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24th International Symposium for Production Research, ISPR 2024

作者： Köse, Nilüfer Zeren, Belkis İclal Çiçeklidağ, Paşa Gürbüzer, Selman Süha Sahin, Merve Department of Industrial Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Computer Science and Software Engineer The University of Western Australia Perth Australia Department of Management Ibn Haldun University Istanbul Turkey

ISBN: (纸本)9783031835827

This study utilizes the SERVQUAL model to conduct a comprehensive analysis of the impact of service quality on customer satisfaction and loyalty within Türkiye’s rapidly expanding e-cosmetics sector. As the industry experiences substantial growth due to digital transformation and changing consumer behaviors, the importance of high service quality in maintaining customer loyalty has never been more critical. This research collected data from 95 participants through meticulously designed online surveys conducted between May and June 2024, employing an adapted SERVQUAL survey to assess service quality across five key dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Utilizing advanced statistical methods with SmartPLS 4.0, the study explores both the direct and indirect effects of these dimensions on customer satisfaction (CS) and customer loyalty (CL). The findings reveal that while tangibles, reliability, and assurance have strong positive impacts on customer satisfaction, other factors such as product quality, brand image, and user interface design also significantly affect customer satisfaction and loyalty. This study provides actionable insights into how e-cosmetics companies can enhance their service quality to better meet consumer expectations, thereby fostering sustained customer satisfaction and loyalty. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User experience

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Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

Synthetic Aircraft Trajectory Generation Using Time-Based VQ...

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Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Abdulmajid Murad Massimiliano Ruocco Department of Software Engineering Safety and Security SINTEF Digital Trondheim Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798331534738

ISBN: (纸本)9798331534745

In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In this paper, we propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE). Our approach leverages time-frequency domain processing, vector quantization, and transformer-based priors to capture both global and local dynamics in flight data. By discretizing the latent space and integrating transformer priors, the model learns long-range spatiotemporal dependencies and preserves coherence across entire flight paths. We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator. Results indicate that TimeVQVAE outperforms a temporal convolutional VAE baseline, generating synthetic trajectories that mirror real flight data in terms of spatial accuracy, temporal consistency, and statistical properties. Furthermore, the simulator-based assessment shows that most generated trajectories maintain operational feasibility, although occasional outliers underscore the potential need for additional domain-specific constraints. Overall, our findings underscore the importance of multi-scale representation learning for capturing complex flight behaviors and demonstrate the promise of TimeVQVAE in producing representative synthetic trajectories for downstream tasks such as model training, airspace design, and air traffic forecasting.

关键词： Training Time-frequency analysis Atmospheric modeling Vector quantization Transformers Aircraft navigation Vectors Trajectory Air traffic control Aircraft

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Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

arXiv

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arXiv 2025年

作者： Murad, Abdulmajid Ruocco, Massimiliano Department of Software Engineering Safety and Security SINTEF Digital Trondheim Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

关键词： Aerobraking

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Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

Synthetic Aircraft Trajectory Generation Using Time-Based VQ...

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2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Murad, Abdulmajid Ruocco, Massimiliano SINTEF Digital Department of Software Engineering Safety and Security Trondheim Norway Norwegian University of Science and Technology Department of Computer Science Trondheim Norway

ISBN: (纸本)9798331534738

关键词： Aerobraking

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One Gray Code Fits All: Optimizing Access Time with Bi-Directional Programming for QLC SSDs

One Gray Code Fits All: Optimizing Access Time with Bi-Direc...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Shaoqi Li Tianyu Wang Yongbiao Zhu Chenlin Ma Yi Wang Zhaoyan Shen Zili Shao College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Gray code, a voltage-level-to-data-bit translation scheme, is widely used in QLC SSDs. However, it causes the four data bits in QLC to exhibit significantly different read and write performance with up to 8 × latency variation, severely impacting the worst-case performance of QLC SSDs. This paper presents BDP, a novel Bi-Directional Programming scheme. Based on a fixed Gray code, BDP combines both the normal (forward) and reverse programming directions to enable runtime programming direction arbitration. Experimental results show that BDP can effectively improve the read and write performance of SSD compared to representative schemes.

关键词： Translation Runtime Europe Bidirectional control Programming Reflective binary codes

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Implementation of mho characteristics in FPAA-based reconfigurable new generation static relay

Implementation of mho characteristics in FPAA-based reconfig...

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19th IET Conference on Developments in Power System Protection (DPSP Europe 2025)

作者： Prasoon Arya Sarasij Das Tarlochan Sidhu Department of Electrical Engineering Indian Institute of Science Bangalore India Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781837243167

Static relays are reliable, robust and fast compared to digital relays. However, they are bulky and hard to configure. The discovery of microprocessor-based relays overcame these limitations, although they are prone to cyber-attacks as they are digital computers. It is tough to detect malicious code and malware in digital computers; conversely, analog circuits are immune to these attacks. Before microprocessor-based relays, static relays were used in power systems protection; they are still considered reliable, robust, fast and immune to cyber threats as they didn’t have any digital components. With the developments in analog technology, such as Field Programmable analog Array (FPAA), there is now an opportunity for analog relays to rebound with reduced size, ease of design and reconfigurability. This work proposes an FPAA-based new generation analog mho relay demonstrating a fully analog implementation of mho distance characteristics on FPAA. The line-ground (LG) and line-line (LL) elements are modelled in the AN231K04-QUAD4 kit and tested on the IEEE 9-Bus system. The results are promising. Relay shows dependability over the designed range of its operation. Besides, this work also proposes using FPAA-based static relays along with the existing digital relays to enhance the cyber security of the protection schemes.

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A Practical Learning-Based FTL for Memory Constrained Mobile Flash Storage

A Practical Learning-Based FTL for Memory Constrained Mobile...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Zelin Du Kecheng Huang Tianyu Wang Xin Yao Renhai Chen Zili Shao Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR College of Computer Science and Software Engineering Shenzhen University Shenzhen China Huawei Technologies China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapidly growing mobile market is pushing flash storage manufacturers to expand capacity into the terabyte range. However, this presents a significant challenge for mobile storage management: more logical-to-physical page mappings are desired to be efficiently managed and cached while the available caching space is extremely limited. This motivates us to shift toward a new learning-based paradigm: rather than maintaining mappings for individual pages, the learning-based approach can represent mapping relationships for a set of continuous pages. However, to construct linear models, existing methods that either consume the already-limited memory space or reuse flash garbage collection demonstrate poor model construction capabilities or significantly degrade flash performance, making them impractical for real-world use. In this paper, we propose LFTL, a practical, learning-based on-demand flash translation layer design for flash management in mobile devices. In contrast to prior work that centered around gathering sufficient mappings for linear model construction, our key insight is that linear patterns can be extracted and refined by leveraging the orderly, LPA-aligned write stream typical of mobile devices. By doing this, highly accurate linear models can be constructed regardless of the constraints of mobile device's cache limitation. We have implemented a fully functional prototype of LFTL based on FEMU. Our evaluation results show that LFTL is more adaptable to memory-constrained storage devices than state-of-the-art learning-based approaches.

关键词： Performance evaluation Adaptation models Maximum likelihood detection Translation Accuracy Storage management Prototypes Nonlinear filters Mobile handsets Load modeling

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A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory

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Knowledge and Information Systems 2025年 1-32页

作者： Asadi, Mehdi Heidari, Arash Jafari Navimipour, Nima Department of Computer Engineering Islamic Azad University Khameneh Branch Khameneh Iran Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan

Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.

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Performance of pre-trained deep learning models for land use land cover classification using remote sensing imaging datasets

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Environmental Earth sciences 2025年第11期84卷 1-36页

作者： Haider, Irfan Khan, Muhammad Attique Masood, Saleha Algamdi, Shabbab Ali Alasiry, Areej Marzougui, Mehrez Nam, Yunyoung Department of Computer Science HITEC University Taxila Pakistan Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia IRC for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Software Engineering College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of

This study presents a comparative analysis of ten pre-trained convolutional neural network (CNN) models, evaluated across three remote sensing datasets: EuroSat, NWPU, and Earth Hazards (Land Sliding). We investigate the interplay between model architecture and classifier selection by incorporating five different neural network (NN) classifiers, emphasizing their impact on predictive accuracy and computational efficiency. Due to its densely connected architecture, DenseNet201 achieved the highest accuracy—97% on EuroSat, 99.40% on NWPU, and 97.80% on Earth Hazards. In contrast, MobileNetV2, while slightly less accurate, demonstrated superior computational efficiency, recording the shortest prediction times of 39.943 s on EuroSat, 27.482 s on NWPU, and 2.8986 s on Earth Hazards. Additionally, classifier choice significantly influenced performance, with the Wide NN classifier excelling in diverse datasets and the Medium NN classifier optimizing speed. Our findings underscore the importance of balancing accuracy and efficiency in selecting CNN models for remote sensing applications, suggesting future research should explore ensembling techniques and lightweight models to enhance performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： software design

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Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities

arXiv

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arXiv 2025年

作者： Chen, Cheng Tian, Geng Qin, Shaoxiang Geng, Dingyang Zhan, Dongxue Yang, Jinqiu Vidal, David Wang, Liangzhu Concordia University Department of Building Civil and Environmental Engineering Department of Computer Science and Software Engineering Montreal Canada Polytechnique Montreal Department of Mechanical Engineering Montreal Canada

Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model’s ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model’s generalizability for different wind directions and urban layouts. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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