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Conference on Model Driven Engineering languages and Systems Companion (MODELS-C)

作者： Jakob Pietron Alexander Raschke Joeri Exelmans Matthias Tichy Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Department of Computer Science University of Antwerp – Flanders Make Antwerp Belgium

CONTEXT: Versioning allows users to efficiently create and evolve artifacts, not only but especially in the domain of model-driven engineering. Due to collaboration tools, the place and time where users jointly work at their artifacts do not play an important role anymore. Objective: We systematically elaborated a classification framework for collaboration and versioning tools. The intention is threefold: First, it should be possible to classify existing approaches. Second, deriving from user goals requirements to be met, and capabilities to be supported by a new tool. Third, given a set of capabilities, highlight which user goals can be achieved. Method: According to Kang and Lee, we systematically elaborated a problem space and a solution space and created mappings between both spaces and their sub-spaces. To demonstrate the applicability, we classified existing VCS like git, Google Docs, and several MDE-specific approaches. Results: The created feature diagram covers 238 features and contains about 87 constraints. Conclusion: The developed framework supports researchers and developers in classifying their tools, revealing new opportunities to improve their tools, and guiding the development of new tools.

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Analyzing Customer Psychological and Behavioral Attributes in Corporate Social Responsibility in Supply Chain Management Using a Multilayer Perceptron Approach 2

Analyzing Customer Psychological and Behavioral Attributes i...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Agnihotri, Kuldeep Kamidi, Deepthi Ranjan, Radha Vasudevan Unni, Manu Babu K, Ramesh Vijaya Jayanthi, N. ISBA Group of Institutes Department of Commerce & Management Madhya Pradesh Indore India Vignan Institute of Technology and Science Department of Computer Science and Engineering Telangana Hyderabad India Amity University Law Amity Law School Bihar Patna India Department of Management Karnataka Bangalore India SRKR Engineering College Department of English and Foreign Languages Andhra Pradesh Bhimavaram India Periyar Maniammai Institute of Science and Technology Deemed to be University Department of Commerce Tamilnadu Thanjavur India

ISBN: (纸本)9798331534950

Despite the fact that CSR is currently important in the retail industry, academic research on its influence is sparse. Retailers have a great interest in understanding the influence of customer opinion on CSR initiatives because the system serves as a conduit between producers and consumers. Understanding the impact of CSR projects on store bottom lines is critical for contextualising the system's results. The suggested technique includes preprocessing, feature selection, and model training. This step involved cleaning the dataset with a text-processing method that assigned each document three numerical values: document length, token number, and token length. The best approach is to model feature selection using Latent Dirichlet Allocation (LDA). The MLP was used to train the model in the specified approach. The suggested model has an average accuracy rate of 94.35%, which is greater than cutting-edge alternatives like as SVM and CNN. © 2024 IEEE.

关键词： Sales

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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

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Materials Today: Proceedings 2022年 56卷 2213-2216页

作者： Boddu, Raja Sarath Kumar Karmakar, Partha Bhaumik, Ankan Nassa, Vinay Kumar Vandana Bhattacharya, Sumanta Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India Government of West Bengal Bikash Bhawan Salt Lake W.B. Kolkata India Dept. of Applied Mathematics With Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Computer Science Engineering South Point Group of Institutions Sonepat Haryana India Department of Mathematics Dasmesh Khalsa College Zirakpur India Department of Science and Technology and Biotechnology MAKAUT India

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic

关键词： COVID-19

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Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information

arXiv

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arXiv 2023年

作者： Pastor, Francisco García-González, Jorge Gandarias, Juan M. Medina, Daniel Closas, Pau García-Cerezo, Alfonso J. Gómez-De-Gabriel, Jesús M. The Robotics and Mechatronics Group University of Málaga Spain The Department of Computer Languages and Computer Science University of Málaga Spain Germany The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Object recognition

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The Tale of Errors in Microservices: Extended Abstract 25

The Tale of Errors in Microservices: Extended Abstract

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Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of computer Systems

作者： I-Ting Angelina Lee Zhizhou Zhang Abhishek Parwal Milind Chabbi Department of Computer Science & Engineering Washington University in St. Louis St. Louis USA Programming Systems Group Uber Technologies Inc. Sunnyvale USA Marketplace Configurations Uber Technologies Inc. Sunnyvale USA

ISBN: (纸本)9798400715938

Microservice architectures have become the de facto paradigm for building scalable, service-oriented systems. Although their decentralized design promotes resilience and rapid development, the inherent complexity leads to subtle performance challenges. In particular, non-fatal errors - internal failures of remote procedure calls that do not cause top-level request failures - can accumulate along the critical path, inflating latency and wasting *** this work, we analyze over 11 billion RPCs across more than 6,000 microservices at Uber. Our study shows that nearly 29% of successful requests experience non-fatal errors that remain hidden in traditional monitoring. We propose a novel latency-reduction estimator (LR estimator) to quantify the potential benefit of eliminating these errors. Our contributions include a systematic study of RPC error patterns, a methodology to estimate latency reductions, and case studies demonstrating up to a 30% reduction in tail latency.

关键词： RPC errors

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The Regularization of Small Sub-Constraint Satisfaction Problems 22nd

The Regularization of Small Sub-Constraint Satisfaction Prob...

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22nd International Conference on Applications of Declarative programming and Knowledge Management, INAP 2019, the 33rd Workshop on Logic programming, WLP 2019, and the 27th Workshop on Functional and Constraint Logic programming, WFLP 2019

作者： Löffler, Sven Liu, Ke Hofstedt, Petra Department of Mathematics and Computer Science MINT Programming Languages and Compiler Construction Group Brandenburg University of Technology Cottbus-Senftenberg Konrad-Wachsmann-Allee 5 Cottbus03044 Germany

ISBN: (纸本)9783030467135

This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints. The purpose of this approach is to reduce the number of fails in the resolution process, to improve the inferences made during search by the constraint solver by strengthening constraint propagation, and to maintain the level of propagation while reducing the cost of propagating the constraints. Our experimental results show improvements in terms of the resolution speed compared to the original CSPs and a competitiveness to the recent tabulation approach [1, 15]. Besides, our approach can be realized in a preprocessing step, and therefore wouldn’t collide with redundancy constraints or parallel computing if implemented. © 2020, Springer Nature Switzerland AG.

关键词： Constraint satisfaction problems

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Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers

arXiv

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arXiv 2022年

作者： Daggitt, Matthew L. Kokke, Wen Atkey, Robert Arnaboldi, Luca Komendantskya, Ekaterina Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Mathematically Structured Programming Group University of Strathclyde Glasgow United Kingdom Laboratory for Foundations of Computer Science University of Edinburgh Edinburgh United Kingdom

Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of networks with hundreds of thousands of nodes. In theory this opens the door to the verification of larger control systems that make use of neural network components. However, although work has managed to incorporate the results of these verifiers to prove larger properties of individual systems, there is currently no general methodology for bridging the gap between verifiers and interactive theorem provers (ITPs). In this paper we present Vehicle, our solution to this problem. Vehicle is equipped with an expressive domain specific language for stating neural network specifications which can be compiled to both verifiers and ITPs. It overcomes previous issues with maintainability and scalability in similar ITP formalisations by using a standard ONNX file as the single canonical representation of the network. We demonstrate its utility by using it to connect the neural network verifier Marabou to Agda and then formally verifying that a car steered by a neural network never leaves the road, even in the face of an unpredictable cross wind and imperfect sensors. The network has over 20,000 nodes, and therefore this proof represents an improvement of 3 orders of magnitude over prior proofs about neural network enhanced systems in ITPs. © 2022, CC BY.

关键词： Problem oriented languages

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A Systematic Review on Security and Safety of Self-Adaptive Systems

SSRN

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SSRN 2022年

作者： Pekaric, Irdin Groner, Raffaela Witte, Thomas Adigun, Jubril Gbolahan Raschke, Alexander Felderer, Michael Tichy, Matthias University of Innsbruck Department of Computer Science Technikerstraße 21a InnsbruckA-6020 Austria Ulm University Institute of Software Engineering and Programming Languages James-Franck-Ring 9 Ulm89081 Germany

Context: Cyber-physical systems (CPS) are increasingly self-adaptive, i.e. they have the ability to introspect and change their behavior. This self-adaptation process must be considered when modeling the safety and security aspects of the ***: This study collects and compares security attacks and safety hazards on self-adaptive systems (SAS) described in the literature. In addition, mitigation and treatment strategies as well as the modeling and analysis approaches are ***: We conducted a systematic literature review on 21 selected papers. The selection process involved a database search and snowballing methodologies. The coding scheme to analyze the content of the papers was obtained through our research questions, existing domain-specific taxonomies, and open ***: Safety and Security are not jointly modeled in the context of self-adaptive systems. The adaptation process is often not considered in the attack and hazard analysis due to naïve assumptions and modeling. The proposed approaches are mostly verified and validated through ***: A thorough and joint modeling approach for safety and security in self-adaptive systems is still an open challenge that needs to be addressed. Further work is needed to address the gap between safety and security modeling in self-adaptive systems. © 2022, The Authors. All rights reserved.

关键词： Adaptive systems

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Analyzing Customer Psychological and Behavioral Attributes in Corporate Social Responsibility in Supply Chain Management Using a Multilayer Perceptron Approach

Analyzing Customer Psychological and Behavioral Attributes i...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Kuldeep Agnihotri Deepthi Kamidi Radha Ranjan Manu Vasudevan Unni Ramesh Vijaya Babu K N. Jayanthi Department of Commerce & Management ISBA Group of Institutes Indore Madhya Pradesh India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad Telangana India Law Amity Law School Amity University Patna Bihar India Department of Management St. Claret College (Autonomous) Bangalore Karnataka India Department of English and Foreign Languages SRKR Engineering College Bhimavaram Andhra Pradesh India Department of Commerce Periyar Maniammai Institute of Science and Technology Deemed to be University Thanjavur Tamilnadu India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

关键词： Training Support vector machines Supply chain management Accuracy Computational modeling Supply chains Psychology Feature extraction Numerical models Resource management

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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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