nils-herrmann/clean_scirepeval huggingface dataset:
Perceived Service Quality in a University Web Portal: Revising the E-Qual Instrument

Online service quality is a much-studied concept. Despite this, dimensions that make up service quality, and the items used to measure those dimensions have proven unstable. It is widely suggested that service quality measurement scales need to be instantiated differently in different business domains. In addition, the nature of online services is continually changing. Universities have been at the forefront of this change, with university Websites increasingly acting as a portal for a wide range of online transactions for a wide range of stakeholders. In this work-in-progress, qualitative study, we conduct focus groups with a range of stakeholders in university Web portals with a view to adapting the e-qual instrument for use in a university Web portal environment. We find support for a new service quality dimension, and for additional items in existing scales. We conclude by proposing a revised instrument that can form the basis for a more extensive quantitative study
A sensory feedback system for prosthetic hand based on evoked tactile sensation

The lack of reliable sensory feedback has been one of the barriers in prosthetic hand development. Restoring sensory function from prosthetic hand to amputee remains a great challenge to neural engineering. In this paper, we present the development of a sensory feedback system based on the phenomenon of evoked tactile sensation (ETS) at the stump skin of residual limb induced by transcutaneous electrical nerve stimulation (TENS). The system could map a dynamic pattern of stimuli to an electrode placed on the corresponding projected finger areas on the stump skin. A pressure transducer placed at the tip of prosthetic fingers was used to sense contact pressure, and a high performance DSP processor sampled pressure signals, and calculated the amplitude of feedback stimulation in real-time. Biphasic and charge-balanced current pulses with amplitude modulation generated by a multi-channel laboratory stimulator were delivered to activate sensory nerves beneath the skin. We tested this sensory feedback system in amputee subjects. Preliminary results showed that the subjects could perceive different levels of pressure at the tip of prosthetic finger through evoked tactile sensation (ETS) with distinct grades and modalities. We demonstrated the feasibility to restore the perceptual sensation from prosthetic fingers to amputee based on the phenomenon of evoked tactile sensation (ETS) with TENS.
Material Recognition : Bayesian Inference or SVMs ?

Material recognition is an important subtask in computer vision. In this paper, we aim for the identification of material categories from a single image captured under unknown illumination and view conditions. Therefore, we use several features which cover various aspects of material appearance and perform supervised classification using Support Vector Machines. We demonstrate the feasibility of our approach by testing on the challenging Flickr Material Database. Based on this dataset, we also carry out a comparison to a previously published work [Liu et al., ”Exploring Features in a Bayesian Framework for Material Recognition”, CVPR 2010] which uses Bayesian inference and reaches a recognition rate of 44.6% on this dataset and represents the current state-of the-art. With our SVM approach we obtain 53.1% and hence, significantly outperform this approach.
A magnetic climbing robot for steel bridge inspection

Corrosion can cause section loss or cracks in the steel members which is one of the most important causes of deterioration of steel bridges. For some critical components of a steel bridge, it is fatal and could even cause the collapse of the whole bridge. Nowadays the most common approach to steel bridge inspection is visual inspection by inspectors with inspection trucks. This paper mainly presents a climbing robot with magnetic wheels which can move on the surface of steel bridge. Experiment results shows that the climbing robot can move on the steel bridge freely without disrupting traffic to reduce the risks to the inspectors.
Solving a bi-objective multi-product vehicle routing problem with heterogeneous fleets under an uncertainty condition

This paper presents a novel bi-objective multi-product capacitated vehicle routing problem with uncertainty in demand of retailers and volume of products (UCVRP) and heterogeneous vehicle fleets. The first of two conflict fuzzy objective functions is to minimize the cost of the used vehicles, fuel consumption for full loaded vehicles and shortage of products. The second objective is to minimize the shortage of products for all retailers. In order to get closer to a real-world situation, the uncertainty in the demand of retailers is applied using fuzzy numbers. Additionally, the volume of products is applied using robust parameters, because the possible value of this parameter is not distinct and belongs to a bounded uncertainty set. The fuzzy-robust counterpart model may be larger than the deterministic form or the uncertain model with one approach and it has with further complexity; however, it provides a better efficient solution for this problem. The proposed fuzzy approach is used to solve the bi-objective mixed-integer linear problem to find the most preferred solution. Moreover, it is impossible to improve one of the objective functions without considering deterioration in the other objective functions. In order to show the conflict between two objective functions in an excellent fashion, a Pareto-optimal solution with the ε-constraint method is obtained. Some numerical test problems are used to demonstrate the efficiency and validity of the presented model.
Cerebral Microvascular Accumulation of Tau Oligomers in Alzheimer’s Disease and Related Tauopathies

The importance of vascular contributions to cognitive impairment and dementia (VCID) associated with Alzheimer's disease (AD) and related neurodegenerative diseases is increasingly recognized, however, the underlying mechanisms remain obscure. There is growing evidence that in addition to Aβ deposition, accumulation of hyperphosphorylated oligomeric tau contributes significantly to AD etiology. Tau oligomers are toxic and it has been suggested that they propagate in a "prion-like" fashion, inducing endogenous tau misfolding in cells. Their role in VCID, however, is not yet understood. The present study was designed to determine the severity of vascular deposition of oligomeric tau in the brain in patients with AD and related tauopathies, including dementia with Lewy bodies (DLB) and progressive supranuclear palsy (PSP). Further, we examined a potential link between vascular deposition of fibrillar Aβ and that of tau oligomers in the Tg2576 mouse model. We found that tau oligomers accumulate in cerebral microvasculature of human patients with AD and PSP, in association with vascular endothelial and smooth muscle cells. Cerebrovascular deposition of tau oligomers was also found in DLB patients. We also show that tau oligomers accumulate in cerebral microvasculature of Tg2576 mice, partially in association with cerebrovascular Aβ deposits. Thus, our findings add to the growing evidence for multifaceted microvascular involvement in the pathogenesis of AD and other neurodegenerative diseases. Accumulation of tau oligomers may represent a potential novel mechanism by which functional and structural integrity of the cerebral microvessels is compromised.
Effectiveness of animal-assisted therapy: A systematic review of randomized controlled trials.

The objectives of this review were to summarize the evidence from randomized controlled trials (RCTs) on the effects of animal-assisted therapy (AAT). Studies were eligible if they were RCTs. Studies included one treatment group in which AAT was applied. We searched the following databases from 1990 up to October 31, 2012: MEDLINE via PubMed, CINAHL, Web of Science, Ichushi Web, GHL, WPRIM, and PsycINFO. We also searched all Cochrane Database up to October 31, 2012. Eleven RCTs were identified, and seven studies were about "Mental and behavioral disorders". Types of animal intervention were dog, cat, dolphin, bird, cow, rabbit, ferret, and guinea pig. The RCTs conducted have been of relatively low quality. We could not perform meta-analysis because of heterogeneity. In a study environment limited to the people who like animals, AAT may be an effective treatment for mental and behavioral disorders such as depression, schizophrenia, and alcohol/drug addictions, and is based on a holistic approach through interaction with animals in nature. To most effectively assess the potential benefits for AAT, it will be important for further research to utilize and describe (1) RCT methodology when appropriate, (2) reasons for non-participation, (3) intervention dose, (4) adverse effects and withdrawals, and (5) cost.
Molecular epidemiology of African swine fever in East Africa

African swine fever (ASF) a lethal, viral hemorrhagic disease of domestic pigs, first reported from East Africa in 1921, is still widespread in this region. In order to assess field heterogeneity at the regional level, nucleotide sequences corresponding to the C-terminal end of the p72 gene were determined for 77 ASF viruses of diverse temporal and species origin occurring in eight East African countries. The number of sites completely conserved across all East African sequences characterized in this study was 84.2% and 86.8% on nucleotide and amino acid level, respectively. Phylogenetic analysis of a homologous 404 bp region revealed the presence of thirteen East African genotypes, of which eight appear to be country specific. An East African, pig-associated, homogeneous virus lineage linked to outbreaks in Mozambique, Zambia and Malawi over a 23 year period was demonstrated. In addition, genotype I (ESACWA) viruses were identified in East African sylvatic hosts for the first time which is significant as this genotype was previously thought to be restricted to the West African region where it occurs only in domestic pigs. The presence of discrete epidemiological cycles in East Africa and recovery of multiple genotypes affirms the epidemiological complexity of ASF in this region.
Model predictive control of a multi-rotor with a slung load for avoiding obstacles

This paper addresses the control problem of transportation of a slung load by a multi-rotor. Control of the slung load system cannot be achieved with usual controllers for multi-rotors because motion of the load is highly coupled with the multi-rotor. Although many controllers have been developed for the system, there still exist several problems. For instance, in trajectory-following control, many previous works make trajectory beforehand, and perform trajectory-following control afterward. Since the time spent to generate trajectories is too long for real-time control applications, alternative efficient algorithms are required. With this in mind, we adopt a fast nonlinear Model Predictive Control (MPC) algorithm. This control framework solves a nonlinear optimal control problem with a finite time horizon using Sequential Linear Quadratic (SLQ) solver, in MPC. The originally proposed work optimizes the implementation time and successfully conducts real-time waypoint flight of general multi-rotor. Here, we apply the framework to the slung load system. Also, an obstacle-avoidance algorithm suitable for the slung load system is developed for cluttered environments. Numerical simulations are conducted to validate the developed method.
Earthquake-induced loss assessment of steel frame buildings with special moment frames designed in highly seismic regions

This paper discusses an analytical study that quantifies the expected earthquake-induced losses in typical office steel frame buildings designed with perimeter special moment frames in highly seismic regions. It is shown that for seismic events associated with low probabilities of occurrence, losses due to demolition and collapse may be significantly overestimated when the expected loss computations are based on analytical models that ignore the composite beam effects and the interior gravity framing system of a steel frame building. For frequently occurring seismic events building losses are dominated by non-structural content repairs. In this case, the choice of the analytical model representation of the steel frame building becomes less important. Losses due to demolition and collapse in steel frame buildings with special moment frames designed with strong-column/weak-beam ratio larger than 2.0 are reduced by a factor of two compared with those in the same frames designed with a strong-column/weak-beam ratio larger than 1.0 as recommended in ANSI/AISC-341-10. The expected annual losses (EALs) of steel frame buildings with SMFs vary from 0.38% to 0.74% over the building life expectancy. The EALs are dominated by repairs of accelerationsensitive non-structural content followed by repairs of drift-sensitive non-structural components. It is found that the effect of strong-column/weak-beam ratio on EALs is negligible. This is not the case when the present value of life-cycle costs is selected as a loss-metric. It is advisable to employ a combination of loss-metrics to assess the earthquake-induced losses in steel frame buildings with special moment frames depending on the seismic performance level of interest.
The risk for a cruciate ligament injury of the knee in adolescents and young adults: a population-based cohort study of 46 500 people with a 9 year follow-up.

The knee joint is the most common site for injury among younger people, the injury often resulting in expensive (surgical) treatment, long-term rehabilitation and permanent functional impairment and disability.



We investigated the incidence and risk factors for a major knee-ligament injury in an adolescent Finnish population. A population-based cohort of 46 472 adolescents was followed for an average of 9 years. All patients hospitalised with the diagnosis of anterior or posterior cruciate ligament injury (ACL or PCL injury) were included in the analysis.



265 (0.6%) people (194 male and 71 female subjects) from the total cohort of 46 472 were treated for a cruciate ligament injury of the knee during the follow-up period, giving an injury incidence of 60.9 (95% CI 53.6 to 68.2) per 100 000 person-years. When the socioeconomic, health and lifestyle background variables were taken into account, the adjusted hazard ratio for a cruciate ligament injury of the knee was 8.5 (95% CI 4.3 to 16.4) for female and 4.0 (95% CI 2.7 to 6.1) for male subjects who participated in organised sports > or =4 times/week.



The general risk for a cruciate ligament injury of the knee is relatively low among adolescents and young adults, but participation in organised sports increases the risk significantly. The risk is especially high in active young women. Preventive measures should be adopted to decrease the short-term and long-term burden of these severe injuries.
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

In this paper, we consider first-order methods for solving stochastic non-convex optimization problems. The key building block of the proposed algorithms is firstorder procedures to extract negative curvature from the Hessian matrix through a principled sequence starting from noise, which are referred to NEgative-curvatureOriginated-from-Noise or NEON and are of independent interest. Based on this building block, we design purely first-order stochastic algorithms for escaping from non-degenerate saddle points with a much better time complexity (almost linear time in the problem’s dimensionality) under a bounded variance condition of stochastic gradients than previous first-order stochastic algorithms. In particular, we develop a general framework of first-order stochastic algorithms with a secondorder convergence guarantee based on our new technique and existing algorithms that may only converge to a first-order stationary point. For finding a nearly second-order stationary point x such that ‖∇F (x)‖ ≤ and ∇F (x) ≥ − √ I (in high probability), the best time complexity of the presented algorithms is Õ(d/ ), where F (·) denotes the objective function and d is the dimensionality of the problem. To the best of our knowledge, this is the first theoretical result of first-order stochastic algorithms with an almost linear time in terms of problem’s dimensionality for finding second-order stationary points, which is even competitive with existing stochastic algorithms hinging on the second-order information.
Estimation of heart rate from foot worn photoplethysmography sensors during fast bike exercise

This paper presents a new method for estimating the average heart rate from a foot/ankle worn photoplethysmography (PPG) sensor during fast bike activity. Placing the PPG sensor on the lower half of the body allows more energy to be collected from energy harvesting in order to give a power autonomous sensor node, but comes at the cost of introducing significant motion interference into the PPG trace. We present a normalised least mean square adaptive filter and short-time Fourier transform based algorithm for estimating heart rate in the presence of this motion contamination. Results from 8 subjects show the new algorithm has an average error of 9 beats-per-minute when compared to an ECG gold standard.
Bayesian Paragraph Vectors

Word2vec (Mikolov et al., 2013b) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such as documents, paragraphs, and sentences. In this work, we propose a novel interpretation for neural-network-based paragraph vectors by developing an unsupervised generative model whose maximum likelihood solution corresponds to traditional paragraph vectors. This probabilistic formulation allows us to go beyond point estimates of parameters and to perform Bayesian posterior inference. We find that the entropy of paragraph vectors decreases with the length of documents, and that information about posterior uncertainty improves performance in supervised learning tasks such as sentiment analysis and paraphrase detection.
Learning Facial Expressions with 3D Mesh Convolutional Neural Network

Making machines understand human expressions enables various useful applications in human-machine interaction. In this article, we present a novel facial expression recognition approach with 3D Mesh Convolutional Neural Networks (3DMCNN) and a visual analytics-guided 3DMCNN design and optimization scheme. From an RGBD camera, we first reconstruct a 3D face model of a subject with facial expressions and then compute the geometric properties of the surface. Instead of using regular Convolutional Neural Networks (CNNs) to learn intensities of the facial images, we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We design a geodesic distance-based convolution method to overcome the difficulties raised from the irregular sampling of the face surface mesh. We further present interactive visual analytics for the purpose of designing and modifying the networks to analyze the learned features and cluster similar nodes in 3DMCNN. By removing low-activity nodes in the network, the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and analyze the effectiveness of our method by studying representative cases. Testing on public datasets, our method achieves a higher recognition accuracy than traditional image-based CNN and other 3D CNNs. The proposed framework, including 3DMCNN and interactive visual analytics of the CNN, can be extended to other applications.
Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination

This paper presents a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate the problem as pairwise (who's better?) and overall (who's best?) ranking of video collections, using supervised deep ranking. We propose a novel loss function that learns discriminative features when a pair of videos exhibit variance in skill, and learns shared features when a pair of videos exhibit comparable skill levels. Results demonstrate our method is applicable across tasks, with the percentage of correctly ordered pairs of videos ranging from 70% to 83% for four datasets. We demonstrate the robustness of our approach via sensitivity analysis of its parameters. We see this work as effort toward the automated organization of how-to video collections and overall, generic skill determination in video.
The Challenges of Global-Scale Data Management

Global-scale data management (GSDM) empowers systems by providing higher levels of fault-tolerance, read availability, and efficiency in utilizing cloud resources. This has led to the emergence of global-scale data management and event processing. However, the Wide-Area Network (WAN) latency separating data is orders of magnitude larger than conventional network latencies, and this requires a reevaluation of many of the traditional design trade-offs of data management systems. Therefore, data management problems must be revisited to account for the new design space. In this tutorial we survey recent developments in GSDM focusing on identifying fundamental challenges and advancements in addition to open research opportunities.
A Finger Exoskeleton Robot for Finger Movement Rehabilitation

In this study, a finger exoskeleton robot has been designed and presented. The prototype device was designed to be worn on the dorsal side of the hand to assist in the movement and rehabilitation of the fingers. The finger exoskeleton is 3D-printed to be low-cost and has a transmission mechanism consisting of rigid serial links which is actuated by a stepper motor. The actuation of the robotic finger is by a sliding motion and mimics the movement of the human finger. To make it possible for the patient to use the rehabilitation device anywhere and anytime, an ArduinoTM control board and a speech recognition board were used to allow voice control. As the robotic finger follows the patients voice commands the actual motion is analyzed by Tracker image analysis software. The finger exoskeleton is designed to flex and extend the fingers, and has a rotation range of motion (ROM) of 44.2◦.
Coarse-to-fine optical flow estimation with image structure tensor

This paper proposes a novel coarse-to-fine optical flow method based on the image local structure tensor. The data term of the optical flow model is projected by combining the proposed structure tensor constancy assumption and the grey value constancy assumption with the robust penalty function, and an isotropic nonlinear function is planned to smoothing term. With the coarse-to-fine multiscale warping strategy, a linear iteration scheme is established. Several experimental results of the Middlebury sequences show that the presented method is high accuracy and good robustness.
Segmenting Time Series: A Survey and Novel Approach

In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature.