UID:
almahu_9949560761602882
Format:
1 online resource (327 pages)
Edition:
1st ed. 2023.
ISBN:
3-031-37649-8
Series Statement:
Lecture Notes in Networks and Systems, 710
Content:
This open access book presents the proceedings of the 10th Machine Intelligence and Digital Interaction Conference. Artificial intelligence (AI) is rapidly affecting more aspects of our lives as a result of significant advancements in its research and the widespread usage of interactive technologies. This has led to the birth of several new social phenomena. Many nations have been working to comprehend these phenomena and discover solutions for moving artificial intelligence development in the proper direction to benefit individuals and communities at large. These efforts necessitate multidisciplinary approaches, encompassing not only the scientific fields involved in the creation of artificial intelligence and human–computer interaction but also strong collaboration between academics and practitioners. Because of this, the primary objective of the MIDI conference, which was conducted online on December 13–15, 2022, is to combine two up until recently distinct disciplines of research—artificial intelligence and human–technology interaction.
Note:
Intro -- Foreword -- Contents -- Machine Intelligence -- Light Fixtures Position Detection Using a Camera -- 1 Introduction -- 1.1 Use Case -- 1.2 Programming Light Show -- 1.3 Light Network Control -- 2 State of the Art -- 3 Software -- 3.1 Programming Environment -- 3.2 Light Fixture Detection -- 3.3 GUI and User Input -- 4 Experiment -- 4.1 Lights Setup -- 4.2 Camera -- 5 Results -- 6 Discussion -- 7 Conclusion -- 8 Limitations -- 9 Future Research -- 10 Declarations -- References -- Improved Vehicle Logo Detection and Recognition for Complex Traffic Environments Using Deep Learning Based Unwarping of Extracted Logo Regions in Varying Angles -- 1 Introduction -- 2 Related Works -- 3 Working Methodology -- 3.1 Selected Models -- 3.2 Dataset -- 4 Results and Discussions -- 4.1 Test Cases -- 4.2 Comparative Analysis -- 4.3 Pose Variation Calculation -- 5 Conclusion and Future Work -- References -- Predicting Music Using Machine Learning -- 1 Introduction -- 2 Data -- 3 Feature Representation -- 3.1 Basic Music Notation -- 3.2 Note and Chord Representation -- 4 Methods and Modeling -- 4.1 Markov Chain Model -- 4.2 LSTM Model -- 4.3 LSTM Encoder-Decoder Model -- 4.4 Inference Modeling -- 4.5 Other Techniques -- 5 Results and Observations -- 6 Conclusion and Future Work -- References -- A Novel Process of Shoe Pairing Using Computer Vision and Deep Learning Methods -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 3.1 Deep Multiview Representation Learning -- 3.2 Clustering -- 4 Results -- 5 Conclusions -- References -- Representation of Observations in Reinforcement Learning for Playing Arcade Fighting Game -- 1 Introduction -- 2 Environment Setup for KOF '97 -- 2.1 Interaction with Arched Emulator -- 2.2 Action Space -- 2.3 Observation -- 2.4 Graphical Representation of the Input and ACT Sequences -- 2.5 Reward System.
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2.6 Proposed Network Structure -- 3 Experiment -- 3.1 Training Process -- 3.2 Results -- 3.3 Discussion -- 4 Conclusions -- References -- AI4U: Modular Framework for AI Application Design -- 1 Motivation -- 2 Related Work -- 3 Proposed Approach -- 4 The Application Area -- 4.1 Watchman Scenario -- 4.2 Parkmonitor Scenario -- 4.3 Tracker Scenario -- 4.4 Mapping the Environment Scenario -- 4.5 Vehicle Counting -- 4.6 Space Surveyer -- 4.7 Mission to Mars -- 4.8 First Conclusions -- 5 Conclusions and Further Work -- References -- A Competent Deep Learning Model to Detect COVID-19 Using Chest CT Images -- 1 Introduction -- 2 Literature Review -- 3 Our Proposed Methodology -- 3.1 Dataset Description and Preprocessing -- 3.2 Methodology -- 4 Results -- 5 Future Work and Conclusion -- References -- AI in Prostate MRI Analysis: A Short, Subjective Review of Potential, Status, Urgent Challenges, and Future Directions -- 1 Introduction -- 2 The Potential of Artificial Intelligence in MpMRI Analysis -- 2.1 Prostate Segmentation -- 2.2 Prostate Lesion Detection and Characterization -- 3 Urgent Challenges -- 3.1 Datasets -- 3.2 Defining Ground Truth -- 3.3 Different Evaluation Criteria -- 3.4 Limited Multireader Studies and Prospective Evaluation -- 4 Future Directions -- 5 Conclusions -- References -- Performance of Deep CNN and Radiologists in Prostate Cancer Classification: A Comparative Pilot Study -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Radiological Assessment Study -- 2.3 Deep CNN Model -- 2.4 Probability Mapping -- 2.5 Statistical Analysis -- 3 Results -- 3.1 Results of Raw CNN Predictions -- 3.2 CNN Performance Compared to Human Raters -- 3.3 Diagnostic Accuracy of Combined Assessment -- 4 Discussion and Conclusion -- References -- Assessing GAN-Based Generative Modeling on Skin Lesions Images -- 1 Introduction -- 2 Materials and Methods.
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2.1 International Skin Imaging Collaboration Database -- 2.2 Training Details -- 2.3 Evaluation Protocol -- 3 Results -- 3.1 GANs Trainings -- 3.2 Predictive Performance with Classifier -- 3.3 Explanations of the Predictions -- 4 Discussion -- 5 Conclusions -- References -- Prostate Cancer Detection Using a Transformer-Based Architecture and Radiomic-Based Postprocessing -- 1 Introduction -- 2 Material and Methods -- 2.1 Preprocessing -- 2.2 Deep Learning Architecture -- 2.3 Optimization -- 2.4 Hyperparameter Selection -- 2.5 Postprocessing -- 3 Results and Discussion -- 4 Conclusions -- References -- Sales Forecasting During the COVID-19 Pandemic for Stock Management -- 1 Introduction -- 2 Dataset -- 3 Methodology -- 3.1 Problem Identification -- 3.2 Data Preparation -- 3.3 Exploratory Data Analysis -- 3.4 Feature Extraction -- 3.5 Dataset Separation -- 3.6 Regression and Machine Learning Model -- 3.7 Performance Evaluation -- 4 Findings and Interpretation -- 5 Conclusion -- References -- Digital Interaction -- Seeking Emotion Labels for Bodily Reactions: An Experimental Study in Simulated Interviews -- 1 Introduction -- 1.1 Research Goal and Motivation -- 1.2 Hypotheses and Research Question -- 2 Theoretical Background -- 3 Methodology -- 3.1 Experiment Design -- 3.2 Procedure -- 3.3 Participants -- 3.4 Data Collection -- 3.5 Preprocessing Data -- 3.6 Analysis -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- "NAO Says": Designing and Evaluating Multimodal Playful Interactions with the Humanoid Robot NAO -- 1 Introduction -- 2 Design and Programming -- 2.1 Design -- 2.2 Programming -- 3 Methods and Studies -- 4 Results -- 4.1 Perceptions of the NAO Robot and the Game "NAO Says" -- 4.2 Perceived Level of Stress Before and After the Game -- 5 Conclusions -- References.
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Representation of Air Pollution in Augmented Reality: Tools for Population-Wide Behavioral Change -- 1 Introduction -- 1.1 Household Related Air Pollution -- 1.2 Air Pollution Representation -- 1.3 Augmented Reality -- 2 VAPE Augmented Reality App Design -- 2.1 Purpose of the AR Application -- 2.2 Accompanying Poster -- 2.3 Implementation of Air Pollution Visual Representation -- 2.4 Application Development and Implementation -- 3 Pretest Results -- 4 Conclusions -- References -- Ukrainian Version of the Copresence Scale -- 1 Introduction -- 2 Theoretical Context -- 2.1 War Migration from Ukraine in Poland -- 2.2 Copresence -- 2.3 Mediated Communication and War Migration -- 3 Method -- 3.1 Sample and Data Collection -- 3.2 Measures -- 3.3 Data Analysis -- 4 Results -- 4.1 Descriptive Statistics -- 4.2 Validation of the Ukrainian Perceived Copresence Scale (PCS-U) -- 5 Discussion -- References -- Modular Platform for Teaching Robotics -- 1 Motivation -- 2 Observations -- 3 Idea -- 4 Construction -- 5 Algorithm's Working Principles -- 6 Tests -- 7 Conclusions -- References -- A Method for Co-designing Immersive VR Environments with Users Excluded from the Main Technological Discourse -- 1 Introduction -- 2 Related Work -- 3 RAPID Approach Outline -- 3.1 I. Preliminary Phase: Team Formation -- 3.2 II. Main Phase: RAPID IERE Development -- 3.3 III. Closing Phase: XR Product Delivery and Testing -- 4 Discussion -- 4.1 Phase I: Team Formation -- 4.2 Phase II Development -- 4.3 Phase III Closing -- 4.4 Other Considerations -- 4.5 General Discussion -- 5 Conclusions -- References -- Improving the Usability of Requests for Consent to Use Cookies -- 1 Introduction -- 2 Privacy and Security Regulation -- 3 Dark Patterns in Cookies Consent Requests -- 4 Evaluation of Consent Requests in Lithuanian News Portals -- 5 Design Guidelines for Cookie Consent Requests.
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6 Conclusions -- References -- Transdisciplinary Approach to Virtual Narratives - Towards Reliable Measurement Methods -- 1 Introduction: Motivation and Related Work -- 2 Overview of the Cinematic VR Research Method -- 2.1 The Research Application -- 2.2 Screening -- 2.3 Measures Used Before VR Experience -- 2.4 Baseline Measures -- 2.5 Cinematic VR Experience Test -- 2.6 Measures Applied After VR Experience -- 2.7 Digital Markers Calibration -- 3 Current Research - Method -- 3.1 Participants -- 3.2 Materials and Apparatus -- 3.3 Procedure -- 4 Results -- 4.1 User Experience - Experimental Setting and Equipment Evaluation -- 4.2 User State - Emotion, Arousal and Control -- 4.3 Presence -- 5 Discussion -- 6 Conclusions -- References -- Towards Gestural Interaction with 3D Industrial Measurement Data Using HMD AR -- 1 Introduction -- 2 Related Work -- 2.1 Gestural Interaction and Data Visualization in AR Systems -- 2.2 Augmented Reality in an Industrial Setting -- 3 Experimental Study Description -- 4 Results Overview -- 5 Discussion and Conclusions -- References -- Polish Adaptation of the Cybersickness Susceptibility Questionnaire (CSSQ-PL) -- 1 Introduction -- 1.1 Measuring Cybersickness -- 1.2 Predicting Cybersickness -- 2 Method -- 2.1 Participants and Apparatus -- 2.2 Measures -- 2.3 Stimuli and Procedure -- 2.4 Validation -- 3 Results -- 3.1 Language Adaptation -- 3.2 Reliability -- 3.3 Distributions -- 3.4 Validity -- 4 Discussion and Future Directions -- References -- Special Session: Advances in Collaborative Robotics -- NARX Recurrent Neural Network Model of the Graphene-Based Electronic Skin Sensors with Hysteretic Behaviour -- 1 Introduction -- 2 Graphene-Based Electronic Skin -- 3 Neural-Network Modelling -- 4 Results -- 4.1 Research Method -- 4.2 Modelling -- 4.3 Discussion -- 5 Summary -- References.
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Proximity Estimation for Electronic Skin Placed on Collaborative Robot Conductive Case.
Additional Edition:
ISBN 3-031-37648-X
Language:
English
DOI:
10.1007/978-3-031-37649-8
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