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11th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2024

作者： Basuki, Setio Sari, Zamah Indrabayu, Rizky Fauzan, Reza Wardhana, Aulia Arif Tsuchiya, Masatoshi Universitas Muhammadiyah Malang Informatics Engineering Malang Indonesia Politeknik Negeri Banjarmasin Department of Electical Engineering Banjarmasin Indonesia Doctoral School Wroclaw University of Science and Technology Wroclaw Poland Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9798331520311

This paper develops a Machine Learning model to estimate the citation counts of research papers. The model uses citation functions, representing the intentions of the paper's author when making citations of previous works, to estimate the number of citations. These intentions include introducing a research topic, comparing, and criticizing previous works, etc. Three predictors have been developed based on citation functions: citing sentence, regular sentence, and reference. The prediction is seen as a regression and classification problem by pre-grouping the number of citations into high-count, medium-count, and low-count. The dataset was obtained from the International Conference on Learning Representations (ICLR) 2017-2020 containing 5,156 accepted and rejected papers. This paper uses only the accepted papers since the main task is to predict the number of citations of accepted/published papers. To obtain the number of citations one year after publication, this paper uses the API provided by Semantic Scholar. According to experiments, the best results in classification reach 98.33% accuracy, and in regression, the results reach 0.3 on both RMSE and MAE. The feature labeled 'citing paper dominant,' which reflects the superiority of the citing paper over the cited paper, has proven highly effective in delivering the best prediction results, even though it is sparsely represented in the dataset. In conclusion, citation function-based predictors are effective in estimating the future impact of a paper. © 2024 IEEE.

关键词： Contrastive Learning

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Classification of Bird Species using K-Nearest Neighbor Algorithm 10

Classification of Bird Species using K-Nearest Neighbor Algo...

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Budiman, Ichsan Ramdania, Diena Rauda Gerhana, Yana Aditia Putra, Alif Rakasha Pratama Faizah, Nisairrizqy Nabilah Harika, Maisevli Uin Sunan Gunung Djati Bandung Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

Birds play an essential role in the functioning of the world's ecosystems by directly impacting human health, economy, and food production and benefiting millions of other species. The diversity of bird habitats shows that there are many types of bird species. This study aims to provide a new treasure for the ontology field by applying a machine learning algorithm to classify bird species based on K-Nearest Neighbor (KNN). A total of 400 species of bird images with a total of 58388 images of data were tested in this study. The test scenario was carried out in 3 stages: 400, 200, and 100 species. The results of testing the accuracy of the K-Nearest Neighbor model applied to the bird image dataset are 26.846%, with a value of K=1. The comparison of training data with test data is 95:5 percent (%). This result shows that the KNN algorithm can classify bird species. © 2022 IEEE.

关键词： Birds

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Personalized User Experience and Engagement in Interactive Mobile Augmented Reality Software Through Fuzzy Weights

Personalized User Experience and Engagement in Interactive M...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Papakostas, Christos Troussas, Christos Krouska, Akrivi Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031896200

The integration of digital content into the real world through mobile augmented reality (AR) systems presents captivating prospects, yet user engagement and experience within such environments are largely contingent upon individual computer skills, which can vary significantly among users, thereby influencing their proficiency in utilizing this technology. This paper aims to bridge this gap by examining the impact of users’ computer skills on interactions within mobile AR settings. To this end, a novel fuzzy logic-based model is introduced to evaluate and refine users’ computer skills within the realm of mobile AR systems. By modeling users’ computer aptitude, the system is capable of delivering tailored assistive prompts and feedback. These prompts are strategically aligned with established fuzzy weights, thereby augmenting users’ interactions within the mobile AR sphere. The proposed methodology is seamlessly integrated into a personalized mobile AR platform designed for spatial ability training. Evaluation findings reveal promising outcomes, underscoring the substantial potential of fuzzy logic modeling to elevate user experiences and propel advancements in mobile augmented reality technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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Estimating Uncertainty in Deep Learning Methods and Applications

Estimating Uncertainty in Deep Learning Methods and Applicat...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alqarafi, Abdulrahman Batool, Hina Abbas, Tahir Janjua, Jamshaid Iqbal Ramay, Sadaqat Ali Ahmed, Munir College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia Times Institute Department of Computer Science Multan Pakistan Lahore Pakistan College of Informatics Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9798350367560

Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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Partition Strategies for Vertex-Centric Historical Graph Systems 25

Partition Strategies for Vertex-Centric Historical Graph Sys...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Alexandros Spitalas Georgios Tsolas Kostas Tsichlas Computer Engineering and Informatics Department University of Patras Rio Achaia Greece

ISBN: (纸本)9798400706295

This paper explores partitioning strategies for vertex-centric historical graph systems within distributed environments, focusing on efficient data management and query execution. Historical graphs, which capture the dynamic evolution of vertices and edges over time, present unique challenges for storage and computation due to their constantly changing nature. We investigate two partitioning approaches for offline and online environments. Both offline and online algorithms aim to minimize an appropriately defined notion of edge cuts within the historical graph setting; the former algorithm is based on BFS, while the latter is based on a greedy partitioning approach. Both algorithms are compared qualitatively and quantitatively to hash-based methods that are mainly used in this setting. They are evaluated on real-world datasets, using metrics such as weighted edge cut score ratio and load balance ratio. Our experiments reveal that our online method consistently reduces edge cuts with minimal overhead, making it suitable for real-time distributed processing systems for historical graphs. Similarly, the offline algorithm achieves much better results for weighted edge cuts, but it requires severely more computational resources to achieve this performance. Additionally, the study highlights how varying the number of workers and partitioning thresholds impacts system performance across different datasets. All our experiments have been carried out in a simulation environment. The results provide valuable insights into optimizing partition strategies for historical graphs, paving the way for more efficient graph analytics in distributed systems.

关键词： partitioning strategies

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Performance and Radiation Testing of the Coral TPU Co-processor for AI Onboard Satellites

Performance and Radiation Testing of the Coral TPU Co-proces...

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2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023

作者： Lentaris, George Leon, Vasileios Sakos, Chronis Soudris, Dimitrios Tavoularis, Antonios Costantino, Alessandra Polo, Cesar Boatella University of West Attica Department of Informatics Computer Engineering Greece National Technical University of Athens School of Electrical Computer Engineering Greece European Space Research Technology Centre European Space Agency Netherlands

ISBN: (纸本)9789090379241

COTS HW and SW components become crucial for advancing AI in space applications. The CAIRS21 ESA project considers Coral TPU as a candidate AI co-processor in avionics and examines its suitability in terms of performance, programmability/productivity, and radiation tolerance. The current paper focuses on our custom methodology for evaluating TPU, including TID and SEE effects. We assume representative AI benchmarks and irradiation profiles for LEO missions. We define a series of actions and measurements to derive thorough conclusions while avoiding increased time/budget procedures. Additionally, the paper summarizes key findings and a preliminary analysis of results from actual tests. Overall, TPU shows significant benefits when executing embedded AI, as well as promising Krad(Si) and SEE cross-section values. © 2023 ESA.

关键词： Space applications

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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(ω1,ω2)-temporal random hyperbolic graphs

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Physical Review E 2024年第2期110卷 024309-024309页

作者： Sofoclis Zambirinis Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics <a href="https://***/05qt8tf94">Cyprus University of Technology</a> 3036 Limassol Cyprus.

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities ω1 and ω2. This extension, while conceptually simple, poses analytical challenges involving the Appell F1 series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The incorporation of ω1 and ω2 enables more flexible tuning of the average contact and intercontact durations, and of the average time-aggregated degree, providing a finer control for exploring the effect of temporal network dynamics on dynamical processes. Overall, our results provide new insights into the analysis of temporal networks and contribute to a more general representation of real-world scenarios.

关键词： Evolving networks Networks & random structures Random graphs Evolving network models

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Design of an AI-Assisted Platform that will Facilitate Educators in Creating Minecraft Education Courses

Design of an AI-Assisted Platform that will Facilitate Educa...

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Artificial Intelligence and Education (WAIE), International Workshop on

作者： Konstantinos Roumpas Michalis Xenos Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9798350378894

ISBN: (纸本)9798350378900

The potential of Education Technology has reached a point where teachers cannot keep up with the latest trends, nor they can spend the time to get accustomed to innovative technologies that might become obsolete in a few years. With the rise in the popularity of Minecraft Education, an educational platform based on the engine of the world-famous video-game: Minecraft, as well as the benefits of gamification in classrooms for all student ages, it seems that an application that would help teachers create educational video games that are run on Minecraft Education, without any code and with artificial intelligence assistance, could be just the thing that they need to update their teaching techniques. We propose the creation of a platform like that, and we present its potential design, after being consulted by the available research that has been done to this day regarding instructional design, user interface and applications that utilize large language models.

关键词： Video games Codes Large language models Conferences User interfaces Market research Educational courses Engines

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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