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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haibin He Maoyuan Ye Jing Zhang Juhua Liu Bo Du Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science at Nanyang Technological University

ISBN: (纸本)9798331314385

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets.

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Optimized Reading for ADHD and Dyslexia Users In VR

Optimized Reading for ADHD and Dyslexia Users In VR

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Computer science, Computer Engineering, & Applied computing (CSCE), Congress in

作者： Kyler Hoang Leonidas Deligiannidis School of Engineering Wentworth Institute of Technology Boston MA USA School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

To combat the rising population of ADHD and dyslexic patients, current advancements in Unity and Oculus Integration are investigated and employed to design a user-friendly reading experience. Unlike physical novels that lack visual aid, our prototype ensures a distraction-free and minimalistic VR environment. It utilizes *** speech recognition to aid ADHD and dyslexic patients in mechanical and comprehensive reading. Users can also change the background color and music to enhance their facilitated experience. This approach allows the user to constantly focus on the text without averting their gaze. We believe that further development of this prototype can create a powerful tool that redefines reading E-books.

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Smartphone Fundus Photography Enhancement for Retinopathy of Prematurity Disease Diagnosis Using Deep Learning

Smartphone Fundus Photography Enhancement for Retinopathy of...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Elizabeth Ndunge Mutua Bernard Shwabwabo Kasamani Christoph Reich School of Computing & Engineering Sciences Strathmore University Nairobi Kenya Institute for Data Science Cloud Computing & IT Society Furtwangen University Furtwangen Germany

Retinopathy of Prematurity (ROP) is the highest cause of childhood blindness globally with babies born preterm having a higher probability of contracting the disease. The disease diagnosis remains an economic burden to many countries, lack of enough ophthalmologists for the disease diagnosis coupled with non-existent national screening guidelines still remains a challenge. To diagnose the disease, a fundus photography is conducted, printout images are analyzed to determine the presence or absence of the disease. With the increase in the development of smartphones having advanced image capturing and processing features, the utilization of smartphones to capture retina image for disease diagnosis is becoming a common trend. For regions where ophthalmologists are few and/or for low resource regions with few or no retina capturing equipment, the use of smartphones to capture retina images for retina diseases is an effective method. This, however, is challenged: different smartphones produce images of different resolutions; some images are darker others lighter and with different resolution. A smartphone retina image capturing has a smaller field of view ranging between 450–900 which is a major limitation. A lens to support a bigger view can be combined with this approach to provide a wide view of 1300. This enlargement however distorts the image quality and may result in losing some image features. To overcome these challenges, this work develops an improved U-Net model to preprocess images captured using smartphones for ROP disease diagnosis. Our focus is to determine the presence or absence of the disease from smartphone captured images. Because the images are captured under a smaller field of view (FOV), we develop an improved U-Net model by adding patches to enhance image circumference and extract all features from the image and use the extracted features to train a U-Net model for the disease diagnosis. The model results outperformed similar recent developments wit

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Rough Set Model and Approximations in Fuzzy Formal Contexts 5

Rough Set Model and Approximations in Fuzzy Formal Contexts

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5th Asia Conference on Machine Learning and computing, ACMLC 2022

作者： Syau, Yu-Ru Lin, En-Bing National Formosa University Department of Information Management Yunlin Huwei63201 Taiwan Wentworth Institute of Technology School of Computing and Data Science BostonMA02132 United States

ISBN: (纸本)9798350333923

Given a set of objects G with a set of attributes M and a binary relation I, a formal context (G, M, I) corresponds to a Boolean-valued information table in rough set theory, and the kernel relation of the function from G to M which maps each object of G to its afterset. This coincides with the indiscernibility relation of the Boolean-valued information table. Rough set model and Variable-Precision model (VP-model) under a formal context can be formulated based on the associated kernel relation on the objects set. α-fuzzified rough set model and VP-model under a fuzzy formal context are formulated analogously. We present an example to demonstrate the proposed α-fuzzified rough set model and VP-model under a fuzzy formal context. © 2022 IEEE.

关键词： Rough set theory

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Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Bhanu Pratap Singh Rathore Rutul Dave Dhanasekaran K Priyadarsini K Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively.

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Vehicle Insurance Damage Estimation Using YOLOv5

Vehicle Insurance Damage Estimation Using YOLOv5

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： R Subash Banu Priya Prathaban Ganeshkumar N Gopinathan S Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management GITAM (Deemed to be University) Bengaluru Karnataka India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Accident assessments for insurance companies are currently conducted manually, which can be time-consuming and prone to human error. The proposed method seeks to automate this process, aiming to save time and enhance accuracy. This paper introduces an innovative approach to analyzing object damage by leveraging the advanced YOLOv5 (You Only Look One Level) deep learning architecture. The research is meticulously crafted to address the imperative need for robust and precise detection and classification of damaged objects in images, crucial for effective assessment of structural and environmental integrity. Leveraging the unparalleled real-time object detection capabilities of YOLOv5, the framework is finely tuned and trained on a comprehensive dataset encompassing diverse scenarios of damaged objects. Through exhaustive experimentation and validation, our methodology demonstrates substantial advancements, offering unparalleled efficiency and accuracy in the domain of object damage analysis. The findings of this study hold significant implications for a broad spectrum of applications, ranging from infrastructure monitoring to disaster response, underscoring the transformative potential of deep learning in enhancing critical assessment processes.

关键词： YOLO Deep learning Accuracy Insurance Process control Estimation Companies Real-time systems Distance measurement Monitoring

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Correction to: Consistent Prompt Tuning for Generalized Category Discovery (International Journal of Computer Vision, (2025), 10.1007/s11263-024-02343-w)

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International Journal of Computer Vision 2025年 1-10页

作者： Yang, Muli Yin, Jie Gu, Yanan Deng, Cheng Zhang, Hanwang Zhu, Hongyuan Singapore Singapore School of Electronic Engineering Xidian University Xi’an China Norinco Group Testing and Research Institute Xi’an China College of Computing and Data Science Nanyang Technological University Singapore Singapore

The original publication of this article was published with several errors and has been corrected as follows. In the Section 4.3.1, the hyperlink for the Section 5 citation was missing and has been included. In Table 6, the significance ‘Reimplemented using CLIP’s image encoder’ was incorrectly provided for the method ‘CPT (Ours) and has been removed. In Table 12, the method name was incorrectly listed as ‘CLIP-GC’ but should read as ‘CLIP-GCD’. Additionally, the significance ‘Reimplemented using (Open/Meta) CLIP’s image encoder’ was missing for the method ‘CLIP-GCD’ and has been included. In Table 15, the incorrect significance ‘Reimplemented using CLIP’s image encoder’ was provided for the methods GCD (2022a), SimGCD (2023) and GPC (2023). This should be removed and the correct significance ‘Training with estimated category number’ has been included. In Table 22, the species "andrias acropora acuminata (staghorn coral)" was missing and has been included before the species "andrias davidianus (chinese giant salamander)." For completeness and transparency, the old incorrect and the corrected versions of Tables are displayed below. The original article has been corrected. Incorrect Table 6: GCD accuracy (%) in the customized setting on NICO dataset dataset → NICO-10 NICO-33 NICO-100 Method ↓ All Old New All Old New All Old New GCD (2022a) 95.1 97.3 94.0 38.6 58.4 25.1 48.0 67.0 39.1 PromptCAL (2023b) 89.6 80.9 93.6 30.5 42.4 22.5 45.3 53.4 41.5 SimGCD (2023) 97.3 97.0 97.4 45.9 67.7 46.3 65.1 37.4 PIM (2023) 96.4 97.2 96.0 42.2 67.6 24.9 49.5 67.5 41.0 GCD† 98.0 39.3 65.0 21.8 51.6 54.5 PromptCAL† 97.5 97.1 29.2 41.8 20.6 48.9 64.4 41.7 SimGCD† 97.5 46.6 29.1 69.4 44.6 PIM† 97.5 97.0 71.8 50.0 39.2 CPT (Ours)† 98.5 Bold value indicates the best results and underlined value indicates the second best results †Reimplemented using CLIP’s image encoder Corrected Table 6: GCD accuracy (%) in the customized setting on NICO dataset dataset → NICO-10 NICO-33 NICO-100 Method ↓

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Parks: A Doubly Infinite Family of NP-Complete Puzzles and Generalizations of A002464

arXiv

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arXiv 2024年

作者： Minevich, Igor Cunningham, Gabe Karan, K. Aditya Gyllinsky, Joshua V. The School of Computing & Data Science Wentworth Institute of Technology BostonMA02115 United States EAi Technologies Vienna VA United States

The Parks Puzzle is a paper-and-pencil puzzle game that is classically played on a square grid with different colored regions (the parks). The player needs to place a certain number of "trees" in each row, column, and park such that none are adjacent, even diagonally. We define a doubly-infinite family of such puzzles, the (c, r)-tree Parks puzzles, where there need be c trees per column and r per row. We then prove that for each c and r the set of (c, r)-tree puzzles is NP-complete. For each c and r, there is a sequence of possible board sizes m × n, and the number of possible puzzle solutions for these board sizes is a doubly-infinite generalization of OEIS sequence A002464, which itself describes the case c = r = 1. This connects the Parks puzzle to chess-based puzzle problems, as the sequence describes the number of ways to place non-attacking kings on a chessboard so that there is exactly one in each column and row (i.e. to place non-attacking dragon kings in shogi). These findings add yet another puzzle to the set of chess puzzles and expands the list of known NP-complete problems described. © 2024, CC BY.

关键词： Trees (mathematics)

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Heterogeneous Face Recognition Algorithm: Convolution Neural Network Approach 5

Heterogeneous Face Recognition Algorithm: Convolution Neural...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickam, M. Ani Minisha, R. Rekha, K.S. Indumathy, T. Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rr and Dr Sr Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India Department of Medical Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350385649

Heterogeneous face recognition, also known as HFR, is an approach to facial recognition that attempts to match facial pictures that have been gathered through a variety of sensory modalities. HFR is primarily used for forensics, network security, and all commercial applications. The heterogeneous face recognition (HFR) algorithm is robust in handling challenges that exist in conventional face recognition. This is since heterogeneous face images display an enormous amount of intra- class variation, and it does not support hybrid modality of face images. This study provides a unique technique to transfer learning as a means of mastering invariant facial feature characteristics in visual (VIS) face pictures. A wrapping procedure combines end to end connections of two components for deep network. A discriminator has the details of fine-grained data for improving the quality of image which depends on wavelets. The system is made up of two different modules: one deals with face recognition, while the other determines a person's gender. It analysis about the characteristics of gender from the image using Convolutional Neural Networks that have been pre-trained. To train CNN in the face recognition module, in particular, the use of the open-source datasets Labelled Faces within the Wild, YouTube Face, and VGGFace2 results in an improvement in accuracy. Deep learning techniques and a substantial number of photographic examples of people's faces are what make it feasible for the proposed recognition to function so admirably. The problem of studying recognition remains challenging due to the limited size of the sample and the information gap between the senses. © 2024 IEEE.

关键词： Face recognition

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