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arXiv 2023年

作者： Nagy, Attila Lakatos, Dorina Barta, Botond Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungary

Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a novel augmentation method, which generates new sentences by swapping objects and subjects across bisentences. This is performed simultaneously based on the dependency parse trees of the source and target sentences. We name this method TreeSwap. Our results show that TreeSwap achieves consistent improvements over baseline models in 4 language pairs in both directions on resource-constrained datasets. We also explore domain-specific corpora, but find that our method does not make significant improvements on law, medical and IT data. We report the scores of similar augmentation methods and find that TreeSwap performs comparably. We also analyze the generated sentences qualitatively and find that the augmentation produces a correct translation in most cases. Our code is available on Github. © 2023, CC BY.

关键词： Neural machine translation

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Innovative Approaches to Arabic Author Identification: A Comprehensive Evaluation of Classical and Deep Learning Approaches 2

Innovative Approaches to Arabic Author Identification: A Com...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Almanaseer, Waref Alsekait, Deema Mohammed Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Abdelaziz, Amr Elsabbagh, Belal Essam, George Nader, Nour Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Riyadh11671 Saudi Arabia Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Benha University Faculty of Computers and Artificial Intelligence Benha Egypt MEU Research Unit Middle East University Amman11831 Jordan

ISBN: (纸本)9798350362633

The amount of written text on the internet has grown exponentially. When pairing that with the difficulties in sourcing the author of a text, a need emerges to be able to verify claimed author of text and attribute anonymous text to its author. These tasks fall under the umbrella of author identification, which is the process of identifying who, among a set of authors, wrote a given text. For many reasons, it is a challenging task in the field of Natural Language Processing (NLP). This paper compares the performance of different machine learning and deep learning models on the task of Arabic author identification between. The results surprisingly show that most models failed except for the feedforward neural network and Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Arabic Deep Learning Machine Learning Natural Language Processing

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A Survey on Image Captioning Using Object Detection and NLP

A Survey on Image Captioning Using Object Detection and NLP

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Advanced Research in Computing (ICARC), International Conference on

作者： Vathila De Silva T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo 06 Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

Recent years have seen the emergence of image captioning as a revolutionary technical development that seamlessly blends computer vision and natural language processing. The integration of various domains facilitates the production of captivating captions for pictures, promoting a more profound comprehension of visual material. This study offers a thorough analysis of the rapidly developing field of image captioning, examining its uses in various settings such as social media, online platforms, assistive technology, and content indexing. It emphasizes the critical importance of advanced techniques like You Only Look Once (YOLO) for accurate item detection and Natural Language Processing (NLP) for creating subtle captions. While NLP gives the resulting text a layer of contextual depth, YOLO guarantees precise object detection, which helps with caption accuracy. Using these cutting-edge methods boosts the overall efficacy of image captioning systems and represents a noteworthy trend in the literature. Examining the evolution of image captioning, the review paper highlights how important it is for multimodal comprehension. Image captioning is a transformative tool that enhances the readability of visual content and influences contemporary digital experiences in various contexts. The development of image captioning is summarized in this abstract, which also highlights the tendency to use cutting-edge techniques for more accurate and nuanced caption production.

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Missing Data Imputation: A Comprehensive Review

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Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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Integrated Smart Parking System Using Internet of Things (IoT) and Digital Image Processing with Faster Region Based Convolutional Neural Network (Faster R-CNN) and Optical Character Recognition (OCR)

Integrated Smart Parking System Using Internet of Things (Io...

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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Fahrurrozi Lubis Erna Budhiarti Nababan Pauzi Ibrahim Nainggolan Mohammad Fadly Syah Putra Romi Fadillah Rahmat Riza Sulaiman Albert Lukas Talupan Pangaribuan Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Institute of Visual Informatics Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

Parking is an integral part of driving, especially in urban areas. However, inefficient parking systems often cause drivers to spend a significant amount of time just to find a parking spot, even up to 10 hours per month in major cities like Jakarta. Besides wasting time, prolonged parking searches also increase fuel consumption and CO2 emissions. This study aims to develop smart parking application integrated with Internet of Things and computer Vision. The system uses ultrasonic sensors connected to an ESP32 microcontroller to detect real-time parking slot availability, sending status updates to Firebase. Additionally, Faster Region Based Convolutional Neural Network (Faster R-CNN) and Easy OCR technologies are used to detect and identify vehicle license plates and parking slot markers, ensuring consistency between reservations and actual parking slot usage. The research findings indicate that the sensors system has the fastest measurement time of 106.3 milliseconds and data transmission time of 73.6 milliseconds. The Faster R-CNN model shows and accuracy of 86% for detecting license plates and parking slot markers, while EasyOCR achieves 100% accuracy in recognizing the characters of license plates and parking slot markers. The average total time for inference and character extraction is 2.7 seconds. This integrated approach provides significant solution for both users and parking vendor.

关键词： Accuracy Microcontrollers Urban areas Time measurement Sensor systems Real-time systems Internet of Things Data communication Optical sensors License plate recognition

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Exploiting Direct Link in RIS-Aided Two-Way Communications with Traffic Asymmetry

Exploiting Direct Link in RIS-Aided Two-Way Communications w...

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International Conference on Radioelektronika

作者： Suneel Yadav Radhika Gour Devendra S. Gurjar Juraj Gazda Department of ECE Indian Institute of Information Technology Allahabad Prayagraj India Department of ECE National Institute of Technology Silchar Assam India Department of Computer and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

In this paper, we examine a two-way communication system empowered by a reconfigurable intelligent surface (RIS), where two half-duplex nodes communicate to each other through an RIS with $N$ reflective elements and a direct link. To exploit the advantage of direct link, the overall information exchange between two end nodes takes two time phases. By considering different target rates at two-end nodes in view of traffic asymmetry, and different powers at two-end nodes, we present the overall outage probability (OOP) analysis for the considered system in a Rayleigh fading environment. To explore further insights into the system's performance, we perform an asymptotic outage analysis in the high signal-to-noise ratio regime. This analysis reveals that the system's diversity order does not depend on the target rates of two end nodes, and only varies with the number of RIS's reflecting elements. Lastly, the derived expressions are verified via simulation studies, and reveal the impact of various parameters on the OOP performance.

关键词： Communication systems System performance Half-duplex system Rayleigh channels Reconfigurable intelligent surfaces Probability Power system reliability Information exchange Signal to noise ratio

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Ant Colony Optimization for Service Composition in Multi-cloud Environments 15

Ant Colony Optimization for Service Composition in Multi-clo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Maurya, Mahesh Vidhya, G. Akram, Faiz Saravanan, M. Venkata Ramana, K. Rajakumari, S. K.C. College of Engineering Department of Computer Engineering Mith Bunder road Thane East Maharashtra Kopri India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Jimma Institute of Technology Jimma University Faculty of Computing and Informatics Department of Computer Science Oromia Region Jimma Ethiopia Sri Eshwar College of Engineering Department of Ece Tamilnadu Coimbatore India Qis College of Engineering and Technology Department of Cse Andrapradesh Ongole India R.M.D. Engineering College Department of Mathematics India

ISBN: (纸本)9798350370249

the Ant Colony Optimization (ACO) algorithm is a famous metaheuristic technique that has been successfully applied in various optimization issues. It mimics the foraging conduct of ants to locate the shortest path among meals assets and their colony. Recently, ACO has been increasingly more applied for provider composition in multi-cloud environments. This involves the composition of offerings from different cloud carriers to satisfy the service necessities of a person. By way of using ACO, services can be correctly selected and composed from a couple of carriers, considering elements including cost, excellent, and availability. ACO has proven promising outcomes in enhancing the overall performance and reliability of carrier composition in multi-cloud environments, making it a precious technique in todays tremendously distributed computing landscape. © 2024 IEEE.

关键词： Distributed cloud

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Hybrid Approaches to Emotion Recognition: A Comprehensive Survey of Audio-Textual Methods and Their Application

Hybrid Approaches to Emotion Recognition: A Comprehensive Su...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sahan Hewage Wewelwala T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo 06 Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

This survey study provides a comprehensive analysis of emotion recognition, documenting its progression from conventional techniques to innovative hybrid algorithms that effectively combine textual and audio information. This work distinguishes itself with its thorough examination of the innovative combination of deep learning-based textual sentiment analysis and sophisticated aural signal processing approaches. This unique combination greatly improves the precision of emotion recognition from intricate sources like social media and consumer feedback. The study addresses the difficulties of processing real-time data and reducing bias in varied datasets. It provides new perspectives on the powers and limitations of neural networks and machine learning in this specific scenario. Our research represents a notable advancement in the utilization of these technologies for human-computer interaction, facilitating the creation of digital interfaces that are more empathic and attuned to human emotions. The final statements underscore the pressing requirement for sophisticated multimodal methodologies and the incorporation of developing technology, underscoring our distinctive contribution to the progress of emotion identification systems. This report offers a thorough analysis of the present condition of the subject and outlines a direction for future advancements, highlighting the crucial significance of emotion identification in improving human-computer interaction.

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A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation

arXiv

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arXiv 2024年

作者： Abeysiriwardana, Miuru Sumanathilaka, Deshan School of Computing Informatics Institute of Technology Colombo06 Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models. Focusing extensively on Word Sense Disambiguation (WSD), it outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet. The paper introduces cutting-edge methodologies like word sense extension (WSE) and neuromyotonic approaches, enhancing disambiguation accuracy by predicting new word senses. It examines specific applications in biomedical disambiguation and language-specific optimisation and discusses the significance of cognitive metaphors in discourse analysis. The research identifies persistent challenges in the field, such as the scarcity of sense-annotated corpora and the complexity of informal clinical texts. It concludes by suggesting future directions, including using large language models, visual WSD, and multilingual WSD systems, emphasising the ongoing evolution in addressing lexical complexities in NLP. This thinking perspective highlights the advancement in this field to enable computers to understand language more accurately. © 2024, CC BY.

关键词： Natural language processing systems

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TripTractix: Optimizing Outing Planning with Advanced Computational Techniques

TripTractix: Optimizing Outing Planning with Advanced Comput...

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International Conference on Information technology Research (ICITR)

作者： Faizan Muthaliff T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo 06 Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Event planning and travel applications often lack critical medical and travel-related information, despite the need to consider factors like weather and infectious disease risks, as highlighted during the COVID-19 pandemic. In Sri Lanka, there is also a scarcity of travel applications that fully utilize Natural Language Processing (NLP) to handle dynamic user requests. To bridge these gaps, this work proposes an outing application that leverages NLP to generate optimal travel recommendations. The system uses Named Entity Recognition (NER) and semantic analysis to extract keywords from user inputs, mapping activity preferences with the help of Large Language Models (LLMs) and zero-shot classification techniques. Recommendations are ranked using the TOPSIS model, while location data is integrated from the Google Place API. This approach aims to deliver personalized, data-driven outing suggestions by combining NLP, advanced recommendation algorithms, and LLMs.

关键词： Infectious diseases Pandemics Semantics Personal voice assistants Named entity recognition Natural language processing User experience Real-time systems Planning Meteorology

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