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检索条件"任意字段=7th International Conference on Advanced Language Processing and Web Information Technology"

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Real Time Fake news Detection web App Enhanced by Machine Learning Algorithms

Real Time Fake news Detection Web App Enhanced by Machine Le...

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Intelligent Technologies (CONIT), international conference on

作者： V Gunasekaran D Karthikeyan School of Information and Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this era, the pervasive proliferation of fabricated news has come to light as a formidable adversary, corroding the credibility of journalistic sources, sowing confusion, and distorting public discourse. To counteract this escalating predicament, this research introduces a pioneering and comprehensive solution-an interactive web application engineered to detect and mitigate the impact of fabricated news. through the strategic integration of sophisticated machine learning algorithms, this platform not only identifies spurious news content but also operates in real time, enabling swift responses to the proclamation of misinformation. the key aim of this study is to elevate the precision and efficiency of fabricated news identification. this is achieved through a meticulously devised amalgamation of advanced natural language processing (NLP) techniques, meticulous feature engineering strategies, and the robust framework of ensemble learning methods. the fusion of these techniques equips the system with an arsenal of tools to scrutinize news story, disentangling the intricate web of linguistic nuances that often obscure deceptive content. the real-time facet of the application is pivotal, ensuring that the detection process transpires in sync with the constant influx of news story across digital platforms. this immediacy minimizes the potential amplification of false narratives, thereby curbing the negative impact of fabricated news proclamation. the heart of the system lies in its fabricated news detection capability, fortified by the intricacies of modern machine learning. By training on vast repositories of verified news data and contrasting it with known instances of false information, the algorithms learn to recognize patterns that distinguish authentic news from falsified narratives. Accessed through standard web browsers, the web application interface offers an intuitive and accessible medium for users to engage with the detection process.

关键词： Training Heart Machine learning algorithms Linguistics Real-time systems Natural language processing Pattern recognition

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Implementation Analysis of Encryption and Decryption Algorithm Based on python language 22

Implementation Analysis of Encryption and Decryption Algorit...

引用

Proceedings of the 2022 7th international conference on Systems, Control and Communications

作者： Kai Xiao Wuhan Railway Vocational College of Technology China

ISBN: (纸本)9781450397247

Facing the Internet information transmission of WAN or LAN, it is difficult to ensure the security and stability of data transmission after key encryption by using traditional one-way password encryption and two-way stream password encryption. therefore, a hybrid encryption algorithm of RSA, DES and BASE64 developed based on python language is proposed, and the encryption and decryption scheme of information transmission between network client and client, client and server is set up, including the encryption and decryption implementation of digital certificate accuracy, server registration, user login and communication, file transmission, password modification and log processing, etc., to improve the encryption and decryption security level of data file transmission and log processing, and to maximize the protection of network instant messaging Security and stability.

关键词： RSA algorithm Implementation of encryption and decryption DES algorithm python language

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Quality of information for data fusion in net centric publish and subscribe architectures

Quality of information for data fusion in net centric publis...

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international conference on information Fusion

作者： M.E. Johnson K.C. Chang Advanced Engineering and Sciences ITT Industries Reston VA USA Department of SEOR George Mason University Fairfax VA USA

this paper examines data fusion and target tracking issues involved within net centric publish and subscribe architectures with respect to the quality of information (QOI) provided to the end user. these architectures are commonly selected in shared knowledge environments to enable access to all information by all users. DoD, DOJ, FAA, and other government organizations are pushing net centric concepts to move from stovepiped, closed architectures with limited data accessibility toward open architectures where dissemination of new data sources is intrinsically supported. Although these net centric initiatives (such as DoD's GIG) promise tremendous aggregate bandwidth, sensor-to-user bandwidth, end-user processing power and storage capacity, and other real world issues are expected to impact operations. For example, the soldier of the future may require moving target updates on a bandwidth-limited wireless PDA. this paper examines how the Air Force Research Laboratory's Joint Battlespace Infosphere (JBI), a publish-and-subscribe system in conjunction with the OGC's sensor web enablement (SWE) initiative, can be used to update clients in a quality-of-information (QOI) paradigm rather than a quality-of-service (QOS) paradigm. We conclude that net centric architectures can support operational flows that improve the quality of information while reducing resource usage over traditional stovepipe systems. We also conclude that net centric tool implementations should specifically address QOI.

关键词： Quality of service Bandwidth Communication channels Delay Data engineering Target tracking FAA Government Aggregates Wireless sensor networks

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CDSCREM: A Cross-Domain Relation Extraction Model Integrating Syntax and Context

CDSCREM: A Cross-Domain Relation Extraction Model Integratin...

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

作者： Nan Zhang Yinchong Li School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Cross-domain relation extraction, an emerging research task, evaluates the generalization ability of relation extraction models across different domains. However, existing studies lack a comprehensive and efficient model. To address this gap, this study proposes the Cross-Domain Syntactic and Contextual Relation Extraction Model (CDSCREM), which enhances cross-domain relation extraction by combining syntactic structure and contextual information. CDSCREM employs a domain aware syntactic graphical encoder, analyzing syntactic dependencies between entities using graph convolutional networks (GCN) to extract domain-specific syntactic information. Additionally, the model introduces an adaptive multidimensional attention mechanism to dynamically adjust attention to syntax and context, enhancing multidimensional data processing. the model also integrates a gated recurrent unit (GRU) for bi-directional context fusion, effectively combining forward and backward contextual information to enhance text-dependent capture. Results from the CrossRE 1.0 and 2.0 datasets demonstrate that CDSCREM significantly outperforms the baseline and other existing models across several test domains. Ablation experiments further confirm the key contribution of each component to the model's performance.

关键词： Adaptation models Attention mechanisms Accuracy Syntactics Logic gates information retrieval Natural language processing Data mining Context modeling Testing

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Semantic Topic Extraction from Research Artifacts 23

Semantic Topic Extraction from Research Artifacts

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Proceedings of the 2023 7th international conference on Computer Science and Artificial Intelligence

作者： Daiveek Sai P Anouksha Rajesh IIT Kharagpur India SRM University India

ISBN: (纸本)9798400708688

the GENESIS project introduces a novel framework for Research Artifact Data Semantics to enhance semantic web technologies within academic institutions. this paper introduces a system developed as part of the GENESIS challenge, focusing on extracting and labelling topics from diverse research artifacts. the approach employs knowledge graphs, topic models, and named entity recognition to infer meaningful topics. Evaluation encompasses measures such as topic coherence and semantic similarity. Future work includes refining entity linking techniques, optimizing system performance, and implementing advanced graph centrality algorithms. this contribution advances semantic understanding in various research contexts.

关键词： knowledge graph linked data natural language processing text summarization topic modelling

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Facilitating Comprehension: An NLP-Based Converter for 7th Grade Students to Transform English Sentences into Mathematical Expressions

Facilitating Comprehension: An NLP-Based Converter for 7th G...

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IEEE international conference on System Engineering and technology

作者： Paula Jean C. Mendoza Jasmin A. Caliwag Roxanne A. Pagaduan Gerald T. Cayabyab Hazel San L. Patilano John Michael F. De Borja College of Computer Studies Technological Institute of the Philippines Quezon City Metro Manila Philippines

Based on research findings, a significant obstacle for students in learning mathematics is their difficulty understanding word problems. Converting verbal explanations into mathematical equations becomes difficult, especially when several procedures are necessary. thus, this study developed a supplemental tool for grade 7 students that is designed to assist them in effectively converting English language phrases into mathematical expressions, thereby enhancing their learning experience. this study encompasses both a web application and a translation algorithm. the web application is an educational tool that delivers content that teaches the mathematical concept of “Translating English Phrases and Sentences into Mathematical Equations and Statements”. Meanwhile, the translation algorithm, developed using Stanza, a Python Natural language processing (NLP) package, is tasked with transforming given English mathematical phrases into their corresponding mathematical expressions. the system was created using the Rapid Application Development (RAD) software development model to implement progressive improvements within three development iterations, and it was then assessed for functional suitability, usability, and reliability using the ISO/IEC-25010 System and software quality model. the outcomes of the survey evaluation revealed that the system proved to be fitting for learning math phrase translations. It proved dependability, convenience, and usability. Furthermore, the algorithm’s precision was evaluated, with 175 predictions yielding an accuracy rate of 81.14%. In summary, this study introduces a valuable resource for math educators and learners, enhancing their comprehension of math phrase translations.

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Rating Estimation from Review Texts Using Long Short-Term Memory

Rating Estimation from Review Texts Using Long Short-Term Me...

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IIAI international conference on advanced Applied Informatics (IIAIAAI)

作者： Ryo Takada Teruhisa Hochin Hiroki Nomiya Graduate School of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Sciences Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9781728173986

A lot of reviews of products have been posted on various web sites and services because of the spread of the Internet, and the estimation of ratings from review texts is actively performed. However, there are few such studies on Japanese review texts without limiting the product genre. In this paper, we propose a neural network model that takes as input a general Japanese product review text and estimates rating for it without limiting the product genre. By using Long Short-Term Memory (LSTM), which is one of the regression type neural network models that can handle sequential data, we analyze words in sentences considering their order. the rating estimation model is realized mainly by segmentation of texts, conversion to distributed representations, an LSTM layer, and a fully connected layer. In addition, we conduct evaluation experiments of the created model and consider the results.

关键词： Limiting Neural networks Bit error rate Estimation Machine learning Data models Natural language processing

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A Joint Model of Entity Recognition and Predicate Mapping for Chinese Knowledge Base Question Answering

A Joint Model of Entity Recognition and Predicate Mapping fo...

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international conference on Behavior, Economic and Social Computing (BESC)

作者： Hongjing Li Lin Li Wuhan University Of Technology Wuhan China

ISBN: (数字)9781728186054

ISBN: (纸本)9781728186061

Knowledge base question answering(KBQA) is the key technology of natural language processing. How to understand the semantic information of the natural language problem and capture the semantic relationship between the problem and the structured triples are the problems that KBQA needs to solve. the boundary of subject entities in Chinese questions is not as clear as English, which increases the difficulty of entity recognition. Besides, the variable Chinese grammar makes predicate mapping more difficult for semantic analysis. Existing KBQA is usually implemented using a pipeline model, which has two disadvantages: (1) Errors caused by entity recognition will be propagated to predicate mapping. (2) Neither entity recognition nor predicate mapping can benefit from the information available to each other. So we propose a BERT-based KBQA to joint entity recognition and predicate mapping tasks that use their dependencies to improve model performance. BERT can solve the semantic ambiguity of the Chinese Q&A databases and improve the accuracy of Chinese Knowledge Base Question Answering(CKBQA). the model achieved an F1 score of 92.04% on the NLPCC 2016 KBQA dataset.

关键词： Social computing Computational modeling Semantics Knowledge based systems Pipelines Natural language processing Task analysis

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MotorIA: Automatic E-Learning Course Generation System 20

MotorIA: Automatic E-Learning Course Generation System

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6th international conference on Computer and technology Applications, ICCTA 2020

作者： Del Carmen Rodríguez-Hernández, María De La Vega Rodrigálvarez-Chamarro, María Vea-Murguia Merck, Jorge I. Ballano, Ángel Esteban Lafuente, Marta Arguedas Del Hoyo-Alonso, Rafael ITAINNOVA María de Luna 7-8 Zaragoza50018 Spain FORTEC Emilia Pardo Bazán 16 Zaragoza50018 Spain

ISBN: (纸本)9781450377492

Recently, through the availability of open data sources and the existence of advanced techniques of Natural language processing (NLP) and Artificial Intelligence, several tools have emerged to support the educational learning process of students and lecturers (experts in different knowledge domains). However, there are very few works which propose tools that automate the e-learning course content development process. Hence, experts in the field of educational training have shown great interest in the emergence of applications that ease this type of process. this paper presents MotorIA, a system which uses NLP and Machine Learning techniques as a means to automate online course content generation from different domains (e.g., computer science, biology, mathematic, etc.). MotorIA is supplied with unstructured information extracted from both Wikipedia services and PDF documents. During the e-learning course content generation process, textual information is pre-processed and structured in a knowledge graph, which must satisfy a set of constraints predefined by experts in the field. In a posterior phase, MotorIA was validated by expert users obtaining good evaluation results. © 2020 ACM.

关键词： Knowledge graph

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Explainable Artificial Intelligence Techniques for Image Classification Models in Diverse Domains

Explainable Artificial Intelligence Techniques for Image Cla...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Pranjali P. Joshi Anant M. Bagade Department of Computer Engineering SCTR's Pune Institute of Computer Technology Pune India Department of Information Technology SCTR's Pune Institute of Computer Technology Pune India

Over the past few decades, Deep Learning (DL), which is a subpart of Machine Learning (ML), has gained recognition as the dominant technique in diverse domains such as Computer Vision, Natural language processing, and Image Recognition. However, despite the exceptional performance of DL models, they are often referred to as of “Black Box“ nature, due to their limited ability to explain the decisions. this has led to a need for explainability of DL models. therefore to address this issue of lack of explainability in DL models, Explainable Artificial Intelligence (XAI) techniques have been improving in recent era. Unlike DL, XAI has the ability to provide both decision-making and model explanations for data. this research review comprises articles that utilize XAI techniques for image classification tasks. the purpose of this paper is to showcase the utility of the most recent XAI techniques employed in Image Classification Models (ICM). this paper will assist researchers to develop a better intepretation of XAI techniques for image classification as well as related challenges and future research directions.

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