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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE Computer Society. All rights reserved.

关键词： Image coding

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Formation of Use Case Scenario Based on Use Case Diagram Using Text Semantics for IdVar4CL Application Development

Formation of Use Case Scenario Based on Use Case Diagram Usi...

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International Conference on Signal Processing and Communication (ICSC)

作者： Maritza Amalia Ika Laksana Yudi Priyadi Yanuar Firdaus Arie Wibowo Department of Information Technology Telkom University Bandung Indonesia Department of Software Engineering Telkom University Bandung Indonesia Department of Informatics Telkom University Bandung Indonesia

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In forming a Use Case Scenario, alignment is needed between two artefacts, the Requirement Elicitation and the Use Case Diagram, so the software development meets the client's needs. There is a process for forming a Use Case Scenario that can be inconsistent with the Use Case Diagram. This inconsistency can occur due to differences in the interpretation of an artefact and a lack of validity and reliability between the software developer and the client's needs in representing it in a Use Case Diagram. This research aims to create a Use Case Scenario using a semantic text process based on a Use Case Diagram. After completing all stages of this research process, four main points emerged as results: ten Use Case Names accompanied by Use Case Diagrams with similar names, tabulation of the Use Case Scenario format, which is the result of the formation of related artefacts, the alignment of the Use Case Scenario formed based on the Use Case Diagram produces a comparison value of 0.39 “fair agreement” and a value of 0.75 “substantial agreement”, and references that produce artefact improvements related to the Requirement Specification documentation for the formation of the Use Case Scenario. The results of this research are expected to help developers enhance development and documentation, making it more effective in consistently meeting user needs.

关键词： Semantics Documentation Signal processing software software reliability software development management

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Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

Performance Comparison of Interpolation Methods for Enrichin...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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Core Temperature Estimation of Lithium-Ion Batteries Using Long Short-Term Memory (LSTM) Network and Kolmogorov-Arnold Network (KAN)

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IEEE Transactions on Transportation Electrification 2025年

作者： Karnehm, Dominic Samanta, Akash Rosenmuller, Christian Neve, Antje Williamson, Sheldon University of Bundeswehr Munich Electrical Engineering and Technical Informatics Department Neubiberg85577 Germany Ontario Tech University Electrical Computer and Software Engineering ONL1G 0C5 Canada Munich80335 Germany

Health-conscious battery management systems (BMS) that rely on surface temperature measurements are insufficient for managing automotive lithium-ion batteries (LIBs). Experimental studies have shown temperature differences of up to 10°C between surface and core of cylindrical LIBs. BMSs that consider only surface temperature overlook critical thermal information. The missing monitoring can delay detecting thermal events within the cell, accelerating battery degradation and increasing the risk of thermal runaway. This paper introduces two deep learning algorithms to address this: Kolmogorov-Arnold Network (KAN) and interconnected Long Short-Term Memory (LSTM) network. Both approaches estimate the core temperature of LIBs without requiring surface temperature feedback to the neural network. Experimental validation revealed a core temperature mean absolute error (MAE) of 0.55 °C with a computational cost of 2.9 ms to 3.2 ms for KAN. The proposed interconnected LSTM reached a MAE of 0.80 °C. The performance of the two core temperature estimation techniques was further evaluated under dynamic loading profile using UDDS drive cycle. The KAN method achieved a MAE of 0.325 °C, demonstrating its adaptability to dynamic operating conditions. The two proposed methods, primarily KAN, are both adaptive and computationally efficient, making them suitable for integrating onboard BMS and cloud-enabled digital-twin-based BMS systems. © 2015 IEEE.

关键词： Long short-term memory

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Incorporating multilevel dynamic game and robust multi-objective optimization for cross-border river governance: A Lancang–Mekong case study

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Journal of Cleaner Production 2025年 513卷

作者： Yang, Ruijia Feng, Jingchun Tang, Jiansong Saga, Ryosuke Business School Hohai University Nanjing211100 China College of Computer Science and Software Engineering Hohai University Nanjing211100 China of Informatics Osaka Metropolitan University Osaka559-8531 Japan

Transboundary river governance faces increasingly complex economic, ecological, and political challenges, yet existing single-round approaches often fail to capture the dynamic negotiations and extreme uncertainty inherent in multi-country resource management. This paper proposes an integrated decision-making framework that couples a multilevel dynamic game with robust multi-objective optimization in the Lancang–Mekong River Basin, aiming to generate politically feasible and stable water-allocation strategies even under severe drought or market shocks. First, an initial robust optimization identifies a Pareto set balancing economic returns, equitable distribution, and ecological flow requirements. These solutions feed into a multi-round dynamic game model, which simulates strategic adjustments and compensation mechanisms among upstream and downstream countries. A subsequent second-stage optimization incorporates negotiation feedback to further improve both political acceptability and resilience. Results indicate that compensation incentives can effectively induce upstream stakeholders to increase critical dry-season releases by 5–10 %, thereby raising downstream ecological flow satisfaction by 10–15 percentage points and boosting agricultural production by up to 8–10 % under drought conditions. Sensitivity analyses confirm that multi-round game negotiations coupled with robust optimization maintain cooperation cohesion and risk tolerance under diverse hydro-economic scenarios. The findings underscore the utility of phased compensation, flexible reservoir releases, and multilateral funding to foster enduring cross-border cooperation in international river basins. © 2025 Elsevier Ltd

关键词： Risk perception

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software Process as a Service: Towards A software Process Ecosystem

arXiv

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arXiv 2025年

作者： Greulich, Oliver Knieke, Christoph Rafie, Bassel Rausch, Andreas Kuhrmann, Marco Technische Universität Clausthal Institute for Software and Systems Engineering Clausthal-Zellerfeld Germany Reutlingen University Faculty of Informatics Herman Hollerith Center Böblingen Germany

In large-scale projects operated in regulated environments, standard development processes are employed to meet strict compliance demands. Since such processes are usually complex, providing process users with access to their required process, which should be tailored to a project’s needs is a challenging task that requires proper tool support. In this paper, we present a process ecosystem in which software processes are provided as web-based services. We outline the general idea, describe the modeling approach, and we illustrate the concept’s realization using a proof-of-concept case based on a large software process line that is mandatory to use for IT projects in the German public sector. The suitability is evaluated with three experts that valued the improved accessibly and usability of the process and the end-user support tool. © 2025, CC BY.

关键词： software prototyping

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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GRCMO: Global Routing Optimization by Median-Based Cell Movement

GRCMO: Global Routing Optimization by Median-Based Cell Move...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Arthur Lourenco Felipe Savi Felipe Nascimento Erfan Aghaeekiasaraee Upma Gandhi Renan Netto Laleh Behjat Tiago Augusto Fontana José Luís Güntzel Dept. of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC) Florianópolis Brazil Dept. of Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

In the physical design of VLSI circuits, the placement and routing steps are responsible for finding cell positions and interconnecting cell pins. They significantly impact the circuit layout quality. Placement and routing were originally solved separately in a divide-and-conquer approach to cope with design complexity, resulting in a decoupling that often leads to poor solutions. To mitigate this problem, a few techniques seeking to integrate those steps have recently emerged. Unfortunately, some of those techniques do not consider the detailed placement information, making it difficult to evaluate their outcomes. In this paper, we present GRCMO, a technique that optimizes Global Routing by moving a few cells to their medians, respecting the detailed placement rules, and rerouting the affected nets. GRCMO was implemented in the well-established open-source platform OpenROAD, making its use within a complete RTL-to-GDSII flow possible. Experimental results using the ISPD 2018 Contest circuits showed that, in comparison to the standard global routing solutions from OpenROAD, GRCMO is able to reduce the estimated wirelength by 0.43%, on average, and from 0.5% up to 0.7% for the biggest circuits, requiring less than 20 iterations and moving 2.61% of the cells, on average.

关键词： Costs Layout Integrated circuit interconnections Very large scale integration Routing Pins Complexity theory Optimization Standards Physical design

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What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

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Session-Based Recommendation with Graph Neural Networks with an Examination of the Impact of Local and Global Vectors 5th

Session-Based Recommendation with Graph Neural Networks wit...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European Conference on Advances in Databases and Information Systems, ADBIS 2024

作者： Glogowska, Justyna Kobiela, Dariusz Mielewczyk, Szymon Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Multimedia Systems and Department of Software Engineering Gabriela Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (纸本)9783031704208

This study investigates the application of graph neural networks (GNN) in session-based recommendation systems (SR), focusing on a key modification involving the use of a global vector. Session-based recommendation systems often face challenges in accurately capturing user behavior due to the limited data available within individual sessions. The SR-GNN model, originally designed for automatic feature extraction from session graphs by leveraging rich connections between nodes, addresses these challenges effectively. In our experiments, we replaced the local vector with a global vector representing the entire session sequence, not just the last element. Our results show that both local and global vectors perform comparably, suggesting that the global vector is sufficient to capture the session context. Additionally, our study indicate that the SR-GNN algorithm maintains consistent performance across various datasets, with minor fluctuations depending on the dataset characteristics. The conducted experiments highlight the resilience and adaptability of the SR-GNN model in diverse scenarios, demonstrating its potential for use in session-based recommendation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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