Miracle-dz/UniFAR-FTUs huggingface dataset:
A design of e-procurement infrastructure model for public service sector

Procurement is one of major business operations in public service sector. The advance of Information and Communication Technology (ICT) pushes this business operation to increase its efficiency and foster collaborations between the organization and its suppliers. This leads to a shift from the traditional procurement transactions to an e-procurement paradigm. Such change impacts on business process, information management and decision making. E-procurement involves various stakeholders who engage in activities based on different social and cultural practices. Therefore, a design of e-procurement system may involve complex situations analysis. This paper describes an approach of using the problem articulation method to support such analysis. This approach is applied to a case study from UAE.
Moving an application to the cloud: an evolutionary approach

When planning to move a legacy style application to the cloud various challenges arise. The potential size and complexity of such a project might especially discourage small or medium companies trying to benefit from the advantages the cloud promises. In addition, the field they have to address is still young and very dynamic and related technologies are rapidly changing. Based on on-going work in the context of the MODAClouds EU project, this paper describes an evolutionary, iterative approach to accomplish the migration of an existing application to a cloud based environment. Model based techniques are used to support the steps of this transition process by providing a baseline for the development of appropriate deployment architectures and the selection of suitable cloud providers. In addition they provide necessary abstractions in order to be less dependent on a specific technology stack or cloud provider. In order to show how we imagine the developed approach to be applied in practice we describe an existing traditional 3-tier application based on the meta-modeling platform ADOxx and how it could be moved to the cloud from the perspective of a medium-sized software manufacturing company.
Compartiendo la mirada: una experiencia en práctica reflexiva para formación permanente

The training of trainers is necessary for school improvement. In this sense, the teacher training team of Malaga Teacher Training Centre developed a course on reflective practice. It began with a theoretical stage so that the team could become familiar with the methodology. Then they carried out a practical task applying this theory in various types of training events and working directly with teachers in planning, monitoring and evaluating these actions, according to a description of the process in detail and an analysis of the results on two aspects: our teachers regarding future training activities and the teacher training team. Despite some difficulties, reflected in this article, it has been a very enriching experience both for the teacher trainers and the participating teachers. Also, we consider the reflective practice is a methodology that requires in-depth training to be applied in a truly effective way.
Named Entity Relation Extraction Based on Multiple Features

For the limited availability of established Mongolian named entity dictionary, with the increase of the new terminology and network vocabulary, Mongolian named entity dictionary was not able to update in time. This paper puts forward a method of named entity recognition, which based on a combination of multiple features and CRF. The method of named entity recognition proposed in this paper firstly cluster the entities that never appear in the repository with the method of hierarchical converged clustering, then carry through the normal words recognition and adjust the results of feature base.
Noiseless Codelength inWavelet Denoising

We propose an adaptive, data-driven thresholding method based on a recently developed idea of Minimum Noiseless Description Length (MNDL). MNDL Subspace Selection (MNDL-SS) is a novel method of selecting an optimal subspace among the competing subspaces of the transformed noisy data. Here we extend the application of MNDL-SS for thresholding purposes. The approach searches for the optimum threshold for the data coefficients in an orthonormal basis. It is shown that the optimum threshold can be extracted from the noisy coefficients themselves. While the additive noise in the available data is assumed to be independent, the main challenge in MNDL thresholding is caused by the dependence of the additive noise in the sorted coefficients. The approach provides new hard and soft thresholds. Simulation results are presented for orthonormal wavelet transforms. While the method is comparable with the existing thresholding methods and in some cases outperforms them, the main advantage of the new approach is that it provides not only the optimum threshold but also an estimate of the associated mean-square error (MSE) for that threshold simultaneously.
Annotating Images through Adaptation: An Integrated Text Authoring and Illustration Framework

This paper presents concepts to support authors illustrating their texts. Our approach incorporates content- and feature-based retrieval techniques in multimedia databases containing 2D images and 3D models. Moreover, we provide tools (i) to adapt the retrieval results to contextual requirements and (ii) to ease their integration into target documents. For 3D models the adaptation comprises aspects of the image composition (i. e., the selection of an appropriate view and the spatial arrangement of visual elements) and the selection of appropriate parameters for the rendering process. In addition, secondary elements (e. g., textual annotations or associated visualizations) are smoothly integrated into adapted 2D or 3D illustrations. These secondary elements reveal details about the semantic content of illustrations and author’s communicative intentions. They can ease the retrieval, reuse, and adaptation of illustrations in multimedia databases and are explicitly stored in conjunction with the adapted illustrations. Moreover, we developed a novel technique to support the mental reconstruction of complex spatial configurations by shape icons. With this illustration technique, shape properties of salient objects can be conveyed using abstractshaped models. We present retrieval techniques to determine appropriate 3D models to be displayed for shape icons. These shape icons along with the other secondary elements are smoothly integrated into the illustration that can be interactively explored by the user.
Microstructure and cleavage in lath martensitic steels

Abstract In this paper we discuss the microstructure of lath martensitic steels and the mechanisms by which it controls cleavage fracture. The specific experimental example is a 9Ni (9 wt% Ni) steel annealed to have a large prior austenite grain size, then examined and tested in the as-quenched condition to produce a relatively coarse lath martensite. The microstructure is shown to approximate the recently identified ‘classic’ lath martensite structure: prior austenite grains are divided into packets, packets are subdivided into blocks, and blocks contain interleaved laths whose variants are the two Kurjumov–Sachs relations that share the same Bain axis of the transformation. When the steel is fractured in brittle cleavage, the laths in the block share {100} cleavage planes and cleave as a unit. However, cleavage cracks deflect or blunt at the boundaries between blocks with different Bain axes. It follows that, as predicted, the block size governs the effective grain size for cleavage.
Implementing predictive learning analytics on a large scale: the teacher's perspective

In this paper, we describe a large-scale study about the use of predictive learning analytics data with 240 teachers in 10 modules at a distance learning higher education institution. The aim of the study was to illuminate teachers' uses and practices of predictive data, in particular identify how predictive data was used to support students at risk of not completing or failing a module. Data were collected from statistical analysis of 17,033 students' performance by the end of the intervention, teacher usage statistics, and five individual semi-structured interviews with teachers. Findings revealed that teachers endorse the use of predictive data to support their practice yet in diverse ways and raised the need for devising appropriate intervention strategies to support students at risk.
Combinging Fisheye with List: Evaluating the Learnability and User Satisfaction

The paper describes a location-aware travel guide prototype that was implemented into a Nokia Symbian Series 60 phone with Location API (JSR-179) support. It contains relevant travelling information that could assist users while they are travelling or when they arrive at a particular destination. The objectives of the study were to discover user preferences towards fisheye and list style menus in the user interface as well as user satisfaction on the prototype. A field test to evaluate the prototype was done with participants ranging from 10 to 39 years old. The results indicated that the fisheye style and the prototype itself were easily comprehended and accepted by the participants.
DMN Operational Synchrony Relates to Self-Consciousness: Evidence from Patients in Vegetative and Minimally Conscious States

The default mode network (DMN) has been consistently activated across a wide variety of self-related tasks, leading to a proposal of the DMN’s role in self-related processing. Indeed, there is limited fMRI evidence that the functional connectivity within the DMN may underlie a phenomenon referred to as self-awareness. At the same time, none of the known studies have explicitly investigated neuronal functional interactions among brain areas that comprise the DMN as a function of self-consciousness loss. To fill this gap, EEG operational synchrony analysis [1, 2] was performed in patients with severe brain injuries in vegetative and minimally conscious states to study the strength of DMN operational synchrony as a function of self-consciousness expression. We demonstrated that the strength of DMN EEG operational synchrony was smallest or even absent in patients in vegetative state, intermediate in patients in minimally conscious state and highest in healthy fully self-conscious subjects. At the same time the process of ecoupling of operations performed by neuronal assemblies that comprise the DMN was highest in patients in vegetative state, intermediate in patients in minimally conscious state and minimal in healthy fully self-conscious subjects. The DMN’s frontal EEG operational module had the strongest decrease in operational synchrony strength as a function of selfconsciousness loss, when compared with the DMN’s posterior modules. Based on these results it is suggested that the strength of DMN functional connectivity could mediate the strength of self-consciousness expression. The observed alterations similarly occurred across EEG alpha, beta1 and beta2 frequency oscillations. Presented results suggest that the EEG operational synchrony within DMN may provide an objective and accurate measure for the assessment of signs of self-(un)consciousness in these challenging patient populations. This method therefore, may complement the current diagnostic procedures for patients with severe brain injuries and, hence, the planning of a rational rehabilitation intervention.
ECHO at the LSHTC Pascal Challenge 2

A classification system called ECHO has been designed for our participation to the first LSHTC Pascal challenge. This system is based on a principle of echo. It computes the score of a document for a class by combining a bottom-up and top-down propagation of activation in a very simple neural network. For this second edition of the challenge, the system has been refined in order to improve its performances. The main improvement concerns the definition of a method of calibration integrating the distribution of the echo scores of the training documents for each class. Another improvement concerns the management of the relative importance of bottom-up and top-down propagation which we make vary with the class.
Interaction, simulation and invention: a model for interactive music

This paper describes the incremental development of a model for interactive music – music instantiated in real-time on the basis of local performance and environmental information. Music is understood as a dynamical complex of interacting situated embodied behaviours. These behaviours may be physical or virtual, composed or emergent, or of a time scale such that they figure as constraints or constructs. All interact in the same space by a process of mutual modelling, redescription, and emergent restructuring. The model is implemented as a complex adaptive system in the Swarm simulation environment.
An Analytical Approach to the Outage Probability of Amplify-and-Forward Relaying with an MRC Receiver

A novel analytical approach is proposed for the performance analysis of AF relaying systems with MRC receiver in the destination. We first derive PDF and CDF of equivalent SNR of the equivalent S-R-D channel. However, both the PDF and CDF include modified Bessel functions which are not easily tractable. In order to derive an analytical statistical model for the PDF of the total SNR at the output of MRC receiver, a novel approach is introduced to rewrite the modified Bessel function of second kind in the form of infinite series using simple elementary functions. By substituting novel series representation of modified Bessel function in the PDF of equivalent S-R-D channel, the performance of the overall system which includes MRC combining in the destination is analytically studied and closed form expressions are derived for the outage probability of the system. Interestingly, the infinite series approaches its asymptotic result rather accurately with a few terms only. Numerical simulations are provided to verify the accuracy of the novel theoretical approach.
The impact of scaffolding and nonscaffolding strategies on the EFL learners' listening comprehension development

ABSTRACT Drawing on sociocultural theory, and a large number of empirical studies conducted on the effectiveness of scaffolding on second or foreign language learning, the authors investigated the application of different forms of scaffolding to improve listening comprehension of the Iranian intermediate English as a foreign language (EFL) learners. To this end, 90 intermediate EFL learners were randomly assigned to 2 experimental groups and a control group. During 15 treatment sessions of listening comprehension instruction, expert peers in the first experimental group assisted their less knowledgeable peers in their listening comprehension tasks. The participants in the second experimental group were asked to assist their coequal peers while doing the listening comprehension tasks, but the participants in group 3 did not interact with each other or even their teacher and no scaffolding scheme was used when they were doing the listening comprehension tasks. One-way analysis of variance and post hoc analyses revealed that the expert peers' scaffolding was the most effective procedure for the intermediate EFL learners' listening comprehension development. Furthermore, coequal peers' scaffolding was proved to be the second effective procedure for the listening comprehension improvement. The findings underscore the relative efficacy of different forms of scaffolding procedure and the superiority of the expert peers' scaffolding over coequals' scaffolding. The qualitative analysis of the recorded data indicated that the implicit scaffolding strategies were more frequently used by the expert peers. However, the explicit scaffolding strategies were more frequently used to by the coequals. The findings of the study underscore the positive potentiality of scaffolding in its different forms for the EFL learners' listening comprehension development.
Time-Contrastive Learning Based Unsupervised DNN Feature Extraction for Speaker Verification

In this paper, we present a time-contrastive learning (TCL)based unsupervised bottleneck (BN) feature extraction method for speech signals with an application to speaker verification. The method exploits the temporal structure of a speech signal and more specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting the signal without using any label information, in contrast to conventional DNN based BN feature extraction methods that train DNNs using labeled data to discriminate speakers or passphrases or phones or a combination of them. We consider different strategies for TCL and its combination with transfer learning. Experimental results on the RSR2015 database show that the TCL method is superior to the conventional speaker and pass-phrase discriminant BN feature and Mel-frequency cepstral coefficients (MFCCs) feature for text-dependent speaker verification. The unsupervised TCL method further has the advantage of being able to leverage the huge amount of unlabeled data that are often available in real life.
Towards calibration-free geo-localization of stationary outdoor webcams

This study proposes two strategies for determining the approximate geographical location of outdoor webcams based on time-series comprising regularly sampled images. The strategies require an accurate account of universal time and the date to be known, then the intensity of the images are used to estimate the sunrise and sunset times. Given accurate sunrise and sunset times a celestial model can be used to extract the latitude and longitude of the webcam. Webcams vary in quality, dynamic pixel depth and sensitivity to light. Two strategies are therefore proposed for avoiding the need to perform calibration. The first technique involves normalizing and noise-damping the image intensity measurements. The second technique employs a self-normalizing brightness ratio. The brightness ratio is computed from the overall brightness of the upper part of the image in relation to the bottom part. During day the sky is much brighter than the ground, while at night the sky is much darker than the ground if the ground is lit up. Experiments demonstrate that the intensity normalization strategy is the most robust and it is capable of determining the geographical location of webcams with an accuracy of approximately 2 degrees.
The Visual Rhetoric of Self-advocacy Organisations on Poverty: All about Courage?

At the beginning of the 1990s, several European welfare states installed a policy on poverty that explicitly recognised the voice and life knowledge of people in poverty. The idea of talking ‘with’ the poor came to prominence instead of talking ‘about’ or ‘to’ people in poverty. Beresford and Croft (1995) proclaimed a possible paradigm shift from advocacy to self-advocacy . In Belgium, in the aftermath of the General Report on Poverty (1994), grassroots organisations such as ATD Fourth World and BMLIK (Movement of People with Low Income and Children ) gained recognition as ‘organisations where people in poverty take the floor’. BMLIK launched the photobook Courage (1998) which contains prominent black and white photographs portraying families in deep poverty combined with oral testimonies. The central question we ask is whether and how this photobook can be considered an emblematic case for the framing of poverty as a violation of human rights, and for the way the self-advocacy paradigm has been materialised in this. Through a visual-rhetorical analysis (Foss, 1994) of Courage we present our research findings wherein the ‘pedagogical aesthetic’ (Trachtenberg, 1990) of socially engaged photography comes to the fore, as well as how this contributes to social change and justice.
Synthesis of a super broadband model for on-chip spiral inductors

A modified T-section equivalent circuit is proposed to model on-chip spiral inductors with extremely large bandwidth. This developed model combines a five-element modified T-section to include up to half-wavelength long transmission-line effects, and parallel and series feedback resonators to account for higher-order resonances present in the coil-coupling and ground-return paths. The modeled S parameters show excellent agreement with measured results over the entire measurement frequency range of 20 GHz and 50 GHz for a series of planar spiral inductors implemented on silicon and InGaAs substrates, respectively. The effective modeling bandwidth is at least several times larger than in a conventional PI-section model.
Supporting Adaptive Sampling in Wireless Sensor Networks

Adaptive sampling is proposed to improve the power efficiency of wireless sensor networks in that the sampling rate of a sensor node can change in response to the changes in the environment. To transmit the sampled data promptly, the nodes with different and dynamically changing sampling rates have to transmit at different and changing rates correspondingly, which in turn causes severe packet loss and power consumption. To address this problem, we propose a routing-layer scheduling scheme, SPAS, to support adaptive sampling. In SPAS, each node keeps a record of packets to be forwarded and wakes up at scheduled times to transmit and to receive. Furthermore, each node can dynamically optimize its route to the sink based on the transmission rates of its neighboring nodes. Our simulation results show that SPAS achieves both high power efficiency and a low packet loss rate in adaptive sampling.
Inferring the causal decomposition under the presence of deterministic relations

The presence of deterministic relations pose problems for current algo- rithms that learn the causal structure of a system based on the observed conditional independencies. Deterministic variables lead to information equivalences; two sets of variables have the same information about a third variable. Based on information content, one cannot decide on the direct causes. Several edges model equally well the dependencies. We call them equivalent edges. We propose to select among the equivalent edges the one with the simplest descriptive complexity. This ap- proach assumes that the descriptive complexity increases along a causal path. As confirmed by our experimental results, the accuracy of the method depends on the chance of accidental matches of complexities.