Learning to Detect Complex Events with Expert Advice

Systems for symbolic event recognition detect occurrences of events in streaming input using a set of event patterns in the form of temporal logical rules. Algorithms for online learning/revising such patterns should be capable of updating the current event pattern set without compromising the quality of the provided service, i.e. the system’s online predictive performance. Towards this, we present an approach based on Prediction with Expert Advice. The experts in our approach are logical rules representing event patterns, which are learnt online via a single-pass strategy. To handle the dynamic nature of the task, an Event Calculus-inspired prediction/event detection scheme allows to incorporate commonsense principles into the learning process.We present a preliminary empirical assessment with promising results

Predicting the Evolution of Communities with Online Inductive Logic Programming

In the recent years research on dynamic social network has increased, which is also due to the availability of data sets from streaming media. Modeling a network\u27s dynamic behaviour can be performed at the level of communities, which represent their mesoscale structure. Communities arise as a result of user to user interaction. In the current work we aim to predict the evolution of communities, i.e. to predict their future form. While this problem has been studied in the past as a supervised learning problem with a variety of classifiers, the problem is that the "knowledge" of a classifier is opaque and consequently incomprehensible to a human. Thus we have employed first order logic, and in particular the event calculus to represent the communities and their evolution. We addressed the problem of predicting the evolution as an online Inductive Logic Programming problem (ILP), where the issue is to learn first order logical clauses that associate evolutionary events, and particular Growth, Shrinkage, Continuation and Dissolution to lower level events. The lower level events are features that represent the structural and temporal characteristics of communities. Experiments have been performed on a real life data set form the Mathematics StackExchange forum, with the OLED framework for ILP. In doing so we have produced clauses that model both short term and long term correlations

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Interactive extreme-scale analytics: towards battling cancer

Summarization: A synergetic understanding of cancer evolution and the effect of combination drug therapies on the disease is the cornerstone for developing effective personalized treatments, which can radically improve patients' well-being and their quality of (work and social) life. By extension, improving the treatment of patients indirectly enhances the quality of life for families, friends, and careers. Moreover, personalizing effective therapeutic approaches reduces treatment duration, cutting down healthcare monetary costs, which can be redirected to other health and social services. Given that three out of four U.S. families will at some point experience a family member suffering from cancer (http://natamcancer.org/NAP_Native_American_Priorities.pdf), the potential impact of improved cancer treatment is of considerable socio-economic and organizational significance.Παρουσιάστηκε στο: IEEE Technology and Society Magazin