nils-herrmann/clean_scirepeval huggingface dataset:
Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications.



Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts.



We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall(®), oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time.



Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall(®): 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time.



Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.
Using Green's function method for leaky-wave antenna analysis based on partially-filled slitted rectangular waveguide

A leaky-wave antenna based on a rectangular waveguide with partially dielectric filling and long slit on the narrow wall is investigated. The Green's function method for dispersion equations and antenna radiation analysis is suggested. A protective dielectric cover on the screen with slit is taken into account. A complex propagation constant is defined by matching of magnetic fields in the slit.
A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws

As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.
Mechanical properties, shrinkage stress, cuspal strain and fracture resistance of molars restored with bulk-fill composites and incremental filling technique.

To compare bulk-fill with incremental filling techniques for restoring large mesio-occlusal-distal (MOD) restorations.



Seventy-five molars with MOD preparations were divided into five groups: Z350XT, incrementally filled with Filtek Z350XT and four bulk-fills-FBF/Z350XT, Filtek Bulk Fill/Filtek Z350XT; VBF/CHA, Venus Bulk Fill/Charisma Diamond; SDR/EST-X, SDR/Esthet-X HD; TEC, TetricEvoCeram Bulk Fill. Cuspal strains were measured using strain-gauges (n=10): CSt-Re, during restorative procedure; CSt-100N, during 100N occlusal loading; CSt-Fr, at fracture load. Before fracture load, teeth were load-cycled. Fracture resistance, fracture mode, and enamel cracks were recorded. The other five teeth were used for Elastic modulus (E) and Vickers hardness (VH). Post-gel shrinkage (Shr), diametral tensile strength (DTS) and compressive strength (CS) were determined (n=10). Shrinkage stresses were analyzed using finite element analysis.



SDR had similar CS values as TEC, lower than all other composites. CHA had similar DTS values as Z350XT, higher than all other composites. Z350XT had the highest mean Shr and SDR the lowest Shr. New enamel cracks and propagation was observed after the restoration, regardless of filling technique. Z350XT had lower fracture resistance than bulk-fill composite techniques. No significant differences in failure modes were found. E and VH were constant through the depth for all techniques. Bulk-filling techniques had lower stresses compared to Z350XT.



Flowable bulk-fill composites had lower mechanical properties than paste bulk-fill and conventional composites. All bulk-fill composites had lower post-gel shrinkage than conventional composite. Bulk-fill filling techniques resulted in lower cusp strain, shrinkage stress and higher fracture resistance.



Using bulk-fill composites cause lower CSt wich indicates lower stress in restored tooth. Furthermore, bulk-fill composites have a higher fracture resistance. Therefore, clinicians may choose the bulk-fill composite to decrease undesirable effects of restoration while simplifying filling procedure.
Doubly Stochastic Variational Inference for Deep Gaussian Processes

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to overfitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalizations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm that does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.
Analysis and Design of Ultra Thin Electromagnetic Absorbers Comprising Resistively Loaded High Impedance Surfaces

High-impedance surfaces (HIS) comprising lossy frequency selective surfaces (FSS) are employed to design thin electromagnetic absorbers. The structure, despite its typical resonant behavior, is able to perform a very wideband absorption in a reduced thickness. Losses in the frequency selective surface are introduced by printing the periodic pattern through resistive inks and hence avoiding the typical soldering of a large number of lumped resistors. The effect of the surface resistance of the FSS and dielectric substrate characteristics on the input impedance of the absorber is discussed by means of a circuital model. It is shown that the optimum value of surface resistance is affected both by substrate parameters (thickness and permittivity) and by FSS element shape. The equivalent circuit model is then used to introduce the working principles of the narrowband and the wideband absorbing structure and to derive the best-suited element for wideband absorption.
A method of driving fatigue detection based on eye location

For a driver fatigue monitoring system, one of the most important problems to solve is eye location. This paper presents a method of eye location based on AAM and a method of driver fatigue detection based on an improved method to calculate PERCOLS. AAM (Active Appearance Model) model is composited by the appearance model and fitting algorithm which is an efficient algorithm to extract facial feature points. First establish the AAM model, then use the fitting algorithm to detect the position of face and locate the position of eye, next calculate the improved physical PERCLOS which is used to measure the driver fatigue to determine the current state of driver is fatigue or not.
Computational thinking (CT): on weaving it in

Paul Curzon Queen Mary University of London Mile End, London, E1 4NS UK +44 (20) 7882 5212 pc@dcs.qmul.ac.uk Joan Peckham Harriet Taylor NSF 4201 Wilson Blvd. Arlington, VA 22230, USA (703) 292-7344 jpeckham/htaylor@nsf.gov Amber Settle School of Computing DePaul University 243 S. Wabash Avenue Chicago, IL 60604, USA (312) 362-5324 asettle@cdm.depaul.edu Eric Roberts Computer Science Stanford University 202 Gates Stanford, CA 94305-901 650-723-3642 eroberts@cs.stanford.edu
A Controlled Pilot-Outcome Study of Sensory Integration ( SI ) in the Treatment of Complex Adaptation to Traumatic Stress

This study tested whether sensory integration (SI) treatment combined with psychotherapy would improve symptom outcome over psychotherapy alone in the treatment of complex posttraumatic stress, as measured by the Disorders of Extreme Stress Not Otherwise Specified (DESNOS) clinical construct in a group of 10 adult patients with histories of childhood abuse. DESNOS symptoms were assessed at three time periods (T1, baseline; T2, after experimental group SI treatment; and T3, after wait-list control group SI treatment) using the Structured Interview for Disorders of Extreme Stress (SIDES). The Sensory Learning Program (SLP), developed by the Sensory Learning Institute of Boulder, Colorado, was used as the SI treatment modality. Results indicated significant differential improvement for the group treated with SLP in SIDES Total Score (T1/T2 and T2/T3), Self Perception (T1/T2 and T2/T3), Affect Regulation (T2/T3), and Alterations in Meaning (T1/T2).
Exploring autoencoders for unsupervised feature selection

Feature selection plays an important role in pattern classification. It is especially an important preprocessing task when there are large number of features in comparison to number of patterns as is the case with gene expression data. A new unsupervised feature selection method has been evolved using autoencoders since autoencoders have the capacity to learn the input features without class information. In order to prevent the autoencoder from overtraining, masking has been used and the reconstruction error of masked input features has been used to compute feature weights in moving average manner. A new aggregation function for autoencoder has also been introduced by incorporating correlation between input features to remove the redundancy in selected features set. Comparative performance evaluation on benchmark image and gene expression datasets shows that the proposed method outperforms other unsupervised feature selection methods.
Optical see-through head up displays' effect on depth judgments of real world objects

Recent research indicates that users consistently underestimate depth judgments to Augmented Reality (AR) graphics when viewed through optical see-through displays. However, to our knowledge, little work has examined how AR graphics may affect depth judgments of real world objects that have been overlaid or annotated with AR graphics. This study begins a preliminary analysis whether AR graphics have directional effects on users' depth perception of real-world objects, as might be experienced in vehicle driving scenarios (e.g., as viewed via an optical see-through head-up display or HUD). Twenty-four participants were asked to judge the depth of a physical pedestrian proxy figure moving towards them at a constant rate of 1 meter/second. Participants were shown an initial target location that varied in distance from 11 to 20 m and were then asked to press a button to indicate when the moving target was perceived to be at the previously specified target location. Each participant experienced three different display conditions: no AR visual display (control), a conformal AR graphic overlaid on the pedestrian via a HUD, and the same graphic presented on a tablet physically located on the pedestrian. Participants completed 10 trials (one for each target distance between 11 and 20 inclusive) per display condition for a total of 30 trials per participant. The judged distance from the correct location was recorded, and after each trial, participants' confidence in determining the correct distance was captured. Across all conditions, participants underestimated the distance of the physical object consistent with existing literature. Greater variability was observed in the accuracy of distance judgments under the AR HUD condition relative to the other two display conditions. In addition, participant confidence levels were considerably lower in the AR HUD condition.
Making sense in a complex landscape: how the Cynefin Framework from Complex Adaptive Systems Theory can inform health promotion practice.

Health promotion addresses issues from the simple (with well-known cause/effect links) to the highly complex (webs and loops of cause/effect with unpredictable, emergent properties). Yet there is no conceptual framework within its theory base to help identify approaches appropriate to the level of complexity. The default approach favours reductionism--the assumption that reducing a system to its parts will inform whole system behaviour. Such an approach can yield useful knowledge, yet is inadequate where issues have multiple interacting causes, such as social determinants of health. To address complex issues, there is a need for a conceptual framework that helps choose action that is appropriate to context. This paper presents the Cynefin Framework, informed by complexity science--the study of Complex Adaptive Systems (CAS). It introduces key CAS concepts and reviews the emergence and implications of 'complex' approaches within health promotion. It explains the framework and its use with examples from contemporary practice, and sets it within the context of related bodies of health promotion theory. The Cynefin Framework, especially when used as a sense-making tool, can help practitioners understand the complexity of issues, identify appropriate strategies and avoid the pitfalls of applying reductionist approaches to complex situations. The urgency to address critical issues such as climate change and the social determinants of health calls for us to engage with complexity science. The Cynefin Framework helps practitioners make the shift, and enables those already engaged in complex approaches to communicate the value and meaning of their work in a system that privileges reductionist approaches.
Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function

One aspect of frontal cortical function that has been given less attention than it is due, both in this review and in the literature in general, is the biological basis of the generation and manipulation of abstract frontal representations. Response properties of some PFC cells have been shown to correspond with abstract rules (Wallis et al, 2001), and models such as those of Norman & Shallice (1980, 1986; Shallice, 1982; Shallice & Burgess, 1991, 1996), Grafman (2002) and Fuster (1997) have emphasized the requirement that frontal cortex dynamically generate and manipulate abstract
Feature covariance matrix-based dynamic hand gesture recognition

Over the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible solutions for HGR, it is still very challenging to satisfy the requirements of both high efficiency and accuracy for real-world HGR systems. In this paper, we propose a novel method using the feature covariance matrix for effective and efficient dynamic HGR. We extract a set of local feature vectors that represent local motion patterns to construct the feature covariance matrix efficiently, which also provides a compact representation of a dynamic hand gesture. By tracking hand keypoints in three successive frames and calculating their motion features, our method can be extended to both 2D dynamic HGR and 3D dynamic HGR. To evaluate the effectiveness of the proposed framework, we perform extensive experiments on three publicly available datasets (one 2D dataset and two 3D datasets). The experimental results demonstrate the effectiveness of our proposed method.
The multi-tasking paradox : perceptions , problems and strategies

The purpose of this article is to describe multi-tasking behaviour in the workplace; to link its cause to the increasing prevalence of low-cost information and communications technologies and to the changing organizational structures that have evolved to meet the demands and opportunities of these technologies.  This article is a presentation of the current literature on multi-tasking behaviour among knowledge workers with a selective bibliography addressing empirical research into the behavioural, managerial and technological aspects of this phenomenon. It then expands to comprehensive coverage of the literature on past and current thinking about task structuring, strategies for coping in a multi-tasking environment and the changing nature of work and organizations, which fuels the need to multi-task in response to these changes.  Among knowledge workers, multi-tasking behaviour appears to be an inevitable consequence of the presence of increasingly easy access to information. Despite the detrimental effect that multi-tasking has on specific task completion, the paradox is that this does not seem to have an effect on overall organizational productivity. For the USA at least, an average 4 per cent growth rate over the past several years of the late twentieth and early twenty-first centuries shows that productivity has increased in tandem with an increase in multi-tasking behaviour and information technologies.  Multi-tasking behaviour needs to be understood in the context of its manifestation as a variable that is at least partially dependent on the existence of relatively “cheap” information. In essence, in an information economy, task completion by knowledge workers to a set deadline may be counterproductive to the interests of the organization as a whole. This article describes certain strategies that can be used to minimize the harmful aspects of continuous task switching and to maximize the returns to experience that multi-tasking can bring to an organization.  Multi-tasking behaviour and its link to complexity theory may lead to a new understanding of organizations as highly fluid and variable entities that are impossible to design or maintain centrally and yet whose goals lead to the moment by moment creation of micro-organizational structures that accomplish tasks in a manner that engages the full resources of knowledge workers.
[Skin signs in child abuse].

Child abuse is far more prevalent today than is generally recognized. Up to 90% of victims suffer physical abuse that can be observed in signs on the skin. Dermatologists are particularly qualified to identify these signs and distinguish them from other conditions that can mimic abuse. This review covers the signs of child abuse that can be observed on the skin. We discuss clues that can help differentiate between lesions caused by abuse and those that are accidental, and we describe the skin conditions that mimic physical abuse.
Design of impedance matching circuits for RF energy harvesting systems

This paper presents a systematic design methodology for impedance matching circuits of an RF energy harvester to maximize the harvested energy for a range of input power levels with known distribution. Design of input matching networks for RF rectifier differs from those for traditional RF circuits such as low-noise amplifier because the transistors in the RF rectifiers operate in different regions. In this work, it is shown that the input impedance of the rectifier estimated based on small signal simulation of transistors at the peak of input signal in the rectifier when the transistors are biased with stable DC operating voltage provides the largest harvested energy from a fixed input power. For variable input power levels, a matching network selection strategy is proposed to maximize expected harvested energy given the probability density distribution of input power levels. As an experimental sample, an RF energy harvester circuit is designed to maximize the harvested energy in the 902–928 MHz band employing an off-chip impedance matching circuit designed using the proposed methodology. The proposed RF energy harvester exhibits a measured maximum power conversion efficiency (PCE) of 32% at −15 dBm (32 μW) and delivers an output DC voltage of 3.2 V to a 1 MΩ load.
Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial.

For children with attention deficit/hyperactivity disorder (ADHD), a reduction of inattention, impulsivity and hyperactivity by neurofeedback (NF) has been reported in several studies. But so far, unspecific training effects have not been adequately controlled for and/or studies do not provide sufficient statistical power. To overcome these methodological shortcomings we evaluated the clinical efficacy of neurofeedback in children with ADHD in a multisite randomised controlled study using a computerised attention skills training as a control condition.



102 children with ADHD, aged 8 to 12 years, participated in the study. Children performed either 36 sessions of NF training or a computerised attention skills training within two blocks of about four weeks each (randomised group assignment). The combined NF treatment consisted of one block of theta/beta training and one block of slow cortical potential (SCP) training. Pre-training, intermediate and post-training assessment encompassed several behaviour rating scales (e.g., the German ADHD rating scale, FBB-HKS) completed by parents and teachers. Evaluation ('placebo') scales were applied to control for parental expectations and satisfaction with the treatment.



For parent and teacher ratings, improvements in the NF group were superior to those of the control group. For the parent-rated FBB-HKS total score (primary outcome measure), the effect size was .60. Comparable effects were obtained for the two NF protocols (theta/beta training, SCP training). Parental attitude towards the treatment did not differ between NF and control group.



Superiority of the combined NF training indicates clinical efficacy of NF in children with ADHD. Future studies should further address the specificity of effects and how to optimise the benefit of NF as treatment module for ADHD.