Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical versus psychosocial stress: effects on hormonal, autonomic, and psychological parameters in healthy young men

Abstract As a time-efficient training system, high intensity interval training (HIIT) is well known for several beneficial effects. However, the literature on the stress-generating effects of HIIT shows a research deficit. A standardized comparable stressor and different kinds of stress-parameters are needed for quantifying the results. The present study examined the hormonal, autonomic, and psychological stress outcomes of HIIT compared to a standardized psychosocial stressor and tested the cross-stressor-adaptation (CSA) hypothesis which implies a stress-buffering effect at a good fitness level. In a sample of 32 healthy young males (24.31 ± 3.35 years of age) stress was induced with a multiple Wingate (WG), as a HIIT all-out performance test, involving four 30 sec all-out exercise bouts. In addition, the Trier Social Stress Test (TSST), which consists of a mock job interview and mental arithmetic performance, was used for stress induction. Cortisol, heart rate variability (HRV), and stress-related questionnaires were assessed before, during, and after stress induction. Both the Wingate as well as the TSST led to a highly significant change in time and stressor for cortisol and HRV. Furthermore, a significantly higher delta during Wingate was identified. In part, the TSST had a significantly higher impact on the psychological measurements than the WG. In contrast to the literature, this study was not able to confirm the stress-buffering effect of the CSA hypothesis. These findings prove the stressful effect of HIIT. The prevention of negative health effects needs to be taken into consideration in sports training methods and programs as well as in stress-related research Lay summary By using a well-estimated psychosocial stressor for comparison and several stress parameters, this study is able to show the strong stress-generating effect of high intensity interval training (HIIT). Interestingly, subjective stress perception differed from objective stress response. This research is an important step towards understanding stress-related disorders in elite sport and making recommendations for reducing autonomic as well as hormonal stress in high intensity sport.
Privacy-Preserving Top- $k$  Route Computation in Indoor Environments

Most of us live our daily lives in various types of indoor environments. With the rapidly increasing air pollution and abnormal weather conditions occurring all over the world, the importance of indoor space is expected to increase in the future. As the importance of indoor spaces has increased, there have been extensive efforts to understand the indoor behavior patterns of individuals by collecting and analyzing their indoor positioning data. However, indiscriminate collection of indoor location information may raise significant privacy issues, because it is possible to infer sensitive information by analyzing their indoor location data. Thus, there is a need to develop a technology capable of: 1) collecting individuals’ indoor positioning data while not violating privacy requirements and 2) analyzing their movement patterns, based on the indoor positioning data set collected in a privacy-preserving manner. Recently, there has been a growing interest in local difference privacy (LDP) that can protect individual privacy in the process of data collection. The main concept of LDP is adding carefully designed noise to sensitive data at the data contributor’s side and directly sending the noisy data to a data collector, guaranteeing that the original data are not exposed to the outside. In this paper, we first develop a method to collect individuals’ indoor positioning information by leveraging LDP, and then, we introduce an effective approach to compute top- $k$ routes between two points in indoor environments by leveraging the indoor positioning data sets collected in an LDP manner. Experimental results show that the proposed approach well exploits the indoor positioning data collected in an LDP-compliant manner, and a good estimation of top- $k$ routes can be achieved with the proposed method and a large data set.
Interactive Intent Modeling for Exploratory Search

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.
Single Image Dehazing via Adaptive Transmission Optimization with Deep Prior

Many conventional single image dehazing methods use human-designed priors to estimate transmission and recover the latent scene. However, it is hard to design a universal prior for different real-work hazy scenarios. Though convolutional neural networks (CNNs) have also been utilized to perform end-to-end dehazing, which are dependent on the training data tightly. In this work, we provide a novel framework to incorporate deep priors into iteration scheme to adaptively optimize the transmission map. Specifically, we first establish our fundamental iteration by solving the implicitly regularized transmission optimization model based on the widely used alternating direction method of multiplier (ADMM). We then design a residual CNN to extract deep priors from a small training dataset (compared with existing dehazing networks) to guide the ADMM iterations, resulting to our final adaptive transmission optimization formulation. Extensive experimental results show that our proposed approach can produce compelling results superior to those obtained by others.
Solar Irradiance Prediction from Historical Trends Using Deep Neural Networks

Accurate prediction of solar irradiance is difficult because of the uncertainties in weather parameters. In this paper we present a forecasting model for solar irradiance based on historical data analysis and meteorological features, with the help of deep neural networks. A unique feature of the method is the use of historical trends of solar irradiance, which significantly improves the accuracy of our prediction model. We have validated the performance of our model using the data obtained from National Solar Radiation Database (NSRDB). As an application, the forecasted irradiance values are used to predict the power output of a photovoltaic (PV) plant set up in Kharagpur, India, within a low margin of error.
Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem

The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision. This article is part of a theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.
Decentralized Neighborhood Energy Management With Coordinated Smart Home Energy Sharing

This paper introduces a day-ahead energy management algorithm for the coordination of smart homes with renewable energy sources and energy storage systems in neighborhood areas. The aim of this paper is to establish a day-ahead decentralized coordination method with appliance scheduling and energy sharing (among smart homes) to minimize the electricity bills of consumers under dynamic pricing. The energy sharing algorithm focuses on increasing the utilization of renewable sources by controlling storage units. A multi-agent system is used to model entities (smart homes, aggregator, and utility) as agents and the optimization problem is solved in a decentralized manner by home agents with a genetic algorithm. The performance of the coordination algorithm is evaluated annually with and without considering forecasting errors.
Primal Averaging: A New Gradient Evaluation Step to Attain the Optimal Individual Convergence

Many well-known first-order gradient methods have been extended to cope with large-scale composite problems, which often arise as a regularized empirical risk minimization in machine learning. However, their optimal convergence is attained only in terms of the weighted average of past iterative solutions. How to make the individual convergence of stochastic gradient descent (SGD) optimal, especially for strongly convex problems has now become a challenging problem in the machine learning community. On the other hand, Nesterov’s recent weighted averaging strategy succeeds in achieving the optimal individual convergence of dual averaging (DA) but it fails in the basic mirror descent (MD). In this paper, a new primal averaging (PA) gradient operation step is presented, in which the gradient evaluation is imposed on the weighted average of all past iterative solutions. We prove that simply modifying the gradient operation step in MD by PA strategy suffices to recover the optimal individual rate for general convex problems. Along this line, the optimal individual rate of convergence for strongly convex problems can also be achieved by imposing the strong convexity on the gradient operation step. Furthermore, we extend PA-MD to solve regularized nonsmooth learning problems in the stochastic setting, which reveals that PA strategy is a simple yet effective extra step toward the optimal individual convergence of SGD. Several real experiments on sparse learning and SVM problems verify the correctness of our theoretical analysis.
Locating Clone-and-Own Relationships in Model-Based Industrial Families of Software Products to Encourage Reuse

Companies often develop similar product variants that share a high degree of functionality (i.e., features) by copying and modifying code (the clone-and-own approach). In an industrial context with a large amount of variants, software reuse can become complex for engineers. Identifying the clone-and-own relationships between the same features in different product variants can encourage reuse (e.g., suggesting improvements on how features are reused or detecting feature reuse impediments). This paper presents our approach to locate the clone-and-own relationships. To do this, our approach proposes an algorithm that combines feature location and code-comparison techniques. We evaluated our approach in three model-based industrial families of two domains (firmware for induction hobs and train control software). In our evaluation, we measure the performance (in terms of precision and recall) and compare our approach with its previous version (baseline), which uses a different technique to compare the code of each feature with its variants. The results show that our approach is able to locate clone-and-own relationships in different domains of real-world environments, and it outperforms the baseline up to 65.37% in terms of precision.
The BIPM Kibble Balance for the Realization of the Redefined Kilogram

This paper reports the latest status of the Kibble balance experiment that the BIPM is developing for the realization of the upcoming redefined mass unit. The apparatus is now capable of working with a mass of 1 kg and fully operational in vacuum with a day-to-day repeatability at the 10−7 level. Studies are being conducted to evaluate various corrections such as those due to the coil-current effect and the self-attraction correction.
On Dynamic Pitch Benefit for Speech Recognition in Speech Masker

Previous work demonstrated that dynamic pitch (i.e., pitch variation in speech) aids speech recognition in various types of noises. While this finding suggests dynamic pitch enhancement in target speech can benefit speech recognition in noise, it is of importance to know what noise characteristics affect dynamic pitch benefit, and who will benefit from enhanced dynamic pitch cues. Following our recent finding that temporal modulation in noise influences dynamic pitch benefit, we examined the effect of speech masker characteristics on dynamic pitch benefit. Specifically, the first goal of the study was to test the hypothesis that dynamic pitch benefit varies depending on the availability of pitch cues in the masker and the intelligibility of masker. The second goal of this study was to investigate whether older listeners as a group can benefit from dynamic pitch for speech recognition in speech maskers. In addition, individual factors of hearing loss and working memory capacity were examined for their impact on older listeners' dynamic pitch benefit. Twenty-three younger listeners with normal hearing and 37 older listeners with varying levels of hearing sensitivity participated the study, in which speech reception thresholds were measured with sentences in speech maskers. While we did not find an effect of masker characteristics on dynamic pitch benefit, the results showed older listeners can benefit from dynamic pitch for recognizing speech in speech maskers. The data also suggest that among those older listeners with hearing loss, dynamic pitch benefit is stronger for individuals with higher working memory capacity. This can be attributed to their ability to exploit facilitated lexical access in processing of degraded speech signal.
Mainumby: un Ayudante para la Traducción Castellano-Guaraní

A wide range of applications play an important role in the daily work of the modern human translator. However, the computational tools designed to aid in the process of translation only benefit translation from or to a small minority of the 7,000 languages of the world, those that we may call "privileged languages". As for those translators who work with the remaining languages, the marginalized languages in the digital world, they cannot benefit from the tools that are speeding up the production of translation in the privileged languages. We may ask whether it is possible to bridge the gap between what is available for these languages and for the marginalized ones. This paper proposes a framework for computer-assisted translation into marginalized languages and its implementation in a web application for Spanish-Guarani translation. The proposed system is based on a new theory for phrase-level translation in contexts where adequate bilingual corpora are not available: Translation by Generalized Segments (referred to as Minimal Dependency Translation in previous work).
Catching Flies With Honey(dew): Adult Marsh Flies (Diptera: Sciomyzidae) Utilize Sugary Secretions for High-Carbohydrate Diets

Abstract Marsh flies are a diverse family that provide valuable ecosystem services, including the biological control of mollusks that are agricultural pests and vectors of animal and human parasitic diseases. In addition, some species may serve as important ecological bioindicators. Despite the extensive research on this family, most have centered on larval diet and behavior, as this is the life stage primarily used for biological control; virtually nothing is known about the natural dietary components of adult marsh flies. Our study aimed to close this knowledge gap by examining the dietary range and preference of adult marsh fly species. Individual flies were provided with five food choices in cafeteria-style food choice trials, consisting of crushed snail, freshly killed slug, glucose solution, honey-yeast mixture (the standard laboratory rearing diet), or water. Sciomyzidae at family level displayed significant differences in food selection (P = 0.0212), with carbohydrates (honey-yeast and glucose solution) significantly preferred over protein options (mollusk tissue) or the water control (P < 0.001). This suggests that marsh flies may naturally maintain a carbohydrate-rich diet. Because many plants typical at field sites produce little or no nectar, a second experiment aimed to determine the source of these carbohydrates in nature. When presented with honeydew harvested from aphids (Hemiptera: Auchenorrhycha), Tetanocera elata Fabricius (Diptera: Sciomyzidae) individuals were observed to feed on dry honeydew and honeydew solution significantly more frequently than the water control (P < 0.001 and P = 0.01969, respectively), suggesting that honeydew may play an important role in adult marsh fly diet.
Overview of Voltage Support Strategies in Grid-Connected VSCs under Unbalanced Grid Faults Considering LVRT and HVRT Requirements

Increasing utilization of grid-connected voltage source converters (VSCs) necessitates their incorporation in supporting the power system and providing ancillary services. This paper first overviews the existing strategies in the recent literature for supporting the voltage under unbalanced conditions by grid-connected VSCs. These strategies are categorized into five groups and discussed in this paper. Second, the compliance of each category with the well-known low-voltage ride-through (LVRT) and high-voltage ride-through (HVRT) requirements is examined. Comparative simulation results illustrate the pros and cons of each category in supporting the connection voltage under the LVRT and HVRT schemes. Two main findings are obtained: first, although all strategies can provide the LVRT capability in grid-connected VSCs, most of them are incapable of proper regulation in terms of the HVRT under unbalanced faults. Second, the dynamic voltage regulation strategy has found to be the most effective one in complying with the LVRT and HVRT requirements since it dynamically controls both positive and negative-sequence current injection based on the unbalanced voltage sag characteristics.
Event-Based Distributed Active Power Sharing Control for Interconnected AC and DC Microgrids

This paper investigates the issue of active power sharing among a cluster of microgrids formed by a set of ac and dc microgrids network-interconnected through a set of interlinking converters (ICs). An event-based distributed consensus control approach is proposed to address this issue, in which each IC is assigned to be an agent that needs only the information of its local and neighboring ICs. We first construct the agent system for each IC and design its consensus control protocol. This control mechanism uses a cooperative approach to indirectly adjust the active power load of individual microgrid and, accordingly, navigate the active power flow among them. Thus active power sharing among the interconnected microgrids can be achieved proportionally. Then, an event-based control scheme is utilized to design the consensus protocol, which can dramatically reduce the communication between ICs. Communication time delays and reactive power support are also considered. The proposed distributed control method allows a sparse communication structure and higher reliability and flexibility operation. Simulation results are presented to demonstrate the proposed control method.
Active Sampling for Subjective Video Quality Assessment

This paper presents an active sampling framework to achieve economic and robust subjective Video Quality Assessment (VQA). To overcome the main drawback of paired comparison that the number of pairs grows exponentially with the number of videos under test, the proposed methodology does not require the participants to perform the complete comparison of all the paired videos. Instead, we first ask some participants to perform random sampling of all possible paired comparisons. With a sufficiency of coverage satisfied motivated by Erdos-Renyi random graph, HodgeRank may give reliable results that can be used to pick out confusing pairs. Subsequently, participants will only need to commit to these confusing ones thus could save much time and labor. In other words, our interest is minimizing the number of pairs needed to learn the ranking. We demonstrate the effectiveness of the proposed framework on LIVE Database and experimental results show that it is a promising and applicable method for efficient subjective VOA.
Virtual Associations of Prosumers for Smart Energy Networks Under a Renewable Split Market

Feed-in-tariff (FIT) policies are currently employed to internalize the positive externalities of renewable energy sources (RESs). However, FIT is not time varying, failing to model the dynamics of the electricity market. Thus, the concept of the aggregator has been adopted to act as a mediator between the market and RES producers. In this paper, RES aggregation is performed through virtual associations (VAs), which are dynamic clusters of prosumers created through ICT. VAs support the prosumers’ active participation in the market, the dynamic formation of the clusters to maximize prosumers’ profit and participation, and the fair competition among the VAs and among the prosumers. A VA does not have to be a separate profit-seeking entity, and thus its interests can be perfectly aligned with those of the prosumers comprising it. The fair sharing scheme used favors the most competitive VAs and prosumers, without excluding less competitive ones from the market. Different algorithms to form VAs are examined based on a min-max optimization strategy and fair sharing. Fair sharing provides: 1) incentives to the VAs to increase their competitiveness; 2) increased prosumers’ participation; and 3) dynamic interaction with the market. Experimental results obtained on realistic traces reveal the advantages of the proposed market models.
Activity Recognition Based on Thermopile Imaging Array Sensor

In this paper, a low resolution infrared array sensor is used to develop an activity recognition system for elderly people. The sensor is composed of a $32\times 32$ thermopile array with the corresponding $33^{\circ}\times 33^{\circ}$ field of view. The outputs of the sensor are sequential images in which each pixel contains a temperature value. According to the thermopile images, the activity recognition system first determines whether the target is within the tracking area; if the target is within the tracking area, the location of the target will be detected and three kinds of activities will be identified.