nils-herrmann/clean_scirepeval huggingface dataset:
Does the EU sugar policy reform increase added sugar consumption? An empirical evidence on the soft drink market.

Whereas National Health authorities recommend a decrease in the consumption of 'added' sugar, a reform on the sugar market will lead to a 36% decrease of the sugar price in the EU. Using French data on soft drinks purchases, this paper investigates the anticipated impact of this reform on the consumption of sugar-sweetened beverages. The reform of the EU sugar policy leads to a decrease in regular soft drink prices by 3% and varies across brands. To assess substitution within this food category, we use a random-coefficients logit model that takes into account a large number of differentiated products and heterogeneity in consumers' behavior. Results suggest that price changes would lead to an increase in market shares of regular products by 7.5% and to substitutions between brands to the benefit of products with the highest sugar content. On the whole, it would raise consumption of regular soft drinks by more than 1 litre per person per year and consumption of added sugar by 124 g per person per year, this increase being larger in households composed of overweight and obese individuals.
Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)

Electroencephalogram (EEG) signals are frequently used in brain–computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by <inline-formula><tex-math notation="LaTeX">$10.02-19.77\%$</tex-math></inline-formula>, and at the same time increase the correlation to the true response speed by <inline-formula><tex-math notation="LaTeX">$19.39-86.47\%$</tex-math></inline-formula>.
Electronic model of human brain using verilog

Computers are man-made machines which work according to the given set of inputs and perform some operation on the input to generate a new set of outputs. They can be programmed to perform huge and complex task yet they lack imagination and ability to understand things. On the hand human brain is a machine which can learn new tasks by process of acquiring knowledge and understanding through thought, experience and the senses. To make computer work or think like human, human brain modeling of machine is necessary. We need to know how human brain works. The basic component of human brain is neuron, which make us think and do smart things. Brains have billions of neurons and they communicate with each other using electrical impulse. These impulses are responsible for making brain think and to have a consciousness. This paper proposes such a model of a part of the brain.
In-vehicle information system as a driver's secondary activity: Case study

A robust methodology for detecting and evaluating driver distraction induced by in-vehicle information system using artificial neural network and fuzzy logic is introduced in this paper. An artificial neural network is used to predict driver's performance on a specific road segment. The predicted performance-based measures are compared to the driving with secondary task accomplishment. Fuzzy logic is applied to fuse the variables into a single output, which constitutes a level of driver distraction in percentage. The technique was tested on a vehicle simulator by ten drivers that exploited in-vehicle information system as a secondary activity. The driver-in-the-loop experiment outcomes are discussed.
Construction and evaluation of a robust multifeature speech/music discriminator

We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on system performance and the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound.
Counterfactual Fairness

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.
inAir: measuring and visualizing indoor air quality

Good indoor air quality is a vital part of human health. Poor indoor air quality can contribute to the development of chronic respiratory diseases such as asthma, heart disease, and lung cancer. Complicating matters, poor air quality is extremely difficult for humans to detect through sight and smell alone and existing sensing equipment is designed to be used by and provide data for scientists rather than everyday citizens. We propose inAir, a tool for measuring, visualizing, and learning about indoor air quality. inAir provides historical and real-time visualizations of indoor air quality by measuring tiny hazardous airborne particles as small as 0.5 microns in size. Through user studies we demonstrate how inAir promotes greater awareness and motivates individual actions to improve indoor air quality.
Hand Gesture Recognition for Real Time Human Machine Interaction System

Real Time Human-machine Interaction system using hand gesture Recognition to handle the mouse event , media player , image viewer .Users have to repeat same mouse and keyboard actions, inducing waste of time. Gestures have long been considered as an interaction technique that can potentially deliver more natural. A fast gesture recognition scheme is proposed to be an interface for the human-machine interaction (HMI) of systems. The system presents some lowcomplexity algorithms and gestures to reduce the gesture recognition complexity and be more suitable for controlling real-time computer systems. In this paper we use the webcam for capturing the image. After capturing the image it converts into the binary image. A gesture is a specific combination of hand position.
Trichoscopic findings in alopecia areata and their relation to disease activity, severity and clinical subtype in Turkish patients.

Trichoscopic studies of alopecia areata are helpful in the non-invasive diagnosis and prediction of the course of the disease. The objective was to determine the relationship of trichoscopic findings in alopecia areata with disease activity, severity and clinical subtype in Turkish patients.



Trichoscopic examinations of 39 patients with alopecia areata were compared with 309 alopecia patients including psoriasis (n = 31), seborrhoeic dermatitis (n = 112), female androgenetic alopecia (n = 138), male androgenetic alopecia (n = 63), female androgenetic alopecia of male pattern (n = 5), telogen effluvium (n = 22) and trichotillomania (n = 4). A χ(2) test and logistic regression analysis were used for the statistical analysis. The odds ratios were calculated by cross tabulation.



There was no relationship between the alopecia areata subtype and trichoscopic findings. On the other hand, a honeycomb hyperpigmentation pattern, cumulus-like clustered white dots, white dots and black dotted pigmentation related to severe disease, while exclamation mark hairs related to mild disease. Exclamation mark hairs were found to be related to active disease while atypical red vessels and white dots were negatively related to disease activity.



In our study, further characteristic trichoscopic findings were detected in alopecia areata such as clustered white dots, multi-hair follicular unit, hidden hairs and black dotted pigmentation, in addition to previous findings. Hence, it is concluded that the identification and prediction of alopecia areata might be straightforward with the help of these new signs such as activation and severity findings.
Bandwidth Modeling in Large Distributed Systems for Big Data Applications

The emergence of Big Data applications provides new challenges in data management such as processing and movement of masses of data. Volunteer computing has proven itself as a distributed paradigm that can fully support Big Data generation. This paradigm uses a large number of heterogeneous and unreliable Internet-connected hosts to provide Peta-scale computing power for scientific projects. With the increase in data size and number of devices that can potentially join a volunteer computing project, the host bandwidth can become a main hindrance to the analysis of the data generated by these projects, especially if the analysis is a concurrent approach based on either in-situ or in-transit processing. In this paper, we propose a bandwidth model for volunteer computing projects based on the real trace data taken from the Docking@Home project with more than 280,000 hosts over a 5-year period. We validate the proposed statistical model using model-based and simulation-based techniques. Our modeling provides us with valuable insights on the concurrent integration of data generation with in-situ and in-transit analysis in the volunteer computing paradigm.
Transformational leadership and organizational commitment : mediating role of psychological empowerment and moderating role of structural distance

Using a sample of 520 staff nurses employed by a large public hospital in Singapore, we examined whether psychological empowerment mediated the effects of transformational leadership on followers’ organizational commitment. We also examined how structural distance (direct and indirect leadership) between leaders and followers moderated the relationship between transformational leadership and organizational commitment. Results from HLM analyses showed that psychological empowerment mediated the relationship between transformational leadership and organizational commitment. Similarly, structural distance between the leader and follower moderated the relationship between transformational leadership and organizational commitment. Implications for research and practice of our findings are discussed.
Thematic development for measuring cohesion and coherence between sentences in English paragraph

Writing is a skill of marking coherent words on a paper and composing a text. There are several criteria of good writing, two of them are cohesion and coherence. Research of cohesion and coherence writing has been conducted by using Centering Theory and Entity Transition Value method. However, the result can still be improved. Therefore, in this research, we tried to use Thematic Development approach which focused on the use of Theme and Rheme as a method to analyze coherence level of a paragraph, combined with CogNIAC rules and Centering Theory to analyze its cohesion. Besides improving the result of previous methods, this research aims to help users in evaluating and assessing their writing text. To achieve these objectives, the proposed method is compared to previous works as well as human judge. Based on the experiment, the proposed method yields average result of 91% which is nearly equivalent to human judge which is 92%. Thematic Development also yields better result compared to Centering Theory which is 29% and Entity Transition Value which is 0%, given the same data set of beginner and intermediate simple writing.
The hidden treasure in nursing leadership: informal leaders.

The goal of the present article was to generate awareness of characteristics of informal leaders in healthcare with the emphasis on nurses in acute care settings. There is limited research or literature regarding informal leaders in nursing and how they positively impact nursing management, the organization and, ultimately, patient care. Identification of nurses with leadership characteristics is important so that leadership development and mentoring can occur within the nursing profession.



More than ever, nursing needs energetic, committed and dedicated leaders to meet the challenges of the healthcare climate and the nursing shortage. This requires nurse leaders to consider all avenues to ensure the ongoing profitability and viability of their healthcare facility.



This paper discusses clinical nurses as informal leaders; characteristics of the informal nurse leader, the role they play, how they impact their unit and how they shape the organization.


Informal nurse leaders are an underutilized asset in health care. If identified early, these nurses can be developed and empowered to impact unit performance, efficiency and environmental culture in a positive manner.
Disturbance of Emotion Processing in Frontotemporal Dementia: A Synthesis of Cognitive and Neuroimaging Findings

Accurate processing of emotional information is a critical component of appropriate social interactions and interpersonal relationships. Disturbance of emotion processing is present in frontotemporal dementia (FTD) and is a clinical feature in two of the three subtypes: behavioural-variant FTD and semantic dementia. Emotion processing in progressive nonfluent aphasia, the third FTD subtype, is thought to be mostly preserved, although current evidence is scant. This paper reviews the literature on emotion recognition, reactivity and expression in FTD subtypes, although most studies focus on emotion recognition. The relationship between patterns of emotion processing deficits and patterns of neural atrophy are considered, by integrating evidence from recent neuroimaging studies. The review findings are discussed in the context of three contemporary theories of emotion processing: the limbic system model, the right hemisphere model and a multimodal system of emotion. Results across subtypes of FTD are most consistent with the multimodal system model, and support the presence of somewhat dissociable neural correlates for basic emotions, with strongest evidence for the emotions anger and sadness. Poor emotion processing is evident in all three subtypes, although deficits are more widespread than what would be predicted based on studies in healthy cohorts. Studies that include behavioural and imaging data are limited. Future investigations combining these approaches will help improve the understanding of the neural network underlying emotion processing. Presently, longitudinal investigations of emotion processing in FTD are lacking, and studies investigating emotion processing over time are critical to understand the clinical manifestations of disease progression in FTD.
FPGA-Based Sensorless PMSM Speed Control Using Reduced-Order Extended Kalman Filters

This paper presents the design and implementation of a field-programmable gate array (FPGA)-based architecture for the speed control of sensorless permanent-magnet synchronous motor (PMSM) drives. For the reduction of computation resources, as well as accuracy improvement in the rotor position estimation, a parallel reduced-order extended Kalman filter (EKF) is proposed in this work. Compared with an EKF, the system order is reduced and the iteration process is greatly simplified, resulting in significant savings of resource utility, while maintaining high estimation performance. The whole control system includes a current-control-and-coordinate-transformation unit, a proportional-integral (PI) speed controller, and other accessory modules, all implemented in a single FPGA chip. A hardware description language is adopted to describe advantageous features of the proposed control system. Moreover, the finite-state-machine method is applied with the purpose to reduce logic elements used in the FPGA chip. The validity of the approach is verified through simulation based on the Modelsim/Simulink cosimulation method. Finally, experimental results are obtained on an FPGA platform with an inverter-fed PMSM to show the feasibility and effectiveness of the proposed system-on-programmable-chip for PMSM drives.
Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs

We present a 3-week user study in which we tracked the locations of 27 subjects and asked them to rate when, where, and with whom they would have been comfortable sharing their locations. The results of analysis conducted on over 7,500 h of data suggest that the user population represented by our subjects has rich location-privacy preferences, with a number of critical dimensions, including time of day, day of week, and location. We describe a methodology for quantifying the effects, in terms of accuracy and amount of information shared, of privacy-setting types with differing levels of complexity (e.g., setting types that allow users to specify location- and/or time-based rules). Using the detailed preferences we collected, we identify the best possible policy (or collection of rules granting access to one’s location) for each subject and privacy-setting type. We measure the accuracy with which the resulting policies are able to capture our subjects’ preferences under a variety of assumptions about the sensitivity of the information and user-burden tolerance. One practical implication of our results is that today’s location-sharing applications may have failed to gain much traction due to their limited privacy settings, as they appear to be ineffective at capturing the preferences revealed by our study.
Autonomous Offroad Navigation Under Poor GPS Conditions

This paper describes an approach for autonomous offroad navigation for the Civilian European Landrobot Trial 2009 (C-ELROB). The main focus in this paper is how to cope with poor GPS conditions such as occurring in forest environments. Therefore the expected and detected problems are stated, and methods how to achieve good autonomous driving performance due to less weigthing of inaccurate GPS information are presented. But first, an introduction how to combine obstacle-avoidance and path following behavior within the reactive so-called tentacles approach is given. Extensions for utilizing visual information for tentacle evaluation as well as the integration of geodetic information to gather information on the road network are presented afterwards. Finally the results gathered from the successful C-ELROB trials are analyzed.
Death by complete decapitation of motorcyclist wearing full face helmet: case report.

We describe a case of complete decapitation following a motorcycle accident in which the victim was wearing a full face helmet. A young man lost control of his motorcycle and was thrown about 20 m, hitting his head against the barrier separating a tramline from the road. The resulting trauma caused his decapitation, the only fatal wound ascertained by the various forensic investigations. The authors present this rare case and compare it against the other two cases reported in the literature, providing some observations on the ways in which this injury can come about. The absence of abrasions or signs that the wound edges came into contact with a metal structure, the presence of signs of impact on the side of the helmet and the finding of a transversal fracture at the base of the skull point to the violent action of a side-to-side opposite force, due to the resistance provided by the lower edge of the protective helmet.
Narrative Planning: Balancing Plot and Character

Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors – logical and aesthetic – that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience’s suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem – to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm – the Intent-based Partial Order Causal Link (IPOCL) planner – that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.
A Predetermined LMS Digital Background Calibration Technique for Pipelined ADCs

A digital background calibration technique for pipelined analog-to-digital converters (ADCs) is proposed to correct the capacitor mismatch, finite dc gain, and nonlinearity of residue amplifiers. It divides the pipelined ADC into two equal channels and changes the decision points of sub-ADCs with a pseudorandom sequence to perform the digital background calibration. The difference between the digital outputs of the channels is used to drive the least mean square (LMS) machine to correct the mentioned errors and also the mismatch between the channels. In order to speed up the error correction, an accurate estimation for the errors is identified. The estimation is done by utilizing a piecewise linear model and a slope mismatch measurement technique in the digital domain. Behavioral simulations of a 12-bit 100-MS/s split pipelined ADC show that the convergence time of the proposed LMS calibration technique is significantly reduced in comparison with the conventional LMS algorithm for the same signal-to-noise-and-distortion ratio.