Mobile Robots and Cobots Integration: A Preliminary Design of a Mechatronic Interface by Using MBSE Approach

Enabling technologies that drive Industry 4.0 and smart factories are pushing in new equipment and system development also to prevent human workers from repetitive and non-ergonomic tasks inside manufacturing plants. One of these tasks is the order-picking which consists in collecting parts from the warehouse and distributing them among the workstations and vice-versa. That task can be completely performed by a Mobile Manipulator that is composed by an industrial manipulator assembled on a Mobile Robot. Although the Mobile Manipulators implementation brings advantages to industrial applications, they are still not widely used due to the lack of dedicated standards on control and safety. Furthermore, there are few integrated solutions and no specific or reference point allowing the safe integration of mobile robots and cobots (already owned by company). This work faces the integration of a generic mobile robot and collaborative robot selected from an identified set of both systems. The paper presents a safe and flexible mechatronic interface developed by using MBSE principles, multi-domain modeling, and adopting preliminary assumptions on the hardware and software synchronization level of both involved systems. The interface enables the re-using of owned robot systems differently from their native tasks. Furthermore, it provides an additional and redundant safety level by enabling power and force limiting both during cobot positioning and control system faulting.

SERIES

Requirement defects are one of the major sources of failure in any software development process, and the main objective of this chapter is to make requirement analysis phase exhaustive by estimating risk at requirement level by analyzing requirement defect and requirement inter-relationships as early as possible to using domain modeling to inhibit them from being incorporated in design and implementation. To achieve this objective, this chapter proposes a tool to assist software developers in assessing risk at requirement level. The proposed tool, software risk estimator, SERIES in short, helps in early identification of potential risk where preventive actions can be undertaken to mitigate risk and corrective actions to avoid project failure in collaborative manner. The entire process has been supported by a software case study. The results of the proposed work are promising and will help software engineers in ensuring that all business requirements are captured correctly with clear vision and scope.

REST API Composition for Effective Testing the Cloud

Cloud offers many ready-made REST services for the end users. This offer realizes the service composition through implementation somewhere on internet based on Service Level Agreement (SLA). For ensuring this SLA, a software testing is a useful means for attesting a non-functional requirement that guarantees quality assurance from end user's perspective. However, test engineer experiences only what goes in and out through an interface that contains a high level behaviors separated from its underlying details. Testing with these behaviors become an issue for classical testing procedures. So, REST API through composition is an alternative new promising approach for modeling behaviors with parameters against the cloud. This new approach helps to devise test effectiveness in terms of REST based behavior-driven implementation. It aims to understand functional behaviors through API methods based on input domain modeling (IDM) on the standard keyboard pattern. By making an effective REST design the test engineer sends complete test inputs to its API directly on application, and gets test responses from the infrastructure. We consider NEMo mobility API specification to design an IDM, which represents pattern match of mobility search URL API path scope. With this scope, sample mobility REST API service compositions are used. Then, the test assertions are implemented to validate each path resource to test the components and the end-to-end integration on the specified service.

Empathy-based counterspeech can reduce racist hate speech in a social media field experiment

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation—either by governments or social media companies—can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies—empathy, warning of consequences, and humor—or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.

General 1-D hydrogeological model for temporal near-surface loess saturation assessment for the needs of radon potential mapping

Radon gas has high mobility and is driven by advection and diffusion with the soil gas throughout connected and water-unsaturated pores and/or cracks in permeable rocks and soils. Hence the radon potential of the area could be dependent on not only geology as a constant source of radon but also from the changes of the saturation state of the ground. The loess complex, characterized by its permeability and usual state of unsaturation, covers 10% of the Bulgarian territory. The study deals with the principles of unsaturated domain modeling. An attempt of generic vertical infiltration model coinciding with the most upper part of loess vadose zone was performed.

Understanding the role of individual learner in adaptive and personalized e-learning system

Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.

Spatio-Temporal Pattern Representation from AI Inspired Brain Model in Spiking Neural Network

Neuronal population activity in the brain is the combined response of information in the spatial domain and dynamics in the temporal domain. Modeling such Spatio-temporal mechanisms is a complex process because of the complexity of the brain and the limitations of the hardware. In this paper, we demonstrate how information processing principles adapted from the brain can be used to create a brain-inspired artificial intelligence (AI) model and represent Spatio-temporal patterns. The same is demonstrated by designing the tiny brain using spiking neural networks, where activated neuronal populations represent information in the spatial domain and transmitting signals represent dynamics in the temporal domain. Spatially located sensory neurons excited by input visual stimuli further activate motor neurons to trigger a motor response that causes behavior modification of the robotic agent. Initially, an isolated brain network is simulated to understand the excitation part from sensory to motor neurons while plotting waveform between membrane potential and time. The response of the network to stimulate robot body movements is also plotted to demonstrate representation. The simulation shows how the response of particular visual stimuli modifies behavior and helps us understand the body and brain synchronization. The perceived environment and resultant behavior response allow us to study body interaction with the environment.

GUM: The generalized upper model

GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling decisions for individual domains may need to be responsive to domain-specific criteria, a linguistically-motivated ontology offers a characterization that generalizes across domains because its design criteria are derived independently both of domain and of application. With respect to this mediating role, the use of GUM resembles (and partially predates) the adoption of upper ontologies as tools for mediating across domains and for supporting domain modeling. This paper briefly introduces the ontology, setting out its origins, design principles and applications. The example cases for this special issue are then described, illustrating particularly some of the principal differences and similarities of GUM to non-linguistically motivated upper ontologies.

An Implementation of Deep-Connections for Multi-Level Modeling

<p>Traditional object-oriented programming languages only support two logical domain classification levels, i.e. classes and objects. However, if the problem involves more than two classification levels, then to model a multi-level scenario within two classification levels, a mapping approach is required which introduces accidental complexity and destroys the desirable property of “direct mapping”. Therefore “Multi-level modeling” was proposed. It supports an unbounded number of classification levels, that can support “direct mapping” without introducing accidental complexity. Many supporting features have been proposed for “multi-level” modeling such as “deep instantiation”, potency, clabjects, etc. To date most of the research effort was focusing on the entities (clabjects), while the relationships between entities were receiving much less attention and remained under-explored. The “Melanee” tool was developed to support multi-level modeling both for academics and practitioners. “Melanee” supports an unbounded number of classification levels for domain modeling and it treats relationships like clabjects. It mainly supports “constructive modeling” by creating models using a “top-down” approach, whereas “explanatory modeling”, which is creating models using “bottom-up” approach, is not well supported and lacks support to ensure the integrity of the created models. Hence, to further explore relationships in multi-level modeling and to provide a better modeling environment, there are two main focuses in this thesis: First, based on existing, I further explore relationships between entities and extend the LML (Level Agnostic Modeling Language) supported by Melanee accordingly. Second, I extend Melanee’s functionality to support “explanatory modeling”. Considering that Melanee is an open source tool I first discuss Melanee’s structure and its principles in order contribute to future extensions to Melanee. The knowledge of Melanee is currently known by its principle developer, Ralph Gerbig, with whom I had contacts in the beginning phase of the “deep-connection” development for advices. Next I use the work proposed in the paper “A Unifying Approach to Connections for Multi-Level Modeling” by Atkinson et al. as a foundation and stepping stone, to further explore relationships between entities. I extended Melanee to support the “Deep-connections” feature by adding potency to connections and their monikers, and further allow connections to have “deep-multiplicities”. I developed these features, as well as respective validation functions to ensure the well-formedness of models. Then I extended LML so that user-specified type names can be used to indicate the names of types for clabjects. Instead of relying on modelers to fully manually define type- of classification relations between different levels, I introduce “connection conformance” and “entity conformance” to introduce classification support to Melanee. Potentially matching types are calculated and ordered per their matching scores. Respective suggestions to modelers including messages for each possible matching type about how to fix the current connection instance so that it matches the potential type whenever applicable. The suggestions are made available as so-called “quick-fixes” and I extended this approach with a second-stage dialog that allows modelers to select amongst many fix alternatives. Finally, I evaluate my design using model sets taken from existing papers and a systematic exploration involving 57 different scenarios.</p>