Seminars of Innovation and Research in Informatics

All human activities have implications on our society. Research activity not only does but also the overall research system is based on trust. Then, responsible research conduct is of outmost importance. The goal of the course is to provide an overview of the rules and professional practices that are considered as responsible conduct of research and engineering. Ph. D. students, post-doctoral researchers and everybody involved in research and innovation must know the principles and practices that define a responsible conduct and the ones that are not appropriate and are identified as misconduct. Topics include: Introduction on Responsible Research and Innovation Research misconduct Authorship Plagiarism Peer reviews Conflicts of interest Mentor/mentee relationship Collaborative research Presenting the results Intellectual Property Information Privacy

Frriday, 11:00-13:00

This is a multi-month hardware security contest. The organisers provide the RTL code of a "buggy" SoC (last year it was the RISC-V Ariane with some vulnerabilities injected) together with a security specification. The participants, who can be from industry or students, are organised in teams of 4 under the supervision of an academic advisor and they have to perform security verification, so they need to find, fix and report as many security vulnerabilities as they can. The top teams of the first phase will be invited to compete in a similar, one/two days event at DAC (if the conference will be organised in a physical form) and receive monetary prizes. In that case, the travel cost will be covered by a travel grant from the competition sponsors and the local organiser from UPC will accompany the qualified teams.

Self paced project development and weekly meetings with the project advisor.

This is a multi-month hardware security contest. The organisers provide the RTL code of a "buggy" SoC (last year it was the RISC-V Ariane with some vulnerabilities injected) together with a security specification. The participants, who can be from industry or students, are organised in teams of 4 under the supervision of an academic advisor and they have to perform security verification, so they need to find, fix and report as many security vulnerabilities as they can. The top teams of the first phase will be invited to compete in a similar, one/two days event at DAC (if the conference will be organised in a physical form) and receive monetary prizes. In that case, the travel cost will be covered by a travel grant from the competition sponsors and the local organiser from UPC will accompany the qualified teams.

Self paced project development and weekly meetings with the project advisor.

The ACACES Summer School is organized by the HiPEAC Network in collaboration with the TETRAMAX Innovation Action. ACACES 2021 is the seventeenth edition of the Summer School. The ACACES Summer School is a one week summer school for computer architects and tool builders working in the field of high performance computer architecture and compilation for computing systems. The school aims at the dissemination of advanced scientific knowledge and the promotion of international contacts among scientists from academia and industry. A distinguishing feature of this Summer School is its broad scope ranging from low level technological issues to advanced compilation techniques. In the design of modern computer systems one has to be knowledgeable about architecture as well as about the quality of the code, and how to improve it. This summer school offers the ideal mix of the two worlds ¿ both at the entry level and at the most advanced level. The summer school is open to everybody but previous training and/or experience in computer science as well as a background in computer architecture or compilation is indispensable.

Daily full day lectures

Learn how to create and mod video games using Unreal Engine 4, even if you're a complete beginner. Unreal is a free-to-use game development engine used by AAA studios and indie developers worldwide. It can be a complex beast to get into, but we break it down step-by-step

22/03/2021. Big Data: background and defintions. Least squares: background and the Gauss-Markov Theorem. Lp-norm estimators and the Exponential Power Function that is the Generalized Error Distribution. 23/03/2021. Main characteristics of Big Data and its basic management. Important suggestions are developed for a quality control before to extract significant samples for subsequent analysis. 24/03/2021. Big Data applications: Big Data and forensics: an innovative approach for a predictable jurisprudence. Big Data for Social and Economics Science. 25/03/2021. Introduction to Exponential Power Function (E.P.F.) and Lp¿norm estimators. Comparison of the Lp¿norm estimators with the Least Squares method (L2) by simulation studies, introducing a linear regression model in the case of multicollinearity. A comparative analysis applying three estimation methods to evaluate the empirical distribution of the regression parameters is considered. 26/03/2021. Practice of the usage of the normalp package of R. Test of fit-of- godness of the model we present. R environment has a set of tools for dealing with the Exponential Power Function. In this package there are functions to compute the density function, the distribution function and the quantiles from an Exponential Power Function and to generate pseudo-random numbers from the same distribution. 29/03/2021. A Generalized Error Distribution Copula-based method for portfolios risk assessment. Comparison of G.E.D. with Gaussian distribution to financial markets and examples. Applications with R. 30/03/2021. Clustering Time Series using the Skewed Generalized Error Distribution (S.G.E.D): a fuzzy approach based on higher conditional moments. 31/03/2021. Discussion and comments on the collective project titled:"Add a new distribution (S.G.E.D) to the GAS package using R studio". Interactive lesson with final evaluation.

22/03/2021. Big Data: background and defintions. Least squares: background and the Gauss-Markov Theorem. Lp-norm estimators and the Exponential Power Function that is the Generalized Error Distribution. 23/03/2021. Main characteristics of Big Data and its basic management. Important suggestions are developed for a quality control before to extract significant samples for subsequent analysis. 24/03/2021. Big Data applications: Big Data and forensics: an innovative approach for a predictable jurisprudence. Big Data for Social and Economics Science. 25/03/2021. Introduction to Exponential Power Function (E.P.F.) and Lp¿norm estimators. Comparison of the Lp¿norm estimators with the Least Squares method (L2) by simulation studies, introducing a linear regression model in the case of multicollinearity. A comparative analysis applying three estimation methods to evaluate the empirical distribution of the regression parameters is considered. 26/03/2021. Practice of the usage of the normalp package of R. Test of fit-of- godness of the model we present. R environment has a set of tools for dealing with the Exponential Power Function. In this package there are functions to compute the density function, the distribution function and the quantiles from an Exponential Power Function and to generate pseudo-random numbers from the same distribution. 29/03/2021. A Generalized Error Distribution Copula-based method for portfolios risk assessment. Comparison of G.E.D. with Gaussian distribution to financial markets and examples. Applications with R. 30/03/2021. Clustering Time Series using the Skewed Generalized Error Distribution (S.G.E.D): a fuzzy approach based on higher conditional moments. 31/03/2021. Discussion and comments on the collective project titled:"Add a new distribution (S.G.E.D) to the GAS package using R studio". Interactive lesson with final evaluation.

In this course, you will learn how to set up and use a Vulkan application, as well as implement several key features exclusive to Vulkan itself, such as Descriptor Sets, Push Constants, and Subpasses. Each aspect is explained in great amounts of detail, to ensure you really understand what all the parts mean, and how they all connect together.

Programa de DS especialitzat en DS, on s'apliquen neural networks i packages de python (tensorflow, keras, scikit...)