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Chinese Short Text Classification Method Based on Enhanced Prompt Learning

Chinese Short Text Classification Method Based on Enhanced P...

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Computer information Science and Application technology (CISAT), international conference on

作者： Erdan Kong Jun Zhang Daiwei Yu Mengjia Shen Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In the current era of rapid development of information technology, the exponential growth of text data poses unprecedented challenges to text classification technology. As the core research content of natural language processing, text classification technology is crucial for its application in various fields such as public opinion monitoring and sentiment analysis. this paper proposes an innovative classification method that combines keyword extraction and prompt learning strategies to address the challenges of Chinese short text classification. this method relies on the rich prior knowledge of pre trained language models and significantly improves the performance of text classification through a carefully designed input data template reconstruction strategy. the experimental results show that the keyword extraction enhanced hint learning method proposed in this paper improves accuracy to varying degrees under both discrete and continuous hint templates. this article optimizes the performance of short text classification and provides effective strategies and practical references for the practical application of pre trained language models in text classification tasks.

关键词： Learning systems Sentiment analysis information science Accuracy Text categorization Machine learning Vectors Data models information technology Monitoring

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advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN-LSTM) Model

Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN...

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Machine Learning and Natural language processing (MLNLP), international conference on

作者： Bo Yu Yiyu Shan Hemn Barzan Abdalla Baha Ihnaini School of Mathematical Sciences College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China IAINLP Department of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

With the proliferation of Internet technology and social media platforms, information can be easily exchanged across regions at unprecedented speeds, but this can also reduce the quality of information - especially in an era when false and misleading content spreads like wildfire on social media. False news spreads among people, misleads individuals and causes great harm in politics, economy, public health, and other fields. Despite the challenges of fake news detection and intervention, excellent technical support is provided in this regard thanks to recent advances in big data, natural language processing (NLP), deep learning paradigms, and more. Currently, however, the technology that can be used to determine whether news is true or false has been developed primarily in English - and there is an urgent need to optimize doing this in China at a non-minimal scale and complexity. the motivation and purpose of this study is to achieve the same high accuracy of fake news detection in the Chinese context. this article suggests a high-precision Chinese fake news detection system that innovatively combines the ERNIE 3.0, TextCNN, and BiLSTM models. Each model has a distinct role in our system: ERNIE 3.0 collects sophisticated semantic information using its robust pre-trained language model, which is particularly useful for comprehending Chinese text; TextCNN is good at extracting local features from text and identifying essential patterns. BiLSTM excels in identifying long-distance dependencies and contextual information in text. We employ a progressive training technique, initially training and optimizing the performance of each model independently, and then in the integrated model by weighting the outputs of these independent models so that they learn from one another, hence enhancing overall predictive power. As a result, our model can effectively integrate the benefits of each, improve the complete analysis capability of the complex characteristics of fake news, and

关键词： Training Analytical models Accuracy Social networking (online) Biological system modeling Bidirectional long short term memory Predictive models Feature extraction Natural language processing Fake news

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Integrating Multiple Cloud Platforms to Build a Data Pipeline for Recommendation Systems

Integrating Multiple Cloud Platforms to Build a Data Pipelin...

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Data Science and information technology (DSIT), international conference on

作者： Ranjeet Nagarkar Colin Bennie Kejia Wang Mark Lam Daniel Gonzalez Mahesh B. Chaudhari The Data Institute University of San Francisco San Francisco USA

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Data Science and Data Engineering are a few of the high-demand skills that are still prevalent in current market economics. there has been an increasing demand for software engineers to combine skills from both these areas to drive better and more accurate Machine Learning algorithms over an automated, distributed data-platform. this paper presents one such experience in combining these skills to build a data infrastructure that forms the foundation for developing and deploying machine learning algorithms. As part of this work, we present how to develop an automated data pipeline using multiple cloud services to drive a recommendation system for a social publishing platform (https://***/) dataset, allowing for enhanced user experience through personalized content suggestions. Utilizing machine learning (ML) and natural language processing (NLP), the system analyzes article content and user behavior to recommend similar articles, writers, and similar content based on user interests. Preliminary results show the system’s effectiveness in fresh content recommendation based on articles, authors, and user preferences. the automated data pipeline pulls in the latest information using APIs over the Medium website such that the dataset for the recommendation engine is always kept up-to date. thus, the integration of cloud-based data processing with advanced analytical techniques improves the overall digital content interaction and discoverability.

关键词： Cloud computing Machine learning algorithms Publishing Pipelines Machine learning Data science Data processing Natural language processing User experience Recommender systems

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Local parallelization of pleasingly parallel stream processing on multiple CPU cores

Local parallelization of pleasingly parallel stream processi...

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international conference and Workshop on Computing and Communication (IEMCON)

作者： Ruediger Gad Martin Kappes Inmaculada Medina-Bulo Terma GmbH Space Darmstadt Germany Frankfurt University of Applied Sciences Frankfurt am Main Germany Universidad de Cádiz Cádiz Spain

ISBN: (纸本)9781509009978

Data stream processing addresses the need for high-throughput near real-time data processing, which can be considered as one part of Big Data or Fast Data. In this paper, we study the local parallelization of stream processing on a single multi-core Central processing Unit (CPU) computer system, which, in our opinion, was not sufficiently addressed yet. In distributed systems, optimizing the local throughput can help to improve the overall system. In less resource demanding scenarios, it may be beneficial to use more lightweight local parallelization instead of more complex distributed approaches. We present our work-in-progress on locally parallelizing stream processing on multiple CPU cores and on ways for further improving the local data processing. In order to study the fundamental mechanisms and effects, we focused on pleasingly parallel workloads. While pleasingly parallel tasks, by definition, can be easily parallelized, our results show that stream processing adds important aspects and that the outcomes strongly vary depending on use case and parallelization approach. Furthermore, we present early stages of a stream transformation Domain Specific language and of a self-adaptive mechanism for easing and optimizing the processing. We published our implementations as Open Source Software.

关键词： Pipelines Data processing throughput Central processing Unit DSL Parallel processing Concurrent computing

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Sign language Translator using the Back Propagation Algorithm of an MLP

Sign Language Translator using the Back Propagation Algorith...

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IEEE international conference on Future Internet of things and Cloud Workshops (FiCloudW)

作者： Gaby Abou Haidar Roger Achkar Dian Salhab Antoine Sayah Fadi Jobran American University of Science and Technology Beirut Lebanon Department of Computer and Communications Engineering American University of Science and Technology AUST Beirut Lebanon American University of Science and Technology

ISBN: (数字)9781728144115

ISBN: (纸本)9781728144122

Although technology has been evolving rapidly in this information age, deaf/mute people still use sign language as their only way of communication. Using sign language as a communication tool can be beneficial among those who are familiar with this language, but the problem remains when communicating with the wider community. Sign language Translator is the appropriate solution that enables deaf/mute people to communication fluently through technology in different languages. It is a combination of two gloves with the required sensors and a smart phone that contains the mobile application that translates the hand gestures into spoken words, is based on the Lebanese Sign language; Artificial Neural Networks plays a big role in releasing the exact output.

关键词： backpropagation data gloves handicapped aids language translation mobile computing multilayer perceptrons natural language processing sign language recognition smart phones Smartphone Handicapped accessibility data gloves back propagation handicapped aids Multilayer perceptrons Natural language processing Mobile Computing interpreters sign language recognition CSRP3 gene Communication tools translator

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Feeding Two Birds with One Scone: Using AWE to Enhance Writing and Creativity among Pre-university Students

Feeding Two Birds with One Scone: Using AWE to Enhance Writi...

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international conference on information Science and technology (ICIST)

作者： Norhazwanie Jatin Hafizoah Kassim Nor Yazi Khamis Asiah Kassim Centre for Modern Languages Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia International Islamic University Malaysia Centre for Languages and PreUniversity Academic Development Gambang Pahang Malaysia

the artificial intelligence (AI) revolution has redefined the practices of teaching and learning writing in academia. AI tools being so accessible to students appear to have desirable effects for self-learning, yet they are also capable of veering them off course. this paper is a pilot study to reveal the potency of employing Automated Writing Evaluation (AWE), an AI-based tool with the application of natural language processing (NLP) to analyse and generate feedback on various aspects of writing in English language writing instructions to improve writing proficiency and creative performance. Pre-test for writing and creativity were administered to eleven CEFR B1 second language learners. Upon using an AWE tool as formative assessment, post-tests and interview protocol were conducted to obtain a richer analysis of the data. the results indicate improvements in writing and creativity scores. the participants believed that the use of AWE particularly improved their writing proficiency due to these features: unlimited revision, interactivity, immediate feedback. this study serves as a springboard for writing teachers to incorporating AI tools in their writing lessons while acknowledging the potential of creativity to be stimulated in those lessons.

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Comparative Study of Data Reduction Methods in Electrical Impedance Tomography For Hand Sign Recognition

Comparative Study of Data Reduction Methods in Electrical Im...

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international conference on advanced Technologies for Signal and Image processing (ATSIP)

作者： Bilel Ghoul Bilel Ben Atitallah Salwa Sahnoun Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Ons City Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Several studies have shown that electrical impedance tomography (EIT) on the human forearm can be used to classify hand signs without the need for cameras or gloves. However, the amount of measured data is in relatively high, leading to long execution times and high processing complexity. In this study, we investigate the influence of reducing the number of EIT measurements on the classification accuracy. Four different algorithms were used to halve the number of measurements, namely Principal Component Analysis (PCA), Chi-square tests, Minimum Redundancy Maximum Relevance (MRMR) and Laplacian scores. these algorithms were applied to a dataset of forearm EIT measurements corresponding to the practice of American Sign language (ASL) in a healthy subject. the results show a slight drop in accuracy when using the quadratic support vector machine (SVM) classifier, with accuracy dropping from 95.3% to 93.4% in the best cases. A method based on a genetic algorithm (GA) was also used. this approach exploits the strengths of genetic algorithms in exploring a large solution space and converging on the near-optimal combination. this approach resulted in an increase in accuracy to 96.7%. the study demonstrated that the number of EIT measurements can be reduced without compromising overall accuracy.

关键词： Support vector machines Electrical impedance tomography Sign language Accuracy Image recognition Laplace equations Assistive technologies

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Enhancing the Automatic Identification of Common Math Misconceptions Using Natural language processing 24th

Enhancing the Automatic Identification of Common Math Miscon...

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

作者： Gorgun, Guher Botelho, Anthony F. Univ Alberta Edmonton AB T6G 2G5 Canada Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9783031363351;9783031363368

In order to facilitate student learning, it is important to identify and remediate misconceptions and incomplete knowledge pertaining to the assigned material. In the domain of mathematics, prior research with computer-based learning systems has utilized the commonality of incorrect answers to problems as a way of identifying potential misconceptions among students. Much of this research, however, has been limited to the use of close-ended questions, such as multiple-choice and fill-in-the-blank problems. In this study, we explore the potential usage of natural language processing and clustering methods to examine potential misconceptions across student answers to both close- and open-ended problems. We find that our proposed methods show promise for distinguishing misconception from non-conception, but may need further development to improve the interpretability of specific misunderstandings exhibited through student explanations.

关键词： misconceptions sentence-BERT intelligent tutoring system natural language processing

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A Directional Recognition Algorithm of Semantic Relation for Literature-Based Discovery

A Directional Recognition Algorithm of Semantic Relation for...

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7th international conference on Swarm Intelligence (ICSI)

作者： Liu, Xiaoyong Fu, Hui Jiang, Chaoyong Guangdong Polytech Normal Univ Dept Comp Sci Guangzhou 510665 Guangdong Peoples R China

ISBN: (纸本)9783319410098;9783319410081

Literature-Based Discovery (LBD), a kind of knowledge discovery algorithm, is proposed by Don R. Swanson, which can assist the researchers to recognize implicit knowledge connection and further accelerate the generation of new knowledge. However, most of algorithms in the field of LBD mainly start from the co-occurrence of terms to find connections between terms, and barely consider the semantic relation actually existing between pairs of terms. In this paper, a kind of directional recognition algorithm of semantic relation is put forward to recognize the directionality of semantic relation existing between pairs of terms. this algorithm will automatically judge the direction of semantic relation based on WordNet and JWNL. the numerical experiment results have indicated that the algorithm proposed in this paper can well recognize the directionality of the semantic relation.

关键词： Data mining Natural language processing Semantic relation WordNet JWNL

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A framework of Universiti Kebangsaan Malaysia patent: UKM patent

A framework of Universiti Kebangsaan Malaysia patent: UKM pa...

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international conference on advanced Communication technology (ICACT)

作者： Rosilah Hassan Abdullah A. Al-khatib Wan Mohd Hirwani Wan Hussain Faculty of Information Science and Technology (FTSM) Research Centre for Software Technology and Management (SOFTAM) Universiti Kebangsaan Malaysia Bangi Selangor MY

ISBN: (纸本)9781509048922

A Meta-Search engine is an ideal tool that takes search expressions and sends them out to multiple search engines. It then returns the categorized results to the user. For example, we established a Universiti Kebangsaan Malaysia (UKM) Patent meta-search engine that takes patents from multiple search engines, such as Google patent and Patent Mall. this website allows UKM students to research patents inside Patent Mall, the UKM database and the Google patent search engine. this study focuses on web design and its development using several tools and programming languages, such as open source *** and SQL Server Database. A few of these are used to retrieve information from search engines, for example, the Google patent application programming interface (API) and Post Query to Patent Mall. In this project, we establish a new framework for the first meta-search engine that specialises in patents, which will minimize useless pages in search results. Additionally, the UKM database contains private university UKM patents. the UKM patent meta-search will extract (or retrieve) these patents depending on a language-Integrated Query (LINQ) that is used for conveniently extracting and processing patents from the UKM database. Finally, the UKM meta-search is a new solution to the problem of obtaining requested information quickly.

关键词： Patents Search engines Metasearch Engines Databases Google Graphical user interfaces

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