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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingcheng Fang Ying He F. Richard Yu Jianqiang Li Victor C. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

With the increasingly sophisticated algorithms of simultaneous localization and mapping (SLAM), it is difficult for mobile terminals with limited resources to exploit the performance of SLAM algorithms fully. Traditional deep reinforcement learning (DRL)-based approaches have offloaded SLAM tasks to servers. However, existing solutions suffer from low data efficiency and poor generalization problems. This paper proposes a novel approach based on recent advances in rewardless active inference for SLAM back-end optimization. Specifically, the reward function is replaced with simple rewardless guidance in active inference. In addition, instead of simply considering the SLAM task as a whole, we delve into the sub-tasks of back-end optimization of SLAM for offloading and resource allocation. Simulation results show the superior performance of the proposed scheme.

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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Development of a Query Delay Injection System for the MEC Simulator of the LWMECPS Platform

Development of a Query Delay Injection System for the MEC Si...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ivan Filianin Vladimir Bogatyrev Matvey Lesonen Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

In this paper the problem of testing decision making systems for MEC platforms was formulated. Methods and means of organizing the introduction of network delays as part of the emulation system of MEC platforms LWMECPS were analyzed. Test applications mec-test-app and mec-test-client are developed for testing and evaluation of the introduced network delays. Developed mec-orch-app to provide test application management and metrics collection. Experiments have been conducted to estimate the insertion latency and manage the test applications using mec-orch-app. Special attention was paid to the organization of all parts of the system as a future laboratory bench for testing decision systems using reinforcement learning machine learning algorithms.

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Large Language Models (LLMs) Inference Offloading and Resource Allocation in Cloud-Edge Networks: An Active Inference Approach

Large Language Models (LLMs) Inference Offloading and Resour...

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IEEE Conference on Vehicular Technology (VTC)

As the research and applications of large language model (LLM) become increasingly sophisticated, it is difficult for resource-limited mobile terminals to run large-model inference tasks efficiently. Traditional deep reinforcement learning (DRL) based approaches have been used to offload LLM inference tasks to servers. However, existing solutions suffer from data inefficiency, insensitivity to latency requirements, and non-adaptability to task load variations. In this paper, we propose an active inference with rewardless guidance algorithm using expected future free energy for offloading decisions and allocating resources for the LLM inference task offloading and resource allocation problem of cloud-edge networks systems. Experimental results show that our proposed method has superior performance over mainstream DRLs, improves in data utilization efficiency, and is more adaptable to changing task load scenarios.

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S-Faster R-CNN: Intra-Spectral Similarity Learning for Audio Copy-Move Forgery Localization in IoT Security

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IEEE Internet of Things Journal 2025年

作者： Qiu, Xin Shi, Canghong Li, Xiaojie Wu, Min Abdullahi, Sani M. Liu, Yong Xihua University School of Computer and Software Engineering Chengdu610039 China Chengdu University of Information Technology School of Computer Science Chengdu610225 China University of Southern Denmark M Rsk McKinney Moller Institute Odense5230 Denmark Shanghai University School of Communication and Information Systems 200444 China

In the Internet of Audio Things, communication security of the audio control terminal is vulnerable to copy-move threats, and detecting and locating audio copy-move forgery remains challenging nowadays. The forgery detection method based on deep learning achieves higher detection accuracy but fails to localize forged regions. To address this issue, this article proposes an S-Faster R-CNN model for audio copy-move forgery detection and localization. We integrate a novel Similarity Computation Module (SCM) into the Faster R-CNN framework, forming the S-Faster R-CNN model. Obtaining the integration of the SCM, which allows the S-Faster R-CNN to precisely localize forgery regions within the spectrogram. Finally, the image coordinate transformation algorithm is used to map these forged regions to the corresponding locations of the original audio waveform, thus completing the audio copy-move forgery detection and localization. Evaluated on three datasets, our method achieves an average recall of 90%, an average precision of 84%, and an average F1-score of 87%, respectively. Experimental results indicate that the S-Faster R-CNN outperforms state-of-the-art methods in both forgery detection accuracy and especially in localization. Moreover, the proposed method shows good robustness under multiple post-processing. © 2014 IEEE.

关键词： Spectrographs

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Quantum Reinforcement Learning with Quantum World Model

Quantum Reinforcement Learning with Quantum World Model

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Victor C.M. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.

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Process Discovery Automation: Benefits and Limitations

Process Discovery Automation: Benefits and Limitations

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Intelligent Methods, systems, and Applications (IMSA)

作者： Reem Kadry Montasser Iman M. A. Helal Software Engineering Department Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

Process discovery algorithms incorporating domain knowledge can have varying levels of user involvement. It ranges from fully automated algorithms to interactive approaches where the user makes critical decisions about the process model. Designing domain knowledge using process discovery techniques faces various challenges. These challenges could cause some issues with existing approaches. Acquiring domain knowledge with domain experts, integrating domain knowledge with process data, scalability to handle large complex data sets, and ensuring data quality are examples of these challenges. In this survey, we assess recent work with varying levels of automation in process discovery to enhance the analysis and understanding of business processes within an organization. Current work can be classified into two categories: fully automated or semi-automated process discovery. We conclude that semi-automated process discovery gives a better opportunity for involving users. Also, the use of deep learning algorithms in automation gives better performance than machine learning algorithms.

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Towards Unified Field-Testing and Monitoring for Safe and Secure Robotic Applications

Towards Unified Field-Testing and Monitoring for Safe and Se...

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Workshop on Robotics software engineering (RoSE)

作者： Marco Stadler Stefan Biffl Michael Vierhauser Johannes Sametinger LIT SCSL / Inst. of Business Informatics - Software Eng. Johannes Kepler University Linz Austria CDL-SQI Institute of Information Systems Engineering TU Wien and CDP Vienna Austria Department of Computer Science University of Innsbruck Austria

ISBN: (数字)9798331537951

ISBN: (纸本)9798331537968

Problems and failures that emerge in Cyber-Physical systems (CPSs), particularly in robotic applications, may originate from various sources, including software bugs, security incidents, hardware malfunctions, and human errors. As robotic systems are deployed in various domains and application contexts, such as manufacturing sites, shop floors, agriculture, and autonomous vehicles, ensuring their safe and secure operation is a crucial aspect. While high-fidelity simulations are frequently used to validate system behavior and to perform tests, the “simulator-to-reality gap” presents significant challenges, requiring additional field testing to validate a system under realistic conditions. As simulations alone are insufficient for performing comprehensive testing and for ensuring adherence to both functional and non-functional requirements, real-world field testing helps to alleviate these issues. However, compared to wellestablished unit testing approaches, field testing typically is still a rather ad hoc process, with insufficient support from tools and frameworks. Field tests often heavily rely on human observations, hence risking overlooking critical issues. There is a pressing need for structured, guided field-testing processes combined with adaptive runtime monitoring to capture the data required for effective error diagnosis and analysis. This paper introduces initial concepts for the Smart Unified Runtime Monitoring Infrastructure for Guided Field-Testing (SMURF) framework designed for robotic applications, combining structured test execution with automated, adaptable monitors, to ensure the efficient collection of data required for post-test analysis. Building on prior efforts in drone field-testing frameworks, we extend our scope to identify essential features for testing and monitoring ROS-based systems. Future work shall further refine this process and implement a practical framework to support developers and testers in achieving reliable, safe, and secure

关键词： Runtime Pressing software Safety Manufacturing Security Reliability Robots Monitoring Testing

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An Architectural Approach for Enhanced Data Interoperability Across Building systems

An Architectural Approach for Enhanced Data Interoperability...

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Cloudification of the Internet of Things (CIoT)

作者： Peter Yefi Sikandar Ejaz Ramanunni Parakkal Menon Ursula Eicker Yann-Gaël Guéhéneuc Computer Science and Software Engineering Dept. Concordia University Montreal Canada Institute for Information Systems Engineering Concordia University Montreal Canada German Aerospace Center Institute for Solar Research Jülich Germany NextGen Cities Institute Concorida University Montreal Canada

ISBN: (数字)9798350356717

ISBN: (纸本)9798350356724

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems’ data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners.

关键词： Temperature sensors Meters Architecture Buildings Time series analysis Data models Energy efficiency Sensors Stakeholders Building management systems

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