Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Learning: A Scaffolding Methodology for CLIL and LSP Teaching and Learning

When dealing with Language for Specific Purposes (LSP), teachers always have to confront with issues which are strictly linked to the specificities of the language of a given field. This is not only the case of language teachers, but it is also and particularly true for CLIL teachers in Italy, who are subject teachers sharing with language teachers some aspects of pupils’ language education. Corpus-based methodology in CLIL classes (the LSP learning environment par excellence) means to empower both teachers and students to develop competences in moving away from mere surface features of text to selecting and understanding meanings and structures. In doing so, they use texts with specific intentions, becoming familiar with tools such as corpora to compensate, for instance, the deficiencies of general dictionaries. Over 25 years ago Tim Johns advocated the learning-centered value of Data-driven Learning (DDL), calling “every student a Sherlock Holmes”. In fact, DDL good practices perfectly align with current theories and practices of SLA, namely the constructivist and learner-centered approaches to language acquisition. It underpins the mandate in contemporary communicative language instruction for the use of authentic language materials and for the development of metalinguistic knowledge and learner autonomy. The uses and benefits of corpora for language learning are widely reported in the literature, although there is still little field practice, in Italy at least. This paper is grounded in the discussion of DDL as a scaffolding method to support the language aspects of CLIL, and the role of data-driven materials in enhancing learning in general. A case study where DDL was successfully used in a vocational context will be presented. The reference corpus was based on oral and written productions by English native speakers, elicited from a picture specifically sketched for the activity (a hairdresser’s salon with multiple actions and objects). Both lexical items and syntactic structures were extracted by students who were confronted with the data and had to deal with different tasks for their analysis. Results were encouraging and students who were exposed to DDL engaged in an involving activity that considerably improved their language skills in their actual working practice.
Multiscale Refinement Network for Water-Body Segmentation in High-Resolution Satellite Imagery

Water-body segmentation in high-resolution satellite imagery is challenging because of the significant variations in the appearance, size, and shape of water bodies. In this letter, a novel multiscale refinement network (MSR-Net) is proposed for water-body segmentation. Similar to most learning-based methods, the MSR-Net resorts to the multiscale information for segmentation, but it improves existing networks in two ways: First, it uses the multiscale information in a new perspective. Instead of the traditional one-off manner that concatenates features and conducts segmentation on one uniform scale, the MSR-Net adopts a new multiscale refinement scheme that makes full use of the multiscale features for more accurate water-body segmentation. In addition, a novel erasing-attention module is designed for an effective feature embedding during the refinement scheme. Experiments on the Gaofen Image Data Set and the DeepGlobe Data Set demonstrate the superiority of MSR-Net when compared with the other state-of-the-art semantic segmentation methods, including U-Net, SegNet, DeepLabv3+, and ExFuse.
Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement

In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.
Quantum teleportation of single-electron states

We consider a scheme for on-demand teleportation of a dual-rail electron qubit state, based on single-electron sources and detectors. The scheme has a maximal efficiency of 25%, which is limited both by the shared entangled state as well as the Bell-state measurement. We consider two experimental implementations, realizable with current technology. The first relies on surface acoustic waves, where all the ingredients are readily available. The second is based on Lorentzian voltage pulses in quantum Hall edge channels. As single-electron detection is not yet experimentally established in these systems, we consider a tomographic detection of teleportation using current correlators up to (and including) third order. For both implementations we take into account environmental effects.
2D GeSe/SnS2(SnSe2) broken-gap heterostructures for tunnel field-effect transistors applications

Two-dimensional (2D) materials-based van der Waals heterostructures (vdWHs) provide a platform to realize novel physical phenomena for future nanoelectronic devices. Here, our theoretical results indicate that the 2D GeSe/SnS2(SnSe2) vdWHs possess the broken-gap band alignment and can be potentially utilized in the tunnel field-effect transistors (TFETs). Moreover, negative electric field can keep type-III band alignment and enlarge the tunneling window, while the positive electric field can transform the type-III to type-I or type-II band alignments for the GeSe/SnS2(SnSe2) vdWHs. Thus, these studies open the way to fabricate the 2D materials-based TFETs and the possibility of multi-functional nanodevices applications.
Building Detection in SAR Images Based on Bi-Dimensional Empirical Mode Decomposition Algorithm

This letter proposes a new synthetic aperture radar (SAR) image building detection method based on the bi-dimensional empirical mode decomposition (BEMD) algorithm, well adapted to nonlinear and nonstationary signals. First, the SAR image is decomposed by the BEMD algorithm to generate intrinsic mode functions (IMFs), and the IMFs are combined to extract bright regions and dark regions from the SAR image. Next, the Markov random field (MRF) model is used to cluster the SAR image, and according to the centroid of the dark region, the dark category with a complete edge is located. Finally, the building is detected by combining the bright region, the dark region, and the given shadow direction. Analytical and experimental evidence show that the proposed method has high detection accuracy and has wide applicability for medium, large, and irregular shaped buildings with shadows.
Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases

In this paper, we present a workflow for evaluating resting-state brain functional connectivity with different community detection algorithms and their strengths to discriminate between health and Parkinson's disease (PD) and mild cognitive impairment preceding Alzheimer's disease (AD-MCI). We further analyze the complexity of particular pipeline steps aiming to provide guidelines for both execution on computing infrastructure and further optimization efforts. On a dataset of 50 controls and 70 patients we measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying AD-MCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.
TINGKAT KECEMASAN MAHASISWA TINGKAT III KEPERAWATAN YANG AKAN MENGIKUTI UJI KOMPETENSI DIPLOMA III KEPERAWATAN

Kecemasan merupakan kondisi gangguan psikologis dan fisiologis yang ditandai dengan gangguan kognitif, somatic, emosional dan komponen dari rangkaian tingkah laku. Kecemasan menghadapi uji kompetensi merupakan suatu keadaan yang membuat mahasiswa D-III keperawatan merasa tidak tenang, khawatir, takut dan tegang ketika akan menghadapi uji kompetensi yang baru pertama kali akan dilaksanakan oleh calon para lulusan perawat. Penelitian ini merupakan penelitian kuantitatif dengan menggunakan rancangan penelitian deskriptif kuantitatif. Penelitian ini merupakan penelitian yang bertujuan menjelaskan fenomena yang ada dengan menggunakan angka-angka untuk menggambarkan karakteristik individu. Penelitian ini berlansung pada 2 s.d 31 Agustus 2018 dengan populasi dalam penelitian ini adalah seluruh mahasiswa tingkat III yang ada di lokasi penelitian. Teknik pengambilan sampel yang digunakan adalah total sampling dikarenakan jumlah populasi yang sedikit. Adapun sampel dalam penelitian ini adalah semua mahasiswa tingkat III yang akan mengikuti uji kompetensi perawat Diploma III. Hasil Penelitian menunjukkan bahwa bahwa 100% mahasiswa tingkat III Mengalami kecemasan dalam menghadapi uji kompetensi yang tersebar dalam kategori kecemasan ringan (29,41%), kecemasan sedang (58,82%) dan kecemasan berat (11,76%) dan didukung oleh respon afektif, kognitif, fisiologi dan perilaku yang muncul.
Spoken language interaction: effects of vocabulary size, user experience, and expertise on user acceptance and performance

To enable novice and expert nursing staff to enter patient information into the healthcare database by speech, a prototype spoken language interface (SLI) has been designed. The prototype permits continuous spoken data to be entered using a speech recognition system. Feedback from the speech recognition system is provided by an auditory beep. The prototype interface contains two available vocabularies, a small specific vocabulary and a large inclusive vocabulary. A split-plot experimental design tests the effects of the two levels of vocabulary available to the prototype spoken language interface, the two levels of nursing expertise (i.e., novice and expert) and two levels of experience with the interface. These three independent variables; vocabulary size, expertise, and experience, are investigated for their impact on the users performance and acceptance. Each subject performs five cardiovascular assessments; a hands-busy and eyes busy task on a non-responding patient. Twenty one data items are gathered and entered into the spoken language system while performing the assessment. Performance is measured by time on task. Acceptance in obtained by the completion of a subjective acceptance questionnaire. Three factors were found to affect the performance and acceptance of an SLI user. Increases in performance were found when the user was an expert in the domain, had experience with the SLI, or was using a SLI with a small vocabulary. A high degree of acceptance was found when the user was experienced with the SLI, was using a large vocabulary, or was a novice in the domain. No effects were found for comparisons of vocabulary and expertise or vocabulary and experience. The overall acceptance of the SLI was relatively high with mean acceptance percentage scores around 70%.
Measurement of multi-antenna communication channels - study of upper bound channel capacity based on eigenvalue decomposition

The content of the work is a study on quality of the wireless communication inside a building with stress put on the multi–antenna systems. The thesis contains a theoretical basis of the propagation of the electromagnetic waves in such Multiple–Input Multiple–Output (MIMO) channel, taking into the account only relevant properties of such propagation. The emphasis is put on the diversity and gain of the systems and capacity of the MIMO channel. A linear algebra numerical tool called Singular Value Decomposition (SVD) is used to calculate channel capacity. The simulation part utilizes SVD to determine a capacity of the defined scenario and then the results are compared to the measurement in the anechoic chamber. The practical part consists of the measurement in the real scenario and calculating of the channel capacity using same tools as in the simulation part. At the end, the results are discussed with regards to possible practical use of those results.
Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups

Sunspots are darker areas on the Sun’s photosphere and most of solar eruptions occur in complex sunspot groups. The Mount Wilson classification scheme describes the spatial distribution of magnetic polarities in sunspot groups, which plays an important role in forecasting solar flares. With the rapid accumulation of solar observation data, automatic recognition of magnetic type in sunspot groups is imperative for prompt solar eruption forecast. We present in this study, based on the SDO/HMI SHARP data taken during the time interval 2010-2017, an automatic procedure for the recognition of the predefined magnetic types in sunspot groups utilizing a convolutional neural network (CNN) method. Three different models (A, B, and C) take magnetograms, continuum images, and the two-channel pictures as input, respectively. The results show that CNN has a productive performance in identification of the magnetic types in solar active regions (ARs). The best recognition result emerges when continuum images are used as input data solely, and the total accuracy exceeds 95%, for which the recognition accuracy of Alpha type reaches 98% while the accuracy for Beta type is slightly lower but maintains above 88%.
Nonlinear Attitude and Pose Filters with Superior Convergence Properties

In this thesis, several deterministic and stochastic attitude filtering solutions on the special orthogonal group SO(3) are proposed. Firstly, the attitude estimation problem is approached on the basis of nonlinear deterministic filters on SO (3) with guaranteed transient and steady-state measures. The second solution to the attitude estimation problem considers nonlinear stochastic filters on SO (3) with superior convergence properties with two filters being developed in the sense of Ito, and one in the sense of Stratonovich. This thesis also presents several deterministic and stochastic pose filtering solutions developed on the special Euclidean group SE(3). The first solution includes two nonlinear deterministic pose filters on SE (3) with predefined transient as well as steady-state performance, while the second one involves a nonlinear stochastic filter on SE (3) in the sense of Stratonovich. The proposed nonlinear deterministic filters on SO(3) and SE(3) guarantee that attitude and pose error are trapped to initially start within a known large set and converge systematically and asymptotically to the equilibrium point from almost any initial condition, respectively. The proposed stochastic filters ensure that errors of the estimates and attitude or errors of the estimates and pose are semi-globally uniformly ultimately bounded in mean square, and they converge to a small neighborhood of the origin from almost any initial condition. The output performance of the proposed filters is examined and simulated considering high level of uncertainties in the measurements and large error in initialization. The above-mentioned consideration makes the proposed filters a good fit for measurements obtained from low-cost inertial measurement units or low-cost inertial vision systems.
Dynamics of the large-scale structures and associated noise emission in airfoil slats

We investigate the slat narrowband peak noise generating mechanism. Unsteady flow data were generated by a lattice-Boltzmann-based commercial code for four configurations, accounting for variations of the airfoil angle of attack and slat overlap. Comparison with experimental results indicates that the aspects of the flow field relevant for the generation of the narrowband peaks were accurately captured. Frequency-domain proper orthogonal decomposition (POD) is applied to identify dominant large-scale structures in the frequency range dominated by the peaks. The combined use of the two POD metrics, namely, the turbulent kinetic energy in the turbulent flow region and the acoustic pressure in the far field, demonstrated that the structures most correlated with the noise resemble spanwise coherent Kelvin–Helmholtz vortices which dominate the slat cove only at the frequency of the narrowband peaks. Time evolution of the structures educed using the acoustic pressure correlation provides detailed evidence of the hydrodynamic and acoustic steps of a Rossiter-like feedback mechanism between the slat cusp and trailing edge. The combined analysis of results for the different slat configurations provides an explanation for the effect of the slat configuration on the amplitude of the narrowband peaks observed in previous studies, particularly the influence of the main-element suction peak.
Existence of Richardson elements for seaweed Lie algebras of finite type

Seaweed Lie algebras are a natural generalisation of parabolic subalgebras of reductive Lie algebras. A well‐known theorem of Richardson says that the adjoint action of a parabolic group has a dense open orbit in the nilpotent radical of its Lie algebra (Richardson, Bull. Lond. Math. Soc. 6 (1974) 21–24. ). Elements in the open orbit are called Richardson elements. In (Jensen, Su and Yu, Bull. Lond. Math. Soc. 42 (2009) 1–15. ) together with Yu, we generalised Richardson's Theorem and showed that Richardson elements exist for seaweed Lie algebras of type A . Using GAP, we checked that Richardson elements exist for all exceptional simple Lie algebras except E8 , where we found a counterexample.
Optimizing Analytical Queries over Semantic Web Sources

Data has always been a key asset for a variety of industries and businesses but lately it is giving data owners a true competitive advantage over others. Nowadays companies collect big volumes of data and store them in large multidimensional databases called data warehouses. A data warehouse presents aggregated data as a cube where cells of the cube contain facts and contextual information such as dates, locations, customer and supplier info, etc. Data warehouse solutions successfully employ Online Analytical Processing (OLAP) to analyze these large sets of data, e.g., sales data can be aggregated along the location and/or time dimension. Currently, new challenges are set by recent trends in technology and the Web. Much information is available on the Web in a machine-processable form (Semantic Web) and Business Intelligence (BI) tools need to be able to discover and retrieve relevant information and present it to users to aid in proper analysis of the situation. Many government and other organizations make their data openly available, identify their data with Uniform Resource Identifiers (URI), and interlink data to other data. This collection of interrelated datasets on the Web is called Linked Data [1]. These datasets are based on the Resource Description Framework (RDF) – a standard format for data interchange on the Web [2]. SPARQL, a query language and protocol for RDF [3], is used to query and manipulate RDF datasets stored in SPARQL endpoints. SPARQL 1.1 Federated Query [4] also defines an extension for executing queries distributed over several SPARQL endpoints. Thus, current standards enable complex analytical queries over multiple data sources and integrating these data into the analysis process becomes a necessity for BI tools. However, due to the amount and complexity of data available on the Web, incorporation and utilization of these data are not easy and straightforward. Therefore, an efficient OLAP solution over Semantic Web sources is needed to improve BI tools. This PhD thesis focuses on the challenges related to the optimization of analytical queries that retrieve data from multiple SPARQL endpoints. First, the thesis proposes a framework for the discovery, integration, and analytical querying of Linked Data – this type of OLAP was termed Exploratory
Stabilisation for cascade of nonlinear ODEs and counter-convecting transport dynamics

ABSTRACT We consider stabilisation for a nonlinear ordinary differential equation (ODE) and counter-convecting transport partial differential equations (PDEs) cascaded system in which the transport coefficients depend on the ODE state. Stability analysis of the closed-loop system is based on the infinite-dimensional backstepping transformations and a Lyapunov functional. A predictor control is proposed such that the closed-loop system is globally asymptotically stable. The proposed design method is illustrated by a single-link manipulator.
High Baud Rate All-Silicon Photonics Carrier Depletion Modulators

We achieved high performance high speed silicon photonics carrier-depletion Mach-Zehnder modulation with commercial foundry by co-optimization of doping and device design assisted with an accurate electro-optical (EO) model. We demonstrated high performance IQ modulators operating at 85 Gbaud 16 QAM and 64 Gbaud 64 QAM with extinction ratio of over 25 dB. For the design of the high performance all-silicon carrier depletion modulator, we developed modeling and design tools to provide not only accuracy, but also efficiency in the simulation of distributed optical and electronic characteristics of travelling waveguides with different designs of optical and microwave waveguides under various doping conditions, which allow the co-design of velocity phase match between optical and microwave waveguides and the impedance match between microwave travelling waveguide and terminal impedance. Our experimental characterization test data agreed well with the model simulation data. More recently, with practical Nyquist filter and linear compensation in commercial arbitrary wave generator (AWG) and optical modulation analyzer (OMA), we demonstrated 100 Gbaud 32 QAM with an all-silicon IQ modulator, which has 6 dB electro-optical bandwidth of 50 GHz and BER achieving FEC threshold with a modern FEC, showing the potential for Tb/s applications.
Une nouvelle métrique pour l'analyse discriminante sur données de dissimilitude

Statistical pattern recognition traditionally relies on a features based representation. For many applications, such vector representation is not available and we have only proximity data (distance, dissimilarity, similarity, ranks .). In this paper, we consider a particular point of view on discriminant analysis from dissimilarity data. Our approach is inspired by the Mahalanobis distance. We define decision rules to mimic the behaviour of a linear or a quadratic Gaussian classifier. The number of parameter is limited (two per class). Numerical experiments on real data show an interesting behaviour compared to a KNN classifier (i) lower or equivalent error rate, (ii) better robustness with sparse dissimilarity data.