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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Michael Vierhauser Kristof Meixner Stefan Biffl Department of Computer Science University of Innsbruck Austria CDL-SQI Vienna Austria Institute of Information Systems Engineering TU Wien Vienna Austria CDP Vienna Austria

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

Testing and validating Cyber-Physical systems (CPSs) in the aerospace domain, such as field testing of drone rescue missions, poses challenges due to volatile mission environments, such as weather conditions. While testing processes and methodologies are well established, structured guidance and execution support for field tests are still weak. This paper identifies requirements for field testing of drone missions and introduces the Field Testing Scenario Management (FiTS) approach for adaptive field testing guidance. FiTS aims to provide sufficient guidance for field testers as a foundation for efficient data collection to facilitate quality assurance and iterative improvement of field tests and CPSs. FiTS shall leverage concepts from scenario-based requirements engineering and Behavior-Driven Development to define structured and reusable test scenarios, with dedicated tasks and responsibilities for role-specific guidance. We evaluate FiTS by (i) applying it to three use cases for a search-and-rescue drone application to demonstrate feasibility and (ii) interviews with three experienced drone developers to assess its usefulness and collect further requirements. The study results indicate FiTS to be feasible and useful to facilitate drone field testing and data analysis.

关键词： Quality assurance Navigation Data collection Requirements engineering Iterative methods Interviews Testing Drones software engineering Meteorology

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Organic-Perovskite Bilayer Color Conversion Media for Full-Color Displays

Organic-Perovskite Bilayer Color Conversion Media for Full-C...

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International Conference on Display Technology, 2024

作者： Liu, Siyang Ting Tam, Bryan Siu Zhu, Mingchao Ye, Fanghao Yan Yeung, Fion Sze Li, Guijun Dong, Shou-Cheng State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Shenzhen China Institute for Advanced Study Hong Kong University of Science and Technology Kowloon Hong Kong Co. Ltd. Suzhou China

Color conversion media (CCM) are crucial for realizing cost-effective, high-resolution micro-LED and OLED displays. In this work, we developed green and red organic-perovskite bilayer CCM that combine the PLQY and color purity of perovskite quantum dots (PeQDs) and the strong blue absorption of perylene-based organic emitters. The bilayer with thickness of 300 nm showed over 99% blue absorption and PLQY of 77% and 61% for green and red CCM, respectively. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Perovskite

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Support Vector Machine Based Automatic Classification Method for IoT Big Data Features

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Journal of computers (Taiwan) 2023年第5期34卷 15-27页

作者： Xu, Yong-Hua School of Computer Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China

As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation. © 2023 computer Society of the Republic of China. All rights reserved.

关键词： Internet of things

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Multi Sensor-Based Implicit User Identification

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computers, Materials & Continua 2021年第8期68卷 1673-1692页

作者： Muhammad Ahmad Rana Aamir Raza Manuel Mazzara Salvatore Distefano Ali Kashif Bashir Adil Khan Muhammad Shahzad Sarfraz Muhammad Umar Aftab Department of Computer Science National University of Computer and Emerging SciencesChiniot35400Pakistan Department of Computer Science Bahauddin Zakariya UniversityMultan66000Pakistan Institute of Software Development and Engineering Innopolis UniversityInnopolis420500Russia Dipartimento di Matematica e Informatica—MIFT University of MessinaMessina98121Italy Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK

Smartphones have ubiquitously integrated into our home and work environments,however,users normally rely on explicit but inefficient identification processes in a controlled ***,when a device is stolen,a thief can have access to the owner’s personal information and services against the stored *** a result of this potential scenario,this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors.A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features,then further optimized using a non-linear unsupervised feature selection *** selected features create an underlying gait biometric representation able to discriminate among individuals and identify them *** classifiers are adopted to achieve accurate legitimate user *** experiments on a group of 16 individuals in an indoor environment show the effectiveness of the proposed solution:with 5 to 70 samples per window,KNN and bagging classifiers achieve 87–99%accuracy,82–98%for ELM,and 81–94%for *** proposed pipeline achieves a 100%true positive and 0%false-negative rate for almost all classifiers.

关键词： Sensors smartphone legitimate user identification

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Merging Parameter Estimation and Classification Using LASSO 63

Merging Parameter Estimation and Classification Using LASSO

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Le Wang, Ying Qiu, Yu Li, Mian Hjalmarsson, Hakan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm10044 Sweden UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China Saic Motor Rd Innovation Headquarters Shanghai201807 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China Centre for Advanced Bio Production Stockholm10044 Sweden

ISBN: (纸本)9798350316339

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not a priori be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness. © 2024 IEEE.

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Interference reduction in directional wireless networks 17th

Interference reduction in directional wireless networks

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17th International Conference on Distributed Computing and Internet Technology, ICDCIT 2021

作者： Basappa, Manjanna Mishra, Sudeepta Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Hyderabad Campus Shameerpet500078 India Department of Computer Science and Engineering Indian Institute of Technology Ropar Nangal Road Rupnagar140001 India

ISBN: (纸本)9783030656201

In a wireless network using directional transmitters, a typical problem is to schedule a set of directional links to cover all the receivers in a region, such that an adequate data rate and coverage are maintained while minimizing interference. We can model the coverage area of a directional transmitter as an unit triangle and the receiver as a point in the plane. Motivated by this, we first consider a minimum ply covering (MPC) problem. We propose a 2-approximation algorithm for the MPC problem in O((opt+ n) m14opt+1(log opt)) time, where m is the number of transmitters and n is the number of receivers given in the plane, and opt is the maximum number of triangles, in the optimal solution, covering a point in the plane. We also show that the MPC problem is NP-hard, and is not (2 - ϵ) -approximable for any ϵ> 0 unless P = NP. We also study channel allocation in directional wireless networks by posing it as a colorable covering problem, namely, 3-colorable unit triangle cover (3CUTC). We propose a simple 4-approximation algorithm in O(m30n2) time, for this problem. © Springer Nature Switzerland AG 2021.

关键词： Approximation algorithms

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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EMGSense: A Low-Effort Self-Supervised Domain Adaptation Framework for EMG Sensing

EMGSense: A Low-Effort Self-Supervised Domain Adaptation Fra...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Di Duan Huanqi Yang Guohao Lan Tianxing Li Xiaohua Jia Weitao Xu City University of Hong Kong Shenzhen Research Institute Department of Computer Science City University of Hong Kong Department of Software Technology Delft University of Technology Department of Computer Science and Engineering Michigan State University

This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and label-free) manner. To alleviate the burden of data collection and avoid labor-intensive data annotation, we propose two EMG-specific data augmentation methods to simulate the EMG signals generated in various conditions and scope the exploration in label-free scenarios. We model combating biological heterogeneity-caused performance degradation as a multi-source domain adaptation problem that can learn from the diversity among source users to eliminate EMG heterogeneous biological features. To relearn the target-user-specific biological features from the unlabeled data, we integrate advanced self-supervised techniques into a carefully designed deep neural network (DNN) structure. The DNN structure can seamlessly perform two training stages that complement each other to adapt to a new user with satisfactory performance. Comprehensive evaluations on two sizable datasets collected from 13 participants indicate that EMGSense achieves an average accuracy of 91.9% and 81.2% in gesture recognition and activity recognition, respectively. EMGSense outperforms the state-of-the-art EMG-oriented domain adaptation approaches by 12.5%-17.4% and achieves a comparable performance with the one trained in a supervised learning manner.

关键词： Degradation Training Pervasive computing Deep learning Supervised learning Neural networks Gesture recognition

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Resource Allocation Driven by Large Models in Future Semantic-Aware Networks

arXiv

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arXiv 2025年

作者： Zhang, Haijun Ni, Jiaxin Wu, Zijun Liu, Xiangnan Leung, V.C.M. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing10083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden College of Computer Science and Software Engineering Shenzhen University Shenzhen51806 China VancouverBCV6T 1Z4 Canada

Large model has emerged as a key enabler for the popularity of future networked intelligent applications. However, the surge of data traffic brought by intelligent applications puts pressure on the resource utilization and energy consumption of the future networks. With efficient content understanding capabilities, semantic communication holds significant potential for reducing data transmission in intelligent applications. In this article, resource allocation driven by large models in semantic-aware networks is investigated. Specifically, a semantic-aware communication network architecture based on scene graph models and multimodal pre-trained models is designed to achieve efficient data transmission. On the basis of the proposed network architecture, an intelligent resource allocation scheme in semantic-aware network is proposed to further enhance resource utilization efficiency. In the resource allocation scheme, the semantic transmission quality is adopted as an evaluation metric and the impact of wireless channel fading on semantic transmission is analyzed. To maximize the semantic transmission quality for multiple users, a diffusion model-based decision-making scheme is designed to address the power allocation problem in semantic-aware networks. Simulation results demonstrate that the proposed large-model-driven network architecture and resource allocation scheme achieve high-quality semantic transmission. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Web

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