Deep Active Learning

Deep Active Learning PDF
Author: Kayo Matsushita
Publisher: Springer
ISBN: 9811056609
Size: 30.53 MB
Format: PDF, ePub
Category : Education
Languages : en
Pages : 226
View: 7173

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Book Description: This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.

Ecai 2020

ECAI 2020 PDF
Author: G. De Giacomo
Publisher: IOS Press
ISBN: 164368101X
Size: 77.58 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 3122
View: 1689

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Book Description: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.

Deep Learning In Healthcare

Deep Learning in Healthcare PDF
Author: Yen-Wei Chen
Publisher: Springer Nature
ISBN: 3030326063
Size: 46.13 MB
Format: PDF, ePub, Mobi
Category : Technology & Engineering
Languages : en
Pages : 218
View: 1848

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Book Description: This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Deep Learning Applications

Deep Learning Applications PDF
Author: M. Arif Wani
Publisher: Springer Nature
ISBN: 9811518165
Size: 16.84 MB
Format: PDF, ePub, Docs
Category : Technology & Engineering
Languages : en
Pages : 178
View: 797

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Book Description: This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Secure And Privacy Aware Machine Learning

Secure and Privacy Aware Machine Learning PDF
Author: Xuhui Chen
Publisher:
ISBN:
Size: 41.23 MB
Format: PDF, Mobi
Category : Cloud computing
Languages : en
Pages : 112
View: 3884

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Book Description: With the onset of the big data era, designing efficient and secure machine learning frameworks to analyze large-scale data is in dire need. This dissertation considers two machine learning paradigms, the centralized learning scenario, where we study the secure outsourcing problem in cloud computing, and the distributed learning scenario, where we explore blockchain techniques to remove the untrusted central server to solve the security problems. In the centralized machine learning paradigm, inference using deep neural networks (DNNs) may be outsourced to the cloud due to its high computational cost, which, however, raises security concerns. Particularly, the data involved in DNNs can be highly sensitive, such as in medical, financial, commercial applications, and hence should be kept private. Besides, DNN models owned by research institutions or commercial companies are their valuable intellectual properties and can contain proprietary information, which should be protected as well. Moreover, an untrusted cloud service provider may return inaccurate and even erroneous computing results. To address above issues, we propose a secure outsourcing framework for deep neural network inference called SecureNets, which can preserve both a user's data privacy and his/her neural network model privacy, and also verify the computation results returned by the cloud. Specifically, we employ a secure matrix transformation scheme in SecureNets to avoid privacy leakage of the data and the model. Meanwhile, we propose a verification method that can efficiently verify the correctness of cloud computing results. Our simulation results on four- and five-layer deep neural networks demonstrate that SecureNets can reduce the processing runtime by up to 64%. Compared with CryptoNets, one of the previous schemes, SecureNets can increase the throughput by 104.45% while reducing the data transmission size by 69.78% per instance. We further improve the privacy level in SecureNets and implement it in a practical scenario. The Internet of Things (IoT) emerge as a ubiquitous information collection and processing paradigm that can potentially exploit the collected massive data for various applications like smart health, smart transportation, cyber-physical systems, by taking advantage of machine learning technologies. However, these data are usually unlabeled, while the labeling process is usually both time and effort consuming. Active learning is one approach to reduce the data labeling cost by only sending the most informative samples to experts for labeling. In this process, two most computation-intensive operations, i.e., sample selection and learning model training, hinder the use of active learning on resource-limited IoT devices. To address this issue, we develop a secure outsourcing framework for deep active learning (SEDAL) by considering a general active learning framework with a deep neural network (DNN) learning model. The improved SecureNets is adopted in the model inferences in sample selection and DNN learning phases. Compared with traditional homomorphic encryption based secure outsourcing schemes, our scheme reduces the computational complexity at the user from O(n^3) to O(n^2). To evaluate the performance of the proposed system, we implement it on an android phone and Amazon AWS cloud for an arrhythmia diagnosis application. Experiment results show that the proposed scheme can obtain a well-trained classifier using fewer queried samples, and the computation time and communication overhead are acceptable and practical. Besides the centralized learning paradigms, in practice, data can also be generated by multiple parties and stored in a geographically distributed manner, which spurs the study of distributed machine learning. Traditional master-worker type of distributed machine learning algorithms assumes a trusted central server and focuses on the privacy issue in linear learning models, while privacy in nonlinear learning models and security issues are not well studied. To address these issues, in this work, we explore the blockchain technique to propose a decentralized privacy-preserving and secure machine learning system, called LearningChain, by considering a general (linear or nonlinear) learning model and without a trusted central server. Specifically, we design a decentralized Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the blockchain. In decentralized SGD, we develop differential privacy based schemes to protect each party's data privacy, and propose an l-nearest aggregation algorithm to protect the system from potential Byzantine attacks. We also conduct theoretical analysis on the privacy and security of the proposed LearningChain. Finally, we implement LearningChain and demonstrate its efficiency and effectiveness through extensive experiments.

Artificial Neural Networks And Machine Learning Icann 2018

Artificial Neural Networks and Machine Learning     ICANN 2018 PDF
Author: Věra Kůrková
Publisher: Springer
ISBN: 3030014215
Size: 51.65 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 632
View: 3239

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Book Description: This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Visible Learning For Teachers

Visible Learning for Teachers PDF
Author: John Hattie
Publisher: Routledge
ISBN: 1000039331
Size: 65.64 MB
Format: PDF, Mobi
Category : Education
Languages : en
Pages : 302
View: 4445

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Book Description: In November 2008, John Hattie’s ground-breaking book Visible Learning synthesised the results of more than fifteen years research involving millions of students and represented the biggest ever collection of evidence-based research into what actually works in schools to improve learning. Visible Learning for Teachers takes the next step and brings those ground breaking concepts to a completely new audience. Written for students, pre-service and in-service teachers, it explains how to apply the principles of Visible Learning to any classroom anywhere in the world. The author offers concise and user-friendly summaries of the most successful interventions and offers practical step-by-step guidance to the successful implementation of visible learning and visible teaching in the classroom. This book: links the biggest ever research project on teaching strategies to practical classroom implementation champions both teacher and student perspectives and contains step by step guidance including lesson preparation, interpreting learning and feedback during the lesson and post lesson follow up offers checklists, exercises, case studies and best practice scenarios to assist in raising achievement includes whole school checklists and advice for school leaders on facilitating visible learning in their institution now includes additional meta-analyses bringing the total cited within the research to over 900 comprehensively covers numerous areas of learning activity including pupil motivation, curriculum, meta-cognitive strategies, behaviour, teaching strategies, and classroom management. Visible Learning for Teachers is a must read for any student or teacher who wants an evidence based answer to the question; ‘how do we maximise achievement in our schools?’

200 Active Learning Strategies And Projects For Engaging Students Multiple Intelligences

200  Active Learning Strategies and Projects for Engaging Students   Multiple Intelligences PDF
Author: James Bellanca
Publisher: Corwin Press
ISBN: 1412968844
Size: 52.15 MB
Format: PDF, ePub, Mobi
Category : Education
Languages : en
Pages : 346
View: 5797

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Book Description: Organized by intelligence area, this resource provides more than 200 new and enhanced strategies to help teachers increase students' motivation and transform them into active learners.

Writing Across Distances And Disciplines

Writing Across Distances and Disciplines PDF
Author: Joyce Magnotto Neff
Publisher: Routledge
ISBN: 1135596778
Size: 50.38 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 200
View: 5274

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Book Description: Writing Across Distances and Disciplines addresses questions that cross borders between onsite, hybrid, and distributed learning environments, between higher education and the workplace, and between distance education and composition pedagogy. This groundbreaking volume raises critical issues, clarifies key terms, reviews history and theory, analyzes current research, reconsiders pedagogy, explores specific applications of WAC and WID in distributed environments, and considers what business and education might teach one another about writing and learning. Exploring the intersection of writing across the curriculum, composition studies, and distance learning , it provides an in-depth look at issues of importance to students, faculty, and administrators regarding the technological future of writing and learning in higher education.