ABSTRACT This study advances multidisciplinary contributions to understanding inhibitory control in children with neurodevelopmental disorders. A serious game was developed to provide cognitive and behavioral characterization in educational contexts. Study 1 provides guidelines on best practices for serious game design in applied cognition and neurodevelopmental support. Study 2 examined the game's feasibility for measuring inhibitory control and its concurrent psychometric links to behavioral and emotional functioning. Results suggest suitability for schools, though psychometric evaluation is required. Study 3 implemented an exploratory twenty‐session design, using heart rate variability to examine physiological correlates, which showed that higher sympathetic activation and stress relate to lower performance. Collectively, these data advance applied cognition by specifying how gamified tasks can be tailored to the functional developmental levels of neurodevelopmental children. By utilizing personalized, data‐driven pathways, this game provides educators a viable tool for cognitive screening, supporting practical classroom observation for students with ADHD and co‐occurring disorders.

The preservation of Cultural Heritage artefacts is a very complex endeavour that requires significant resources and knowledge from various domains, ranging from cultural to scientific and political aspects. These complex interactions involve many individuals that need to collaborate closely to achieve the best result. In the ChemiNova Project, funded by European Union, we are developing a FAIR compliant Cultural Heritage oriented platform that allows specialists to store digital information about the artefacts (digital tweens, accompanying documents, previous condition reports, etc.) and to collaborate in real-time or asynchronously on the available information. Proposed solution has at its core the concept of an Enriched 3D Model, which represents a 3D representation of the artefact enriched with information retrieved from: (1) scans performed with different sensors (RTI, hyperspectral, infrared, RGB, etc) which are synchronized over the same 3D mesh; (2) support documents (provenance, surroundings, previous restorations, etc); or (3) added by specialists directly into the platform within the analysis process in the form of annotations (digital marks placed on the 3D mesh and accompanied by any type of document – pictures, archives, numerical values, etc) or condition reports. Specialists can contribute collaboratively with information to the same artefact, while indicating the licencing available for the provided data. Our approach proposes an extendable architecture which is based on the concept of connected digital tools. Each tool encapsulates specific functionalities, technologies, data visualization capabilities and user interaction techniques, and communicates with all the other components through a secured and dedicated API, which ensures the consistency and security of the data stored into the Platform. This approach ensures that new tools can be developed and securely integrated into the platform at any time, through Single Sign On implementation, addressing specific needs and incorporating new technologies.

It is vital that working memory capacity can be assessed and trained with motivating resources, in a personalized way in children with neurodevelopmental disorders. This paper illustrates the possibility of a serious game, such as WorM, to assess and train children’s working memory capacity. WorM was designed, developed and evaluated through three studies (Study 1: informative, Study 2: validation and Study 3: user studies). The first two qualitative studies employ in-depth interviews and focus group discussions, to identify, argue and validate suitable design aspects guided by interviews from teachers and psychologists. The third study presents the usefulness of WorM based on the Item Response Theory analysis and answers from 23 end-user, typical children. Examples of important design aspects identified in Study 1 revealed the role of captivating interests, customization, short duration, rewards, feedback and clear instructions for designing tasks with right images and sound effects. From these, Study 2 enhanced the role of interactive visual feedback for correct and incorrect sorting, using images, keyboard keys, multiple colors, or the phase of the trial. Study 3 illustrates how Correct Pepper Classification tasks show some variability in difficulty, where the range of difficulties was higher in the game tasks than in the standardized Childhood Executive Functioning Inventory (CHEXI) task, usually applied for identifying the NDD characteristics in the examined users. While both instruments showed good reliability with high Person and Item Separation Reliability and Cronbach’s alpha values, the findings of this paper suggest that serious games can offer promising prospects to provide meaningful contexts to assess and train working memory by the WorM game.

The vision behind this research is to develop a platform that supports children with neurodevelopmental disorders (NDD) in handling their difficulties. This will be done by informing their teachers about each child's NDD specificity, personal ability, and learning progress through standardized tasks that allow tailoring the tasks to personalized requirements. The aim of this work in progress is to 1) examine the role of using eye tracking (ET) technologies to inform teachers to support NDD children through a platform, 2) provide an example for using ET data, and 3) show how ET data can be combined with Serious Games (SG) output in a pipeline. The results show the necessary requirements for the experimental setup with a focus on informing the teachers, the influence of inherent limitations of the participant pool, and illustrate how the ET and SG results can be used to communicate status for sustained attention.

Near-Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth's vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep convolutional neural networks for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We applied transfer learning and fine-tuning on these pre-existing deep convolutional networks, and from the results that we obtained, the potential of using deep convolutional neural networks in the process of asteroid classification can be seen. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we lose the least number of valid asteroids.

Chemistry is a very complex and difficult field to understand in the natural sciences. We all know the popular expression: everything around us is chemistry. Any object we see or touch is chemistry, the food we eat is chemistry, the air we breathe is chemistry, the emotions we experience are chemistry, our body is chemistry. But little do we know or understand how this actually manifests in reality. The current research presents an original solution for chemistry learning, based on Augmented Reality and Gamification principles. The main purpose of this approach is to help students visualize the microscopic molecule structures and describe them in an interactive manner. After a short overview of Augmented Reality initiatives in education the focus switches to the chemistry related initiatives. Finally, the details of the proposal are being discussed.

In 2015 we started a PhD thesis aiming to write a moving objects processing system (MOPS) aimed to detect near Earth asteroids (NEAs) in astronomical surveys planned within the EURONEAR project. Based on this MOPS experience, in 2017 we proposed the NEARBY project to the Romanian Space Agency, which awarded funding to the Technical University of Cluj-Napoca (UTCN) and the University of Craiovafor building a cloud-based online platform to reduce survey images, detect, validate and report in near real time asteroid detections and NEA candidates. The NEARBY platform was built and is available at UTCN since Feb 2018, being tested during 5 pilot surveys observed in 2017-2018 with the Isaac Newton Telescope in La Palma. Two NEAs were discovered in Nov 2018 (2018 VQ1 and 2018 VN3), being recovered and reported to MPC within 2 hours. Other 4 discovered NEAs were found from a few dozen possible NEA candidates promptly being followed, allowing us to discover 22 Hungarias and 7 Mars crossing asteroids using the NEARBY platform. Compared with other few available software, NEARBY could detect more asteroids (by 8-41%), but scores less than human detection (by about 10%). Using resulted data, the astrometric accurancy, photometric limits and an INT NEA survey case study are presented as guidelines for planning future surveys.

Due to the development of digital tools in the information society, all arenas of society are constantly changing, including the process and methods of education and learning. Both students and educators familiar with the use of digital devices have a significant presence of digital technology in their daily lives, not only in connection with teaching and learning activities, but also outside the classroom. This paper review, highlights and summarizes several Cognitive InfoCommunication technologies that can be effective facilitators in meeting the expectations of Education 4.0.

Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.References:[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., “Soil Classification Techniques in Transylvania Area Based on Satellite Data”. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).[2] Bacu V., Stefanut T., Gorgan D., “Building soil classification maps using HorusApp and Sentinel-2 Products”, Proceedings of the Intelligent Computer Communication and Processing Conference – ICCP, in IEEE press (2019).[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., “Soil classification based on Sentinel-2 Products using HorusApp application”, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).

Eye movement tracking systems offer the opportunity to observe and study a complex cognitive process such as programming. As a consequence of the growing number of program systems, software developers need to be able to use more and more programming technologies effectively. The paper analyses one of the possibilities of the C# programming language, the two types of the Language-Integrated Query data abstraction layer, that is the readability and comprehensibility of query and method syntax-based queries by evaluating the knowledge level and evaluating eye movement parameters.

Emotional and behavioral problems are common in children and adolescents and can impact their quality of life. The motivation behind this paper is to develop a better understanding for screening support contributing to the identification of the emotional and behavioral symptoms as well as executive functions, emotion regulation strategies, and attentional patterns. We have conducted a study in which we enrolled 32 children (with the mean age of 10.54 years) with autism spectrum disorder (ASD). By investigating if the difficulties regarding executive functions are related to other emotional and behavioral problems, this study shows interdependencies, between executive functions and emotional and behavioral problems. By showing the interrelation between functional and cognitive problems, and the possibility to use technologies to support screening by objective measurements, this paper argues for the need for further developing a platform that includes the different screening results for the individuals. This paper argues for the possibility of cross dependencies between the different executive and behavioral problems. However, further research is needed to demonstrate the feasibility for such a platform and the validity of combining the measurements from the included technologies.

Machine learning algorithms are widely used in the domain of robotics. In particular, applications using machine learning and artificial intelligence algorithms have led to promising results in industrial applications, cognitive robotics and thus gained attention in recent years. In this context, the purpose of this article is to present the technologies and architectures used in the design and development of cognitive robots by students. This study highlights the difficulties encountered by future engineers in developing research projects in robotics.

The main purpose of an Internet of Things(IoT) network is to make people's work and life easier by providing processes and services as close as possible to their needs. Globally, it is stated that the Internet of Things (IoT) must be available everywhere. As the Internet is almost ubiquitous today, this is not an unreasonable requirement. But to create such a network, it would be necessary for all devices, regardless of the date of creation or manufacturer to be able to be inter-connected to a common platform and made accessible securely through the Internet. In the current article we are proposing an architecture that responds to this need for the interconnectivity of devices and facilitates secure communication through its components. Through the installation of dedicated board/boards in the desired space and through the connection of the electrical devices on different pins to them, the “objects” are connected to the internet and the user can control their ON/OFF state remotely. So this purpose the proposed architecture features four main components: (1) on-site boards that control the electrical items and the internet connection; (2) a server that orchestrates the communication between all the other components; (3) a web application for electrical items management; (4) a smartwatch application for electrical items control. Author

Developing software products that provide meaningful, relevant and user-targeted experiences to users has lately become a trend. This paper proposes a centralized standalone system that yields personalized user experience to a target application under logs analysis context. We introduce a real-time Sequential Pattern Mining approach to gather knowledge about on-going user’s habits. The modules that comprise a personalization system are introduced, emphasizing the usage mining module. Solutions and limitations on integrating personalization aspects in a to-bepersonalized environment will be presented as well. The applicability of the personalized gathered knowledge will be exemplified in the context of a Web application used to manage and configure products in a company.

The survey of the nearby space and continuous monitoring of the Near Earth Objects (NEOs) and especially Near Earth Asteroids (NEAs) are essential for the future of our planet and should represent a priority for our solar system research and nearby space exploration. More computing power and sophisticated digital tracking algorithms are needed to cope with the larger astronomy imaging cameras dedicated for survey telescopes. The paper presents the NEARBY platform that aims to experiment new algorithms for automatic image reduction, detection and validation of moving objects in astronomical surveys, specifically NEAs. The NEARBY platform has been developed and experimented through a collaborative research work between the Technical University of Cluj-Napoca (UTCN) and the University of Craiova, Romania, using observing infrastructure of the Instituto de Astrofisica de Canarias (IAC) and Isaac Newton Group (ING), La Palma, Spain. The NEARBY platform has been developed and deployed on the UTCN's cloud infrastructure and the acquired images are processed remotely by the astronomers who transfer it from ING through the web interface of the NEARBY platform. The paper analyzes and highlights the main aspects of the NEARBY platform development, and the results and conclusions on the EURONEAR surveys.

An efficient agricultural management is based on a good, reach and accurate information on the environment and especially on the soil. The need for up-to-date and high-resolution soil information and the direct access to this information in a flexible and simple manner is imperative for pedology and agriculture specialists. This paper presents the HORUSApp application supporting the integration of multispectral data coming from satellite images (in particular from Sentinel-2 satellite) into the soil analysis and classification process.

Soil classification maps (i.e. pedological maps) describe soil characteristics based on observable features in the field such as color or texture, and on the distribution of the different horizons (i.e. layers) within the soil. Such maps are the result of a soil survey consisting in measuring soil characteristics in a very limited number of geographical locations and using them to classify soil in that particular area. HorusApp supports the development of pedological maps by combining field studies and measurements with Sentinel-2 data. HorusApp integrates ESA’s SNAP software tool used to process remotely the sensing data. Soil classification can be performed at different levels (class, type and subtype) based on machine learning techniques. HorusApp consists of several modules: Measurement Points Editor, Soil Classification System, Map Generator and Map Evaluator. Measurement Points Editor allows the editing of the set of measurement points used for calibration of the Soil Classification System. The user specifies a point by selecting a position on the map and a set of attributes such as latitude/longitude, soil class, soil type, soil subtype is automatically computed. Soil Classification System performs soil classification using satellite data extracted from the measurement points based on machine learning techniques. Map Generator infers the soil type for each location in the map. Map Evaluator evaluates the quality of the generated pedological map. The evaluation is done according to a metric that compares two pedological maps. HorusApp uses the HORUS platform which enables specialists to scale the processing over a cloud infrastructure running Kubernetes and Docker containers. The development of the HORUS platform and application have been supported and funded by the Romanian Space Agency (ROSA). Several case studies were conducted in the Transylvanian plain using Sentinel-2 images from the ESA repository and existing pedological maps provided by University of Agricultural Sciences and Veterinary Medicine ClujNapoca (USAMV).