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作者： Tan-Sang Ha Hai Nguyen-Truong Tuan-Anh Vu Sai-Kit Yeung Department of Computer Science and Engineering The Hong Kong University of Science and Technology Division of Integrative Systems and Design (ISD) The Hong Kong University of Science and Technology

Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we present MarineVRS, a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. In addition, unlike the conventional video retrieval system, which only permits users to index a collection of images or videos and search using a freeform natural language sentence, our retrieval system includes an additional Explainability module that outputs the segmentation masks of the objects that the input query referred to. This feature allows users to identify and isolate specific objects in the video footage, leading to more detailed analysis and understanding of their behavior and movements. Finally, with its adaptability, explainability, accuracy, and scalability, MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species.

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Sign Language Vocabulary Recognition Only with Tactile Sensing Glove

Sign Language Vocabulary Recognition Only with Tactile Sensi...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Motoki Kagami Zeping Yu Sim Teck Ceng Lei Jing School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Graduate School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Management and Design University of Aizu Junior College Division Aizu-Wakamatsu Japan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

A significant challenge in communication arises between deaf individuals and hearing individuals. Sign language recognition is expected to be an important research field for eliminating barriers. In this field, data collection is primarily conducted using two methods: sensor-based and vision-based approaches. In this study, we adopted a sensor-based approach by using a tactile sensing glove due to its portability, costeffectiveness, and ability to capture fine finger movements. Subsequently, we applied LSTM and k-NN respectively to evaluate the recognition accuracy using this method. As a result, LSTM achieved a test accuracy of approximately 76%, while k-NN demonstrated an average accuracy of 87%. These findings highlight the potential of sensor-based SLR technology in reducing communication barriers and advancing inclusively in communication tools.

关键词： Sign language Vocabulary Accuracy Multicore processing Fingers Auditory system Data collection Sensors Long short term memory

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HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation

arXiv

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arXiv 2024年

作者： Liu, Changkun Chen, Shuai Zhao, Yukun Huang, Huajian Prisacariu, Victor Braud, Tristan Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Division of Integrated Systems Design The Hong Kong University of Science and Technology Hong Kong Active Vision Laboratory Department of Engineering Science University of Oxford United Kingdom

Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these unreliable predictions. However, existing uncertainty modelling techniques are often coupled with a specific APR architecture, resulting in suboptimal performance compared to state-of-the-art (SOTA) APR methods. This work introduces a novel APR-agnostic framework, HR-APR, that formulates uncertainty estimation as cosine similarity estimation between the query and database features. It does not rely on or affect APR network architecture, which is flexible and computationally efficient. In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR. The extensive experiments demonstrate the effectiveness of our framework, reducing 27.4% and 15.2% of computational overhead on the 7Scenes and Cambridge Landmarks datasets while maintaining the SOTA accuracy in single-image APRs. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation

HR-APR: APR-agnostic Framework with Uncertainty Estimation a...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Changkun Liu Shuai Chen Yukun Zhao Huajian Huang Victor Prisacariu Tristan Braud Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Engineering Science Active Vision Laboratory University of Oxford United Kingdom Division of Integrated Systems Design The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

关键词： Training Uncertainty Accuracy Databases Pipelines Estimation Network architecture

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Identifying Critical Points of Departure for the design of Self-Fashioning Technologies 25

Identifying Critical Points of Departure for the Design of S...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Rebeca Blanco Cardozo Rachael Garrett Mafalda Samuelsson-Gamboa Kashyap Haresamudram Dominika Lisy Maria Rogg Claudia Núñez-Pacheco Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Interaction Design CSE Chalmers University of Technology University of Gothenburg Gothenburg Sweden Department of Technology and Society Lund University Lund Sweden Department of Thematic Studies Division of Gender Studies Linköping University Linköping Sweden Department for Informatics and Media Uppsala University Uppsala Sweden Department of Computer Science and Media Technology Malmö University Malmö Sweden

ISBN: (纸本)9798400713941

designing technologies that clothe, adorn, or are otherwise placed on the body raises questions concerning the role they will play in dressing ourselves. We situate self-fashioning - or the process through which we stylise and present our bodies - as a complex practice where a series of social, material, and contextual factors shape how we present ourselves. Informed by reflective discussions and projective design tools, we contribute three critical points of departure for self-fashioning technologies: (i) Purposeful examining discomfort as an ongoing phenomenon, (ii) Supporting mimesis and visibility as qualities to be negotiated, and (iii) Envisioning the multiplicity of the body. We call for the design community to help devise fashionable technologies that are sensitive, caring, and responsive to the complexities of fashioning our bodies. © 2025 Copyright held by the owner/author(s).

关键词： self-fashioning

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Histopathology-driven prostate cancer identification: A VBIR approach with CLAHE and GLCM insights

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computers in Biology and Medicine 2024年 182卷 109213-109213页

作者： Rangaiah, Pramod K.B. Pradeep Kumar, B.P. Augustine, Robin Microwaves in Medical Engineering Group Division of Solid State Electronics Department of Electrical Engineering Uppsala University Box 65 UppsalaSE-751 03 Sweden Department of Computer Science and Design Atria Institute of Technology Bengaluru560024 India

Efficient extraction and analysis of histopathological images are crucial for accurate medical diagnoses, particularly for prostate cancer. This research enhances histopathological image reclamation by integrating Visual-Based Image Reclamation (VBIR) techniques with contrast-limited adaptive Histogram Equalization (CLAHE) and the Gray-Level Co-occurrence Matrix (GLCM) algorithm. The proposed method leverages CLAHE to improve image contrast and visibility, crucial for regions with varying illumination, and employs a non-linear Support Vector Machine (SVM) to incorporate GLCM features. Our approach achieved a notable success rate of 89.6%, demonstrating significant improvement in image analysis. The average execution time for matched tissues was 41.23 s (standard deviation 36.87 s), and for unmatched tissues, 21.22 s (standard deviation 29.18 s). These results underscore the method's efficiency and reliability in processing histopathological images. The findings from this study highlight the potential of our method to enhance image reclamation processes, paving the way for further research and advancements in medical image analysis. The superior performance of our approach signifies its capability to significantly improve histopathological image analysis, contributing to more accurate and efficient diagnostic practices. © 2024 The Authors

关键词： Urology

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Enhancing ESG Reporting Analysis: Leveraging GPT and Resampling Methods for Improved Multi-Label Classification

Enhancing ESG Reporting Analysis: Leveraging GPT and Resampl...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Jun-Wei Wu Lydia Hsiao-Mei Lin Richard Tzong-Han Tsai Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Interaction Design National Taipei University of Technology Taipei Taiwan Center for GIS Research Center for Humanities and Social Sciences Academia Sinica Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

ESG reporting, covering environmental, social, and governance aspects, is a crucial resource for investors, companies, and governments to understand a company's value. However, the sheer volume of data and information in the reports makes it difficult to retrieve specific information on ESG. ESG-BERT models are developed to categorize the content of ESG reports based on the relevant field. However, the accurate prediction of multiple classifications is difficult due to unevenly distributed labels. To address this problem, we used a self-made assistant based on GPT to label the data and then resampling technology to adjust the balance of the data set. After the data set was balanced, we adjusted to ESG-BERT to improve its multi-label classification accuracy. We evaluated multiple resampling methods and determined the most suitable strategy for classifying ESG report content. Thus, the accuracy of ESG content analysis was improved through more refined model adjustment and preprocessing methods. The results indicated that balancing the data for improved classification ensures fairness and objectivity in distributing content for ESG reports with multiple labels.

关键词： Analytical models Accuracy Government Training data Companies Big Data Data models

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal Data

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Rajesh Munirathnam Meenakshi Maindola Bhargav H K Prachi Ramesh Patil Pooja Bhatt Natrayan L Data & Analytics Division TechnoGen Inc. New Jersey USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Industry Institute Interaction Cell(IIIC) Tumakuru India Department of Electronics and Telecommunication Engineering Jagdambha College of Engineering and Technology Yavatmal India Department of Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019–2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures.

关键词： Epidemics Accuracy Surveillance Real-time systems Vectors Spatiotemporal phenomena Risk management Public healthcare Long short term memory Diseases

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FetchAid: Making Parcel Lockers More Accessible to Blind and Low Vision People With Deep-learning Enhanced Touchscreen Guidance, Error-Recovery Mechanism, and AR-based Search Support

arXiv

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arXiv 2024年

作者： Guan, Zhitong Xiong, Zeyu Fan, Mingming Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Parcel lockers have become an increasingly prevalent last-mile delivery method. Yet, a recent study revealed its accessibility challenges to blind and low-vision people (BLV). Informed by the study, we designed FetchAid, a standalone intelligent mobile app assisting BLV in using a parcel locker in real-time by integrating computer vision and augmented reality (AR) technologies. FetchAid first uses a deep network to detect the user's fingertip and relevant buttons on the touch screen of the parcel locker to guide the user to reveal and scan the QR code to open the target compartment door and then guide the user to reach the door safely with AR-based context-aware audio feedback. Moreover, FetchAid provides an error-recovery mechanism and real-time feedback to keep the user on track. We show that FetchAid substantially improved task accomplishment and efficiency, and reduced frustration and overall effort in a study with 12 BLV participants, regardless of their vision conditions and previous experience. © 2024, CC BY-NC-ND.

关键词： Augmented reality

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Enhancing Functional and Extra Motor Abilities: A Focus Group Study on the Re-design of an Extra-Robotic Finger

Enhancing Functional and Extra Motor Abilities: A Focus Grou...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sjoerd Hendriks Basma Hasanen Naqash Afzal Irfan Hussain Mohammad Obaid Department of Computer Science and Engineering Interaction Design Unit Chalmers University of Technology Gothenburg Sweden Department of Mechanical & Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The utilization of extra-robotic limbs to regain and extend motor abilities poses challenges. Nonetheless, the evidence suggesting the extension of these abilities without functional interference holds promise for the advancement of extra-robotic fingers or limbs. To gain insights into design improvement from diverse perspectives, we conducted a focus group study utilizing a prototype supernumerary robotic finger (SRF). The main purpose of the study is to gain knowledge on the current prototype, and then use the outcomes in a future co-designing process of SRF having the potential to be used as an assistive and augmented tool. Fifteen healthy subjects with an engineering background participated in the current study. We analyzed the collected data through a thematic analysis process to generate key design recommendations. These recommendations contain suggestions for designing for personal and social acceptance, adaptability, and multi-modal interaction. The results serve as a subset of future subsequent studies to be carried out with other stakeholders of the prototype, including patients, clinicians, and designers. Future work focuses on using the recommendations in a co-design process to inform the prototyping of SRF technologies supporting and extending motor abilities in upper limbs.

关键词： Fingers Prototypes Interference Motors Stakeholders Robots

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