The last decade was marked by rapid growth and development of technology. One example of that is the automotive industry. This industry has made an enormous progress, and its main goal is to achieve safer and better driving. The vehicle incorporates GPS devices that send information about the current location and speed of the vehicle. Large amounts of collected data can be used in companies for tracking vehicles and various analysis and statistics. Sometimes, however, GPS data is not accurate. In this paper, the potential of real data sets will be used to analyze possible anomalies that may occur when reading GPS position of vehicles. The approach for solving this problem used in this paper consists of calculating distance and time, based on GPS measurements, then calculating average speed based on these two values, and comparing that speed with the speed given by GPS device.

Paper illustrates the process of topic modeling and text classification. Specifically, the dataset used is a corpus consisting of scientific publications published by Neural Information Systems Processing Conference. Topic modeling itself is performed using Latent Dirichlet Allocation model. It is followed by optimization of a number of topics on the basis of topic coherence, a quality measure of human interpretability. Results of topic modeling are used for labeling data prior to text classification. Labels are determined based on the distribution of assigned papers' topics over time. Specifically, peak changes used for differentiating between time periods dominated by specific topics are calculated as a Kullback-Leibler divergence. Finally, transforming data into the feature vectors, several different text classification approaches are evaluated. As observed, the greatest accuracy score is recorded for the use of extreme gradient boosting classifier being 77.1%.

In this work we will show a novel method for improving the performance of content-based image retrieval using a deep neural network. The main focus of the method is training the distance function using the deep neural network and transfer learning. An existing pretrained network for image classification is used as a basis. One of the method's benefits is the fact that the existing network is not retrained and features for the distance function are the neural network's layers which were trained and stored before. The method is tested with the publicly available VGG19 deep neural network. Obtained results are comparable or in some cases better than the state of the art methods with a similar approach.

Abstract The aim of the paper is to reveal that humour processing is an exertion which requires both, mental and emotional capacities. To prove the point, two theories for humour processing were employed: the conceptual integration theory and the benign violation theory. The paper shows that theories, though different, possess certain common elements and represent useful tools in humour processing. The conceptual integration theory, with its input spaces, blending processes and generic space, together with the benign violation theory and its detection of humorous elements within tragic situations, could be valuable assets in understanding how people find humour in intimidating and life-threatening situations. The paper also sheds some light on how different age groups perceive black humour showing that age and life experience facilitate understanding of black humour.

Introduction: Neurophysiological tests allow accurate assessment of the function of the peripheral nervous system. Detection of neurophysiological changes allows us to understand the neurological clinical symptoms and signs of patients with type 1 and type 2 diabetes and the possibility for their symptomatic treatment. Aim: Evaluate the effect of diabetes mellitus on the “cutaneous silent period” in detecting diabetic polyneuropathy. Material and Methods: The study included 150 subjects, 90 suffering from diabetes, divided into three groups of 30, depending on the disease duration, and a control group of 60 respondents not suffering from diabetes or other polyneuropathies. The control group are referred for EMG analysis on another basis (cervical radiculopathy, brachialgia, etc.). Group 1 consisted of 30 subjects with diabetes mellitus type 2 and duration of illness up to 5 years. Group 2 consisted of 30 subjects with type 2 diabetes mellitus 2 and illness duration from 5 to 10 years. Group 3 consisted of 30 patients with type 1 diabetes mellitus. The study groups consisted of patients referred for EMNG analysis to the EMG office of the Clinical Center of Sarajevo University, Neurology Clinic and the Neurophysiology Laboratory in Ljubljana, from July 1, 2011 to May 1, 2016. All patients were examined neurologically and electroneurographic analysis was performed. Results: A statistically significant difference was found in the incidence of pathologic CSP with respect to the study groups, χ2 = 26.153; p=0.001. Pathologic CSP was more common in group 1 and group 2 of subjects (56.17%) compared to group 3 and control subjects, where it occurred in 13.3% of the cases. Conclusion: The pathological cutaneous period of silence was more frequent in subjects of group 1 and group 2, that is, in subjects with DM type 2, compared to subjects with DM type 1.

Group recommender systems generate recommendations for a group by aggregating individual members’ preferences and finding items that are liked by most of the members. In this paper we introduce a new approach to preference aggregation and group choice prediction that is based on a new form of weighting individuals’ preferences. The approach is based on network science, and, in particular, it relies on the computation of node centrality scores in preferences similarity networks of groups. We also motivate and introduce a non-linear (exponential) remapping of the individuals’ preferences. Based on offline experiments we demonstrate: 1) non-linear remapping of preferences is useful to better predict group choices and generate recommendations; and 2) our weighted approach predicts the actual group choices more accurately than current state-of-the-art methods for group recommendations.CCS CONCEPTS• Information systems → Recommender systems; • Humancentered computing → User studies; User models; Social network analysis.

Smart Ecosystem reflects in the control decisions of entities of different nature, especially of its software components. Particularly, the malicious behavior requires a more accurate attention. This paper discusses the challenges related to the evaluation of software smart agents and proposes a first solution leveraging the monitoring facilities for a) assuring conformity between the software agent and its digital twin in a real-time evaluation and b) validating decisions of the digital twins during runtime in a predictive simulation.

Teleoperation of multi-robot systems, e.g. dual manipulators, in cooperative manipulation tasks requires haptic feedback of multi-contact interaction forces. Classical haptic devices restrict the workspace of the human operator and provide only one contact point. An alternative solution is to enable the operator to command the robot system via free-hand motions which extends the workspace of the human. In such a setting, a multi-contact haptic feedback may be provided to the human through multiple wearable haptic devices, e.g. fingertip devices that display forces on the human fingertips. In this paper we evaluate the benefit of using wearable haptic fingertip devices to interact with a bimanual robot setup in a pick-and-place manipulation task. We show that haptic feedback through wearable devices improves task performance compared to the base condition of no haptic feedback. Therefore, wearable haptic devices are a promising interface for guidance of multi-robot manipulation systems.