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Sentiment Analysis of Bangladeshi Digital Newspaper by Using Machine Learning and Natural language processing

Sentiment Analysis of Bangladeshi Digital Newspaper by Using...

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international conference on Electrical Engineering and information & Communication technology (ICEEICT)

作者： Tasfia Samin Neshat Anjuman Mouri Farzana Haque Md. Shariful Islam Department of Electrical & Computer Engineering North South University Dhaka Bangladesh Department of Mathematics & Physics North South University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

the modern era has witnessed a heightened level of global news exposure in recent years. We all are updated with any news, be it the economic news of our country, the political news, national or international calamities, disease outbreaks, accidents, or homicides through various means of communication. Despite the advancements in news media, the enduring appeal and sustained demand for newspapers have not waned over time. A notable segment of the population still relies on the regular distribution of newspapers to their homes as a means of staying updated on domestic and global events. the profound influence of print media on the community and its perception of the nation is a matter of great significance. Within the realm of news, it is evident that the presence of positive and negative news yields distinct effects on individuals. this study focuses on conducting sentiment analysis of news articles featured on the front pages of prominent newspapers in Bangladesh. the polarity of news stories from five distinct newspapers was examined individually, as well as the collective polarity of the full dataset. the overall dataset consists of positive, negative, and neutral news, accounting for 54.305 % , 42.434 % , and 3.259% respectively. the classification has been done on a unique customized dataset containing the news for six consecutive months on the front pages of the leading five newspapers and the analysis was performed using six classification models: Logistic Regression, Multinomial Naive Bayes, Support Vector Machine (SVM), Support Vector Classifier (SVC), Random Forest, and K Nearest Neighbors. Overall, these models achieved accuracies of approximately 85.19 % , 77.39 % , 83.17%, 84.23 % , 80.60%, and 58.78% respectively.

关键词： Support vector machines Sentiment analysis Publishing Biological system modeling Sociology Static VAr compensators Media

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A Contrastive Self-distillation BERT with Kernel Alignment-Based Inference 23rd

A Contrastive Self-distillation BERT with Kernel Alignment-...

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23rd international conference on Computational Science, ICCS 2023

作者： Xu, Yangyan Yuan, Fangfang Cao, Cong Su, Majing Lu, Yuhai Liu, Yanbing Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China The 6th Research Institute of China Electronic Corporations Beijing China

ISBN: (纸本)9783031359941

Early exit, as an effective method to accelerate pre-trained language models, has recently attracted much attention in the field of natural language processing. However, existing early exit methods are only suitable for low acceleration ratios due to two reasons: (1) the shallow classifiers in the model lack semantic information. (2) Exit decisions in the intermediate layers are unreliable. To address the above issues, we propose a Contrastive self-distillation BERT with kernel alignment-based inference (CsdBERT), which aims to let shallow classifiers learn deep semantic knowledge to make comprehensive predictions. Specifically, we classify the early exit classifiers into teachers and students based on classification loss to distinguish the representation ability of the classifiers. Firstly, we present a contrastive learning approach between teacher and student classifiers to maintain the consistency of class similarity between them. then, we introduce a self-distillation strategy between these two kinds of classifiers to solidify learned knowledge and accumulate new knowledge. Finally, we design a kernel alignment-based exit mechanism to identify samples of different difficulty for accelerating BERT inference. Experimental results on the GLUE and ELUE benchmarks show that CsdBERT not only achieves state-of-the-art performance, but also maintains 95 % performance at 4 × speed. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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TPC-NAS: Simple and Effective Neural Architecture Search Based on Total Path Count

TPC-NAS: Simple and Effective Neural Architecture Search Bas...

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IEEE international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Ming-Shan Huang Pi-Chuan Chen Tzi-Dar Chiueh National Taiwan University Taipei Taiwan

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

With the explosive growth of neural network (NN) research and application areas, there is a pressing need to automate the NN model search process in order to attain optimal performance. Nevertheless, existing neural architecture search (NAS) algorithms are time-consuming, resource-intensive, and predominantly tailored for image-related applications. this paper presents the Total Path Count (TPC) score, a straightforward yet highly efficient accuracy predictor solely reliant on the architectural information of a model. the effectiveness of the TPC score is underscored by a robust rank correlation of 0.96 between TPC scores and the accuracies of CIFAR100 architectures. We further introduce TPC-NAS, a zero-shot NAS method that can complete a NAS task in under five CPU minutes without training and inference. TPC-NAS has found wide-ranging applications and it outperforms many other NAS solutions. In image classification, TPC-NAS achieves 78.3% ImageNet top-1 accuracy with 399M FLOPs, while in object detection, it improves mAP by at least 2% over other NAS-derived models. Moreover, TPC-NAS successfully discovers a super-resolution architecture with < 300K parameters and achieves 32.09dB PSNR. In NLP, TPC-NAS delivers a model that matches tinyBERT’s FLOPs but outperforms it by almost 10% in accuracy. these experiments illustrate TPC-NAS’s ability to rapidly generate high-performance CNN/transformer architectures for various applications.

关键词： Training Accuracy Superresolution Object detection Artificial neural networks Natural language processing Central processing Unit

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Engage Learn: An AI-Based English Proficiency Improviser

Engage Learn: An AI-Based English Proficiency Improviser

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Ubiquitous Computing and Intelligent information Systems (ICUIS), international conference on

作者： Balasaheb Jadhav Manas Jain Adhip Jajoo Devika Kadam Harshvardhan Kadam Toshish Kakkad Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In this research, the new and highly effective web-based English learning and teaching platform called Engage Learn is introduced with the main purpose of avoiding the mistakes of the traditional models and to help users gain the ability to write and speak English more effectively. Engage Learn employs a marked-defined approach, that involves the application of several advanced technologies. Generative models including ChatGPT 3.5 turbo and Gemini 1.5 flash get enhanced presence when trained using Retrieval-Augmented Generation (RAG) technique. First, the ability for users to speak English makes for an almost informal procedure when learning since the user and the computer are communicating with one another by use of words. In addition to this, targeted feedback modules make an assessment of the user's input and give an immediate-feedback of what is required in terms of improving a user's vocabulary, grammar, and fluency. It offers another learning approach since it presents the content in a manner that is suited more to the current ability level of the user and the usage of preferred learning objectives. Engage Learn also provides flexibility of use based on learning modes as encompassed by general conversation practice, focused grammar correction and vocabulary buildup. this paper reviews the system architecture, explaining features of the different parts of the system, including the speech recognition, LLM with RAG, output analyzing, and the feedback-giving modules on grammar, vocabulary, and reading preferences. Further the paper discusses the tuning of the LLMs in a carefully selected set for language acquisition. this fine-tuning makes sure that the LLMs shall have adequate capability and knowledge required so as to get a feel about how spoken English is and about the particular feedbacks that different user may require out of them. In the last section of the abstract, the planned evaluation methods are presented with the focus on the responsibility of user te

关键词： Vocabulary Reviews Computational modeling Systems architecture Speech recognition Ubiquitous computing Grammar Speech processing Tuning Testing

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NLP Based Sentiment Analysis Using Particle Swarm Optimization with Logistic Regression for Digital Education

NLP Based Sentiment Analysis Using Particle Swarm Optimizati...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： G. Clara Barathi Priyadharshini Prasanalakshmi Balaji Mousmi Ajay Chaurasia Subhranginee Das Sushama Rani Dutta Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Department of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

Identification and categorization of user sentiments from a text passage is known as sentiment analysis into several sentiments. For example, using feelings like happiness, sadness, anger, or positive, negative, or neutral to gauge how the user feels about a certain topic or item. these days, a variety of websites and applications collectively referred to as online educational platforms allow users to obtain a higher degree of education across a range of areas. For this reason, digital education provides a platform for the dissemination of knowledge. Among the most active areas within NLP, web mining, and text mining research is sentiment analysis. It is significant in a variety of subjects, including as the social sciences and management sciences, where evaluating the efficacy of learning technology depends heavily on student input. Due to the proliferation of educational institutions, many students have become enthralled with online learning portals that provide cost-free courses. Every year, a huge number of students enrol in these expansive online courses and then revisit their opinions regarding the course material and level of instruction. Additionally, share ideas on blogs on how to raise the standard of instruction by expressing both good and negative opinions. However, an assortment of ML techniques are available to assess these ratings and ascertain which virtual education platform is the most user-friendly and optimal. this study examined natural language processing (NLP)-based sentiment analysis and social media evaluations that were manually gathered from educational applications. To categorise the reviewer’s comment for digital education, Particle Swarm Optimisation with Logistic Regression (PSOLR) outperformed all other existing Naïve Bayes, Decision Tree, and Logistic Regression algorithms in terms of accuracy (93.75%).

关键词： Text mining Sentiment analysis Logistic regression Machine learning algorithms Accuracy Education web mining Bayes methods Decision trees Particle swarm optimization

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Fine-Tuning LLMs for Code Mutation: A New Era of Cyber threats

Fine-Tuning LLMs for Code Mutation: A New Era of Cyber Threa...

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IEEE international conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Mohammad Setak Pooria Madani Computer Science Ontario Tech University Oshawa Canada Business and Information Technology Ontario Tech University Oshawa Canada

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

Recent advancements in Large language Models (LLMs) have significantly improved their capabilities in natural language processing and code synthesis, enabling more complex applications across different fields. this paper explores the application of LLMs in the context of code mutation, a process where the structure of program code is altered without changing its functionality. Traditionally, code mutation has been employed to increase software robustness in mission-critical applications. Additionally, mutation engines have been exploited by malware developers to evade the signature-based detection methods employed by malware detection systems. Existing code mutation engines, often used by such threat actors, typically result in only limited variations in the malware, which can still be identified through static code analysis. However, the agility demonstrated by an LLM-based code synthesizer could significantly change this threat landscape by allowing for more complex code mutations that are not easily detected using static analysis. One can increase variations of codes synthesized by a pre-trained LLM through fine-tuning and retraining. this process is what we refer to as code mutation training. In this paper, we propose a novel definition of code mutation training tailored for pre-trained LLM-based code synthesizers and demonstrate this training on a lightweight pre-trained model. Our approach involves restructuring (i.e., mutating) code at the subroutine level, which allows for more manageable mutations while maintaining the semantic integrity verified through unit testing. Our experimental results illustrate the effectiveness of our approach in improving code mutation capabilities of LLM-based program synthesizers in producing varied and functionally correct code solutions, showcasing their potential to transform the landscape of code mutation and the threats associated with it.

关键词： Training Codes Synthesizers Semantics Transforms Static analysis Malware Security Engines Testing

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Enhancing G.C.E. Ordinary Level Mathematics through Adaptive Learning in Sri Lanka

Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive...

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Advancements in Computing (ICAC), international conference on

作者： P.S.D. Fernando H.P.D.E.R. Amarasekara M.D.B. Rathnakumara B.W. Amarakoon Samantha Rajapaksha Jenny Krishara Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. this application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. the suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka.

关键词： Adaptive learning Visualization three-dimensional displays Writing Mathematics Natural language processing Mobile applications Problem-solving Stakeholders Research and development

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Digital safeguard of laminated historical manuscripts: the treatise “Poem in Rajaz on medicine” as a case study

Digital safeguard of laminated historical manuscripts: the t...

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international conference on information Science and technology (ICIST)

作者： Angelo Mario del Grosso Driss Fassi Fihri Mohammed el Mohajir Ouafae Nahli Anna Tonazzini Institute for Computational Linguistic Italian National Research Council Pisa Italy University of Al-Qarawiyyin Fez Morocco Sidi Mohamed Ben Abdellah University Fez Morocco Institute of Information Science and Technologies Italian National Research Council Pisa Italy

ISBN: (数字)9781728166469

ISBN: (纸本)9781728166476

In this paper, we analyze and discuss the characteristics of a system for the effective digital preservation and fruition of historical manuscripts degraded by the process of lamination. As a case study, we will make reference to the “Poem in Rajaz on medicine”, written by Abubacer in the XII century, and conserved in the Al Quaraouiyine Library located in Fez, Morocco. the conceived system should have at least four main functionalities: image acquisition (i.e. digitization), image enhancement, text encoding, and linguistic analysis. Based on the evaluation of the manuscript damages, the acquisition set up should be designed in such a way to be able to avoid reflections as much as possible. Suitable digital image processing techniques should also be devised to correct the residual degradations and enhance the text for an easier legibility. Finally, semi-automatic transcription, scholarly encoding and linguistic analysis, to be performed on the virtually restored pages, should adapt existing tools to the specificity of the primary source writing system and language. the feasibility study for the realization of such a system is of general utility, in that it can provide guidelines for the digitization, the enhancement and the text encoding of the many laminated manuscripts conserved in other historical archives. On the other hand, from the cultural heritage point of view, the experimentation on the “Poem in Rajaz on medicine” could foster the systematic philological and ontological study of a unique piece of our documental heritage: the longest poem of medieval Islamic medical literature.

关键词： Image coding Lamination Linguistics Reflection Libraries Digital preservation Biomedical imaging

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Analyzing Users' Interaction withWriting Feedback and their Effects on Writing Performance 24th

Analyzing Users' Interaction withWriting Feedback and Their ...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Jiang, Yang Klebanov, Beata Beigman Livne, Oren E. Hao, Jiangang Educ Testing Serv Princeton NJ 08541 USA

ISBN: (纸本)9783031363351;9783031363368

With the advance of technology, computer-based tools are increasingly used for writing instruction. However, there is a gap between their practical application and research on how they are used and their effects on writing skills. In the present study, we examined the use of Writing Mentor (R) (WM), a free Google Docs add-on designed to support academic writing through automated feedback. We used event logs to explore the activities that users-in-the-wild engaged inwhile revising their submissions. We found that the quality of users' written products significantly improved from the first submission to the last. Viewing feedback related to the writing being well-edited more frequently and more time spent in WM were significantly associated with a bigger improvement in writing quality. Our findings have implications for the development of writing feedback and the design of AI-assisted tools to support writing.

关键词： Writing feedback automated scoring writing analytics natural language processing automated writing evaluation Writing Mentor

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Experimental design of emotion recognition based on machine learning 23

Experimental design of emotion recognition based on machine ...

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Proceedings of the 2023 6th international conference on Machine Learning and Natural language processing

作者： Jintao Yu Mingze Jiang Tingwei Liang School of Computer and Information Engineering Harbin University of Commerce China School of Astronautic Harbin Institute of Technology China

ISBN: (纸本)9798400709241

In this paper, a wrist bracelet was used to measure the subjects' heart rate signals and skin electrical signals, and three types of emotions (positive, neutral and negative) were induced by designing an experimental environment. the heart rate and skin electrical signals corresponding to emotions were collected through the bracelet, and the recognition accuracy of the three types of emotions was finally obtained through data preprocessing and the support vector machine model in machine learning. At present, how to build an experimental environment that can fully induce subjects' emotions is a major difficulty in emotion recognition, so this paper provides a specific emotion recognition environment design and detailed steps. Meanwhile, through a large number of experiments, the kernel function in the support vector machine model, namely Gaussian kernel function, is determined, and grid search is introduced to search for hyperparameter C. To get the optimal parameters and results. A total of 20 people were measured in this experiment. the experimental results obtained by SVM model were positive: the recognition accuracy of skin electrical signal was 0.8422, the recognition accuracy of heart rate signal was 0.8345, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion was 0.8832. Negative: the recognition accuracy of skin electrical signal is 0.9812, the recognition accuracy of heart rate signal is 0.9385, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion is 0.9902. Neutral: the recognition accuracy of skin electrical signal was 0.6403, the recognition accuracy of heart rate signal was 0.5308, and the recognition accuracy of skin electrical signal and heart rate signal feature fusion was 0.5438

关键词： Emotion recognition Experimental design Machine learning Physiological signal

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