nils-herrmann/clean_scirepeval huggingface dataset:
Factors Affecting the Adoption of Internet Banking in Jordan : An Extended TAM Model

This study aims to investigate how customers perceive and adopt internet banking (IB) in Jordan. An extended Model, based on the Technology Acceptance Model (TAM), was developed. Three more constructs were added to the Model, namely; Perceived Risk (PR), Perceived Trust (PT) and Bank Credibility (BC). To empirically test the Model’s ability to predict customers’ intention to adopt and use internet banking, a questionnaire was developed and used. A randomly 500 graduate students at four Jordanian Universities were surveyed. An exploratory factor analysis, correlation matrix, and a regression analysis were used to test the robustness of the model as well as to test the hypothesized relationships among variables. The results provide support to the extended TAM model and confirm its robustness in predicting customers’ intention to adopt and use internet banking. This study contributes to the body of literature about internet banking, and its results provide useful information for bank managers on how to deal with internet challenges in Jordan. Since this empirical study was performed with a time constraint, it is not without limitation.
Mining communities and their relationships in blogs: A study of online hate groups

Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.
NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.



The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default.



We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses.



The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
k-Nearest Neighbors in Uncertain Graphs

Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer k-nearest neighbor queries (k-NN), which is the problem of computing the k closest nodes to some specific node. In this paper we introduce a framework for processing k-NN queries in probabilistic graphs. We propose novel distance functions that extend well-known graph concepts, such as shortest paths. In order to compute them in probabilistic graphs, we design algorithms based on sampling. During k-NN query processing we efficiently prune the search space using novel techniques. Our experiments indicate that our distance functions outperform previously used alternatives in identifying true neighbors in real-world biological data. We also demonstrate that our algorithms scale for graphs with tens of millions of edges.
Propeller-hull interaction in a single-screw vessel

Unfavorable wake and separated flow from the hull might cause a dramatic decay of the propeller performance in single-screw propelled vessels such as tankers, bulk carriers and containers. For these types of vessels, special attention has to be paid to the design of the stern region, the occurrence of a good flow towards the propeller and rudder being necessary to avoid separation and unsteady loads on the propeller blades and, thus, to minimize fuel consumption and the risk for cavitation erosion and vibrations. The present work deals with the analysis of the propeller inflow in a single-screw chemical tanker vessel affected by massive flow separation in the stern region. Detailed flow measurements by Laser Doppler Velocimetry (LDV) were performed in the propeller region at model scale, in the Large Circulating Water Channel of CNR-INSEAN. Tests were undertaken with and without propeller in order to investigate its effect on the inflow characteristics and the separation mechanisms. In this regard, the study concerned also a phase locked analysis of the propeller perturbation at different distances upstream of the propulsor. The study shows the effectiveness of the 3 order statistical moment (i.e. skewness) for describing the topology of the wake and accurately identifying the portion affected by the detached flow.
The dual PI3K/mTOR inhibitor PKI-587 enhances sensitivity to cetuximab in EGFR-resistant human head and neck cancer models

Cetuximab is the only targeted agent approved for the treatment of head and neck squamous cell carcinomas (HNSCC), but low response rates and disease progression are frequently reported. As the phosphoinositide 3-kinase (PI3K) and the mammalian target of rapamycin (mTOR) pathways have an important role in the pathogenesis of HNSCC, we investigated their involvement in cetuximab resistance.Different human squamous cancer cell lines sensitive or resistant to cetuximab were tested for the dual PI3K/mTOR inhibitor PF-05212384 (PKI-587), alone and in combination, both in vitro and in vivo.Treatment with PKI-587 enhances sensitivity to cetuximab in vitro, even in the condition of epidermal growth factor receptor (EGFR) resistance. The combination of the two drugs inhibits cells survival, impairs the activation of signalling pathways and induces apoptosis. Interestingly, although significant inhibition of proliferation is observed in all cell lines treated with PKI-587 in combination with cetuximab, activation of apoptosis is evident in sensitive but not in resistant cell lines, in which autophagy is pre-eminent. In nude mice xenografted with resistant Kyse30 cells, the combined treatment significantly reduces tumour growth and prolongs mice survival.Phosphoinositide 3-kinase/mammalian target of rapamycin inhibition has an important role in the rescue of cetuximab resistance. Different mechanisms of cell death are induced by combined treatment depending on basal anti-EGFR responsiveness.
Health benefits of fermented foods: microbiota and beyond.

Fermented foods and beverages were among the first processed food products consumed by humans. The production of foods such as yogurt and cultured milk, wine and beer, sauerkraut and kimchi, and fermented sausage were initially valued because of their improved shelf life, safety, and organoleptic properties. It is increasingly understood that fermented foods can also have enhanced nutritional and functional properties due to transformation of substrates and formation of bioactive or bioavailable end-products. Many fermented foods also contain living microorganisms of which some are genetically similar to strains used as probiotics. Although only a limited number of clinical studies on fermented foods have been performed, there is evidence that these foods provide health benefits well-beyond the starting food materials.
Decision Making as a Window on Cognition

A decision is a commitment to a proposition or plan of action based on information and values associated with the possible outcomes. The process operates in a flexible timeframe that is free from the immediacy of evidence acquisition and the real time demands of action itself. Thus, it involves deliberation, planning, and strategizing. This Perspective focuses on perceptual decision making in nonhuman primates and the discovery of neural mechanisms that support accuracy, speed, and confidence in a decision. We suggest that these mechanisms expose principles of cognitive function in general, and we speculate about the challenges and directions before the field.
Automated Guide Vehicles Dynamic Scheduling Based on Annealing Genetic Algorithm

Dispatching automated guided vehicles (AGVs) is the common approach for AGVs scheduling in practice, the information about load arrivals in advance was not used to optimize the performance of the automated guided vehicles system (AGVsS). According to the characteristics of the AGVsS, the mathematical model of AGVs scheduling was established. A heuristic algorithm called Annealing Genetic Algorithm (AGA) was presented to deal with the AGVs scheduling problem, and applied the algorithm dynamically by using it repeatedly under a combined rolling optimization strategy. the performance of the proposed approach for AGVs scheduling was compared with the dispatching rules by simulation. Results showed that the approach performs significantly better than the dispatching rules and proved that it is really effective for AGVsS.
Designing for Depth Perceptions in Augmented Reality

Augmented reality technologies allow people to view and interact with virtual objects that appear alongside physical objects in the real world. For augmented reality applications to be effective, users must be able to accurately perceive the intended real world location of virtual objects. However, when creating augmented reality applications, developers are faced with a variety of design decisions that may affect user perceptions regarding the real world depth of virtual objects. In this paper, we conducted two experiments using a perceptual matching task to understand how shading, cast shadows, aerial perspective, texture, dimensionality (i.e., 2D vs. 3D shapes) and billboarding affected participant perceptions of virtual object depth relative to real world targets. The results of these studies quantify trade-offs across virtual object designs to inform the development of applications that take advantage of users' visual abilities to better blend the physical and virtual world.
GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects

The aim of this article is to develop a GPS/IMU Multisensor fusion algorithm, taking context into consideration. Contextual variables are introduced to define fuzzy validity domains of each sensor. The algorithm increases the reliability of the position information. A simulation of this algorithm is then made by fusing GPS and IMU data coming from real tests on a land vehicle. Bad data delivered by GPS sensor are detected and rejected using contextual information thus increasing reliability. Moreover, because of a lack of credibility of GPS signal in some cases and because of the drift of the INS, GPS/INS association is not satisfactory at the moment. In order to avoid this problem, the authors propose to feed the fusion process based on a multisensor Kalman filter directly with the acceleration provided by the IMU. Moreover, the filter developed here gives the possibility to easily add other sensors in order to achieve performances required.
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally

Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging. Annotating biomedical images is not only tedious and time consuming, but also demanding of costly, specialty - oriented knowledge and skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method called AIFT (active, incremental fine-tuning) to naturally integrate active learning and transfer learning into a single framework. AIFT starts directly with a pre-trained CNN to seek worthy samples from the unannotated for annotation, and the (fine-tuned) CNN is further fine-tuned continuously by incorporating newly annotated samples in each iteration to enhance the CNNs performance incrementally. We have evaluated our method in three different biomedical imaging applications, demonstrating that the cost of annotation can be cut by at least half. This performance is attributed to the several advantages derived from the advanced active and incremental capability of our AIFT method.
Dual Attention Network for Visual Question Answering

Visual Question Answering (VQA) is a popular research problem that involves inferring answers to natural language questions about a given visual scene. Recent neural network approaches to VQA use attention to select relevant image features based on the question. In this paper, we propose a novel Dual Attention Network (DAN) that not only attends to image features, but also to question features. The selected linguistic and visual features are combined by a recurrent model to infer the final answer. We experiment with different question representations and do several ablation studies to evaluate the model on the challenging VQA dataset.
Adversarial Network Embedding

Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other highorder proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks. The source code will be available online.
Early detection of lung cancer: clinical perspectives of recent advances in biology and radiology.

Lung cancer is the most common cause of cancer death in developed countries. The prognosis is poor, with less than 15% of patients surviving 5 years after diagnosis. The poor prognosis is attributable to lack of efficient diagnostic methods for early detection and lack of successful treatment for metastatic disease. Most patients (>75%) present with stage III or IV disease and are rarely curable with current therapies. Within the last decade, rapid advances in molecular biology, pathology, bronchology, and radiology have provided a rational basis for improving outcome. These advancements have led to a better documentation of morphological changes in the bronchial epithelium before development of clinical evident invasive carcinomas. This has changed our concept of lung carcinogenesis and emphasized the multistep carcinogenesis approach on several levels. Combined with the technical developments in bronchoscopic techniques, e.g., laser-induced fluorescence endoscope (LIFE) bronchoscopy, we now have improved methods to localize preinvasive and early-invasive bronchial lesions. With the LIFE bronchoscope, a new morphological entity (angiogenic squamous dysplasia) has been recognized, which might be an important biomarker and target for antiangiogenic chemopreventive agents. To reduce the mortality of lung cancer, these new technologies have been taken into the clinic in different scientific settings. The use of low-dose spiral computed tomography in the screening of a high-risk population has demonstrated the possibility of diagnosing small peripheral tumors that are not seen on conventional X-ray. A shift in the therapeutic paradigm from targeting advanced clinically manifest lung cancer toward asymptomatic preinvasive and early-invasive cancer is occurring. The present article reviews the recent advances in the diagnosis of preinvasive and early-invasive cancer to identify biomarkers for early detection of lung cancer and for chemoprevention studies.
Computer and video game addiction-a comparison between game users and non-game users.

Computer game addiction is excessive or compulsive use of computer and video games that may interfere with daily life. It is not clear whether video game playing meets diagnostic criteria for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV).



First objective is to review the literature on computer and video game addiction over the topics of diagnosis, phenomenology, epidemiology, and treatment. Second objective is to describe a brain imaging study measuring dopamine release during computer game playing.



Article search of 15 published articles between 2000 and 2009 in Medline and PubMed on computer and video game addiction. Nine abstinent "ecstasy" users and 8 control subjects were scanned at baseline and after performing on a motorbike riding computer game while imaging dopamine release in vivo with [123I] IBZM and single photon emission computed tomography (SPECT).



Psycho-physiological mechanisms underlying computer game addiction are mainly stress coping mechanisms, emotional reactions, sensitization, and reward. Computer game playing may lead to long-term changes in the reward circuitry that resemble the effects of substance dependence. The brain imaging study showed that healthy control subjects had reduced dopamine D2 receptor occupancy of 10.5% in the caudate after playing a motorbike riding computer game compared with baseline levels of binding consistent with increased release and binding to its receptors. Ex-chronic "ecstasy" users showed no change in levels of dopamine D2 receptor occupancy after playing this game.



This evidence supports the notion that psycho-stimulant users have decreased sensitivity to natural reward.



Computer game addicts or gamblers may show reduced dopamine response to stimuli associated with their addiction presumably due to sensitization.
A review on locomotion robophysics: the study of movement at the intersection of robotics, soft matter and dynamical systems

Discovery of fundamental principles which govern and limit effective locomotion (self-propulsion) is of intellectual interest and practical importance. Human technology has created robotic moving systems that excel in movement on and within environments of societal interest: paved roads, open air and water. However, such devices cannot yet robustly and efficiently navigate (as animals do) the enormous diversity of natural environments which might be of future interest for autonomous robots; examples include vertical surfaces like trees and cliffs, heterogeneous ground like desert rubble and brush, turbulent flows found near seashores, and deformable/flowable substrates like sand, mud and soil. In this review we argue for the creation of a physics of moving systems-a 'locomotion robophysics'-which we define as the pursuit of principles of self-generated motion. Robophysics can provide an important intellectual complement to the discipline of robotics, largely the domain of researchers from engineering and computer science. The essential idea is that we must complement the study of complex robots in complex situations with systematic study of simplified robotic devices in controlled laboratory settings and in simplified theoretical models. We must thus use the methods of physics to examine both locomotor successes and failures using parameter space exploration, systematic control, and techniques from dynamical systems. Using examples from our and others' research, we will discuss how such robophysical studies have begun to aid engineers in the creation of devices that have begun to achieve life-like locomotor abilities on and within complex environments, have inspired interesting physics questions in low dimensional dynamical systems, geometric mechanics and soft matter physics, and have been useful to develop models for biological locomotion in complex terrain. The rapidly decreasing cost of constructing robot models with easy access to significant computational power bodes well for scientists and engineers to engage in a discipline which can readily integrate experiment, theory and computation.