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Features of the Development of Intelligent Scientific and Educational Internet Resources 6

Features of The Development of Intelligent Scientific and Ed...

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6th international conference on Computer Science and Engineering, UBMK 2021

作者： Sadirmekova, Z.H.B. Tussupov, J.A. Sambetbayeva, M.A. Yerimbetova, A.S. Zagorulko, Y.A. L.N. Gumilyov Eurasian National University Nur-Sultan Kazakhstan Institute of Information and Computational Technologies CSMES RK Almaty Kazakhstan Institute of Information and Computational Technologies CSMES RK Al-Farabi Kazakh National University Almaty Kazakhstan A.P. Ershov Institute of Informatics Systems Siberian Branch Russian Academyof Sciences Novosibirsk Russia

ISBN: (纸本)9781665429085

the purpose of this work is to develop methods, technologies and tools for creating and maintaining intelligent scientific and educational Internet resources (ISEIR) based on a service-oriented approach and Semantic web technologies. the main purpose of ISEIR is to provide meaningful access to scientific and educational information resources of a given field of knowledge and integrated information processing services. According to the preliminary concept, an intelligent scientific and educational Internet resource will be an information system accessible via the Internet, which provides ontology-based systematization and integration of scientific knowledge, data and information resources into a single information space, meaningful effective access to them, as well as supporting their use in solving various scientific and educational tasks. ISEIR is equipped with an ergonomie web-based user interface and special editors designed to manage the knowledge integrated into it. the proposed approach to the construction of intelligent scientific and educational Internet resources is the basis of the developed technology for creating and maintaining information environments for distributed learning. © 2021 IEEE

关键词： Ontology

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Code Generation Based on Quality-Aware Reordering

Code Generation Based on Quality-Aware Reordering

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Yuan Song Luo Yu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. this method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. the experimental results show that the approach proposed in this paper obtains a significant improvement in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improvement in solving the problem of selecting the correct output from natural language to code generation, effectively improves the accuracy of evaluation by automating the generation of test cases, and provides an effective solution to improving the correctness of code generation.

关键词： Codes Accuracy Computational modeling Estimation Signal processing Benchmark testing

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Bangla Abusive language Detection using Machine Learning on Radio Message Gateway 6

Bangla Abusive Language Detection using Machine Learning on ...

引用

6th IEEE international conference on Communication and Electronics Systems, ICCES 2021

作者： Ritu, Sumaiya Salim Mondal, Joysurya Mia, Md. Moinu Marouf, Ahmed Al Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665435871

In the era of modern technology, machine learning and natural language processing has been adopted to be applied in several application areas. Natural language processing consists of diversified techniques such as text classification, text summarization, named entity recognition, sentiment analysis. Text classification is considered to be the area of research where the text gets segmented into different category sentences or paragraphs from a single text genre. this paper presents a mechanism for detecting Bangla abusive language from a realtime radio message gateway. Online radio stations nowadays accept communications and voices of their target audience from web-based applications or social media platforms, such as Facebook or Twitter pages. this paper has created a dataset with more than 45000 Bangla sentences, which are labeled as abusive and non-abusive. Sample online radio message gateway has been been introduced and machine learning algorithms such as multinomial naive bias (MNB), logistic regression (LR), and random forest (RF) classifiers are utilized to predict the abusive languages. One of the significant prospects of this work would be applied during live radio programs where listeners try to communicate by sending live messages. Our proposed mechanism can check and map the live messages with the dataset and segregate the positive comments or messages only, by filtering the abusive comments. Among the applied classifiers, it has been found that the random forest classifier has performed better than the other two classifiers by leveraging approximately 76% accuracy. © 2021 IEEE.

关键词： Random forests

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Research on Text Communication Security Based on Deep Learning Model 1

引用

6th EAI international conference on Machine Learning and Intelligent Communications, MLICOM 2021

作者： Yu, Guanghua Cong, Wanjuan Heihe University Heilongjiang Heihe China School of Computer and Information Engineering College HeiHe University Heihe China

ISBN: (数字)9783031044090

ISBN: (纸本)9783031044083

In response to the current spam flooding problem, this paper uses Python language machine learning and natural language processing technology to study the identification classification of spam messages. the Jieba algorithm is used to distinguish the Chinese word, and the TF-IDF algorithm is used to conduct feature extraction. On the basis of the analysis of the classifier algorithm, the experimental data is finalized. the results show that the classification effect of the polynomial plain Bayes classifier is optimal, and the identification of garbage text is best optimized. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Bayesian networks

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Enhanced Sign language Recognition and Translation Framework Utilizing CNN-LSTM for Healthcare Integration

Enhanced Sign Language Recognition and Translation Framework...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Fiza Asmat Muhammad Shahid Bhatti Areeba Aleem Quratulain Marran Aldossari Kazim Ali Department of Computer Science COMSATS University Islamabad Lahore Campus Lahore Pakistan Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Punjab Education Department Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Humans interact with each other through communication using languages. All over the world, each country has its own natural language and some standards for communicating with others. there is sign language for a deaf person to communicate with a hearing person in their daily routine. the importance of understanding sign language is just as significant as speaking a natural language for over 70 million people worldwide. In this age, various intelligent features are being integrated into daily routines to minimize the communication gap for impaired people. this study presents the use of Machine Learning and Computer Vision techniques to recognize and translate sign language using a hybrid CNN-LSTM approach, which deals with both spatial and temporal features of dynamic gestures. the primary focus of the system is to help hearing-impaired people communicate smoothly with medical professionals. Our method applies preprocessing techniques on a customized dataset consisting of diverse health related gestures, achieving 70 percent accuracy in real-time prediction with a delay of 300 ms. Using NLP to convert text and GTTS to convert audio into English and Urdu, this translator effectively becomes the voice of the deaf and the ears of the mute.

关键词： Sign language Translation Accuracy Text recognition Education Medical services Computer architecture web conferencing Real-time systems Tokenization

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Sparse Transformer Visual Tracking Network Based on Second-Order Attention

Sparse Transformer Visual Tracking Network Based on Second-O...

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Natural language processing (ICNLP), international conference on

作者： Xiaolin Yang Zhiqiang Hou Fan Guo Sugang Ma Wangsheng Yu Xiaobao Yang School of Computer Science & Technology Xi’an University of Posts & Telecommunications Xi’an China Information & Navigation College Air Force Engineering University Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

the Transformer-based visual tracking has demonstrated exceptional performance, but there is still space for further improvement in target feature expression. To address this issue, this paper proposes a Sparse Transformer visual tracking network based on second-order attention to enhance the feature expression capability of the tracking algorithm. Firstly, the spatio-temporal motion information is integrated in this paper to model the motion of the target in the video sequence, thereby enhancing the feature expression of the target. Secondly, the proposed method improves the Transformer structure in the visual tracking by utilizing a sparse self-attention mechanism that focuses on the most crucial information in the target area. More importantly, to further enhance the discriminative ability of the tracker for the target, a mixed second-order pooling (MSOP) module is embedded in the encoder-decoder structure. the proposed method has been extensively evaluated on multiple datasets, achieving success rates of 70.9%, 67.9%, and 64.8% on OTB100, UAV123, and LaSOT, respectively. Furthermore, the method obtains EAO score of 0.485 on VOT2018. the experimental results demonstrate that the proposed method has better tracking performance and is more robust in various complex scenarios.

关键词： Visualization Target tracking Computational modeling Video sequences Semantics Interference Computer architecture Transformers Robustness Natural language processing

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Generating Short Video Description using Deep-LSTM and Attention Mechanism 6

Generating Short Video Description using Deep-LSTM and Atten...

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6th international conference for Convergence in technology (I2CT)

作者： Yadav, Naveen Naik, Dinesh Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

ISBN: (纸本)9781728188768

In modern days, extensive amount of data is produced from videos, because most of the populations have video capturing devices such as mobile phone, camera, etc. the video comprises of photographic data, textual data, and auditory data. Our aim is to investigate and recognize the visual feature of the video and to generate the caption so that users can get the information of the video in an instant of time. Many technologies capture static content of the frame but for video captioning, dynamic information is more important compared to static information. In this work, we introduced an Encoder-Decoder architecture using Deep-Long Short-Term Memory (Deep-LSTM) and Bahdanau Attention. In the encoder, Convolution Neural Network (CNN) VGG16 and Deep-LSTM are used for deducing information from frames and Deep-LSTM combined with attention mechanism for describing action performed in the video. We evaluated the performance of our model on MSVD dataset, which shows significant improvement as compared to the other video captioning model.

关键词： Computer Vision Machine Translation Recurrent Neural Network Natural language processing Video Captioning

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Research on Text Classification and Topic Extraction Model of Medical English Corpus Based on Natural language processing

Research on Text Classification and Topic Extraction Model o...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Xinli Zhang Lingyue Xie Guangdong Medical University Dongguan China South China University of Technology Guangzhou China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In the medical field, a large amount of professional literature stores a wealth of knowledge, and effectively organizing and classifying this information is of great significance for medical research and practice. this paper proposes a deep learning-based text classification and topic extraction model for processing medical English corpora. We first obtained a corpus of 1000 medical texts, and then used natural language processing techniques to preprocess these texts. We propose a convolutional neural network (CNN) based text classification model that is capable of automatically learning and extracting features from text. Meanwhile, we utilize Latent Dirichlet Allocation (LDA) model for topic extraction. On the test set, our model outperforms other common text classification models such as Naive Bayes, Support Vector Machines, and Logistic Regression in both accuracy and F1-score. the research in this paper provides an effective method for the automatic classification and topic extraction of medical English texts, which has wide practicability and application value.

关键词： Support vector machines Computational modeling Text categorization Neural networks Feature extraction Natural language processing Data models Convolutional neural networks Resource management Text processing

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Research on Online Education Model and Crisis Intervention System in Universities Perspective on Mental Health 22

Research on Online Education Model and Crisis Intervention S...

引用

6th international conference on Education and Multimedia technology, ICEMT 2022

作者： Tai, Ruiting Chen, Chizhi Hwang, Zhiheng School of Information and Software Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781450396455

Due to COVID-19, online education has become the main and new teaching method in the lockdown zone. However, teachers and students inevitably feel difficult and distressful about these new ways. there are also some other issues, such as inadequate interactions in the online classroom, causing poor knowledge absorption. Compared with traditional offline education, students cannot release their emotions or psychological pressure. What's more, teachers aren't able to comprehensively evaluate students' psychological conditions, which lead to belated awareness and solutions of their psychological problems. therefore, from the perspective of mental health, this paper is designed to analyze the needs of college students for online education based on Maslow's hierarchy of needs theory, and to explore the corresponding mode and interventional strategies of psychological crisis under the impact of COVID-19 epidemic. Equipped with emotion recognition and attention recognition based on computer vision technology, an intelligent interactive online classroom is constructed. At the same time, by using social big data, natural language processing (NLP) emotion analysis is achieved, which has made multi-index mental health prediction and crisis intervention realized. Although under the influence of COVID-19, this will still help educators implement more comprehensive and convenient online education and provides a guarantee for the overall development for students. © 2022 ACM.

关键词： Students

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Sequence Recognition of Scene Text Based on CRNN and CTPN Models 6

Sequence Recognition of Scene Text Based on CRNN and CTPN Mo...

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6th international conference on Electronic information technology and Computer Engineering, EITCE 2022

作者： Liu, Yiyi Faculty of Science and Technology Beijing Normal University Hong Kong Baptist University United International College Guangdong Province Zhuhai China

ISBN: (纸本)9781450397148

Image-based sequence recognition has lately emerged as a prominent study subject in the science of computer vision, while text detection and identification in natural situations has emerged as an active research field. Based on scene text data, this paper addresses the theory of deep learning-based CRNN and CTPN models and the process of processing text. Using CRNN, text recognition can be turned into a time-dependent sequence learning issue, which is commonly employed for indeterminate-length text sequences. Contextual relationships between text images are learned using BLSTM and CTC, thus effectively improving text recognition accuracy and making the model more robust. It also excels in text recognition tests for wordless and lexical-based scenes, as it is not constrained by any predefined language. It produces a more efficient, but smaller, model that is more suited to real-world settings. CRNN recognition accuracy is lower for short texts with large morphological changes, such as artistic words, or texts with large changes in natural scenes. Because of the Anchor setting, CTPN can only detect horizontally distributed text, but a small improvement can detect vertical text by adding horizontal Anchor. As a result of the limitations of the framework, the irregularly inclined text can be detected very broadly. © 2022 Association for Computing Machinery.

关键词： Optical character recognition

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