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Measuring Text-to-SQL Semantic Parsing Model on the Question Generalizability 22

Measuring Text-to-SQL Semantic Parsing Model on the Question...

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Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： thanakrit Julavanich Akiko Aizawa Department of Computer Science Graduate School of Information Science and Technology The University of Tokyo Japan Aizawa Laboratory National Institute of Informatics Japan and Department of Computer Science Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9781450397629

One of the challenges in NLP tasks, such as text-to-SQL semantic parsing, is generalization. In the text-to-SQL task, having separate training and testing data can measure one aspect of the generalization: how well the model generalizes to unseen databases. Other aspects, however, remain unaccounted for. We propose a new dataset and a more challenging and thorough evaluation process that focuses on the two challenges of generalizing the text-to-SQL model: database content references and question patterns. We create SPIDER-QG, an augmented dataset that employs three techniques, to assess generalizability. First, we replace the set of values in the existing test set with other values from the same column in the same database. Second, we use the synonym of each value as a replacement instead. third, we generate new questions for the existing SQL query by back-translating the original question. Our evaluation setup demonstrates the generalization challenges and struggles of the current models.

关键词： datasets model generalizability text-to-SQL

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Generating Bangla Image Captions with Deep Learning Techniques

Generating Bangla Image Captions with Deep Learning Techniqu...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Md. Anwar Hossain Mirza AFM Rashidul Hasan Sajeeb Kumar Ray Naima Islam Dept of Information and Communication Engineering Pabna University of Science and Technology Bangladesh Dept of Information and Communication Engineering University of Rajshahi Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

the importance of BangIa image captioning is rooted in the need to bridge the gap between visual content and textual descriptions in Bengali, thereby facilitating more accessible and inclusive technologies for Bengali-speaking individuals. Image captioning entails the creation of textual descriptions for images through the application of deep learning techniques that combine methodologies from computer vision and natural language processing to accurately recognize and depict visual content. this study presents an innovative methodology that employs EfficientNetB4 and ResNet-50 architectures for the extraction of features. the selection of these models followed a comprehensive evaluation of numerous alternatives, as they exhibited exceptional performance, rendering them well-suited for the objectives of this investigation. A pivotal contribution of this analysis is the introduction of the newly established BangIa View dataset, designed specifically for BangIa captioning tasks, used alongside the widely recognized Flickr30k dataset. the empirical results reveal that EfficientN etB4 surpasses ResN et-50, attaining a BLEU score of 0.54 after merely 10 training epochs, underscoring its efficacy in generating coherent BangIa captions. these outcomes not only illustrate technological progress but also highlight the potential of this methodology to stimulate innovations that promote linguistic diversity and improve user experiences in domains such as accessibility and digital communication.

关键词： Deep learning Training Visualization Technological innovation Computational modeling Neural networks Computer architecture Feature extraction Rendering (computer graphics) Natural language processing

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PHONE-DURATION-BASED CONFIDENCE MEASURES FOR EMBEDDED APPLICATIONS

PHONE-DURATION-BASED CONFIDENCE MEASURES FOR EMBEDDED APPLIC...

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6～(th) international conference on Spoken language processing

作者： Silke Goronzy Krzysztof Marasek Ralf Kompe Andreas Haag Sony International (Europe GmbH Advanced Technology Center Stuttgart (ATCS Home Network Company Europe Man Machine Interfaces Hedelfinger Str. 61 D-70327 StuttgartvGermany Sony International (Europe GmbH Advanced Technology Center Stuttgart (ATCS Home Network Company Europe Man Machine Interfaces Hedelfinger Str. 61 D-70327 StuttgartvGermany Sony International (Europe GmbH Advanced Technology Center Stuttgart (ATCS Home Network Company Europe Man Machine Interfaces Hedelfinger Str. 61 D-70327 StuttgartvGermany Sony International (Europe GmbH Advanced Technology Center Stuttgart (ATCS Home Network Company Europe Man Machine Interfaces Hedelfinger Str. 61 D-70327 StuttgartvGermany

In order to detect misrecognitions that may result from a mismatch between training and testing data. we use a confidence measure (CM) that collects a set of features during recognition and from the N-best list that is output by the recognizer. A neural network (NN) then calculates the probability that the utterance was recognized correctly based on these features. Since for misrecognized utterances the resuiting phoneme alignments are often erroneous, we introduced some new features that are based on phoneme durations. the durations found by the recognizer are compared to the durations present in the training data base and the resuits of these comparisons serve as input for the NN. A great advantage of the duration-related features is that they are independent of the recognizer in contrast to e.g. acoustic scorebased features. We also use some score-related fearures that have proven to be useful in the past. Simultaneously with determining the confidence for a recognition resilt, we try to detect if in case of a misrecognition the utterance was an out of vocabulary (OOV) utterance. Using the complete set of 46 features we can achieve a correct classification rate of 90%. the word error rate can be reduced by 92% at a false rejection rate of 5.1% on a test task that consists of 35 speakers and includes more than 50% OOV utterances. OOV words were detected correctly in 91% of the cases. the presented CM is also used in a semi-supervised speaker adaptation scheme.

关键词： PHONE-DURATION-BASED CONFIDENCE MEASURES FOR EMBEDDED APPLICATIONS

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Enhancing Bangla-English Code-Mixed Sentiment Analysis with Cross-Lingual Word Replacement and Data Augmentation

Enhancing Bangla-English Code-Mixed Sentiment Analysis with ...

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

作者： Babe Sultana Khondaker A. Mamun Department of Computer Science & Engineering United International University Dhaka Green University of Bangladesh Dhaka

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Sentiment analysis employs natural language processing, computational linguistics, and other textual analysis approaches to locate and extract subjective information to assess the text's conveyed attitude or emotional state. the prevalence of internet-based media and online businesses encourages extensive user engagement. Individuals frequently express their opinions, thoughts, ideas, attitudes, feelings, and more using familiar languages on various social media platforms. the majority of individuals tend to articulate their opinions using a combination of languages, often incorporating English with their native tongue. the lack of annotated code-mixed data poses a significant problem for automated sentiment analysis in languages with limited resources such as Bangla. To tackle this challenge, we aim to gather code-mixed content in Bangla and English. Subsequently, we plan to utilize data augmentation techniques to expand the dataset. Following this, we aim to conduct a comparative analysis by employing a variety of machine-learning techniques. this research delves into four common machine learning techniques—Support Vector Machine (SVM), Decision Tree (DT), Stochastic Gradient Descent (SGD), and Random Forest (RF)—employing feature extraction named TF-IDF methods. the experimental results indicate that Random Forest with TF-IDF gained the highest accuracy, reaching 83%, outperforming other techniques.

关键词： Support vector machines Sentiment analysis Tongue Social networking (online) Media Data augmentation Feature extraction

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Breast cancer patients using mobile applications: An automated biomedical literature curation model (BLCM)

Breast cancer patients using mobile applications: An automat...

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Women in Data Science at Prince Sultan University (WiDS PSU), international conference of

作者： Dina A. Salem Computer and Software Engineering Misr University for Science and Technology (MUST) 6th of October Egypt

Natural language processing (NLP) is increasingly drawing researchers’ attention to mine for valuable knowledge in the massive literature arising in our technological era. Text mining is one of the NLP tools that can take the lead over manual curation of articles from different medical databases. It offers appreciable help by automating the whole information extraction process to get the required information saving effort and time. As Mobile Health (mHealth) for breast cancer patients is showing a clear positive impact on their treatment and recovery journey, text mining techniques can help in the continuous tracking of related research articles to figure out the drawbacks and highlight future directions. therefore, this article proposes a model that initiate a deep search to curate articles dataset using National Center for Biotechnology information (NCBI) database applying five different queries. the model preprocesses the curated dataset to remove outliers and extract the target articles only. In addition, it analyzes the articles’ titles to extract required information. Total number of retrieved articles was 610 that are further reduced to 13 after the applied filtration steps. Two of which are review articles that are concerned with effect of mobile apps on patients undergoing chemotherapy which is a cancer critical stage. the retrieval process took from few seconds to several minutes according to the number of articles related to the entered query, while the preprocessing stage is achieved in one second.

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PDF Fusion: Revolutionizing Document Management with Chat Interfaces and Voice Recognition

PDF Fusion: Revolutionizing Document Management with Chat In...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Jyotsna Vilas Barpute Vanita Kshirsagar Sneha Salunke Suraj Vedpathak Krishna Varma Vidit Solanki Dept. Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

“PDF Fusion” introduces an innovative system that integrates PDF handling, chat interfaces, and voice recognition technologies to revolutionize document management. Recent techniques in document management include advanced OCR (Optical Character Recognition) for text extraction, natural language processing for context understanding, and AI-driven summarization tools. Despite these advancements, challenges such as information overload, complex document structures, and limited interactivity persist. Our proposed system addresses these challenges by offering an intuitive and efficient platform for interacting with multiple PDF files. Key features include multiple file management, voice recognition, PDF summaries, and links to relevant Wikipedia and YouTube content in response to user queries. the objective is to enhance productivity and user experience through seamless navigation, comprehensive search capabilities, and interactive communication via chat interfaces and voice commands. Rigorous empirical evaluations and user testing demonstrate the practical usability and effectiveness of our integrated approach. By bridging the gap between traditional document management systems and modern interactive platforms, “PDF Fusion” significantly advances human-computer interaction, contributing valuable insights into optimizing digital document workflows and improving accessibility and usability across various domains.

关键词： Video on demand Optical character recognition Text summarization Speech recognition Portable document format Natural language processing User experience Internet web sites Usability

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Profiling Fake News Spreader on Twitter Using Stochastic Gradient Descent

Profiling Fake News Spreader on Twitter Using Stochastic Gra...

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

作者： Niladree Datta Muhammad Anwarul Azim Abu Nowshed Chy Mohammad Khairul Islam Sajib Kumar Das Department of Computer Science and Engineering University of Chittagong Chattogram Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

A fake news spreader is a person who deliberately shares misleading information to misguide people. Fake news spreaders can be considered a potential threat to our society. Any misleading information floating in social media can create a serious misconception in society. It can damage the reputation of a person and create cybersecurity threats. To ensure a safe and sound social networking environment for everyone, it is inevitable to detect fake news spreaders. Numerous state-of-the-art approaches have been done to address this issue. they employed several classification algorithms to detect the fake news spreaders. Usually, social media data often lack proper grammar due to noise and informal language. In most of the prior work, the authors did not use the proper text preprocessing techniques which leads to poor performances. To solve this problem, we describe our proposed framework which includes extensive text preprocessing techniques and statistical features along with the stochastic gradient descent (SGD) classifier on top to detect the fake news spreader efficiently. We choose SGD because of its capability to handle large datasets as well as the stochastic phenomenon helps to escape local minima and performs better generalization in complicated optimization scenarios. Experimental results on the benchmark PAN 20 fake news spreader dataset show that our proposed approach outperformed most of the prior works with a 76.50% accuracy thus validating its effectiveness.

关键词： Social networking (online) Noise Stochastic processes Benchmark testing Feature extraction Natural language processing Classification algorithms

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Where event processing grand challenge meets real-time web: PLAY event marketplace 12

Where event processing grand challenge meets real-time web: ...

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Proceedings of the 6th ACM international conference on Distributed Event-Based Systems

作者： Nenad Stojanovic Roland Stühmer Françoise Baude Philippe Gibert FZI Research Center for Information Technology Karlsruhe Germany Université de Nice Sophia-Antipolis France Orange Labs SOP/BIZZ/DIAM Sophia-Antipolis France

ISBN: (纸本)9781450313155

the Grand Challenge in Event processing serves as a common goal and mechanism for coordinating research across the spectrum of people working on event processing. the Grand Challenge is a single, though broad, challenge that impacts society which the community can use as a basis for measuring progress in research and *** this paper we will present an analysis of the recent development in the realization of the Grand Challenge and elaborate on its implementation using the current development in the so called Real-time web - a decentralized, global, Internet-like infrastructure, built upon widely-accepted open *** present a particular solution that has been developed in the scope of the EU project PLAY (***), that is aiming to develop an elastic and reliable platform for dynamic complex event-driven interaction in large highly distributed and heterogeneous federated service systems. the main outcome is an Event Marketplace Platform which offers and consumes real-time data of all sorts and across the whole *** explain the theoretical principles behind the Event Marketplace, discuss the research and technological challenges required for its efficient development, present the implementation and evaluation details and demonstrate several applications that have been built on the top of the Platform so far.

关键词： distributed computing

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Adaptive Learning Tool to Enhance Educational Outcomes for Students with Inattentive Attention Deficit Hyperactivity Disorder (ADHD)

Adaptive Learning Tool to Enhance Educational Outcomes for S...

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Advancements in Computing (ICAC), international conference on

作者： Oshada thawalampola Danuja Jayasuriya Supun Kariyawasam Madushi Makawita Dinuka Wijendra Jenny Joseph Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

this paper presents an adaptive learning tool to enhance educational outcomes for students with inattentive ADHD. the platform comprises four key components: content chunking and simplification with interactive content, an adaptive quizzing system, a personalized ADHD identifier and break recommendation system, and a tool to maximize learning potential. the content chunking component transforms traditional educational materials into ADHD-friendly formats using Natural language processing (NLP) technologies, making them more accessible and engaging. the adaptive quizzing system employs a modified spaced repetition algorithm to optimize memory retention and engagement through personalized quizzes. the personalized ADHD identifier uses a hybrid Convolutional Neural Network (CNN) model to analyze webcam data, tracking head movements and facial emotions to provide real-time break recommendations, enhancing focus and cognitive performance. the final component integrates machine learning and data mining to adaptively assign tasks and provide personalized feedback, fostering a supportive learning environment. Preliminary evaluations indicate significant improvements in academic performance and engagement among ADHD-diagnosed students. this research underscores the potential of adaptive learning technologies in creating inclusive educational environments addressing the unique challenges faced by students with ADHD.

关键词： Adaptive learning Adaptive systems webcams Tracking Transforms Natural language processing Real-time systems Magnetic heads Convolutional neural networks Recommender systems

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PRBJE: A Novel Pre-Relation Bidirectional Joint Extraction Model

PRBJE: A Novel Pre-Relation Bidirectional Joint Extraction M...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Qiang Yu Wenhui Zhou Shuai Dong Dongyuan Li School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Joint extraction of entities and their semantic relationships to form factual triples from unstructured text is a fundamental task in information extraction. However, existing methods either ignore the interactions between relationships and entities or extract the subjects and objects in a fixed sequential order, which makes these approaches too sensitive to the extraction results of subjects. To address the two issues, we propose a pre-relation bidirectional joint extraction model (PRBJE). Specifically, we first detect potential relationships from the sentence to assist in the subsequent extraction of head and tail entities. then, we extract all possible entity pairs maintained in the text using two complementary directions: extracting the tail entity based on the head entity and vice versa. Finally, we assign relationships to all entity pairs by a biaffine model, thereby forming complete relational triples. Extensive experimental results demonstrate that our model outperforms state-of-the-art methods, with improved F1 scores across four public datasets, particularly achieving gains of 0.9 and 0.7 on NYT and webNLG, respectively.

关键词： Semantics Refining Tail Predictive models information retrieval Natural language processing Data mining Artificial intelligence Robots Intelligent control

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