This course studie human-computer interaction (HCI) with a focus on increasing the efficiency of the communication between users and computing services. Technology appears to be unlimited regarding which form of user interfaces can be implemented. However, users have strong limitations regarding what they can perceive and what they can do in a given amount of time. Human Centered Interaction approaches the HCI problem as the *optimization of the human input/output bandwidth* through better HCIs. Content Lectures Human sensorimotor capabilities and limits: perception, control. Modeling the interaction between users and computers. Optimizing the interaction with touch, gestural, mobile and 3D interaction. Benefits and limits of tangible interaction Future of tangible interaction Implementation of tangible interaction with Arduino Benefits of multimodal interaction Design elements of multimodal interaction Project Students work in groups of 2 during the whole semester on an HCI study. They chose an HCI problem of their choice (moderated by the professors), analyze the problem, propose a new interaction, prototype and evaluate it, and they present their work to the class at the end of the semester. Evaluation Session 1: Project (75%), written exam 2h (25%) Session 2: The project grade is kept (75%), oral exam 0.5h (25%). Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IH36TTHR 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Bérengère DUC
ri-im2ag@univ-grenoble-alpes.fr

Operating systems are the foundation of computer systems, often complemented with middleware systems to help with more domain-specific features. Operating systems wrap the underlying hardware platforms into an effective software platform, creating an illusion, hidding hardware details away and offering instead high-value services. In the end, operating systems create an effective virtual world for software developers and end users alike. As such, operating systems are virtual machines. Virtual platforms come in many shapes and sizes, creating virtual platforms with different specifics, for different application domains. Some virtual platforms are real-time operating systems for mission-critical systems such as avionics or aerospace systems. Others are sheer veneers above very specific hardware like in Game consoles or Aduino-like embedded systems for the Do-It-Yourself communities. Others are combining operating system kernels with high-level languages, such as the Google Android platform that combines the Linux kernel and the Java virtual machine. Others are for world-scale cloud infrastructures, often associating modified Linux and hypervisors, along with advanced distributed services such as shared storage or shared FPGA accelerators. Across this massive domain, there are only few key enabling technologies, technologies that we will help you learn and master in this course. The course starts with understanding some of the key evolutions of current hardware platforms; platforms facing the challenge to deliver increasing performance while keeping the energy consumption under control. The course then discusses what is an operating system kernels and what are the architectural options that have been tried so far, such as discussing monolithic kernels, micro-kernels, and hypervisors. We will discuss these approach, debating their original design goals and comparing them with the characteristics of their implementations. Then the course moves onto the key enabling technologies for the Cloud infrastructures. These technologies are the enablers of popular online services such as search engines, social networks, or streaming services. They are also the enablers of Big Data applications. All these complex systems share similar requirements such as requiring large amount of computing resources and having stringent constraints in terms of reliability, availability and performance. To fulfill such requirements, these complex systems are implemented above Cloud platforms that exploit large numbers of servers hosted in a data center, forming so-called “rack-scale” or even “warehouse-scale” platforms. These platforms are at the heart of companies like Google, Facebook, Twitter or Amazon. Everyday, these companies face the challenge of exploiting data center resources efficiently and reliably through well-designed software infrastructures. While a few challenges are specific to the massive size of the these giants, most of the design principles they rely on are also of interest for smaller scale systems. Through this course, you will learn about these design principles and get a chance to understand the underlying theoretical and practical challenges, including the study of scalability, fault tolerance, and data consistency—all in the context of virtualized hardware platforms. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IGDOSOJ0 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Bérengère DUC
ri-im2ag@univ-grenoble-alpes.fr

1. Symmetric cryptology : overview of design and cryptanalysis techniques of block ciphers – Theoretical foundations – Cryptanalysis aspects – Design elements 2. Asymmetric cryptography – Cryptosystems based on the discrete logarithm problem : . standard groups used . DDH, ElGamal, security assumptions, signature schemes… . bilinear maps, identity-based encryption . generic attacks, index calculus, special focus on elliptic curves – Post-quantum cryptography : . super-singular isogeny Diffie-Hellman key exchange . multivariate cryptography and polynomial system solving : isomorphism of polynomials problem, MQ-schemes, Gröbner bases and their computation Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IFNPDYKV 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Bérengère DUC
ri-im2ag@univ-grenoble-alpes.fr

In this course, we will first focus on linear methods for image denoising. In this regard, we will investigate some properties of the heat equation and of the Wiener filter. We will then introduce nonlinear partial equations such as the Perona­Malick model for noise removal, and some other similar models. A last part of the course will be devoted to edge detection for which we will consider the Canny approach and, more precisely, we will deal in details with active contours and level sets methods. Computer Science, Mathematics and Applied Mathematics (UFR IM²AG) Grenoble – Domaine universitaire IGNFYEFF 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Bérengère DUC
ri-im2ag@univ-grenoble-alpes.fr

Introduction
1. Introduction to SOC VLSI interconnect design and analysis
2. SOC design challenges and potential solutions
3. Power and signal integrity analysis and optimization

Interconnect modeling and simulation
4. Formulation of circuit equation
5. Solution of linear equations
6. Interconnect delay models
7. Laplace transformation and analysis
8. Transient simulation
9. Model order reduction
10. Interconnect modeling I: capacitance extraction
11. Interconnect modeling II: inductance extraction
12. Iterative solution of linear equations & application

Nonlinear circuit simulation
13. Solution of non-linear equations
14. Transient analysis of nonlinear dynamic circuits
15. Consistency, stability, convergence, local truncation error

Circuit optimization
16. Mathematical programming I: linear programming
17. Mathematical programming II: nonlinear programming
18. Mathematical programming III: geometric programming
19. Combinatorial optimization I: greedy algorithm
20. Combinatorial optimization II: dynamic programming College of Electrical Engineering & Computer Science Main Campus Chung-Ping Chen 25 Wednesday 7,8,9 EE5043 3 Half Graduate Institute of Electrical Engineering, Graduate Institute of Biomedical Electronics and Bioinfornatics http://www.ee.ntu.edu.tw/en/

I open several related courses. Please visit our lab website for more detailed info on how to choose my lectures based on your preference.

http://homepage.ntu.edu.tw/~tksmiki/for_Students_%28zhong_wen%29.html

The objective is to provide students with computer skills for dynamical modeling of populations and communities, which are governed by difference equation, ordinary differential equation, or partial differential equation.

This is an introductory course intended for undergraduate and graduate students with knowledge of basic ecology. We will learn basic skills of computer programming (C language) with Linux. If necessary, we will also learn mathematical theories of numerical calculations. Every student needs to bring his/her own notebook PC/Mac with enough memory size (2GB in total is recommended) and empty part of hard disk. Ubuntu does not work in a sufficient speed in some of Netbook (e.g. old Eee PC). All applications that are necessary for this course will be provided. Each lecture will include:

1. Setting up your computer

2. Basic commands in Linux

3-9. Basic grammar and algorithms in C-language

10. How to use gnuplot (an application for graphics)

11. Numerical calculations for difference equations

12-13. Mathematical theories of numerical calculations of ordinary differential equations

14. Numerical calculations for population dynamics of a single species

15. Numerical calculations for population dynamics of multiple species

16. Numerical calculations for reaction-diffusion models To learn computer skills for dynamical modeling of populations and communities, which are governed by difference equation, ordinary differential equation, or partial differential equation. College of Science Main Campus Takeshi Miki 15 Thursday 6,7 OCEAN5069 2 Half Graduate Institute of Oceanography, Marine Biology & Fisheries Division http://www.oc.ntu.edu.tw/?lang=en

This is a course intended for students with basic knowledge of ecology, statistics, differential equations, and computer programming techniques and had some experience on modeling. We will discuss the application of mathematical modeling and computer programming techniques to investigate ecological questions. We will also discuss statistical analyses for identifying ecological patterns. Students will select a subject base on his/her own interest and present the progress of the chosen topic. The class is mainly in the form of discussion. Students are required to do oral presentation on a topic of ecological modeling and participate discussion. College of Science Main Campus Takeshi Miki 25 Tuesday 7,8 Ocean7152 2 Half Graduate Institute of Oceanography, Marine Biology & Fisheries Division http://www.oc.ntu.edu.tw/?lang=en

Many society areas use computational applications in order to manipulate different media types, in a integrated fashion or not. The information volume generated by those applications and the need for inter-media synchronization make necessary to use coding techniques as well as standards to guarantee efficiency and interoperability. The knowledge about those techniques and standards are essential for graduated students develop research in the Multimedia area. The goal of this course is to present multimedia fundamentals, approaching important issues regarding different media types and coding methods. It is also in the scope to analyze features and limitations of available tools, applications and systems. After the course we expect the student to be capable to discuss recent related research topics. Institute of Mathematical and Computer Sciences (ICMC) São Carlos campus Mutimedia definition. Introduction to digitization. Compression of different media types. Basic compression techniques. Spatial and temporal coding.Standards. Multimedia segmentation and adaptation. Multimedia authoring. Maria da Gra_a Campos Pimentel, Rudinei Goularte, Marcelo Garcia Manzato 30 SCC5909 7 The final grade will be obtained calculating a weighted average among exams, projects and seminars. http://conteudo.icmc.usp.br/Portal/conteudo/1079/538/foreign-scholars

Interactive systems are present in the daily lives of individuals who make explicit or implicit use of a variety of computing devices. This course provides students with a comprehensive overview of the key concepts, techniques and methods that can be used in the design and evaluation of such systems. The course aims at presenting the fundamental concepts, techniques and methods for the design, development and evaluation of interactive systems. Institute of Mathematical and Computer Sciences (ICMC) São Carlos campus Interaction design. User experience. Conceptual models. Metaphors. Paradigms. Cognitive, social and emotional aspects. Interface types. Natural interfaces. Interfaces for mobile devices. Techniques for identification and analysis requirements. Design, prototyping and construction. Agile UX. Design patterns. Avalia__o: inspection techniques and usability testing. Maria da Gra_a Campos Pimentel 30 SCC5912 8 The complementary course “Human-computer Interaction II: practice” allows students to develop a project while applying the concepts tackled in this course. Weighted average among exams, seminars and practical work. http://conteudo.icmc.usp.br/Portal/conteudo/1079/538/foreign-scholars

Riemannian Geometry is a basic course for any graduate student in Mathematics who wants to study Geometry, Topology or Dynamic Systems, and is also a relevant course for students of Analysis and Applied Mathematics. Provide to the student the basic tools and some fundamental results of Riemannian Geometry. Institute of Mathematical and Computer Sciences (ICMC) São Carlos campus Program: Riemannian metrics; Connections; Completeness; Curvature; Isometric immersions; Variational calculus; Applications. Detailed program: (1) Riemannian metrics; Examples of Riemannian manifolds: the Euclidean space R^n, the sphere S^n, the real hyperbolic space H^n, product of Riemannian manifolds, conformal metrics, Riemannian coverings, flat tori, the Klein bottle, Riemannian submersions, the Hopf fibration and the complex projective space, quotient manifolds, Lie groups. (2) Connections; Parallel transport along a curve; Geodesics; Isometries and Killings vector fields; Induced connections. (3) Completeness; The Hopf-Rinow theorem; Cut locus, Examples. (4) The Riemann-Christoffel curvature tensor; The Ricci tensor and scalar curvature; Covariant derivative of tensors; Examples. (5) Isometric immersions; The second fundamental form; The fundamental equations. (6) Variational calculus; The energy functional; Jacobi vector fields; Conjugate points; Examples. (7) Space forms; The Synge theorem; The Bonnet-Myers theorem; Nonpositively curved manifolds. Fernando Manfio, Irene Ignazia Onnis 35 SMA5947 8 Two written tests. http://conteudo.icmc.usp.br/Portal/conteudo/1079/538/foreign-scholars

Many systems in the real world are already organized in networks, for example, electricity transmission and distribution networks, road networks, social networks, computer networks, and neural networks. With the growth of these networks, the science and engineering deal with more and more problems modeled by complex networks (large sparse graphs). Thus, the study of complex networks is important and of general interests to various scientific areas. In computer science, complex networks can be applied to various research fields, such as, data mining, image processing, information retrieval, pattern recognition, bioinformatics and grid computing. With the in-depth study of the theory of complex networks, we can obtain a basis for the development of research in complex network field it own, in computer science, as well as in engineering and other sciences. Due to the broad interests and wide range of applications of complex networks, we intend to offer this course to all areas of computer science and computational mathematics. Presenting to the students the basic and intermediate levels of techniques for complex network analysis, as well as presenting network modeling methods for solving real computational problems involving complex networks. Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP) Ribeirão Preto campus The aim of this course is to explore the concepts, techniques and applications involved in complex networks. 1) Introduction: Basic Concept of Complex Networks; Evolution of Complex Networks; 2) Complex Networks Models and Generation Algorithms: Random Networks; Small-World Networks; Scale-Free Networks; Clustered Networks; 3) Complex Network Measures: Centrality; Connectivity; Transitivity; Assortativity; Local Density ; Betweenness; Other Measures; 4) Advanced Network Analysis Techniques: Searching Methods for Complex Networks; Graph Isomorphism and Networks Similarity; Flow Optimization in Complex Networks; Community Detection in Complex Networks; Spectrum Analysis; Generating Functions; Other Techniques; 5) Applications: Data Mining; Machine Learning; Information Retrieval; Image Processing and Pattern Recognition; Grid Computing; Network Security; Bioinformatics; Other Applications; Antonio Carlos Roque da Silva Filho, Alexandre Souto Martinez, Zhao Liang 33 5955012 8 Evaluation: 01 written test and 02 practical tasks. The final grade will be calculated by the weighted average of the test and the practical tasks. https://www.google.com.br/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiBp_-p9NzYAhWHkZAKHY_oACkQFggnMAA&url=http%3A%2F%2Fwww.ffclrp.usp.br%2Fdown.php%3Fid%3D1430%26d&usg=AOvVaw3-C7BSHGAhorxoB-Rfx8dD

This course introduces students to the fundamentals of three important techniques of artificial intelligence (AI), namely, artificial neural networks (ANN), genetic algorithm (GA), and fuzzy logic. These techniques have been successfully applied by many industries in consumer products and industrial systems. ANN provides strong generalization and discriminant properties and offer a simple way of developing system models and function approximation. GA is adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics for optimization and search problems. Fuzzy logic offers flexibility in developing rule-based systems using natural language type of rules. They are highly applicable for many pattern recognition applications. This course gives the students appropriate knowledge and skills to develop, design and analyze effectively these AI techniques for practical problems with some degree of accuracy. The students will also be given a hands-on programming experience in developing fuzzy logic and neural networks system as well as genetic algorithm, to effectively solve real world problems. 1. Design systems using ANN, GA and fuzzy logic for real world applications based on theoretical framework. 2. Demonstrate the ability to acquire information from various resources about the development of ANN, GA and fuzzy logic for various applications. 3. Demonstrate the ability to develop fuzzy logic, ANN and/or GA using appropriate programming languages or software tools for solving application.

Malaysia-Japan International Institute of Technology UTMKL Lecture and Discussion, Co-operative and Collaborative Method, Problem Based Method. week 1, week 2, etc. Prof. Datin Dr. Rubiyah Yusof conditional SMJE 3203 3 Sem 6 1. J. McCarthy, What is Artificial Intelligence http://www-formal.stanford.edu/jmc/whatisai/whatisai.html 2. S. N. Sivanandam, S. Sumathi and S. N. Deepa, Introduction to Fuzzy Logic using MATLAB, Springer-Verlag, Berlin, 2007 3. Fuzzy Logic Toolbox For Use with MATLAB® , The Mathworks Inc., 2006 4. Neural Network Toolbox For Use with MATLAB® , The Mathworks Inc., 200 Test, Assignment, Project, Final Examination Prof. Datin Dr. Rubiyah Yusof
Dr. Mohd Ibrahim Shapiai mailto:rubiyah.kl@utm.my,md_ibrahim83@utm.my