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Reasoning of intelligent Virtual Agents with Exceptional Behavior in Rare Scenarios 22nd

Reasoning of Intelligent Virtual Agents with Exceptional Beh...

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22nd IFIP TC 14 international conference on Entertainment Computing (ICEC)

作者： Lysek, Alexander Seele, Sven Herpers, Rainer Bonn Rhein Sieg Univ Appl Sci Inst Visual Comp St Augustin Germany Univ New Brunswick Fac Comp Sci Fredericton NB Canada York Univ Dept Elect Engn & Comp Sci Toronto ON Canada

ISBN: (纸本)9789819982479;9789819982486

This paper presents a concept for temporal reasoning. Temporal reasoning enables non-player characters (NPCs) to infer new knowledge based on previously acquired information and apply it within their decision making process. The approach aims to enhances the credibility of NPC behavior by allowing them to make flawed decisions based on ambiguous or misleading information. As a proof of concept a pedestrian crossing scenario with occluded vision was realized and evaluated inside the FIVIS bicycle simulator system. It could be demonstrated that so far rare simulation scenarios such as traffic accidents could be simulated in a more plausible and nondeterministic nor randomized way. These scenarios only occur if certain conditions are met and even in these cases only if the decision making process has been influenced by incomplete, error prone or ambiguous input data, so that proper decisions were difficult to derive. That means our approach is able to model human failures and ambiguity in the decision making process.

关键词： Serious Games Games and Simulations for learning Traffic Simulations Virtual Agents Reasoning

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Nanotribological characterization of an X39CrMo17-1 steel thin-film via measurement-based machine learning methods 22

Nanotribological characterization of an X39CrMo17-1 steel th...

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22nd international conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Percic, Marko Zelenika, Saša Mezic, Igor University of Rijeka Faculty of Engineering Centre for Micro- and Nanosciences and Technologies Vukovarska 58 Rijeka51000 Croatia University of Rijeka Centre for Artificial Intelligence and Cybersecurity Laboratory for AI in Mechatronics Radmile Matejcic 2 Rijeka51000 Croatia UC Santa Barbara Department of Mechanical Engineering Santa BarbaraCA93105 United States

ISBN: (纸本)9781998999118

Frictional characteristics are an important factor in the design and exploitation of precision positioning systems and components. In fact, friction is a complex stochastic phenomenon depending on an intricate interplay between materials' physical and chemical properties, as well as on the contact conditions influenced by the exerted normal forces, sliding velocities, contact area, temperature etc. Experimental measurements of the nanoscale friction force of an X39CrMo17-1 high-alloy steel thin film, deposited on a silicon wafer, are obtained in this work via elaborated lateral force microscopy (LFM) measurements performed on a scanning probe microscope using silicon-nitride microcantilever probes. The thin-film samples are synthesised by using pulsed laser deposition, which enables obtaining precise stochiometric elemental properties. The LFM measurements allow obtaining single-asperity contact conditions in the studied tribo-pair, which is studied experimentally considering the mentioned external influences as the variable process parameters. In order to obtain an in-depth insight into the dependencies of nanoscale friction on these parameters, the obtained experimental data is used to train various machine learning (ML) algorithms. The resulting predicted values of the nanoscale friction force are therefore obtained by using random forest, multilayer perceptron and support vector regression ML methods. In order to assess their predictive performances, all the used algorithms are validated on a separately measured test dataset. The best predictive performances are obtained by using the support vector regression algorithm, resulting in the highest achieved coefficient of determination (R2), allowing the prediction of 90 % of the variance of the experimental measurements. © European Society for Precision engineering and Nanotechnology, conference Proceedings - 22nd international conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Thin films

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Virtual Sensor for Door Opening Detection and Anomaly Detection Using Machine learning

Virtual Sensor for Door Opening Detection and Anomaly Detect...

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international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Almir Neto Luis Gomes Zita Vale Federal Institute of Education Science and Technology of Maranhão (IFMA) São Luís Brazil GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (P.PORTO) Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

Currently, several studies are being developed using machine learning with the aim of replacing real sensors with virtual sensors. One of the biggest challenges is obtaining quality data to enable the system modeling. This article proposes the replacement of a light dependent resistor sensor, applied to a refrigerator, which detects whether the door is open or closed and an anomaly test when the door is forgotten open for too long. The use of tiny machine learning is applied to ESP32 to enable the use of regression and classification models to detect the door opening and anomaly. The tested metrics for the regression were the root mean squared error. The comparative results between the virtual and real sensors are satisfactory with an accuracy of 99.59% for classification and a root mean squared error value of 0.01 for regression. Furthermore, a prototype was developed, and the model was embedded in the ESP32 microcontroller.

关键词： Temperature sensors Temperature measurement Microcontrollers Soft sensors Tiny machine learning Neural networks Prototypes Virtualization intelligent sensors Anomaly detection

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Evaluating Deep Neural Nets and Optimized Hyperparameters Random Forest for Decision Support Systems 22

Evaluating Deep Neural Nets and Optimized Hyperparameters Ra...

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22nd IEEE international conference on Information Reuse and Integration for data Science (IRI)

作者： Ajila, Samuel A. Vijay, Nidheesh Carleton Univ Sytems & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781665438759

The use of telemarketing by business enterprises can be intrusive to customers and time consuming for the businesses. A way to mitigate it, is to use an intelligent decision support system (DSS) that will effectively target potential customers likely to buy into the business. Therefore, there is need for a targeted, effective, and trained predictive Decision Support Systems DSS. This research work uses deep learning and optimized hyperparameters Random Forest to build predictive DSS and compared the performance accuracy and ROC Area to that of Support Vector Machines (SVM). The research uses a dataset from a Portuguese bank collected over a period of 2008 to 2013 at the onset of a financially difficult time for banks. The experimental results show that there are no significant differences between the performance accuracy of the different deep learning algorithms. However, the deep learning algorithms performance accuracy and ROC Area are better than that of SVM (which was used in a previous work on the same dataset). The optimized hyperparameters Random Forest has the best accuracy of 99.74% and ROC Area of 100% compared to the best deep learning algorithm with 90.87% (for RNN and Seq2Seq), and ROC Area of 92.64.

关键词： Decision Support Systems Deep learning SVM RNN LSTM Sequence-to-Sequence (Seq2Seq) Random Forest, and Hyperparameters

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Advancing Singular Value Decomposition Techniques for Enhanced data Mining in Recommender Systems 22nd

Advancing Singular Value Decomposition Techniques for Enhanc...

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22nd international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Beshley, Mykola Hordiichuk-Bublivska, Olena Beshley, Halyna Ivanochko, Iryna Lviv Polytech Natl Univ Dept Telecommun UA-79013 Lvov Ukraine Comenius Univ Dept Informat Syst Fac Management Bratislava 82005 Slovakia

ISBN: (纸本)9783031425073;9783031425080

The development of effective algorithms for processing large volumes of information is a very urgent problem for modern information systems. The work examines the problems of content analysis and data mining by recommender systems. Features of automatic systems management using machine learning algorithms are presented. The importance of using recommender systems for more accurate prediction of goods or services of interest to users, determination of interrelationships between various production factors, etc. have been determined. The operation of the FunkSVD algorithm, which allows accurate and fast processing of sparse data sets of a large volume, has been studied. A modification of FunkSVD is proposed to use fewer data about users and items when forming recommendations. The simulation of the proposed algorithm was carried out and it was determined that it works faster than the usual one, while maintaining fairly high accuracy, so it can be used in the processing of large data. It is also proposed to increase the privacy and accuracy of FunkSVD recommendations in distributed systems by using the Fed SVD algorithm. The results of the experiments showed the high accuracy of the proposed algorithm but at the expense of a certain increase in the duration of calculations. According to the conducted research, it was concluded that both modifications can be used in systems with different requirements for data mining and distributed architecture.

关键词： Big data Recommender Systems data Mining Funk SVD Fed SVD

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Using Deep learning for Building Stock Classification in Seismic Risk Analysis 22nd

Using Deep Learning for Building Stock Classification in Sei...

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22nd EPIA conference on Artificial Intelligence (EPIA)

作者： Lopes, Jorge Gouveia, Feliz Silva, Vitor Moreira, Rui S. Torres, Jose M. Guerreiro, Maria Reis, Luis Paulo Univ Fernando Pessoa IRIS Porto Portugal Univ Porto LIACC Porto Portugal Univ Aveiro Aveiro Portugal

ISBN: (纸本)9783031490101;9783031490118

In the last decades most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral datasets, and local surveys. The first approach is only updated every 10 years and does not provide building locations, the second type of data is only available for restricted urban centers, and the third approach requires surveyors with an engineering background, which is cost-prohibitive for large-scale risk studies. It is thus clear that methods to characterize the built environment for large-scale risk analysis at the asset level are currently missing, which hampers the assessment of the impact of natural hazards for the purposes of risk management. Some recent efforts have demonstrated how deep learning algorithms can be trained to recognize specific architectural and structural features of buildings, which is needed for earthquake risk analysis. In this paper we describe how convolutional neural networks can be combined with data from OpenStreetMap and Google Street View to help develop exposure models for multi-hazard risk analysis. This project produced an original comprehensively annotated (15 characteristics) dataset of approximately 5000 images of buildings from the parish of Alvalade (Lisbon, Portugal). The dataset was used to train and test different deep learning networks for building exposure models. The best results were obtained with ResNet50V2, InceptionV3 and DenseNet201, all with accuracies above 82%. These results will support future developments for assessing exposure models for seismic risk analysis. The novelty of our work consists in the number of characteristics of the images in the dataset, the number of deep learning models trained and the number of classes that can be used for building exposure models.

关键词： Deep learning Convolutional Neural Networks Computer Vision Building Exposure Models Seismic Risk Analysis

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Proceedings - 2022 international conference on Advanced learning Technologies, ICALT 2022

Proceedings - 2022 International Conference on Advanced Lear...

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22nd international conference on Advanced learning Technologies, ICALT 2022

ISBN: (纸本)9781665495196

The proceedings contain 121 papers. The topics discussed include: advances in readability research: a new readability web app for English;anonymizing student team data of online collaborative learning in slack;application of first principles of instruction in the mathematics flipped classroom: student perceptions in online modality;automatic classification of OER for metadata quality assessment;developing computational thought in mathematics through educational robotics in basic education: a practical research analysis;developing digital competencies and future skills: an international project-based learning through open virtual mobilities in pandemic times;do learners really have different preferences?;novel handcrafted features for cognitive attention analysis of students in flipped classroom;should IEEE establish learning engineering as a new engineering profession?;and striving for platform independence in the e-learning landscape: a study on a flexible exercise creation system.

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Enhanced Computer-Aided Diagnosis Model on Ultrasound Images through Transfer learning and data Augmentation Techniques for an Accurate Breast Tumors Classification 27

Enhanced Computer-Aided Diagnosis Model on Ultrasound Images...

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27th international conference on Knowledge Based and intelligent Information and engineering Sytems, KES 2023

作者： Ben Ahmed, Ikram Ouarda, Wael Ben Amar, Chokri Sousse University Hammam Sousse4011 Tunisia Tunisia Sfax University Sfax3038 Tunisia

Cancer is a critical global public health problem with meager median survival. It is therefore quite essential to detect this disease at an early stage to improve diagnostic results and consequently avoid serious complications. For this purpose, various researchers have implemented automated methods with the use of different medical imaging modalities. Accordingly, the expansion of deep learning techniques grants opportunities to enhance diagnosis, cure, and prevention. In this study, a diagnostic system for accurate classification of ultrasound breast abnormalities based on the powerful ResNet-50 CNN is proposed with the aim of providing early detection of breast cancer decease. The contribution of this work lies in the novel approach taken to improve the performance of the ResNet50 model in the classification of ultrasound breast cancer images. Transfer learning allows for the model to leverage pre-existing knowledge, while the application of data augmentation techniques enhances the diversity and quality of the training data. Additionally, the optimization of the batch size as a hyperparameter ensures that the model is able to effectively learn from the training data, leading to improved accuracy and efficiency in the classification process. This approach is crucial in the early detection and treatment of breast cancer. Quantitative and qualitative evaluations have been detailed in this study using Breast Ultrasound dataset BUSI. Our presented work shows interesting results in terms of accuracy, specificity, sensitivity, and AUC which exceed the performance of other compared works. Moreover, the proposed method helps boost the clinical diagnosis of breast cancer. It may integrate a radiologist network, allowing them to constantly follow up on the patient's medical history. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： Breast Cancer Classification data augmentation Deep learning Transfer learning Ultrasound

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Scaffolds and Nudges: A Case Study in learning engineering Design Improvements 22nd

Scaffolds and Nudges: A Case Study in Learning Engineering D...

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Fancsali, Stephen E. Pavelko, Martina Fisher, Josh Wheeler, Leslie Ritter, Steven Carnegie Learning Inc Pittsburgh PA 15219 USA

ISBN: (纸本)9783030782702;9783030782696

We present a brief case study of a multi-year learning engineering effort to iteratively redesign the problem-solving experience of students using the "Solving Quadratic Equations" workspace in Carnegie learning'sMATHia intelligent tutoring system. We consider two design changes, one involving additional scaffolds for the problem-solving task and the next involving a "nudge" for learners to more rapidly and readily engage with these scaffolds and discuss resulting changes in the relative proportion of students who fail to master skills associated with this workspace over the course of two school years.

关键词： learning engineering intelligent tutoring system Instructional design

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Multi-initial-Center Federated learning with data Distribution Similarity-Aware Constraint 22nd

Multi-initial-Center Federated Learning with Data Distribut...

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22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Li, Xiaoying Chen, Xiaojun Wang, Shaopu Ding, Yangyang Li, Kaiyun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031226762

Federated learning (FL) has recently attracted high attention since it allows clients to collaboratively train a model while the training data remains local. However, due to the inherent heterogeneity of local data distributions, the trained model usually fails to perform well on each client. Clustered FL has emerged to tackle this issue by clustering clients with similar data distributions. However, these model-dependent clustering methods tend to perform poorly and be costly. In this work, we propose a distribution similarity-based clustered federated learning framework FedDSMIC, which clusters clients by detecting the client-level underlying data distribution based on the model’s memory of training data. Furthermore, we extend the assumption about data distribution to a more realistic cluster structure. The center models are learned as good initial points to obtain common data properties in the cluster. Each client in a cluster gets a more personalized model by performing one step of gradient descent from the initial point. The empirical evaluation on real-world datasets shows that FedDSMIC outperforms popular state-of-the-art federated learning algorithms while keeping the lowest communication overhead. © 2023, Springer Nature Switzerland AG.

关键词： learning systems

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