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arXiv 2024年

作者： Liu, Mugeng Huang, Xiaolong He, Wei Xie, Yibing Zhang, Jie M. Jing, Xiang Chen, Zhenpeng Ma, Yun Ministry of Education School of Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Department of Informatics King’s College London London United Kingdom National Key Laboratory of Data Space Technology and System Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Artificial Intelligence Peking University Beijing China

The Software engineering (SE) community has been embracing the open science policy and encouraging researchers to disclose artifacts in their publications. However, the status and trends of artifact practice and quality remain unclear, lacking insights on further improvement. In this paper, we present an empirical study to characterize the research artifacts in SE publications. Specifically, we manually collect 1,487 artifacts from all 2,196 papers published in top-tier SE conferences (ASE, FSE, ICSE, and ISSTA) from 2017 to 2022. We investigate the common practices (e.g., URL location and format, storage websites), maintenance activities (e.g., last update time and URL validity), popularity (e.g., the number of stars on GitHub and characteristics), and quality (e.g., documentation and code smell) of these artifacts. Based on our analysis, we reveal a rise in publications providing artifacts. The usage of Zenodo for sharing artifacts has significantly increased. However, artifacts stored in GitHub tend to receive few stars, indicating a limited influence on real-world SE applications. We summarize the results and provide suggestions to different stakeholders in conjunction with current guidelines. © 2024, CC BY.

关键词： Software engineering

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Security Analysis for Threats to Patient Data in the Medical Internet of Things 11

Security Analysis for Threats to Patient Data in the Medical...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Shrivastava, Abhudaya Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India The State University of New Jersey Department of Computer Science CamdenNJ United States Gheorghe Asachi Technical University of Iasi Neagu Bogdan Constantin Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

The current model of the healthcare system, which is being changed, is based on hospitals and specialists. The new model will be less centralised and will put more attention on each patient. The Medical Internet of Things (MIoT) makes it possible for machines to talk to each other and for humans to intervene in real time. These changes will soon make medical care much easier to get, cheaper, and more reliable. The promise of the Medical Internet of Things (MIoT) is that it will make patients happier with their healthcare overall by making it easier for them to get personalized e-health services no matter where they are or what time it is. The Medical Internet of Things (MIoT), also called the healthcare Internet of Things, is made up of medical devices and software that are all connected to each other. Many Internet of Things (IoT) applications are based on the management of personally identifiable information, such as a patient's name, address, medical history, and health conditions. This is one reason why IoT applications have a special place in the field of healthcare services. When it comes to the Internet of Things (IoT), protecting the privacy and safety of patients without compromising data security is one of the hardest things to do. The privacy and safety of the information that IoT devices collect is still a big concern, both when the information is being sent to the cloud for storage and when it is being sent there. This is because there is a lot of data and it is important and sensitive. In this paper, we look at the technical flaws and research challenges for future work in security and privacy, as well as the current challenges and needs related to the flow of data in the MIoT. To proposed approach for Security Analysis for Threats to Patient Data in the Medical Internet of Things © 2022 IEEE.

关键词： Internet of things

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Error Density-dependent Empirical Risk Minimization

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Expert Systems with Applications 2024年 254卷

作者： Chen, Hong Zhang, Xuelin Gong, Tieliang Gu, Bin Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen518000 China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518000 China School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Artificial Intelligence Jilin University Jilin130012 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Empirical Risk Minimization (ERM) with the squared loss has become one of the most popular principles for designing learning algorithms. However, the existing mean regression models under ERM usually suffer from poor generalization performance due to their sensitivity to atypical observations (e.g., outliers). For alleviating this sensitivity, some strategies have been proposed by utilizing the quantitative relationship of error values with different observations to form robust learning objectives. Instead of focusing on error values, this paper considers the error density to uncover the structure information of observations and proposes a new learning objective, called Error Density-dependent Empirical Risk Minimization (EDERM), for robust regression under complex data environments. Property characterizations and experimental analysis validate the robustness and competitiveness of the proposed EDERM-based learning models. The implemented codes can be found at https://***/zhangxuelincode/EDERM. © 2024 Elsevier Ltd

关键词： Learning algorithms

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An Optimized Approach to Hourly Temperature and Humidity Setpoint Generation for Reducing Tomato Disease and Saving Power Cost in Greenhouses

SSRN

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SSRN 2024年

作者： Sánchez-Molina, Jorge Antonio Wang, Hui Laktionov, Ivan Díaz, Francisco Rodríguez Li, Ming BIPP Institute of Plant Protection Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Department of Computer Systems Software Dnipro University of Technology DniproUA49005 Ukraine Department of Informatics University of Almería ceiA3 CIESOL Almería04120 Spain Beijing Research Center for Information Technology in Agriculture National Engineering Research Centre for Information Technology in Agriculture National Engineering Laboratory for Agri-product Quality Traceability Key Laboratory of Agri-informatics Ministry of Agriculture Beijing100097 China

Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops. The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop. Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems. The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day-1 in warm weather condition, as well as 3.74 €*day-1 in cold weather condition, the uninfected LAI (m2[Leaves](m-2[soils]*day-1)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose. The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth. © 2024, The Authors. All rights reserved.

关键词： Crops

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Generalized regular Turán numbers

arXiv

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arXiv 2023年

作者： Gerbner, Dániel Karim, Hilal Hama Alfréd Rényi Institute of Mathematics Hun-Ren Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

We combine two generalizations of ordinary Turán problems. Given graphs H and F and a positive integer n, we study rex(n, H, F), which is the largest number of copies of H in F -free regular n-vertex graphs. Copy...

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Color Models for Skin Lesion Classification from Dermatoscopic Images 8th

Color Models for Skin Lesion Classification from Dermatoscop...

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8th International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2018 held in conjunction with the 30th IEEE International Conference on Tools with Artificial Intelligence, IEEE ICTAI 2018

作者： Mporas, Iosif Perikos, Isidoros Paraskevas, Michael Communications and Intelligent Systems Group School of Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Computer Engineering and Informatics Department University of Patras Patras Greece Computer Technology Institute and Press ‘Diophantus’ Patras Greece Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9789811519178

In this paper, we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image preprocessing for natural hair removal and image segmentation for extraction of the skin lesion area followed by computation of statistical values of colors as features. The color-based features were extracted from several well-known and widely used color models. Several classification algorithms were evaluated with the best performing classification algorithm being the AdaBoost with random forest classifier with classification accuracy equal to 73.08% when using RGB-based features only and 74.26% when combining RGB, HSV, and YIQ color model-based features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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Progress in Applied Electrical engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Tuukka Ruotsalo Luis A. Leiva Adam Sudoł Edward Jacek Gorzelańczyk Adam Łysiak Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Department of Computer Science University of Luxembourg Luxembourg Luxembourg Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.

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Human Factors in Model-Driven engineering: Future Research Goals and Initiatives for MDE

arXiv

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arXiv 2024年

作者： Liebel, Grischa Klünder, Jil Hebig, Regina Lazik, Christopher Nunes, Inês Graßl, Isabella Steghöfer, Jan-Philipp Exelmans, Joeri Oertel, Julian Marquardt, Kai Juhnke, Katharina Schneider, Kurt Gren, Lucas Happe, Lucia Herrmann, Marc Wyrich, Marvin Tichy, Matthias Goulão, Miguel Wohlrab, Rebekka Kalantari, Reyhaneh Heinrich, Robert Greiner, Sandra Rukmono, Satrio Adi Chakraborty, Shalini Abrahão, Silvia Amaral, Vasco Department of Computer Science Reykjavik University Menntavegur 1 Reykjavik102 Iceland Software Engineering Group Leibniz University Hannover Welfengarten 1 Hannover30167 Germany Department of Software Engineering University of Rostock Albert-Einstein-Straße Rostock18057 Germany Department of Software Engineering Humboldt-Universität zu Berlin Unter den Linden 6 Berlin10099 Germany Frankfurt Germany Department of Software Engineering II University of Passau Innstraße 33 Passau94032 Germany XITASO GmbH Austraße 35 Augsburg86153 Germany Department of Computer Science University of Antwerp Middelheimlaan 1 Antwerp2020 Belgium Department of Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany SMT-EMI6 Carl Zeiss SMT GmbH Rudolf-Eber-Straße 2 Oberkochen73447 Germany Department of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg41127 Sweden Department of Interdisciplinary Didactics Karlsruhe Institute of Technology Engesserstr. 2 Karlsruhe75131 Germany Department of Computer Science Saarland University Saarland Informatics Campus Saarbrücken66123 Germany Institute of Software Engineering and Languages Ulm University James-Franck-Ring Ulm89081 Germany Department of Computer Science NOVA School of Science & Technology Campus de Caparica Caparica2829-516 Portugal Department of Engineering Design and Teaching Innovation University of Ottawa 800 King Edward Avenue OttawaK1N6N5 Canada KASTEL - Institute of Information Security and Dependability Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany Department of Computer Science University of Bern Bern3012 Switzerland Department of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven5600 MB Netherlands Instituto Universitario Mixto de Tecnología Informática Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

Purpose: Software modelling and Model-Driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values. © 2024, CC BY.

关键词： Software design

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A secure and multi-objective virtual machine placement framework for cloud data centre

arXiv

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arXiv 2021年

作者： Saxena, Deepika Gupta, Ishu Kumar, Jitendra Singh, Ashutosh Kumar Wen, Xiaoqing Department of Computer Applications National Institute of Technology Kurukshetra India Department of Computer Applications Nit Tiruchirappalli Tamilnadu India Department of Creative Informatics Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka8208502 Japan

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%. © 2021, CC BY.

关键词： Electric power utilization

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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