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

Биомаркери на воÑпаление кај деца Ñо туберкулоза

The aim of this  study was to evaluate  the efficacy of using  appropriate biomarkers of inflammation for diagnosis of tuberculosis in children, monitoring the disease and the effects of the applied  therapy.Material and methods: In this  study, 60 patients were included and divided into  two groups: control group  of 40 healthy children and a group of 20 children with tuberculosis. The subjects from both  groups were aged from 1 to 15 years, matched by age. The children with tuberculosis were hospitalized for diagnostic and therapeutic treatment. None of them received corticosteroid therapy before treatment. Blood samples from patients with tuberculosis were taken  just  before  the therapy and after 2 months. The concentration of three biomarkers close-related with the evolution of the disease was analyzed in blood sera: tumor necrosis factor alpha (TNF-α), C-reactive  protein  (CRP) and procalcitonin (PCT). Serum  concentrations of TNF-a were  measured  in  vitro using IMMULITE 1000 analyzer  for quantitative measurement of TNF-α in serum  (EIA). Determination of CRP and PCT in serum  was done with the quantitative method (ELFA) using i-CHROMA - immune analyzer.Results: A statistically significant difference was found in the concentrations of TNFa serum before and after treatment  (18.54 ± 1.94  v.s. 10.02± 0.640 pg/ml, p<0.05), СRP before  and after therapy (99.59 ± 9.23 v.s 9.13 ± 1.13 mg/L,  p<0.05), and РСТ (0.31 ± 0.02 v.s. 0.04 ± 0,01 ng/ml, p<0.05).  Conclusion: The results showed that tested biomarkers of inflammation are important for early diagnosis and monitoring the effects of the anti-tuberculosis therapy.Целта на трудот беше да Ñе оцени ефикаÑноÑта на употребата на Ñоодветни биомаркери на воÑпаление за дијагноза на туберкулозата кај деца, Ñледење на болеÑта и ефектите од применетата терапија.Материјал и методи: Во рамките на оваа иÑтражувачка Ñтудија беа вклучени 60 иÑпитаници, поделени во две групи: контролна група од 40 здрави деца и група од 20 деца Ñо туберкулоза. ИÑпитаниците од двете групи беа на возраÑÑ‚ од 1 до 15 години, компарабилни по возраÑÑ‚. Децата Ñо туберкулоза беа хоÑпитализирани  Ð¿Ð¾Ñ€Ð°Ð´Ð¸ дијагноÑтички и терапевтÑки третман. Ðиту едно од нив не примаше кортикоÑтероидна терапија пред третманот. Крв за анализа кај пациентите Ñо туберкулоза беше земена непоÑредно пред започнувањето Ñо терапијата и по 2 меÑеца. Беа анализирани три биомаркери во крвниот Ñерум кои Ñе во теÑна релација Ñо еволуцијата на болеÑта: тумор некрозниот фактор алфа (TNF-α), С- реактивниот протеин (CRP) и прокалцитонин (PCT) во крвниот Ñерум. СерумÑките концентрации  Ð½Ð° TNF-a беа одредувани in vitro Ñо IMMULITE 1000 анализаторот за квантитативно мерење на TNF-α во Ñерум (EIA). Одредувањето на CRP и РСТ во Ñерум Ñе правеше Ñо квантитативната метода (ELFA), на i-CHROMA имунолошки анализатор.Ре зултати: Беше утврдена ÑтатиÑтички  Ñигнификантна разлика во ÑерумÑката концентрација на TNFa пред и по терапијата (18,54 ± 1,94 v.s. 10,02 ± 0,64 pg/ml,  p<0,05), на СRP пред и по терапијата (99,59 ± 9,23 v.s 9,13 ± 1,13 mg/L,  p<0,05), и на РСТ (0,31 ± 0,02 v.s. 0,04 ± 0,01 ng/ml,  p<0,05).Заклучок: Резултатите покажаа дека иÑпитуваните биомаркери на инфламација кои беа одредувани Ñе значајни за рано дијагноÑтицирање  Ð¸ Ñледење на ефектите од антитуберкулозната терапија

Detecting Falls with Location Sensors and Accelerometers

Due to the rapid aging of the population, many technical solutions for the care of the elderly are being developed, often involving fall detection with accelerometers. We present a novel approach to fall detection with location sensors. In our application, a user wears up to four tags on the body whose locations are detected with radio sensors. This makes it possible to recognize the user’s activity, including falling any lying afterwards, and the context in terms of the location in the apartment. We compared fall detection using location sensors, accelerometers and accelerometers combined with the context. A scenario consisting of events difficult to recognize as falls or non- falls was used for the comparison. The accuracy of the methods that utilized the context was almost 40 percentage points higher compared to the methods without the context. The accuracy of pure location-based methods was around 10 percentage points higher than the accuracy of accelerometers combined with the context

Recognising lifestyle activities of diabetic patients with a smartphone

Diabetes is both heavily affected by the patients' lifestyle, and it affects their lifestyle. Most diabetic patients can manage the disease without technological assistance, so we should not burden them with technology unnecessarily, but lifestyle-monitoring technology can still be beneficial both for patients and their physicians. Because of that we developed an approach to lifestyle monitoring that uses the smartphone, which most patients already have. The approach consists of three steps. First, a number of features are extracted from the data acquired by smartphone sensors, such as the user's location from GPS coordinates and visible wi-fi access points, and the physical activity from accelerometer data. Second, several classifiers trained by machine learning are used to recognise the user's activity, such as work, exercise or eating. And third, these activities are refined by symbolic reasoning encoded in Event Calculus. The approach was trained and tested on five people who recorded their activities for two weeks each. Its classification accuracy was 0.88