nils-herrmann/clean_scirepeval huggingface dataset:
Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit

Online banking (Internet banking) has emerged as one of the most profitable e-commerce applications over the last decade. Although several prior research projects have focused on the factors that impact on the adoption of information technology or Internet, there is limited empirical work which simultaneously captures the success factors (positive factors) and resistance factors (negative factors) that help customers to adopt online banking. This paper explores and integrates the various advantages of online banking to form a positive factor named perceived benefit. In addition, drawing from perceived risk theory, five specific risk facets – financial, security/privacy, performance, social and time risk – are synthesized with perceived benefit as well as integrated with the technology acceptance model (TAM) and theory of planned behavior (TPB) model to propose a theoretical model to explain customers’ intention to use online banking. The results indicated that the intention to use online banking is adversely affected mainly by the security/privacy risk, as well as financial risk and is positively affected mainly by perceived benefit, attitude and perceived usefulness. The implications of integrating perceived benefit and perceived risk into the proposed online banking adoption model are discussed.
Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction

The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is different from the training data. If the range of noise levels of training data is extended to solve the problem, the maximum performance of a produced deep-CNN is limited. To solve the tradeoff, we propose a deep-CNN that is adjustable to the noise level of the input image immediately. We use soft shrinkage for activation functions of our deep-CNN. The soft shrinkage has thresholds proportional to the noise level given by the user. We also propose an optimization method for proportionality coefficients for the thresholds of soft shrinkage. Our method optimizes the coefficients for various noise levels simultaneously. In our experiment using a test set whose noise level is from 5 to 50, the proposed method showed higher PSNR than that in the case of the conventional method using only one deep-CNN, and PSNR comparable to that in the case of the conventional method using multiple noise-level-specific CNNs.
Internet geolocation: Evasion and counterevasion

Internet geolocation technology aims to determine the physical (geographic) location of Internet users and devices. It is currently proposed or in use for a wide variety of purposes, including targeted marketing, restricting digital content sales to authorized jurisdictions, and security applications such as reducing credit card fraud. This raises questions about the veracity of claims of accurate and reliable geolocation. We provide a survey of Internet geolocation technologies with an emphasis on adversarial contexts; that is, we consider how this technology performs against a knowledgeable adversary whose goal is to evade geolocation. We do so by examining first the limitations of existing techniques, and then, from this base, determining how best to evade existing geolocation techniques. We also consider two further geolocation techniques which may be of use even against adversarial targets: (1) the extraction of client IP addresses using functionality introduced in the 1.5 Java API, and (2) the collection of round-trip times using HTTP refreshes. These techniques illustrate that the seemingly straightforward technique of evading geolocation by relaying traffic through a proxy server (or network of proxy servers) is not as straightforward as many end-users might expect. We give a demonstration of this for users of the popular Tor anonymizing network.
A Hybrid-Control Approach to the Parking Problem of a Wheeled Vehicle Using Limited View-Angle Visual Feedback

In this paper we consider the mobile robot parking problem, i.e., the stabilization of a wheeled vehicle to a given position and orientation, using only visual feedback from low-cost cameras. We take into account the practically most relevant problem of keeping the tracked features in sight of the camera while maneuvering to park the vehicle. This constraint, often neglected in the literature, combines with the non-holonomic nature of the vehicle kinematics in a challenging controller design problem. We provide an effective solution to such a problem by using a combination of previous results on non-smooth control synthesis and recently developed hybrid control techniques. Simulations and experimental results on a laboratory vehicle are reported, showing the practicality of the proposed approach. parking of wheeled robots, visual servoing, hybrid control, non-holonomic systems
Molecular graph convolutions: moving beyond fingerprints

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.
Efficacy and safety of over-the-counter whitening strips as compared to home-whitening with 10 % carbamide peroxide gel—systematic review of RCTs and metanalysis

The study aims to compare the efficacy and safety of over-the-counter whitestrips with the American Dental Association (ADA)-recommended home-whitening using the 10 % carbamide peroxide gel. Randomized controlled trials (RCTs) comparing the clinical efficacy and safety of the whitestrips with the 10 % carbamide peroxide (10 % CP) gel applied on tray for tooth whitening in adults were searched at PubMed and Cochrane Central Register of Controlled Trials databases and selected up to October 2014. Efficacy of the whitening techniques was assessed through ∆E, ∆L, and ∆b parameters, while side effects were analyzed as dichotomous variables. Data was extracted independently by two reviewers. Metanalysis was performed using random- and fixed-effect models (RevMan 5.3). Eight studies were included in the metanalysis. The metanalysis revealed no significant difference between the intervention groups for tooth-whitening efficacy measured as ΔE (mean difference [MD]−0.53; 95 % CI [−1.72;0.66]; Z = 0.88; p = 0.38) and ΔL (MD−0.22; 95 % CI [−0.81;0.36]; z = 0.75; p = 0.45); reduction of yellowing was higher with the whitestrips (MD−0.47; 95 % CI [−0.89; −0.06]; Z = 2.25; p = 0.02). Tooth sensitivity (risk ratio [RR] 1.17; 95 % CI [0.81–1.69]; Z = 0.81; p = 0.42) and gingival sensitivity (RR 0.76; 95 % CI [0.53–1.10]; Z = 1.44; p = 0.15) were similar, regardless of the whitening method used. The observed gingival irritation was higher when the 10 % CP gel was applied on tray (RR 0.43; 95 % CI [0.20–0.93]; Z = 2.14; p = 0.03). The quality of evidence generated was rated very low for all outcomes. There is no sound evidence to support the use of the whitening strips in detriment of the ADA-recommended technique based on the 10 % carbamide peroxide gel applied on tray. To the moment, there is no sound evidence in dental literature to suggest that the ADA-recommended whitening technique based on 10 % carbamide peroxide gel could be substituted by the whitening strips. The existing studies, with their limitations, revealed similar tooth whitening and tooth and gingival sensitivity for both whitening techniques.
Pyramidal neurons: dendritic structure and synaptic integration

Pyramidal neurons are characterized by their distinct apical and basal dendritic trees and the pyramidal shape of their soma. They are found in several regions of the CNS and, although the reasons for their abundance remain unclear, functional studies — especially of CA1 hippocampal and layer V neocortical pyramidal neurons — have offered insights into the functions of their unique cellular architecture. Pyramidal neurons are not all identical, but some shared functional principles can be identified. In particular, the existence of dendritic domains with distinct synaptic inputs, excitability, modulation and plasticity appears to be a common feature that allows synapses throughout the dendritic tree to contribute to action-potential generation. These properties support a variety of coincidence-detection mechanisms, which are likely to be crucial for synaptic integration and plasticity.
AMR Parsing as Graph Prediction with Latent Alignment

meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclicmeaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational autoencoding framework and a continuous relaxation of the discrete alignments. We show that joint modeling is preferable to using a pipeline of align and parse. The parser achieves the best reported results on the standard benchmark (73.6% on LDC2016E25).
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.



Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.



The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings.



The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
Use of 'natural' products as alternatives to antibiotic feed additives in ruminant production.

The banning in 2006 of the use of antibiotics as animal growth promoters in the European Union has increased demand from producers for alternative feed additives that can be used to improve animal production. This review gives an overview of the most common non-antibiotic feed additives already being used or that could potentially be used in ruminant nutrition. Probiotics, dicarboxylic acids, enzymes and plant-derived products including saponins, tannins and essential oils are presented. The known modes of action and effects of these additives on feed digestion and more especially on rumen fermentations are described. Their utility and limitations in field conditions for modern ruminant production systems and their compliance with the current legislation are also discussed.
Bacteriocin-Antimicrobial Synergy: A Medical and Food Perspective

The continuing emergence of multi-drug resistant pathogens has sparked an interest in seeking alternative therapeutic options. Antimicrobial combinatorial therapy is one such avenue. A number of studies have been conducted, involving combinations of bacteriocins with other antimicrobials, to circumvent the development of antimicrobial resistance and/or increase antimicrobial potency. Such bacteriocin-antimicrobial combinations could have tremendous value, in terms of reducing the likelihood of resistance development due to the involvement of two distinct mechanisms of antimicrobial action. Furthermore, antimicrobial synergistic interactions may also have potential financial implications in terms of decreasing the costs of treatment by reducing the concentration of an expensive antimicrobial and utilizing it in combination with an inexpensive one. In addition, combinatorial therapies with bacteriocins can broaden antimicrobial spectra and/or result in a reduction in the concentration of an antibiotic required for effective treatments to the extent that potentially toxic or adverse side effects can be reduced or eliminated. Here, we review studies in which bacteriocins were found to be effective in combination with other antimicrobials, with a view to targeting clinical and/or food-borne pathogens. Furthermore, we discuss some of the bottlenecks which are currently hindering the development of bacteriocins as viable therapeutic options, as well as addressing the need to exercise caution when attempting to predict clinical outcomes of bacteriocin-antimicrobial combinations.
Functional connectivity hubs of the mouse brain

Recent advances in functional connectivity methods have made it possible to identify brain hubs - a set of highly connected regions serving as integrators of distributed neuronal activity. The integrative role of hub nodes makes these areas points of high vulnerability to dysfunction in brain disorders, and abnormal hub connectivity profiles have been described for several neuropsychiatric disorders. The identification of analogous functional connectivity hubs in preclinical species like the mouse may provide critical insight into the elusive biological underpinnings of these connectional alterations. To spatially locate functional connectivity hubs in the mouse brain, here we applied a fully-weighted network analysis to map whole-brain intrinsic functional connectivity (i.e., the functional connectome) at a high-resolution voxel-scale. Analysis of a large resting-state functional magnetic resonance imaging (rsfMRI) dataset revealed the presence of six distinct functional modules related to known large-scale functional partitions of the brain, including a default-mode network (DMN). Consistent with human studies, highly-connected functional hubs were identified in several sub-regions of the DMN, including the anterior and posterior cingulate and prefrontal cortices, in the thalamus, and in small foci within well-known integrative cortical structures such as the insular and temporal association cortices. According to their integrative role, the identified hubs exhibited mutual preferential interconnections. These findings highlight the presence of evolutionarily-conserved, mutually-interconnected functional hubs in the mouse brain, and may guide future investigations of the biological foundations of aberrant rsfMRI hub connectivity associated with brain pathological states.
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.
Who Said What: Modeling Individual Labelers Improves Classification

Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label and to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in samplespecific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computeraided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona (2010); Mnih and Hinton (2012). Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels
SDPT3 — a Matlab software package for semidefinite-quadratic-linear programming, version 3.0

This document describes a new release, version 3.0, of the software SDPT3. This code is designed to solve conic programming problems whose constraint cone is a product of semidefinite cones, second-order cones, and/or nonnegative orthants. It employs a predictor-corrector primal-dual path-following method, with either the HKM or the NT search direction. The basic code is written in Matlab, but key subroutines in Fortran and C are incorporated via Mex files. Routines are provided to read in problems in either SeDuMi or SDPA format. Sparsity and block diagonal structure are exploited, but the latter needs to be given explicitly. ∗Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA (reha+@andrew.cmu.edu). Research supported in part by NSF through grant CCR-9875559. †Department of Mathematics, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. (mattohkc@math.nus.edu.sg). Research supported in part by the Singapore-MIT Alliance. ‡School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853, USA (miketodd@cs.cornell.edu). Research supported in part by NSF through grant DMS-9805602 and ONR through grant N00014-96-1-0050.
Barriers to and facilitators of physical activity program use among older adults.

Regular physical activity (PA) is important for maintaining long-term physical, cognitive, and emotional health. However, few older adults engage in routine PA, and even fewer take advantage of programs designed to enhance PA participation. Though most managed Medicare members have free access to the Silver Sneakers and EnhanceFitness PA programs, the vast majority of eligible seniors do not utilize these programs. The goal of this qualitative study was to better understand the barriers to and facilitators of PA and participation in PA programs among older adults.



This was a qualitative study using focus group interviews.



Focus groups took place at three Group Health clinics in King County, Washington.



Fifty-two randomly selected Group Health Medicare members between the ages of 66 to 78 participated.



We conducted four focus groups with 13 participants each. Focus group discussions were audio-recorded, transcribed, and analyzed using an inductive thematic approach and a social-ecological framework.



Men and women were nearly equally represented among the participants, and the sample was largely white (77%), well-educated (69% college graduates), and relatively physically active. Prominent barriers to PA and PA program participation were physical limitations due to health conditions or aging, lack of professional guidance, and inadequate distribution of information on available and appropriate PA options and programs. Facilitators included the motivation to maintain physical and mental health and access to affordable, convenient, and stimulating PA options.



Older adult populations may benefit from greater support and information from their providers and health care systems on how to safely and successfully improve or maintain PA levels through later adulthood. Efforts among health care systems to boost PA among older adults may need to consider patient-centered adjustments to current PA programs, as well as alternative methods for promoting overall active lifestyle choices.
Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms