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  1. Article ; Online: Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series.

    Andrienko, Natalia / Andrienko, Gennady / Artikis, Alexander / Mantenoglou, Periklis / Rinzivillo, Salvatore

    IEEE computer graphics and applications

    2024  Volume PP

    Abstract: Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we ... ...

    Abstract Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier. We evaluate our approach through a case study focused on detecting trawling activities in fishing vessel trajectories, demonstrating significant improvements in pattern recognition by leveraging domain knowledge and incorporating human reasoning and feedback. Our contribution is a novel framework that integrates human expertise and analytical reasoning with ML or AI techniques, advancing the field of data analytics.
    Language English
    Publishing date 2024-03-20
    Publishing country United States
    Document type Journal Article
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2024.3379851
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

    Katzouris, Nikos / Artikis, Alexander / Paliouras, Georgios

    2021  

    Abstract: Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty- ... ...

    Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER datasets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

    Comment: Under consideration in Theory and Practice of Logic Programming (TPLP)
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-03-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Early Time-Series Classification Algorithms

    Akasiadis, Charilaos / Kladis, Evgenios / Michelioudakis, Evangelos / Alevizos, Elias / Artikis, Alexander

    An Empirical Comparison

    2022  

    Abstract: Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. Such methods can be employed to obtain classification forecasts in many time-critical applications. However, ...

    Abstract Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. Such methods can be employed to obtain classification forecasts in many time-critical applications. However, available techniques are not equally suitable for every problem, since differentiations in the data characteristics can impact algorithm performance in terms of earliness, accuracy, F1-score, and training time. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets originating from the life sciences and maritime domains. Our goal is to provide a framework for the evaluation and comparison of ETSC algorithms and to obtain intuition on how such approaches perform on real-life applications. The presented framework may also serve as a benchmark for new related techniques.

    Comment: 18 pages, 11 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2022-03-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Complex Event Forecasting with Prediction Suffix Trees

    Alevizos, Elias / Artikis, Alexander / Paliouras, Georgios

    Extended Technical Report

    2021  

    Abstract: Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before ... ...

    Abstract Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting (CEF). Our framework combines two formalisms: a) symbolic automata which are used to encode complex event patterns; and b) prediction suffix trees which can provide a succinct probabilistic description of an automaton's behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. Our experimental results demonstrate the benefits, in terms of accuracy, of being able to capture such long-term dependencies. This is achieved by increasing the order of our model beyond what is possible with full-order Markov models that need to perform an exhaustive enumeration of all possible past sequences of a given order. We also discuss extensively how CEF solutions should be best evaluated on the quality of their forecasts.
    Keywords Computer Science - Databases ; Computer Science - Artificial Intelligence ; Computer Science - Formal Languages and Automata Theory ; F.4.3 ; G.3 ; I.2.6 ; I.2.4
    Subject code 006
    Publishing date 2021-09-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Symbolic Register Automata for Complex Event Recognition and Forecasting

    Alevizos, Elias / Artikis, Alexander / Paliouras, Georgios

    2021  

    Abstract: We propose an automaton model which is a combination of symbolic and register automata, i.e., we enrich symbolic automata with memory. We call such automata Symbolic Register Automata (SRA). SRA extend the expressive power of symbolic automata, by ... ...

    Abstract We propose an automaton model which is a combination of symbolic and register automata, i.e., we enrich symbolic automata with memory. We call such automata Symbolic Register Automata (SRA). SRA extend the expressive power of symbolic automata, by allowing Boolean formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers. SRA also extend register automata, by allowing arbitrary Boolean formulas, besides equality predicates. We study the closure properties of SRA under union, intersection, concatenation, Kleene closure, complement and determinization and show that SRA, contrary to symbolic automata, are not in general closed under complement and they are not determinizable. However, they are closed under these operations when a window operator, quintessential in Complex Event Recognition, is used. We show how SRA can be used in Complex Event Recognition in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics, and that allows for a systematic treatment of such automata. We also show how the behavior of SRA, as they consume streams of events, can be given a probabilistic description with the help of prediction suffix trees. This allows us to go one step beyond Complex Event Recognition to Complex Event Forecasting, where, besides detecting complex patterns, we can also efficiently forecast their occurrence.
    Keywords Computer Science - Formal Languages and Automata Theory ; Computer Science - Artificial Intelligence ; Computer Science - Databases
    Subject code 511
    Publishing date 2021-10-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Optimizing Vessel Trajectory Compression

    Fikioris, Giannis / Patroumpas, Kostas / Artikis, Alexander

    2020  

    Abstract: In previous work we introduced a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. This methodology can provide reliable trajectory synopses with little deviations ... ...

    Abstract In previous work we introduced a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. This methodology can provide reliable trajectory synopses with little deviations from the original course by discarding at least 70% of the raw data as redundant. However, such trajectory compression is very sensitive to parametrization. In this paper, our goal is to fine-tune the selection of these parameter values. We take into account the type of each vessel in order to provide a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. Furthermore, we employ a genetic algorithm converging to a suitable configuration per vessel type. Our tests against a publicly available AIS dataset have shown that compression efficiency is comparable or even better than the one with default parametrization without resorting to a laborious data inspection.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 006
    Publishing date 2020-05-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: A Formal Specification of Dynamic Protocols for Open Agent Systems

    Artikis, Alexander

    2010  

    Abstract: Multi-agent systems where the agents are developed by parties with competing interests, and where there is no access to an agent's internal state, are often classified as `open'. The member agents of such systems may inadvertently fail to, or even ... ...

    Abstract Multi-agent systems where the agents are developed by parties with competing interests, and where there is no access to an agent's internal state, are often classified as `open'. The member agents of such systems may inadvertently fail to, or even deliberately choose not to, conform to the system specification. Consequently, it is necessary to specify the normative relations that may exist between the agents, such as permission, obligation, and institutional power. The specification of open agent systems of this sort is largely seen as a design-time activity. Moreover, there is no support for run-time specification modification. Due to environmental, social, or other conditions, however, it is often required to revise the specification during the system execution. To address this requirement, we present an infrastructure for `dynamic' specifications, that is, specifications that may be modified at run-time by the agents. The infrastructure consists of well-defined procedures for proposing a modification of the `rules of the game', as well as decision-making over and enactment of proposed modifications. We evaluate proposals for rule modification by modelling a dynamic specification as a metric space, and by considering the effects of accepting a proposal on system utility. Furthermore, we constrain the enactment of proposals that do not meet the evaluation criteria. We employ the action language C+ to formalise dynamic specifications, and the `Causal Calculator' implementation of C+ to execute the specifications. We illustrate our infrastructure by presenting a dynamic specification of a resource-sharing protocol.
    Keywords Computer Science - Multiagent Systems
    Subject code 006
    Publishing date 2010-05-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book: Logic programs, norms and action

    Artikis, Alexander / Sergot, Marek J

    essays in honor of Marek J. Sergot on the occasion of his 60th birthday

    (Lecture notes in computer science : Festschrift ; 7360)

    2012  

    Author's details Alexander Artikis ... (eds.)
    Series title Lecture notes in computer science : Festschrift ; 7360
    Keywords Logischer Schluss ; Temporale Logik ; Logische Programmierung ; Normative Logik ; Deontische Logik ; Künstliche Intelligenz ; Argumentation
    Language English
    Size XX, 424 S., graph. Darst.
    Publisher Springer
    Publishing place Heidelberg u.a.
    Document type Book
    Note Literaturangaben
    ISBN 9783642294136 ; 9783642294143 ; 3642294138 ; 3642294146
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  9. Book ; Online: Wayeb

    Alevizos, Elias / Artikis, Alexander / Paliouras, Georgios

    a Tool for Complex Event Forecasting

    2018  

    Abstract: Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real-time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely ... ...

    Abstract Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real-time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Formal Languages and Automata Theory ; F.1.1
    Publishing date 2018-12-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Parallel Model Exploration for Tumor Treatment Simulations

    Akasiadis, Charilaos / Ponce-de-Leon, Miguel / Montagud, Arnau / Michelioudakis, Evangelos / Atsidakou, Alexia / Alevizos, Elias / Artikis, Alexander / Valencia, Alfonso / Paliouras, Georgios

    2021  

    Abstract: Computational systems and methods are being applied to solve biological problems for many years. Incorporating methods of this kind in the research for cancer treatment and related drug discovery in particular, is shown to be challenging due to the ... ...

    Abstract Computational systems and methods are being applied to solve biological problems for many years. Incorporating methods of this kind in the research for cancer treatment and related drug discovery in particular, is shown to be challenging due to the complexity and the dynamic nature of the related factors. Usually, there are two objectives in such settings; first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. We combine a multi-scale simulator for tumor cell growth and a Genetic Algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in a parallel manner on high performance computing infrastructures, since large-scale computational and storage capabilities are necessary in this domain. After using the GA for calibration, our goal is to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Results from experiments on high performance computing infrastructure illustrate the effectiveness and timeliness of the approach.

    Comment: 15 pages, 9 figures
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Quantitative Biology - Tissues and Organs
    Subject code 006
    Publishing date 2021-03-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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