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How Variables Graphs May Help to Correct Erroneous MAS Specifications

How Variables Graphs May Help to Correct Erroneous MAS Speci...

引用

Intelligent systems Conference (IntelliSys)

作者： Mermet, Bruno Simon, Gaele CNRS GREYC UMR 6072 Caen France Univ Le Havre Caen France

ISBN: (纸本)9783031477171;9783031477188

This paper is situated in the context of multi-agent systems validation using theorem proving techniques. This document presents a preliminary case study, which is a part of a broader investigation aiming to explore whether such techniques could aid developers in detecting and characterizing errors in MAS specifications. Indeed, regardless of the verification system used (model checking or theorem proving), understanding the reason of a failure in order to correct the specification is most of the time rather difficult. In this article, we propose a method that may help in this task. This method relies on the variables (and their dependencies) that appear in proof obligations generated by GDT4MAS, a specification and verification method dedicated to Multi-Agent systems. Graphs generated thanks to dependencies between variables occurring in an unproved theorem may indeed help to identify certain types of mistakes, giving a way to correct the specification.

关键词： Multi-agent systems Proof failure Debugging

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Power Consumption in HPC-AI systems 6th

Power Consumption in HPC-AI Systems

引用

6th International Conference on Cloud Computing and Artificial Intelligence-Technologies and Applications

作者： Nana, Roblex T. Tadonki, Claude Dokladal, Petr Mesri, Youssef Mines Paris PSL Ctr Rech Informat CRI Fontainebleau France Mines Paris PSL Ctr Morphol Math CMM Fontainebleau France Mines Paris PSL Ctr Mise Forme Mat CEMEF Sophia Antipolis France

ISBN: (纸本)9783031786976;9783031786983

Beside the need for speed that has led to HPC, energy has become a crucial concern that is addressed in HPC hardware and software solutions. There are several aspects when it comes to energy in computing systems: cost, source, heat, carbon, lifetime, and more. From the standpoint of embedded systems, devices are battery-powered, thus the available amount of energy to proceed with is limited. In critical cases like these, it is vital to optimize all sources of substantial power consumption. Regarding more standard computing systems, including supercomputers, the question of energy saving mainly translates into electricity cost and carbon emission. Indeed, the overall energy required to run an HPC infrastructure including cooling systems represents an important part of the maintenance budget. In addition, considering heat dissipation, lifetime of the hardware is reduced as well as MTBF (mean time between failures). One the major topic from the application standpoint that HPC has to consider carefully is artificial intelligence (AI). Indeed, this processing paradigm has tremendously grew up with more and more ambitious perspectives. The pervasiveness of AI solutions and the noticeable computing time required for at least the training phases exacerbate the concern of power consumption in this context. The purpose of this chapter is to explore and illustrate the issue of power consumption in HPC-AI systems so as to make the issue more clear to the reader and highlight the main solutions and perspectives.

关键词： HPC AI energy power consumption carbon footprint

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Galactostack: A 3-In-1 Stack Game for Tackling Social Media Addiction in Children Post-pandemic 8th

Galactostack: A 3-In-1 Stack Game for Tackling Social Media ...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Mudlapur, Vaishnavi Kumar, Tarun PES Univ Bangalore 560085 India Indian Inst Sci Bangalore India

ISBN: (纸本)9789819713288;9789819713295

After the global coronavirus pandemic, children are using social media a lot more than usual, and this worries parents because it might not be good for their minds, feelings, and bodies. 'Galactostack' is a novel game that attempts to reduce this addiction by employing interactive elements and gamification. Instead of children spending too much time on social media, they can play this game which is fun and educational. The game combines three unique parts: a game to play, things to learn, and elements to make them feel better. The idea is to keep them engaged and happy, so they do not use social media and reduce screen time. This study discusses the core concepts of the game, its features, and how it could positively impact children's online behaviour, reactions and emotions. User evaluation and feedback show positive results: children became more aware and felt more engaged after playing the game. In the post-pandemic era, this design approach offers an innovative and practical way to reduce the adverse impacts of social media addiction. It helps children build healthier connections with technology and the people in their lives.

关键词： Social media addiction Gamification Post-pandemic Mental health Design Interactive learning

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Prognosis of Dengue Incidences in India Using LSTM with Multivariate Data 4th

Prognosis of Dengue Incidences in India Using LSTM with Mult...

引用

4th Congress on Intelligent systems (CIS)

作者： Sharma, Shruti Gupta, Yogesh Kumar Banasthali Vidhyapith Dept Comp Sci Jaipur Rajasthan India SVKMs NMIMS Indore STME Indore India

ISBN: (纸本)9789819990399;9789819990405

Monitoring climatic change and its impacts unique to the disease is required for predicting the expansion of dengue. One of the infectious diseases transmitted by vectors with the fastest rate of transmission is dengue. In this study, we have proposed a forecasting model for the prediction of dengue cases by considering the climatic variables for all the states of India for last 12 years. For our study, we have accumulated data with factors like temperature (minimum and maximum), relative humidity, mean wind speed, dengue cases and deaths. For the prediction, the different models ANN, RNN, LSTM, Stacked LSTM, BiLSTM for forecasting time series prediction are implemented and then compared. During our study, it is found that humidity and maximum temperature have a strong correlation with the number of dengue cases. BiLSTM model outperformed all other deep learning model with an accuracy of 98.91%. This study will assist the healthcare practitioners to take informed decisions beforehand.

关键词： Machine learning Forecasting Dengue disease Epidemic disease outbreak Time series

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Simulation of the Lubrication of High-Speed Jet-Lubricated Gears: Study of the Impact of the Oil Droplet Suspension on the Power Losses 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Concli, Franco Della Torre, Augusto Free Univ Bolzano Bozen Fac Engn I-39100 Bolzano Italy Politecn Milan Fac Energy Via Lambruschini 4 I-20156 Milan Italy

ISBN: (数字)9783031704628

ISBN: (纸本)9783031704611;9783031704628

With the rising power density and the need for increasingly reliable mechanical geared transmissions, lubrication has become as a primary design focus. An efficient optimization of lubricant supply involves not just minimizing power losses and enhancing system efficiency but also lowering operating temperatures and improving overall reliability. This paper aims at presenting two Finite Volume Eulerian compressible multi-phase approaches developed in the open-source environment OpenFOAM((R)) for the simulation of the windage power losses of a jet-lubricated spur gear pair operating at high speed. The numerical results have been compared with experimental measurements performed by the Ohio State University on a back-to-back test rig under different operating conditions. The analysis has underlined the fundamental role of the droplet concentrations (oil suspension) in the generation of windage power losses.

关键词： Gears Windage Churning Power Losses Jet-Lubrication

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Novel Hybrid Methods for Journal Article Summarization Combining Graph Method and Rough Set TFIDF Method with Pegasus Model 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Kurian, K. Sheena Mathew, Sheena Cochin Univ Sci & Technol Sch Engn Kochi Kerala India

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

Journal article summarization shortens an article, including all the relevant and significant topics. As the number of journal articles published every year is increasing rapidly, the significance of research on journal article summarization also increases. This work generates a summary of journal articles from the ScisummNet dataset using extractive, abstractive and novel hybrid methods. The summary generated by these methods is then analyzed using word overlap and semantic similarity scores to find the best summary. The precision, recall and f-measure scores of rouge-1, rouge-2, rouge-L and rouge-Lsum scores are analyzed to find the word overlap score and precision, recall and f-measure of the BERT score to find the semantic similarity score. The summary generated by the novel hybrid methods combining the extractive methods like the graph with the sum of weights, the graph with bushy path and the rough set TFIDF methods with the abstractive Pegasus fine-tuned model gives the best rouge and BERT scores among the other hybrid methods.

关键词： Abstractive Fine-tuning Graph methods Rough set Pre-trained models Summarization

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Prediction Cows Estrus Images Using Convolutional Neural Network with Optimized Parameters by the Artificial Immune System Algorithm 1

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20th International Conference on Computing and Information Technology (IC2IT2)

作者： Ninphet, Watchara Amm-Dee, Nopadol Sangsongfa, Adisak Muban Chom Bueng Rajabhat Univ Fac Ind Technol Dept Ind Technol Management Chom Bueng 70150 Thailand

ISBN: (数字)9783031585616

ISBN: (纸本)9783031585609;9783031585616

The factor that affects the milk production of dairy cows is the breeding of cows. Before the cows can be bred, the cows must show signs of estrus first. Farmers must detect estrus in time because the period of estrus is It did not take long, so this research has developed the prediction of estrus in dairy cows using artificial intelligence methods, that is, using convolutional neural networks to help in the prediction. This research has a Comparison between the convolutional neural networks before adjusting the parameters, which achieved an accuracy of 95.082% and when using the Artificial Immunity System algorithm to adjust the parameters, the accuracy was 98.361%. The prediction used 4 types of dairy cow movements for prediction.

关键词： Convolutional Neural Network Cow Estrus Artificial Immunity System

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Dataset Generation and Bonobo Classification from Weakly Labelled Videos

Dataset Generation and Bonobo Classification from Weakly Lab...

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Intelligent systems Conference (IntelliSys)

作者： Martin, Pierre-Etienne Max Planck Inst Evolutionary Anthropol CCP Dept D-04103 Leipzig Germany

ISBN: (纸本)9783031477232;9783031477249

This paper presents a bonobo detection and classification pipeline built from the commonly used machine learning methods. Such application is motivated by the need to test bonobos in their enclosure using touch screen devices without human assistance. This work introduces a newly acquired dataset based on bonobo recordings generated semi-automatically. The recordings are weakly labelled and fed to a macaque detector in order to spatially detect the individual present in the video. Handcrafted features coupled with different classification algorithms and deep-learning methods using a ResNet architecture are investigated for bonobo identification. Performance is compared in terms of classification accuracy on the splits of the database using different data separation methods. We demonstrate the importance of data preparation and how a wrong data separation can lead to false good results. Finally, after a meaningful separation of the data, the best classification performance is obtained using a fine-tuned ResNet model and reaches 75% of accuracy.

关键词： Bonobos classification Machine learning Convolutional neural networks Data splitting Automatic dataset generation

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Edge AI on Constrained IoT Devices: Quantization Strategies for Model Optimization

Edge AI on Constrained IoT Devices: Quantization Strategies ...

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Intelligent systems Conference

作者： Shabir, Muhammad Yasir Torta, Gianluca Damiani, Ferruccio Univ Torino Dept Comp Sci Cso Svizzera 185 I-10149 Turin Italy Univ Kotli AJ&K Kotli Pakistan

ISBN: (纸本)9783031664274;9783031664281

In the field of deep learning (DL), the deployment of complex Neural networks (NN) models on memory-constrained devices presents a significant challenge. TinyML focuses on optimizing DL models for such environments, where computational and storage resources are limited. The main key aspect of this optimization involves reducing the size of the models without compromising their performance too much. We have investigated the efficacy of various quantization techniques in optimizing DL models for deployment on memory-constrained devices. To understand the challenges of memory requirements of standard deep learning models, we conducted comprehensive literature reviews and identified quantization methods as a potent approach for model size reduction. Our study targets popular NN architectures such as ResNetV1 and V2, MobileNetV1 and V2, and introduces a custom-designed model, examining their suitability to TinyML constraints. We have analyzed CIFAR10 and MNIST datasets to assess the impact of four distinct quantization techniques on model size and accuracy. These techniques include Dynamic Range Quantization, Full Integer Quantization, Float16 Quantization, and Integer 16x8 Quantization. Our aim is to contribute valuable insights into model optimization for efficient deployment in resourcelimited environments.

关键词： Quantization Optimization Deep learning Memory constrained devices Machine learning AI

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Gamification in Higher Education Assessment Through Kahoot 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Salazar-Vallejo, Geovanna Rivera-Rogel, Diana Univ Tecn Particular Loja Fac Social Sci Educ & Humanities CP 11-01-608 Loja Ecuador

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

The approach of gamified assessments has significant implications in student learning and encourages educators to develop dynamic learning environments with permanent feedback, establishing a formative context that, through dynamic techniques, evaluates the content taught and measures the progress of learning. Responding to this background, the present research determines through a quantitative methodology, the potential of Kahoot in the university evaluation of Clinical Psychology students of the Universidad Tecnica Particular de Loja (UTPL) and establishes a comparison between their average bimonthly evaluation score on the platform and the bimonthly written exam developed in the classroom. It is concluded that students improve their learning with the use of the Kahoot platform, presenting high levels of success in the topics raised throughout the academic course, which demonstrates its feasibility to reinforce the academic results obtained and process knowledge during professional training. In addition, it was established that the use of Kahoot is feasible in preparing students for a final written evaluation.

关键词： Education Gamification Evaluation

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