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arXiv 2024年

作者： Peng, Yubo Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun School of Intelligent Software and Engineering Nanjing University Suzhou China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Department of Computer Science Brunel University London United Kingdom

Semantic Communication (SC) focuses on transmitting only the semantic information rather than the raw data. This approach offers an efficient solution to the issue of spectrum resource utilization caused by the various intelligent applications on Mobile Users (MUs). Generative Artificial Intelligence (GAI) models have recently exhibited remarkable content generation and signal processing capabilities, presenting new opportunities for enhancing SC. Therefore, we propose a GAI-assisted SC (GSC) model deployed between MUs and the Base Station (BS). Then, to train the GSC model using the local data of MUs while ensuring privacy and accommodating heterogeneous requirements of MUs, we introduce Personalized Semantic Federated Learning (PSFL). This approach incorporates a novel Personalized Local Distillation (PLD) and Adaptive Global Pruning (AGP). In PLD, each MU selects a personalized GSC model as a mentor tailored to its local resources and a unified Convolutional Neural Networks (CNN)-based SC (CSC) model as a student. This mentor model is then distilled into the student model for global aggregation. In AGP, we perform network pruning on the aggregated global model according to real-time communication environments, reducing communication energy. Finally, numerical results demonstrate the feasibility and efficiency of the proposed PSFL scheme. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Thematic Domain Analysis for Ocean Modeling

arXiv

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arXiv 2022年

作者： Jung, Reiner Gundlach, Sven Hasselbring, Wilhelm Software Engineering Group Department of Computer Science Kiel University Christian-Albrechts-Platz 4 Kiel24118 Germany

Ocean science is a discipline that employs ocean models as an essential research asset. Such scientific modeling provides mathematical abstractions of real-world systems, e.g., the oceans. These models are then coded as implementations of the mathematical abstractions. The developed software systems are called models of the real-world system. To advance the state in engineering such ocean models, we intend to better understand how ocean models are developed and maintained in ocean science. In this paper, we present the results of semi-structured interviews and the Thematic Analysis (TA) of the interview results to analyze the domain of ocean modeling. Thereby, we identified developer requirements and impediments to model development and evolution, and related themes. This analysis can help to understand where methods from software engineering should be introduced and which challenges need to be addressed. We suggest that other researchers extend and repeat our TA with model developers and research software engineers working in related domains to further advance our knowledge and skills in scientific modeling. © 2022, CC BY-NC-ND.

关键词： Oceanography

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Day-ahead PV Output Power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Hossein Nourollahi Hokmabad Oleksandr Husev Dmitri Vinnikov Juri Belikov Eduard Petlenkov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R 2 score compared to standalone LSTM models.

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation

arXiv

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arXiv 2025年

作者： Cao, Hongye Feng, Fan Huo, Jing Yang, Shangdong Fang, Meng Yang, Tianpei Gao, Yang National Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

Model-based offline Reinforcement Learning (RL) constructs environment models from offline datasets to perform conservative policy optimization. Existing approaches focus on learning state transitions through ensemble models, rollouting conservative estimation to mitigate extrapolation errors. However, the static data makes it challenging to develop a robust policy, and offline agents cannot access the environment to gather new data. To address these challenges, we introduce Model-based Offline Reinforcement learning with AdversariaL data augmentation (MORAL). In MORAL, we replace the fixed horizon rollout by employing adversaria data augmentation to execute alternating sampling with ensemble models to enrich training data. Specifically, this adversarial process dynamically selects ensemble models against policy for biased sampling, mitigating the optimistic estimation of fixed models, thus robustly expanding the training data for policy optimization. Moreover, a differential factor is integrated into the adversarial process for regularization, ensuring error minimization in extrapolations. This data-augmented optimization adapts to diverse offline tasks without rollout horizon tuning, showing remarkable applicability. Extensive experiments on D4RL benchmark demonstrate that MORAL outperforms other model-based offline RL methods in terms of policy learning and sample efficiency. © 2025, CC BY.

关键词： Adversarial machine learning

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Classifying Psychiatric Patients Using Machine Learning

Classifying Psychiatric Patients Using Machine Learning

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Information Technology (ACIT)

作者： Hmmam M. Alrjoob Raed Alazaidah Radwan Batyha Hayel Khafajeh Esraa Abu Elsoud Ala'a Saeb Al-Sherideh Ali Elrashidi Muhyeeddin Alqaraleh Waleed Amer Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyper Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty on Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.

关键词： Machine learning algorithms Correlation Psychology Feature extraction Classification algorithms Bayes methods Information technology Random forests Bagging Logistics

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Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplink NOMA IoT Systems

Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplin...

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IEEE International Conference on Communications (ICC)

作者： Tianheng Xu Yinjun Xu Jingyi Wu Haijun Zhang Ting Zhou Honglin Hu Victor C.M. Leung Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Frontier lnnovation Research Institute Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Microelectronics Shanghai University Shanghai China Department of Electrical and Computer Engineering The University of British Columbia Canada College of Computer and Software Engineering Shenzhen University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

关键词： 6G mobile communication NOMA Closed-form solutions Simulation Throughput Orbits Sensors

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Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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Advancements in Computing (ICAC), International Conference on

作者： Aparna Jayawardena Kasuni Ganegoda Sakuni Imbulana Gavin Gunapala Nuwan Kodagoda Thilini Jayasinghe Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries; leaf disease identification; cover crop identification; and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology.

关键词： Image recognition Crops Machine learning computer architecture Mobile handsets Mobile applications Rubber

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Artificial Intelligence computer-Aided Diagnosis to automatically predict the Pediatric Wrist Trauma using Medical X-ray Images

Artificial Intelligence Computer-Aided Diagnosis to automati...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Elif Merve Erzen Ertan BÜtÜn Mugahed A. Al-Antari Radhwan A. A. Saleh Daniel Addo Department of Computer Engineering Fırat University Elazığ Turkey Department of Artificial Intelligence Sejong University Seoul Korea Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

Pediatric wrist trauma (PWT) is a common injury that occurs in hospital emergency departments, with fractures being among the most frequent cases. The traditional diagnostic process for these injuries involves the collaboration of radiologists and surgeons. However, recent advancements in deep learning-based computer vision algorithms have shown potential in automating and expediting this diagnosis. In this paper, we propose an automatic end-to-end computer-aided diagnosis (CAD) system that can simultaneously detect and classify various types of pediatric wrist injuries, including bone anomalies, bone lesions, foreign bodies, fractures, metallic artifacts, periosteal reactions, pronator signs, soft tissue abnormalities, textual elements, and the axis. To train and evaluate the proposed CAD system, we utilize the public GRAZPEDWRI-DX dataset. The design of our CAD system involves careful steps such as medical X-ray data collection, data labeling, preprocessing, prediction AI model optimization, and model evaluation. The core component used for the detection and classification task in our system is YOLOv8, the advanced and state-of-the-art version of YOLO for object detection. Experimental results demonstrate that our proposed CAD system achieves promising evaluation results in terms of precision (77.80%), recall (54.60%), mAP@50 (59.10%), and mAP50@95 (37.20%). These encouraging evaluation results can serve as a future direction to provide practical solutions for improving the capabilities of the healthcare system in rapidly and accurately predicting pediatric wrist injuries.

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Towards an Intelligent Industrial Environment: Blockchain-Empowering Human-Centered Artificial Intelligence for Risk Assessment and Strategic Evaluation

SSRN

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SSRN 2023年

作者： Khan, Abdullah Ayub Chen, Yen-Lin Hajjej, Fahima Shaikh, Aftab Ahmed Yang, Jing Ku, Chin Soon Por, Lip Yee Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

With the recent introduction of high-level human control and high-level IoT control in intelligent transportation systems, their association with blockchain increases the performance of the Internet of Vehicles (IoVs) and E-Healthcare, which leads to wider adaptation in the future as well. In this paper, the contributions of this systematic article are divided into three different folds: first, this paper evaluates more than a hundred previously published articles and highlights the gaps in futuristic concerns. Second, the paper presents a novel and secure human-centered AI-enabled risk assessment framework for the IoVs with blockchain distributed ledger technology (DLT). Third, a distributed public permissionless network with two operational channels of inter-communication for vehicle-to-vehicle (V2V) is proposed. It is considered an extension in the Vehicle-to-Everything (V2X) environment. Along with that, the pseudo-development of chaincodes to automate human-centered IoV node transactions. In addition, a Practical Byzantine Fault Tolerance (PBFT) consensus protocol and related controlled executions are programmed for fast scheduling, offloading, managing, and optimizing with reliability. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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