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Applying Model-Driven engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations: The Case for Microservice Architecture

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SN computer science 2021年第6期2卷 459页

作者： Sorgalla, Jonas Wizenty, Philip Rademacher, Florian Sachweh, Sabine Zündorf, Albert IDiAL Institute University of Applied Sciences and Arts Dortmund Otto-Hahn-Straße 27 Dortmund 44227 Germany Department of Computer Science and Electrical Engineering University of Kassel Wilhelmshöher Allee 73 Kassel 34121 Germany

Microservice architecture (MSA) denotes an increasingly popular architectural style in which business capabilities are wrapped into autonomously developable and deployable software components called microservices. Microservice applications are developed by multiple DevOps teams each owning one or more services. In this article, we explore the state of how DevOps teams in small and medium-sized organizations (SMOs) cope with MSA and how they can be supported. We show through a secondary analysis of an exploratory interview study comprising six cases, that the organizational and technological complexity resulting from MSA poses particular challenges for small and medium-sized organizations (SMOs). We apply model-driven engineering to address these challenges. As results of the second analysis, we identify the challenge areas of building and maintaining a common architectural understanding, and dealing with deployment technologies. To support DevOps teams of SMOs in coping with these challenges, we present a model-driven workflow based on LEMMA—the Language Ecosystem for Modeling Microservice Architecture. To implement the workflow, we extend LEMMA with the functionality to (i) generate models from API documentation;(ii) reference remote models owned by other teams;(iii) generate deployment specifications;and (iv) generate a visual representation of the overall architecture. We validate the model-driven workflow and our extensions to LEMMA through a case study showing that the added functionality to LEMMA can bring efficiency gains for DevOps teams. To develop best practices for applying our workflow to maximize efficiency in SMOs, we plan to conduct more empirical research in the field in the future. © 2021, The Author(s).

关键词： Development process DevOps Microservice architecture Model-driven engineering

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Sensor-based Complex Human Activity Recognition from Smartwatch Data using Hybrid Deep Learning Network 36

Sensor-based Complex Human Activity Recognition from Smartwa...

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36th International Technical Conference on Circuits/Systems, computers and Communications, ITC-CSCC 2021

作者： Mekruksavanich, Sakorn Jitpauanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665435536

Sensor-based human activity recognition (HAR) research is being used for tasks like healthcare tracking, fall detection, and misbehavior prevention. Because of the sophistication of hand gesture signals, complex human activity (CHA) recognition is a difficult task in HAR research. Compared to simple human behavior (SHA), the CHA has more input and long sequential information to deal with. To solve the CHA problem, we proposed a hybrid deep learning model that combines a CNN network and an LSTM network in this paper. The proposed model is an end-to-end model that automatically extracts high features. The model is tested on the daily human activity (DHA) dataset, which is a publicly accessible dataset of complex human activities. The results of the experiments show that the proposed hybrid deep learning model outperforms the current state-of-the-art recognition model. © 2021 IEEE.

关键词： Long short-term memory

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Reducing Effective System Dimensionality with Long-Range Collective Dipole-Dipole Interactions

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Physical Review Letters 2024年第17期132卷 173803-173803页

作者： Ashwin K. Boddeti Yi Wang Xitlali G. Juarez Alexandra Boltasseva Teri W. Odom Vladimir Shalaev Hadiseh Alaeian Zubin Jacob Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Graduate Program in Applied Physics Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA School of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d¯=2.20(12), despite the emitters being distributed in 3D. This contrasts with the homogeneous environment, where the apparent dimension is d¯=3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters.

关键词： Light-matter interaction Long-range interactions Metamaterials Optics & lasers Photonics Plasmonics Quantum optics

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Honeypot-based IDS for cyber attack detection

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AIP Conference Proceedings 2025年第1期3204卷

作者： M. Karthigha M. Indira Priyadharshini M. Rohini J. Reema Nafeesa B. ShreeAkshaya R. Yagavi Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India. Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India. Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India.

As technology elaborates daily, maintaining enormous amounts of data has become a great challenge. Security becomes vulnerable starting from zero-day attacks to malware threats. It is necessary to bring a solution to this without compromising data integrity and data security. This calls for an intrusion detection system that can detect suspicious activities while alerting the users. Honeypots are one such system that enables us to carry out the same. It is a type of network security system that is becoming increasingly popular in modern network architecture due to its ability to detect malicious activities from external sources. Honeypots simulate a high-value target and lure potential attackers away from critical systems. Thus, a honeypot, in simple words, is a fake system that acts as trap alluring intruders to seek the system/data. It entices the users to perform attacks instead where information about attacks is recorded. The honeypot creates a decoy environment to divert intruders to detect, track, deter, and prevent unauthorized access to the network. As evidenced by its growing popularity in security, the honeypot is an invaluable addition to a comprehensive security plan. The project proposes a honeypot system that imitates a telnet port. This telnet port works by deceiving attackers and retrieving information about them.

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InstaSight: Leveraging AI to See Beyond Sight for Blind Users on Social Media

InstaSight: Leveraging AI to See Beyond Sight for Blind User...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Chi, Chun-Nan Hsu, Jia-Lien Hung, Chi-Huang Hsu, Chia-Hsun Phuong, To Nguyen Tan Juan, Wei-Chih Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Department of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Dept. of Information Technology Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of International High School Kang Chiao International School New Taipei City Taiwan Dept. Electronical Engineering Lee-Ming Institute of Technology New Taipei City Taiwan Department of Information Management Hwa Hsia University of Technology New Taipei City Taiwan

ISBN: (纸本)9798331521165

This study introduces "InstaSight," a system enabling visually impaired individuals to engage with social media focused on visual content. InstaSight integrates a Single Board computer, head-mounted webcam, and Bluetooth headset for seamless voice interactions. By utilizing the APIs of social media, speech recognition, and Large Language Models (LLMs), users can post photos and videos with voice-to-text captions and listen to comments converted from text or images. This system fosters social engagement, helping blind users connect and share experiences, improving their psychological well-being. © 2025 IEEE.

关键词： Social networking (online)

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Fall Detection from Smart Wearable Sensors using Deep Convolutional Neural Network with Squeeze-And-Excitation Module 25

Fall Detection from Smart Wearable Sensors using Deep Convol...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Charoenphol, Adisaya Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Older people are at a higher risk of injury or death due to falls than the general population. As the world's population ages, new fall detection and prevention technology must be developed quickly. The emerging technology sector is devoted to the development of tools that improve the lives of the elderly. The goal of a fall prevention system is to foresee and reduce the risk of a person falling. A fall detection system, on the other hand, tracks the fall. It alerts the user in the event of a fall to assist. The purpose of this study is to demonstrate the efficacy of fall detection utilizing wearable sensors. We developed the SE-DeepConvNet model, a hybrid deep convolutional neural network, to enhance the fall detection capability using squeeze-And-excitation modules. The proposed model uses wearable sensor data from the SisFall dataset, a publicly available benchmark dataset. We performed a series of experiments using various deep learning models, including the proposed approach. The experimental findings demonstrate that the introduced SE-DeepConvNet model performs better than other deep learning models (99.201% accuracy and 99.202% F1-score). © 2021 IEEE.

关键词： Fall detection

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Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems

arXiv

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arXiv 2023年

作者： Kičić, Ivica Vlachas, Pantelis R. Arampatzis, Georgios Chatzimanolakis, Michail Guibas, Leonidas Koumoutsakos, Petros Computational Science and Engineering Laboratory Eth Zürich CH-8092 Switzerland School of Engineering and Applied Sciences Harvard University 29 Oxford Street CambridgeMA02138 United States Department of Computer Science Stanford University 353 Serra Hall CA94035 United States

Predictive simulations are essential for applications ranging from weather forecasting to material design. The veracity of these simulations hinges on their capacity to capture the effective system dynamics. Massively parallel simulations predict the systems dynamics by resolving all spatiotemporal scales, often at a cost that prevents experimentation. On the other hand, reduced order models are fast but often limited by the linearization of the system dynamics and the adopted heuristic closures. We propose a novel systematic framework that bridges large scale simulations and reduced order models to extract and forecast adaptively the effective dynamics (AdaLED) of multiscale systems. AdaLED employs an autoencoder to identify reduced-order representations of the system dynamics and an ensemble of probabilistic recurrent neural networks (RNNs) as the latent time-stepper. The framework alternates between the computational solver and the surrogate, accelerating learned dynamics while leaving yet-to-be-learned dynamics regimes to the original solver. AdaLED continuously adapts the surrogate to the new dynamics through online training. The transitions between the surrogate and the computational solver are determined by monitoring the prediction accuracy and uncertainty of the surrogate. The effectiveness of AdaLED is demonstrated on three different systems - a Van der Pol oscillator, a 2D reaction-diffusion equation, and a 2D Navier-Stokes flow past a cylinder for varying Reynolds numbers (400 up to 1200), showcasing its ability to learn effective dynamics online, detect unseen dynamics regimes, and provide net speed-ups. To the best of our knowledge, AdaLED is the first framework that couples a surrogate model with a computational solver to achieve online adaptive learning of effective dynamics. It constitutes a potent tool for applications requiring many computationally expensive simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Weather forecasting

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Enhancement of Human Complex Activity Recognition using Wearable Sensors Data with InceptionTime Network 2

Enhancement of Human Complex Activity Recognition using Wear...

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2nd International Conference on Big Data Analytics and Practices, IBDAP 2021

作者： Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665428415

Effective human activity recognition can be incredibly beneficial in big data applications like ambient healthcare-supported living. Deep learning (DL) techniques have considerably advanced research in human activity recognition (HAR). These deep learning algorithms outperform conventional machine learning methods in terms of automatic feature extraction. Many deep learning models have recently been demonstrated as state-of-the-art approaches to efficiently classify simple and complex human behaviors to address the HAR. This work proposes a new sensor-based HAR to classify complex activities with high performance using a DL model called InceptTime network. The proposed architecture is evaluated on a public benchmark complex activity dataset named PAMAP2. The experimental outcomes show that the proposed InceptTime model is significantly better than other baseline D L models on the same dataset with the highest accuracy of 88 %. © 2021 IEEE.

关键词： Wearable sensors

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Electron and neutrino scattering off the deuteron in a relativistic framework

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Physical Review C 2023年第2期107卷 024617-024617页

作者： A. Grassi J. Golak W. N. Polyzou R. Skibiński H. Witała H. Kamada M. Smoluchowski Institute of Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University PL-30348 Kraków Poland Department of Physics and Astronomy The University of Iowa Iowa City Iowa 52242 USA Department of Physics Faculty of Engineering Kyushu Institute of Technology Kitakyushu 804-8550 Japan

We build a relativistic model to perform calculations of exclusive, semiexclusive, and inclusive unpolarized cross sections and various polarization observables in electron and neutrino scattering experiments with deuteron targets. The strong interaction dynamics is defined by an explicit dynamical unitary representation of the Poincaré group, where representations of space translations and rotations in the interacting and noninteracting representations are the same. The Argonne V18 potential is used to construct a relativistic nucleon-nucleon interaction reproducing the experimental deuteron binding energy and nucleon-nucleon scattering observables. Our formalism does not include the pion production channel and neglects two-body contributions in the electromagnetic as well as in the weak nuclear current operator. We show that it is applicable to processes at kinematics, where the internal two-nucleon energy remains below the pion production threshold but the magnitude of the three-momentum transfer extends at least to several GeV.

关键词： Electroweak interactions in nuclear physics Nuclear reactions Polarization phenomena Special relativity Few-body systems

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Cnn-Gnn Convolution Bridge: An Effective Algorithmic Migration Strategy from Cnns to Gnns

SSRN

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SSRN 2023年

作者： Zhang, Kuijie Pang, Shanchen Yang, Huahui Lin, Jerry Chun-Wei Zhang, Yuanyuan Wu, Wenhao Li, Hengxiao Shandong 266580 China School of Qilu Transportation Shandong University Shandong 250002 China Department of Computer Science Electrical Engineering and Mathematical Science Western Norway University of Applied Sciences Bergen264200 Norway School of Information and Control Engineering Qingdao University of Technology Shandong 266520 China

Graph neural networks (GNNs), as rising stars in machine learning, are widely used in relational data models and have achieved outstanding performance in graph tasks. GNNs continuously receive mature models from other domains such as computer vision and natural language processing to motivate the development of graph algorithms. However, due to the data structures various from different domains, the cross-domain migration of models has to go through a long period of disassembly and reconstruction, which may not yield the desired results. To preserve the excellent properties of convolution and optimize the migration process from convolutional neural networks (CNNs) to GNNs, we propose a CNN-GNN Convolution Bridge. The CNN-GNN Convolution Bridge realizes the data alignment from CNN to GNN, so that the CNN-based model can be efficiently migrated to the graph structure model. To demonstrate the effectiveness of our migration strategy, we migrated the Inception module and U-Net architecture from CNNs to GNNs, named GraInc and GraU-Net, for the node-level task and the graph-level task, respectively. Experimental results show that GraInc and GraU-Net are highly competitive with the current state-of-the-art models, particularly on dense graph datasets. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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