Miracle-dz/UniFAR-FTUs huggingface dataset:
Building task-oriented machine translation systems

The main goal of this thesis is to develop computer assisted translation and machine translation systems which present a more robust synergy with their potential users. Hence, the main purpose is to make current state-of-the-art systems more ergonomic, intuitive and efficient, so that the human expert feels more comfortable when using them. For doing this, different techniques are presented, focusing on improving the adaptability and response time of the underlying statistical machine translation systems, as well as a strategy aiming at enhancing human-machine interaction within an interactive machine translation setup. All of this with the ultimate purpose of filling in the existing gap between the state of the art in machine translation and the final tools that are usually available for the final human translators. Concerning the response time of the machine translation systems, a parameter pruning technique is presented, whose intuition stems from the concept of bilingual segmentation, but which evolves towards a full parameter re-estimation strategy. By using such strategy, experimental results presented here prove that it is possible to achieve reductions of up to 97% in the number of parameters required without a significant loss in translation quality. Being robust across different language pairs, these results evidence that the pruning technique presented is effective in a traditional machine translation scenario, and could be used for instance in a post-editing setup. Nevertheless, experiments carried out within a simulated interactive machine translation environment are slightly less convincing, since a trade-off between response time and translation quality is needed. Two orthogonally different approaches are presented with the purpose of increasing the adaptability of the statistical machine translation systems. On the one hand, we investigate how to increase the adaptability of the language model, by subdividing it into several smaller language models which are then interpolated in translation time according to the source sentence to be translated. The specific sub-models are built either by taking advantage of supervised information present in certain bilingual corpora, or by performing unsupervised clustering on the training set, with the aim of uncovering specific sub-topics or language
Globally optimal triangulations of minimum weight using Ant Colony Optimization metaheuristic

Globally optimal triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) problem of a given set of n points in the plane. Our aim is to show how the Ant Colony Optimization (ACO) metaheuristic can be used to search for globally optimal triangulations of minimum weight. We present an experimental study for a set of instances for MWT problem. We create these instances since no reference to benchmarks for this problem were found in the literature. We assess through the experimental evaluation the applicability of the ACO metaheuristic for MWT problem.
Non-hemodynamic predictors of blood pressure in recreational sport practitioners in Cotonou, Benin Republic

Currently, there are evidences that regular physical activity is an efficient means to control high blood pressure. This cross-sectional study aims at identifying in subjects who exercise in non-institutional structures at Cotonou, the main factors that account for the inter-individual variations of the blood pressure. Four adiposity indexes, indirectly assessed VO 2 max, atherogenous blood lipids, resting heart rate and blood pressure (SBP and DBP) were assessed in 120 recreational sport practitioners (RS), aged 13 to 52 years. The characteristics of the recreational sport practitioners and the history of cardiovascular disease were also assessed in all participants. Descriptive statistics (means ± standard error of the mean) were computed for all the variables. Pearson’s coefficients of correlation were computed and a linear multiple regression was used to identify the best predictors of blood pressure. The results indicate that transportation by car or motorcycle and the length of daily walking less than 30 min correlated with high diastolic blood pressure. Age was an independent predictive variable of arterial blood pressure in this study sample (R 2 = 0.095, p < 0.001 for SBP; R 2 = 0.18, p < 0.001 for DBP). Together, and after adjustment for age, neck circumference and HDL-cholesterol were the most powerful predictors of the SBP (R 2 = 0.14, p < 0.05) and the sum of skinfold thickness along with the DBP (R 2 = 0.21, p < 0.05). It is concluded that exercises in non-institutional structures can be effective in managing hemodynamic health problems. However, this should be combined with meaningful dietary practices.
Private and public families

EnglishSocial workers around the world work with families and family complexities in their everyday practice. In this cross-national study, we explore social workers’ family intervention practices related to family definitions and functions, and how social workers balance children’s and parents’ rights and social policies in the proper context. Data derives from focus group interviews with child welfare workers from Norway, Lithuania, Chile and England based on discussions of a common fictitious complex family case (vignette). The four countries chosen for this comparative study are examples of four different welfare systems/regimes. The findings related to this broad area of caring topics are related to how the dimensions of a ‘private’ and a ‘public’ family manifest in social work in the four countries. Social workers in Chile and Lithuania refer to the idea of the private family, while their Norwegian counterparts lean more to the public family. English social workers combine public and private family conceptions in their focus groups, reflecting a system that is partly de-familialized. SpanishFamilias públicas y privadas. La visión de los trabajadores sociales sobre la posición de hijos y padres en Chile, Inglaterra, Lituania y Noruega.Los trabajadores sociales en todo el mundo lidian con familias y sus complejidades en su práctica cotidiana. En este estudio de comparación internacional exploramos las prácticas de intervención en familias de trabajadores sociales en relación con sus definiciones y funciones, así como los modos en que los trabajadores sociales equilibran los derechos de padres e hijos y las políticas sociales en el contexto apropiado. Los datos provienen de entrevistas grupales con trabajadores sociales de servicios de bienestar infantil en Noruega, Lituania, Chile e Inglaterra, basadas en la discusión acerca de un caso común ficticio de familia compleja (viñeta). Los cuatro países seleccionados para este estudio comparativo ejemplifican cuatro regímenes o sistemas de bienestar. Los resultados relativos a esta amplia área del tema del cuidado tienen que ver con cómo las dimensiones de “familia privada” y “familia pública” se manifiestan en el trabajo social de estos cuatro países. Los trabajadores sociales de Chile y Lituania hacen referencia a la idea de la familia privada, mientras sus colegas noruegos se inclinan más hacia la familia pública. Los trabajadores sociales ingleses apuntan hacia una combinación de las concepciones de familia pública y privada en sus grupos focales, reflejando un sistema que es parcialmente de-familiarizado.
Structure discovery in hidden Markov models.

The Baum-Welch algorithm for training hidden Markov models (HMMs) requires model topology and initial parameters to be specified, and iteratively improves the model parameters. Sometimes prior knowledge of the process being modeled allows such specification, but often this knowledge is unavailable. Experimentation and guessing are resorted to. Techniques for discovering the model structure from observation data exist but their use is not commonplace. We propose a state splitting approach to structure discovery, where states are split based on two heuristics: within-state autocorrelation and a measure of Markov violation in the state path. Statistical hypothesis testing is used to decide which states to split, providing a natural termination criterion and taking into account the number of observations assigned to each state, splitting states only when the data demands it.
Most of Them Are Junk Food but We Did Put Fruit on There and We Have Water

Purpose – The Australian Dietary Guidelines support good health and disease prevention. Children with healthy eating habits established early in life have been shown to continue these habits into adulthood compared with those children who have poor eating habits in their younger years. The nutritional intake of many Australian children is not in accordance with the national guidelines. The reasons children make the food choices they do are unclear from the literature. The paper aims to discuss these issues. Design/methodology/approach – This study used participatory action research methods to explore why primary school-aged children make the food choices that they do. A non-government primary school requested assistance in encouraging their children to make healthier choices from the school canteen menu. The authors gathered opinions from the children in two different ways; a group discussion during class and a “discovery day” that involved four class grades. The authors identified children’s food prefere.
Multimodal neuroimaging transcends time in thrombolysis for pediatric stroke

Abstract Pediatric stroke is particularly challenging because of its variable presentation and relative rarity. Diagnosis of pediatric stroke is often delayed while other diagnoses are considered, unfortunately leading to a loss of therapeutic opportunity. Recent research advances in adult stroke such as multimodal imaging and a clinical trial showing benefits in extending the therapeutic window for thrombolytics are likely to have important implications in pediatric stroke. We describe the case of a 13-year-old boy presenting with acute stroke where multimodal magnetic resonance imaging with perfusion and permeability images allowed safe treatment with thrombolysis outside the standard treatment time window.
Novel labeling strategies for hierarchical representation of multidimensional data analysis results

Hyperbolic visualization represents a useful tool for the interpretation of complex data analysis results, whenever it can be combined with efficient labeling strategies. In this paper, we firstly present a new approach combining original hypertree construction techniques for multidimensional clustering results visualization with novel cluster labeling techniques based on the use of cluster content evaluation criteria, like the F-measure on cluster properties. The first part of the paper briefly presents the cluster hypertree construction principle. The main part of the paper focuses on the presentation of the labeling techniques. It illustrates that the scope of the proposed techniques can be extended from single cluster labeling to labeling of hierarchical structures, like hypertrees. Finally, using specific evaluation criteria, we show the better efficiency of the proposed methods, as compared to usual labeling methods, both for single cluster labeling and for hierarchical labeling. The experimental context of the paper is a bibliographic database of 2127 PASCAL references related to the geological domain.
Increased inflammatory markers in adolescents born extremely preterm and small for gestational age

Abstract Low birth weight has been associated with chronic low-grade inflammation and later cardiovascular disease. Whether this is related to low birth weight due to premature birth, being born small for gestational age (birth weight below the 10th percentile for gestational age) or variations in postnatal growth patterns is unknown. The objective of this study was to explore the impact of fetal growth restriction versus low birth weight due to prematurity on inflammatory status in later life. We investigated systemic markers of cell-mediated immune activation, kynurenine/tryptophan ratio and high-sensitive CRP (hs-CRP), in two population-based cohorts of children aged 10 and 17 years, who were born preterm (gestational age ≤ 28 weeks) or with an extremely-low-birth weight (< 1000 g) (n = 4). The controls were sex-and age-matched term-born with appropriate for gestational age birth weight children (n = 75). Children born premature and small for gestational age had higher Kyn/Trp and hs-CRP compared to both age-matched preterms with appropriate for gestational age birth weight (p = 0.01 and p = 0.002, for Kyn/Try ratio and CRP respectively) and controls (p < 0.001 and p = 0.001). No significant differences in inflammatory markers were observed between appropriate for gestational age preterms and controls. Our observations suggest that the chronic low-grade immune activation observed in adults born with a low birth weight may be related to fetal growth restriction rather than low birth weight due to prematurity.
Anycast group membership management protocol

A group of hosts which belong to a particular IP address and inter-networked need to be coordinated, organized and managed for their network activities. Multicast and anycast are the popular group services used for group network activities. This group of hosts in one or more networks requires a management service that allows new members to join or leave as they wish. This information is then used by end host to acquire group services. In IPA and IPv6 multicast, Internet Group Management Protocol (IGMP) and Multicast Listener Discovery (MLD) are being used for group membership management. However the new communication paradigm anycast, is lack of such mechanism for its Group Membership Management (GMM). This paper proposes a new protocol known as Anycast Group Membership Management Protocol (AGMP) to support global anycast GMM. Existing multicast GMM has been explored and distinguished with our proposed AGW. AGW solution is not expensive in terms of memory and computation. It is scalable to support large numbers of groups and members per group. Design and simulated implementation results of the group membership behavior is presented in this paper.
Virtual Reality Therapy: an Effective Treatment for the Fear of Public Speaking

The major goal of this research was to investigate the efficacy of virtual reality therapy (VRT) in the treatment of the fear of public speaking. After an extensive two-stage screening process, sixteen subjects were selected from the pool. They were assigned to two treatment conditions: VRT (N=8) and comparison group (N=8). Fourteen subjects completed the study. The VRT group was exposed to the virtual reality public speaking scene while the comparison group was exposed to a trivial virtual reality scene and guided by the experimenters to manage their phobia either by using visualization techniques or selfexposure to the situation they feared. The VRT and comparison group sessions were conducted on an individual basis over a five week period. Two assessment measures were used in this study. The first measure used was the Attitude Towards Public Speaking (ATPS) Questionnaire. The second measure used was the eleven-point Subjective Units of Disturbance (SUD) scale. These measurements assessed the anxiety, avoidance, attitudes and disturbance associated with their fear of public speaking before and after treatments. In addition, objective measures such as heart rate was collected in each stage of the treatment. Significant differences between the six subjects who completed the VRT sessions and comparison group were found on all measures. The VRT group showed significant improvement after five weeks of treatment. The comparison group did not show any meaningful changes. The authors concluded that VRT was successful in reducing the fear of the public speaking.
Data flow analysis for verifying correctness properties of concurrent programs

Developers of modern software systems are increasingly employing concurrency to meet demanding system requirements. To deal with the inherent complexity that results from concurrency, developers require cost-effective automated analysis techniques to gain confidence in the quality of their concurrent software. We present an approach, called FLAVERS, that is able to provide cost-effective analysis of concurrent programs with respect to a rich class of explicitly stated correctness properties. FLAVERS is based on a family of polynomial-time, conservative data flow analysis algorithms. Unlike existing analysis approaches for concurrent software, FLAVERS allows developers to control the tradeoff between analysis cost and the precision of analysis results by incrementally introducing additional information into the analysis. One strength of this approach is the flexibility allowed in choosing and combining a variety of sources of information about the program and property being analyzed, so as to increase precision without making the cost of analysis impractical. We have extended the theoretical foundations of data flow analysis to support the description and solution of data flow analysis problems for concurrent programs. We have engineered a domain-specific software architecture that greatly reduces the cost of developing data flow analyzers. These advances are widely applicable. We have leveraged off of them to produce an implementation of FLAVERS with which we have been able to successfully validate the feasibility of the analysis approach.
Mining and querying multimedia data

The emerging popularity of multimedia data, as digital representation of text, image, video and countless other milieus, with prodigious volumes and wild diversity, exhibits the phenomenal impact of modern technologies in reforming the way information is accessed, disseminated, digested and retained. This has iteratively ignited the data-driven perspective of research and development, to characterize perspicuous patterns, crystallize informative insights, and realize elevated experience for end-users, where innovations in a spectrum of areas of computer science, including databases, distributed systems, machine learning, vision, speech and natural languages, has been incessantly absorbed and integrated to elicit the extent and efficacy of contemporary and future multimedia applications and solutions. Under the theme of pattern mining and similarity querying, this manuscript presents a number of pieces of research concerning multimedia data, to address an array of practical tasks encompassing automatic annotation, outlier detection, community discovery, multi-modal retrieval and learning to rank, in their respective contexts including satellite image analysis, interne traffic surveillance, image bioinformatics, and Web search. A repertoire of extant and novel techniques pertaining to graph mining, clustering analysis, tensor decomposition and probabilistic graphical models has been developed or adapted, which satisfactorily met differing quality and efficiency requisites postulated by specific application settings, best exemplified by the 40 times speed-up in annotating satellite images and the up to 30% performance improvement in predicting web search user clicks, yet without the loss of generality to similar and related scenarios.
Amyotrophic Lateral Sclerosis Overview

The purpose of this overview is to increase the awareness of clinicians regarding genetic causes of amyotrophic lateral sclerosis (ALS) and related genetic counseling issues.The following are the goals of this overview: Goal 1 Describe the clinical characteristics of ALS. Goal 2 Review genetic causes of ALS. Goal 3 Provide an evaluation strategy to identify the genetic cause of ALS in a proband (when possible). Goal 4 Inform genetic counseling of family members of an individual with ALS. Goal 5 Provide a high-level view of management of ALS.
Bayes Risk for Large Scale Hierarchical Top-K Image Classification

Despite numerous efforts and recent progress, image classification remains a challenging problem, where computers are still outperformed by humans. In particular, the recent trend of large-scale image classification (thousands of images, hundreds of classes, high dimensional features), made popular by the ImageNet dataset [13] has recently received growing interest. Yet, the standard evaluation protocol, which reports only the misclassification rate, fails to produce well-behaved classifiers. The inherent difficulty of large-scale datasets, causes human beings to sometimes fail at the classification task (the three classes of ImageNet “softball”, “hardball” and “professional baseball” are for instance almost indistinguishable). Even when failing, humans nevertheless always predict an output semantically similar to the correct one. A hierarchy between concepts was therefore introduced to define a inter-class distance, to penalize classifiers outputing farfetched labels, as for instance in the 2010 and 2011 editions of the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). Another recent trend in classification protocols is to allow classifiers to output several guesses, only taking the best one into account. Also in use in ILSVRC, this leniency allows for datasets imperfections and ambiguous images. The purpose of this master thesis is twofold. In a first part, we introduce Minimum Bayes Risk prediction to solve the problem of large-scale hierarchical top-K classification. Using an approximation of a submodular score and posterior class-probabilities given by a Logistic Regression, we get significant improvements over the naive prediction. In a second part, we report a preliminary work on improving the determination of the posterior probabilities with a new classifier called the Bayes Risk Machine. We report good improvements on top-1.
Students' Perspectives on the Role of English-Medium Instruction in English Learning: A Case Study

The current study attempts to identify undergraduate students’ needs in English-medium instruction (EMI) from the perspectives of both past and future of their English learning and use. Selecting one elective EMI course in the Department of English Language and Literature at Waseda University, 15 undergraduate students completed a set of questionnaires covering their prior experiences with formal language instruction and their perceptions of achievements and difficulties in the target EMI course. The study revealed three major findings. First, there was individual variability in target language use domains (e.g., English for Language Teaching, English for Business Purposes). Second, most of them had an expectation of linguistic outcomes through attending the target EMI course (i.e., by-product ; Taguchi, 2014a). Third, students had difficulty particularly with group discussion activities in the EMI course. These findings lead to propose several pedagogical implications for more fine-grained integration of curricula for EMI content courses and regular English language courses.
Preimplantation genetic diagnosis: where are we?

Clinical application of the concept of pre implantation genetic diagnosis (PGD) was introduced in humans after the successful application of in vitro fertilization over the past 20 years. Today, PGD can be used for the screening of structural chromosome abnormalities (aneuploidy, transloca-tions), detection of single gene defects, for the diagnosis of late-onset diseases, pre implantation HLA typing and other non-traditional prenatal testing. Procedure, technique, accuracy of the tests and indications had been evaluated in the passage. The present PGD experience includes thousands of clinical cycles performed for several disorders, suggesting that PGD is a safe, accurate and reliable technique, with a growing value for prevention, especially when couples cannot accept prenatal diagnosis and termination of pregnancy as only option.