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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories Computer architecture Threat assessment Real-time systems Resource management Reliability Computer security Protection Standards software engineering

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A QoE evaluation of augmented reality for the informational phase of procedure assistance

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Quality and User Experience 2023年第1期8卷 1-13页

作者： Hynes, Eoghan Flynn, Ronan Lee, Brian Murray, Niall Faculty of Engineering and Informatics Technological University of the Shannon: Midlands Midwest Athlone Co. Westmeath Ireland Software Research Institute Technological University of the Shannon: Midlands Midwest Athlone Co. Westmeath Ireland

Augmented reality (AR) is an emerging technology that has significant potential as a solution for novel procedure assistance in mass customisation. Procedure assistance is a series of steps or instructions required to aid a person to complete a task. The informational phase of a procedure is the period when a user is trying to understand instructions, in advance of implementing them. With AR as a potential solution to communicate these steps, it is important to understand the factors that influence user acceptability and experience. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two approaches for informational phase assistance: AR procedure assistance and paper-based procedure assistance (control group). Each approach presented a procedure to solve a Rubik’s Cube® in the minimum number of steps. As part of the evaluation methodology of these procedure assistance methods, different metrics were captured. These included the user’s physiological ratings, facial expression features and self-reported measures in terms of affect, task load and QoE. The results show that AR-based assistance yielded significantly reduced procedure completion times and increased success rates compared to paper-based instructions. Several correlations were discovered between physiological and self-reported measures. For example, frustration and mental task load components were seen to correlate to both electrodermal and interbeat interval ratings. The findings from this work will stimulate experimentation and theoretical discussion on the use of physiological ratings and facial expressions as indicators of task load and QoE.

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Emotional Preferences in Metaverse Library Interface: A Kansei Analysis 10th

Emotional Preferences in Metaverse Library Interface: A Kans...

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10th International Conference on Kansei engineering and Emotion Research, KEER 2024

作者： Ahmad, Nik Azlina Nik Lokman, Anitawati Mohd Suhaimi, Ahmad Iqbal Hakim Abdullah, Munaisyah Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur50250 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam40450 Malaysia

ISBN: (纸本)9789819798896

While metaverse technology continues to advance, catching user attention ultimately relies on the appealing qualities of its user interface (UI), making it essential to include emotional perception. A well-designed UI aligned with user preferences fosters user engagement and contributes to product success. However, creating a UI that prioritizes emotions remains an ongoing challenge. For that reason, this study attempts to explore the relationship between UI design factors and the user perception of the demand experience. This research utilizes Kansei engineering (KE) method to investigate user preferences on the metaverse library UI and examine the correlation between emotional responses. A three-step methodology consisting of instrument, evaluation, and analysis was applied in this study. The evaluation utilized a total of 28 specimens and 60 Kansei Words (KWs). The data were analyzed using multivariate statistical analysis;Cronbach's Alpha, Principal Component Analysis (PCA) and Factor Analysis (FA) to identify the significant UI design for the metaverse library. The results of this research take the form of recommendations for UI design concepts based on the most dominant emotional factors. The data analysis results revealed that "radiance", "orderliness", and "conciseness" were the most significant emotional factors for the metaverse library application. Through the application of KE analysis, the emotionally demanding UI concepts within the metaverse library can be determined. This analysis provides valuable guidance for designing or redesigning the library interface, which in turn, contributes to an enhanced metaverse experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Product design

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Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD

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Physical Review Materials 2023年第5期7卷 053805-053805页

作者： Vinay I. Hegde Christopher K. H. Borg Zachary del Rosario Yoolhee Kim Maxwell Hutchinson Erin Antono Julia Ling Paul Saxe James E. Saal Bryce Meredig Citrine Informatics 2629 Broadway Redwood City California 94063 USA Olin College of Engineering 1000 Olin Way Needham Massachusetts 02492 USA Molecular Sciences Software Institute Virginia Tech Blacksburg Virginia 24061 USA

A central challenge in high-throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and postprocessing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the Materials Project (MP), and the Open Quantum Materials Database (OQMD), and compare reported properties across each pair of databases for materials calculated using the same initial crystal structure. We find that HT-DFT formation energies and volumes are generally more reproducible than band gaps and total magnetizations; for instance, a notable fraction of records disagree on whether a material is metallic (up to 7%) or magnetic (up to 15%). The variance between calculated properties is as high as 0.105 eV/atom (median relative absolute difference, or MRAD, of 6%) for formation energy, 0.65 Å3/atom (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and 0.15μB/formula unit (MRAD of 8%) for total magnetization, comparable to the differences between DFT and experiment. We trace some of the larger discrepancies to choices involving pseudopotentials, the DFT+U formalism, and elemental reference states, and argue that further standardization of HT-DFT would be beneficial to reproducibility.

关键词： Density functional theory

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Recursive Broadcasting Approach 33

Recursive Broadcasting Approach

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Concordia University Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada Institute for Informatics and Automation Problems NAS RA Yerevan0014 Armenia

ISBN: (纸本)9798331524937

Broadcasting is one of the fundamental information dissemination primitives in interconnection networks, where a message is passed from one node (called originator) to all other nodes in the network. Following the increasing interest in interconnection networks, extensive research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this study, we will mainly focus on determining the broadcast time and nearoptimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this study, we consider the broadcast time problem on graphs that comprise some recursive structures. We initiate a novel direction to designing broadcasting algorithms on recursively defined graphs. We provide a theoretical foundation for future broadcasting studies, as well as discuss several practical applications of the approach we introduce. © 2025 IEEE.

关键词： Interconnection networks (circuit switching)

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Design Amharic Text Sentiment Analysis Model Using Machine Learning Techniques. In Case of Restaurant Reviews

Design Amharic Text Sentiment Analysis Model Using Machine L...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Birku Gedif Abebe Alemu Yibeltal Assefa Shegaw Nibret Information Science College Of Informatics University of Gondar Computer Science College Of Informatics University of Gondar Software Engineering Kombolcha Institute of Technology Wollo University ICT4D Research Center Bahir Dar Institute of Technology Bahir Dar University

Sentiment analysis is a type of natural language processing for tracking the attitude of the public about a particular product, service or topic. It is also highly challenging as natural language processing research topic, and covers many novel sub-problems. Now business organizations and academics are putting forward their efforts to find the best system for sentiment analysis. The focus of this study was an Amharic unstructured restaurant review on the web. The objective of the paper was to design Amharic text sentiment analysis model using supervised machine learning techniques and evaluate the performance of classifiers. This paper explored the supervised machine learning classification approaches (naive Bayes, support vector machine and k-nearest neighbor) with different feature selection schemes to obtain a sentiment analysis model for domain specific restaurant review dataset at sentence level. The proposed model has the following components: Data preparation, preprocessing such as tokenization, normalization, filter stop words, feature extraction and selection to prepare feature vector, polarity classification. Performance analysis carried out on classifiers, based on n-grams proposed. From the results of the experimental studies, all algorithms are known to be highly effective classifiers, and are able to achieve good accuracy in this experiment. The experiments show that Term frequency (TF) and the TF -IDF scheme gives maximum accuracy 80.43 % and 79.49 % respectively for SVM in bigram features. Term frequency and term occurrence also give maximum accuracy 78.37 % and 78.00 % respectively for Naive Bayes classifier at bigram features. TF-IDF also give maximum accuracy 78.00% for KNN at 4-gram. The challenge was opinion holders sometimes give objective text to express their opinion, but the classifier did not identify those facts from opinions. These kinds of complexities of natural languages make sentiment mining systems more challenging and to resolving t

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A Query Language for software Architecture Information (Extended version)

arXiv

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arXiv 2023年

作者： Ammermann, Joshua Jordan, Sven Linsbauer, Lukas Schaefer, Ina Karlsruhe Germany Group IT Solution & Enterprise Architecture Volkswagen AG Wolfsburg Germany Institute of Software Engineering and Automotive Informatics Technische Universität Braunschweig Braunschweig Germany

software maintenance is an important part of a software system's life cycle. Maintenance tasks of existing software systems suffer from architecture information that is diverging over time (architectural drift). The Digital Architecture Twin (DArT) can support software maintenance by providing up-to-date architecture information. For this, the DArT gathers such information and co-evolves with a software system, enabling continuous reverse engineering. But the crucial link for stakeholders to retrieve this information is missing. To fill this gap, we contribute the Architecture Information Query Language (AIQL), which enables stakeholders to access up-to-date and tailored architecture information. We derived four application scenarios in the context of continuous reverse engineering. We showed that the AIQL provides the required functionality to formulate queries for the application scenarios and that the language scales for use with real-world software systems. In a user study, stakeholders agreed that the language is easy to understand and assessed its value to the specific stakeholder for the application scenarios. © 2023, CC BY.

关键词： Query languages

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A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings

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Energy informatics 2021年第2期4卷 1-19页

作者： Clausen, Anders Arendt, Krzysztof Johansen, Aslak Sangogboye, Fisayo Caleb Kjærgaard, Mikkel Baun Veje, Christian T. Jørgensen, Bo Nørregaard Center for Energy Informatics Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense M5230 Denmark Software engineering unit Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense M5230 Denmark

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort. © 2021, The Author(s).

关键词： Energy efficiency

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Leveraging Big Data for Effective Healthcare Management with Artificial Intelligence 1

Leveraging Big Data for Effective Healthcare Management with...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Krati Kasar, Chaitali B. Thulluri, Guravaiah Yesugad, Anuradha Karishma, Mehaboob Padhi, Dipanjali Manipal University Jaipur Department of Information Technology Jaipur India Dr. D. Y. Patil Institute of Technology Department of Electronics and Telecommunication Engineering Pune411018 India Embedded Senior Software Developer Sak Informatics Hyderabad India Pune India Sir C R R College Department of Computer Application Eluru India Dhole Patil College of Engineering Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798350330915

To improve clinical judgment, resource allocation, and patient outcomes, this study investigates the integration of big data with artificial intelligence (AI) for medical management. Utilizing a descriptive design with an emphasis on secondary data collecting, the study adopts an interpretive perspective and a deductive approach. Data collection, AI algorithm implementation, integration into the system, and infrastructure issues are all included in the technical setup. Accuracy, sensitivity, quickness of processing, and scalability are examples of performance measures, and the influence on patients and healthcare providers is highlighted by user viewpoints and experiences. Informed permission, data security, and legal compliance are all included in the ethical considerations. The results highlight the potential advantages of data-driven medical services, but they also highlight how crucial ethical precautions and user acceptance are. Upcoming studies ought to concentrate on improving algorithms, protecting privacy, and evaluating long-term effects © 2023 IEEE.

关键词： artificial intelligence big data clinical decision support ethical considerations healthcare management

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Trustworthy Model Elements as Basis for Seamless Bim Collaboration

SSRN

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SSRN 2024年

作者： Paskaleva, Galina Mazak-Huemer, Alexandra Huemer, Christian TU Wien Institute of Information Systems Engineering Favoritenstr. 9-11 ViennaA-1040 Austria Johannes Kepler University Linz Department of Business Informatics - Software Engineering Altenberger Strasse 69 Science Park 3 LinzA-4040 Austria

Current applications of BIM in the Architecture engineering and Construction (AEC) industry require multiple stakeholders to collaborate on the same digital model over multiple project phases, sharing the responsibility for the same model element. This requires trust, both between the stakeholders and between each stakeholder and the BIM technology. Lack of trust often manifests as poor collaboration, model duplications and break-down of model continuity. In this work, we present a method for the introduction of trust into the digital twin of a built structure by creating trustworthy model elements. The source of trust can be any technical or legal requirement. Such trusted model elements can offer a formal guarantee that the corresponding requirement has been fulfilled. Furthermore, this guarantee can be renewed automatically after each update by any project stakeholder. Finally, we introduce a standardized workflow for the maintenance and update of already established trusted elements in a digital twin. © 2024, The Authors. All rights reserved.

关键词： Architectural design

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