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DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences

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arXiv 2023年

作者： Liu, Wentao Tian, Tong Wang, Lemeng Xu, Weijin Li, Lei Li, Haoyuan Zhao, Wenyi Tian, Siyu Pan, Xipeng Deng, Yiming Gao, Feng Yang, Huihua Wang, Xin Su, Ruisheng School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Aeronautics and Astronautics Dalian University of Technology Dalian China Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing China Ultrasonic Department The Fourth Hospital of Hebei Medical University Hebei Tumor Hospital Shijiazhuang China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Radiology The Netherlands Cancer Institute Amsterdam Netherlands Department of Radiology & Nuclear Medicine Erasmus MC University Medical Center Rotterdam Netherlands Medical Image Analysis group Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://***/10.5281/zenodo.11396520 and https://***/lseventeen/DIAS. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

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Research Square

Research Square 2021年

作者： Ryan, Conor Kshirsagar, Meghana Vaidya, Gauri Cunningham, Andrew Sivaraman, R. Biocomputing and Developmental Systems Group Lero The Science Foundation Ireland Research Centre for Software Computer Science and Information System Department University of Limerick LimerickV94 T9PX Ireland Intel Research and Development Ireland Limited LeixlipW23 CX68 Ireland Department of Electronics Communication Engineering School of Electrical and Electronics Engineering Shanmugha Arts Science Technology and Research Academy Deemed to be University Thanjavur613401 India

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve. © 2021, CC BY.

关键词： Monte Carlo methods

Correction to “Performance Evaluation of LoRaWAN for Green Internet of Things”

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IEEE Access 2022年 10卷 125400-125400页

作者： Zulfiqar Ali Shagufta Henna Adnan Akhunzada Mohsin Raza Sung Won Kim Department of Software Engineering Bahria University Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan Telecommunications Software and Systems Group (TSSG) Waterford Institute of Technology (WIT) Waterford Ireland Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Design Engineering and Mathematics Middlesex University London London U.K Department of Information and Communication Engineering Yeungnam University Gyeongsan South Korea

In the above article [1] , we want to change the affiliation of the first author to “department of software engineering, Bahria University, Islamabad, 46000, Pakistan” and add a new affiliation for the second author, which is “department of computer science, Bahria University, Islamabad, 46000, Pakistan.” The first author (ZULFIQAR ALI; zulfijobs@***) is a Ph.D. student at Bahria University Islamabad, Pakistan, and the second author (SHAGUFTA HENNA; shaguftahenna@***) is his Ph.D. Supervisor.

关键词： Performance evaluation computer science Internet of Things software engineering

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

Facial Landmark Localization Robust on the Eyes with Position Regression Network

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Facial Landmark Localization Robust on the Eyes with Positio...

International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Chanwoong Kwak Jaeyoon Jang Hosub Yoon Department of Computer Software and Engineering Korea University of Science and Technology Daejeon Republic of Korea Human-Machine Interaction Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

Facial landmark localization is essential for robot-human interaction. In particular, the human eye is more important because it can grasp a person's interests. However, the traditional method does not consider eye changes from the dataset, so the limitation is clear, this paper presents a data augmentation method for acquiring various eye images and a method for creating a robust eye landmark model with 2-stage training. Experiments on augmented 300W-LP datasets show that our method outperforms performance than the previous method.

关键词： Face Training Three-dimensional displays Two dimensional displays Measurement computer vision

An empirical study on progressive sampling for just-in-time software defect prediction 7

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An empirical study on progressive sampling for just-in-time ...

7th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Shi, Kai Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Alibaba Group Hangzhou China

Just-in-time software defect prediction (JIT-SDP) is an active research topic in the field of software engineering, aiming at identifying defect-inducing code changes. Most existing JIT-SDP work focuses on improving the prediction performance of the model by improving the model. However, a frequently ignored problem is that collecting large and high quality defect data sets is costly. Specifically, when labelling the samples, experts in the field are required to carefully analyze the defect report information and log of code modification, which requires a lot of effort. Therefore, how to build a high-performance JIT-SDP model with a small number of training samples is an issue worth studying, which can reduce the size of the defect data sets and reduce the cost of data sets acquisition. This work thus provides a first investigation of the problem by introducing a progressive sampling method. Progressive sampling is a sampling strategy that determines the minimum number of training samples while guaranteeing the performance of the model. However, progressive sampling requires that the learning curve of the prediction model be well behaved. Thus, we validate the availability of progressive sampling in the JIT-SDP issue based on six open-source projects with 227417 changes. experimental results demonstrate that the learning curve of the prediction model is well behaved. Therefore, the progressive sampling is feasible to tackle the JITSDP problem. Further, we investigate the optimal training sample size derived by progressive sampling for six projects. Empirical results demonstrate that a high-performance prediction model can be built using only a small number of training samples. Thus, we recommend adopting progressive sampling to determine the size of training samples for the JIT-SDP problem. Copyright © 2019 for this paper by its authors.

关键词： Defects

Towards automated metamorphic test identification for ocean system models

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arXiv 2021年

作者： Hiremath, Dilip Jagadeeshwarswamy Claus, Martin Hasselbring, Wilhelm Rath, Willi MarDATA - Helmholtz School for Marine Data Science GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel University Kiel Germany Software Engineering Group Computer Science Department Kiel University Kiel Germany Ocean Circulation and Climate Dynamics Department GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel University Kiel Germany Ocean Circulation and Climate Dynamics Department GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel Germany

Metamorphic testing seeks to verify software in the absence of test oracles. Our application domain is ocean system modeling, where test oracles rarely exist, but where symmetries of the simulated physical systems are known. The input data set is large owing to the requirements of the application domain. This paper presents work in progress for the automated generation of metamorphic test scenarios using machine learning. We extended our previously proposed method [1] to identify metamorphic relations with reduced computational complexity. Initially, we represent metamorphic relations as identity maps. We construct a cost function that minimizes for identifying a metamorphic relation orthogonal to previously found metamorphic relations and penalize for the identity map. A machine learning algorithm is used to identify all possible metamorphic relations minimizing the defined cost function. We propose applying dimensionality reduction techniques to identify attributes in the input which have high variance among the identified metamorphic relations. We apply mutation on these selected attributes to identify distinct metamorphic relations with reduced computational complexity. For experimental evaluation, we subject the two implementations of an ocean-modeling application to the proposed method to present the use of metamorphic relations to test the two implementations of this *** Codes J.2 © 2021, CC BY-SA.

关键词： Cost functions

software requirements selection using different patterns of pairwise comparison matrices of analytic hierarchy process 2nd

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Software requirements selection using different patterns of ...

2nd International Conference on Communication and Computing systems, ICCCS 2018

作者： Mohammad, C.W. Khan, S. Sadiq, M. Computer Science and Technology Research Group Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia [A Central University New Delhi India Software Engineering Research Group UPFET Jamia Millia Islamia [A Central University New Delhi India

ISBN: (纸本)9780367001476

Analytic hierarchy process (AHP) is an important multi-criteria decision making algorithm which is employed to select the alternatives or software requirements (SR) based on distinct criteria like cost, usability, security, performance, etc. In industrial applications, AHP has been considered as useful and trustworthy method for the SR selection. In AHP, pairwise comparison matrices (PCM) are used to specify the preferences of the alternatives or SR on the basis of different criteria. In literature, we identify that during the SR selection process less attention is given to check whether the PCM of AHP is consistent or not. Therefore, to address this issue we proposed a method for the selection of SR. In our work, we use the goal oriented method for the elicitation of the SR of the Institute Examination System. © 2019 Taylor & Francis group, London.

关键词： Analytic hierarchy process