Parsing With Clause and Intra-clausal Coordination Detection

We present a new dependency parsing algorithm based on the decomposition of large sentences into smaller units such as clauses and intraclausal coordinations. For the identification of these units, new methods combining machine learning techniques and heuristic rules were developed. The algorithm was evaluated on the Slovene dependency treebank text corpus. Compared to the MSTP parser, currently the most accurate for Slovene, parsing accuracy was improved by 1.27 percentage points, which equals 6.4 % relative error reduction

Intelligent System for Playing Tarok

We present an advanced intelligent system for playing three-player tarok card game. The system is based on alpha-beta search with several enhancements such as fuzzy transposition table, which clusters strategically similar positions into generalised game states. Unknown distribution of other players’ cards is addressed by Monte Carlo sampling. Experimental results show an additional reduction in size of expanded 9-ply game-tree by a factor of 184. Human players judge the resulting program to play tarok reasonably well

Parsing Aided by Intra-Clausal Coordination Detection

Proceedings of the Sixth International Workshop on Treebanks and Linguistic Theories. Editors: Koenraad De Smedt, Jan Hajič and Sandra Kübler. NEALT Proceedings Series, Vol. 1 (2007), 79-84. © 2007 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/4476

Discovering Strategic Behaviour of Multi-Agent Systems in Adversary Settings

Can specific behaviour strategies be induced from low-level observations of two adversary groups of agents with limited domain knowledge? This paper presents a domain-independent Multi-Agent Strategy Discovering Algorithm (MASDA), which discovers strategic behaviour patterns of a group of agents under the described conditions. The algorithm represents the observed multi-agent activity as a graph, where graph connections correspond to performed actions and graph nodes correspond to environment states at action starts. Based on such data representation, the algorithm applies hierarchical clustering and rule induction to extract and describe strategic behaviour. The discovered strategic behaviour is represented visually as graph paths and symbolically as rules. MASDA was evaluated on RoboCup. Both soccer experts and quantitative evaluation confirmed the relevance of the discovered behaviour patterns

A Novel Way of Using Simulations to Support Urban Security Operations

The growing importance of security operations in urban terrain has triggered many attempts to address the perceived gaps in the readiness of security forces for this type of combat. One way to tackle the problem is to employ simulation techniques. Simulations are widely used to support both mission rehearsal and mission analysis, but these two applications tend to be seen as distinctly separate. We argue that integrating them in a unified framework can bring significant benefits for end-users. We perform a structured walk-through of such a unified system, in which a novel approach to integration through the behaviour cloning enabled the system to capture the operational knowledge of security experts, which is often difficult to express verbally. This capability emerged as essential for the operation of the integrated system. We also illustrate how the interplay between the system components for the mission analysis and mission rehearsal is realized

Competitive Live Evaluation of Activity-recognition Systems

In order to ensure the validity and usability of activity recognition approaches, an agreement on a set of standard evaluation methods is needed. Due to the diversity of the sensors and other hardware employed, designing and accepting standard tests is a difficult task. This article presents an initiative to evaluate activity recognition systems: a living-lab evaluation established through an annual competition − EvAAL-AR (Evaluating Ambient Assisted Living Systems through Competitive Benchmarking − Activity Recognition). In the competition, each team brings their own activity-recognition system, which is evaluated live on the same activity scenario performed by an actor. The evaluation criteria attempt to capture the practical usability: recognition accuracy, user acceptance, recognition delay, installation complexity, and interoperability with ambient assisted living systems. The article also presents the competing systems with emphasis on two best-performing ones: (i) a system that achieved the best recognition accuracy, and (ii) a system that was evaluated as the best overall. Finally, the article presents lessons learned from the competition and ideas for future development of the competition and of the activity recognition field in general

Holistic health record for Hidradenitis suppurativa patients.

Hidradenitis suppurativa (HS) is a recurrent inflammatory skin disease with a complex etiopathogenesis whose treatment poses a challenge in the clinical practice. Here, we present a novel integrated pipeline produced by the European consortium BATMAN (Biomolecular Analysis for Tailored Medicine in Acne iNversa) aimed at investigating the molecular pathways involved in HS by developing new diagnosis algorithms and building cellular models to pave the way for personalized treatments. The objectives of our european Consortium are the following: (1) identify genetic variants and alterations in biological pathways associated with HS susceptibility, severity and response to treatment; (2) design in vitro two-dimensional epithelial cell and tri-dimensional skin models to unravel the HS molecular mechanisms; and (3) produce holistic health records HHR to complement medical observations by developing a smartphone application to monitor patients remotely. Dermatologists, geneticists, immunologists, molecular cell biologists, and computer science experts constitute the BATMAN consortium. Using a highly integrated approach, the BATMAN international team will identify novel biomarkers for HS diagnosis and generate new biological and technological tools to be used by the clinical community to assess HS severity, choose the most suitable therapy and follow the outcome.This work was supported by a Biomolecular Analyses for Tailored Medicine in AcneiNversa (BATMAN) project, funded by ERA PerMed (JTC_2018) through the Italian Ministry of Health, the “Fondazione Regionale per la Ricerca Biomedica” (FRRB), the Slovenian Ministry of Education, Science, and Sport (MIZŠ), the Austrian Science fund (I 4229), the Federal Ministry of Education and Research Germany (BMBF), and ANR automate (ANR-20-CE15-0018-01). This work was also supported by and by a grant from the Institute for Maternal and Child Health IRCCS ‘Burlo Garofolo/Italian Ministry of Health (RC16/2018) and by a Starting Grant (SG-2019-12369421) founded by the Italian Ministry of Health. Figures were created with BioRender.com