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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans

Implementation of a Hip Fracture Classification Network Base...

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International Conference on computer and Communications (ICCC)

作者： Kui Zhao Meng Yang Ti Zhang Die Hu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China Department of Orthopedics Peking University Third Hospital Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition.

关键词： Deep learning Image segmentation Adaptation models Osteoporosis Accuracy Computed tomography Transfer learning Predictive models Older adults Hip

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Understanding the use of message passing interface in exascale proxy applications

Understanding the use of message passing interface in exasca...

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作者： Sultana, Nawrin Rüfenacht, Martin Skjellum, Anthony Bangalore, Purushotham Laguna, Ignacio Mohror, Kathryn Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science and Engineering University of Tennessee at Chattanooga ChattanoogaTN United States Department of Computer Science University of Alabama at Birmingham BirminghamAL United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

The Exascale computing Project (ECP) focuses on the development of future exascale-capable applications. Most ECP applications use the message passing interface (MPI) as their parallel programming model with mini-apps serving as proxies. This paper explores the explicit usage of MPI in such ECP proxy applications. We empirically analyze 14 proxy applications from the ECP Proxy Apps Suite. We use the MPI profiling interface (PMPI) to collect MPI usage patterns in ECP proxy apps. Our analysis shows that a small subset of features from MPI is commonly used in the proxies of exascale-capable applications, even when they reference third-party libraries. This study is intended to provide a better understanding of the use of MPI in current exascale applications. The findings can help focus software investments made for exascale systems in the MPI middleware including optimization, fault-tolerance, tuning, and hardware-offload. © 2020 John Wiley & Sons Ltd

关键词： Message passing

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Correction to: An investigation of online and offline learning models for online just-in-time software defect prediction

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Empirical Software Engineering 2024年第6期29卷 1-2页

作者： Cabral, George G. Minku, Leandro L. Oliveira, Adriano L. I. Pessoa, Dinaldo A. Tabassum, Sadia Department of Computing Federal Rural University of Pernambuco Recife Brazil School of Computer Science University of Birmingham Edgbaston Birmingham UK Center for Informatics Federal University of Pernambuco Recife Brazil Banco Central do Brasil Recife Brazil Department of Informatics King’s College London London UK

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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A Review in E-Learning Context: Gamification Elements for Language Learning 1

A Review in E-Learning Context: Gamification Elements for La...

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1st International Conference on Technology, Engineering, and computing Applications, ICTECA 2023

作者： Zamahsari, Gamal Kusuma Romadhon, Sahrul Amalia, Mufidah Nur Rifah, Lailatul Prihatini, Arti Saputra, Ardi Wina Faculty of Humanities Bina Nusantara University Digital Language Learning Center Communication Science Department Jakarta Indonesia Indonesian Language Education Study Program Institute of Islamic Religion Madura Pamekasan Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Computer Science Department Jakarta Indonesia Bina Nusantara University Digital Language Learning Center Visual Communication Design Faculty of Humanities Jakarta Indonesia Unversity of Muhammadiyah Malang Indonesian Language Education Faculty of Teacher Training and Education Malang Indonesia Widya Mandala Catholic University Indonesian Language Education Study Program Surabaya Indonesia

ISBN: (纸本)9798350360554

Recent advancements in e-learning have highlighted the significance of incorporating gamification into language acquisition processes. It is essential to examine the application of learning game features within classroom settings. The foremost goal of this systematic review of literature is to ascertain and thoroughly delineate the elements of gamification considered suitable for incorporation into environments for language learning. This investigation focuses on two main objectives: firstly, to examine the influence of diverse elements of gamification on the enhancement of language abilities, and secondly, to comprehend how these elements shape the educational approaches and experiences of students in a gamified learning environment. The review methodology employed in this study is a systematic literature review of the current literature to comprehensively analyze and map out the effective utilization of gamification in the context of language teaching. The results of our study demonstrate that the deliberate integration of game features can have a substantial impact on student motivation and engagement. Nevertheless, the efficacy of these components is contingent upon the individual attributes of students and the educational goals in question. The paper finishes by providing advice for customizing gamification tactics to accommodate the varying learner profiles in language teaching. Additionally, it suggests potential areas of future research that can contribute to the ongoing development and enhancement of this pedagogical approach. © 2023 IEEE.

关键词： computer aided instruction

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Dynamic network embedding via incremental skip-gram with negative sampling

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science China(Information sciences) 2020年第10期63卷 89-107页

作者： Hao PENG Jianxin LI Hao YAN Qiran GONG Senzhang WANG Lin LIU Lihong WANG Xiang REN Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Southern California

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O（1/n;）, and the second order moment of the objective difference can be stabilized in order of O（1）. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to

关键词： dynamic network embedding bound and convergence analysis multi-label classification link prediction

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Reverse Engineering of Mobile Banking Applications

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computer Systems science & Engineering 2021年第9期38卷 265-278页

作者： Syeda Warda Asher Sadeeq Jan George Tsaramirsis Fazal Qudus Khan Abdullah Khalil Muhammad Obaidullah National Center for Cyber Security Department of Computer Science&Information TechnologyUniversity of Engineering&TechnologyPeshawar25120Pakistan Higher Colleges of Technology Abu Dhabi Women’s CollegeAbu DhabiUAE Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Software Engineering University of Engineering&TechnologyMardan23200Pakistan

Software reverse engineering is the process of analyzing a software system to extract the design and implementation *** engineering provides the source code of an application,the insight view of the architecture and the third-party *** a security perspective,it is mostly used for finding vulnerabilities and attacking or cracking an *** process is carried out either by obtaining the code in plaintext or reading it through the binaries or ***,reverse engineering is widely used for mobile applications and is considered a security *** Open Web Application Security Project(OWASP),a leading security research forum,has included reverse engineering in its top 10 list of mobile application *** applications are used in many sectors,e.g.,banking,education,*** particular,the banking applications are critical in terms of security as they are used for financial transactions.A security breach of such applications can result in huge financial losses for the customers as well as the *** exist various tools for reverse engineering of mobile applications,however,they have deficiencies,e.g.,complex configurations,lack of detailed analysis *** this research work,we perform an analysis of the available tools for reverse engineering of mobile *** dataset consists of the mobile banking applications of the banks providing services in *** results indicate that none of the existing tools can carry out the complete reverse engineering process as a standalone *** addition,we observe significant differences in terms of the execution time and the number of files generated by each tool for the same file.

关键词： Reverse engineering mobile banking applications security analysis

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning

arXiv

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arXiv 2024年

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaiane, Osmar Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art (SOTA) methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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