nils-herrmann/clean_scirepeval huggingface dataset:
Examining the relationship between violent video games , presence , and aggression

The level of presence, or immersion, a person feels with media influences the effect media has on them. This project examines both the causes and consequences of presence in the context of violent video game play. In a between subjects design, 227 participants were randomly assigned to play either a violent or a non violent video game. Causal modeling techniques revealed two separate paths to presence. First, individual differences predicted levels of presence: men felt more presence while playing the video game, as did those who play video games more frequently. Secondly, those who perceived the game to be more violent felt more presence. Those who felt more presence, felt more resentment, were more verbally aggressive, and that led to increased physically aggressive intentions. Presence as immersion, video games, aggressive affect, violence, aggression, and social learning theory.
Dark Current Sharing and Cancellation Mechanisms in CMOS Image Sensors Analyzed by TCAD Simulations

Technology computer aided design (TCAD) simulations are conducted on a 4T PPD pixel, on a conventional gated photodiode (PD), and finally on a radiation hardened pixel. Simulations consist in demonstrating that it is possible to reduce the dark current due to interface states brought by the adjacent gate (AG), by means of a sharing mechanism between the PD and the drain. The sharing mechanism is activated and controlled by polarizing the AG at a positive OFF voltage, and consequently the dark current is reduced and not compensated. The drawback of the dark current reduction is a reduction of the full well capacity of the PD, which is not a problem when the pixel saturation is limited by the readout chain. Some measurements performed on pixel arrays confirm the TCAD results.
Developing language in a developing body: the relationship between motor development and language development.

ABSTRACTDuring the first eighteen months of life, infants acquire and refine a whole set of new motor skills that significantly change the ways in which the body moves in and interacts with the environment. In this review article, I argue that motor acquisitions provide infants with an opportunity to practice skills relevant to language acquisition before they are needed for that purpose; and that the emergence of new motor skills changes infants' experience with objects and people in ways that are relevant for both general communicative development and the acquisition of language. Implications of this perspective for current views of co-occurring language and motor impairments and for methodology in the field of child language research are also considered.
CLPsych 2015 Shared Task: Depression and PTSD on Twitter

This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.
Aurora: Providing Trusted System Services for Enclaves On an Untrusted System

Intel SGX provisions shielded executions for securitysensitive computation, but lacks support for trusted system services (TSS), such as clock, network and filesystem. This makes enclaves vulnerable to Iago attacks  in the face of a powerful malicious system. To mitigate this problem, we present Aurora, a novel architecture that provides TSSes via a secure channel between enclaves and devices on top of an untrusted system, and implement two types of TSSes, i.e. clock and end-to-end network. We evaluate our solution by porting SQLite and OpenSSL into Aurora, experimental results show that SQLite benefits from a microsecond accuracy trusted clock and OpenSSL gains end-to-end secure network with about 1ms overhead.
BIM AND LEAN INTERACTIONS FROM THE BIM CAPABILITY MATURITY MODEL PERSPECTIVE : A CASE STUDY

Building Information Modeling (BIM) and Lean Thinking have been used separately as key approaches to overall construction projects’ improvement. Their combination, given several scenarios, presents opportunities for improvement as well as challenges in implementation. However, the exploration of eventual interactions and relationships between BIM as a process and Lean Construction principles is recent in research. The objective of this paper is to identify BIM and Lean relationship aspects with a focus on the construction phase and from the perspective of the general contractor (GC). This paper is based on a case study where BIM is already heavily used by the GC and where the integration of Lean practices is recent. We explore areas of improvement and Lean contributions to BIM from two perspectives. First, from Sacks et al.’s (2010) Interaction Matrix perspective, we identify some existing interactions. Second, based on the Capability Maturity Model (CMM) of the National Building Information Modeling Standard (NBIMS), we measure the level of the project’s BIM maturity and highlight areas of improvement for Lean. The main contribution of the paper is concerned with the exploration of the BIM maturity levels that are enhanced by Lean implementation.
Offline Policy-search in Bayesian Reinforcement Learning

Faculty of Applied Sciences Department of Electrical Engineering and Computer Science Doctor of Computer Science Offline Policy-search in Bayesian Reinforcement Learning by Michael CASTRONOVO This thesis presents research contributions in the study field of Bayesian Reinforcement Learning — a subfield of Reinforcement Learning where, even though the dynamics of the system are unknown, the existence of some prior knowledge is assumed in the form of a distribution over Markov decision processes. In this thesis, two algorithms are presented: OPPS (Offline Priorbased Policy Search) and ANN-BRL (Artificial Neural Networks for Bayesian Reinforcement Learning), whose philosophy consists to analyse and exploit the knowledge available beforehand prior to interacting with the system(s), and which differ by the nature of the model they make use of. The former makes use of formula-based agents introduced by Maes et al. in (Maes, Wehenkel, and Ernst, 2012), while the latter relies on Artificial Neural Networks built via SAMME (Stagewise Additive Modelling using a Multi-class Exponential loss function) — an AdaBoost algorithm developed by Zhu et al. in (Zhu et al., 2009). Moreover, we also describe a comprehensive benchmark which has been created to compare Bayesian Reinforcement Learning algorithms. In real life applications, the choice of the best agent to fulfil a given task depends not only on their performances, but also on the computation times required to deploy them. This benchmark has been designed to identify the best algorithms by taking both criteria into account, and resulted in the development of an open-source library: BBRL (Benchmarking tools for Bayesian Reinforcement Learning)1.
An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.
A comprehensive search for calcium binding sites critical for TMEM16A calcium-activated chloride channel activity

TMEM16A forms calcium-activated chloride channels (CaCCs) that regulate physiological processes such as the secretions of airway epithelia and exocrine glands, the contraction of smooth muscles, and the excitability of neurons. Notwithstanding intense interest in the mechanism behind TMEM16A-CaCC calcium-dependent gating, comprehensive surveys to identify and characterize potential calcium sensors of this channel are still lacking. By aligning distantly related calcium-activated ion channels in the TMEM16 family and conducting systematic mutagenesis of all conserved acidic residues thought to be exposed to the cytoplasm, we identify four acidic amino acids as putative calcium-binding residues. Alterations of the charge, polarity, and size of amino acid side chains at these sites alter the ability of different divalent cations to activate the channel. Furthermore, TMEM16A mutant channels containing double cysteine substitutions at these residues are sensitive to the redox potential of the internal solution, providing evidence for their physical proximity and solvent accessibility.
Towards a swarm of agile micro quadrotors

We describe a prototype 73 gram, 21 cm diameter micro quadrotor with onboard attitude estimation and control that operates autonomously with an external localization system. We argue that the reduction in size leads to agility and the ability to operate in tight formations and provide experimental arguments in support of this claim. The robot is shown to be capable of 1850◦/sec roll and pitch, performs a 360◦ flip in 0.4 seconds and exhibits a lateral step response of 1 body length in 1 second. We describe the architecture and algorithms to coordinate a team of quadrotors, organize them into groups and fly through known three-dimensional environments. We provide experimental results for a team of 20 micro quadrotors.
Abstract : Wireless Sensor Network-Based Tunnel Monitoring

In this paper we describe the development and deployment of a wireless sensor network (WSN) to monitor a train tunnel duri ng adjacent construction activity. The tunnel in question is a pa rt of the London Underground system. Construction of tunnels beneat h the existing tunnel is expected to cause deformations. The expe cted deformation values were determined by a detailed geotechnica l analysis. A real-time monitoring system, comprising of 18 sensin g u its and a base-station, was installed along the critical zone of the tunnel to measure the deformations. The sensing units report th ei data to the base-station at periodic intervals. The system was us ed for making continuous measurements for a period of 72 days. This window of time covered the period during which the tunnel bor ing machine (TBM) was active near the critical zone. The deploye d WSN provided accurate data for measuring the displacements and this is corroborated from the tunnel contractor’s data.
Optimization of the aerosolization properties of an inhalation dry powder based on selection of excipients.

The aim of this study was to investigate the influence of formulation excipients on physical characteristics of inhalation dry powders prepared by spray-drying. The excipients used were a series of amino acids (glycine, alanine, leucine, isoleucine), trehalose and dipalmitoylphosphatidylcholine (DPPC). The particle diameter and the powder density were assessed by laser diffraction and tap density measurements, respectively. The aerosol behaviour of the powders was studied in a Multi-Stage Liquid Impinger. The nature and the relative proportion of the excipients affected the aerosol performance of the powders, mainly by altering powder tap density and degree of particle aggregation. The alanine/trehalose/DPPC (30/10/60 w/w/w) formulation showed optimal aerodynamic behaviour with a mass median aerodynamic diameter of 4.7 microm, an emitted dose of 94% and a fine particle fraction of 54% at an airflow rate of 100 L/min using a Spinhaler inhaler device. The powder had a tap density of 0.10 g/cm(3). The particles were spherical with a granular surface and had a 4 microm volume median diameter. In conclusion, optimization of the aerosolization properties of inhalation dry powders could be achieved by appropriately selecting the composition of the particles.
Advances in the conceptualization and measurement of religion and spirituality. Implications for physical and mental health research.

Empirical studies have identified significant links between religion and spirituality and health. The reasons for these associations, however, are unclear. Typically, religion and spirituality have been measured by global indices (e.g., frequency of church attendance, self-rated religiousness and spirituality) that do not specify how or why religion and spirituality affect health. The authors highlight recent advances in the delineation of religion and spirituality concepts and measures theoretically and functionally connected to health. They also point to areas for areas for growth in religion and spirituality conceptualization and measurement. Through measures of religion and spirituality more conceptually related to physical and mental health (e.g., closeness to God, religious orientation and motivation, religious support, religious struggle), psychologists are discovering more about the distinctive contributions of religiousness and spirituality to health and well-being.
Virtualization and Containerization of Application Infrastructure : A Comparison

Modern cloud infrastructure uses virtualization to isolate applications, optimize the utilization of hardware resources and provide operational flexibility. However, conventional virtualization comes at the cost of resource overhead. Container-based virtualization could be an alternative as it potentially reduces overhead and thus improves the utilization of datacenters. This paper presents the results of a marco-benchmark performance comparison between the two implementations of these technologies, namely Xen and LXC, as well as a discussion on their operational flexibility.
Intelligence Measure of Cognitive Radios with Learning Capabilities

Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing "cognition", the research on measuring CR cognition capabilities and intelligence is largely open. Deriving the intelligence capabilities of CR not only can lead to the development of new CR technologies, but also makes it possible to better configure the networks by integrating CRs with different intelligence capabilities in a more cost- efficient way. In this paper, for the first time, we propose a data-driven methodology to quantitatively analyze the intelligence factors of the CR with learning capabilities. The basic idea of our methodology is to run various tests on the CR in different spectrum environments under different settings and obtain various performance results on different metrics. Then we apply factor analysis on the performance results to identify and quantize the intelligence capabilities of the CR. More specifically, we present a case study consisting of sixty three different types of CRs. CRs are different in terms of learning-based dynamic spectrum access strategies, number of sensors, sensing accuracy, and processing speed. Based on our methodology, we analyze the intelligence capabilities of the CRs through extensive simulations. Four intelligence capabilities are identified for the CRs through our analysis, which comply with the nature of the tested algorithms.
The Computational Linguistics Summarization Pilot Task

The Computational Linguistics (CL) Summarization Pilot Task was created to encourage a community effort to address the research problem of summarizing research articles as “faceted summaries” in the domain of computational linguistics. In this pilot stage, a handannotated set of citing papers was provided for ten reference papers to help in automating the citation span and discourse facet identification problems. This paper details the corpus construction efforts by the organizers and the participating teams, who also participated in the task-based evaluation. The annotated development corpus used for this pilot task is publicly available at: https://github.com/WING-
A Preliminary Investigation of a Specialized Music Therapy Model for Children with Disabilities Delivered in a Classroom Setting

Music therapy is gaining popularity as an intervention strategy for children with developmental disabilities, including autism spectrum disorder (ASD). This study was a pilot investigation of a classroom-based music-based intervention, Voices Together®, for improving communication skills in children with ASD and children with intellectual disabilities. Four local public elementary school special education classrooms, serving 5 children with a classification of autistic disorder and 32 children with intellectual disability without autism, were randomly selected to receive one of two levels of exposure to Voices Together music therapy: "long-term" (15 weeks beginning in January 2015 (Time 1), n = 14) or "short-term" (7 weeks beginning 7 weeks later in February (Time 2), n = 17). Using observational ratings, investigators reliably scored participants live in terms of their level of verbal responsiveness to prompts during three songs featured each week of the program. Both groups demonstrated increases in verbal responses over time; however, only the long-term group demonstrated significant within-group increases. Preliminary findings suggest that music therapy delivered in a classroom in 45-minute weekly sessions for 15 weeks can promote improvements in verbal responsiveness among individuals with autism and other developmental disabilities. Findings warrant further investigation into the efficacy of classroom-based music therapy programs.
MaxTract: Converting PDF to $\mbox\LaTeX$ , MathML and Text

In this paper we present the first public, online demonstration of MaxTract; a tool that converts PDF files containing mathematics into multiple formats including LTEX, HTML with embedded MathML, and plain text. Using a bespoke PDF parser and image analyser, we directly extract character and font information to use as input for a linear grammar which, in conjunction with specialised drivers, can accurately recognise and reproduce both the two dimensional relationships between symbols in mathematical formulae and the one dimensional relationships present in standard text. The main goals of MaxTract are to provide translation services into standard mathematical markup languages and to add accessibility to mathematical documents on multiple levels. This includes both accessibility in the narrow sense of providing access to content for print impaired users, such as those with visual impairments, dyslexia or dyspraxia, as well as more generally to enable any user access to the mathematical content at more re-usable levels than merely visual. MaxTract produces output compatible with web browsers, screen readers, and tools such as copy and paste, which is achieved by enriching the regular text with mathematical markup. The output can also be used directly, within the limits of the presentation MathML produced, as machine readable mathematical input to software systems such as Mathematica or Maple.
Differential domain analysis for non-uniform sampling

Sampling is a core component for many graphics applications including rendering, imaging, animation, and geometry processing. The efficacy of these applications often crucially depends upon the distribution quality of the underlying samples. While uniform sampling can be analyzed by using existing spatial and spectral methods, these cannot be easily extended to general non-uniform settings, such as adaptive, anisotropic, or non-Euclidean domains.
 We present new methods for analyzing non-uniform sample distributions. Our key insight is that standard Fourier analysis, which depends on samples' spatial locations, can be reformulated into an equivalent form that depends only on the distribution of their location differentials. We call this differential domain analysis. The main benefit of this reformulation is that it bridges the fundamental connection between the samples' spatial statistics and their spectral properties. In addition, it allows us to generalize our method with different computation kernels and differential measurements. Using this analysis, we can quantitatively measure the spatial and spectral properties of various non-uniform sample distributions, including adaptive, anisotropic, and non-Euclidean domains.
Compassion training in healthcare: what are patients’ perspectives on training healthcare providers?

The purpose of this qualitative study was to investigate advanced cancer patients' perspectives on the importance, feasibility, teaching methods, and issues associated with training healthcare providers in compassionate care.



This study utilized grounded theory, a qualitative research method, to develop an empirical understanding of compassion education rooted in direct patient reports. Audio-recorded semi-structured interviews were conducted to obtain an in-depth understanding of compassion training from the perspectives of hospitalized advanced cancer patients (n = 53). Data were analyzed in accordance with grounded theory to determine the key elements of the underlying theory.



Three overarching categories and associated themes emerged from the data: compassion aptitude, cultivating compassion, and training methods. Participants spoke of compassion as an innate quality embedded in the character of learners prior to their healthcare training, which could be nurtured through experiential learning and reflective practices. Patients felt that the innate qualities that learners possessed at baseline were further fashioned by personal and practice experiences, and vocational motivators. Participants also provided recommendations for compassion training, including developing an interpersonal relationship with patients, seeing the patient as a person, and developing a human connection. Teaching methods that patients suggested in compassion training included patient-centered communication, self-reflection exercises, and compassionate role modeling.



This study provides insight on compassion training for both current and future healthcare providers, from the perspectives of the end recipients of healthcare provider training - patients. Developing a theoretical base for patient centred, evidence-informed, compassion training is a crucial initial step toward the further development of this core healthcare competency.