nils-herrmann/clean_scirepeval huggingface dataset:
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches

The naïve Bayes classifier is built on the assumpti on of conditional independence between the attributes given the class. The algorithm has been shown to be surprisingly robust to obvious violations of this condition, but it is natural to ask if it is possib le to further improve the accuracy by relaxing this assumption. We examine an approach where naïve Bayes is augmented by the addition of correlation a rcs between attributes. We explore two methods for finding the set of augmenting arcs, a greedy hillclimbing search, and a novel, more computationally efficient algorithm that we call SuperParent. We compare these methods to TAN; a state-of the-art distribution-based approach to finding the augmenti ng arcs.
Associations between young adults' use of sexually explicit materials and their sexual preferences, behaviors, and satisfaction.

This study examined how levels of sexually explicit material (SEM) use during adolescence and young adulthood were associated with sexual preferences, sexual behaviors, and sexual and relationship satisfaction. Participants included 782 heterosexual college students (326 men and 456 women; M(age) = 19.9) who completed a questionnaire online. Results revealed high frequencies and multiple types and contexts of SEM use, with men's usage rates systematically higher than women's. Regression analyses revealed that both the frequency of SEM use and number of SEM types viewed were uniquely associated with more sexual experience (a higher number of overall and casual sexual intercourse partners, as well as a lower age at first intercourse). Higher frequencies of SEM use were associated with less sexual and relationship satisfaction. The frequency of SEM use and number of SEM types viewed were both associated with higher sexual preferences for the types of sexual practices typically presented in SEM. These findings suggest that SEM use can play a significant role in a variety of aspects of young adults' sexual development processes.
System-level Max POwer (SYMPO) - a systematic approach for escalating system-level power consumption using synthetic benchmarks

To effectively design a computer system for the worst case power consumption scenario, system architects often use hand-crafted maximum power consuming benchmarks at the assembly language level. These stressmarks, also called power viruses, are very tedious to generate and require significant domain knowledge. In this paper, we propose SYMPO, an automatic SYstem level Max POwer virus generation framework, which maximizes the power consumption of the CPU and the memory system using genetic algorithm and an abstract workload generation framework. For a set of three ISAs, we show the efficacy of the power viruses generated using SYMPO by comparing the power consumption with that of MPrime torture test, which is widely used by industry to test system stability. Our results show that the usage of SYMPO results in the generation of power viruses that consume 14-41% more power compared to MPrime on SPARC ISA. The genetic algorithm achieved this result in about 70 to 90 generations in 11 to 15 hours when using a full system simulator. We also show that the power viruses generated in the Alpha ISA consume 9-24% more power compared to the previous approach of stressmark generation. We measure and provide the power consumption of these benchmarks on hardware by instrumenting a quad-core AMD Phenom II X4 system. The SYMPO power virus consumes more power compared to various industry grade power viruses on x86 hardware. We also provide a microarchitecture independent characterization of various industry standard power viruses.
Gesture recognition in smart home using passive RFID technology

Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.
Comparisons of Probabilistic and Non-probabilistic Hough Transforms

A new and eecient version of the Hough Transform for curve detection, the Randomized Hough Transform (RHT), has been recently suggested. The RHT selects n pixels from an edge image by random sampling to solve n parameters of a curve and then accumulates only one cell in a parameter space. In this paper, the RHT is related to other recent developments of the Hough Transform by experimental tests in line detection. Hough Transform methods are divided into two categories: probabilistic and non-probablistic methods. Four novel extensions of the RHT are proposed to improve the RHT for complex and noisy images. These apply the RHT process to a limited neighborhood of edge pixels. Tests with synthetic and real-world images demonstrate the high speed and low memory usage of the new extensions, as compared both to the basic RHT and other versions of the Hough Transform.
Extracting Drug-Drug Interactions with Attention CNNs

We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12%, which is competitive with the state-of-the-art models.
Nurse staffing, burnout, and health care-associated infection.

Each year, nearly 7 million hospitalized patients acquire infections while being treated for other conditions. Nurse staffing has been implicated in the spread of infection within hospitals, yet little evidence is available to explain this association.



We linked nurse survey data to the Pennsylvania Health Care Cost Containment Council report on hospital infections and the American Hospital Association Annual Survey. We examined urinary tract and surgical site infection, the most prevalent infections reported and those likely to be acquired on any unit within a hospital. Linear regression was used to estimate the effect of nurse and hospital characteristics on health care-associated infections.



There was a significant association between patient-to-nurse ratio and urinary tract infection (0.86; P = .02) and surgical site infection (0.93; P = .04). In a multivariate model controlling for patient severity and nurse and hospital characteristics, only nurse burnout remained significantly associated with urinary tract infection (0.82; P = .03) and surgical site infection (1.56; P < .01) infection. Hospitals in which burnout was reduced by 30% had a total of 6,239 fewer infections, for an annual cost saving of up to $68 million.



We provide a plausible explanation for the association between nurse staffing and health care-associated infections. Reducing burnout in registered nurses is a promising strategy to help control infections in acute care facilities.
SoK: Single Sign-On Security — An Evaluation of OpenID Connect

OpenID Connect is the OAuth 2.0-based replacement for OpenID 2.0 (OpenID) andone of the most important Single Sign-On (SSO) protocols used for delegatedauthentication. It is used by companies like Amazon, Google, Microsoft, andPayPal. In this paper, we systematically analyze well-known attacks on SSOprotocols and adapt these on OpenID Connect. Additionally, we introduce twonovel attacks on OpenID Connect, Identity Provider Confusion and MaliciousEndpoints Attack, abusing flaws in the current specification and breaking thesecurity goals of the protocol. In 2014 we communicated with the authors of theOpenID Connect specification about these attacks and helped to repair the issue(currently an RFC Draft). We categorize the described attacks into two classes: Single-Phase Attacksabusing a lack of a single security check and Cross-Phase Attacks requiring acomplex attack setup and manipulating multiple messages distributed across thewhole protocol workflow. We provide an evaluation of officially referencedOpenID Connect libraries and find 75% of them vulnerable to at least oneSingle-Phase Attack. All libraries are susceptible to Cross-Phase Attacks, which is not surprising since the attacks abuse a logic flaw in the protocoland not an implementation error. We reported the found vulnerabilities to thedevelopers and helped them to fix the issues. We address the existing problemsin a Practical Offensive Evaluation of Single Sign-On Services (PrOfESSOS). PrOfESSOS is our open source implementation for a fully automatedEvaluation-as-a-Service for SSO. PrOfESSOS introduces a generic approach toimprove the security of OpenID Connect implementations by system-aticallydetecting vulnerabilities. In collaboration with the IETF OAuth and OpenIDConnect working group, we integrate PrOfESSOS into the OpenID Connect certification process. PrOfESSOS is available at https://openid.sso-security.de.
An Optical 6-axis Force Sensor for Brain Function Analysis using fMRI

This paper presents an 6-axis optical force sensor which can be used in fMRI. Recently, fMRIs are widely used for studying human brain function. Simultaneous measurement of brain activity and peripheral information, such as grip force, enables more precise investigations in studies of motor function. However, conventional force sensors cannot be used in fMRI environment, since metal elements generate noise which severely contaminate the signals of fMRI. An optical 2-axis force sensor has been developed using photo sensors and optical fibers by Tada et. ], that resolved these problems. The developed force sensor removed all magnetic components from the sensing part. It detected minute displacements by measure amount of light and light traveled through the optical fibers. However, there still remain several problems on this optical force sensor. Firstly, the accuracy is not high compared to the conventional force sensors. Secondly, the robustness is not enough against the contact force to the optical fibers. In this paper, the problems concerning to the accuracy and the sensor output stability has been improved by novel methods of fixing fibers and arithmetic circuit. Furthermore, an optical 6-axis force sensor is developed based on these improvements, and usefulness of our sensor for brain function analysis is confirmed in fMRI experimentations.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.
Comparative evaluation of soft-switching concepts for bi-directional buck+boost dc-dc converters

Soft-switching techniques are an enabling technology to further reduce the losses and the volume of automotive dc-dc converters, utilized to interconnect the high voltage battery or ultra-capacitor to the dc-link of a Hybrid Electrical Vehicle (HEV) or a Fuel Cell Vehicle (FCV). However, as the performance indices of a power electronics converter, such as efficiency and power density, are competing and moreover dependent on the underlying specifications and technology node, a comparison of different converter topologies naturally demands detailed analytical models. Therefore, to investigate the performance of the ARCP, CF-ZVS-M, SAZZ and ZCT-QZVT soft-switching converters, the paper discusses in detail the advantages and drawbacks of each concept, and the impact of the utilized semiconductor technology and silicon area on the converter efficiency. The proposed analytical models that correlate semiconductor, capacitor and inductor losses with the component volume furthermore allow for a comparison of power density and to find the η-ρ-Pareto-Front of the CF-ZVS-M converter.
Metric visual-inertial navigation system using single optical flow feature

This paper introduces a state estimation framework that allows estimating the attitude, full metric speed and the orthogonal metric distance of an IMU-camera system with respect to a plane. The filter relies only on a single optical flow feature as well as gyroscope and accelerometer measurements. The underlying assumption is that the observed visual feature lies on a static plane. The orientation of the observed plane is not required to be known a priori and is also estimated at runtime. The estimation framework fuses visual and inertial measurements in an Unscented Kalman Filter (UKF). The theoretical limitations of the UKF are investigated using a nonlinear observability analysis based on Lie-derivatives. Simulations using realistic sensor noise values successfully demonstrate the performance of the filter as well as validate the findings of the observability analysis. It is shown that the state estimate is converging correctly, even in presence of substantial initial state errors. To the authors' knowledge, this paper documents for the first time the estimation of the heading and metric distance to a wall with no range sensor, relying solely on optical flow as the only exteroceptive sensing modality.
Spurious group differences due to head motion in a diffusion MRI study

Diffusion-weighted MRI (DW-MRI) has become a popular imaging modality for probing the microstructural properties of white matter and comparing them between populations in vivo. However, the contrast in DW-MRI arises from the microscopic random motion of water molecules in brain tissues, which makes it particularly sensitive to macroscopic head motion. Although this has been known since the introduction of DW-MRI, most studies that use this modality for group comparisons do not report measures of head motion for each group and rely on registration-based correction methods that cannot eliminate the full effects of head motion on the DW-MRI contrast. In this work we use data from children with autism and typically developing children to investigate the effects of head motion on differences in anisotropy and diffusivity measures between groups. We show that group differences in head motion can induce group differences in DW-MRI measures, and that this is the case even when comparing groups that include control subjects only, where no anisotropy or diffusivity differences are expected. We also show that such effects can be more prominent in some white-matter pathways than others, and that they can be ameliorated by including motion as a nuisance regressor in the analyses. Our results demonstrate the importance of taking head motion into account in any population study where one group might exhibit more head motion than the other.
Complementarities Between Organizational IT Architecture and Governance Structure

T study addresses the theoretically neglected interplay between organizational information technology (IT) architecture and IT governance structure in shaping IT alignment. We theoretically develop the idea that IT architecture modularity helps sustain IT alignment by increasing IT agility, and that decentralization of IT governance strengthens this relationship. IT architecture therefore complements IT governance structure. Tests of the proposed mediated-moderation model using data from 223 organizations support these ideas. Implications for theory and practice are also discussed.
Deep Convolutional Factor Analyser for Multivariate Time Series Modeling

Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.
Properties and performance of a center/surround retinex

The last version of Land's (1986) retinex model for human vision's lightness and color constancy has been implemented and tested in image processing experiments. Previous research has established the mathematical foundations of Land's retinex but has not subjected his lightness theory to extensive image processing experiments. We have sought to define a practical implementation of the retinex without particular concern for its validity as a model for human lightness and color perception. We describe the trade-off between rendition and dynamic range compression that is governed by the surround space constant. Further, unlike previous results, we find that the placement of the logarithmic function is important and produces best results when placed after the surround formation. Also unlike previous results, we find the best rendition for a "canonical" gain/offset applied after the retinex operation. Various functional forms for the retinex surround are evaluated, and a Gaussian form is found to perform better than the inverse square suggested by Land. Images that violate the gray world assumptions (implicit to this retinex) are investigated to provide insight into cases where this retinex fails to produce a good rendition.
Dependency-Based Open Information Extraction

Building shallow semantic representations from text corpora is the first step to perform more complex tasks such as text entailment, enrichment of knowledge bases, or question answering. Open Information Extraction (OIE) is a recent unsupervised strategy to extract billions of basic assertions from massive corpora, which can be considered as being a shallow semantic representation of those corpora. In this paper, we propose a new multilingual OIE system based on robust and fast rule-based dependency parsing. It permits to extract more precise assertions (verb-based triples) from text than state of the art OIE systems, keeping a crucial property of those systems: scaling to Web-size document collections.
Social network, social trust and shared goals in organizational knowledge sharing

The aim of our study was to further develop an understanding of social capital in organizationalknowledge-sharing. We first developed a measurement tool and then a theoretical framework in which three social capital factors (social network, social trust, and shared goals) were combined with the theory of reasoned action; their relationships were then examined using confirmatory factoring analysis. We then surveyed of 190 managers from Hong Kong firms, we confirm that a social network and shared goals significantly contributed to a person’s volition to share knowledge, and directly contributed to the perceived social pressure of the organization. The social trust has however showed no direct effect on the attitude and subjective norm of sharing knowledge.  Corresponding author.  E-mail addresses: vwschow@hkbu.edu.hk (W.S. Chow), sherry.ls.chan@gmail.com (L.S. Chan).
My changed body: breast cancer, body image, distress and self-compassion.

Bodily changes after breast cancer treatment can lead to long-term distress. Self-compassion, the ability to be kind to oneself, is an internal resource that may enhance a woman's ability to adjust to cancer-related bodily changes. The aim of the present study was to test the hypothesis that self-compassion mediates the relationship between body image and distress, controlling for alternate plausible mediators.



Members of a nationwide breast cancer consumer network were invited to participate. A total of 279 women who had finished active cancer treatment completed the online survey. Assessments included the Body Image Scale; Self-compassion Scale; Depression, Anxiety and Stress Scale and items measuring perceived normative pressure and comfort with one's weight. Possible mediating effects of proposed variables on the body image-distress relationship were assessed.



Tests using a bootstrapping approach with multiple mediators were significant for self-compassion on distress. Body image disturbance was indirectly associated with distress through low self-compassion.



Body image disturbance and lower self-compassion were associated with increased psychological distress among these breast cancer survivors. This study provides preliminary evidence for a mediating role of self-compassion between body image disturbance and psychological distress, suggesting a potentially protective effect of higher levels of self-compassion for women at risk of experiencing body image disturbance.