Giovambattista Ianni | University of Calabria, Italy | Wednesday, February 19th, 2020 | 14:00 s.t. | room S.1.42

Abstract:
The availability of advanced features for controlling technical aspects of grounding engines and solver modules for the answer set semantics allows great flexibility and enables the possibility to scale in otherwise out of reach applications. Nonetheless, the presence of nonobvious heuristic tuning „knobs“ deepens and widens the gap between knowledge representation technologies and software developers. In this talk, we present our recent introduction of incremental grounding techniques for answer set solvers in the perspective of videogame development, a challenging applicative domain in which design-time fast prototyping and run-time speed are at the highest priority.

Same as in-stream reasoning, videogames require repeated and fast-paced executions of decision-making tasks. In this context, we illustrate an incremental grounding approach for the answer set semantics. We focus on the possibility of maintaining incrementally larger ground logic programs; so-called „overgrounded programs“ can be generated, updated and reused transparently to the user and in combination with deliberately many different sets of inputs. The update burden of overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repeated on a series of inputs similar to each other.

CV: Giovambattista Ianni is a full professor of Computer Science in the Department of Mathematics and Computer Science at the University of Calabria, Italy. Prof. Ianni’s current research interests include knowledge representation, reasoning, and coupling of hybrid systems. He recently focused his research interests on Artificial Intelligence in videogames with particular attention to the issue of complex and time-consuming incremental reasoning in real-time contexts. He has contributed to the DLV system and the DLVHEX system, especially dealing with the issue of dealing with, often non-symbolic, external information to knowledge bases. He has been involved in several national and international research projects and has been acknowledged with research awards such as the ICLP Test-of-time award 2018 and the Artificial Intelligence Journal Prominent Paper Award 2013.

Björn Thór Jónsson, Associate Professor

IT University of Copenhagen, Denmark & Reykjavik University, Iceland

02.03.2020 | 10.00 | S.2.42

Abstract:

Scalable retrieval of high-dimensional feature vectors is an important component of many applications in multimedia and other fields, but also a very challenging problem. In this talk, we discuss the challenges of high-dimensional indexing at scale, and then present two approximate indexing methods designed for large-scale retrieval. We present results from experiments with the two largest feature collections reported in the literature, 28.5 billion SIFT features on a single server and 42.9 billion SIFT features in a distributed setting, and demonstrate an application with interactive retrieval over the 99.2 million images of the YFCC100M collection.

CV:

Björn Þór Jónsson is an Associate Professor at the IT University of Copenhagen, Denmark, and Reykjavík University, Iceland. Björn works in the broad field of Multimedia Analytics, applying multi-dimensional analysis concepts and techniques to large-scale multimedia collections.

Previously, Björn studied scalability of multimedia retrieval, where he was involved with the two largest feature vector collections reported in the literature. Björn has a special interest in promoting demonstrations, live events, and reproducibility, e.g. serving as Reproducibility Chair for ACM Multimedia 2019 and 2020. He served as general co-chair for MMM 2017 and CBMI 2019, and will co-organize ACM ICMR 2020 and SISAP 2020.

Prof. Daniele Fontanelli (Università degli Studi di Trento) 

24.01.2020 | 09.00 | B04a.1.06 (Lakeside Park)

Abstract:

Service robots are becoming more and more pervasive in modern societies. One of the ever increasing field of application are service robots able to help seniors in their daily duties. Indeed, ageing is generally associated with a decrease in mobility and social interaction: a growing body of research suggests that reduced levels of out-of-home mobility can have widespread, detrimental effects for older adults. With the median age in Europe projected to grow from 37.7 (2003) to 52.3 (2050), the population asking for mobility aids at an affordable price is becoming substantial.

In this talk, we briefly introduce our solution conceived for autonomous mobility: the FriWalk (i.e. Friendly Walker). Stemming from this example, we will present the fundamental problems for autonomous robots, i.e. localization, planning and control, with application-related scenarios. In particular, we will focus on three aspects of the technological solutions: the localisation problem using different low-cost sensing solutions, together with an optimal landmarks placement algorithm; the set of controlled guidance solutions implementing the authority sharing paradigm and modelled as hybrid systems; the activity and reactive planning approaches in actual application scenarios.

CV:

Daniele Fontanelli received the MSc degree in Information Engineering in 2001, and the Ph.D. degree in Automation, Robotics and Bioengineering in 2006, both from the University of Pisa, Italy.  He was a Visiting Scientist with the Vision Lab of the University of California at Los Angeles, US, and an Associate Researcher with the Interdepartmental Research Center „E. Piaggio“, University of Pisa.  From 2008 he joined the University of Trento, Italy, where he is now an Associate Professor at the Department of Industrial Engineering.   He is currently an Associate Editor for the IEEE Transactions on Instrumentation and Measurement and for the IET Science, Measurement & Technology Journal.His research interests include localisation algorithms, service robotics, motion planning, human motion modelling, real-time control and estimation, and resource aware control.

Assoc.-Prof. Rui Abreu (Universität Lissabon) | 19.12.2019 | 10:00 Uhr | S.1.37

Abstract:

In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 2,141 bugs from 5 benchmarks. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts, which we used to find causes of non-patch generation. These causes are reported in this presentation, which can help repair tool designers to improve their approaches and tools. This work was presented at ESEC/FSE19 and was given an ACM SIGSOFT Distinguished Paper Award.

CV:

Rui Abreu holds a Ph.D. in Computer Science – Software Engineering from TU Delft, The Netherlands, and a M.Sc. in Computer and Systems Engineering from the U.Minho, Portugal. His research revolves around software quality, with emphasis on automating the testing and debugging phases of the software development life-cycle as well as self-adaptation. He is the recipient of 7 Best Paper Awards (including a 2019 FSE  Distinguished Paper Award). Before joining IST, U.Lisbon as an Associate Professor (with habilitation), he was a member of the Model-Based Reasoning group at PARC’s System and Sciences Laboratory. He has co-founded DashDash in January 2017, a platform to create web apps using only spreadsheet skills. Currently, he is enjoying a sabbatical leave as a Visiting Scientist at Google NY’s. He is also passionate about soccer and a FC Porto fan.