Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the adoption of quantified self-tracking wearable devices in the organization environment: an empirical case study

Prior research on wearable devices has focused heavily on the consumer market. This study makes a unique empirical contribution to wearables research by extending the knowledge on factors that contribute to the adoption of quantified self-tracking wearable devices in an organizational environment. A wearable acceptance model (WAM) and factors that can influence the individual's decision to adopt quantified self-tracking wearable devices and self-monitoring practices were tested with an online survey of 129 university employees (faculty, administration) and students. Partial least squares path modeling was applied in an analysis to test nine hypotheses to validate the WAM. The factors in the individual context i.e. attitude plays a significant mediating role between the intention to use and the other influential factors of technology, implementation and risk context. The factors of the fashnology (wearability, aesthetic/design), individual (attitude) and risk context (privacy concern) tend to have strong and direct effects on the int use the devices, whereas factors of risk context (privacy and technology context (performance expectancy) moderate influence on the intention to use through Organizational facilitating conditions have a significant influence on the intention to use. Surprisingly, effort expectancy does not have any effect on attitude.
Improving face recognition of artificial social companions for smart working and living environments

In this paper we address the topic of improving the performance of human-machine interaction functionalities, focusing on the example of face recognition, using a context based approach to reduce the search space for algorithms performing face identification in large face data sets. We apply this feature in the customization of the behaviour of a virtual assistant, which performs distinct "animations" depending on the identified user. The results presented in the paper refer mainly to the comparison of a face identification algorithm, Eigenfaces, while performing in various scenarios, without and with the context based approach. The conclusions of this work, point in the direction that clustering the search space by defining constrains based on context features lead to improved performance of the identification algorithm while adding some degree of simple first-order logic to the actions performed afterwards (i.e. the behaviour performed by the virtual assistant).
Relationship Between Stressful Life Events and Sleep Quality: Rumination as a Mediator and Resilience as a Moderator

Purpose: The aim of this study was to investigate the relationship between stressful life events and sleep quality and to probe the role of rumination and resilience in the relationship. Method: The Adolescent Self-Rating Life Events Checklist, Ruminative Responses Scale, Connor–Davidson Resilience Scale, and Pittsburgh Sleep Quality Index were used among 1,065 college students. Statistical Product and Service Solutions (SPSS) 20.0 and the SPSS macro Process, which were specifically developed for assessing complex models including both mediators and moderators, were used to analyze the data. Results: High scores of stressful life events predicted worse sleep quality. Rumination partially mediated the relations between stressful life events and sleep quality. Resilience moderated the direct and indirect paths leading from stressful life events to sleep quality. Conclusions: The results demonstrate that stressful life events can directly affect the sleep quality of college students and indirectly through rumination. Additionally, increasing psychological resilience could decrease both the direct effect and the indirect effect of stressful life events affecting sleep quality. The results of this study may contribute to a better understanding of the effects, as well as the paths and conditions, of stressful life events on sleep quality in college students. Moreover, these findings can provide constructive suggestions for improving college students’ sleep quality.
Crowd Learning: Improving Online Decision Making Using Crowdsourced Data

We analyze an online learning problem that arises in crowdsourcing systems for users facing crowd-sourced data: a user at each discrete time step t can choose K out of a total of N options (bandits), and receives randomly generated rewards dependent on user-specific and option-specific statistics unknown to the user. Each user aims to maximize her expected total rewards over a certain time horizon through a sequence of exploration and exploitation steps. Different from the typical regret/bandit learning setting, in this case a user may also exploit crowdsourced information to augment her learning process, i.e., other users' choices or rewards from using these options. We consider two scenarios, one in which only their choices are shared, and the other in which users share full information including their choices and subsequent rewards. In both cases we derive bounds on the weak regret, the difference between the user's expected total reward and the reward from a user-specific best single-action policy; and show how they improve over their individual-learning counterpart. We also evaluate the performance of our algorithms using simulated data as well as the real-world movie ratings dataset MovieLens.
VibrAid: comparing temporal and spatial tactile cues in control room environments

We present VibrAid, a miniaturized wrist-worn haptic display that provides vibrotactile alert cues for operators of large dashboard environments. We interviewed three control room operators in a steam plant on what a vibrotactile attention guidance system for the control room should look like and designed our system and the laboratory evaluation environment accordingly. We conducted an experiment (24 participants) comparing task completion time with temporal and spatial attention cues to the standard visual attention cues that are used in most modern monitoring systems. While temporal attention cues did not have an effect on task completion time, spatial cues led to an significant decrease in average task completion time, as well as significantly increasing trust in the ability to keep up with tasks even at a more rushed pace.
Deep Reason: A Strong Baseline for Real-World Visual Reasoning

This paper presents a strong baseline for real-world visual reasoning (GQA), which achieves 60.93% in GQA 2019 challenge and won the sixth place. GQA is a large dataset with 22M questions involving spatial understanding and multi-step inference. To help further research in this area, we identified three crucial parts that improve the performance, namely: multi-source features, fine-grained encoder, and score-weighted ensemble. We provide a series of analysis on their impact on performance.
Drive by maintenance: towards adaptive work environments with improved industrial HCI

Although automation is the backbone of the modern industrial complex, humans remain the indispensable shepherds of the machine park. One operator may be responsible for several machines that need to run continuously. Unfortunately, the complexity of the machines and processes increased faster than the improvement in the corresponding interaction technologies. We propose a multi-modal adaptive human computer interface system for the modern industry that relies on position, activity and other relevant contextual information, in order to provide a more flexible interaction in industrial scenarios. Information about the machine, is transmitted to the worker using a vibrotactile display on his wrist whenever the line of sight is broken. The context in which the user and the machine currently reside in, are determined by software artefacts (Apps). We leverage modern software concepts and an App based eco-system platform on the manufacturing shop floor to organize the software systems described in this paper. We evaluate transmitting arbitrary information via vibrotactile displays and come to the conclusion that such a system is feasible in industrial settings and may be beneficial as it has a very low distraction rate.
ALGORITHM FOR MINING TIME VARYING FREQUENT ITEMSETS

A popular subfield of data mining is the area of association mining that searches for frequently cooccurring items in a market-basket type database. A market basket is the list of items a customer purchases at the super market. We can analyze past transaction data to discover customer behaviors such that quality of business decision can be improved. The Input of Association mining is large set of transactions each consisting of a list of items a customer has purchased at a supermarket. This paper focuses on the composition of the list of frequent itemsets that may change in time as the purchasing habits get affected by season, fashion and introduction of new products. The output of this algorithm is to get the frequent items which occur after a particular item or item sets.
Bicycle-Related Injuries: a Local Scene

To determine the profile of bicycle injury and possible risk factors for severe injuries, we studied patients with bicycle-related accidents attending the Accident and Emergency Department of North District Hospital between 1 May 1999 to 31 October 1999. Of the 424 patients, 52 (12.3%) were admitted and one (0.24%) died. Comparing to those not admitted, significantly more of the admitted cases suffered from major injuries, rode on rainy days and involved in collision with motor vehicles. A total of 160 patients (37.8%) had documented head injuries. However the extremely low utilisation rates of helmet at the time of injury implied that action should be taken in this direction to reduce bicycle-related morbidity and mortality.
Evaluation of different types of haptic feedback influencing the task-based presence and performance in virtual reality

Haptic feedback may support immersion and presence in virtual reality (VR) environments. The emerging market of consumer devices offers first devices which are expected to increase the degree of feeling being actually present in a virtual environment. In this paper we introduce a novel evaluation that examines the influence of different types of haptic feedback on presence and performance regarding manual tasks in VR. Therefore, we conducted a comprehensive user study involving 14 subjects, who performed throwing, stacking and object identification tasks in VR with visual (i.e., sensory substitution), vibrotactile or force feedback. We measured the degree of presence and task-related performance metrics. Our results indicate that regarding presence vibrotactile feedback outperforms haptic feedback which performs better than visual feedback only. In addition, force feedback significantly lowered the execution time for the throwing and the stacking task. In object identification tasks, the vibrotactile feedback increased the detection rates compared to the vibrotactile and force feedback, but also increased the required time of identification. Despite the inadequacies of the still young consumer technology, there were nevertheless strong indications of connections between presence, task fulfillment and the type of haptic feedback.
Itération sur les Politiques Optimiste et Apprentissage du Jeu de Tetris. (Optimistic Policy Iteration and Learning the Game of Tetris)

Cette these s'interesse aux methodes d'iteration sur les politiques dans l'apprentissage par renforcement a grand espace d'etats avec approximation lineaire de la fonction de valeur. Nous proposons d'abord une unification des principaux algorithmes du controle optimal stochastique. Nous montrons la convergence de cette version unifiee vers la fonction de valeur optimale dans le cas tabulaire, ainsi qu'une garantie de performances dans le cas ou la fonction de valeur est estimee de facon approximative. Nous etendons ensuite l'etat de l'art des algorithmes d'approximation lineaire du second ordre en proposant une generalisation de Least-Squares Policy Iteration (LSPI) (Lagoudakis et Parr, 2003). Notre nouvel algorithme, Least-Squares [lambda] Policy Iteration (LS[lambda]PI), ajoute a LSPI un concept venant de [lambda]-Policy Iteration (Bertsekas et Ioffe, 1996) : l'evaluation amortie (ou optimiste) de la fonction de valeur, qui permet de reduire la variance de l'estimation afin d'ameliorer l'efficacite de l'echantillonnage. LS[lambda]PI propose ainsi un compromis biais-variance reglable qui peut permettre d'ameliorer l'estimation de la fonction de valeur et la qualite de la politique obtenue. Dans un second temps, nous nous interessons en detail au jeu de Tetris, une application sur laquelle se sont penches plusieurs travaux de la litterature. Tetris est un probleme difficile en raison de sa structure et de son grand espace d'etats. Nous proposons pour la premiere fois une revue complete de la litterature qui regroupe des travaux d'apprentissage par renforcement, mais aussi des techniques de type evolutionnaire qui explorent directement l'espace des politiques et des algorithmes regles a la main. Nous constatons que les approches d'apprentissage par renforcement sont a l'heure actuelle moins performantes sur ce probleme que des techniques de recherche directe de la politique telles que la methode d'entropie croisee (Szita et Lorincz, 2006). Nous expliquons enfin comment nous avons mis au point un joueur de Tetris qui depasse les performances des meilleurs algorithmes connus jusqu'ici et avec lequel nous avons remporte l'epreuve de Tetris de la Reinforcement Learning Competition 2008
Design and implementation of a wireless tensiometer with smartphone interface

In this paper an implementation of a wireless tensiometer with smartphone interface for personal use is present. The prototype has been developed to allow the final user to keep a register of its own blood pressure measurement. The application also can generate a report with the measurement data that can be sent to a doctor or to an online database historical profile keeping, which may be useful for remote diagnosis procedures. The device establishes the communication with the smartphone via Bluetooth. The smartphone interface was developed in Worklight, which gives the possibility to synthetize the application on different operating systems for smartphone such as iOS, Blackberry OS and Android OS.
Menarquia y factores asociados Menarche and associated factors

Introducción: la menarquia es el indicador de maduración sexual más comúnmente utilizado, influida por factores genéticos y ambientales, y se asocia al riesgo de desarrollar enfermedades como la obesidad, el cáncer de mama, las enfermedades cardiovasculares, entre otras. Objetivos: caracterizar demográficamente las adolescentes en estudio; evaluarlas nutricionalmente utilizando el índice de masa corporal y la circunferencia de cintura, así como identificar la relación entre la menarquia, el color de la piel, el bajo peso al nacer, el exceso de peso corporal, la obesidad abdominal y la presencia de acantosis nigricans. Métodos: se realizó un estudio observacional, descriptivo transversal en la Secundaria Básica Urbana "José María Heredia", desde junio de 2010 a enero de 2011. La población estuvo constituida por 85 adolescentes, a las cuales se les preguntó acerca de la menarquia, se les realizaron las mediciones antropométricas, y se buscó la presencia de acantosis nigricans. Resultados: la edad media de las adolescentes fue 12,05 años, de las cuales el 63,5 % había experimentado la menarquia, y su edad media de presentación fue de 11,00±0,801 años. El 17,6 % presentó exceso corporal, el 34,1 % adiposidad abdominal y el 12,9 % acantosis nigricans, asociadas estadísticamente con la aparición de la menarquia. Conclusiones: la mayoría de las adolescentes que habían experimentado la menarquia eran maduradoras tempranas, y de color de piel blanca; mostraron un riesgo incrementado de desarrollar exceso de peso corporal, obesidad abdominal y signos clínicos de resistencia a la insulina. La aparición de la menarquia no estuvo influida por el bajo peso al nacer.
Intraoperative Protective Mechanical Ventilation for Prevention of Postoperative Pulmonary Complications: A Comprehensive Review of the Role of Tidal Volume, Positive End-expiratory Pressure, and Lung Recruitment Maneuvers

Postoperative pulmonary complications are associated with increased morbidity, length of hospital stay, and mortality after major surgery. Intraoperative lung-protective mechanical ventilation has the potential to reduce the incidence of postoperative pulmonary complications. This review discusses the relevant literature on definition and methods to predict the occurrence of postoperative pulmonary complication, the pathophysiology of ventilator-induced lung injury with emphasis on the noninjured lung, and protective ventilation strategies, including the respective roles of tidal volumes, positive end-expiratory pressure, and recruitment maneuvers. The authors propose an algorithm for protective intraoperative mechanical ventilation based on evidence from recent randomized controlled trials.
Regulation of potassium channel trafficking in the distal nephron

Purpose of reviewPotassium channels in the distal nephron are precisely controlled to regulate potassium secretion in accord with physiological demands. In recent years, it has become evident that membrane trafficking processes play a fundamental role. This short review highlights recent developments in elucidating the underlying mechanisms. Recent findingsNovel sorting signals in the renal potassium channels, and the elusive intracellular trafficking machinery that read and act on these signals have recently been identified. These new discoveries reveal that independent signals sequentially interact with different intracellular sorting, retention and internalization machineries to appropriately ferry the channels to and from the apical and basolateral membrane domains in sufficient numbers to regulate potassium balance. SummaryA new understanding of the basic mechanisms that control potassium channel density at polarized membrane domains has emerged, providing new insights into how potassium balance is achieved and how it goes awry in disease.
Ascorbic acid reduces redox potential in human spermatozoa subjected to heat‐induced oxidative stress

Oxidation–reduction potential (ORP) is a new measure of oxidative stress. It is a balance between the total available oxidants and reductants. This study measures the efficiency of ascorbic acid (AA) against oxidative stress induced by either heat alone or heat and hydrogen peroxide in sperm suspensions using the MiOXSYS System. Two concentrations of ascorbic acid (400 and 600 μmol/L) were tested against heat‐ and heat plus hydrogen peroxide‐induced oxidative stress in sperm suspensions after 2 and 4 hr of incubation. Sperm motility and static oxidation reduction potential (sORP) were measured at 2 and 4 hr of incubation at three different temperatures. A significant decrease in sORP was observed as a function of AA concentration. The 600 μmol/L AA had more pronounced reduction in sORP compared to 400 μmol/L AA (p = .001). Significant decreases in sperm motility ranging from 4.89% to 14.02% were observed both as a function of incubation time and addition of H2O2 (p < .001). Ascorbic acid is efficacious to reduce heat‐induced oxidative stress in sperm preparations in vitro. The supplementation of ascorbic acid may be advantageous for semen preparations in IUI, IVF and ICSI.
Scalable telemetry and network autonomics in ACTN SDN controller hierarchy

Abstraction and Control of TE Networks (ACTN) refers to the set of virtual network operations needed to operate, control and manage large-scale multi-domain, multi-layer and multi-vendor TE networks, so as to facilitate network programmability, automation, efficient resource sharing. This paper provides scalable telemetry streaming mechanisms that describe Key Performance Indicator (KPI) telemetry and network autonomics in the context of ACTN SDN controller hierarchy.
Insulin resistance in chronic hepatitis C: association with genotypes 1 and 4, serum HCV RNA level, and liver fibrosis.

BACKGROUND & AIMS Our study was designed to test the association between insulin resistance (IR) and hepatitis C virus (HCV) genotypes, serum HCV RNA level and liver fibrosis stage in a large prospective cohort of chronic hepatitis C (CHC) patients. METHODS Six hundred consecutive patients (CHC, n = 500; chronic hepatitis B (CHB), n = 100) were evaluated on the day of liver biopsy. IR (Homeostasis Model for Assessment of Insulin Resistance) and all components of the metabolic syndrome were assessed. By logistic regression, independent factors associated with IR and those associated with significant fibrosis were assessed in nondiabetic and noncirrhotic CHC, respectively. Parameters of IR were compared between hepatitis B and 240 CHC matched by epidemiologic, metabolic, and histologic features. RESULTS IR was present in 32.4% of the 462 nondiabetic CHC and associated with the metabolic syndrome, genotypes 1 and 4, significant fibrosis, and severe steatosis. IR was diagnosed in 15% of 145 CHC without metabolic syndrome or significant fibrosis, and associated with genotypes 1 and 4, high serum HCV RNA level, and moderate-severe necroinflammation. Significant fibrosis was present in 51.1% of the 454 noncirrhotic CHC patients and associated with male sex, age >40 years, IR, moderate-severe necroinflammation, and severe steatosis. IR was less frequent in CHB than in matched CHC (5% vs 35%, respectively, P < .001). CONCLUSIONS IR is a specific feature of CHC, associated with genotypes 1 and 4 and high serum HCV RNA level. Significant fibrosis is associated with IR independent from steatosis.
Prepregnancy overweight and obesity are associated with impaired child neurodevelopment

The authors examined the relationship of prepregnancy body mass index (BMI) and gestational weight gain (GWG) with child neurodevelopment. Mother-child dyads were a subgroup (n = 2,084) of the Child Health and Development Studies from the Oakland, California, area enrolled during pregnancy from 1959 to 1966 and followed at child age 9 years. Linear regression was used to examine associations between prepregnancy BMI, GWG, and standardized Peabody Picture Vocabulary Test and Raven Progressive Matrices scores and to evaluate effect modification of GWG by prepregnancy BMI. Before pregnancy, 77% of women were normal weight, 8% were underweight, 11% were overweight, and 3% were obese. Associations between GWG and child outcomes did not vary by prepregnancy BMI, suggesting no evidence for interaction. In multivariable models, compared to normal prepregnancy BMI, prepregnancy overweight and obesity were associated with lower Peabody scores (b: -1.29; 95% CI [-2.6, -0.04] and b: -2.7; 95% CI [-5.0, -0.32], respectively). GWG was not associated with child Peabody score [b: -0.03 (95% CI: -0.13, 0.07)]. Maternal BMI and GWG were not associated with child Raven score (all P >0.05). Maternal prepregnancy overweight and obesity were associated with lower scores for verbal recognition in mid-childhood. These results contribute to evidence linking maternal BMI with child neurodevelopment. Future research should examine the role of higher prepregnancy BMI values and the pattern of pregnancy weight gain in child cognitive outcomes.