Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Personalization to support Learners’ Motivation on Gamified MOOC Platform

Online learning expands along with the development of information technology. One model of online education that widely used because it is open and massive today is the Massive Open Online Courses (MOOCs). However, the problems that arise at the MOOC are related to the high drop out rate caused by the lack of motivation of users to complete the course. Various approaches are taken to deal with this, one of which is by utilising elements of the game through gamification and personalisation to understand the factors needed by learners in the learning environment. This research proposes a personalised design through self-determination. The results of the study have implemented on a new Gamified MOOC platform that pays attention to learner personalisation regarding competency, autonomy and relatedness.
Effects of obstruent voicing on vowel F0: Implications for laryngeal realism

It is sometimes argued that languages with two-way laryngeal contrasts can be classified according to whether one series is realized canonically with voicing lead or the other with voicing lag. In languages of the first type, such as French, the phonologically relevant feature is argued to be [voice], while in languages of the second type, such as German, the relevant feature is argued to be [spread glottis]. A crucial assumption of this position is that the presence of certain contextually stable phonetic cues, namely voicing lead or lag, can be used to diagnose the which feature is phono-logically active. In this paper, we present data on obstruent-intrinsic F0 perturbations (CF0) in two [voice] languages, French and Italian. Voiceless obstruents in both languages are found to raise F0, while F0 following (pre)voiced obstruents patterns together with sonorants, similar to the voiceless unaspirated stops of [spread glottis] languages like German and English. The contextual stability of this cue implies that an active de-voicing gesture is common to languages of both the [voice] and [spread glottis] types, and undermines the idea that a strict binary dichotomy between true voicing and aspirating languages can be reliably inferred based on properties of the surface phonetics.
A performance assessment of dynamic heuristic optimisers on the IEEE CEC 2015 niching competition test problems

We present the performance results of five multi-modal heuristic optimisers on the 20 benchmark functions of the IEEE CEC 2015 competition on niching methods for multimodal optimisation. All the algorithms compared here are pre-defined in existing works, are dynamic in their niche maintenance, and exploit ‘hill-valley’ niche detection. Code and files of output statistics required by the competition are also provided online. We find that although the recently developed Niching Migratory Multi-Swarm Optimiser (NMMSO) algorithm performs best overall, other optimisers perform better on some of the problems, with the main divergence in performance apparent between homogeneous and heterogeneous problem landscapes.
Application Design for the Quantified Pet Domain from a User Centered Design Perspective

Quantifying personal information is a rapidly emerging lifestyle that has now extended to tracking non-personal information as well. The Quantified Pet (QP) domain enables pet owners to gain insights in their pet’s behavior and wellbeing. This study investigates how to design a QP application using the User Centered Design Method. To gain understanding in the inner drivers that form the human-pet relationship, five dog owners are interviewed. Three inner drivers that trigger interaction are revealed: habits, love and guilt. By surveying 104 users of existing QP applications, this study examined what motivates users to use a pet activity tracker and what sustains this usage. After performing a thematic analysis on this data, it was found that Activities, interest in activity data, and Health, improving and ensuring the dog’s health, are dominantly portraited in the results. These themes provide the foundation for the establishment of two personas, of which the Health persona is chosen to be the primary design target. Next to user requirements following from the persona design, a heuristic evaluation is performed on one QP application (FitBark) to provide an additional set of design requirements. From these requirements, a design solution is proposed and evaluated amongst ten participants by means of a task list, semi-structured interview and a questionnaire. This revealed high usability for navigation and successful implementation of most requirements. However, design flaws, in specific of data visualization, and some misunderstanding of informational components remain. Future work proposes an improved design and provides additional suggestions for implementation and examination.
PREPARING FOR ORGANIZATIONAL CHANGE: EVOLUTION OF THE ORGANIZATIONAL CAPACITY FOR CHANGE CONSTRUCT.

This study examines a promising new dynamic capability known as organizational capacity for change (OCC). It is anticipated that organizations will find such a capability valuable if they find it necessary to adapt to rapidly changing environments. Recognizing that scale construction is one of the most challenging problems in the study of organizations, we follow the framework established by Hinkin (1998) in order to develop a scale that satisfies the standards of organization science. First, we provide detail on the generation of items and the collection and analysis of an extensive multi-industry database of over 3,500 respondents within 150 organizational units from 2000-05. Next, we explore the nomological network of variables surrounding OCC for 51 organizational units in a single industry study. The end result is a reliable and valid measure of a promising new construct in the literature for measuring and tracking organizational capacity for change.
Understanding the Impact of Determinants in Game Learning Acceptance: An Empirical Study

This study aimed to integrate Innovation Diffusion Theory (IDT) and Expectation Confirmation Model (ECM) on user intention of game learning. This study framed 10 hypotheses to investigate the objectives of this study. The results of 8 hypotheses were positive and had a significant impact on students’ behavior in the use of game learning. The method used in this research was empirical, with 164 valid respondents. The results found in this research was students’ continued intention in the use of game learning (CI), which was directly influenced by student perceived usefulness (PU), student satisfaction (SAT) and student habitual (HAB) while student satisfaction (SAT) was influenced by student perceived usefulness (PU) and triability (TRA). In addition, the findings of this research were predicted to offer useful validation from the antecedents involved in the process of deciding the user continuous intention in game learning. This research also provided a theoretical framework and understanding of the field of user behavior intention for academia and practitioners, who will benefit from the findings of this study and develop applications/software for a more adequate and competent game learning.
Investigation of Pov-Ray data structures and algorithms

Ray tracing is the most powerful technique for producing photo-realistic images. POV-Ray is a popular freeware and open source ray tracing software capable of producing stunningly realistic images. While the user documentation is descriptive and thorough, not much explained for the advanced users who want to study the source code. This paper hopes to provide a detailed description of the ray tracing data structures and implementation of various features and to give pointers for further study.
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests

It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifier, that strikes a balance between predictive power and interpretability by simultaneously performing classification, feature selection, feature interaction detection and visualization. It builds parsimonious yet flexible models using tree-structured Bayesian networks, and samples an ensemble of such models using Markov chain Monte Carlo. We build in feature selection by dividing the trees into two groups according to their relevance to the outcome of interest. Our method performs competitively on classification and feature selection benchmarks in low and high dimensions, and includes a visualization tool that provides insight into relevant features and interactions.
Movement Control of Accompanying Robot Based on Artificial Potential Field Adapted to Dynamic Environments

This paper focuses on mobile robots that can accompany a person, that is, it addresses how to control the position of the robot relative to the accompanied person according to the dynamic environment. The robot is expected to move side by side with the person in a normal situation, but a position in front or behind the person might be better if there are obstacles. The shape of the artificial potential field of the accompanied person is devised to smoothly control the robot position in a unified way. The Laplace potential method is also used subsidiarily to cope with extremely cluttered environments. Simulations and experiments in real space show the usefulness of the proposed method.
Robot-Assisted Surgical Training Over Several Days in a Virtual Surgical Environment with Divergent and Convergent Force Fields

Surgical procedures require a high level of technical skill to ensure efficiency and patient safety. Due to the direct effect of surgeon skill on patient outcomes, the development of cost-effective and realistic training methods is imperative to accelerate skill acquisition [1]. Teleoperated robotic devices allow for intuitive ergonomic control, but the learning curve for these systems remains steep. Recent studies in motor learning have shown that visual or physical exaggeration of errors helps trainees to learn to perform tasks faster and more accurately [2]. In this study, we extended the work from two previous studies [3,4] to investigate the performance of subjects in different force field training conditions, including convergent (assistive), divergent (resistive), and no force field (null). We hypothesized that the group who trained in the divergent force field would show superior performance to those in the null and convergent fields.
LiveRAC : live reorderable accordion drawing

LiveRAC is a scalable focus+context approach for monitoring computer systems and networking data that provides user-directed data reordering and details-ondemand. LiveRAC maps alarm and metric data from network devices including servers, routers and switches into a visual metaphor called accordion drawing. In accordion drawing, users interact with the display as though it were a rubber sheet tacked down at the borders. Regions of the display can be stretched and compressed, but the fixed borders ensure the visibility of the entire information space. Compressed regions of the display aggregate the underlying data. We implement guaranteed visibility, a mechanism for ensuring the visibility of important features such as critical alarms. LiveRAC extends existing accordion drawing techniques in two ways: it can add, remove and reorder objects to the data set in logarithmic time, and provides an infrastructure for semantic zoom, a visualization technique where an optimal data representation is selected based on the screen space available to a data cell. Using a client-server approach we allow the user to query the underlying data in a context-rich, visually salient metaphor while maintaining interactive frame rates.
On the design of GaN Chireix power amplifiers using an embedding device model

As wireless cellular communication keeps expanding toward higher bandwidth, multiband signals, and high frequencies of operation, the design of power efficient radio frequency power amplifiers (PAs) for cellular phone basestations is submitted to more stringent requirements. This paper discusses the promising technique of nonlinear embedding, which may help stream line the design of such PAs. To this order, the design of a GaN radio frequency PA from device modeling to circuit design is presented. The large signal modeling of GaN high‐electron‐mobility transistors including thermal and trapping memory effects is discussed first. The nonlinear embedding device model is then introduced using the concept of an anti‐circuit transfer network. This embedding device model is then applied to the design of a Chireix amplifier. New Chireix design equations are developed to work with the memoryless inner core of the embedding device model, and their validity is confirmed in circuit simulations. The Chireix amplifier is then designed using the multi‐harmonic impedance terminations predicted by the embedding device model for the package reference planes. Finally, the resulting Chireix amplifier is implemented in a circuit simulator with the original GaN high‐electron‐mobility transistors device model and verified in simulations to have a performance approaching that of the originally targeted Chireix at the current reference planes. These theoretical and simulation results demonstrate the potential of the nonlinear embedding PA design technique in the design of Chireix power amplifiers. Copyright © 2016 John Wiley & Sons, Ltd.
Proteins in Calcium Phosphates Biomineralization

Biomineralization is a process of creating crystalline structures under biological control. The process takes place in hard tissues, such as bones, cartilages, and teeth. Biominerals are a combination of a crystal phase deposited onto an organic matrix. Inorganic components are mainly responsible for the biomineral hardness, while the organic matrix controls the shape, size, and polymorph of the crystals. Within the organic matrix, proteins exhibit a special biomineralization activity. Among them, one can distinguish insoluble collagen and soluble noncollagenous proteins. It is particularly noteworthy that noncollagenous proteins are intrinsically disordered proteins. High flexibility, acidic nature, and susceptibility to modifications make them especially adapted to the biomineralization control. This review paper is dedi-cated to the proteins which are involved in biomineralization of bones and teeth.
Creative Flow+ Dataset

We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth. Our dataset includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. Our dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size. While modern techniques for tasks such as optical flow estimation achieve impressive performance on realistic images and video, today there is no way to gauge their performance on non-photorealistic images. Creative Flow+ poses a new challenge to generalize real-world Computer Vision to messy stylized content. We show that learning-based optical flow methods fail to generalize to this data and struggle to compete with classical approaches, and invite new research in this area. Our dataset and a new optical flow benchmark will be publicly available at: www.cs. toronto.edu/creativeflow/. We further release the complete dataset creation pipeline, allowing the community to generate and stylize their own data on demand.
Hybrid evolutionary approach for designing neural networks for classification

An approach for the automatic design of artificial neural networks is presented where a hybrid evolutionary algorithm (HEA) is applied to the structural and parametric learning of networks. The HEA combines genetic algorithms and evolutionary programming on the basis of a real-valued multi-matrix representation. Experimental results show that the proposed approach has a good generalisation and a low computational cost.
Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Because of the bottleneck in the increase of clock frequency, multi-cores emerged as a way of improving the overall performance of CPUs. In the recent decade, many-cores begin to play a more and more important role in scientific computing. The highly costeffective nature of many-cores makes them extremely suitable for data-intensive computations. Specifically, many-cores are in the forms of GPUs (e.g., NVIDIA or AMD GPUs) and more recently, coprocessers (Intel MIC). Even though these highly parallel architectures offer significant amount of computation power, it is very hard to program them, and harder to fully exploit the computation power of them. Combing the power of multi-cores and many-cores, i.e., making use of the heterogeneous cores is extremely complicated. Our efforts have been made on performing optimizations to important sets of applications on such parallel systems. We address this issue from the perspective of communication patterns. Scientific applications can be classified based on the properties (communication patterns), which have been specified in the Berkeley Dwarfs many years ago. By investigating the characteristics of each class, we are able to derive efficient execution strategies, across different levels of the parallelism. We design a high-level programming API, as well as implement an efficient runtime system with pattern-specific optimizations, considering the characteristics of the hardware platform. Thus, instead of providing a general programming model, we provide separate APIs for each communication pattern.
Competition among universities on the educational services market in the circumstances of economics crisis and increased transition difficulties

Universities are the kind of institutions where values, as well as social mission, play an essential role. A wide range of changes, especially marketisation and massification of the higher education system, globalisation, internalisation, turbulence on the labour market, has resulted in universities existing in an atmosphere of strong competitiveness. It re-evaluated higher education as a kind of a service. This process also led to a new way of treating higher education aims, and an innovative style of managing HE institutions became necessary. Moreover, the quality of the HE educational service significantly influences both the private and professional life of individuals, society and the economic development in the country. The aim of the paper is to present an analysis of key strategies and tools of competition among universities on the labour market, especially in the aspect of providing their graduates with a successful transition into the labour market. The principal consideration will be devoted to instruments of building and managing long-term relations with broadly understood customers as a fundamental source of competitiveness.
Representing Human Cognition in Complex Man-Machine Environments

A major problem in attempts to understand human cognition and performance in man-machine systems is that of adequately representing the processes contributing to the performance in question. Such a representation should reflect not only the identity of the processes under study; but also the interactions and dependencies between the processes themselves and between the processes and the environment, the sequence in which the processes occur, and the information flow relevant to each process. Additionally, a rigorous representation of such processes should be predictive; preferably at a mathematical level.
Real‐world preferences for linked bilateral processing

Decades of research have substantiated that binaural hearing is superior to monaural hearing. This research has focused on areas such as sound detection, localization, speech intelligibility, and sound quality (see Blauert for review1). Considerable research on the advantages of bilateral hearing instrument fittings also has been conducted. In laboratory studies, advantages for bilateral hearing aids have been similar to the findings observed for binaural hearing.2-6 As discussed by Dillon, the three major factors contributing to the benefit of bilateral hearing instruments are: elimination of the head shadow effect, binaural squelch, and binaural redundancy.7 In recent years there has been an emphasis on using realworld data rather than laboratory studies to substantiate hearing aid benefit. Keeping with this trend, Noble recently reviewed the field evidence related to the benefits of bilateral amplification.8 Although the results of the studies he reviewed are not consistent, he concludes that for most people with a moderate-to-severe hearing loss, or whose lives include critical listening in dynamic listening tasks, bilateral hearing aids will be preferred. He adds that for people with milder hearing loss, or those who typically face less complex listening tasks, the preference for bilateral amplification may not be as great. One factor that could have impacted the outcome of these earlier field studies, however, is the type of signal processing employed. That is, more recently linked bilateral hearing processing has become available, and reports have suggested this feature may enhance bilateral benefits.9
Mapping of State Machines to Code: Potentials and Challenges

There is a big number of testing methods which are based on the state machine formalism. State machines serve as a strong means in behavior modeling of computer systems. However, this strength is lost as we go down the abstraction level from models to code. This is essentially due to the inherent semantic gap between state machine models and code, and that it is generally a challenging task to track states and transitions at runtime. In this paper, we discuss the benefits and challenges of having a mechanism for mapping state machines to code. The main intention with such a mechanism is to enable tracking of state changes at runtime. As we explain in this paper, the mapping provides for several important testing features such as verifying the actual runtime behavior of the system against its state machine models. Its importance becomes further emphasized remembering that model-based static analysis techniques rely on models as the source of information and design assumptions, and therefore, any mismatch between the actual behavior of the system and its models can also imply invalidation of the analysis results.