Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution a l'integration des preferences dans le raisonnement argumentatif

Le raisonnement argumentatif est base sur la justification d'une conclusion plausible par des arguments en faveur de cette conclusion. L'argumentation est donc un modele prometteur pour raisonner avec des connaissances incertaines ou incoherentes ou plus generalement de sens commun. Ce modele est base sur la construction d'arguments et de contre arguments, la comparaison de ces differents arguments et enfin la selection d'arguments juges acceptables. Nous proposons tout d'abord une etude de differents systemes d'argumentation en mettant en evidence deux notions d'acceptabilite complementaires : _ une acceptabilite individuelle, basee sur l'existence de contre arguments directs. _ une acceptabilite conjointe basee sur l'existence de defenseurs. Nous etudions ensuite la comparaison d'arguments par differentes relations de preference. De telles relations peuvent refleter des relations de priorite entre les connaissances. Nous enrichissons les deux notions d'acceptabilite precedentes par la prise en compte de ces relations de preference. Un argument est ainsi juge acceptable s'il n'est pas contrarie, ou s'il est prefere a ses contrariants (cas de defense individuelle) ou encore s'il est defendu par d'autres arguments. Nous illustrons notre approche dans le cadre du traitement de l'incoherence dans des bases de connaissances. Nous proposons dans ce cadre une comparaison avec des methodes basees sur la restauration de coherence. Nous montrons enfin une application des systemes d'argumentation a la modelisation du dialogue entre deux agents.
Farm Bankruptcies and Land Value Trends: The Effects of Land Value Fluctuations on Financial Stress

This study examines the relationship between agricultural land value trends and farm financial stress as proxied by farm bankruptcies and agricultural loan delinquency rates. Farms are becoming more dependent on agricultural land values as evidenced by an increasing share of their assets tied to the value of agricultural land and a rising share of agricultural debt secured by agricultural land. A vector autoregressive (VAR) framework is utilized to facilitate Granger causality between agricultural land and financial stress. Preliminary results are mixed in that farm bankruptcies Granger cause agricultural land changes while agricultural land changes Granger cause loan delinquency changes.
Economic Potential and Development Prospects of Small Businesses in Rural Areas

The article examines the role of small business in the development of rural areas by the example of bakery production. Moreover, it demonstrates the need for the interaction of small bakery businesses with large enterprises of the industry and grain processors. The article reveals the development trends of small bakery production in rural areas. The conditions for the participation of small bakery businesses in rural areas in the development of the production of functional and specialized bread are substantiated. The article proposes the directions and mechanisms of support for small bakery production at the level of rural territories. The development of modern methods of redistribution of bakery production in proportion to the living population, measures of state support for small bakery production and expansion of the range of bread products that contribute to improving the health of the population of Russia, is the theme of this study. The object of the research is the development of small bakery production as a condition for improving the quality of life of the population in rural areas. The subject of the research is production and management relations that ensure the development of small bakery production and its impact on the quality of life of the population in rural areas.
Early Software Quality Prediction Based on a Fuzzy Neural Network Model

For the management of a software development project, a software quality prediction model is very helpful since it can provide the management with useful information needed for decision-makings. Many software quality prediction models and techniques have been proposed and studied in the literature. Nevertheless, the complicated situations of software development process call for a more flexible model that can cater for all factors that have impact on the quality of the target software, including the characteristics of the software product, the characteristics of the development process and the operation conditions. In this paper, a software quality prediction model based on a fuzzy neural network is presented. The proposed model is a hybrid model of Artificial Neural Network (ANN) and Fuzzy Logic (FL), which exploits the advantages of ANN and FL while eliminating their limitations. The model exhibits some favorable features such as being able to deal with objective data collected in the software development process as well as knowledge/experiences obtained from experts or from similar projects, which is the main information that is available in the early phases of a software development process. Using this model, early prediction of software quality becomes feasible and the management can have knowledge of the quality of target software product as early as possible, which helps to identify design errors and avoid expensive rework. Experimental results show that for problems of small-to-medium scale, the proposed model can be easily trained and thus can be of practical use.
Contributing Factors of Production Performance in the Food Processing Industry

The main purpose of this study is to determine the factors that can contribute to the production performance in the food industry. This study can help to identify the contributing factors towards the production performance. Five factors were identified based on the past literature. 100 food processing manufacturers with the majority coming from small and medium industries (SME) have been selected. The purposive sampling technique has been selected for this study with the total number of usable questionnaires received being from 80 respondents. The collected data was analysed using SmartPLS 3.0 to test the relationships between the independent variables and the dependent variable of the study. The results showed that the Total Quality Management (TQM), Lean Management (LM), and Hazard Analysis and Critical Control Point (HACCP)significantly influenced the production performance. The practical implications of these outcomes are further discussed in the study.
Fertility and infant mortality trends in Nicaragua 1964-1993. The role of women's education.

OBJECTIVES: To assess trends in fertility and infant mortality rates (IMR) in León, Nicaragua, and to examine the effect of women's education on these trends during 1964-1993, a period of rapid social change. DESIGN: Cross sectional survey, based on random cluster sampling. A retrospective questionnaire on reproductive events was used. SETTING: The municipality of León, which is the second largest city in Nicaragua, with a total population of 195,000 inhabitants. SUBJECTS: 10,867 women aged 15-49 years, corresponding to 176,281 person years of reproductive life. Their children contributed 22,899 person years under 12 months of age to the IMR analysis. MAIN OUTCOME MEASURES: Fertility rate (number of pregnancies per 1000 person years) for specific age groups and calendar periods, total fertility rate, and IMR. RESULTS: Fertility rates and IMR declined in parallel, especially during the 1980s. However, education specific fertility rates did not decline, but the proportion of educated young women increased from 20% to 46%. This had also an impact on the overall IMR decline, although IMR reduction mainly took place among infants of women without formal education, decreasing from 118 to 69 per 1000 during the observation period. CONCLUSIONS: In this demographic transition over three decades, fertility and IMR declined simultaneously. The decreasing trend in fertility was mainly explained by an increase in women's education, while the IMR decline seemed to be the result of health interventions, specially targeted to poorer groups of women and their infants. Thus, social differences in fertility rates remained unchanged, while equity in chances of child survival increased.
A method of state recognition of dressing clothes based on dynamic state matching

This paper describes a method of clothing recognition using dynamic state matching. One purpose of this research is to develop a function that is used to observe the progress of dressing a bottom at a toilet or a bedside. In this dressing, although we just have to do is to put my or others' legs on the bottom. some undesirable situations may occur. For instance, the feet gets hung up on the part of the bottom, and so on. Assuming to use a single camera, we propose a method that distinguishes whether or not a behavior of dressing a bottom is working in order. The input data is an image stream that captures a dressing sequence. Optical flow is calculated from two consecutive images, and then the distribution of the flows is used to distinguish the present clothing state by matching the optical flow with training dataset. The method outputs whether or not the present clothing is in a successful situation. The effectiveness of the proposed method was proven by means of real image data.
– 1 Problem IN Conv 1 X 1 Conv 3 X 3 Conv 1 X 1 Conv 1 X 1 Conv 3 X 3 Conv 1 X 1 Conv 1 X 1 Conv 3 X 3 Conv

Figure 1: Multi-branch residual block (Baseline) [7]. Deep residual learning [3] have led to a series of successful results for image classification. By adopting a residual learning framework, it has become possible to train very deep convolutional neural networks. The degradation problem caused by the increased depth of the network is no longer a serious obstacle for training very deep models [4]. In addition to the importance of the depth and the width of deep convolutional networks, there have been some attempts to study the effect of another dimension called the "Cardinality" or in other words using multiple parallel branches at the same depth instead of a single layer. Recently, Xie et al. [7] showed through an extensive set of experiments, that using multi-branch residual blocks leads to better performance than using a single branch residual block. Figure 1 shows this multi-branch residual block architecture. This multi-branch block allows the learning of different transformations and features represented by the branches.
A Truthful Online Auction for Tempo-spatial Crowdsourcing Tasks

Mobile crowdsourcing is an emerging paradigm which utilizes the distributed smartphones to monitor diverse phenomena about human activities and surrounding environment, enabling a large number of mobile crowdsourcing applications. For those applications to collect sufficient data, motivating smartphone users to be interested in mobile crowdsourcing campaign becomes very significant. Most of the incentive mechanisms assume that tasks are static in mobile crowdsourcing systems. Even for those studies that take the uncertain arrival of tasks into consideration, they always ignore the important geographic location information of the tasks. In the paper, we propose a near-optimal online incentive mechanism based on a more realistic scenario in which crowdsourcing tasks and users both arrive dynamically with tempo-spatial constraints. Through adequate simulations and rigorous theoretical analysis, the online mechanism is proved to satisfy the properties of truthfulness, computational efficiency, individual rationality, and achieve high social welfare and low total payment.
Multiflots dynamiques avec contraintes de synchronisation de ressources

Nous etudions un probleme de multiflots caracteristique de l’organisation logistique de la messagerie. Ce probleme est mis en situation dans la litterature puis formalise de deux manieres, donnant lieu a l’introduction d’un modele base sur la notion de multiflots et egalement d’un modele indexe dans le temps. Dans un cadre generique applicable a ces deux modelisations, nous proposons des strategies heuristiques pour resoudre notre probleme : heuristique serielle, technique d’echelonnement de capacite et enfin Large Neighborhood Search. La qualite des solutions obtenues est evaluee sur la base de bornes inferieures que nous calculons. Nous exploitons a cette fin des instances de grandes tailles, representatives d’organisations reelles, et issues d’un generateur d’instance dedie.
Ambulatory care visits to physician offices, hospital outpatient departments, and emergency departments: United States, 2001-02.

OBJECTIVE This report presents statistics on ambulatory care visits to physician offices, hospital outpatient departments, and hospital emergency departments. Ambulatory medical care utilization is described in terms of patient, practice, facility, and visit characteristics. Office-based care is further subdivided into the categories of primary care, surgical specialties, and medical specialties. METHODS Data from the 2001 and 2002 National Ambulatory Medical Care Surveys (NAMCS) and National Hospital Ambulatory Medical Care Surveys (NHAMCS) were combined to produce averaged annual estimates of ambulatory medical care utilization. RESULTS Patients in the United States made an estimated 1.1 billion visits per year in 2001 and 2002 (annual average) to physician offices, hospital outpatient departments, and emergency departments, a rate of 3.8 visits per person annually. This marks the first time that the annual estimate of visits has surpassed the billion mark and is also a significant increase from the 1999-2000 estimate. The change was primarily driven by a jump in the number of visits to primary care physicians. The distribution of visits by patient age, sex, race, expected source of payment, geographic region, and whether the visit occurred in a metropolitan statistical area (MSA) varied across ambulatory care settings. Females had higher visit rates than males to all settings except office-based surgical specialists and emergency departments (ED). Black persons had higher visit rates than white persons to hospital outpatient and emergency departments, but lower visit rates to office-based surgical and medical specialists. Visits to emergency departments were more likely to be patient-paid or no charge, possibly reflecting a lack of private health insurance, than were visits to physician offices. Visit rates to office-based medical specialists were more than double in MSAs compared with non-MSAs.
Real time Android app based respiration rate monitor

Respiration rate is monitored in most diseases, especially ones like sleep apnea and respiratory arrest. However, most devices available for monitoring are expensive, fragile and often need trained staff for operation. For obvious reasons, constantly monitoring the patient's breathing rate is also not feasible and our proposed module tends to modify this scenario. The hardware module uses a thermistor to monitor the patient's breathing rate. This is less costly, and also involves low power consumption as well as robust performance. The device also uses cloud computing to transmit the data to the doctor's smartphone app as well as to the patient's relatives' correspondence. A buzzer attached to the system notifies nearby medics in case of abnormal breathing rates. The device is specially adapted to suit rural Indian conditions. The prototype has been substantiated for accurate monitoring of breathing rates through the standard tests.
Competing for Talent

In many labor markets, e.g., for lawyers, consultants, MBA students, and professional sport players, workers get offered and sign long-term contracts even though waiting could reveal significant information about their capabilities. This phenomenon is called unraveling. We examine the link between wage bargaining and unraveling. Two firms, an incumbent and an entrant, compete to hire a worker of unknown talent. Informational frictions prevent the incumbent from always observing the entrant’s arrival, inducing unraveling in all equilibria. We analyze the extent of unraveling, surplus shares, the average talent of employed workers, and the distribution of wages within and across firms.
Anatomical data augmentation for CNN based pixel-wise classification

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.
Sensitivity and Depth Penetration of Continuous Wave Versus Frequency‐domain Photon Migration Near‐infrared Fluorescence Contrast–enhanced Imaging ¶

The development of near‐infrared fluorescent contrast agents and imaging techniques depends on the deep penetration of excitation light through several centimeters of tissue and the sensitive collection of the re‐emitted fluorescence. In this contribution, the sensitivity and depth penetration of various fluorescence‐enhanced imaging studies is surveyed and compared with current studies using continuous wave (CW) and frequency‐domain photon migration (FDPM) measurements with planar wave illumination of modulated excitation light at 100 MHz and area collection of reemitted fluorescent light using a previously developed modulated intensified charge‐coupled device camera system. Fluorescence was generated from nanomolar to micromolar solutions of indocyanine green (ICG) in a 100 μL volume submerged at 1–4 cm depths in a 1% Liposyn® solution to mimic tissue scattering properties. Enhanced depth penetration and sensitivity are achieved with optimal filter rejection of excitation light, and FDPM rejection of background light is not achieved using CW methods. We show the ability to detect as few as 100 fmol of ICG from area illumination of 785 nm light (5.5 mW/cm2) and FDPM area collection of 830 nm fluorescent light generated from 3 cm below the phantom surface. The lowered noise floor of FDPM measurements enables greater sensitivity and penetration depth than comparable CW measurements.
Identification of Trichomonas vaginalis Cysteine Proteases That Induce Apoptosis in Human Vaginal Epithelial Cells*

A secreted cysteine protease (CP) fraction from Trichomonas vaginalis is shown here to induce apoptosis in human vaginal epithelial cells (HVEC) and is analyzed by mass spectrometry. The trichomonad parasite T. vaginalis causes one of the most common non-viral sexually transmitted infection in humans, trichomoniasis. The parasite as well as a secreted cysteine protease (CP) fraction, isolated by affinity chromatography followed by Bio-Gel P-60 column chromatography, are shown to induce HVEC apoptosis, as demonstrated by the Cell Death Detection ELISAPLUS assay and annexin V-fluorescein isothiocyanate flow cytometry analyses. Initiation of apoptosis is correlated with protease activity because the specific CP inhibitor E-64 inhibits both activities. SDS-PAGE analysis of the CP fraction reveals triplet bands around 30 kDa, and matrix-assisted laser desorption ionization time-of-flight MS indicates two closely associated peaks of molecular mass 23.6 and 23.8 kDa. Mass spectral peptide sequencing of the proteolytically digested CPs results in matches to previously reported cDNA clones, CP2, CP3, and CP4 (Mallinson, D. J., Lockwood, B. C., Coombs, G. H., and North, M. J. (1994) Microbiology 140, 2725-2735), as well as another sequence with high homology to CP4 (www.tigr.org). These last two species are the most abundant components of the CP fraction. The present results, suggesting that CP-induced programmed cell death may be involved in the pathogenesis of T. vaginalis infection in vivo, may have important implications for therapeutic intervention.
Developing an iMVT pedagogy for science learning

We summarize an emerging pedagogical approach to learning science subjects as iMVT (Modeling and Visualization Technology Integrated Inquiry-based Learning) based on our school-based research and the literature of technology for science education. The authors use evidence in their chemistry, biology, and physics studies to show the potential of using iMVT as a uniformed theoretical framework for designing effective learning environments to support science learning.
Continuation Curriculum Learning with Question Answering on the SQuAD Dataset

We implement a slightly simplified Bi-Directional Attention Flow Model[4] and a slightly modified Multi-Perspective Context Matching[6] model for Question Answering on the SQuAD dataset. In the Multi-Perspective model, we add perspective matching between forward and backward contexts. We omit the character-level embeddings of both models, and make a few other small simplifications. We briefly looked at the effects of curriculum learning as a continuation method for our BiDAF network. Curriculum learning[1] is inspired by the education system, in which subjects are trained through an organized curriculum of varying tiers of difficulty. This differs from other machine learning approaches where training is done on random samples. We hoped this could improve performance on longer length questions.
Silencing of NUF2 inhibits tumor growth and induces apoptosis in human hepatocellular carcinomas.

BACKGROUND As an important component of the NDC80 kinetochore complex, NUF2 is essential for kinetochore-microtubule attachment and chromosome segregation. Previous studies also suggested its involvement in development of various kinds of human cancers, however, its expression and functions in human hepatocellular carcinoma (HCC) are still unclear. MATERIALS AND METHODS In the present study, we aimed to test the hypothesis that NUF2 is aberrant in human HCCs and associated with cell growth. RESULTS Our results showed significantly elevated expression of NUF2 in human HCC tissues compared to adjacent normal tissues, and high expression of NUF2 in HCC cell lines. Using lentivirus-mediated silencing of NUF2 in HepG2 human HCC cells, we found that NUF2 depletion markedly suppressed proliferation and colony formation capacity in vitro, and dramatically hampered tumor growth of xenografts in vivo. Moreover, NUF2 silencing could induce cell cycle arrest and trigger cell apoptosis. Additionally, altered levels of cell cycle and apoptosis related proteins including cyclin B1, Cdc25A, Cdc2, Bad and Bax were also observed. CONCLUSIONS In conclusion, these results demonstrate that NUF2 plays a critical role in the regulation of HCC cell proliferation and apoptosis, indicating that NUF2 may serve as a potential molecular target for therapeutic approaches.