nils-herrmann/clean_scirepeval huggingface dataset:
Use of alcohol and other drugs among male university students and its meanings.

To understand the meanings that male university students assign to the condition of users of alcohol and other drugs.



An exploratory study using a qualitative approach, with inductive analysis of the content of semi-structured interviews applied to 20 male university students from a public university in the southeast region of Brazil, grounded on the theoretical-methodological referential of interpretive anthropology and ethnographic method.



Data were construed using content inductive analysis for two topics: use of alcohol and/or drugs as an outlet; and use of alcohol and/or other drugs: an alternative for belonging and identity.



Male university students share the rules of their sociocultural environment that values the use of alcohol and/or other drugs as a way of dealing with the demands and stress ensuing from the everyday university life, and to build identity and belong to this social context, reinforcing the influence of culture.



Compreender os significados atribuídos pelos universitários do sexo masculino à condição de usuários de álcool e outras drogas.


MÉTODO
Estudo exploratório de abordagem qualitativa, com análise de conteúdo indutiva de entrevistas semiestruturadas de 20 universitários do sexo masculino, matriculados em uma universidade pública da região sudeste do Brasil, fundamentado no referencial teórico-metodológico da Antropologia Interpretativa e do método etnográfico.


RESULTADOS
Os dados foram interpretados com a análise de conteúdo indutiva em dois temas: O uso do álcool e/ou drogas como válvula de escape; O uso do álcool e/ou outras drogas: alternativa para o pertencimento e para a identidade.



Os universitários do sexo masculino compartilham normas de seu meio sociocultural, que valorizam o uso de álcool e/ou outras drogas, como uma forma de lidar com as exigências e o estresse da vida universitária, criar uma identidade e ter pertencimento neste contexto social, reforçando a influência da cultura.
Digital Color Reproduction

8.5 Printing 304 8.5.1 Overview 304 8.5.2 Inks and subtractive color calculations 304 8.5.2.1 Density 305 8.5.3 Continuous tone printing 306 8.5.4 Halftoning 307 8.5.4.1 Traditional halftoning 307 8.5.5 Digital halftoning 308 8.5.5.1 Cluster dot dither 310 8.5.5.2 Bayer dither and void and cluster dither 310 8.5.5.3 Error diffusion 311 8.5.5.4 Color digital halftoning 312 8.5.6 Print characterization 313 8.5.6.1 Transduction: the tone reproduction curve 313 8.6
Semantic Web and Human Computation: The status of an emerging field

This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this editorial we inspect and reflect on the evolution of research at the intersection of Semantic Web and Human Computation. We use a methodology based on Systematic Mapping Studies to collect quantitative bibliographic data which we analyze through the lens of research topics envisioned by the research manifesto to characterize the evolution of research in this area, thus providing a context for introducing the papers of this special issue. We found evidences of a thriving research field; while steadily maturing, the field offers a number of open research opportunities for work where Semantic Web best practices and techniques are applied to support and improve the state-of-the-art in Human Computation, but also for work that exploits the strength of both areas to address scientifically and societally relevant issues.
An architecture for aggregating information from distributed data nodes for industrial internet of things

The current Internet of Things (IoT) has made it very convenient to obtain information about a product from a single data node. However, in many industrial applications, information about a single product can be distributed in multiple different data nodes, and aggregating the information from these nodes has become a common task. In this paper, we provide a distributed service-oriented architecture for this task. In this architecture, each manufacturer provides service for their own products, and data nodes keep the information collected by themselves. Semantic technologies are adopted to handle problems of heterogeneity and serve as the foundation to support different applications. Finally, as an example, we illustrate the use of this architecture to solve the problem of product tracing.
Learning Abstract Options

Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for addressing this challenge is the options framework [41]. However, only recently in  was a policy gradient theorem derived for online learning of general purpose options in an end to end fashion. In this work, we extend previous work on this topic that only focuses on learning a two-level hierarchy including options and primitive actions to enable learning simultaneously at multiple resolutions in time. We achieve this by considering an arbitrarily deep hierarchy of options where high level temporally extended options are composed of lower level options with finer resolutions in time. We extend results from  and derive policy gradient theorems for a deep hierarchy of options. Our proposed hierarchical option-critic architecture is capable of learning internal policies, termination conditions, and hierarchical compositions over options without the need for any intrinsic rewards or subgoals. Our empirical results in both discrete and continuous environments demonstrate the efficiency of our framework.
Scaling of SOI FinFETs down to fin width of 4 nm for the 10nm technology node

Using a novel replacement gate SOI FinFET device structure, we have fabricated FinFETs with fin width (D<inf>Fin</inf>) of 4nm, fin pitch (FP) of 40nm, and gate length (L<inf>G</inf>) of 20nm. With this structure, we have achieved arrays of thousands of fins for D<inf>Fin</inf> down to 4nm with robust yield and structural integrity. We observe performance degradation, increased variability, and V<inf>T</inf> shift as D<inf>Fin</inf> is reduced. Capacitance measurements agree with quantum confinement behavior which has been predicted to pose a fundamental limit to scaling FinFETs below 10nm L<inf>G</inf>.
Comparison of communication protocols for UAVs and VANETs

This decade sees a growing number of applications of Unmanned Aerial Vehicles (UAVs) or drones. UAVs are now being experimented for commercial applications in public areas as well as used in private environments such as in farming. As such, the development of efficient communication protocols for UAVs is of much interest. This paper compares and contrasts recent communication protocols of UAVs with that of Vehicular Ad Hoc Networks (VANETs) using Wireless Access in Vehicular Environments (WAVE) protocol stack as the reference model. The paper also identifies the importance of developing light-weight communication protocols for certain applications of UAVs as they can be both of low processing power and limited battery energy.
On the use of radio environment maps for interference management in heterogeneous networks

This article addresses the use of REMs to support interference management optimization in heterogeneous networks composed of cells of different sizes and including both cellular and non-cellular (e.g. WiFi) technologies. After presenting a general architecture for including REM databases in different network entities, the article analyzes the achievable benefits in relation to specific interference management techniques, including a discussion on practical considerations such as information exchange requirements, REM ownership, and security aspects. Finally, several research directions derived from the proposed framework are identified.
The role of social cognition in emotion

Although recent research has shown that social cognition and emotion engage overlapping regions of the brain, few accounts of this overlap have been offered. What systems might be commonly or distinctively involved in each? The close functional relationship between social cognition and emotion might be understood in terms of a central role for mental state attribution in the understanding, learning and regulation of emotion. In each of these cases, mental state attributions might be supported by either stimulus-driven or more reflective processes.
The dawn of the liquid biopsy in the fight against cancer

Cancer is a molecular disease associated with alterations in the genome, which, thanks to the highly improved sensitivity of mutation detection techniques, can be identified in cell-free DNA (cfDNA) circulating in blood, a method also called liquid biopsy. This is a non-invasive alternative to surgical biopsy and has the potential of revealing the molecular signature of tumors to aid in the individualization of treatments. In this review, we focus on cfDNA analysis, its advantages, and clinical applications employing genomic tools (NGS and dPCR) particularly in the field of oncology, and highlight its valuable contributions to early detection, prognosis, and prediction of treatment response.
HealthSense: classification of health-related sensor data through user-assisted machine learning

Remote patient monitoring generates much more data than healthcare professionals are able to manually interpret. Automated detection of events of interest is therefore critical so that these points in the data can be marked for later review. However, for some important chronic health conditions, such as pain and depression, automated detection is only partially achievable. To assist with this problem we developed HealthSense, a framework for real-time tagging of health-related sensor data. HealthSense transmits sensor data from the patient to a server for analysis via machine learning techniques. The system uses patient input to assist with classification of interesting events (e.g., pain or itching). Due to variations between patients, sensors, and condition types, we presume that our initial classification is imperfect and accommodate this by incorporating user feedback into the machine learning process. This is done by occasionally asking the patient whether they are experiencing the condition being monitored. Their response is used to confirm or reject the classification made by the server and continually improve the accuracy of the classifier's decisions on what data is of interest to the health-care provider.
Gaussian mixture model for human skin color and its applications in image and video databases

This paper is concerned with estimating a probability density function of human skin color using a nite Gaussian mixture model whose parameters are estimated through the EM algorithm Hawkins statistical test on the normality and homoscedasticity common covariance matrix of the estimated Gaussian mixture models is performed and McLachlan s bootstrap method is used to test the number of components in a mixture Experimental results show that the estimated Gaussian mixture model ts skin images from a large database Applications of the estimated density function in image and video databases are presented
A Comprehensive Review on Smart Meter Communication Systems in Smart Grid for Indian Scenario

In existing power grid, the currently used power meters, are either electronic energy meters or electro-mechanical meters and are limited to record power/energy up to KWh unit only. One of the drawbacks of these power grids is that out of the total power production, 26% of energy gets lost because of various reasons such as power theft, blackouts, etc. To fulfill the continuous drastic demand of energy production and consumption, a new generation power grid is required, which should include communication and information technology (ICT) along with distributed power generation (like solar power and wind power). The concept of new generation smart power grid, so called Smart Grid, has been gaining continuous rapid attention worldwide. Smart Grid is considered as system that can supply bi-directional flow of electricity and information, with better power grid reliability, security, and efficiency of electrical system from generation to transmission to distribution. To meet the requirement of Smart Grid we require the most fundamental components like Smart Meter, in the intelligent energy networks (IENs). Smart Energy meters are used to control and monitor home appliances and other devices by providing two way communications among them. This paper reviews the basics of Smart grid and development and deployment of communication technologies used among various Smart Grid components/networks and provides an insight into the current research on Smart meters with some guidelines and future directions for IENs.  Infrastructure (AMI), Intelligent Energy Advanced metering Networks (IENs), Smart grid, Smart Meter, Zig-
Unsupervised Intuitive Physics from Visual Observations

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times. Some authors have relaxed such requirements by supplementing the model with an handcrafted physical simulator. Still, the resulting methods are unable to automatically learn new complex environments and to understand physical interactions within them. In this work, we demonstrated for the first time learning such predictors directly from raw visual observations and without relying on simulators. We do so in two steps: first, we learn to track mechanically-salient objects in videos using causality and equivariance, two unsupervised learning principles that do not require auto-encoding. Second, we demonstrate that the extracted positions are sufficient to successfully train visual motion predictors that can take the underlying environment into account. We validate our predictors on synthetic datasets; then, we introduce a new dataset, ROLL4REAL, consisting of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). We show that in all such cases it is possible to learn reliable extrapolators of the object trajectories from raw videos alone, without any form of external supervision and with no more prior knowledge than the choice of a convolutional neural network architecture.
Large Field and High Resolution: Detecting Needle in Haystack

The growing use of convolutional neural networks (CNN) for a broad range of visual tasks, including tasks involving fine details, raises the problem of applying such networks to a large field of view, since the amount of computations increases significantly with the number of pixels. To deal effectively with this difficulty, we develop and compare methods of using CNNs for the task of small target localization in natural images, given a limited ”budget” of samples to form an image. Inspired in part by human vision, we develop and compare variable sampling schemes, with peak resolution at the center and decreasing resolution with eccentricity, applied iteratively by re-centering the image at the previous predicted target location. The results indicate that variable resolution models significantly outperform constant resolution models. Surprisingly, variable resolution models and in particular multi-channel models, outperform the optimal, ”budget-free” full-resolution model, using only 5% of the samples.
A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks

The densification of small cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small cell base stations. This involves efficient association and placement of the backhaul hubs that connects these small-cells with the core network. Terrestrial hubs suffer from an inefficient non line of sight link limitations and unavailability of a proper infrastructure in an urban area. Realizing the popularity of flying platforms, we employ here an idea of using networked flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned balloons flying at different altitudes, as aerial backhaul hubs. The association problem of these NFP-hubs and small- cell base stations is formulated considering backhaul link and NFP related limitations such as maximum number of supported links and bandwidth. We then present an efficient and distributed solution of the designed problem, which performs a greedy search in order to maximize the sum rate of the overall network. A favorable performance is observed via a numerical comparison of our proposed method with optimal exhaustive search algorithm in terms of sum rate and run-time speed.
Narrative Review of Dance-based Exercise and Its Specific Impact on Depressive Symptoms in Older Adults

Depression is a chronic condition that results in considerable disability, and particularly in later life, severely impacts the life quality of the individual with this condition. The first aim of this review article was to summarize, synthesize, and evaluate the research base concerning the use of dance-based exercises on health status, in general, and secondly, specifically for reducing depressive symptoms, in older adults. A third was to provide directives for professionals who work or are likely to work with this population in the future.  All English language peer reviewed publications detailing the efficacy of dance therapy as an intervention strategy for older people in general, and specifically for minimizing depression and dependence among the elderly were analyzed.
Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis

Electromyography (EMG) is the subject which deals with the detection, analysis and utilization of electrical signals emanating from skeletal muscles. The field of electromyography is studied in Biomedical Engineering. And prosthesis using electromyography is achieved under Biomechatronics . The electric signal produced during muscle activation, known as the myoelectric signal, is produced from small electrical currents generated by the exchange of ions across the muscle membranes and detected with the help of electrodes. Electromyography is used to evaluate and record the electrical activity produced by muscles of a human body. The instrument from which we obtain the EMG signal is known as electromyograph and the resultant record obtained is known as electromyogram .
Non-verbal communication: the importance of listening.

This article presents the author's personal reflection on how her nursing practice was enhanced as a result of losing her voice. Surprisingly, being unable to speak appeared to improve the nurse/patient relationship. Patients responded positively to a quiet approach and silent communication. Indeed, the skilled use of non-verbal communication through silence, facial expression, touch and closer physical proximity appeared to facilitate active listening, and helped to develop empathy, intuition and presence between the nurse and patient. Quietly 'being with' patients and communicating non-verbally was an effective form of communication. It is suggested that effective communication is dependent on the nurse's ability to listen and utilize non-verbal communication skills. In addition, it is clear that reflection on practical experience can be an important method of uncovering and exploring tacit knowledge in nursing.
An Overview of Granular Computing Using Fuzzy Logic Systems

As Granular Computing has gained interest, more research has lead into using different representations for Information Granules, i.e., rough sets, intervals, quotient space, fuzzy sets; where each representation offers different approaches to information granulation. These different representations have given more flexibility to what information granulation can achieve. In this overview paper, the focus is only on journal papers where Granular Computing is studied when fuzzy logic systems are used, covering research done with Type-1 Fuzzy Logic Systems, Interval Type-2 Fuzzy Logic Systems, as well as the usage of general concepts of Fuzzy Systems.
Neural Random Projections for Language Modelling

Neural network-based language models deal with data sparsity problems by mapping the large discrete space of words into a smaller continuous space of real-valued vectors. By learning distributed vector representations for words, each training sample informs the neural network model about a combinatorial number of other patterns. We exploit the sparsity in natural language even further by encoding each unique input word using a reduced sparse random representation. In this paper, we propose an encoder for discrete inputs that uses random projections to allow for the learning of language models using significantly smaller parameter spaces when compared with similar neural network architectures. Furthermore, random projections also eliminate the dependency between a neural network architecture and the size of a pre-established dictionary. We investigate the properties of our encoding mechanism empirically, by evaluating its performance on the widely used Penn Treebank corpus, using several configurations of baseline feedforward neural network models. We show that guaranteeing approximately equidistant inner products between representations of unique discrete inputs is enough to provide the neural network model with enough information to learn useful distributed representations for these inputs. By not requiring prior enumeration of the lexicon, random projections allow us to face the dynamic and open character of natural languages.
Study on Causes and Effects of Employee Turnover in Construction Industry

The term “employee turnover” is a crucial metric that's usually central to organizations workforce planning and strategy. the explanations why staff leave their current positions; not simply the actual fact that they leave have crucial implications for future retention rates among current staff, job satisfaction and employee engagement and an organization’s ability to draw in proficient folks for job vacancies. The impact of turnover has received substantial attention by senior management, human resources professionals and construction engineers particularly project engineers in construction projects. It’s tried to be one among the foremost expensive and ostensibly intractable human resource challenges braving many organizations globally. The aim of this analysis is so, to seek out the particular reasons behind turnover and its damaging effects on the development industries in Kerala. To explore turnover in larger detail, this text can examine the most sources of turnover rate, its effects and advocate some ways on however a company will retain staff and scale back turnover rate in housing industry in Kerala. The results of this study area unit expected to be helpful for numerous construction firms for taking remedial measures to scale back the worker turnover that is the major resource in determining the general success of a project.