Model repair techniques aim at automatically updating a process model to incorporate behaviors that are observed in reality but are not compliant with the original model. Most state-of-the-art techniques focus on the fitness of the repaired models, with the goal of including single anomalous behaviors observed in a log in the form of the events. This often hampers the precision of the obtained models, which end up allowing much more behaviors than intended. In the quest of techniques avoiding this over-generalization pitfall, some notion of higher-level anomalous structure is taken into account. The type of structure considered is however typically limited to sequences of low-level events. In this work, we introduce a novel repair approach targeting more general high-level anomalous structures. To do this, we exploit instance graph representations of anomalous behaviors, that can be derived from the event log and the original process model. Our experiments show that considering high-level anomalies allows to generate repaired models that incorporate the behaviors of interest while maintaining precision and simplicity closer to the original model.

This demonstration presents a smart wearable armband for hand gesture recognition interfaced with a 6-DOF robotic arm which actuates the user's movements. The armband is composed of seven modules which detect the muscular activity beneath them, fuse the data together, predict the performed gesture, and transmit the high-level information to an external computer via a Bluetooth Low Energy (BLE) communication. There, a software module transforms the sequence of gestures into consistent commands for the robotic arm. The observed responsiveness and accuracy make this armband suitable for the real-time control of robotic limbs in mixed reality scenarios.

We present ConArgLib, a C++ library implemented to help programmers solve some of the most important problems related to extension-based abstract Argumentation. The library is based on ConArg, which exploits Constraint Programming and, in particular, Gecode, a toolkit for developing constraint-based systems and applications. Given a semantics, such problems consist, for example, in enumerating all the extensions, and checking the credulous or sceptical acceptance of an argument passed as parameter. The goal is to let programmers use the library to quickly develop programs on top of it, as, for instance, implementing decision-making procedures based on the strongest arguments, or comparing two frameworks by looking at the differences between their (e.g., stable) semantics. The library features the possibility to use different branching strategies, which we all test and compare on a set of frameworks taken from the International Competition on Computational Models of Argumentation (ICCMA17). Moreover, for some of the tasks, it is possible to perform a parallel search using several workers at the same time: we test the speed-up between using from 1 to 16 threads on a set of ICCMA17 frameworks.

The demonstration presents a surface ElectroMyoGraphy (sEMG) low power wireless system used to control Functional Electrical Stimulation for rehabilitative neuro-muscular applications. An event-driven technique is applied to the bio-signal, minimising power consumption, complexity, and transmitted data. The acquisition board transmits four channels sEMG event data, through a Bluetooth Low Energy wireless module, to a workstation where they are processed in order to control the stimulation pattern of a commercially available functional stimulator. We will show system functionality and efficiency during the execution of some basic functional movements.

This paper presents a minimum complexity hand movement recognition algorithm based on Average Threshold Crossing (ATC) technique. It exploits the number of threshold-crossing events, generated by a full-custom acquisition board, from the surface ElectroMyoGraphic (sEMG) signals of three forearm muscles to detect four different movements of the wrist: flexion, extension, abduction and grasp. A Support Vector Machine (SVM) model has been trained with the signals acquired from ten subjects, who repeated ten times each gesture. To avoid correlation between training and testing dataset, the Leave One Subject Out (LOSO) cross-validation technique has been chosen. The average ATC classifier's accuracy is 92.87 %, only 5.34 % below the results obtained feeding the same model with the sEMG features extracted from the raw sampled signals. The total latency of the algorithm, from the acquisition to the prediction, is 160 ms. Power consumption was considered too: with less than the power budget for one sampled sEMG channel, it is possible to acquire and transmit (through a Bluetooth low energy module) the event-driven data of four sEMG channels, with an effective data rate of only 28B/s. Obtained performance makes this technique suited for wearable systems or Internet-of-Things (IoT) applications.

We test different solvers dedicated to the solution of classical problems in Abstract Argumentation, as enumeration/existence of extensions, and sceptical/credulous acceptance of arguments. We handle a subset of the solvers tested in ICCMA15, and a superset of graphs used in the same competition. The goal is to provide considerations that can help future comparisons and competitions as ICCMA15. We offer a detailed report of this comparison from the point of view of different graphs, solvers, problems and timeouts. We show that the characteristics of graphs impact on the performance of solvers and on their final ranking. In addition, we extract other general considerations, e.g., reducing the computation timeout does not change the same ranking.

We present ConArgLib, a C++ library implemented to help programmers solve some of the most important problems related to extension-based Abstract Argumentation. The library is based on ConArg, which exploits Constraint Programming and, in particular, Gecode, a toolkit for developing constraintbased systems and applications. Given a semantics, such problems consist, for example, in enumerating all the extensions, and checking the credulous or sceptical acceptance of an argument passed as parameter. The goal is to let programmers use the library to quickly develop programs on top of it, as, for instance, implementing decision-making procedures based on the strongest arguments, or comparing two frameworks by looking at the differences between their (e.g., stable) semantics.

ConArg is a tool for solving different problems related to extension-based semantics: e.g., enumeration of extensions, sceptical and credulous acceptance of arguments. We have extended it in order to deal with Weighted Abstract Argumentation Frameworks, where each attack is associated with a strength score. Classical notions of defence and conflict-freeness have been redefined with the purpose to have different (weighted) degrees of their relaxation. The ultimate aim is to let an agent choose between a higher internal consistency or a stronger defence.

We compare four different implementations of reasoning-tools dedicated to Abstract Argumentation Frameworks. These systems are ArgTools, ASPARTIX, ConArg2, and Dung-O-Matic. They have been tested over three different models of randomly-generated grap

In this paper we test four different implementations of reasoning tools dedicated to Abstract Argumentation Frameworks. These systems are ASPARTIX, dynPARTIX, Dung-O-Matic, and ConArg2. The tests are executed over three different models of randomly-generated graphs, i.e., the Erdős-Rényi model, the Kleinberg small-world model, and the scale-free Barabasi-Albert model. We compare these four tools with the purpose to test the search of all the possible stable extensions. Then we benchmark dynPARTIX and ConArg2 on the credulous and skeptical acceptance of arguments. Finally, we also evaluate ConArg2 to check the existence of a stable extension.

We compare three different implementations of reasoning tools dedicated to Abstract Argumentation Frameworks. These systems are ASPARTIX, ConArg2, and Dung-O-Matic. They have been tested over three different random graph-models, corresponding to the Erdös-Rényi model, Kleinberg small-world model, and scale-free Barabasi model.

We provide an extensive testing on how efficiently state-of-the art solvers are capable of solving credulous and sceptical argument-acceptance for lower-order extensions. In fact, as our benchmark we consider three different random graph-models to represent random Abstract Argumentation Frameworks: Barabasi and Erdos-Renyi networks, and, in addition, we also rework balanced trees by randomise their structure, with the purpose to obtain random trees of different height. Therefore, we test two reasoners, i.e., Con Arg2 and dyn PARTIX, on such benchmark, by comparing their performance on NP/co-NP-complete decision problems related to argument acceptance in admissible, complete, and stable semantics.