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7th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2018

作者： Vranić, Valentino Granec, Michal Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovicova 2 Bratislava84216 Slovakia

ISBN: (纸本)9788670314733

This paper introduces an approach to establishing software product lines from a set of existing products based on a reversed Lee Kang design feature based approach to deriving program code from features, Van den Broek's feature model merging combined with partial feature models, and aspect-oriented refactoring strategies cataloged by Monteiro and Fernandes for implementing the features as modules that can be plugged in or out as needed. The approach interlinks the feature model merging cases with the actual implementation of features and speciffic aspect-oriented refactoring strategies used for this. The approach was successfully applied to JForum, establishing a product line from three existing products prepared out of the original, publicly available JForum code. © 2018 by the paper's authors.

关键词： Merging

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QOS Analysis of Routing Protocol Babel on Mobile Ad-Hoc Network (MANET)

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IOP Conference Series: Materials Science and engineering 2020年第1期771卷

作者： A Wijayanto R Adhitama Department of Software Engineering Faculty of Industrial Technology and Informatics Telkom Institute of Technology Purwokerto Indonesia

The unequal access to internet communication infrastructure in most parts of Indonesia can cause difficulties for people in the area to communicate. As an alternative communication, the mobile ad-hoc network requires a reliable routing algorithm. Quality of service is vital to address the problem of communications in the mobile ad-hoc network. This study analyzes the quality of service routing protocol babel on a mobile ad-hoc network. Test carried out using 3 scenarios, namely testing feasibility condition, self-healing, and delay, jitter, packet loss, and throughput on multihop condition. Values delay and jitter generated by the scenario testing conducted according to the standards TIPHON into the category of a very good and good. In the packet loss data capture value generated by the scenario testing conducted according to the standards TIPHON into the category of good and bad, but most of the values obtained into the bad category. At throughput data capture value generated by the scenario testing conducted according to the standards TIPHON into the category of good, medium and bad but mostly the value obtained in the category of good.

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The Business Experiments Navigator (BEN)

The Business Experiments Navigator (BEN)

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2018 IEEE International Conference on engineering, Technology and Innovation, ICE/ITMC 2018

作者： Gutbrod, Matthias Munch, Jurgen Tichy, Matthias Faculty of Informatics Reutlingen University Reutlingen Germany Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

ISBN: (纸本)9781538614693

Early reduction of risks in a startup or an innovation project is highly important. Appropriate means for risk reduction, such as testing business models with different kinds of experiments exist. However, deciding what to test and how to select the right test, is challenging for many startups and innovation projects. This article presents the so-called Business Experiments Navigator (BEN), a toolkit to assist startup and innovation processes. It compliments other tools such as the Business Model Canvas or the Lean Startup process. The main contribution of BEN is to bridge the gap between the riskiest assumptions of a business model and the multitude of available testing techniques by providing assumption templates. The Business Experiments Navigator has been validated in several workshops. Results show that it creates awareness among the workshop participants that a business model is based on assumptions, which impose risks and need to be validated. Further, users of BEN were able to identify relevant assumptions and map different kinds of assumptions to appropriate testing techniques. The process applied in the workshops, as well as the assumption templates, helped the participants understand the main concepts and transfer their learnings, to their own business ideas. © 2018 IEEE.

关键词： Systems engineering

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P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

arXiv

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arXiv 2020年

作者： Ventura, Jonathan Kukelova, Zuzana Sattler, Torsten Baráth, Dániel Department of Computer Science & Software Engineering Cal Poly San Luis Obispo United States Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Computer Vision and Geometry Group ETH Zürich Switzerland

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/. © 2020, CC BY.

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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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

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Energy loss calculation and voltage profile improvement for the rehabilitation of 0.4 kV low voltage distribution network (LVDN)

Measurement: Energy

引用

Measurement: Energy 2024年 4卷

作者： Hasham Khan Main Farhan Ullah Muhammad Saqib Ali Muhammad Shahzaib Shah Muhammad Adnan Khan Muhammad Waseem Ahmed Mohammed Saleh Government College of Technology Abbottabad Pakistan Elementary & Secondary Education Department Khyber Pakhtunkhwa Pakistan College of Electrical Engineering Zhejiang University 310027 China Department of Electrical Engineering University of Engineering and Technology Taxila Taxila 47050 Pakistan School of Electrical Engineering Chongqing University Chongqing 400044 China International Renewable and Energy Systems Integration Research Group (IRESI) Department of Electronic Engineering Maynooth University Co. Kildare Ireland Lero - Science Foundation Ireland Research Centre for Software School of Business Innovation Value Institute (IVI) Maynooth University Co. Kildare Ireland Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BME V1 Building Egry József u. 18 1111 Budapest Hungary

In recent decades, electrical utilities have made significant advancements in Electrical Power Distribution Systems (EPDS). However, energy loss in distribution networks, particularly in 0.4 kV systems, remains a critical challenge. It is increasingly essential for countries to review and update their power loss policies to deliver electrical energy to consumers at the most feasible and economical rates. The performance of Low Voltage Distribution Networks (LVDN) often falls short, resulting in high voltage fluctuations and significant energy losses for end consumers in 0.4 kV systems. This research aims to address these issues by minimizing energy losses and enhancing the voltage profile of the 0.4 kV distribution network. Various rehabilitation techniques have been employed to significantly improve the efficiency of the LVDN. Since most energy losses are directly associated with the LVDN, two 0.4 kV distribution networks in the villages of New Kalyam and Upper Tharjial in the Mandra Sub-Division, under Islamabad Electricity Supply Corporation (IESCO), Pakistan, were selected as case studies. Field surveys were conducted in collaboration with the local utility staff to collect actual data. The analysis was performed using computer-aided tools, including the Computer-Aided Distribution Planning and Design (CADPAD) software and Energy Loss Reduction (ELR) programs. The proposed methodology is straightforward and practical. The case study results demonstrate significant improvements in the voltage profile and reductions in energy losses within the LVDN. These results have been validated and found to be within permissible standard limits, underscoring the effectiveness of the proposed approach.

关键词： CADPAD Distribution networks Distributed generation ELR Energy losses EPDS Financial saving LVDN Power loss Power quality Rehabilitation techniques Technical and non-technical losses Voltage profile improvement

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Representation of a convolutional neuronal network as a high level paralel composition applied to the recognition of DNA sequences 31

Representation of a convolutional neuronal network as a high...

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31st European Modeling and Simulation Symposium, EMSS 2019

作者： Rossainz-López, M. Zúñiga-Herrera, S. Capel-Tuñón, M. Pineda-Torres, I. Faculty of Computer Science Autonomous University of Puebla San Claudio Avenue and South 14th Street San Manuel Puebla Puebla72000 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9788885741263

This work proposes the use of Structured Parallel Programming using the process communication pattern called Pipeline in its version of High-Level Parallel Composition (HLPC) to implement a process composition that represents a convolutional neuronal network or CNN and that is used to solve a specific problem of DNA sequences. The HLPC Pipeline-CNN is then shown, which represents the implementation of a convolutional neural network making use of the three types of parallel objects that make up an HLPC: A manager object, one or more stage objects and a collector object. The manager object represents the HLPC itself and makes an encapsulated abstraction out of it that hides the internal structure, the stage objects are objects of a specific purpose, in charge of encapsulating an client-server type interface that settles down between the manager and the slave-objects and the collector object that is an object in charge of storing the results received from the stage objects to which is connected. To show the usefulness and performance of the HLPC Pipeline-CNN implemented, it was used in the recognition of DNA sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6). The results of this classification were obtained in terms of percentages of training precision and validation precision, as well as performance results in terms of speedup from 1000 to 4000 training steps with 2, 4, 8, 16 and 32 exclusive processors in one parallel machine of up to 64 processors with shared-distributed memory. © 2019 Dime Universita di Genova, DIMEG University of Calabria.

关键词： Managers

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Automatic classification of Lithuanian parliament bills

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International Journal of Information Technology and Management 2018年第1-2期17卷 129-139页

作者： Mackute-Varoneckiene, Aušra Man, Ka Lok Krilavicius, Tomas Informatics Faculty Vytautas Magnus University Kaunas Lithuania Department of Computer Science and Software Engineering China xi'An Jiaotong-Liverpool University Suzhou China Baltic Institute of Advanced Technology Vilnius Lithuania

Quantitative methods are becoming more and more important in political science. However, they are not applicable without computers and computer based systems. In this paper we apply natural language technologies, mainly text classification, to categorise bills of the Lithuanian parliament into the predefined groups for further use in voting analysis and in other text analytic tasks. As only the titles of bills were used, in general it can be claimed that the problem of short text classification, which is poorly explored in consideration with the Lithuanian language, is addressed in this study. © 2018 Inderscience Enterprises Ltd.

关键词： Support vector machines

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A comparative analysis on diagnosis of diabetes mellitus using different approaches – A survey

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informatics in Medicine Unlocked 2020年 21卷

作者： Anwar, Fareeha Qurat-Ul-Ain Ejaz, Muhammad Yasir Mosavi, Amir Department of Computer Science and Software Engineering International Islamic University Islamabad (IIUI) Islamabad 44000 Pakistan School of Economics and Business Norwegian University of Life Sciences Ås 1430 Norway John von Neumann Faculty of Informatics Obuda University Budapest 1034 Hungary Department of Informatics J. Selye University Komarno 94501 Slovakia

Diabetes Mellitus is commonly known as diabetes. It is one of the most chronic diseases as the World Health Organization (WHO) report shows that the number of diabetes patients has risen from 108 million to 422 million in 2014. Early diagnosis of diabetes is important because it can cause different diseases that include kidney failure, stroke, blindness, heart attacks, and lower limb amputation. Different diabetes diagnosis models are found in literature, but there is still a need to perform a survey to analyze which model is best. This paper performs a literature review for diabetes diagnosis approaches using Artificial Intelligence (neural networks, machine learning, deep learning, hybrid methods, and/or stacked-integrated use of different machine learning algorithms). More than thirty-five papers have been shortlisted that focus on diabetes diagnosis approaches. Different datasets are available online for the diagnosis of diabetes. Pima Indian Diabetes Dataset (PIDD) is the most commonly used for diabetes prediction. In contrast with other datasets, it has key factors which play an important role in diabetes diagnosis. This survey also throws light on the weaknesses of the existing approaches that make them less appropriate for a diabetes diagnosis. In artificial intelligence techniques, deep learning is widespread and in medical research, heart rate is getting more attention. Deep learning combined with other algorithms can give better results in diabetes diagnosis and heart rate should be used for other cardiac disease diagnoses. © 2020 The Authors

关键词： Accuracy Deep learning Diabetes mellitus Machine learning

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