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Theoretical Aspects of software engineering (TASE)

作者： Gaigai Tang Lianxiao Meng Huiqiang Wang Shuangyin Ren Qiang Wang Lin Yang Weipeng Cao School of computer science and technology Harbin Engineering University Harbin China National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering Chinese Academy of Military Science Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

software vulnerabilities are usually caused by design flaws or implementation errors, which could be exploited to cause damage to the security of the system. At present, the most commonly used method for detecting software vulnerabilities is static analysis. Most of the related technologies work based on rules or code similarity (source code level) and rely on manually defined vulnerability features. However, these rules and vulnerability features are difficult to be defined and designed accurately, which makes static analysis face many challenges in practical applications. To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. However, there are many types of neural networks, and different data preprocessing methods will have a significant impact on model performance. It is a great challenge for engineers and researchers to choose a proper neural network and data preprocessing method for a given problem. To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i.e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers. Specifically, we found that 1) the training speed of RVFL is always faster than Bi-LSTM, but the prediction accuracy of Bi-LSTM model is higher than RVFL; 2) using doc2vec for vector representation can make the model have faster training speed and generalization ability than using word2vec; and 3) multi-level symbolization is helpful to improve the precision of neural network models.

关键词： Training Neural networks Data preprocessing Static analysis Predictive models Feature extraction software

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A preliminary usability evaluation for online annotation and student clustering system 27

A preliminary usability evaluation for online annotation and...

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27th International Conference on Computers in Education, ICCE 2019

作者： Chang, Miao-Han Kuo, Rita Essalmi, Fathi Chang, Maiga Kumar, Vive Kung, Hsu-Yang School of Computing and Information System Athabasca University Canada Department of Computer Science and Engineering New Mexico Institute of Mining and Technology United States Research Unit of Technologies of Information and Communication University of Kairouan Tunisia Department of Management Information System National Pingtung University of Science and Technology Taiwan

ISBN: (纸本)9789869721448

This research evaluated an annotation recommendation system – GRACE (General Rapid Annotation Clustering Enhancement) – which gives suggestions of what annotations might be missing when students were reading the teaching materials. A pilot is done in two computer science classes in a college in Tunisia. The results show that system’s usability is acceptable for students doing online reading activities on GRACE. © ICCE 2019 - 27th International Conference on Computers in Education, Proceedings. All rights reserved.

关键词： Students

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Narrowband-internet of things (NB-IoT) system for elderly healthcare services 5

Narrowband-internet of things (NB-IoT) system for elderly he...

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5th International Conference on engineering, Applied Sciences and Technology, ICEAST 2019

作者： Manatarinat, Wiraphon Poomrittigul, Suvit Tantatsanawong, Panjai Electrical Engineering Department Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand Software Engineering and Information System Department Faculty of Science and Technology Pathumwan Institute of Technology Bangkok Thailand Computer Technology Department Faculty of Science Silpakorn University Bangkok Thailand

ISBN: (纸本)9781728100678

3G and 4G wireless communication technology, which requires broad network resources and large amounts of available electricity, was designed for high-speed Internet usage. Most narrowband-Internet of Things (NB-IoT) devices do not require high-speed Internet because most IoTs work by sending commands to and receiving commands from a server only. NB-IoT is a low-power wide area (LPWA) technology that was developed to accommodate a broad variety of new IoT devices. A low-power wide area network (LPWAN) uses narrow frequencies that support the use of devices in the 900 MHz frequency range that are inexpensive and that can be used in both buildings and wide areas. NB-IoT devices can also be used to assist medical patients, the elderly, and disabled people that are susceptible to falling. Our newly developed monitoring system has the ability to immediately recognize a fall, and then automatically alert up to 4 contacts via the LINE application. If dispatch of a medical unit is required, the patient's location is automatically available without the need for voice contact by the patient. © 2019 IEEE.

关键词： Internet of things

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Towards the statistical construction of hybrid development methods

arXiv

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arXiv 2021年

作者： Tell, Paolo Klünder, Jil Küpper, Steffen Raffo, David MacDonell, Stephen Münch, Jürgen Pfahl, Dietmar Linssen, Oliver Kuhrmann, Marco Department of Computer Science IT University Copenhagen Denmark Software Engineering Group Leibniz University Hannover Hannover Germany Center for Digital Technologies Clausthal University of Technology Clausthal-Zellerfeld Germany Department of Computer Science School of Business Portland State University PortlandOR United States Department of Information Technology and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science Reutlingen University Reutlingen Germany Institute of Computer Science University of Tartu Tartu Estonia FOM Hochschule für Oekonomie & Management Essen Germany Software Engineering I University of Passau Passau Germany

Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large-scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice. Copyright © 2021, The Authors. All rights reserved.

关键词： software design

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Atmospheric Propagation and Correction of Laser Beams for Communication and Sensing Applications

Atmospheric Propagation and Correction of Laser Beams for Co...

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Applications of Lasers for Sensing and Free Space Communications, LSC 2019 - Part of Laser Congress 2019

作者： Kieleck, Christelle Segel, Max McDonald, Douglas Bellossi, Raphaël Zepp, Andreas Lambert, Andrew Stein, Karin Eichhorn, Marc Gladysz, Szymon Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstr. 1 Ettlingen76275 Germany School of Engineering and Information Technology Unsw Canberra CanberraACT2600 Australia

ISBN: (纸本)9781557528209

Correction of atmospheric effects on the propagation of light can be done by adaptive optics (AO). Nevertheless, challenging scenarios like strong turbulence near the ground lead to high failure rates of the traditional AO systems. Unconventional wavefront sensors and AO strategies are developed at Fraunhofer IOSB to provide alternatives for measuring the wavefront deformation of a laser beam. © 2019 The Authors.

关键词： Adaptive optics

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Efficient Contact Tracing for pandemics using blockchain

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Informatics in Medicine Unlocked 2021年 26卷

作者： Bari, Nida Qamar, Usman Khalid, Ayesha Knowledge & Data Science Research Centre Computer & Software Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan The Centre for Secure Information Technologies (CSIT) ECIT Institute Queen's University of Belfast Belfast BT7 1NN United Kingdom

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File system. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic. © 2021 The Authors

关键词： And pandemic Blockchain Contact tracing COVID-19 Distributed system Interplanetary file system

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SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

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Brain Informatics 2021年第1期8卷 14页

作者： Fabietti, Marcos Mahmud, Mufti Lotfi, Ahmad Kaiser, M. Shamim Averna, Alberto Guggenmos, David J. Nudo, Randolph J. Chiappalone, Michela Chen, Jianhui Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Department of Health Sciences University of Milan Via di Rudinì 8 Milan20142 Italy Department of Physical Medicine and Rehabilitation University of Kansas Medical Center 3901 Rainbow Blvd Kansas City66160 United States Department of informatics Bioengineering Robotics and System Engineering-DIBRIS University of Genova Via All’Opera Pia 13 Genoa16145 Italy Faculty of Information Technology International WIC Institute Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials. © 2021, The Author(s).

关键词： Neural networks

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

arXiv

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arXiv 2024年

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Zhang, Lize Liu, Shuai Feng, Chaoyu Wang, Luyang Chen, Shuan Shao, Guangqi Wang, Xiaotao Lei, Lei Yang, Qirui Cheng, Qihua Xu, Zhiqiang Liu, Yihao Yue, Huanjing Yang, Jingyu Vasluianu, Florin-Alexandru Wu, Zongwei Ciubotariu, George Timofte, Radu Zhang, Zhao Zhao, Suiyi Wang, Bo Zuo, Zhichao Wei, Yanyan Teja, Kuppa Sai Sri Jayakar Reddy, A. Rongali, Girish Mitra, Kaushik Ma, Zhihao Liu, Yongxu Zhang, Wanying Shang, Wei He, Yuhong Peng, Long Yu, Zhongxin Luo, Shaofei Wang, Jian Miao, Yuqi Li, Baiang Wei, Gang Verma, Rakshank Maheshwari, Ritik Tekchandani, Rahul Hambarde, Praful Tazi, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Zhang, Haopeng Hou, Yingli Yao, Mingde Levin, M.S. Sundararajan, Aniruth Hari Kumar, A. Xiaomi Inc. China School of Electrical and Information Engineering Tianjin University China Shenzhen MicroBT Electronics Technology Co. Ltd China Shanghai Artificial Intelligence Laboratory China Computer Vision Lab CAIDAS IFI University of Würzburg Germany Laboratory for Multimedia Computing Hefei University of Technology China Detect Technologies Pvt Ltd India Indian Institute of Technology Madras India Xidian University China Harbin Institute of Technology China Northeastern University China University of Science and Technology of China China Fujian Normal University China Snap Inc. United States Tongji University China Hefei University of Technology China GEC Ajmer India CVPR Lab IIT Ropar India SCSS Trinity College Dublin Ireland Faculty of Robot Science and Engineering Northeastern University China Software College Northeastern University China The Chinese University of Hong Kong China Shiv Nadar University India

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024. Copyright © 2024, The Authors. All rights reserved.

关键词： Image sensors

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Unified feature extraction framework based on contrastive learning

arXiv

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arXiv 2021年

作者： Zhang, Hongjie Qiang, Wenwen Zhang, Jinxin Jing, Ling College of Information and Electrical Engineering China Agricultural University Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China University of Chinese Academy of Sciences Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China National Innovation Center for Digital Fishery China Agricultural University Beijing100083 China College of Science China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture Beijing100083 China

Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. As a popular self-supervised learning method, contrastive learning has recently garnered considerable attention. In this study, we proposed a unified framework based on a new perspective of contrastive learning (CL) that is suitable for both unsupervised and supervised feature extraction. The proposed framework first constructed two CL graph for uniquely defining the positive and negative pairs. Subsequently, the projection matrix was determined by minimizing the contrastive loss function. In addition, the proposed framework considered both similar and dissimilar samples to unify unsupervised and supervised feature extraction. Moreover, we propose the three specific methods: unsupervised contrastive learning method, supervised contrastive learning method 1,and supervised contrastive learning method 2. Finally, the numerical experiments on five real datasets demonstrated the superior performance of the proposed framework in comparison to the existing methods. © 2021, CC BY.

关键词： Feature extraction

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Influence of transformer's operating Mode for value of electrodynamic force

Influence of transformer's operating Mode for value of elect...

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2020 Ural Smart Energy Conference, USEC 2020

作者： Popov, Ivan V. Evdokunin, Georgy A. Popov, Vladislav V. Joint Stock Company Scientific and Technical Center of Unified Power System Power Systems Design and Development Department st.Petersburg Russia Institute of Energy Peter the Great St. Petersburg Polytechnic University st.Petersburg Russia Baltic State Technical University VOENMEH Named after D.F. Ustinov Department of Information Systems and Software Engineering St. Petersburg Russia

ISBN: (纸本)9781728197067

In this article authors considering the issue of the power transformer's windstand capability from the point of view, that wasn't mentioned before. Authors compare windstand forces between short circuit and turning on no-load mode operating modes. The risk of damage of a winding by an inrush current, that occurs during the process of turning on no-load mode was assessed. The values of the inrush current and the short circuit currents were calculated for ORTS417000/750/24/24 transformer. Conclusions about the possibility of reaching a value close to the short - circuit current by inrush currents in certain situations are made. Calculations of magnetic fields and electrodynamic forces in the transformer for these operating modes are performed. Conclusions about the influence of the operating mode of the transformer on the value and place of application of electrodynamic forces are made. It is concluded that it is necessary to take into account the process of turning on noload mode when checking the electrodynamic windstand of transformers. © 2020 IEEE.

关键词： Power transformers

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