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6th Doctoral Symposium on Natural Language Processing from the Proyecto ILENIA, PLN-DS 2023

作者： Estevanell-Valladares, Ernesto Luis Department of Software and Computer Systems University of Alicante Spain Faculty of Mathematics and Computer Science University of Havana Cuba

The popularity of artificial intelligence has led to an increasing need for machine learning models tailored to specific needs. AutoML aims to automate the process of creating effective machine-learning solutions, but current systems need to be more versatile to meet the demand. While more flexible and extensible heterogeneous systems have overcome many limitations of traditional AutoML systems, they lack accessibility due to their programmatic interfaces. We propose a research project to address this issue to develop a heterogeneous AutoML system that can produce optimal machine-learning pipelines using a natural language interface. © 2022 Copyright for this paper by its authors.

关键词： Machine learning

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Online Information-Based Product Evolution Course Mining and Prediction

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International Journal of Information Technology and Decision Making 2024年第2期23卷 599-627页

作者： Chen, Yuh-Jen Chen, Yuh-Min Department of Accounting and Information Systems College of Business Intelligence National Kaohsiung University of Science and Technology Kaohsiung City Taiwan Institute of Manufacturing Information and Systems College of Electrical Engineering and Computer Science National Cheng Kung University Tainan City Taiwan

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success;therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM;it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to

关键词： Forecasting

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FAIRlead: A Conceptual Framework for a Model Driven Software Development Approach in the Field of FAIR Data Management 16

FAIRlead: A Conceptual Framework for a Model Driven Software...

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16th International Conference on Knowledge Management and Information systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Schmidt, Andreas Koubaa, Mohamed Anis Liu, Nan Schmurr, Philipp Stucky, Karl-Uwe Süß, Wolfgang Karlsruhe Germany Department of Computer Science and Business Information Systems Karlsruhe University of Applied Sciences Karlsruhe Germany

ISBN: (纸本)9789897587160

The publication of scientific results together with the underlying experiments is an important source of further research. In 2016, the "FAIR Guiding Principles for scientific data management and stewardship" were published, in which the authors postulate a series of guidelines for improving the (F)indability, (A)ccessibility, (I)nteroperability and (R)eusability of digital information (FAIR). The point (I)nteroperability deals with the prerequisites for the reusability of digital objects. The central point here is the need to have a common understanding of the meaning of digital objects. This understanding is provided by formal languages of knowledge representation (ontologies), which describe the actual data. These descriptions of data are also known as metadata. As part of our current work at the Institute for Automation and Applied computer science (IAI) at KIT, we are implementing novel concepts and technologies for the sustainable handling of research data using high-quality metadata. As part of this work, we plan to develop a software tool that can be used to enrich data with suitable metadata and thus automate the process of making research results available. A key requirement is that the tool must be independent of the underlying domain. In order to be able to deal with data from any domain, we have opted for a model-driven approach in which an ontology, and possibly other platform-specific information, are input for a software generator, which then generates an (interactive) tool for specifying the metadata and linking it to the data itself. The generated tool includes the complete software stack, starting with a user interface, programmatic APIs for connecting additional application logic, and a persistence component. How these individual layers are realized is not specified, but defined by the mapping rules of the software generator, which also opens up the possibility of generating and evaluating different variants of the software. © 2024 by SCITEPRESS

关键词： Ontology

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in Engineering & sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Machine Learning-Enabled Healthcare Decision Support System: An Advanced Disease Prediction with Patient-Doctor Collaboration 1

Machine Learning-Enabled Healthcare Decision Support System:...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Ragavi, V. Preethi Reddy, Panneru Kalva Thanmaye Eswari, Vommina Lakshmi Umaeswari, P. R.M.K Engineering College Computer Science and Business Systems Tamil Nadu Chennai India

ISBN: (纸本)9798350365337

In this research, a revolutionary healthcare software system that gathers various patient symptoms in order to anticipate and assess diseases is presented. To generate predictions, the system uses five different algorithms: Random Forest, XG Boost, Support Vector Machine, Logistic Regression, and Naive Bayes. The most accurate prediction is chosen as the final result after a comparative study of the forecasts and accuracies from each method. Healthcare practitioners can make better decisions when using a graphical representation. A noteworthy development in proactive healthcare is demonstrated by the system's introduction of a function that recommends appropriate doctors to the patient. This invention supports both individual well-being and more general public health goals. © 2024 IEEE.

关键词： Logistic regression

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Enhanced Security through Distributed Fragmentation and Encryption for Cloud based Data Storage 2

Enhanced Security through Distributed Fragmentation and Encr...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Palani, S. Ajay, B. Bhavadharani, B. Varshini, A. Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Integrating cloud computing into organizational data management demands paramount protection of sensitive information. This paper presents a framework to enhance data security through distributed fragmentation and advanced encryption techniques. By segmenting sensitive data into multiple fragments distributed across various cloud storage locations, the approach mitigates risks associated with centralized data repositories. Each fragment is encrypted with robust cryptographic algorithms, ensuring that unauthorized access renders intercepted data unintelligible. This fragmentation not only strengthens security but also improves performance through parallel data processing and retrieval, optimizing resource utilization. Additionally, decentralized storage reduces the impact of single points of failure, enhancing resilience against data loss or breaches. Simulation results demonstrate the effectiveness of this model in countering attack vectors, including data tampering and unauthorized access. Overall, this research contributes to secure cloud storage solutions, offering a robust framework for organizations to protect sensitive information, ultimately fostering trust in cloud technologies and enabling safer data management practices. © 2024 IEEE.

关键词： Cloud security

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Double Pepper: Mediated Human-Robot Interaction and Its Influence on Human Perception 10

Double Pepper: Mediated Human-Robot Interaction and Its Infl...

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10th IEEE International Conference on Behavioural and Social Computing, BESC 2023

作者： Keller, Lisa Muller, Nicholas H. Technical University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Würzburg Germany

ISBN: (纸本)9798350395884

Remote communication solutions have grown in our globalized world. Combining this area with robotics, telepresence robots enable people to feel more present at distant locations and allow the operator to move freely. Seemingly, there is no difference in perceived human affinity whether a person interacts with another person directly or through a telepresence robot, as a preliminary study demonstrates. In contrast to the preliminary study focusing on affinity towards a human interaction partner, the study in this paper deploys a humanoid robot as interaction partner. Hence, this work aims to explore if human affinity towards a humanoid robot varies if people interact with it directly or through a telepresence robot (so called mediated human-robot interaction). A Double 2 robot was used as telepresence robot and a Pepper as humanoid robot interlocutor. To measure participants' mood, the Multidimensional Mood State Questionnaire was applied before and after interacting with Pepper. Also, human affinity was measured through a questionnaire. The results indicate that there is no significant difference regarding mood and perceived human affinity whether someone interacts with a humanoid robot in person or through a telepresence robot. However, participants were significantly more awake after interacting in person. Concluding, the field of mediated human-robot interaction can enable new successful use cases e.g. deploying a humanoid robot in a telepresence robot tour as a guide instead of a person. Thereby, neither attendees nor a human guide have to be onsite which is beneficial in health hazard crises such as the corona pandemic. © 2023 IEEE.

关键词： Human robot interaction

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Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining 4

Video Anomaly Detection Using Pre-Trained Deep Convolutional...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumar, Nampally Vijay Srimukhi, Vaddadi Lakshmi Sai Siruvoru, Vahini Ashritha, Guda Ruthvini, Merugu Sheshu, Thugapindi Shiva Department of Computer Science and Business Systems B V Raju Institute of Technology Narsapur Medak502313 India

ISBN: (纸本)9798331528614

Monitoring of CCTV is readily accessible today, but software is unable to accurately detect aberrant behaviour, such as shoplifting or sudden running, which is referred to as an anomaly. The existing techniques trained the model to recognize abnormal behaviours by using all normal images, but they were strongly dependent on a huge number of training pictures. The author of this work uses three distinct pretrained CNN models/algorithms, dubbed anomaly detection, object detection, and tracking, to get around the problems. The pre-trained models exhibit high efficiency in accurately extracting features, which are subsequently retrained using the De-noising Auto Encoder model (DAE). After DAE has trained on normal images, it will alert users with red bounding boxes if it detects any aberrant frames in the movie. The UCSD pedestrian dataset, which is used in this proposed work to train the model, shows people walking, which is considered normal behaviour. If, however, a person arrives on a bicycle or a tree falls, the model detects this as aberrant behaviour, also known as anomaly detection. The distinction between normal and pathological behaviour, known as spectral and temporal behaviour—spectral denoting place, and temporal denoting TIME—is extracted through the application of context mining. In order to carry out this experiment, we used the UCSD images dataset, which is taken from movies. ©2024 IEEE.

关键词： Deep neural networks

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Integrating robotic process automation (RPA) with hybrid cloud enterprise resource planning (ERP). 5

Integrating robotic process automation (RPA) with hybrid clo...

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5th Technology Innovation Management and Engineering science International Conference, TIMES-iCON 2024

作者： Ratchatawetchakul, Yongyut Bansri, Benjamas Ratchatawetchakul, Kamonwan Satchawatee, Natarpha Ratchatawetchakul, Weerayut Eampoonga, Itthiphol Digital Business and Information System Major Mahasarakham Business School Mahasarakham University Thailand Department of Business Computer Faculty of Administrative Science Kalasin University Kalasin Thailand Business Computer Major Mahasarakham Business School Mahasarakham University Thailand Department of Management Faculty of Management Science Loei Rajabhat University Thailand

ISBN: (纸本)9798350362602

This research aims to 1) apply an enterprise resource planning (ERP) system with cloud computing, 2) implement automated workflow processes (RPA) using UiPath tools within the ERP production cycle, and 3) assess the efficiency of automated workflow processes (RPA) within the ERP production cycle on cloud computing *** results from applying automated workflow processes (RPA with UiPath) within the production cycle of the enterprise resource planning system (ERP with Odoo) hosted on cloud servers (Cloud with Microsoft Azure) indicate that manual processes in the production formula system take 1.05 times longer than automated processes, manual processes in the purchasing system take 1.29 times longer, manual processes in the goods receipt system take 1.22 times longer, and manual processes in the production operations take 1.15 times longer than their automated counterparts ©2024 IEEE.

关键词： Cloud platforms

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Traffic Information Extraction And Estimation In Developing Countries 9

Traffic Information Extraction And Estimation In Developing ...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Minh, Quang Tran Phan, Trong Nhan Bao, Thu Le Thi Faculty of Computer Science and Engineering Department of Information Systems Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331505042

This study aims to investigate and propose solutions to traffic information extraction used for state estimation based on traffic data collected from crowd-source. Concretely, a framework to traffic data collection such as voice and image/videos and the AI techniques to analyze such data to estimate traffic state will be proposed. This traffic information such as congested locations, reason caused such a traffic condition, routing suggestion will then be disseminated to commuters, which helps to eliminate traffic congestions. We have conducted evaluations using our system prototype and the results show the effectiveness of the proposed solutions revealing that the proposed approaches can be applied to real-world applications, especially in developing countries. © 2024 IEEE.

关键词： Data collection

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