diverse systems and architectures accompanied by technical and mathematical explanations of their operations.

关键词： Surveys Hands Statistical analysis Text summarization Computer architecture Writing Linguistics Transformers Natural language processing Mathematical models

来源：评论

学校读者我要写书评

暂无评论

A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2023年第23期17卷 4-19页

作者： Chi, Jing On, Chin Kim Zhang, Haopeng Chai, Soo See Faculty of Computing and Informatics University Malaysia Sabah Sabah Malaysia School Information and Electrical Engineering Hebei University of Engineering Hebei China Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

引用

2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud computing Environment for Efficient Resource Scheduling for Distributed Overloading

引用

Journal of Systems Science and Systems Engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

来源：评论

学校读者我要写书评

暂无评论

Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

引用

2023 International Research Conference on Smart computing and Systems Engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

TeFaHub: Dashboard Applications for Supporting the Teaching Factory Program in Indonesian Vocational High school 2

TeFaHub: Dashboard Applications for Supporting the Teaching ...

引用

2nd International Conference on Software Engineering and Information technology, ICoSEIT 2024

作者： Suwastika, Novian Anggis Qonita, Qori Masrom, Maslin Telkom University School of Computing Bandung Indonesia Public Vocational High School 10 Bandung Broadcasting and Film Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9798350317503

The aim of this research is to develop a dashboard application to support the Teaching Factory (TeFa) program in Vocational High schools in Indonesia, referred to as Teaching Factory Hub (TeFaHub). The study employs the Rapid Application Development (RAD) method to build TeFaHub, with a specific focus on the user requirements and user design phases within the RAD methodology. User requirements for this research are identified through a rapid literature review to gather feedback on the issues and challenges in implementing the TeFa program. A literature review query targeting the implementation of TeFa in vocational high schools in Indonesia, with a focus on information management, was conducted. Through screening and synthesis processes, 21 relevant articles were identified. Analysis of the synthesis results revealed that TeFa program stakeholders consist of four categories: TeFa program managers, industry representatives, teachers, and students. The identified issues were categorized into three stages: preparation, implementation, and evaluation. The analysis results served as a reference for constructing a sitemap, outlining functionalities to address the challenges and issues in implementing TeFa. The findings of this research can serve as a foundation for the development of TeFaHub in the development, construction, and cutover phases. © 2024 IEEE.

关键词： Application programs

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：