Miracle-dz/UniFAR-FTUs huggingface dataset:
Environments for learning motor skills using wearable technologies

This paper discusses work aimed to advance understanding of haptic nonverbal communication (HNVC) and to build environments that take advantage of wearable technologies and simple tactile languages to extend the reach of HNVC and support learning of various skills. Progress includes a taxonomy that organizes extant work on HNVC technologies, a general architecture for an environment oriented to support learning of motor skills using wearable devices, and experimentation in the specific domain of leader-follower dancing. We also discuss collaboration opportunities with other researchers in the Latin American region.
Defining playability of games: functionality, usability, and gameplay

Playability is an ambiguous term that is often used but seldom defined. Used by researchers, practitioners, journalists, and players alike, the term has remained unclear due to different interpretations and definitions. We aim to provide clarity on this issue and propose a definition for playability that is based on a game's functionality, usability, and gameplay. We argue that playability should not cover aspects such as controllers, social contexts, or player experiences, as these are external factors related to hardware, situation, and players. Our definition is based on games as systems paradigm and it focuses only on components that the designer can design into the game. This approach provides clarity between playability and player experience, and it is applicable to all kinds of games - physical or digital. Good playability does not necessarily result in good player experience, while games with poor playability can be enjoyable in the certain contexts.
Embodying Meaningful Digital Media: A Strategy to Design for Product Attachment in the Digital Age

Technological products have become central to the ways in which many people communicate with others, conduct business and spend their leisure time. Despite their prevalence and significance in people's lives, these devices are often perceived to be highly replaceable. From a sustainability perspective, there is value in creating technological products with meaning directly associated with their materiality to reduce the rate of product consumption. We set out to explore the potential for design to promote the formation of product attachment by creating technological devices with meaningful materiality, closely integrating the physical form with the significance of its digital contents. We used the life stories and ongoing input of our intended user as inspiration for the creation of Melo, a bespoke music player. The evaluation and critical reflection of our design process and resulting artefact are used to propose a design strategy for promoting product attachment within the growing sector of technological devices.
How to Put the Data Subject's Sovereignty into Practice. Ethical Considerations and Governance Perspectives

Ethical considerations and governance approaches of AI are at a crossroads. Either one tries to convey the impression that one can bring back a status quo ante of our given "onlife"-era [1,2], or one accepts to get responsibly involved in a digital world in which informational self-determination can no longer be safeguarded and fostered through the old fashioned data protection principles of informed consent, purpose limitation and data economy [3,4,6]. The main focus of the talk is on how under the given conditions of AI and machine learning, data sovereignty (interpreted as controllability [not control (! )] of the data subject over the use of her data throughout the entire data processing cycle [5]) can be strengthened without hindering innovation dynamics of digital economy and social cohesion of fully digitized societies. In order to put this approach into practice the talk combines a presentation of the concept of data sovereignty put forward by the German Ethics Council [3] with recent research trends in effectively applying the AI ethics principles of explainability and enforceability [4-9].
STARS: software technology for adaptable and reusable systems PhD research project

Dynamically Adaptive Systems (DAS) modify their behaviours in response to (sometimes unpredictable) changes in their environment or to the evolution of their own abilities (sensors and actuators). To support adaptation, a reference architecture (such as the MAPE-K model) is paramount. Yet, this is not sufficient as challenges concerning the fine-grained variability management and testability remain. The STARS Ph.D. project aims at developing a variability and context-aware architectural model for DAS that particularly takes into account testability. In particular, we want to adapt tests at runtime in order to assess and prevent the impact of inappropriate adaptations.
WraPr: Spool-Based Fabrication for Object Creation and Modification

We propose a novel fabrication method for 3D objects based on the principle of spooling. By wrapping off-the-shelf materials such as thread, ribbon, tape or wire onto a core structure, new objects can be created and existing objects can be augmented with desired aesthetic and functional qualities. Our system, WraPr, enables gesture-based modelling and controlled thread deposition. We outline and explore the design space for this approach. Various examples are fabricated to demonstrate the possibility to attain a range of physical and functional properties. The simplicity of the proposed method opens the grounds for a light-weight fabrication approach for the generation of new structures and the customization of existing objects using soft materials.
Immersive Technologies in Higher Education: Applications, Challenges, and Good Practices

Immersive technologies are becoming increasingly popular in a wide range of fields from gaming to therapy. While there is a high number of case studies in education with promising results in terms of students' performance and engagement, there has still not been widespread adoption. Indeed, more research focused on large-size samples, assessing and discussing the effects on the medium and large term is required, as well as comparisons among different hardware and software, and guidelines for instructors that want to introduce these technologies in their classrooms. In this paper, we focus on the use of immersive technologies applications in higher education. In particular, we explain the basics of immersive technologies, review the main applications in higher education, discuss benefits and challenges, and describe good practices for instructors.
A review on Mutual Coupling Reduction Methods for Antenna Arrays

In telecommunications domain, device components miniaturization is a major need. In case of the antenna array structures, reducing the overall size of the antenna leads engineers to place them closer to each other. In this case some undesirable electromagnetic phenomena can occur such as antennas interferences in the near field and surface wave propagation between microstrip radiators causing inherent mutual coupling which leads to weak antennas array radiation characteristics. The reduction of this mutual coupling for microstrip patch antenna array is a major challenge. This work presents a summary of the mutual coupling reduction methods for microstrip patch antennas.
Edge Computing with Embedded AI: Thermal Image Analysis for Occupancy Estimation in Intelligent Buildings

With the rise of the IoT, there has been a growing demand for people counting and occupancy estimation in Intelligent buildings for adapting their heating, ventilation and cooling systems. This can have a significant impact on energy consumption at a global scale as such systems consume about 40% of electricity and create about 36% of the CO2 emissions in Europe. Previous approaches to occupancy estimation either utilize methods that do not ensure people's privacy when obtaining high accuracy estimations, such as RGB cameras, or utilize thermal or radar sensors with lower accuracy. Thermal vision for people detection has several advantages. It protects people's privacy while being less affected by changes in the environment. In addition, most of the previous image processing approaches rely on streaming the data to the cloud to be analyzed. However, with the development of the more distributed network paradigms edge and fog computing, there has been a trend in moving computation towards the edge of the network. This process of embedding intelligence into end-devices enables more efficient energy consumption and network load distribution. In this work, we present an embedded algorithm for room occupancy estimation based on a thermal sensor with accuracy over the state-of-the-art. We study the performance of a variety of deep learning models on different embedded processors. We achieve a prediction accuracy of 98.9% for people counting estimation with minimal 2 KB RAM utilization. Furthermore, the proposed algorithm has very low latency achieving execution times under 14 ms.
Hard disk Drive Failure Prediction Challenges in Machine Learning for Multi-variate Time Series

Hard disk drive failure prediction (HDDFP) is an active area of machine learning applications. While recent work shows very promising results with high failure recall (95%) and precision based on SMART attributes, challenges remain that call for improvement in the machine learning pipeline. This paper starts with an introduction of the topic and a summary of recent work. Some challenges applicable to the existing solutions are then illustrated with an example using Backblaze dataset and its HDDFP rule. A main result of the paper is a rigorous formulation of the HDDFP problem as a MIMO dynamic system problem to tackle the challenges. It is also shown that the general formulation can help the existing classification method by enhancing the prediction lead time requirement. Though presented in the context of the HDDFP problem, the findings and thought process are applicable to other dynamic system failure prediction, and in some degree to the IoT and time series based analytics in general.
Evaluating the Usability of an E-Learning Platform within Higher Education from a Student Perspective

With the advancement in information technologies, e-learning has become an integral part of the modern higher education system. As part of this progression, Universities have increasingly adopted Virtual Learning Environments (VLE) that enable learners and tutors to communicate efficiently for learning purposes. However, it has been shown in a number of studies that poor usability is an area that can have a significant impact on the success of this communication and students' learning. The aim of this study therefore is to evaluate a VLE platform used in a higher education setting and provide some recommendations to enhance the interface design, system usability, and subsequent learning performance. Quantitative and qualitative research methods have been used, with a survey of 101 students recruited from all Schools at Keele University to investigate whether the system is usable or not (using the System Usability Scale (SUS)), from which 27 free-text responses were obtained for thematic analysis. The results indicate that the VLE performed below the average usability expectation (SUS score of 62.52). Thematic analysis of comments revealed very negative views of the VLE as well as areas for improvement. Evaluating the effect of usability on e-learning is complicated. However, there is a need to evaluate how usable VLE platforms are to ensure that this component of the platform design does not impede learning and teaching and further work is therefore needed by service providers to identify common usability issues as well as for educators to be more aware of usability issues and appropriate e-learning design principles.
Improving the Accuracy of the Video Popularity Prediction Models through User Grouping and Video Popularity Classification

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ”user grouping” and ”content classification. ” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.
Data Integration and NoSQL Systems: A State of the Art

Data Integration is one of the older research problems in database area. It aims to combine data stored in different data sources and provide a unified view of data to the user. With the spread of a new generation of database systems, called NoSQL systems, data integration becomes more challenging, since we have to integrate data stored in systems that implement different data models and query languages. Inspired by these motivations, we provide in this paper an overview of data integration challenges and solutions in the context of NoSQL systems.
Local foodie: experience design of a mobile augmented reality application for tourists to encourage local food consumption

Food is an essential part of travel experience. Consumption of locally produced food while traveling has the two-fold benefit of providing insight into the local culture and increasing the sustainability of tourism. However, finding local food often requires motivation and effort, as information about food ingredients' origins and supply chains is not easy to discover. This paper presents the three-phase experience design process of a prototype of a mobile augmented reality (MAR) application "Local Foodie" designed to encourage tourists in Finland to consume local food. Adventure, autonomy, and competence were determined as experience goals for the application, and an interactive MAR prototype was created through iterative design. The results of a user evaluation (n=10) of the prototype suggest that the use of the application was intrinsically motivating, and the MAR elements contributed especially to the fulfillment of adventure and autonomy experience goals. Future work could leverage context-awareness and personalization to further enhance the experience of adventure.
Special Issue Introduction: Desperately Seeking the Information in Information Systems Research at HICSS 2019

In this Special Issue, we present some of the important research work curated from the internationally renowned Hawaii International Conference on Systems Science (2019). We take note of the call to action by Petter, Carter, Randolph, and Lee (2018) in the editorial titled "Desperately Seeking the Information in Information Systems Research. " In their editorial, the authors highlight the embrace of the technology artifact in IS research at the expense of "abandonment of that other central component in information systems research - information" (p. 10). Petter et al. argue not to "overlook the role of information" and highlight the necessity to study it in an era of analytics, algorithmic decision making, and fake news.
EDUMAT: gamified web tool for teaching elementary operations in primary school

This research proposes a method for teaching elementary operations based on gamification and information and communication technologies with the aim of promoting best practices in the context of education and improving the performance of basic education students in the area of mathematics, specificamente in the division with successive subtraction, which includes the four elementary operations (addition, subtraction, multiplication and division) in a single procedure. The method was implemented in a web application with an interactive and didactic environment where the skills and knowledge acquired by the students in the classroom were put to the test through the game. The intention was to verify by means of a test of implantation in an educational environment if method is really effective and if it fosters the interest of including didactic strategies of learning in the classrooms.
Motivational Factors of Australian Mobile Gamers

Mobile games are a fast growing industry, overtaking all other video game platforms with year on year increases in revenue. Many studies have been conducted to explore the motivations of why video games players play their selected games. However very little research has focused on mobile gamers. In addition, Australian studies on the topic are sparse. This paper aimed to discover what motivates a mobile gamer from the perspective of the initial motivational factors attracting them to a mobile game, and the motivational factors that provide interest to continue playing and thereby increase game longevity. A survey was conducted online for Australian participants, which attracted 123 respondents. The survey was formulated by focusing on the 12 key subcomponents as motivational factors of the Gamer Motivational Profile v2 model devised by Quantic Foundry. It was discovered that mobile gamers are a completely different breed of gamer in contrast to the general video gamer. Strategy and challenge which are subcomponents of mastery proved popular among all mobile gamers, while destruction and excitement, subcomponents of action, were often the least motivating factors of all. With the newly discovered data, perhaps mobile game developers can pursue the correct avenues of game design when catering to their target audience.
CRUSCom model: A new theoretical model to trace the evolution line of information within the enterprise environment

Individuals and enterprises permanently use and manage information. In fact, individuals need the information to interact with their environment while enterprises consider it the cornerstone of their activities (operation and management) and the backbone of their digital heritage. In general, information takes many forms, which complicates its definition, use and management. In computer science, information is based on the notion of data while its composition depends heavily on its use objectives. Unfortunately, the path of the enterprise digital data/information from its creation to its last evolution phase is vulnerable and poorly defined, which makes its management and security complicated along it. The enterprise therefore needs a mapping of the different routes of its data/information in order to facilitate their management, to define perfectly their use purposes and to specify their vulnerability points. The present paper presents a theoretical model (theoretical tool) which allows the enterprise to segment data/information evolution paths and to well organize some activities of their management and security.
Attack Intent Analysis Method Based on Attack Path Graph

With the rapid development of network technology, network security problems are gradually increasing, and the network attack situation is very severe. In a complex attack scenario, timely detection of potential attack behaviors and timely identification and pre-judgment of attack intentions are important components of security risks. However, the attack behavior in the network presents complexity, multi-step and uncertainty, which brings new technical challenges to attack intent analysis. Aiming at the problem that the attack intention of multi-step complex attack is difficult to identify, this paper proposes an attack intention analysis method based on attack path graph. Firstly, aiming at the multi-step complex attack behavior analysis problem, the key asset assessment technology is used to find out the key assets in the network system, and the hypothetical attack intention is generated according to the security protection requirements of the network system. Then, it is difficult to manually construct the attack path map in the large-scale network, and the automatic generation of the attack path map is realized. Finally, a method of network attack intent identification is proposed and a calculation method of attack intent probability is designed, which improves the efficiency and accuracy of attack intent recognition.
Rotation & Viewpoint Angle Prediction in Capsule Network

Convolutional neural network (CNN) is effective in detecting features and classifying object but less effective in exploring spatial relationships among the features. Capsule network introduces stacking layers called capsules and routing algorithm. Such a capsule structure is proved to handle spatial relationships better than CNN architecture. This paper aims at exploring capsule network ability to adapt with the rotation and viewpoint change in image recognition for MNIST, SmallNORB and CAS-PEAL datasets compared to CNN architecture (VGG-based network). The experimental results show that capsule network performs better than CNN for rotation estimation, whereas CNN architecture performs slightly better than capsule network for viewpoint change. The experiments also show that capsule network may have capability to generalize better in some untrained data for rotation and viewpoint change. Capsule network is quite promising architecture in classification and spatial context.
Build Your Own Hercules: Helping Visitors Personalize their Museum Experience

Museums compete with the entertainment industry to attract a large audience. One solution to make them more attractive is to personalize the visits according to visitors' preferences. Following a user centered design approach with visitors and museum professionals, we designed and implemented Build Your Own Hercules. This tangible prototype helps groups of visitors or individuals choose a visit based on their characteristics and desires. A pilot study in the museum provided first insights about the ease of use, satisfaction and interest within visitor groups.
Influence of RNN structural parameters on blind equalization of shortwave channels

Aiming at the problem of severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels, this paper designs a short-wave channel blind equalizer based on RNN, and focuses on the analysis of the accuracy rate, cross entropy loss and the number of hidden elements in the RNN training process on the equalizer training process and the equalization error rate. By simulating two typical short-wave Rayleigh flat fading and frequency selective fading channels, the results show that: (1) When RNN is used for short-wave fast time-varying channel equalization, improving the SNR can enhance the accuracy of the training process and the stability of the loss curve. Meanwhile, the accuracy of classification can be improved, and the convergence speed is further accelerated. (2) Increase the number of hidden units can improve the convergence speed of RNN; But, excessive hidden units can easily lead to a sharp jump in the training process curve, which not only reduces the stability of the RNN model during training, but also causes over-fitting problems in RNN. (3) For short-wave channels, the BER of the RNNE is lower than that of the CMA equalizer at the same SNR.