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Creating a Self-Service DevOps Platform for Black-Box Testing on Kubernetes 9th

Creating a Self-Service DevOps Platform for Black-Box Testin...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Golis, Tomas Dakic, Pavle Slovak Univ Technol Bratislava Fac Informat & Informat Technol Inst Informat Informat Syst & Software Engn Bratislava Slovakia Singidunum Univ Fac Informat & Comp Belgrade Serbia

ISBN: (纸本)9789819733040;9789819733057

This paper looks at how to integrate black-box testing automation into Kubernetes on multitenancy infrastructure, to enhance software quality and efficiency. Among the primary goals are to examine black-box testing automation methodologies, measure the impact of security test reuse, evaluate containerization and deployment using Kubernetes, and study issues in dynamic YAML artifact file creation. The study also looks at the importance of test automation in aligning with modern software development methodologies and the importance of multitenancy infrastructure in black-box testing automation. The key findings of this study highlight the necessity of black-box testing in ensuring that software meets user needs, with a special emphasis on Kubernetes-based multitenant infrastructures. Additionally, it highlights the challenges of creating and managing a multitenant environment, highlighting the importance of consistent security across all tenant spaces. The study contributes to a thorough understanding of black-box testing automation by providing insights into its use in modern DevOps methods, integrating tools like Terraform SDK and ArgoCD, and demonstrating the potential of self-service platforms in facilitating infrastructure management. This study should lay the foundation for future research and practical applications to improve software quality through improved testing and deployment approaches.

关键词： CI/CD GitOps Kubernetes DevOps Self-service ArgoCD Cloud Testing Black-box testing Infrastructure

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A Mission Planner for Autonomous Tasks in Farms 6th

A Mission Planner for Autonomous Tasks in Farms

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6th Iberian Robotics Conference (Robot)

作者： Emmi, Luis Cordova-Cardenas, Ruth Gonzalez-de-Santos, Pablo Ctr Automat & Robot UPM CSIC Carretera CAMPO REAL Km 02 Madrid 28500 Spain

ISBN: (纸本)9783031591662;9783031591679

This research introduces a Mission Planner, a route optimization system for agricultural robots. The primary goal is to enhance weed management efficiency using laser technology in narrow-row crops like wheat and barley and wide-row crops like beets and maize. The Mission Planner relies on graph-based approaches and incorporates a range of algorithms to generate efficient and secure routes. It employs three key algorithms: (i) Dijkstra algorithm for identifying the most optimal farm route, (ii) Visibility Road-Map Planner (VRMP) to select paths in cultivated fields where visibility is limited, and (iii) an enhanced version of the Hamiltonian path for determining the optimal route between crop lines. This Mission Planner stands out for its versatility and adaptability, owing to its emphasis on graphs and the diverse algorithms it employs for various tasks. This adaptability allows it to provide multiple functions, making it applicable beyond a specific role. Furthermore, its ability to adjust to different agricultural robot sizes and specifications is a significant advantage, as it enables tailored programming to meet safety and movement requirements specific to each robot. These research results affirm the effectiveness of the implemented strategies, demonstrating that a robot can confidently and effectively traverse the entire farm while performing weed management tasks, specifically laser-based weed management.

关键词： Agricultural robotics Route planner Dijkstra's algorithm Visibility Road-Map Planner (VRMP) Hamiltonian path Laser weed removal

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Hybrid Feature Model Based Knee Vibroarthrographic Signal Classification with Signal Level Enhancement Using Morphological Filter 4th

Hybrid Feature Model Based Knee Vibroarthrographic Signal Cl...

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4th Congress on Intelligent systems (CIS)

作者： Shidore, Mrunal Jalnekar, Rajesh Vishwakarma Inst Technol Pune Maharashtra India

ISBN: (纸本)9789819990399;9789819990405

When the human knee is in motion, joint vibration signals are detected and recorded. These vibration signals termed as a Vibroarthrographic (VAG) signals are helpful in assessing the knee joint pathological condition. So, by employing feature extraction and machine learning algorithms, VAG signals can be classified into normal and abnormal, which will assist in non-invasive investigation for medical practitioners. In this paper, we propose a unique morphological denoising filters on VAG signal to improve signal to noise ratio (SNR). Along with gold standard statistical features, features such as RMS values of opening, closing and average morphological filters are computed. Fifteen statistical features are reduced to four using Fischer score algorithm. In this hybrid model of feature combination, four spectral features which are spectral slope (variance), spectral flux (mean), spectral flux (variance) and spectral mean (variance) are also a part of final feature set. Pattern classification performance is tested and compared using Random Forest (RF), Gaussian Naive Bayes (GNB) and Support vector Machine (SVM-RBF) classifiers. Random forest model performed best offering classification accuracy of 93.07%, sensitivity 90% and specificity of 87.88%.

关键词： Baseline Wander CMA filter Gaussian Naive Bayes Fischer score Morphological filter Random Forest SNR Support vector machine

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Evaluation of No-Load Losses in the Single-Sheet, Double-Sheet, and Triple-Sheet Step Lap Joints of the Transformer Core 2nd

Evaluation of No-Load Losses in the Single-Sheet, Double-She...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Dawood, Kamran Kaymaz, Ismet Tursun, Semih Astor Enerji Ankara Turkiye

ISBN: (纸本)9783031628801;9783031628818

Power transformers are crucial pieces of equipment in power grids, and the working of transformers directly impacts the safety of the power system. Transformer-increased losses due to core design can increase the transformer's operational cost. With the development of numerical methods and the growth of finite element analysis techniques, tremendous progress has been made in applying the finite element method in the design of the transformer, which provides new ideas for designing and optimizing the transformer design. This paper analyzes the no-load losses of step lap cores based on finite element analysis and summarizes the impact of flux density distribution on single-sheet step lap joints (SSSL), double-sheet step lap joints (DSSL), and triple-sheet step lap joints (TSSL). Three different step lap joints of power transformers are examined at different magnetic flux densities and give guidelines to select the optimal step joints to achieve low losses and cost-effective design.

关键词： Core, Double-sheet Step Lap Joints Finite Element Analysis Single-sheet Step Lap Joints Transformer, Triple-sheet Step Lap Joints

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Harmony in Complexity: The Collective Intelligence of Living Organisms

Harmony in Complexity: The Collective Intelligence of Living...

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Intelligent systems Conference

作者： Piumetti, Marco Lygeros, Nik Politecn Torino Dept Appl Sci & Technol Corso Duca Abruzzi 24 I-10129 Turin Italy Univ Lyon LGPC UMR 5285 F-69616 Villeurbanne France

ISBN: (纸本)9783031664274;9783031664281

This work delves into the dynamics of collective behavior and structural intelligence within biological systems, offering insights into self-organization phenomena and the emergence of differentiated biosystems over time. Synergistic interactions among components give rise to a unique form of collective intelligence inherent in biosystems since the inception of life on Earth. The manuscript explores concepts of non-linearity, self-organization, and emerging collective structures, emphasizing the pivotal role of synergies in evolutionary processes. From the mathematical representation of open, non-equilibrium systems to the emergence of self-organized patterns and fractal objects, this journey unfolds the fascinating principles underlying life's complexity. Evolutionary game theory provides a lens through which the dynamic interplay of effective interactions among components leads to the formation of collective structures, showcasing the adaptability and resilience of living organisms. Ultimately, this manuscript serves as a comprehensive exploration into the captivating world of collective intelligence, revealing its foundational importance in the complex harmony of biological systems.

关键词： Self-organizing phenomena Collective intelligence Synergistic interactions Complex systems Biosystems

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Salary Prediction with Machine Learning in Teachers Hired from the Region of Cusco Peru 13th

Salary Prediction with Machine Learning in Teachers Hired fr...

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13th Computer Science Online Conference

作者： Canahuire Hilari, Segundo Larico Carbajal, Joel Pineda, Ferdinand Soria, Juan J. Univ Peruana Union Autop Heroes Guerra Pacifico Filial Juliaca Escuela Profes Ingn Sistemas Carretera Salida Arequipa Km 6 Chullunquiani Juliaca Peru

ISBN: (纸本)9783031705946;9783031705953

This article presents an analysis of machine learning (ML) models to predict the salaries of 11,392 contracted teachers appointed at Ugel in the Cusco region of Peru, using recent data from the unique payroll system. The core element of the study is the hired teachers, deliberately excluding the salaries of the appointed teachers from the analysis. A significant result of this research is the identification of a new ML model capable of predicting teacher salaries with considerable accuracy, based on independent variables closely related to salary. This finding is noteworthy because it fills a gap in existing ML applications for salary prediction, indicating a promising direction for future research in this area. Even though the methodology used to analyze salary data is exhaustive, it does not take into account gender differences, which may affect salary variation during the three-year period considered. This oversight suggests that future research should include a broader range of variables, including gender, to improve the accuracy and applicability of salary predictions for both appointed and contracted faculty. Such an approach could provide more nuanced information about the factors that influence teacher salaries and help develop more equitable and effective salary models. One of the key contributions of the article is the detailed examination of the factors that influence the salaries of appointed teachers, including age, position, educational level, modular code, weekly hours, period, and other Dummy variables. The use of Decision Tree Regressor (DTR), Gradient Boosting Regressor (GBR), Random Forest Regressor (RFR), Neural Network Regressor (NNR), and Support Vector Regressor (SVR) models yielded accurate metrics to choose the best model for salary prediction. This research not only advances our understanding of the determinants of teacher salaries in the Cusco-Peru region, but also offers a valuable framework for similar studies in other contexts. Finally,

关键词： Machine Learning Remuneration Gradient Boosting Decision Tree Random Forest Neural Network Support Vector

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HR Analytics: Opportunities and Challenges for Implementation in SME 1

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Bolbotinovic, Zeljko Z. Popovac, Ranka M. Vukmirovic, Dragan V. Comic, Tijana Z. Stanojevic, Nebojsa D. Tekijanka Doo Dunavska 50 Kladovo 19320 Serbia Philip Morris Int Lausanne Switzerland Fac Org Sci Jove Ilica 154 Belgrade 11000 Serbia

ISBN: (数字)9783031608407

ISBN: (纸本)9783031608391;9783031608407

The paper explores opportunities and challenges for implementing human resourcing analytics (HRa) in small and medium enterprises (SMEs) in the first stages of their digital transformation. In the reference literature, this topic is not sufficiently represented, regardless of the importance that SMEs have in the economic system. The solutions presented and analyzed are primarily related to the digital transformation of large companies, which have much more resources at their disposal than SMEs, so these solutions are largely inapplicable to SMEs. For this purpose, an original model for the beginning of digital transformation was defined, which envisages the implementation of HR in SMEs (HRPA - HR People Analytic model). The main components of the model are presented and directions for implementation are indicated. The methodological procedure applied in the research is based on the literature review, the analysis of secondary sources and the results of the author's previous research. Initial hypotheses were set, and recommendations were made for further development of the model.

关键词： HR analytics people analytics SMEs digital transformation model implementation

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Teaching Reinforcement Learning Fundamentals in Vocational Education and Training with RoboboSim 6th

Teaching Reinforcement Learning Fundamentals in Vocational E...

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6th Iberian Robotics Conference (Robot)

作者： Renda, Cristina Prieto, Abraham Bellas, Francisco Univ A Coruna CITIC Res Ctr GII Coruna Spain Univ A Coruna CITENI Res Ctr GII Coruna Spain

ISBN: (纸本)9783031591662;9783031591679

The current paper presents an educational resource to introduce Vocational Education and Training (VET) students to the topic of Reinforcement Learning (RL) through a practical activity. Specifically, they have to program a Q-learning algorithm using Python language to obtain a policy that allows a mobile robot to solve a task autonomously in an industrial-like setup. To this end, a 3D simulation platform called RoboboSim, and the Python libraries of the Robobo educational robot are used. The resource has been developed in the scope of the Erasmus + project called AIM@VET, and it has been tested with a group of 12 VET students from Spain, Portugal, and Slovenia. The obtained results have been successful, and students with no previous background on RL have learned its fundamentals through a purely practical methodology. In addition, some drawbacks have been captured from teachers and students, and the resource has been improved accordingly before it was made available through the project web.

关键词： AI literacy Educational Robotics Reinforcement Learning Project-based learning

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Deep Learning for Commercial Building Load Forecasting: Hyperparameter Fine-Tuning Convolution Neural Network-Multivariate Multilayered Long Short-Term Memory Time-Series Model 7th

Deep Learning for Commercial Building Load Forecasting: Hype...

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7th International Conference on Green Technology and Sustainable Development

作者： Chi Nghiep Le Tan Ngoc Dinh Chandran, Jaideep Seyedmahmodan, Mehdi Stojcevski, Alex Swinburne Univ Technol Sch Sci Comp & Engn Technol Hawthorn Vic Australia

ISBN: (纸本)9783031761966;9783031761973

The existing multivariate multilayered long short-term memory model (M-LSTM) of the Swinburne Energy Laboratory requires improvement in efficiency and performs robustness load forecasting for commercial buildings. Accordingly, this research proposes a hyperparameter fine-tuning (HFT) convolution neural network (CNN) M-LSTM (HFT CNN-M-LSTM). By cascading CNN to M-LSTM, CNN can learn hierarchical features and produce dense representations that M-LSTM leverages to capture long-term dependencies for accurate predictions. In addition, HFT is added to CNN and M-LSTM support to adjust the hyperparameters on the basis of feedback during training. The HFT uses a Bayesian Optimization algorithm for hyperparameter searching. Bayesian Optimization is beneficial in reducing computation *** performance of HFT CNN-M-LSTM is evaluated by comparing it with those of M-LSTM and CNN-M-LSTM by using the energy consumption datasets from Swinburne University of Technology, Hawthorn Campus, i.e., ATC Building (Dataset S1) and AMDC Building (Dataset S2). The metrics applied to the evaluation are normalized root-mean-square error (NRMSE), R-2 score, and mean absolute percentage error (MAPE). For Dataset S1, HFT CNN-M-LSTM exhibits outstanding performance when compared with M-LSTM and CNN-M-LSTM, with NRMSE = 0.0358, R-2 score = 0.8825, and MAPE = 0.1112. For Dataset S2, HFT CNN-M-LSTM exhibits a slight degradation compared with M-LSTM, with NRMSE = 0.0353, R-2 score = 0.6323, and MAPE = 0.1133. However, the research result emphasizes that HFT CNN-M-LSTM is an excellent candidate for long-term commercial building load forecasting because the proposed model can capture complex patterns and extract residual errors.

关键词： building energy consumption forecast power management

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Surveying Impacts of AI in Education and Creative Practices

Surveying Impacts of AI in Education and Creative Practices

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Intelligent systems Conference (IntelliSys)

作者： Deck, Andy SUNY Oneonta NY 13820 USA

ISBN: (纸本)9783031477201;9783031477218

Fields as diverse as art, photography, writing, and design are now confronting the consequences of easily available-and easy to use-generative systems. Text and image products that used to be understood as direct expressions of the minds of a human creator may now be generated synthetically through software and neural networks. The emergence of such 'smart' software poses novel ethical challenges for the evaluation of intellectual products like images and text. There has been a predictable backlash against incursions of automation and generative systems into creative practices that have evolved over centuries. The author surveys critical perspectives on these software systems as they relate to creative practices, and cites examples of how tools and services made possible by neural networks have caused controversy. Further, based on history and trends in mainstream digital culture, the author concludes that popular notions of authorship and creativity will continue to evolve as machine learning and artificial intelligence become increasingly entwined in production tools.

关键词： Art Authorship Creative Practices Artificial Intelligence Automation

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