Miracle-dz/UniFAR-FTUs huggingface dataset:
What starting public service interpreters in the EU should know about legislation: the case of Belgium and the Netherlands

espanolEn la profesion de interprete se cruzan numerosas disposiciones legales. Especialmente en los sectores de inmigracion, tribunales y servicios policiales o sociales, los derechos de terceros desempenan un papel esencial, y la asistencia linguistica es importante para la legalidad de las acciones. Se contrata a interpretes freelance. Estos han optado por hacerse autonomos y por las relaciones de derecho privado con sus clientes. Sin embargo, la esfera del derecho publico esta muy presente en sus vidas, a traves de su formacion, certificacion, despliegue, tarifacion e impuestos. Debido a la creciente profesionalizacion del sector, el nivel exigido a los interpretes es cada vez superior. Hoy en dia, los criterios son mas estrictos que el tradicional juramento de fidelidad. Comentamos lo que los jovenes interpretes necesitan saber al lanzarse como emprendedores en Belgica y los Paises Bajos EnglishThe interpreting profession weaves its way through a tangled web of legal provisions. Especially in the areas of immigration, the court, the police or social services, third-party rights play an important role, and language assistance is needed to act following the law. Freelance interpreters are contracted. These interpreters have opted for entrepreneurship and private-law relationships with their clients. The public-law sphere, however, is very much a part of their activities, through their training, certification, fees they earn, but also taxation. Due to growing professionalization, higher demands are being placed on interpreters. Requirements nowadays are much stricter than an oath of faithful translation. This paper discusses what starting public service interpreters need to know about legal provisions when starting their career as entrepreneurs in Belgium and the Netherlands
Evaluating Post Leaving Certificate Provision in Ireland

This paper summarises the key findings of an evaluation of the Post Leaving Certificate (PLC) Programme, the largest full-time further education (post-secondary non-tertiary) programme in Ireland. This programme has multiple purposes – preparing people for employment, acting as a bridge to higher education and serving as a second-chance route for adult learners. Based on evidence from both administrative data and a unique specially-designed learner survey, we find positive results for the estimated counterfactual impact of PLC provision on employment at 16 per cent relative to similar individuals who entered the labour market directly on completion of their (upper secondary) Leaving Certificate, with a corresponding positive estimate for transitions to higher education standing at 27 per cent. Nevertheless, the evaluation found that PLC courses were often poorly connected to the labour market and that reforms could further improve the labour market prospects of leavers.
The green value chain construct: instrument validation and green practices among Malaysian corporations

This study aims to validate the green value chain construct and developed through semi-structured interviews conducted on 35 public listed companies with green focus in Malaysia. Despite the importance of value chain activities as the cornerstone of value creation for businesses, there are only limited research effort that had been directed at empirically validating the green value chain construct. This study validated the green value chain construct through questionnaire surveys among 198 Malaysian corporations. In addition, the adoption of green practices within the value chain among the corporations are examined. The findings suggest that while the corporations are embedding green practices, mainly in the primary value chain activities, the green practices in the secondary value chain activities are however given lower emphasis. The minimal emphasis on embedding green elements in the technology development and human resource management is of concern which raises the question on the sustainability of green practices among these corporations.
Impact of investor sentiment on decision-making in Indian stock market: an empirical analysis

Purpose The purpose of this paper is to analyze the relationship between the factors influencing investors sentiment and investment decision-making (DM) of the individual investors. This paper proposes a unique conceptual framework that incorporates the herding, market and awareness factors that are leading to investor sentiment (IS) and decision-making process of the individual investors. Design/methodology/approach This study has conducted a questionnaire-based survey to collect data from 875 individual investors through the convenience sampling method. Structural equation modeling was used to evaluate the relationship between factors, namely, market effect, herd behavior, media, social interaction and advocate recommendation that influences IS and DM. Findings The present study found that market effect and herding are the most significantly influencing factors of investors sentiment. Among the sources of awareness, the internet has the lowest influence when compared to media, social interaction and advocate recommendation. Practical implications This study will help individual investors to avoid the problems faced while making an investment decision. The study could help investors to select a suitable investment aid and avoid repeating expensive errors, which arise due to investors’ sentiment. It is recommended to increase the awareness regarding investors’ sentiment among individuals, so as to increase their understanding about the financial settings and to make them confident while investing. The present study also sheds light upon the behavior of Indian individual investors so that policymakers can take appropriate measures to provide the proper guidance. Policymakers can conduct awareness campaigns to increase investors’ knowledge on the market condition and to enhance proper investment DM among them. Originality/value To best of the authors’ knowledge, previous studies have focused on limited factors at a time. The present study has investigated how factors influencing investors sentiment, namely, market factors (MF), herding as well as awareness would influence investment DM among individual investors in India. The influence of these factors has never been studied simultaneously in the context of Indian individual investors’ DM.
Video denoising for security and privacy in fog computing

To reduce heavy noise from degraded video in low or predictable latency and preserve privacy, a powerful and efficient video denoising algorithm is proposed based on fog computing for Visual Internet of Things. The conventional method is to remove noise in the cloud; however, this may overload computation and communication and raise security and privacy issues. The proposed denoising algorithm is distributed to heterogeneous devices at network edges to preserve privacy and avoid security risks as noise can be reduced in the fog rather than the cloud. To address the problems of latency, communication rate, and extremely heavy noise, structure registration, inter‐frame and inner‐frame filters, and distribution compensation are applied in the proposed algorithm. A scheme for encrypting the denoised data at network edges is provided so that security and privacy issues may be avoided during transmission and storage. Compared with other denoising approaches under extremely heavy noise conditions, the experimental results demonstrate that the proposed approach achieves superior denoising performance in terms of peak signal‐noise ratio and visual quality at low computational cost, high bandwidth efficiency, and low‐latency response in a fog computing manner.
Within and Across Salesperson Heterogeneity in Negotiated Prices

In this paper, we study within firm heterogeneity in the discounts offered to consumers. Utilizing transaction level data from a large home appliance retailer, we quantify the extent of both across and within-salesperson heterogeneity in the discounts they negotiate with consumers. We find significant differences across salespeople in the discounts they offer: conditional on purchase, a consumer could realize a 15% higher discount by just negotiating with a different salesperson. We also find a significant effect of salesperson quotas on discounts, but as compared to across-salesperson heterogeneity, the impact of this within-salesperson heterogeneity is small. Using estimates from a structural demand model, we find that the retailer can realize 30% more profit, on average, by optimally allocating salespeople to consumers as compared to random allocation. This increase is economically significant given that a move from fixed pricing to bargaining (with random allocation) results in a 19% increase in profits. Further, strategic allocation based on consumer demographics, an implementable policy, captures about one-sixth of the total potential profit increase. We find that consumer welfare is lower under bargaining with strategic allocation, but it is not significantly different than under a fixed pricing mechanism. Together, these results suggest that, in addition to price discrimination through bargaining, the retailer can also price discriminate by matching salespeople to consumers.
MODELLING MORTALITY DEPENDENCE WITH REGIME-SWITCHING COPULAS

Abstract We propose a two-regime Markov switching copula to depict the evolution of mortality dependence. One regime represents periods of high dependence and the other regime represents periods of low dependence. Each regime features a regular vine (R-vine) copula that, built on bivariate copulas, provides great flexibility for modelling complex high-dimensional dependence. Our estimated model indicates that the years of recovery from extreme mortality deterioration and the years of health care reform more likely fall into the low regime, while the years in which extreme mortality deteriorating events break out and the peaceful years without major mortality-impacting events more likely fall into the high regime. We use a case study to illustrate how the regime-switching copula can be applied to assess the effectiveness of longevity risk hedge with different beliefs about future mortality dependence evolution incorporated.
Music from Aura

Our aim is to fill the gap between computer music and musical emotions. We use a 2D emotional space (2D ES) both to analyze listener's emotions captured from physiological measure and to drive a music generator with a Reinforcement Learning algorithm. We present hereafter the Reinforcement Learning algorithm and the different parts of our framework used as input (emotions classification with Emotiv EEG headset and Kirlian photography device, to improve the machine learning by using data that have no correlation between them), and output of the algorithm (rhythm and scale/chords generation). Some current and future applications are also briefly depicted.
Scalable longitudinal statistical models for genomics data

This thesis establishes statistical models for long longitudinal data coming from genome-wide sequencing-based experiments. It is shown how generalized additive models can be effectively applied to model chromatin immunoprecipitation followed by sequencing data and how these models allow improved differential analysis and principled peak calling. Effective algorithms to fit these models to complete genomes combining approximated parallelization schemes and sparse matrix techniques are provided.
Case-Based Reasoning Approach for Automating Control of Gas-Compressor Unit Within Gas-Compressor Station

The aspects of Case-based reasoning system operation are described. The possibility of application such system to gas-compressor units within gas-compressor stations for revealing emergencies is considered. A functional diagram of intellectual case-based decision-support system is offered. The diagram is to minimize maintenance staff operation during control of technological gas-compression process, which leads to minimizing human factor influence on the quality of decision-making in the field of control in emergency cases.
Deep multi-label 3D ConvNet for breast cancer diagnosis in DBT with inversion augmentation

Digital breast tomosynthesis (DBT) is a pseudo-3D breast image, which is a collection of 2D slice images. It is increasingly being used for detection and diagnosis of breast cancer. In comparison to mammography (2D breast image), DBT provides higher sensitivity (true-positive rate). Recently, there have been many state-of-the-art methods for the detection of masses and calcifications in DBT. However, these previous studies can identify just only one type of breast lesions (abnormality class), while there can be multiple breast lesions simultaneously (multi-label classification); each of them requires different treatment procedures. In this paper, we present an end-to-end multi-label classification approach that takes into account the pseudo-3D nature of DBT and able to automatically detect two major types of malignant lesions: soft tissue and calcifications. The proposed network is a real 3D convolutional network (ConvNet) with two additional strategies: global kernel and global average pooling. Also, an inversion augmentation is invented; it does not only alleviate a small size of training data, but also help an occlusion overlapping issue. Such system can be used to support radiologists in DBT analysis by prompting suspicious locations. Our in-house dataset consists of 115 DBT volumes, including 91 volumes of cancer detected cases (contained biopsy-proven malignant lesions) and 24 volumes of normal cases. The experimental results show that our approach yields a promising result for the classification of malignant lesions: 72% accuracy with f1-score at 0.842. Moreover, a multi-label classification network is able to detect concurrent lesions in 3 of 6 volumes in the testing set.
A Half-Dynamic Classification Method on Obfuscated Malicious JavaScript Detection

The traditional static detection method for malicious JavaScript detection has high efficiency without the need of code executing, but it cannot detect new malicious script. While the dynamic method usually needs to execute code and extract features, which lead to low efficiency and highly difficulty. In this paper, we propose a half-dynamic detection method for classification, which can solve the problem of obfuscated malicious JavaScript. The proposed method starts with obtaining the intermediate-state machine code using the JavaScript interpreter to compile the JavaScript. After extracting the function calling sequence of machine code, the feature model of the sequence is built using N-gram. Then we use k-NN classifier for training and detecting the malicious script. N-gram can directly be used to statically analyze the sequence of the obfuscated JavaScript, but not available to recognize the maliciousness. Then N-gram on the call function sequence of the compiled machine code is proposed as an efficient half-dynamic malicious script detection method. Finally, the efficiency and effectiveness of the proposed method is demonstrated through the experiments.
Application of transcriptome analysis to investigate the effects of long-term low temperature stress on liver function in the tiger puffer (Takifugu rubripes)

The tiger puffer (Takifugu rubripes) is an important economic fish species in northern China. However, it is a warm-temperature species, and low winter temperatures can result in high mortality in aquaculture. Understanding the mechanisms of cold resistance in tiger puffers will thus provide critical information to help cope with winter cold. In this study, we performed transcriptome analysis of livers from puffer fish kept at different temperatures (18°C, 13°C, and 8°C) to identify the key pathways and genes involved in the response to low-temperature stress. We also detected serum levels of proteases, arginine, and proline to obtain further information on the response to cold adaption. Totals of 51, 942, and 195 differentially expressed genes were identified in the 18°C vs 13°C, 18°C vs 8°C, and 13°C vs 8°C groups, respectively. Pathway analysis showed that significantly enriched pathways were mainly related to digestion, metabolism, and environmental adaptation. Most genes in the pathways related to digestion and metabolism were down-regulated, while most genes in the pathways related to environmental adaptation were up-regulated. Serum levels of proteases were significantly lower in the low-temperature groups (13°C and 8°C) compared with the control group (18°C), while arginine and proline levels were significantly higher in the 8°C group compared with the other two groups. These results suggest that low temperature caused digestive and metabolic disorders, as well as adaptive changes to low temperature in tiger puffers. On this premise, we found that some up-regulated genes in the pancreatic secretion pathway, arginine and proline metabolism pathway, and circadian rhythm pathway played important roles in the survival, growth, and development of tiger puffers under low-temperature stress. The accumulation of arginine and proline can maintain metabolism and circulation and resist cold stress. The circadian rhythm is closely related to digestion and metabolism, which is an adaptive change and plays a positive role in the resistance to low temperature. The results of this study provide new insights and a theoretical basis for the study of cold tolerance in tiger puffers.
A Survey of Force-Assisted Robotic Cell Microinjection Technologies

Cell injection plays an important role in genetics, transgenics, molecular biology, drug discovery, reproductive study, and other biomedical fields. Compared with manual cell microinjection and robotic cell microinjection with sole position feedback, force-assisted robotic cell microinjection can improve the success rate and survival rate of cell injection. In this paper, the state-of-the-art research on microinjection of both adherent cells and suspended cells with microforce sensing techniques is reviewed. The significance of force sensors in the robotic cell injection system is also discussed. Five types of prevalent force sensing methods and their applications in cell microinjection are reviewed. The challenges and promising solutions in automating the cell microinjection process are addressed. Note to Practitioners—Microinjection process is a complex task to perform. As the advance progress of high-performance microforce sensors, force-assisted robotic cell injection has been a hot topic in recent years. This paper presents the state-of-the-art survey of recent developments on microforce sensing for robotic cell microinjection to address the research challenges. Based on microforce sensing and control, microinjection of both adherent and suspended cells can benefit from the force-assisted robotic cell injection process. The main challenges and promising solutions in terms of micromanipulator design, injection control design, cell holder design, penetration scheme design, injecting pipette maintenance, injection volume, cost reduction, and microforce sensor calibration issues have been discussed. The related research trends are summarized.
Prison postsecondary education: bridging learning from incarceration to the community

This paper focuses on prison postsecondary education programming, which attempts to address factors that contribute to incarceration and assist with reintegration into society by providing credit and non-credit college-level courses to inmates before their release from prison. Specifically, we describe several postsecondary correctional education programs primarily offered by community colleges, including programs in California, New Mexico, North Carolina, Texas, Virginia, and Washington, and identify both challenges and solutions in providing these services to inmates. We also highlight program features that may improve re-entry outcomes. The paper concludes with a discussion of research areas warranting further attention from researchers and policy makers.
Manipulating plasma thyroid hormone levels alters development of endothermy and ventilation in nestling red-winged blackbirds

Thyroid hormones are key regulators of development and metabolism in vertebrates. During the nestling period, young of altricial species transition from an ectothermic phenotype to an endothermic phenotype. Red-winged blackbirds are an altricial species that exhibit an increase in plasma 3,3’, 5-triiodo-L-thyronine (T3) levels during the first 5 days post-hatch (dph), begin to develop endothermic metabolic responses by 7 dph, and fledge within 10 days of hatching. We propose that thyroid hormones play an important role in regulating development of endothermy during the nestling period in altricial birds. To better understand the effects of thyroid hormones on endothermic metabolic development in an altricial species, we treated nestling red-winged blackbirds on 2, 3, and 5 dph with either methimazole (MMI) to induce hypothyroidism or supplemental T3 to induce hyperthyroidism. We then measured on 5, 7, and 9 dph morphology and whole animal O2 consumption ( V ˙ o 2 ) and ventilation in the thermal neutral zone and during gradual cooling. Treatment of nestlings with MMI resulted in lower plasma T3 levels on 5 dph that recovered by 7 dph, while supplementing with T3 did not affect plasma T3 levels on 5, 7 and 9 dph. Treatment with MMI resulted in smaller nestlings with smaller hearts and structural characters such as wing chord and femur length, but larger lungs and kidneys. Treatment with T3 produced smaller nestlings with smaller body masses and shorter femur and tarsus lengths. The development of V ˙ o 2 and ventilation endothermic responses to gradual cooling in MMI treated nestlings were delayed when compared with control nestlings. In 9 dph nestlings, hypothyroidism resulted in alterations in the responses of ventilation frequency and tidal volume to cooling when compared with the control nestlings. Supplemental T3 had no effect on the development of V ˙ o 2 and ventilation in the thermal neutral zone or in response to cooling. Our data suggest plasma thyroid hormone levels play an active role in the systemic development of endothermic capacity and the development of ventilatory control. In the nestling avian, multiple systems develop in concert to produce an endothermic phenotype, but reduced thyroid hormone delays maturation of endothermic capacity.
The implementation of measures for reduction an inertia of the interaction components of the heterogeneous environment for the automated process control system

Authors have proposed the model of a specialized data management system in the automated process control system. This model is proposed by based on the results of research into the interaction of heterogeneous systems, as well as trends in the development of information support. The software instances, information support, and the interface of their interaction are considered. The implementation of measures for reduction an inertia of the interaction components of these types of a software by applying the formation transactions approach are suggested. The implementation of measures is connected with a revision of an algorithmic essence of the heterogeneous environment components of the automated process control system is expressed by organization of connections pool, which based on using ODBC API functions and the exclusion of gateway transport. The numerical effect of these measures were a reduction the inertia of an inertia of the interaction components of the heterogeneous environment of the automated process control system.