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4th Workshop on Arabic Corpus Linguistics, WACL 2025

作者： Aftiss, Azzedine Lamsiyah, Salima Schommer, Christoph El Alaoui, Said Ouatik Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science FSTM University of Luxembourg Esch-sur-Alzette Luxembourg

ISBN: (纸本)9798891762206

Moroccan Dialect (MD), or "Darija," is a primary spoken variant of Arabic in Morocco, yet remains underrepresented in Natural Language Processing (NLP) research, particularly in tasks like summarization. Despite a growing volume of MD textual data online, there is a lack of robust resources and NLP models tailored to handle the unique linguistic challenges posed by MD. In response, we introduce ***_v2, an expanded version of the *** dataset, containing over 50k articles with their titles across 11 categories. This dataset provides a more comprehensive resource for developing summarization models. We evaluate the application of large language models (LLMs) for MD summarization, utilizing both fine-tuning and zero-shot prompting with encoder-decoder and causal LLMs, respectively. Our findings demonstrate that an expanded dataset improves summarization performance and highlights the capabilities of recent LLMs in handling MD text. We open-source our dataset, fine-tuned models, and all experimental code, establishing a foundation for future advancements in MD NLP. We release the code at https://***/AzzedineAftiss/Moroccan-Dialect-Summarization. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Developing Healthcare Chatbot: Migrant Workers' Study With Design Implication 8

Developing Healthcare Chatbot: Migrant Workers' Study With D...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Jarupreechachan, Weerachaya Surasak, Thattapon Kasetsart University Faculty of Engineering at Kamphaeng Saen Department of Computer Engineering Kamphaeng Saen Campus Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350399646

Migrant workers are often those with low income who are subject to a high risk of virus transmission due to environmental and occupational exposures. The COVID-19 pandemic is reported to exacerbate the existing vulnerabilities of these marginalized groups. This study aims to shed light on how the potential of a conversational agent (Chatbot) could help improve migrant workers' health information seeking and health literacy. The exploration extended to the existing barriers to seeking health information strategies to obtain the needed information during COVID-19. This study conducted an exploratory qualitative study using semi-structured interviews with a scenario-based approach to study the experiences of the migrant Thai low-income workers in Taiwan. A total of 12 Thai stakeholders and migrant workers participated in the study. Migrant Thai workers developed distinct health information behaviors and improved various types of health competencies amid the COVID-19 pandemic. However, the critical barriers to health literacy remain. As a result, this work found that the Chatbot posted several potential features that could improve health literacy on Access. Therefore, Apprise and Apply depend on the understanding, which can be enhanced if the understanding rate increases. This study addresses the need for the development of the potential of a conversational agent. The findings of this study would guide the authority and technological interventions to respond to migrant workers' health information needs with appropriate tools. © 2023 IEEE.

关键词： COVID-19

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Climate-Based AI-Powered Precision Irrigation: Sustainably Smart Agriculture Frameworks for Maximum Crop Yields

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 161-172页

作者： Dhanke, Jyoti A. Srivastava, Diksha Menaga, D. Raj, Roop Kumar, Kambala Vijaya Jangir, Pradeep Mani, P. Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Biotechnology and Chemical Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan K.Ramakrishnan College of Technology Trichy India

A digital transformation is necessary for agriculture to achieve sustainability and increase productivity. With the use of IoT, high-tech detectors and helicopters can monitor crop health and environmental factors in real time, transmitting crucial data to cloud-based systems. Due to their robust computational and storage capabilities, these systems are capable of handling large datasets and carrying out intricate data analytics. Even before the COVID-19 pandemic, the world’s food supply was precariously balanced and insufficient. The present agricultural system prioritizes short-term gains above the preservation of the earth and its ecosystems. To construct agricultural systems that can meet the needs of ten billion individuals in the next 30 years, significant structural and automated changes are required. However, with the support of intelligent technology and the progress of artificial intelligence in agriculture, these obstacles might be overcome. Integration of data, processing images, and neural networks are examples of artificial intelligence technologies that have allowed producers to make better choices using up-to-date information and prediction models. The study emphasizes the need to address the ethical and practical concerns associated with climate-based AI in agriculture to promote its equitable deployment. The results of this research contribute to the ongoing debate about artificial intelligence’s possible applications in farming. New agricultural technologies provide optimism for a future that is environmentally friendly and will be able to feed the growing global population. The integration of renewable energy sources is one of the areas where artificial intelligence is seen to be able to assist in reaching global sustainability goals. Artificial intelligence (AI) has the potential to revitalize both newly developed and existing agricultural land by modifying, deploying, and implementing automated systems and tools. We take stock of where AI is at

关键词： Artificial intelligence Climatic conditions Cloud computing Deep learning Energy Sustainable precision irrigation

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients 2

Empowering Donors: How Blockchain Technology Can Help Ensure...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ajmal, Sahar Muzammil, Muteeb Bin Shoaib, Muhammad Mehmood, Muhammad Hassaan National University of Computer and Emmerging Sciences Department of Software Engineering Faisalabad Pakistan Riphah International University College of Computing Faisalabad Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential. © 2023 IEEE.

关键词： Transparency

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Enhancing Deep Reinforcement Learning with Scenario-Based Modeling

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SN computer science 2023年第2期4卷 1-13页

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem 91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 8410501 Israel

Deep reinforcement learning agents have achieved unprecedented results when learning to generalize from unstructured data. However, the “black-box” nature of the trained DRL agents makes it difficult to ensure that they adhere to various requirements posed by engineers. In this work, we put forth a novel technique for enhancing the reinforcement learning training loop, and specifically—its reward function, in a way that allows engineers to directly inject their expert knowledge into the training process. This allows us to make the trained agent adhere to multiple constraints of interest. Moreover, using scenario-based modeling techniques, our method allows users to formulate the defined constraints using advanced, well-established, behavioral modeling methods. This combination of such modeling methods together with ML learning tools produces agents that are both high performing and more likely to adhere to prescribed constraints. Furthermore, the resulting agents are more transparent and hence more maintainable. We demonstrate our technique by evaluating it on a case study from the domain of internet congestion control, and present promising results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep reinforcement learning Domain expertise Machine learning Rule-based specifications Scenario-based modeling

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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Anomaly Detection in PV Modules: A Comparative Study of DBSCAN, k-means, Isolation Forest, and LOF 7

Anomaly Detection in PV Modules: A Comparative Study of DBSC...

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7th IEEE International Congress on Information science and Technology, CiSt 2023

作者： Hairach, Mohamed Limam El Bellamine, Insaf Tmiri, Amal Laroseri Chouaïb Doukkali University Department of Computer Science Eljadida Morocco Mohammed v University National School of Arts and Crafts Department of Applied Mathematics and Computer Engineering Rabat Morocco

ISBN: (纸本)9781665461337

The increasing adoption of photovoltaic (PV) modules for renewable energy generation highlights the importance of maintaining their performance and efficiency. Anomalies in PV modules can lead to energy losses and reduced system reliability. Classical approaches for detecting PV module anomalies include current-voltage (IV) curve analysis, visual inspection, infrared thermography, and electroluminescence imaging. However, these methods often require employees, manual intervention, and may lack scalability and *** this study, we explore unsupervised machine learning techniques for detecting anomalies in PV modules using data from a PV plant, aiming to develop more efficient and accurate solutions compared to classical approaches. We evaluate four unsupervised machine learning algorithms - Isolation Forest, Local Outlier Factor (LOF), K-Means and DBSCAN, and compare their effectiveness based on metrics such as accuracy, precision, recall, and F1 *** findings reveal that the K-Means and DBSCAN algorithms, when properly tuned, demonstrate superior performance in detecting anomalies, while other algorithms show promise with further optimization. This study provides insights into the potential of unsupervised machine learning algorithms as an alternative to classical approaches for anomaly detection in PV modules, contributing to the development of efficient, accurate, and scalable anomaly detection systems in the solar energy sector and enhancing PV system performance and reliability. © 2023 IEEE.

关键词： Solar energy

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Application of Recurrent Neural Networks to Covariate Software Defect Prediction 28

Application of Recurrent Neural Networks to Covariate Softwa...

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28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023

作者： Salboukh, Fatemeh Silva, Priscila Nagaraju, Vidhyashree Fiondella, Lance Engineering and Applied Science University of Massachusetts Dartmouth MA United States Electrical and Computer Engineering University of Massachusetts Dartmouth MA United States Department of Computer Science Stonehill College MA United States

ISBN: (纸本)9798986576121

Traditional software reliability growth models (SRGM) assess defect discovery as a function of testing time or effort to quantify failure intensity based on the Non-Homogeneous Poisson Process (NHPP). More recently, covariate NHPP models have substantially improved defect prediction by characterizing software defect discovery in terms of underlying testing activities. Traditional NHPP models are based on parametric forms with specific distributional assumptions, which often fail to capture all details present on defect data. Therefore, this paper assesses the effectiveness of neural networks to predict software defects including covariates to encode factors driving defect discovery explicitly. Two neural networks are considered, including recurrent neural networks (RNNs) and long short-term memory (LSTM), which are then compared with the traditional covariate models to validate predictive capability. Our results suggest that LSTM achieves better overall goodness-of-fit measures, such as approximately 6 and 9 times smaller mean square errors and mean absolute percentage error, respectively, compared to traditional models when 75% of the data is used for training. These results suggest that neural networks are able to track and predict defect trends more accurately than traditional methods. © RQD 2023. All rights *** right reserved.

关键词： Software reliability

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Design of syntactic adaptive interactive system based on human perception state estimation within scenario context

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16933-16948页

作者： Jin, Dian Yu, Weiwei Srivastava, Gautam Yang, Xinliang School of Mechanical Engineering Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China Unmanned System Research Institute Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Math Lebanese American University Beirut Lebanon

Adaptive interactive systems can be divided into two categories: Semantic adaptations affect the system’s function, and syntactic adaptations affect the presentation of information through the operator interface without modifying the system behavior. Although automation is becoming more and more important, in many situations, human operators still play an irreplaceable role, making the development of syntactic adaptations indispensable. However, existing syntactic adaptations usually do not relate to the task context, which is a critical factor to achieve syntactic adaptations. Besides, existing studies do not fully consider the operator’s information perception state under highly dynamic scenarios, thus cannot dynamically prompt accurate information to the operators. Aiming at the problems above, a syntactic adaptive interactive system architecture based on perception state estimation and context segmentation is proposed in this paper. To address the real-time context segmentation, a time series segmentation algorithm is proposed, which achieves real-time estimation of the scene and produces highly interpretable results. To address the problem of dynamic perception state estimation, for the first time, the concept of a perception stack is proposed, which can be used to estimate the perception bias in the operator’s working memory based on the eye movement data and generate proper prompt information according to the current context. The proposed design method of an adaptive syntactic interactive system was verified in a flight simulator. The system improves information prompts accuracy and reduces redundancy through these innovative mechanisms mentioned above. Comparative experiments confirm significant improvements in the operator’s control accuracy and control stability according to our evaluation criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： State estimation

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Multi-Agent Reinforcement Learning with Epistemic Priors 9

Multi-Agent Reinforcement Learning with Epistemic Priors

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Walker, Thayne T. Ide, Jaime S. Choi, Minkyu Guarino, Michael Alcedo, Kevin Applied Ai Team University of Denver Department of Computer Science United States Yale University Department of Psychiatry United States University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States

ISBN: (纸本)9798350311402

It is important for autonomous multi-agent teams to coordinate actions so that collaborative goals can be achieved efficiently without conflicts. Practical issues in multi-agent, real-time systems are limited sensing and communication capabilities. A significant number of multi-agent algorithms rely on accurate state information for all agents in order to effectively coordinate. In this paper, we propose an approach called Reinforcement Learning with Epistemic Priors (MARL-EP). MARL-EP incorporates a shared mental model between agents by leveraging epistemic estimation to infer portions of the obser-vation space which are unobservable. We show that MARL-EP allows a very high level of coordination to be achieved with severely impaired sensing and zero communication between agents. © 2023 IEEE.

关键词： Real time systems

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