00997nam0-22003251i-450-99000382639040332101-985070-5-4000382639FED01000382639(Aleph)000382639FED0100038263920010507d2000----km-y0itay50------baengSymbolic computation for statistical inferenceD. F. Andrews and J. E. StaffordOxfordOxford University press2000157 p.24 cmOxford statistical science series21Statistica computazionaleInferenza statisticaInferenza non parametrica519.50285Andrews,David F.144897Stafford,James E.H.382187ITUNINARICAUNIMARCBK990003826390403321VIII-A-238127MASMASSymbolic computation for statistical inference507656UNINA05932oam 2200553 450 991073538520332120190911103509.01-4302-5903-510.1007/978-1-4302-5903-9(OCoLC)864746762(MiFhGG)GVRL6XOM(EXLCZ)99371000000001899220140410d2013 uy 0engurun|---uuuuatxtccrWindows Phone 8 recipes a problem-solution approach /Lori Lalonde and David R. Totzke1st ed. 2013.New York :Apress,2013.1 online resource (xxvi, 400 pages) illustrations (some color)Expert's voice in Windows Phone"Planning a delicious Windows Phone 8 application? We have the recipes to make it happen"--Cover.Includes index.1-4302-5902-7 Includes bibliographical references and index.""Contents at a Glance""; ""Contents""; ""About the Authors""; ""About the Technical Reviewer""; ""Acknowledgments""; ""Introduction""; ""Chapter 1: Introduction to the Windows Phone SDK""; ""1-1. Install the Development Tools""; ""Problem""; ""Solution""; ""How It Works""; ""1-2. Create Your First Windows Phone 8 Application""; ""Problem""; ""Solution""; ""How It Works""; ""1-3. Launch an App in the Windows Phone Emulator""; ""Problem""; ""Solution""; ""How It Works""; ""1-4. Launch an App on a Windows Phone Device""; ""Problem""; ""Solution""; ""How It Works""""1-5. Upgrade a Windows Phone 7.x app to Windows Phone 8""""Problem""; ""Solution""; ""How It Works""; ""1-6. Become Acquainted With the Capabilities and Requirements in the Windows Phone Application Manifest""; ""Problem""; ""Solution""; ""How It Works""; ""Chapter 2: Multi-Resolution Support and Basic User Interface Components""; ""2-1. Managing Resolution Dependent Assets""; ""Problem""; ""Solution""; ""How It Works""; ""2-2. Dynamic Layout""; ""Problem""; ""Solution""; ""How It Works""; ""2-3. Working with the Application Bar""; ""Problem""; ""Solution""; ""How It Works""; ""The Basics""""View Model and Data-Binding Support""""Using Commands""; ""2-4. Navigation""; ""Problem""; ""Solution""; ""How It Works""; ""Basic Navigation""; ""Navigation Events""; ""Passing Information""; ""Fragments""; ""QueryString Parameters""; ""2-5. LongListSelector""; ""Problem""; ""Solution""; ""How It Works""; ""Basic Configuration""; ""Templating""; ""Shaping the Data""; ""2-6. Using the Windows Phone Toolkit""; ""Problem""; ""Solution""; ""How It Works""; ""Installation""; ""Referencing the Controls""; ""Chapter 3: Gestures""; ""3-1. Select The Right Action For Your App""; ""Problem""""Solution""""How It Works""; ""3-2. Tap, DoubleTap, or (â€?Tap-And-)Hold""; ""Problem""; ""Solution""; ""How It Works""; ""Designing the UI""; ""Creating Shapes Programmatically""; ""Registering the Events""; ""Managing Tap-Specific Gestures""; ""Testing the App""; ""3-3. Donâ€?t Be A Drag, Just Flick It""; ""Problem""; ""Solution""; ""How It Works""; ""Designing the UI""; ""Managing the GestureListener Events""; ""Improving the UI""; ""Itâ€?s Time To Flick It""; ""3-4. Pinch to Zoom""; ""Problem""; ""Solution""; ""How It Works""; ""Designing the UI""; ""Creating the GestureListener""""Managing the GestureListenerâ€?s Pinch Events""""Testing the App""; ""3-5. Be Generous With Size and Considerate Of Space""; ""Problem""; ""Solution""; ""How It Works""; ""Chapter 4: Tiles and Lock Screen""; ""4-1. Configure the Default Application Tile""; ""Problem""; ""Solution""; ""How It Works""; ""4-2. Create a Flip Tile""; ""Problem""; ""Solution""; ""How It Works""; ""Mapping Content""; ""Character Content""; ""Visual Layout""; ""4-3. Create an Iconic Tile""; ""Problem""; ""Solution""; ""How It Works""; ""Mapping Content""; ""Character Content""; ""Visual Layout""; ""Icon Design""""4-4. Create a Cycle Tile""Windows Phone 8 Recipes is a problem-solution based guide to the Windows Phone 8 platform. Recipes are grouped according to features of the platform and ways of interacting with the device. Solutions are given in C# and XAML, so you can take your existing .NET skills and apply them to this exciting new venture. Not sure how to get started? No need to worry, there’s a recipe for that! Always wondered what it takes to add cool features like gesture support, maps integration, or speech recognition into your app? We've got it covered! Already have a portfolio of Windows Phone 7 apps that needs to be upgraded? We have a recipe for that too! The book starts by guiding you through the setup of your development environment, including links to useful tools and resources. Core chapters range from coding live tiles and notifications to interacting with the camera and location sensor. Later chapters cover external services including Windows Azure Mobile Services, the Live SDK, and the Microsoft Advertising SDK, so you can take your app to a professional level. Finally, you'll find out how to publish and maintain your app in the Windows Phone Store.  Whether you're migrating from Windows Phone 7 or starting from scratch, Windows Phone 8 Recipes has the code you need to bring your app idea to life.Expert's voice in Windows phone.Application softwareDevelopmentSmartphonesApplication softwareDevelopment.Smartphones.004005.258Lalonde Loriauthttp://id.loc.gov/vocabulary/relators/aut1377130Totzke David R.MiFhGGMiFhGGBOOK9910735385203321Windows Phone 8 Recipes3413756UNINA10814nam 22004693 450 99660156070331620240603084507.03-031-61140-3(MiAaPQ)EBC31359048(Au-PeEL)EBL31359048(CKB)32200402800041(EXLCZ)993220040280004120240603d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Neuroscience and Emotional Systems 10th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024, Olhâo, Portugal, June 4-7, 2024, Proceedings, Part I1st ed.Cham :Springer,2024.©2024.1 online resource (560 pages)Lecture Notes in Computer Science Series ;v.146743-031-61139-X Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Machine Learning in Neuroscience -- Morning Anxiety Detection Through Smartphone-Based Photoplethysmography Signals Analysis Using Machine Learning Methods -- 1 Introduction -- 2 Methods -- 2.1 Data Collection -- 2.2 Pulse Wave Extraction -- 2.3 Feature Extraction -- 2.4 Stress Classification -- 2.5 Fisher's Discriminant Ratio -- 3 Results and Discussion -- 4 Conclusion -- References -- Visualizing Brain Synchronization: An Explainable Representation of Phase-Amplitude Coupling -- 1 Introduction -- 2 Materials and Methods -- 2.1 The LEEDUCA Dataset -- 2.2 Data Preprocessing -- 2.3 Phase-Amplitude Coupling -- 2.4 Visualization of Local PAC Patterns -- 3 Consistency of Temporal Patterns -- 4 Results and Discussion -- 5 Conclusions -- References -- Enhancing Neuronal Coupling Estimation by NIRS/EEG Integration -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Preprocessing -- 2.2 CFS Image Sequences -- 2.3 fNIRS Functional Activation -- 2.4 Classification -- 3 Results -- 4 Conclusions and Future Work -- References -- Causal Mechanisms of Dyslexia via Connectogram Modeling of Phase Synchrony -- 1 Introduction -- 2 Material and Methods -- 2.1 Data Acquisition -- 2.2 Preprocessing -- 2.3 Hilbert Transform -- 2.4 Granger Causality -- 2.5 Feature Selection -- 2.6 Connectograms -- 2.7 Machine Learning Classification -- 3 Results -- 4 Conclusions -- References -- Explainable Exploration of the Interplay Between HRV Features and EEG Local Connectivity Patterns in Dyslexia -- 1 Introduction -- 2 Materials and Methods -- 2.1 Database -- 2.2 Cross Frequency Coupling with ISPC -- 2.3 Heart Rate Variability Descriptors -- 2.4 Explainable Regression Experiments -- 3 Results -- 4 Discussion -- 5 Conclusion -- References.Enhancing Intensity Differences in EEG Cross-Frequency Coupling Maps for Dyslexia Detection -- 1 Introduction -- 2 Materials and Methods -- 2.1 Database -- 2.2 Generation of CFC Maps from EEG Signals -- 2.3 Enhancing Differences in CFS Maps Through Histogram Transformation -- 2.4 Quantification of the Improvement -- 3 Results -- 4 Discussion and Conclusions -- References -- Improving Prediction of Mortality in ICU via Fusion of SelectKBest with SMOTE Method and Extra Tree Classifier -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Dataset Used -- 3.2 Preprocessing -- 3.3 AutoML -- 4 Result and Discussion -- 5 Conclusion -- References -- A Cross-Modality Latent Representation for the Prediction of Clinical Symptomatology in Parkinson's Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Preprocessing -- 2.2 Multi-modal Joint Latent Variable Model -- 2.3 Evaluation -- 3 Results and Discussion -- 4 Conclusions -- References -- Zero-Shot Ensemble of Language Models for Fine-Grain Mental-Health Topic Classification -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experiments and Results -- 5 Conclusions -- References -- Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants, and Applications -- 1 Introduction -- 2 The Most Important Versions of GNP -- 2.1 Some Specific Applications of GNP Algorithm -- 3 Conclusion and Future Works -- References -- Enhancing Coronary Artery Disease Classification Using Optimized MLP Based on Genetic Algorithm -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 The Important Process of This Study -- 4 Results and Discussion -- 5 Conclusion -- References -- Extracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders.1 Introduction -- 2 Material and Methods -- 2.1 Participants -- 2.2 NIRS Acquisition -- 2.3 Extraction of Heart Signal from NIRS -- 2.4 Preprocessing -- 2.5 Classification and Explainability -- 3 Results -- 4 Conclusions and Future Work -- References -- Diagnosis of Parkinson Disease from EEG Signals Using a CNN-LSTM Model and Explainable AI -- 1 Introduction -- 2 Proposed Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Deep Learning Model -- 3 Statistical Metrics -- 4 Experiment Results -- 5 Discussion, Conclusion, and Future Works -- References -- Early Diagnosis of Schizophrenia in EEG Signals Using One Dimensional Transformer Model -- 1 Introduction -- 2 Proposed Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Feature Extraction Based on Transformer -- 2.4 Classification -- 3 Statistical Metrics -- 4 Experiment Results -- 5 Discussion, Conclusion, and Future Works -- References -- Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models -- 1 Introduction -- 2 Proposed Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Feature Extraction and Classification -- 3 Experiment Results -- 4 Discussion, Conclusion, and Future Works -- References -- A Survey on EEG Phase Amplitude Coupling to Speech Rhythm for the Prediction of Dyslexia -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition and Preprocessing -- 2.2 Cross-Frequency Coupling -- 2.3 Feature Aggregation and Classification -- 2.4 Evaluation and Interpretability -- 3 Results and Discussion -- 3.1 Best Performing Stimuli and PAC Measures -- 3.2 Effect of Classification Hyperparameters -- 3.3 Assymetric Differences Between DLX and CTL -- 4 Conclusions -- References -- Comprehensive Evaluation of Stroke Rehabilitation Dynamics: Integrating Brain-Computer Interface with Robotized Orthesic Hand and Longitudinal EEG Changes.1 Introduction -- 2 Material and Methods -- 2.1 Participants -- 2.2 Description of the Experimentation -- 2.3 EEG Data Analysis -- 3 Results -- 3.1 Time Frequency -- 3.2 Topographic Maps -- 3.3 Power Bands -- 4 Discussion, Study Limitations and Future Work -- 5 Conclusion -- References -- PDBIGDATA: A New Database for Parkinsonism Research Focused on Large Models -- 1 Introduction -- 2 Database Description -- 3 Experiments and Results -- 4 Conclusions -- References -- A Comparative Study of Deep Learning Approaches for Cognitive Impairment Diagnosis Based on the Clock-Drawing Test -- 1 Introduction -- 2 Materials and Methods -- 2.1 CDT Databases -- 2.2 Image Preprocessing -- 2.3 Convolutional Neural Network -- 2.4 Attentive Pairwise Interaction Network -- 3 Experiments and Results -- 4 Discussion -- 5 Conclusion -- References -- Artificial Intelligence in Neurophysiology -- Prediction of Burst Suppression Occurrence Under General Anaesthesia Using Pre-operative EEG Signals -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Pre-processing -- 2.3 Training -- 2.4 Models Evaluation -- 2.5 Generating Explanations with SHAP -- 3 Experiments -- 4 Results and Discussion -- 4.1 Models Evaluation -- 4.2 Explainability Analysis -- 4.3 Limitations and Future Work -- 5 Conclusion -- References -- Advances in Denoising Spikes Waveforms for Electrophysiological Recordings -- 1 Introduction -- 2 Methods -- 2.1 Acquisition System and Dataset -- 2.2 Neural Signal Denoising -- 2.3 Spike Sorting -- 2.4 Implementation Details -- 2.5 Software Testing -- 3 Results -- 3.1 Denoising and Spike Extraction Quality Measurement -- 4 Discussion -- 5 Conclusions -- References -- Analysis of Anxiety Caused by Fasting in Obesity Patients Using EEG Signals -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 Procedure -- 2.3 Data Processing -- 3 Results and Discussion.4 Conclusions -- References -- Evolution of EEG Fractal Dimension Along a Sequential Finger Movement Task -- 1 Introduction: Fractal Dimension and its Application on EEG Signals Analysis -- 2 Materials and Methods -- 2.1 Dataset and Motor Task -- 2.2 EEG Signals Preprocessing -- 2.3 EMG Signals Preprocessing -- 2.4 Katz Fractal Dimension -- 2.5 KFD Time Windows -- 3 Results -- 4 Conclusion -- References -- Neuromotor and Cognitive Disorders -- Stress Classification Model Using Speech: An Ambulatory Protocol-Based Database Study -- 1 Introduction -- 2 Methods -- 2.1 Database -- 2.2 Feature Extraction -- 2.3 Supervised Classification -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Exploring Spatial Cognition: Comparative Analysis of Agent-Based Models in Dynamic and Static Environments -- 1 Introduction -- 2 Methodology -- 2.1 EvoJAX -- 2.2 Materials and Method -- 2.3 Results -- 3 Conclusions and Future Directions -- References -- Machine Learning for Personality Type Classification on Textual Data -- 1 Introduction -- 2 Background -- 3 Materials and Methods -- 3.1 Data Collection: -- 3.2 Feature Extraction -- 3.3 Target Classes -- 3.4 Classification Models -- 3.5 Performance Evaluation Metrics -- 4 Results -- 5 Conclusions -- References -- Grad-CAM Applied to the Detection of Instruments Used in Facial Presentation Attacks -- 1 Introduction -- 2 Related Work and Background -- 2.1 PAD Techniques -- 2.2 XAI Techniques -- 3 Methods and Materials -- 3.1 Design and Implementation -- 3.2 eXpainable Artificial Intelligence Grad-CAM -- 3.3 Procediment -- 3.4 Statistical Analysis -- 3.5 Database -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Comparison of an Accelerated Garble Embedding Methodology for Privacy Preserving in Biomedical Data Analytics -- 1 Introduction -- 1.1 State of the Art -- 2 Theoretical Framework.2.1 Information Comparison.Lecture Notes in Computer Science SeriesFerrández Vicente José Manuel1740228Val Calvo Mikel1740229Adeli Hojjat784299MiAaPQMiAaPQMiAaPQBOOK996601560703316Artificial Intelligence for Neuroscience and Emotional Systems4165663UNISA05590nam 22006855 450 991088859990332120260331110739.09783031601798(ebook)303160179310.1007/978-3-031-60179-8(MiAaPQ)EBC31682236(Au-PeEL)EBL31682236(CKB)36043960600041(DE-He213)978-3-031-60179-8(OCoLC)1458447184(MiFhGG)9783031601798(EXLCZ)993604396060004120240919d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierGenerative AI in Higher Education Innovation Strategies for Teaching and Learning /by Adebowale Owoseni, Oluwaseun Kolade, Abiodun Egbetokun1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Palgrave Macmillan,2024.1 online resource (209 pages)Business and Management Series9783031601781 3031601785 Chapter 1: Generative AI and its Implications for Higher Education Students and Educators -- Chapter 2: Applications of Generative AI in Lesson Preparation and Content Development -- Chapter 3: Applications of Generative AI in Formative Learning and Assessment -- Chapter 4: Applications of Generative AI in Summative Assessment -- Chapter 5: Enhancing Personalised Learning and Student Engagement using Generative AI -- Chapter 6: Responsible Use of Generative AI for Educators and Students in Higher Education Institutions -- Chapter 7: Emergent Issues and Future Considerations.“This book is a must-read for those committed to embracing the digital transformation in education with a responsible and forward-thinking approach.” David Mba, Vice Chancellor, Birmingham City University, UK “The book by Owoseni, Kolade, and Egbetokun is an important and timely contribution to this debate and it positively differs from much of the debate by exploring the opportunities and benefits that generative AI can offer.” Bernd Stahl, Professor of Critical Research in Technology, School of Computer Science, University of Nottingham, UK With the integration of generative artificial intelligence (AI), teachers and learners now have access to powerful tools to enhance their productivity and effectiveness in their work. To meet the demands of this dynamic educational landscape, teachers must embrace AI to handle repetitive tasks, freeing them to focus on more intelligent and humanistic responsibilities. For learners, responsible use of AI could make learning more fun, personalized, flexible, and enriching. This insightful new book explores the evolving role of educators in higher education in a world of rapid technological advancements and provides a practical outline of the available technologies. By integrating Generative AI into teaching and learning, Higher Education Institutions can contribute to achieving inclusive and equitable quality education, a target of the UN Sustainable Development Goals, and promote lifelong learning opportunities for all. Generative AI can be used to enhance teaching and learning experiences, foster creativity, and develop new learning experiences in higher education. This book is a valuable resource for educators navigating the ever-changing landscape of education technology. With scientific background, practical insights and actionable tips, this book will be of interest to scholars of emerging technologies and innovation in education. It will also be of practical use to instructors seeking to harness the power of generative AI, enhancing productivity and transforming their approach to personalized learning. Adebowale Owoseni is a Senior Lecturer in Information Systems at De Montfort University, Leicester, UK, and a Senior Fellow of the Higher Education Academy. He transitioned to academia in 2019 after a 13-year career in fintech. Oluwaseun Kolade is a Professor of Entrepreneurship and Digital Transformation at Sheffield Hallam University, UK. He has authored more than 70 academic outputs spanning digital transformation, AI, circular economy, and SMEs strategies. Abiodun Egbetokun is Senior Lecturer in Business Management at De Montfort University, Leicester, UK, and a Senior Fellow of the Higher Education Academy. His current research examines the implications of Large Language Models in research and higher education.Technological innovationsArtificial intelligenceEducation, HigherEducational technologyInnovation and Technology ManagementArtificial IntelligenceHigher EducationDigital Education and Educational TechnologyTechnological innovations.Artificial intelligence.Education, Higher.Educational technology.Innovation and Technology Management.Artificial Intelligence.Higher Education.Digital Education and Educational Technology.370.28563Owoseni Adebowale1770737Kolade Oluwaseun1359807Egbetokun Abiodun1770738MiAaPQMiAaPQMiAaPQWlCmTSDBOOK9910888599903321Generative AI in Higher Education4253671UNINA