Communicating with Humans and Robots: A Motion Tracking Data Glove for Enhanced Support of Deafblind

In this work, we discuss the design and development of a communication system for enhanced support of the deafblind. The system is based on an advanced motion tracking Data Glove that allows for high fidelity determination of finger postures with consequent identification of the basic Malossi alphabet signs. A natural, easy-to-master alphabet extension that supports single-hand signing without touch surface sensing is described, and different scenarios for its use are discussed. The focus is on using the extended Malossi alphabet as a communication medium in a Data Glove-based interface for remote messaging and interactive control of mobile robots. This may be of particular interest to the deafblind community, where distant communications and robotized support and services are rising. The designed Data Glove-based communication interface requires minimal adjustments to the Malossi alphabet and can be mastered after a short training period. The natural interaction style supported by the Data Glove and the popularity of the Malossi alphabet among the deafblind should greatly facilitate the wider adoption of the developed interface

UML consistency rules: a systematic mapping study

Context: The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard tool for objectoriented modeling and documentation. Since the various UML diagrams describe different aspects of one, and only one, software under development, they are not independent but strongly depend on each other in many ways. In other words, the UML diagrams describing a software must be consistent. Inconsistencies between these diagrams may be a source of the considerable increase of faults in software systems. It is therefore paramount that these inconsistencies be detected, ana

Exploring Immersive Learning Experiences: A Survey

Immersive technologies have been shown to significantly improve learning as they can simplify and simulate complicated concepts in various fields. However, there is a lack of studies that analyze the recent evidence-based immersive learning experiences applied in a classroom setting or offered to the public. This study presents a systematic review of 42 papers to understand, compare, and reflect on recent attempts to integrate immersive technologies in education using seven dimensions: application field, the technology used, educational role, interaction techniques, evaluation methods, and challenges. The results show that most studies covered STEM (science, technology, engineering, math) topics and mostly used head-mounted display (HMD) virtual reality in addition to marker-based augmented reality, while mixed reality was only represented in two studies. Further, the studies mostly used a form of active learning, and highlighted touch and hardware-based interactions enabling viewpoint and select tasks. Moreover, the studies utilized experiments, questionnaires, and evaluation studies for evaluating the immersive experiences. The evaluations show improved performance and engagement, but also point to various usability issues. Finally, we discuss implications and future research directions, and compare our findings with related review studies

model driven reverse engineering approaches a systematic literature review

This paper explores and describes the state of the art for what concerns the model-driven approaches proposed in the literature to support reverse engineering. We conducted a systematic literature review on this topic with the aim to answer three research questions. We focus on various solutions developed for model-driven reverse engineering, outlining in particular the models they use and the transformations applied to the models. We also consider the tools used for model definition, extraction, and transformation and the level of automation reached by the available tools. The model-driven reverse engineering approaches are also analyzed based on various features such as genericity, extensibility, automation of the reverse engineering process, and coverage of the full or partial source artifacts. We describe in detail and compare fifteen approaches applying model-driven reverse engineering. Based on this analysis, we identify and indicate some hints on choosing a model-driven reverse engineering approach from the available ones, and we outline open issues concerning the model-driven reverse engineering approaches

Hybrid human-AI driven open personalized education

Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

Verslo procesų prognozavimo ir imitavimo taikant sisteminių įvykių žurnalų analizės metodus tyrimas

Business process (BP) analysis is one of the core activities in organisations that lead to improvements and achievement of a competitive edge. BP modelling and simulation are one of the most widely applied methods for analysing and improving BPs. The analysis requires to model BP and to apply analysis techniques to the models to answer queries leading to improvements. The input of the analysis process is BP models. The models can be in the form of BP models using industry-accepted BP modelling languages, mathematical models, simulation models and others. The model creation is the most important part of the BP analysis, and it is both time-consuming and costly activity. Nowadays most of the data generated in the organisations are electronic. Therefore, the re-use of such data can improve the results of the analysis. Thus, the main goal of the thesis is to improve BP analysis and simulation by proposing a method to discover a BP model from an event log and automate simulation model generation. The dissertation consists of an introduction, three main chapters and general conclusions. The first chapter discusses BP analysis methods. In addition, the process mining research area is presented, the techniques for automated model discovery, model validation and execution prediction are analysed. The second part of the chapter investigates the area of BP simula-tion. The second chapter of the dissertation presents a novel method which automatically discovers Bayesian Belief Network from an event log and, furthermore, automatically generates BP simulation model. The discovery of the Bayesian Belief Network consists of three steps: the discovery of a directed acyclic graph, generation of conditional probability tables and their combination. The BP simulation model is generated from the discovered directed acyclic graph and uses the belief network inferences during the simulation to infer the execution of the BP and to generate activity data dur-ing the simulation. The third chapter presents the experimental research of the proposed network and discusses the validity of the research and experiments. The experiments use selected logs that exhibit a wide array of behaviour. The experiments are performed in order to test the discovery of the graphs, the inference of the current process instance execution probability, the predic-tion of the future execution of the process instances and the correctness of the simulation. The results of the dissertation were published in 9 scientific publica-tions, 2 of which were in reviewed scientific journals indexed in Clarivate Analytics Science Citation Index

Gamification and Advanced Technology to Enhance Motivation in Education

This book, entitled “Gamification and Advanced Technology to Enhance Motivation in Education”, contains an editorial and a collection of ten research articles that highlight the use of gamification and other advanced technologies as powerful tools for motivation during learning. Motivation is the driving force behind many human activities, especially learning. Motivated students are ready to make a significant mental effort and use deeper and more effective learning strategies. Numerous studies indicate that playing promotes learning, since when fun pervades the learning process, motivation increases and tension is reduced. Therefore, games can be very powerful tools in the improvement of learning processes from three different and complementary perspectives: as tools for teaching content or skills, as an object of the learning project itself and as a philosophy to be taken into account when designing the training process. Each contributions presented in this book falls into one of these categories; that is to say, they all deal with the use of games or related technologies, and they all study how playing enhances motivation in education

Model of Information System Architecture for Land Administration System Based on Blockchain Technology

У оквиру дисертације проучава се могућност примене блокчејн технологије у катастру непокретности. Врши се идентификација проблема катастра непокретности које је могуће ублажити применом блокчејн технологије. Предлог решења за ублажавања изабраних проблема дат је у форми паметног уговора. Паметни уговор је тестиран кроз примену у дефинисаним случајевима употребе. Предложен је модела информационог система катастра непокретности, са компонентом засновану на блокчејн технологији која је задужена за управљање трансакцијама.U okviru disertacije proučava se mogućnost primene blokčejn tehnologije u katastru nepokretnosti. Vrši se identifikacija problema katastra nepokretnosti koje je moguće ublažiti primenom blokčejn tehnologije. Predlog rešenja za ublažavanja izabranih problema dat je u formi pametnog ugovora. Pametni ugovor je testiran kroz primenu u definisanim slučajevima upotrebe. Predložen je modela informacionog sistema katastra nepokretnosti, sa komponentom zasnovanu na blokčejn tehnologiji koja je zadužena za upravljanje transakcijama.In this dissertation, the possibility of applying blockchain technology to the real estate cadastre is studied. Real estate cadastre problems that can be alleviated by applying blockchain technology are being identified. The proposed solution for alleviating the selected problems is given in the form of a smart contract. The smart contract has been tested through implementation in defined use cases. A model of the information system of the real estate cadastre with a component based on blockchain technology that is responsible for managing transactions was proposed

Advanced PID Control Optimisation and System Identification for Multivariable Glass Furnace Processes by Genetic Algorithms

This thesis focuses on the development and analysis of general methods for the design of optimal discrete PID control strategies for multivariable glass furnace processes, where standard genetic algorithms (SGAs) are applied to optimise specially formulated objective functions. Furthermore, a strong emphasis is given on the realistic model parameters identi cation method, which is illustrated to be applicable to a wide range of higher order model parameters identi cation problems. A complete, realistic and continuous excess oxygen model with nonlinearity effect was developed and the model parameters were identified. The developed excess oxygen model consisted of three sub-models to characterise the real plant response. The developed excess oxygen model was evaluated and compared with real plant dynamic response data, which illustrated the high degree of accuracy of the developed model. A new technique named predetermined time constant approximation was proposed to make an assumption on the initial value of a predetermined time constant, whose motive is to facilitate the SGAs to explore and exploit an optimal value for higher order of continuous model's parameters identi cation. Also, the proposed predetermined time constant approximation technique demonstrated that the population diversity is well sustained while exploring the feasible search region and exploiting to an optimal value. In general, the proposed method improves the SGAs convergence rate towards the global optimum and illustrated the effectiveness. An automatic tuning of decentralised discrete PID controllers for multivariable processes, based on SGAs, was proposed. The main improvement of the proposed technique is the ability to enhance the control robustness and to optimise discrete PID parameters by compensating the loop interaction of a multivariable process. This is attained by adding the individually optimised objective function of glass temperature and excess oxygen processes as one objective function, to include the total effect of the loop interaction by applying step inputs on both set points, temperature and excess oxygen, at two different time periods in one simulation. The effectiveness of the proposed tuning technique was supported by a number of simulation results using two other SGAs conventional tuning techniques with 1st and 2nd order control oriented models. It was illustrated that, in all cases, the resulting discrete PID control parameters completely satisfied all performance specifications. A new technique to minimise the fuel consumption for glass furnace processes while sustaining the glass temperature is proposed. This proposed technique is achieved by reducing the excess oxygen within the optimum thermal efficiency region within 1.7% to 3.2%, which is approximately equal to about 10% to 20% of excess air. Therefore, by reducing the excess oxygen set point within the optimum region, 2.45% to 2%, the fuel consumption is minimised from 0:002942kg/sec to 0:002868kg/sec while the thermal efficiency of the glass temperature is sustained at the desired set point (1550K). In addition, a reduction in excess oxygen within methane combustion guidelines will assure that undesirable emissions are in control throughout the combustion process. The efficiencies of the proposed technique were supported by a number of simulation results applying the three SGAs controller tuning techniques. It was illustrated that, in all cases, the fraction of excess oxygen reduction results in a great minimisation of fuel consumption over long plant operating periods