Miracle-dz/UniFAR-FTUs huggingface dataset:
Debriefing's Influence on Learning in Business Game: An Experimental Design

This paper studies the influence of debriefing in the learning of the participants of business games. Through quasi-experimental study we examined the self-declarations of 112 undergraduate Business Management students undertaking the 8th term, divided into two groups: experimental, exposed to debriefing; and control, not exposed to the debriefing. Mann- Whitney´s tests revealed that the quantum of learning perceived by the members of the experimental group was statistically significantly higher than the members of the control group in seven out of nine learning variables assessed (p smaller than 0.05). The average effect size (d = 0.45) shows an average improvement of 18%. These results suggested that the debriefing positively influence on experiential learning cycle promoted by business games. In the perspective of Kolb’s experiential learning cycle (Kolb, 1984), our findings suggest that Reflective observation and Abstract conceptualization stages can be reinforced by subsequent debriefing activities to the simulation rounds, in order to enhance continuous processes of action and reflection of the participants, according to the spiral experiential learning cycle.
An Automatic Multi-domain Thesauri Construction Method Based on LDA

This paper proposed a method for building domain-specific thesauri automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). This method consists of two steps: 1) discovering domain-specific terms from document collections of multiple domains, and 2) learning hierarchical relations between the associated terms of each domain. The novelty of step 1 lies in the utilization of LDA in selecting terms with high predictive probability of a specific domain via latent topics, which overcomes the drawbacks of unigram model. Meanwhile, the hierarchical relations among domain terms are exploited by a novel approach based on word association analysis in step 2. The proposed method is tested on two datasets in different languages. The experimental results show that the terms obtained by this method are intuitively relevant to the reference domain and many term pairs with hierarchical relations are discovered. And the relations reflect the structure of the domain rather well. Compared to other approaches, the proposed one is more accurate in both domain terms mining and hierarchical relation learning tasks.
Long Horizon Predictability of ExchangeRates : Is it for Real ?

This paper reexamines recent results on the predictability of nominal exchange rate returns by means of fundamental models. Using a monthly sample of the post-Bretton Woods period we show that the in-sample t between long-horizon exchange rate returns and various models is not signiicant if we correct for the persistence that is caused by overlapping data and spurious regression phenomena. The long horizon out-of-sample predictive power of the fundamental exchange rate models is found to be very weak. This is especially the case when we conduct the out-of-sample forecasting tests for a longer time span than that of earlier papers. We show that this failure in forecasting performance, resulting from extending the time span, is due to the absence of cointegration between exchange rates and structural exchange rate models.
The development and real-world application of FROG, the fun robotic outdoor guide

This video details the development of an intelligent outdoor guide robot. The main objective is to deploy an innovative robotic guide which is not only able to show information, but to react to the affective states of the users, and to offer location-based services using augmented reality. The scientific challenges concern autonomous outdoor navigation and localization, robust 24/7 operation, affective interaction with visitors through outdoor human and facial feature detection as well as engaging interactive behaviours in an ongoing non-verbal dialogue with the user.
Understanding students’ perceptions and challenges of Computer-Based Assessments: a case of UKZN

ABSTRACT Assessments are important to academic institutions because they help in evaluating students’ knowledge. The conduct of assessments nowadays has been influenced by the continuous evolution of information technology. Hence, academic institutions now use computers for assessments, often known as Computer-Based Assessments (CBAs), in tandem with Paper-Based Assessments (PBAs). The literature, however, shows that the use of CBAs has received controversial reactions, and also shows that students face challenges when undertaking CBAs. This study was conducted to understand the perceptions of students and the challenges they face when undertaking CBAs. The study employed a descriptive research approach and data was collected through questionnaires. A total of 210 questionnaires were distributed to students. Findings show that students find it easy to undertake CBA and hence prefer it to PBA. However, the findings also show that students experience challenges that include Internet connectivity and mode of presentation of items, when undertaking CBA.
Flexible Data Access in a Cloud Based on Freshness Requirements

Data clouds are newly emerging environments in which commercial providers manage large volumes of data with individual quality of service (QoS) guarantees per customer. These guarantees mainly include keeping several replicas of each data item in different distributed data centers for availability purposes. However, as the cost of maintaining several updateable replicas per data object is very high, cloud providers rather offer only a limited number of synchronously updated replicas (i.e., replicas that are always up-to-date) together with several read-only replicas that are updated in a lazy way and thus might hold stale data. QoS agreements may also include the maintenance of dedicated archives (copies of data which are frozen at some point in time). Stale data allow cloud providers to offer a variety of read operations with different semantics, e.g., read the most recent data, read data not older than / not younger than some timestamp t, or read data produced between t1 and t2, or read data exactly as of t. These read operations can be supported by a read-only site using a stale replica. In this paper we present our approach to cloud data management, based on a recent protocol for data grids. We discuss in detail how the refresh of individual replicas is provided in a completely distributed way. Finally, we present the results of a performance evaluation in a data cloud setting.
A model of the role of error detection and self-regulation in academic performance

ABSTRACT The authors' aim was to determine the extent to which error detection contributes to the explanation of a cognitive and motivational model of student performance in an assessment test. A total of 151 science students of secondary education participated in the investigation. Two causal models were developed using a structural equation analysis. This allowed the authors to estimate the effects and relationships between the different variables involved. In addition, they conducted correlational and descriptive analyses of the study variables to further explore the data obtained for the sample. The results indicate that error detection is an important factor that influences student performance to a greater extent than other strategic and motivational variables that have traditionally been considered strong predictors of performance. The findings show that students' detection of their own errors is a mediatory variable that could act as an engagement in the self-regulation cycle.
Monitoring slowly evolving tumors

Change detection is a critical task in the diagnosis of many slowly evolving pathologies. This paper describes an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ten clinical data sets. We show that the results of our approach highly correlate with expert findings but seem to be less impacted by inter- and intra-rater variability.
Optimal Control of a Multi-Energy District Boiler: A Case Study

Abstract The present paper deals with the optimization of a multi-energy district boiler (La Rochelle, France) which supplies domestic hot water and heats residential and public buildings, using renewable and fossil resources. First, a combination of white, grey and black boxes was used to model the plant, thanks to a modular approach. Next, a stratified thermal storage tank was modelled and completed the just-mentioned plant model. Using these models and forecasted sequences about outdoor temperature and thermal power consumption, a model predictive controller allows optimizing the use of both the tank and the wood boiler. As a result, fossil energy consumption and CO 2 emissions are minimized. Energy is stored during low-demand periods and used when demand is high, instead of consuming gas and fuel oil.
Kernel Additive Models for Source Separation

Source separation consists of separating a signal into additive components. It is a topic of considerable interest with many applications that has gathered much attention recently. Here, we introduce a new framework for source separation called Kernel Additive Modelling, which is based on local regression and permits efficient separation of multidimensional and/or nonnegative and/or non-regularly sampled signals. The main idea of the method is to assume that a source at some location can be estimated using its values at other locations nearby, where nearness is defined through a source-specific proximity kernel. Such a kernel provides an efficient way to account for features like periodicity, continuity, smoothness, stability over time or frequency, and self-similarity. In many cases, such local dynamics are indeed much more natural to assess than any global model such as a tensor factorization. This framework permits one to use different proximity kernels for different sources and to separate them using the iterative kernel backfitting algorithm we describe. As we show, kernel additive modelling generalizes many recent and efficient techniques for source separation and opens the path to creating and combining source models in a principled way. Experimental results on the separation of synthetic and audio signals demonstrate the effectiveness of the approach.
An Algorithm for Finding Process Identification Intervals from Normal Operating Data

Performing experiments for system identification is often a time-consuming task which may also interfere with the process operation. With memory prices going down and the possibility of cloud storage, years of data is more and more commonly stored (without compression) in a history database. In such stored data, there may already be intervals informative enough for system identification. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identification (rather than completely autonomous system identification). For each loop, four stored variables are required: setpoint, manipulated variable, measured process output and mode of the controller. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a hypothesis test to check that there is a causal relationship between process input and output. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from about 200 control loops. It was able to find all intervals in which known identification experiments were performed as well as many other useful intervals in closed/open loop operation.
Answer-Driven Reasoning as a Framework for Intelligent Information Access

This paper presents a general overview of answer-driven reasoning, which may serve as a common framework for a variety of Information Retrieval related activities. The underlying assumption is that a piece of information in a given document is as relevant as the extent to which such information represents the answer to a possible user’s question. The approach relies on the application of answer procedures to a source data (e.g. a text collection) to mine question/answer correlations. The outcome can be exploited for different purposes, ranging from the creation of a Q/A index specifically targeted to intelligent Information Retrieval, Question Answering and Summarization, to the augmentation of existing ontologies in the Semantic Web scenario.
A Computationally Efficient Method for Nonparametric Modeling of Neural Spiking Activity with Point Processes

Point-process models have been shown to be useful in characterizing neural spiking activity as a function of extrinsic and intrinsic factors. Most point-process models of neural activity are parametric, as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can arise. Nonparametric methods are attractive due to fewer assumptions, but computation in general grows with the size of the data. We propose a computationally efficient method for nonparametric maximum likelihood estimation when the conditional intensity function, which characterizes the point process in its entirety, is assumed to be a Lipschitz continuous function but otherwise arbitrary. We show that by exploiting much structure, the problem becomes efficiently solvable. We next demonstrate a model selection procedure to estimate the Lipshitz parameter from data, akin to the minimum description length principle and demonstrate consistency of our estimator under appropriate assumptions. Finally, we illustrate the effectiveness of our method with simulated neural spiking data, goldfish retinal ganglion neural data, and activity recorded in CA1 hippocampal neurons from an awake behaving rat. For the simulated data set, our method uncovers a more compact representation of the conditional intensity function when it exists. For the goldfish and rat neural data sets, we show that our nonparametric method gives a superior absolute goodness-of-fit measure used for point processes than the most common parametric and splines-based approaches.
Stratégies identitaires, trajectoires migratoires et processus d'interculturation : solutions déployées dans le contact culturel par les migrants d'origine japonaise et sénégalaise en France

L’auteur s’interroge sur la facon dont s’effectue la restructuration identitaire du migrant engage dans un processus d’interculturation et sur les consequences de cette operation pour son equilibre identitaire. Articulant sa problematique autour des strategies identitaires, elle cherche a determiner l’impact de la culture d’origine du migrant et de sa trajectoire migratoire sur l’emploi des strategies identitaires et a evaluer la capacite de ces strategies a favoriser son integration psychosociale au pays d’accueil. Une enquete qualitative et quantitative aupres de 200 migrants japonais et senegalais met en evidence differents types d’interculturation qui caracterisent concretement le lien entre le processus d’interculturation et ses consequences identitaires. Enfin, l’auteur pointe la complementarite des variables culture d’origine et trajectoire migratoire et developpe la notion de style strategique fluide pour expliquer les modes de gestion individualises des conflits identitaires.
Na+/H+ Exchanger Regulatory Factor 1 Overexpression-dependent Increase of Cytoskeleton Organization Is Fundamental in the Rescue of F508del Cystic Fibrosis Transmembrane Conductance Regulator in Human Airway CFBE41o- Cells

NHERF1 overexpression increases functional apical expression of F508del CFTR in CFBE41o- cells. Here, we show that this occurs via the formation of the multiprotein complex NHERF1-phosphoezrin-actin, which provides a regulated linkage between F508del CFTR and the actin cytoskeleton resulting in an increased F508del CFTR stability in the membrane.
Moore's curse on textual passwords

Background: Passwords are still the predominant way of authentication in information systems, and are mostly at user's responsibility. They conceive, use, re-use, abuse and forget passwords. In absence of strict password policies and at minimum required user training, passwords tend to be short, easy to remember, connected to the user's personal or professional life and consequently easy to break. The additional problem with passwords is their aging: Moore's law is affecting the available computing power to crack passwords and those deemed secure today may easily be broken in the near future. Objective: The aim of this paper is to study various scenarios of the effect the Moore's law is having on passwords and their security. In addition, advancements in other fields, e.g. quantum computing and Internet of Things, are taken into the account. Method: We analyzed various password types and the lengths required to withstand an off-line brute-force attack. The analysis was performed under various scenarios and combinations thereof: the Moore's law will continue to be in the effect for years to come with varying parameters, quantum computing will become feasible, improvements in hash tables computations will speed up the cracking process, and others. Results: The paper shows the minimum password length in characters for each password type under various scenarios. Even the most optimistic scenario shows that the minimum required password length today should be of 11 randomly drawn characters, rendering most of the passwords inappropriate due to their poor memorability. Conclusion: The current textual passwords are cursed by the Moore's law and other advancements in the field. Soon, classical textual passwords will need to be replaced by other mechanisms, which are, fortunately, already emerging.
Cost-Based Dynamic Job Rescheduling: A Case Study of the Intel Distributed Computing Platform

We perform a trace-driven analysis of the Intel Distributed Computing Platform (IDCP), an Internet-scale data center based distributed computing platform developed by Intel Corporation for massively parallel chip simulations within the company. IDCP has been operational for many years, and currently is deployed “live” on tens of thousands of machines that are globally distributed at various data centers. Our analysis is performed on job execution traces obtained over a one year period collected from tens of thousands of IDCP machines from 20 different pools. Our analysis demonstrates that job completion time can be severely impacted due to job suspension when higher priority jobs preempt lower priority jobs. We then develop costbased dynamic job rescheduling strategies that adaptively restart suspended jobs, which better utilize system resources and improve completion times. Our trace-driven evaluation results show that dynamic rescheduling enables IDCP to significantly reduce job completion times.
Machine Comprehension Using Robust Rule-Based Features and Multiple-Sentence Enhancing

In this project, we designed multiple featurelizers to extract information and answer multiple choice reading comprehension questions. Given a triple of passage question and answer, the featurelizer will generate a set of features which are designed using robust NLP tools. We then feed generated features into a neural network classifier which gives a probability score for each answer. The features we used are improved sliding window, key word distance, syntax feature, word embeddings, multiple sentences and coreference resolution.
Exploring emotional preference for smartphone applications

Emotion is an essential element of human behaviours. In this research, we investigated human behaviours related with the touch interface on a smartphone as a way to understand users' emotional states. As modern smartphones have various embedded sensors such as accelerometer and gyroscope, we aim to utilize data from these embedded sensors for recognizing human emotion and further finding emotional preferences for smartphone applications. We collected 12 attributes from 3 sensors during users' touch behaviours, and recognized seven basic emotions with off-line analysis. Finally, we generated a preference matrix of applications by calculating the difference between prior and posterior emotional states to the application usage. The pilot study showed 0.57 of an average f1-measure score (and 0.82 with decision tree based methods) with 455 cases of touch behaviour. We discovered that a simple touching behaviour has a potential for recognizing users' emotional states.
Impact of Legal Threats on Online Music Sharing Activity: An Analysis of Music Industry Legal Actions*

The music industry has repeatedly expressed concerns over potentially devastating impacts of online music sharing. Initial attempts to control online file sharing have been primarily through consumer education and legal action against the operators of networks that facilitated file sharing. Recent legal action against individual file sharers marked an unprecedented shift in the industry’s strategy. The focus now is on well‐publicized legal threats and actions on a relatively small group of individuals to discourage overall music file sharing. To determine the resulting impact of these legal threats, we passively tracked online file‐sharing behavior of over 2,000 individuals. We found that individuals who share a substantial number of music files react to legal threats differently from those who share a lesser number of files. Importantly, our analysis indicates that even after these legal threats and the resulting lowered levels of file sharing, the availability of music files on these networks remains substantial.
A fast clustering method based on multi-splitting grid

Clustering algorithms based on Grid are attractive for the task of data partition in spatial database. In the background of big data more and more research focuses on how to solve the conflict between efficiency and accuracy of clustering. Existing Grid-based clustering algorithms generally have a high time efficiency without considering the distribution of the data inside a grid. In this paper, a new clustering method based on multi-splitting grid (CBMG) is proposed. In CBMG algorithm grids are further split into cells in order to discover the data distribution in each grid. So if the data in a grid belongs to different clusters, CBMG can easily handle it. Because the number of cells in a grid is limited, CBMG can greatly improve the accuracy of clustering and only take less extra time consuming. Experiments show the better performance of CBMG.
Live HTTP streaming of video and subtitles within a browser

Video streaming has become a very popular application on the web, with rich player interfaces and subtitle integration. Additionally, live streaming solutions are deployed based on HTTP Streaming technologies. However, the integration of video and subtitles in live streaming solutions still poses some problems. This paper describes a demonstration of live streaming of video and subtitle data using the MPEG-DASH technology and its synchronous playback in a web browser. It presents the formats, architecture and technical choices made for this demonstration and shows that it is feasible with upcoming browsers, paving the way for richer web video applications.