Miracle-dz/UniFAR-FTUs huggingface dataset:
A Connected Generation? Digital Inequalities in Elementary and High School Students According to Age and Socioeconomic Level

The objective of this article was to better understand the relationship between students’ age and socioeconomic level, and its influence on students’ digital uses. We conducted a quantitative study of 401 elementary and high school students in Quebec. Four independent variables were initially selected: two related to age (actual age and education level) and two others related to the socioeconomic environment (school poverty index and parents’ employment status). The dependent variable that represented students’ digital uses was the number of different technologies they used weekly. We conducted correlation tests followed by a linear regression analysis. Socioeconomic level appears to have a stronger influence on students’ digital uses compared to age, and explanations for this are proposed. L'objectif de cet article est de mieux comprendre la relation entre l’âge et le milieu socioeconomique des eleves dans leurs usages numeriques. Nous avons mene une etude quantitative aupres de 401 eleves du primaire et du secondaire dans la region de Montreal. Quatre variables independantes ont ete selectionnees initialement, dont les deux premieres renvoient a l'âge (l'âge et l'ordre d'enseignement) alors que les deux dernieres renseignent sur le milieu socioeconomique (l'indice de defavorisation des ecoles et la situation d'emploi des parents d'eleves). La variable dependante permettant de rendre compte des usages numeriques des eleves etait le nombre de technologies qu'ils utilisent sur une base hebdomadaire. Nous avons procede a une regression lineaire precedee de tests de correlation. Il en ressort que le niveau socioeconomique semble influencer davantage les usages numeriques des eleves que l'âge pour plusieurs raisons explorees dans cette recherche.
Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing

The efficient processing of video streams is a key component in many emerging Internet of Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self-driving cars. These applications require real-time high-throughput video processing. This can be attained via a collaborative processing model between the edge and the cloud---called an Edge-Cloud model . To this end, many approaches were proposed to optimize the latency and bandwidth consumption of Edge-Cloud video processing, especially for Neural Networks (NN)-based methods. In this demonstration. We investigate the efficiency of these NN techniques, how they can be combined, and whether combining them leads to better performance. Our demonstration invites participants to experiment with the various NN techniques, combine them, and observe how the underlying NN changes with different techniques and how these changes affect accuracy, latency and bandwidth consumption.
A Review of the Effect of Pricing Strategies on the Purchase of Consumer Goods

This study examined the effect of pricing strategies on the purchase of consumer goods. Also examined in this research is the effect of internet (online presence) on informed purchase decision. The research intended to answer questions on the extent to which competitor's price affects purchase of products, how customers perceive the value-based pricing concept of firms and the extent to which online pricing inform customer purchase decision. This paper being descriptive and historical relied heavily on secondary sources of information. The research utilized a descriptive and historical method and relied heavily and solely on secondary instruments as sources of data. Findings from the data obtained indicate that consumers have a perception of value reflected in prices of firms’ products. It also shows that competitors price affect the purchase of firm products and that online pricing informs and affects purchase decision. This study contributes to knowledge in series of issues associated with pricing strategies and purchase decision process. This research recommends that as much as firms should focus on communicating value to customers through prices, firms should also be on the watch for competitor’s prices and examine how much it affects purchase of their products.
E NHANCING M ULTILINGUAL G RAPHEMIC RNN B ASED ASR S YSTEMS U SING P HONE I NFORMATION

: In the past, we proposed the use of Language Feature Vectors (LFVs) to better adapt multilingual speech recognition systems to languages. Recently, we applied this method to RNN/CTC based systems. The recognition accuracy could be improved by modulating the network using LFVs. In this work, we propose an improvement to this approach by reﬁning the network architecture as well as the training strategy. We ﬁrst evaluated multiple methods for applying the modulation. As we are using bi-directional layers, each unit outputs two values, one per direction. Optimizing the combination of the outputs for each direction did improve the performance. In addition, we propose a method for including phonetic information into the training process of a graphemic system. By pre-training layers using phones as targets, the network did learn features to discriminate phones. Adding more layers and a two stage ﬁne-tuning process using graphemes, we ﬁrst forced the network map phonetic features to graphemes. In the second stage, we allowed the network to update the phonetic feature detectors as well. Both methods improved the performance of our setup. We evaluated our setup using a combination of 4 languages (English, French, German, Turkish), with a joint set of acoustic units.
On the Interrelation Between Listener Characteristics and the Perception of Emotions in Classical Orchestra Music

This study deals with the strong relationship between emotions and music, investigating three main research questions: (RQ1) Are there differences in human music perception (e.g., emotions, tempo, instrumentation, and complexity), according to musical education, experience, demographics, and personality traits? ; (RQ2) Do certain perceived music characteristics correlate (e.g., tension and sadness), irrespective of a particular listener's background or personality? ; (RQ3) Does human perception of music characteristics, such as emotions and tempo, correlate with descriptors extracted from music audio signals? To investigate our research questions, we conducted two user studies focusing on different groups of subjects. We used web-based surveys to collect information about demographics, listening habits, musical education, personality, and perceptual ratings with respect to perceived emotions, tempo, complexity, and instrumentation for 15 segments of Beethoven's 3rd symphony, “Eroica”. Our experiments showed that all three research questions can be affirmed, at least partly. We found strong support for RQ2 and RQ3, while RQ1 could be confirmed only for some perceptual aspects and user groups.
Arsenic- Exposure, Mechanism of Action and Toxicity Analysis: A Prenatal View

Arsenic continues to poison the drinking water of thousands across the globe. Numerous studies have evaluated the carcinogenic potential of lifetime exposure and also the ill effects of prenatal exposure. Population Studies have revealed the strong correlation between the development of skin, bladder, kidney and lung cancer and arsenic exposure in endemic areas. Although the adult exposure outcomes continue to be evaluated, the prenatal exposure aspect is neglected in most studies. Our study aims to explore the co carcinogenic effect of prenatal arsenic exposure. Most experiments conducted so far in this topic utilizes models of adult exposure and very few studies have reported association of prenatal exposure to any type of carcinogenesis. This study will address the underlying mechanisms behind this association. The knowledge derived from the study can be used to develop or design early life detection markers for in utero arsenic exposure and the anomalies that may ensue. Keywords-Arsenic, in utero, biomethylation of arsenic, tumor, carcinogenesis, prenatal.
Passive Choices and Cognitive Spillovers

Passive behavior is ubiquitous - even when facing various alternatives to choose from, people commonly fail to take decisions. This paper provides evidence on the cognitive foundations of such "passive choices" and studies implications for policies that encourage active decision-making. In an experiment designed to study passive behavior, we document three main results. First, we demonstrate that scarcity of cognitive resources leads to passive behavior. Second, policies that encourage active choice succeed in reducing passivity and improve decisions in the targeted domain. Third, however, these benefits of choice-promoting policies come at the cost of negative cognitive spillovers to other domains.
DISCUSSION OF A REGIONAL AGREEMENT FOR SMALL CETACEAN CONSERVATION IN THE INDIAN OCEAN

Multiple and cumulative anthropogenic threats are having an increasing impact on cetacea around the world. While the International Whaling Commission actively manages the twelve great whales, there are few small cetacean-specific regimes. The complexity of threats and the geopolitical ranges these animals inhabit makes an all-encompassing international regime difficult to consider at this stage. Regional agreements under frameworks such as the Convention on the Conservation of Migratory Species of Wild Animals offer a viable alternative. This article explores the efficacy of a regional agreement over the Indian Ocean and associated seas to protect small cetacea from the full range of anthropogenic threats they face. It also advances the proposition that such an agreement would be a valuable step towards global cetacean conservation, through a network of interconnected regional agreements, but also through greater regional cooperation and domestic capacity for marine management.
Phytochemical Constituents, Biological Activities, Therapeutic Potentials and Nutritional Values of Moringa oleifera (Zogale): A Review

Moringa oleifera popularly known as Zogale in Northern Nigeria is one of the most widely distributed and cultivated plant species in the region. Moringa oleifera is a small or middle-sized tree, about 10m in height and is cultivated for different purposes such as vegetable, spice, medicine, for cooking and cosmetic oil. Different parts of this plant contain a profile of important minerals, and are a good source of protein, vitamins, β-carotene, amino acids and various phenolics. It was reported that Moringa trees are used to overcome malnutrition, especially in infants and nursing mothers, due to the presence of essential nutritional composition. Moringa leaves contain more vitamin A, calcium, iron, vitamin C, and potassium than carrots, milk, spinach, oranges and bananas respectively, and the protein quality of Moringa leaves is more than that of milk and eggs. An overview of phytochemical constituents present, biological activities, therapeutic potentials and their nutritional values are given in this paper.
Simultaneous Estimation of T1, T2 and B1 Maps From Relaxometry MR Sequences

Interest in quantitative MRI and relaxometry imaging is rapidly increasing to enable the discovery of new MRI disease imaging biomarkers. While DESPOT1 is a robust method for rapid whole-brain voxel-wise mapping of the longitudinal relaxation time (T1), the approach is inherently sensitive to inaccuracies in the transmitted flip angles, de- fined by the B1 inhomogeneity field, which become more severe at high field strengths (e.g., 3T). We propose a new approach for simultaneously mapping the B1 field, M0 (proton density), T1 and T2 relaxation times based on regular fast T1 and T2 relaxometry sequences. The new method is based on the intrinsic correlation between the T1 and T2 relaxometry sequences to jointly estimate all maps. It requires no additional sequence for the B1 correction. We evaluated our proposed algorithm on simulated and in-vivo data at 3T, demonstrating its improved accuracy with respect to regular separate estimation methods.
Assessment of Immature Granulocytes Percentage to Predict Severe Bacterial Infection in Latvian Children: An Analysis of Secondary Data

Background: Detection of small proportion of serious bacterial infections (SBI) with a potentially life threating course in a large group of children with fever admitted to emergency department (ED) is still complicated. Measurement of immature granulocytes (IG) percentage may be used as a marker of bacterial infections. The aim of the study was to evaluate whether the IG percentage is a useful additional predictive marker of SBI. Methods: This study included 258 children with febrile infections that were admitted to the ED. Clinical follow-up, microbiological and radiological tests were used as reference standards for the definition of SBI. Study population was categorized into two groups: (1) infected patients with no suspicion of SBI (n = 75); (2) patients with suspicion of SBI (n = 183). IG percentage, white blood cell count (WBC), and C-reactive protein (CRP) levels were analyzed from the first routine blood samples at hospital admission. Results: A statistically significant difference in IG percentage levels was observed in children with SBI and those without—the mean IG percentage was 1.2% for the SBI group, 0.3% for those without SBI. The cutoff level of IG percentage to predict SBI was 0.45 (84% specificity, 66% sensitivity, 90% positive predictive value). We combine variables and evaluate their additive values. The sensitivity of WBC to detected SBI improved from 74% to 85% when IG percentage was added to the prediction models. When CRP, WBC, and IG percentage were combined, the sensitivity to predict SBI increased to 93%, the specificity to 86% (95% CI 77%–93%). Receiver operator characteristic analysis to predict SBI showed an area under the curve (AUC) of 0.80 for IG percentage. Conclusion: Addition of IG percentage to traditionally used markers of SBI as WBC and CRP may help to identify children with serious bacterial infections. Furthermore, IG percentage can be rapidly obtained from the traditional full blood count without any extra sampling and costs.
Five Dimensions of Collaboration: Toward a Critical Theory of Coordination and Interoperability in Language Documentation

Documentary linguistics is engaged in an evolving discourse on best practices, methods, and ethics in an effort to establish a theory of language documentation. A developing consensus in the literature is that a language documentation should be comprehensive, data-centered, and usable by diverse audiences of academics, speech community members, and others. Writers have argued that comprehensiveness requires an orientation to many issues that are beyond the standard training and expertise of most linguists and that any successful documentation project will draw on the multiple strengths of interdisciplinary collaboration as well as collaboration with the speech community under study. As a result, “collaboration” has surfaced as a key term in best practices literature in language documentation and is used to describe both interactions between scholars and between researchers and communities. At the moment, collaboration is not the norm broadly in linguistics and for linguists coming to a documentation project the methods of effective collaboration may be difficult to identify. A theory of language documentation must also grapple with its theoretical orientation to collaboration. While the nature of collaboration between researchers and communities is best determined by the situation and individual cultures involved, there should be a theoretical space within the discipline of language documentation to begin to explore best practices for researchers working together. The first aim of this paper is to provide a review of some key literature on the subject of collaboration between researchers in the physical and computer sciences and the humanities. Secondly, the goal is to identify five aspects of academic collaboration—coordination, distribution of labor, standards for interoperation, authorship and authority, and feedback—that have special bearing on the enterprise of language documentation. Finally, the paper investigates these dimensions of collaboration as a starting point for linguists and our collaborators to consider critically what collaboration means for a language documentation project and for the discipline of linguistics. Through reviewing the five dimensions, I will suggest that a true theory of coordination of our efforts and interoperatability of the products of our documentation will require linguists to reorient our assumptions about the value of 'lone-wolf' linguistics, the power of the relationships between researchers, and will emphasize the advantage in engaging and perhaps altering the social and cultural underpinnings of linguistics as a discipline.
Towards 5G Network Slicing for the V2X Ecosystem

The automotive vertical market is currently undergoing key technological transformations as the number of connected and more autonomous vehicles grows, thus realizing the Vehicle-to-Everything (V2X) communication ecosystem. Such a revolution raises unprecedented challenges to the ICT players that have to guarantee ultra-low latency and ultra-high reliable connectivity under high-mobility and high-density conditions. By allowing an operator to flexibly provide dedicated logical networks with customer-specific (virtualized) functionalities over a common physical infrastructure, network slicing candidates itself as a prominent solution to support V2X over upcoming programmable and softwarized 5G systems. In this paper, we share our vision about V2X network slicing, by pinpointing key requirements and providing a set of design guidelines, aligned with ongoing 3GPP standard specifications and network softwarization directions.
Lateral Transfer: A Survey and New Developments

Exponentially accumulating genetic molecular data were supposed to bring us closer to resolving one of the most fundamental issues in biology—the reconstruction of the tree of life. This tree should encompass the evolutionary history of all living creatures on earth and trace back a few billions of years to the most ancient microbial ancestor. Ironically, this abundance of data only blurs our traditional beliefs and seems to make this goal harder to achieve than initially thought. This is largely due to lateral gene transfer, the passage of genetic material between organisms not through lineal descent. Evolution in light of lateral transfer tangles the traditional universal tree of life, turning it into a network of relationships. Lateral transfer is a significant factor in microbial evolution and is the mechanism of antibiotic resistance spread in bacteria species. In this paper we survey current methods designed to cope with lateral transfer in conjunction with vertical inheritance. We distinguish betwe.
Connecting Microscopic L-H Transition Physics to the Power Threshold

A physics-based model of the L-H transition power threshold is needed to confidently extrapolate auxiliary heating requirements for burning plasmas. Two important scalings that need a physics basis are the isotope scaling, and the non-monotonic scaling of the power threshold with plasma density. The goal of this work is to link differences in the microscopic turbulence-flow interaction to the resulting macroscopic power threshold density and isotope scaling. Evidence from several recent experiments has pointed towards a synergistic role of turbulence-driven flows [Zonal Flows (ZFs)] and pressure-gradient-driven flows in the trigger and evolution of the L-H transition [1,3-7]. L-H transitions preceded by limit cycle oscillations (LCO) between the turbulence amplitude and the E×B flow have been obtained in DIII-D [8,9] and other devices near the L-H power threshold Pth, across a large range of plasma density, current, and safety factor (1x10 m ≤
Forecasting Big Time Series: Old and New

Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and workforce scheduling in warehouses, call centers, factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources, posing unprecedented challenges to traditional time series forecasting methods. As such, how can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of "similar" time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes? The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in three related fields: (1) classical modeling of time series, (2) scalable tensor methods, and (3) deep learning for forecasting. Further, we share lessons learned from building scalable forecasting systems. While our focus is on providing an intuitive overview of the methods and practical issues, we also present technical details underlying these powerful tools.
Adaptive TCP Flow Control for Improving Performance of Mobile Cloud Clusters

Significant innovations in mobile technologies are enabling mobile users to make realtime actionable decisions based on balancing opportunities and risks in order to take coordinated actions with others in the workplace. This requires a new distributed analytic framework that collects relevant information from internal and external sources, performs real-time distributed analytics, and delivers a critical analysis to any user at any place in a given time frame through the use of mobile devices such as smartphones and tablets. This work discusses the benefits and challenges of building mobile cloud clusters using recent mobile devices for distributed analytic applications by showing its feasibility with MapReduce framework and also investigates performance issues of Hadoop mobile clusters by conducting extensive experiments using typical Hadoop benchmarks; the newest release of Hadoop software framework with its enhancements is ported to the latest Android-based mobile devices through mobile virtualization technique. In addition, it develops the MapReduce simulator based on the ns-2 network simulator to comprehensively examine the performance and efficiency of mobile cloud clusters in extensive operating environments, which enables it to identify critical performance issues under different cluster (or workload) scales, dynamic node mobility, and various wireless channel conditions. From the performance analysis this work identifies TCP (Transmission Control Protocol) communication problems resulting from distinct traffic patterns of MapReduce-based Hadoop distributed framework and proposes adaptive TCP flow control algorithms for improving the performance of mobile cloud clusters. The overall computing power of the mobile cluster is no longer significantly bounded by typical processing capabilities of each individual mobile node as mobile devices have been constantly enhanced, but the mobile cluster has limitations on interchanging large amounts of analytical data among mobile devices and