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Frontiers of computer Science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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MDEV Model:A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

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computer Systems Science & engineering 2023年第7期46卷 287-302页

作者： Mehwish Shaikh Isma Farah Siddiqui Qasim Arain Jahwan Koo Mukhtiar Ali Unar Nawab Muhammad Faseeh Qureshi Department of Software Engineering Mehran University of Engineering and TechnologyJamshoroPakistan College of Software Sungkyunkwan UniversitySuwonKorea Department of Computer Systems Mehran University of Engineering and TechnologyJamshoroPakistan Department of Computer Education Sungkyunkwan UniversitySeoulKorea

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is *** physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a *** input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’***,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest *** medical classifications,deep convolution neural networks are commonly *** research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and *** MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR *** evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and *** model effectively decreases training loss while increasing *** findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.

关键词： Deep transfer learning convolution neural network image processing computer vision ensemble learning pneumonia classification MDEV model

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Critical Thermal Characterization of Next-Generation Solid-State Batteries for Automotive Battery Management Systems 14

Critical Thermal Characterization of Next-Generation Solid-S...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Chetri, Chandan Williamson, Sheldon Group Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798331516116

Solid-state batteries (SSBs) are at the forefront of next-generation energy storage technologies, promising enhanced safety, energy density, and longevity compared to traditional lithium-ion batteries. However, managing the thermal behavior of SSBs during charging is critical to ensuring their safe and reliable operation. This paper presents an in-depth analysis of the thermal characteristics of SSBs under various charging conditions, based on laboratory experiments conducted in controlled environments. The study specifically evaluates the thermal performance of SSBs in comparison to traditional lithium nickel cobalt aluminum oxide (NCA) batteries, with an emphasis on their potential application in e-mobility. The insights gained from this research are expected to inform the development of advanced thermal management strategies, contributing to the design of safer and more efficient solid-state battery technologies for e-mobility applications. © 2025 IEEE.

关键词： Solid-State Batteries

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Dynamic Synergy: Leveraging RIS and Reinforcement Learning for Secure, Adaptive Underlay Cognitive Radio Networks 15

Dynamic Synergy: Leveraging RIS and Reinforcement Learning f...

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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Tashman, Deemah H. Cherkaoui, Soumaya Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798331520113

Reconfigurable intelligent surfaces (RISs) have recently been employed to facilitate communication and improve performance by reflecting signals through configuring phase shifts toward the intended destination. This article examines the physical layer security of an underlay cognitive radio network aided by an RIS and in the presence of multiple eavesdroppers. The research is conducted under practical conditions, encompassing RIS hardware constraints and cascaded fading channels. An optimization problem is proposed with the objective of maximizing the secrecy rate of secondary users by optimizing the reflection angles of the RIS and the transmission power of the secondary user transmitter. A deep reinforcement learning method, specifically the soft actor-critic, is presented as a solution. The results section demonstrates the effect of altering the number of RIS elements on security. We also analyze the impact of hardware limitations and cascade levels on the secrecy rate. The effect of varying the number of eavesdroppers and the maximum permissible transmission power are also examined. © 2025 IEEE.

关键词： Fading channels

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CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

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Identifying patients in need of psychological treatment with language representation models

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Multimedia Tools and Applications 2025年第1期84卷 397-418页

作者： Aygün, İrfan Kaya, Buket Kaya, Mehmet Department of Software Engineering Celal Bayar University Manisa Turkey Department of Electronics and Automation Fırat University Elazig Turkey Department of Computer Engineering Fırat University Elazig Turkey

Early diagnosis of psychological disorders is very important for patients to regain their health. Research shows that many patients do not realize that they have a psychological disorder or apply to different departments for treatment. The detection of hidden psychological disorders in patients will both increase the quality of life of patients and reduce the traffic of patients who apply to the wrong department. This study aimed to determine whether patients who consult a physician for any reason need psychological treatment. For this purpose, the relationships, and similarities between the sentences of previous psychiatric patients and the sentences of newly arrived patients were analyzed. Domain-based trained ELECTRA language model was used to detect sentence similarities semantically. In the study, the dialogues of patients with physicians in 92 different specialties were analyzed using the MedDialog dataset, which consists of online physician applications, and the DAIC-WOZ dataset. As a result of the experiments, 90.49% success was achieved for the MedDialog dataset and 89.36% for the DAIC-WOZ dataset. With the proposed model, patients in need of psychological treatment were identified and the medical departments where psychological problems were revealed the most were determined. These divisions are Neurology, Sexology, Cardiology, and Plastic Surgery, respectively. With the findings obtained, complications caused by psychological problems and types of diseases that are precursors to psychological disorders were determined. To the best of our knowledge, this article is the first study that aims to analyze all psychological illness instead of focusing on any of the psychological problems (depression, OCD, schizophrenia, etc.) and validated by electronic health records. © The Author(s) 2024.

关键词： Diseases

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DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning

DocTTT: Test-Time Training for Handwritten Document Recognit...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Gu, Wenhao Gu, Li Wang, Ziqiang Suen, Ching Yee Wang, Yang Concordia University Department of Computer Science and Software Engineering Canada

ISBN: (纸本)9798331510831

Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder (MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets. © 2025 IEEE.

关键词： Self-supervised learning

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A remote and cost-optimized voting system using blockchain and smart contract

IET Blockchain

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IET Blockchain 2023年第1期3卷 1-17页

作者： Neloy, Mohammad Nabiluzzaman Wahab, Md. Abdul Wasif, Sheikh All Noman, Abdulla Rahaman, Mustafizur Pranto, Tahmid Hasan Haque, A. K. M. Bahalul Rahman, Rashedur M. Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Software Engineering LUT University Lappeenranta Finland

Traditional voting procedures are non-remote, time-consuming, and less secure. While the voter believes their vote was submitted successfully, the authority does not provide evidence that the vote was counted and tallied. In most cases, the anonymity of a voter is also not sure, as the voter's details are included in the ballot papers. Many voters consider this voting system untrustworthy and manipulative, discouraging them from voting, and consequently, an election loses a significant number of participants. Although the inclusion of electronic voting systems (EVS) has increased efficiency;however, it has raised concerns over security, legitimacy, and transparency. To mitigate these problems, blockchain technology has been leveraged and smart contract facilities with a combination of artificial intelligence (AI) to propose a remote voting system that makes the overall voting procedure transparent, semi-decentralized, and secure. In addition, a system that aids in boosting the number of turnouts in an election through an incentivization policy for the voters have also developed. Through the proposed virtual campaigning feature, the authority can generate a decent amount of revenue, which downsizes the overall cost of an election. To reduce the associated cost of transactions using smart contracts, this system implements a hybrid storage system where only a few cardinal data are stored in the blockchain network. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Smart contract

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Classification of Fetal Cardiac Arrhythmia Using Heart Rate Variability and Machine Learning 5

Classification of Fetal Cardiac Arrhythmia Using Heart Rate ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Shahid, Muhammad Arslan Shaukat, Arslan Akram, Muhammad Usman Butt, Wasi Haider College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350394764

Fetal arrhythmias can lead to cardiac failure or death;thus, early detection is crucial but challenged by noise and artifacts. This paper investigates fetal arrhythmia detection using time, frequency, and non-linear Heart Rate Variability (HRV) features extracted from fetal ECG signals in the NIFEEG dataset. Machine learning classifiers including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, XGBoost, Naive Bayes, and Convolutional Neural Network (CNN) are evaluated. The HRV features provide insights into fetal heart rate patterns. While SVM, KNN, Decision Tree, and XGBoost show promise, CNN demonstrates the highest performance with 98.16% accuracy, 100% sensitivity, 96.15% specificity, and 98.11% F1-score. This highlights CNN's potential for early and reliable fetal arrhythmia detection. Statistical analysis supports the significance of HRV features, paving the way for improved prenatal monitoring. To further improve model generalizability, future work should focus on expanding the dataset size, exploring advanced deep neural network architectures such as LSTMs, and integrating supplementary physiological inputs like uterine contractions. Overall, this study demonstrates the capabilities of HRV-based machine learning approaches, especially CNNs, for robust fetal arrhythmia detection, offering valuable clinical implications for early diagnosis and management. © 2024 IEEE.

关键词： Feature extraction

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Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer

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computer Modeling in engineering & Sciences 2024年第12期141卷 2351-2373页

作者： Mohammed A.Mahdi Shahanawaj Ahamad Sawsan A.Saad Alaa Dafhalla Alawi Alqushaibi Rizwan Qureshi Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Department of Computer and Information Sciences Universiti Teknologi PetronasSeri Iskandar32610Malaysia Center for Research in Computer Vision(CRCV) University of Central FloridaOrlandoFL 32816USA

The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome *** by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical ***,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a *** primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and *** proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer *** models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and *** was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation *** instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model *** 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the

关键词： PET/CT imaging tumor segmentation weighted fusion transformer multi-modal imaging deep learning neural networks clinical oncology

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