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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Shiman Zhang Lakshmikar Reddy Polamreddy Youshan Zhang Graduate Computer Science and Engineering Department Katz School of Science and Health Yeshiva University New York NY

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Canine cardiomegaly, marked by an enlarged heart, poses serious health risks if undetected, requiring accurate diagnostic methods. Current detection models often rely on small, poorly annotated datasets and struggle to generalize across diverse imaging conditions, limiting their real-world applicability. To address these issues, we propose a Confident Pseudo-labeled Diffusion Augmentation (CDA) model for identifying canine cardiomegaly. Our approach addresses the challenge of limited high-quality training data by employing diffusion models to generate synthetic x-ray images and annotate Vertebral Heart Score key points, thereby expanding the dataset. We also employ a pseudo-labeling strategy with Monte Carlo Dropout to select high-confidence labels, refine the synthetic dataset, and improve accuracy. Iteratively incorporating these labels enhances the model's performance, overcoming the limitations of existing approaches. Experimental results show that the CDA model outperforms traditional methods, achieving state-of-the-art accuracy in canine cardiomegaly detection. The code implementation is available at https://***/Shira7z/CDA.

关键词： Heart Deep learning Accuracy Monte Carlo methods Limiting Imaging Training data Diffusion models X-ray imaging Synthetic data

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MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

MSEMG: Surface Electromyography Denoising with a Mamba-based...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Yu-Tung Wang, Kuan-Chen Chao, Rong Siniscalchi, Sabato Marco Yeh, Ping-Cheng Tsao, Yu Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy Research Center for Information Technology Innovation Academic Sinica Taiwan

ISBN: (纸本)9798350368741

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters. © 2025 IEEE.

关键词： Deep neural network ECG interference removal State space model Surface electromyography

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SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing

SST-EM: Advanced Metrics for Evaluating Semantic, Spatial an...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Varun Biyyala Bharat Chanderprakash Kathuria Jialu Li Youshan Zhang Graduate Computer Science and Engineering Department Katz School of Science and Health Yeshiva University New York NY

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Video editing models have advanced significantly, but evaluating their performance remains challenging. Traditional metrics, such as CLIP text and image scores, often fall short: text scores are limited by inadequate training data and hierarchical dependencies, while image scores fail to assess temporal consistency. We present SST-EM (Semantic, Spatial, and Temporal Evaluation Metric), a novel evaluation framework that leverages modern Vision-Language Models (VLMs), Object Detection, and Tempo-ral Consistency checks. SST-EM comprises four components: (1) semantic extraction from frames using a VLM, (2) primary object tracking with Object Detection, (3) focused object refinement via an LLM agent, and (4) temporal consistency assessment using a Vision Transformer (ViT). These components are integrated into a unified metric with weights derived from human evaluations and regression analysis. The name SST-EM reflects its focus on Semantic, Spatial, and Temporal aspects of video evaluation. SST-EM provides a comprehensive evaluation of semantic fidelity and temporal smoothness in video editing. The source code is available in the GitHub Repository.

关键词： Measurement computer vision Adaptation models Computational modeling Source coding Semantics Training data Object detection Transformers Visual perception

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Automatic Teaching Platform on Vision Language Retrieval Augmented Generation

arXiv

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arXiv 2025年

作者： Gokhman, Ruslan Li, Jialu Zhang, Youshan Artificial Intelligence Graduate Computer Science and Engineering Department Yeshiva University NY United States

Automating teaching presents unique challenges, as replicating human interaction and adaptability is complex. Automated systems cannot often provide nuanced, real-time feedback that aligns with students' individual learning paces or comprehension levels, which can hinder effective support for diverse needs. This is especially challenging in fields where abstract concepts require adaptive explanations. In this paper, we propose a vision language retrieval augmented generation (named VL-RAG) system that has the potential to bridge this gap by delivering contextually relevant, visually enriched responses that can enhance comprehension. By leveraging a database of tailored answers and images, the VL-RAG system can dynamically retrieve information aligned with specific questions, creating a more interactive and engaging experience that fosters deeper understanding and active student participation. It allows students to explore concepts visually and verbally, promoting deeper understanding and reducing the need for constant human oversight while maintaining flexibility to expand across different subjects and course material. © 2025, CC0.

关键词： Students

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Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mollah, Muhammad Baqer Wang, Honggang Karim, Mohammad Ataul Fang, Hua University of Massachusetts Dartmouth Department of Electrical and Computer Engineering MA02747 United States Yeshiva University Katz School of Science and Health Department of Graduate Computer Science and Engineering NY10016 United States University of Massachusetts Dartmouth Department of Computer and Information Science MA02747 United States

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications. © 2015 IEEE.

关键词： Beamforming

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Addressing Domain Mismatch in Unsupervised Neural Machine Translation

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 472-482页

作者： Youyuan Lin Rui Wang Chenhui Chu Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Pretrained models have taken full advantage of monolingual corpora and achieved impressive results in training Unsupervised Neural Machine Translation (UNMT) models. However, when adapting UNMT models with in-domain monolingual corpora for domain-specific translation tasks, one of the languages may lack in-domain corpora, resulting in the unequal amount and proportion of in-domain monolingual corpora in each language. This problem situation is known as Domain Mismatch (DM). This study investigates the impact of DM in UNMT. We find that DM causes a translation quality disparity. That is, while in-domain monolingual corpora of a language can enhance the in-domain translation quality into that particular language, this enhancement cannot be generalized to the other language, and the translation quality into the other language remains deficient. To address this problem, we propose Domain-Aware Adaptation (DAA), which can be embedded in the vanilla UNMT model training process. By passing sentence-level domain information to the model during training and inference, DAA gives higher weight to in-domain data from open-domain corpora related to specific domains to alleviate domain mismatch. The experimental results on German-English and Romanian-English translation tasks specified in the IT, Koran, medical, and TED2020 domains demonstrate that DAA can efficiently exploit open-domain corpora to mitigate the quality disparity of translation caused by DM.

关键词： Training Adaptation models Translation Neural machine translation Data models Speech processing

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A Neural Network Model for Autonomous Vehicles Safety Check Using Camera-only/Camera-Lidar Images

A Neural Network Model for Autonomous Vehicles Safety Check ...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Dengyi Liu Honggang Wang Hua Fang Graduate Computer Science and Engineering Department Katz School of Health and Science Yeshiva University NY Dept. of Computer and Information Science University of Massachusetts Dartmouth MA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Camera and LiDAR sensors play a crucial role in vehicle perception systems, enabling accurate detection of obstacles and other vehicles in autonomous driving technologies. Despite their complementary advantages, ensuring the reliability of camera data under varying environmental conditions poses a significant challenge. This paper introduces a novel system for camera data consistency checking through two methods: (1) a camera-only consistency checking mechanism utilizing an effective point-to-point feature extraction neural network inspired by the D2-Net method, and (2) a camera-LiDAR fusion data consistency checking approach employing advanced sensor fusion techniques. By integrating a critical reliability check for camera data and fusing it with LiDAR information, our system leverages the strengths of both sensor types to enhance the robustness and efficiency of autonomous vehicle navigation. Experimental results demonstrate that the proposed methods significantly improve the safety and performance of autonomous driving by ensuring reliable sensor data fusion, enabling safe navigation even in challenging environments.

关键词： Deep learning Accuracy Neural networks Sensor fusion Cameras Feature extraction Sensor systems Sensors Safety Autonomous vehicles

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A Verification Framework Based on EDA Algorithms for E-book Design by Visual Programming Language

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IEEE Software 2025年

作者： Koong, Chorng-Shiuh Hsieh, Tung-Li Wu, Chao-Chin Guo, Cheng-Yan National Taichung University of Education Department of Computer Science and Information Engineering Taichung City403514 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung City807618 Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Changhua50007 Taiwan National Taiwan University Graduate Institute of Electronics Engineering The Graduate School of Advanced Technology Taipei106319 Taiwan

Creating an interactive electronic book (e-book) requires substantial time, skill, and effort. Our study reveals that authoring tools must eliminate users' need for textual programming. We propose a novel interactive e-book design method that uses multimedia reusable components (MRCs) and an e-book description language (EBDL) to implement e-book authoring tools like a circuit design concept. Interactive ebooks can be designed intuitively using MRCs and EBDL without text-based programming, which differs from static syntax analysis of text-based programming languages. The designs using MRCs and EBDL can be verified and tested using the Boolean satisfiability of the electronic design automation algorithm. Notably, verification and debugging are challenging in current visual programming languages. Therefore, our research provides e-book designers reduce the technical requirements of e-book design. The findings can also ensure visual programming languages' consistent behavior and design while detecting design errors. © 1984-2012 IEEE.

关键词： Visual languages

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Damage-Free Neutral Beam Etching for Gate Recess in E-Mode AlGaN/GaN HEMTs

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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A User Interactive Shift Scheduling and Timetabling System Based on Evolutionary Computation for Private Tutoring Schools 29th

A User Interactive Shift Scheduling and Timetabling System ...

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29th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

作者： Suzuki, Yosuke Mizuno, Kazunori Graduate School of Engineering Takushoku University Tokyo Japan Department of Computer Science Faculty of Engineering Takushoku University Tokyo Japan

ISBN: (纸本)9789819645954

A shift schedule is absolutely necessary to manage the work of each employee. However, manually constructing the schedule taken account of employee preference can impose a large burden on a constructor. In private tutoring school, a shift schedule should make arrangement of the requests of students in addition to teachers’ preference, being necessary to construct simultaneously a shift schedule for each teacher and a timetable for each student. In this paper, we have proposed a two-phase optimization method to find such schedules: shift scheduling using memetic algorithms and timetabling using simulated annealing. We have demonstrated that the proposed method, in particular memetic algorithms in the first phase, can be more effective for constructing a shift schedule and timetable that are taken account of both requests of teachers and students than the previous method using simple genetic algorithms and simulated annealing. In addition, we have developed an interface that allows users to interactively check and make minor modifications to the automatically created shift schedule. This user interface can be useful for the user to instantly check where constraints have been violated and assist in making corrections by the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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