Series of seminars Prof. Fernando De La Prieta

E-HEALTH: Progetti di ricerca nel campo dell’informatica applicata alla salute

SERIE DI SEMINARI
Where: Blocco 11
When:

  • Lunedì 28/10, ore 18-19, aula D4.8
  • Martedì 29/10, ore 18-19, aula D4.5
  • Mercoledì 30/10, ore 18-19, aula D4.8
  • Giovedì 31/10, ore 18-19, aula D4.2

Riconosciuta come AFO per il CLM in Medicina e Chirurgia per 0.5 CFU

PROF. FERNANDO DE LA PRIETA
Coordinator – Degree in Computer Engineering
Department of Computer Science, School of
Science – University of Salamanca
BISITE Research Group – http://bisite.usal.es 

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Human behaviour modelling and simulation – an agent-based approach

Speaker:
Visiting Professor Julie Dugdale,
HAwAI research group (Human Aware Artificial Intelligence),
Università di Grenoble.

Quando: Mercoledì 16 Ottobre, 11:30

Dove: Sala Seminari, II piano, edificio Alan Turing

Abstract
This seminar looks at how human behaviours can be simulated in a computational model for the purpose of designing new technologies or procedures. The seminar begins with a short introduction to area of Agent-based Social Simulation (ABSS) and shows its potential for modelling human behaviours in complex socio-technical systems that exhibit emergent phenomena. ABSS is strongly multi-disciplinary area drawing upon the domains of sociology, psychology and computer science (specifically distributed artificial intelligence).  The seminar covers different types of agents (e.g. model based, utility based) and some common architectures, such as belief-desire-intention. Using applications that have been developed in the areas of crisis and emergency management, and shared urban spaces (mixed autonomous vehicles and pedestrians in cities), I will show how aspects such as social attachment, norms, and other forms of cognition can be modelled and simulated.  

Combining Epistemic and Operational Aspects in Compositional Verification of Protocols

Schedule: 1 October, 11:30 – 12.30
Place: GSSI – MLH, v. Francesco Crispi 7

Speaker: Mohammad Reza Mousavi
School of Informatics – Data-Oriented Software Engineering, University of Leicester, UK 
https://www2.le.ac.uk/departments/informatics/people/mohammad-mousavi

Title: Combining Epistemic and Operational Aspects in Compositional Verification of Protocols

Abstract: We propose a framework to reason about what rational agents know or believe, when they observe and relate different operational runs (histories) of the protocol. Our framework is based on a combination of modal mu-calculus and epistemic logic. We investigate decompositional verification methods for this type of reasoning. Finally, we draw parallels to between epistemic reasoning and reasoning about causality and present initial ideas on a decompositional method for reasoning about causality.

(Based on joint work with Georgiana Caltais, Francien Dechesne, Stefan Leue, and Simona Orzan) 

 

Distinguished lectures del Prof. K. GOPAKUMAR

Il Prof. K. Gopakumar, IEEE Fellow e Distinguished Lecturer dell”IEEE Industrial Electronics Society sarà ospite dell’IEEE Italy Chapter e terrà due distiguished lecturers nei giorni:

  • Mercoledì 19 Settembre ore 15.00: Università degli Studi di Firenze, Complesso di Santa Apollonia, Via San Gallo 25, Firenze ( https://goo.gl/maps/FVDPNrfxx2gMmdT79 )
  • Lunedì 23 Settembre ore 9.00: Università degli Studi dell’Aquila, Sala Riunioni DISIM dell’Edificio Alan Touring, Via Vetoio ( https://goo.gl/maps/UdD4X7WTXkurQn5s9 )

 

Titolo: Stacked multilevel inverter topologies for variable speed drives applications

Abstract: Many interesting multilevel topologies have been reported for drive applications. However, still the most popular topology is the NPC three level, especially for medium voltage drives applications. This shows that the industry is still looking for some viable alternative to this, with reduced power circuit complexity and with increased reliability for medium voltage drives applications. This lecture will focus on some of the recent work from my lab on five-level, nine level and forty nine level inverter topologies with reduced DC link voltages for variable speed drive
applications. Elimination of the common point voltage fluctuations due to stacking of cells, with a normal six phase IM drive will also be discussed.

Locandina

Design Languages: A Necessary New Generation of Computer Languages

Title: Design Languages: A Necessary New Generation of Computer Languages 
Bran Selic (Monash University/Malina Software) 
 
When: 11 July 2019, 14:30 
Where: Sala Seminari Alan Turing
 
Abstract: With the increased demand for so-called “smart” systems, which are required to interact with the physical world in ever more complex ways, we are witnessing a corresponding growth in the complexity of the software that is at the core of such systems. Keeping pace with this rise in complexity is proving to be a challenge for current mainstream programming technologies, whose origins are typically rooted in increasingly outdated computing paradigms that can be traced to some of the earliest applications of computers (e.g., solving numerical problems). The first part of this talk examines in detail the primary inadequacies of current mainstream programming technologies, which renders them unsuitable for addressing modern software applications. This is followed by a discussion of emerging trends in computer language development, which point to a new generation of programming languages, referred to herein as design languages. The primary technical requirements for these new languages are explained, as well as the various pragmatic and socio-economic issues associated with their introduction into practice. In addition, a high-level summary of crucial research topics required to realize the full potential of these languages is also provided.

GPU-Programming with CUDA

Short course on GPU programming. The instructor will be Marius Brehler from Dortmund University (Germany), and a course summary is available here

Schedule:

– Session 1: Monday, May 20,   14:30 – 17:30,          Meeting room of Alan Turing building;

– Session 2: Tuesday, May 21,   14:30 – 17:30,          Meeting room of Alan Turing building;

– Session 3: Wednesday, May   22, 14:30 – 17:30,    Room to be defined.

Two seminars on Robotics and Automotive

Title: Effective and User-friendly Specification of Multi-Robot Missions
When: Wednesday 24, April 15:00-16:00
Where: Aula seminari, blocco 0 

Abstract: Mobile robots are increasingly used in everyday life to autonomously realize missions such as exploring rooms, delivering goods, or following certain paths for surveillance. The current robotic market is asking for a radical shift in the development of robotic applications where mission specification is performed by robotic users that are not highly qualified and specialized in robotics or ICT. To this aim, we proposed a Domain Specific Language (DSL) that enables non-technical users to specify missions for a team of autonomous robots in a user- friendly and effective way. The talk will present how we developed such DSL, it semantics, and how we evaluated it.

Bio: Sergio García is a PhD student at the Computer Science and Engineering department of Gothenburg University. His research interests comprise model-driven software development and software architecture for robotics. His research is currently involved with the Co4Robots European Project, from where he often collaborates with industry.

Title: On Interfaces to Support Agile Architecting in Automotive
When: Monday 29, April 15:00-16:00
Where: Aula seminari, blocco 0 

Abstract: In large-scale agile automotive companies, practitioners struggle with creating and evolving an architecture when developing complex and safety-critical systems. A key issue is the trade-off between upfront planning and flexibility to embrace change. In particular, the coordination of interfaces is an important challenge, as interfaces determine and regulate the exchange of information between components, subsystems, and systems, which are often developed by multiple teams. In a fast-changing environment, interfaces between teams can provide the sufficient stability to align software or systems, while maintaining a sufficient degree of autonomy. In this talk, we present an exploratory case study with an automotive OEM to identify characteristics of different interfaces, from non-critical interfaces that can be changed frequently and quickly, to those that are critical and require more stability and a rigorous change process. We identify what dimensions impact how interfaces are changed, what categories of interfaces exist along these dimensions, and how categories of interfaces change over time. We conclude with suggestions for practices to manage the different categories of interfaces in large-scale agile development.

Bio: Rebekka Wohlrab is an industrial PhD student at the Computer Science and Engineering Department of Chalmers University of Technology and Systemite AB. In her research, she focuses on large-scale automotive companies aiming for more agile ways of managing documentation. Her work is funded by the Wallenberg AI, Autonomous Systems and Software Program.

GSSI Seminar by Adrian Rutle

When: Wednesday, April 17, 2019, 15:00 

Where: Library Room, GSSI

Title: Model Repair with Reinforcement Learning
Seminar by Prof. Adrian Rutle, Department of Computing, Mathematics and Physics – Western Norway University of Applied Sciences

Abstract:Model Driven Engineering is an emerging branch of Software Engineering used to handle complex and evolving software systems. The industrial demand is quite high and with the increasing use of Model-Based Development in many domains (e.g., Automotive, Web Applications, Business processes), models are becoming core artifacts of modern software engineering processes. With this, it comes the necessity to keep models free of errors and in case errors happen, to repair them reasonably. This seminar will present our proposed approach to model repair with focus on these three topics:

Model repair methodologyWhen models increase in size and complexity, they tend to become hard to keep free from mistakes.In this seminar, we propose reinforcement learning algorithms as a step forward to achieve repair of broken models allowing both customization of results and automation at the same time. As a proof of concept, we have built a bridge between model repairing and reinforcement learning, designed a system of rewards to support customization of results and implemented a concrete scenario of model repairing. Although we have applied our approach to models defined in the Eclipse Modeling Framework (EMF), the approach is general enough to be extended to other modeling frameworks.We have validated our research by repairing a large set of broken models generated with a mutation tool.

Personalization, customization and transfer learning In the model repairing field there are tools providing automatic repairing of models.Some of them support user personalization, but none stores information from the repairing process.Reusing experience from past repairings would help to avoid duplicated calculations when facing repeated personalization preferences.Our tool PARMOREL, uses reinforcement learning algorithms as a step forward to achieve repair of broken models allowing both personalization of results and automation at the same time. We propose transfer learning as an approach to reuse the experience learned from each execution of PARMOREL.We have built a theoretical approach to support transfer learning in PARMOREL.Also, we have validated our research by repairing a model using different sets of preferences and studying how our tool’s performance evolved when reusing the experience from each repairing.


Using metrics for calibration of rewardsAs part of the seminar, moreover, we present a proposal for integrating quality assurance into PARMOREL.We describe an architecture that would allow PARMOREL to learn to automatically repair models with the highest quality possible.

Seminar by Luca Berardinelli on “Model-Driven Engineering in Practice at Braintribe: tools, challenges, and collaborations”

When: Tuesday, April 16th, 11AM

Where: Meeting Room, “Alan Turing” building

Title: Model-Driven Engineering in Practice at Braintribe: tools, challenges, and collaborations.   


Summary: Braintribe (https://www.braintribe.com) is a SME founded in 2005, with around 70 employees, located in Vienna, Zurich, Belgrade, Bratislava, London, Sao Paolo, and Frankfurt. Braintribe provides two main products, Tribefire and Datapedia that are horizontal, cross-domain modeling/metamodeling and asset (e.g., COTS) sharing platforms based on models and data normalisation approaches.

Their core component, namely Cortex, defines a new technical space in the MDE domain. This seminar aims at showing the platform for the first time to the academy and to MDE experts to discuss tool capabilities and limitations, shape challenges for a wise adoption of MDE principles and best practises, and promote future collaborations.

Speaker:Luca Berardinelli is a PhD in Computer Science. He works as a Technical Lead at BrainTribe, with particular focus on Model/Data integration, with special focus on Smart Cities and Smart Factories.

He received his Ms and PhD degrees in Computer Science from the University of L’Aquila, Italy. He has been a postdoc at the Univ. of L’Aquila (2011-15) and TU Wien (2015-18) working on Model-Driven Engineering (non-functional analyses, context and uncertainty modeling) in many European (PLASTIC,   VISION, PRESTO,  CRAFTERS, U-TEST) and national projects (MAE4ASE/SysML4Industry), participating as representative of his research departments to intermediate and final review meetings.

LinkedIn: https://www.linkedin.com/in/lucaberardinelli/ Research Gate:  https://www.researchgate.net/profile/Luca_Berardinelli

Seminars Prof. Carlo Fischione, Monday 15 and Tuesday 16, from 2 pm to 6 pm

Prof. Carlo Fischione (Royal Institute of Technology – KTH, Sweden) will give two classes on Monday 15 and Tuesday 16, from 2 pm to 6 pm in Aula Seminari DISIM (Alan Turing Building), on the following topics:

Title: Fundamentals of Machine Learning over Networks

Lecturer: Prof. Carlo Fischione

Abstract: This course covers fundamentals of machine learning over networks (MLoNs). It starts from a conventional single-agent setting where one server runs a convex/nonconvex optimization problem to learn an unknown function. We introduce several approaches to address this seemingly, simple yet fundamental, problem. We introduce an abstract form of MLoNs, present centralized and distributed solution approaches to address this problem, and exemplify via training a deep neural network over a network. The course covers various important aspects of MLoNs, including optimality, computational complexity, communication complexity, security, large-scale learning, online learning, MLoN with partial information, and several application areas. As most of these topics are under heavy researches nowadays, the course is not based on a single textbook but builds on a series of key publications in the field. 

Bibliography

[1]        Bubeck, Sébastien. “Convex optimization: Algorithms and complexity.” Foundations and Trends in Machine Learning, vol. 8, no.3-4 (2015): 231-357.
[2]        L. Bottou, F. Curtis, J. Norcedal, “Optimization Methods for Large-Scale Machine Learning”, SIAM Rev., 60(2), 223–311.
[3]        Boyd, Stephen, et al. “Distributed optimization and statistical learning via the alternating direction method of multipliers.” Foundations and Trends in Machine learning 3.1 (2011): 1-122.
[4]        Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, MIT press 2016
[5]        Jordan, Michael I., Jason D. Lee, and Yun Yang. “Communication-efficient distributed statistical inference,” Journal of the American Statistical Association, 2018.
[6]        Smith, Virginia, et al. “CoCoA: A general framework for communication-efficient distributed optimization.” Journal of Machine Learning Research 18 (2018): 230.
[7]        Alistarh, Dan, et al. “QSGD: Communication-efficient SGD via gradient quantization and encoding.” Advances in Neural Information Processing Systems. 2017.
[8]        Schmidt, Mark, Nicolas Le Roux, and Francis Bach. “Minimizing finite sums with the stochastic average gradient.” Mathematical Programming 162.1-2 (2017): 83-112.
[9]        Boyd, Stephen, et al. “Randomized gossip algorithms,” IEEE Transactions on Information Theory, 2006.
[10]     Scaman, Kevin, et al. “Optimal algorithms for smooth and strongly convex distributed optimization in networks,” ICML, 2017.