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Improving Grammatical Error Correction for Multiword Expressions 18

Improving Grammatical Error Correction for Multiword Express...

18th Workshop on Multiword Expressions, MWE 2022 in conjunction with the 13th Language Resources and Evaluation Conference, LREC 2022

作者： Taslimipoor, Shiva Bryant, Christopher Yuan, Zheng ALTA Institute Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics King’s College London United Kingdom

ISBN: (纸本)9791095546900

Grammatical error correction (GEC) is the task of automatically correcting errors in text. It has mainly been developed to assist language learning, but can also be applied to native text. This paper reports on preliminary work in improving GEC for multiword expression (MWE) error correction. We propose two systems which incorporate MWE information in two different ways: one is a multi-encoder decoder system which encodes MWE tags in a second encoder, and the other is a BART pre-trained transformer-based system that encodes MWE representations using special tokens. We show improvements in correcting specific types of verbal MWEs based on a modified version of a standard GEC evaluation approach. © European Language Resources Association (ELRA).

关键词： Error correction

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm 15

Psychological Stress Classification Using Extreme Gradient B...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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Help! I need somebody. A Mapping Study about Expert Identification in Software Development 23

Help! I need somebody. A Mapping Study about Expert Identifi...

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37th Brazilian Symposium on Software Engineering, SBES 2023, held in conjunction with the Brazilian Conference on Software: Theory and Practice, CBSoft 2023

作者： Braga, Carlos Santos Jr, Paulo Barcellos, Monalessa Computer Science Department Federal University of Espírito Santo ES Vitoria Brazil Leds Research Department of Informatics Federal Institute of Education Science and Technology of Espírito Santo ES Serra Brazil

ISBN: (纸本)9798400707872

Context: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. Objective: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. Method: We carried out a systematic literature mapping and analyzed 17 publications. Results: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. Conclusion: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification. © 2023 ACM.

关键词： Knowledge management

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Big data link stability-based path observation for network security

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data Engineering and Communications Technologies 2021年 66卷 459-468页

作者： Arappali, Nedumaran Mekonnen, Melaku Tamene Tefera, Wondatir Teka Sundaram, B. Barani Karthika, P. Department of Electrical and Computer Engineering Wollo University Kombolcha Institute of Technology Kombolcha Ethiopia Department of Computer Science College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Application Kalasalingam Univerity Viruthunagar Tamilnadu India

Wireless ad hoc network [WANET] systems are creating multihop communication structure between the mobiles to transfer the data groups. The remarkable qualities of remote frameworks cause the correspondence to interface among the conflicting mobiles. To manage high convey ability and biological blocks, most physical directing shows will not believe the stable associations during pack communication, which prompts the elevated delay and bundle reducing in the master-mind. The proposed research work recommends a way perception support physical steering convention that specifies POPR for WANET. The anticipated guiding show merges the relative partition, course and mid-expand forwarder center point with transfer thickness to propel data toward the objective in order to recover physical sending among the connection point. Multiplication results illustrate the projected directing convention, which performs superior to the existing arrangements. © The Author(s).

关键词： Routing protocols

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A REPRODUCIBILITY STUDY ON USER-CENTRIC MIR RESEARCH AND WHY IT IS IMPORTANT 23

A REPRODUCIBILITY STUDY ON USER-CENTRIC MIR RESEARCH AND WHY...

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23rd International Society for Music Information Retrieval Conference, ISMIR 2022

作者： Knees, Peter Ferwerda, Bruce Rauber, Andreas Strumbelj, Sebastian Resch, Annabel Tomandl, Laurenz Bauer, Valentin Tang, Fung Yee Bobinac, Josip Ceranic, Amila Dizdar, Riad Faculty of Informatics TU Wien Austria Department of Computer Science and Informatics Jönköping University Sweden School of Music Georgia Institute of Technology United States

ISBN: (纸本)9781732729926

Reproducibility of results is a central pillar of scientific work. In music information retrieval research, this is widely acknowledged and practiced by the community by re-implementing algorithms and re-validating machine learning experiments. In this paper, we argue for an increased need to also reproduce the results and findings of user studies, including qualitative work, especially since these often lay the foundations and serve as justification for choices taken in algorithmic design and optimization criteria. As an example, we attempt to reproduce the study by Kim et al. [1] presented in the RecSys (2020) paper ªDo Channels Matter? Illuminating Interpersonal Influence on Music Recommendationsº. By repeating this study on how interpersonal relationships can affect a user’s assessment of music recommendations on a new sample of n = 142 participants, we can largely confirm and support the validity of the original results. At the same time, we extend the analysis and also observe differences with regards to adoption rates between different channels as well as different factors that influences the adoption rate. From this specific reproducibility study, we conclude that potential cultural differences should be accounted for more explicitly in future studies and that systems development should be more explicitly connected to its intended target audience. © P. Knees, B. Ferwerda, A. Rauber, S. Strumbelj, A. Resch, L. Tomandl, V. Bauer, F. Y. Tang, J. Bobinac, A. Ceranic, and R. Dizdar.

关键词：

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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On Learning Adaptive Service Compositions

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Journal of Systems Science and Systems Engineering 2021年第4期30卷 465-481页

作者： Ahmed Moustafa Department of Computer Science Nagoya Institute of TechnologyGokiso-choShowa-kuNagoyaAichi466-8555Japan Faculty of Informatics Zagazig UniversityZagazigEgypt

Service composition is an important and effective technique that enables atomic services to be combined together to forma more powerful service,i.e.,a composite *** the pervasiveness of the Internet and the proliferation of interconnected computing devices,it is essential that service composition embraces an adaptive service provisioning *** learning has emerged as a powerful tool to compose and adapt Web services in open and dynamic ***,the most common applications of reinforcement learning algorithms are relatively inefficient in their use of the interaction experience data,whichmay affect the stability of the learning process when deployed to cloud *** particular,they make just one learning update for each interaction *** paper introduces a novel approach that aims to achieve greater data efficiency by saving the experience data and using it in aggregate to make updates to the learned *** proposed approach devises an offline learning scheme for cloud service composition where the online learning task is transformed into a series of supervised learning tasks.A set of algorithms is proposed under this scheme in order to facilitate and empower efficient service composition in the cloud under various policies and different *** results of our experiments show the effectiveness of the proposed approach for composing and adapting cloud services,especially under dynamic environment settings,compared to their online learning counterparts.

关键词： Service composition reinforcement learning cloud services offline learning

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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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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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