DIGITAL SAFETY MANAGER: IOT SERVICE TO ASSURE THE SAFE BEHAVIOUR OF MACHINES AND CONTROLS IN THE DIGITAL INDUSTRY

The digital industry requires increasingly complex and reliable software systems. They need to control and make critical decisions at runtime. As a consequence, the verification and validation of these systems has become a major research challenge. At design and development time, model testing techniques are used while run-time verification aims at verifying that a system satisfies a given property. The latter technique complements the former. The solution presented in this paper targets embedded systems whose software components are designed by state machines defined by Unified Modelling Language (UML). The CRESCO (C++ REflective State-Machines based observable software COmponents) platform generates software components that provide internal information at runtime and the verifier uses this information to check system-level reliability/safety contracts. The verifier detects when a system contract is violated and initiates a safeState process to prevent dangerous scenarios. These contracts are defined by internal information from the software components that make up the system. Thus, as demonstrated in the tested experiment, the robustness of the system is increased. All software components (controllers), such as the verifier, have been deployed as services (producers/consumers) of the Arrowhead IoT platform: the controllers are deployed on local Arrowhead platforms (Edge) and the verifier (Safety Manager) is deployed on an Arrowhead platform (Cloud) that will consume controllers on the Edge and ensure the proper functioning of the plant controllers. Keywords: run-time monitoring, robustness, software components, contracts, software models, state machines

Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

<p class="Abstract"><span id="page629R_mcid43" class="markedContent"><span dir="ltr">Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.</span></span></p>

The Dilemma of War and Peace in the Trend of the XXI Century (Russian – Ukrainian case)

In the presented article the author asks how the essence of the relationship between such states of international relations as war and peace has changed under the influence of the trends of the XXI century. A clear empirical example for such an analysis was the modern Russian-Ukrainian war, a manifestation of which we see on the Donbass. This war was largely the result and manifestation of these new trends in international relations at both the regional and global levels. First of all, these trends and their destructive consequences are typical for the security sphere. From so the dilemma of war and peace takes on a new dimension and becomes one of the most pressing problems of the theory of war and peace and the theory of international relations. The purpose of this article is to understand how the essence of the relationship between such states of international relations as war and peace has changed under the influence of the 21st century trends. In order to properly investigate this problem, was chosen as an object, a striking manifestation of which we see on the Donbass. Research questions relate to changing approaches to understanding the dilemma of war and peace and the nature of the relationship between these states of international relations under the influence of the 21st century trends. To address this research challenge, a systematic review of contemporary research on various aspects of war and peace has been carried out. The answers are based on a study of the criteria for determining the state of war and peace and the determinants that influence the dynamics of change in these states. The study used deductive methods, comparative, political and conflict analysis, as well as neo-realistic and neoliberal approaches to treating the dilemma of war and peace. The article based on the assumption that the modern Russo-Ukrainian war became a consequence and manifestation of these new trends in international relations both at the regional and global levels. The conclusions drawn from this study require a conceptual rethinking and a new reading of the dilemma of war and peace, which are becoming hybrid. Therefore, understanding the new quality of these hybrid forms of war and peace is a very important and necessary task. To solve it, it is necessary to determine how the parameters of the relationship between peace and war have changed. Empirical observations show that one of the new features of this relationship is the blurring of the boundaries of war and peace. The objectives of the study are based on the discovery of a new content of the categories of war and peace and their interdependence due to the influence of 21st century trends in the modern system of international relations. The results of the study are based on the analysis of modern research on various aspects of the war and peace, as well as empirical data on the course of the Russian-Ukrainian war. This article provides an overview of current research on various aspects of war and peace, identifies the interrelationships, interdependencies, and boundaries between hybrid warfare and hybrid peace. The author tried to define the criteria for such a distinction between war and peace, based on the neoliberal and neorealist theory of international relations. The scientific novelty of this publication is that the author clarified the methodological reasons for the unresolved dilemma of war and peace in the current trends of the 21st century. The article concludes with a forecast of the consequences of the unresolved dilemma of war and peace for national and international security. Recommendations are given for a possible solution to the problem of war and peace on Donbass. The research presented in this article is an attempt to conceptually rethink and re-read the dilemmas of war and peace that are becoming hybrid. The article greatly expands the understanding of how the parameters of the relationship between peace and war have changed.

Interpretation of Frequency Channel-Based CNN on Depression Identification

Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group.

L’expression de l’opinion

Users of forums, social networks and news portals now have the opportunity to publicly express their opinions on current political events, social issues, or their everyday lives. The analysis of opinion expression, which primarily represented a research topic in the field of language learning, has now become an important research challenge in the field of computational linguistics, which provides relevant solutions for various companies and organizations. The aim of this article is to analyse messages by which users of the social network Twitter reacted to an incident in which Emmanuel Macron was slapped in the face by a man as he went out to meet the public. We analysed the tweets that express agreement, disagreement and a neutral attitude towards the action. The analysis includes 80 tweets and refers to the textual, syntactic and lexical levels. The results show that tweets expressing disagreement have a typical declarative or exclamatory form, simple sentence structure and include explicit vocabulary expressing the author’s opinion (shameful, disrespectful). Tweets demonstrating agreement are more likely to have an exclamatory form, simple sentence structure and include an explicit term (well done, deserve a slap). Opinion-neutral tweets, on the other hand, are more likely to be formulated as declarative sentences with complex sentence structure and do not include an explicit term expressing the author’s opinion. The presented method is established on basic grammatical criteria (number of sentences, sentence structure, sentence form, keywords), which can also be applied to computational analysis of large collections of texts. In the future, the presented model could be applied to investigate various political, societal or healthcare challenges (elections, corruption or pandemic issues).

The Effect of Incorporating Ceramic Particles with Different Morphologies on the Microstructure, Mechanical and Tribological Behavior of Hybrid TaC_ BN/AA2024 Nanocomposites

Improving the mechanical durability and wear resistance of aluminum alloys is a research challenge that can be solved by their reinforcement with ceramics. This article is concerned with the improvement of the mechanical properties and wear resistance of the AA2024 aluminum alloy surface. Surface composites were prepared by incorporating a hybrid of heavy particles (tantalum carbide (TaC), light nanoparticles, and boron nitride (BN)) into the AA2024 alloy using the friction stir process (FSP) approach. Three pattern holes were milled in the base metal to produce the composites with different volume fractions of the reinforcements. The effects of the FSP and the reinforcements on the microstructure, mechanical properties, and wear resistance are investigated. In addition to the FSP, the reinforced particles contributed to greater grain refinement. The rolled elongated grains became equiaxed ultrafine grains reaching 6 ± 1 µm. The refinement and acceptable distribution in the reinforcements significantly improved the hardness and wear resistance of the produced composites. Overall, the hardness was increased by 60% and the wear resistance increased by 40 times compared to the base alloy.

Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

Individual differences in information-seeking

Vast amounts of personalized information are now available to individuals. A vital research challenge is to establish how people decide what information they wish to obtain. Here, over five studies examining information-seeking in different domains we show that information-seeking is associated with three diverse motives. Specifically, we find that participants assess whether information is useful in directing action, how it will make them feel, and whether it relates to concepts they think of often. We demonstrate that participants integrate these assessments into a calculation of the value of information that explains information seeking or its avoidance. Different individuals assign different weights to these three factors when seeking information. Using a longitudinal approach, we find that the relative weights assigned to these information-seeking motives within an individual show stability over time, and are related to mental health as assessed using a battery of psychopathology questionnaires.

Autonomous Obstacle Crossing Strategies for the Hybrid Wheeled-Legged Robot Centauro

The development of autonomous legged/wheeled robots with the ability to navigate and execute tasks in unstructured environments is a well-known research challenge. In this work we introduce a methodology that permits a hybrid legged/wheeled platform to realize terrain traversing functionalities that are adaptable, extendable and can be autonomously selected and regulated based on the geometry of the perceived ground and associated obstacles. The proposed methodology makes use of a set of terrain traversing primitive behaviors that are used to perform driving, stepping on, down and over and can be adapted, based on the ground and obstacle geometry and dimensions. The terrain geometrical properties are first obtained by a perception module, which makes use of point cloud data coming from the LiDAR sensor to segment the terrain in front of the robot, identifying possible gaps or obstacles on the ground. Using these parameters the selection and adaption of the most appropriate traversing behavior is made in an autonomous manner. Traversing behaviors can be also serialized in a different order to synthesise more complex terrain crossing plans over paths of diverse geometry. Furthermore, the proposed methodology is easily extendable by incorporating additional primitive traversing behaviors into the robot mobility framework and in such a way more complex terrain negotiation capabilities can be eventually realized in an add-on fashion. The pipeline of the above methodology was initially implemented and validated on a Gazebo simulation environment. It was then ported and verified on the CENTAURO robot enabling the robot to successfully negotiate terrains of diverse geometry and size using the terrain traversing primitives.