nils-herrmann/clean_scirepeval huggingface dataset:
The processing-speed theory of adult age differences in cognition.

A theory is proposed to account for some of the age-related differences reported in measures of Type A or fluid cognition. The central hypothesis in the theory is that increased age in adulthood is associated with a decrease in the speed with which many processing operations can be executed and that this reduction in speed leads to impairments in cognitive functioning because of what are termed the limited time mechanism and the simultaneity mechanism. That is, cognitive performance is degraded when processing is slow because relevant operations cannot be successfully executed (limited time) and because the products of early processing may no longer be available when later processing is complete (simultaneity). Several types of evidence, such as the discovery of considerable shared age-related variance across various measures of speed and large attenuation of the age-related influences on cognitive measures after statistical control of measures of speed, are consistent with this theory.
A text processing pipeline to extract recommendations from radiology reports

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology recommendations is an important barrier to ensuring timely follow-up of patients especially with non-acute incidental findings on imaging examinations. In this paper, we present a text processing pipeline to automatically identify clinically important recommendation sentences in radiology reports. Our extraction pipeline is based on natural language processing (NLP) and supervised text classification methods. To develop and test the pipeline, we created a corpus of 800 radiology reports double annotated for recommendation sentences by a radiologist and an internist. We ran several experiments to measure the impact of different feature types and the data imbalance between positive and negative recommendation sentences. Our fully statistical approach achieved the best f-score 0.758 in identifying the critical recommendation sentences in radiology reports.
Music transcription modelling and composition using deep learning

We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition. We build and train LSTM networks using approximately 23,000 music transcriptions expressed with a high-level vocabulary (ABC notation), and use them to generate new transcriptions. Our practical aim is to create music transcription models useful in particular contexts of music composition. We present results from three perspectives: 1) at the population level, comparing descriptive statistics of the set of training transcriptions and generated transcriptions; 2) at the individual level, examining how a generated transcription reflects the conventions of a music practice in the training transcriptions (Celtic folk); 3) at the application level, using the system for idea generation in music composition. We make our datasets, software and sound examples open and available: https://github.com/IraKorshunova/folk-rnn.
Mesh-based inverse kinematics

The ability to position a small subset of mesh vertices and produce a meaningful overall deformation of the entire mesh is a fundamental task in mesh editing and animation. However, the class of meaningful deformations varies from mesh to mesh and depends on mesh kinematics, which prescribes valid mesh configurations, and a selection mechanism for choosing among them. Drawing an analogy to the traditional use of skeleton-based inverse kinematics for posing skeletons. we define mesh-based inverse kinematics as the problem of finding meaningful mesh deformations that meet specified vertex constraints.Our solution relies on example meshes to indicate the class of meaningful deformations. Each example is represented with a feature vector of deformation gradients that capture the affine transformations which individual triangles undergo relative to a reference pose. To pose a mesh, our algorithm efficiently searches among all meshes with specified vertex positions to find the one that is closest to some pose in a nonlinear span of the example feature vectors. Since the search is not restricted to the span of example shapes, this produces compelling deformations even when the constraints require poses that are different from those observed in the examples. Furthermore, because the span is formed by a nonlinear blend of the example feature vectors, the blending component of our system may also be used independently to pose meshes by specifying blending weights or to compute multi-way morph sequences.
Factors Influencing the Adoption of Cloud Computing by Saudi University Hospitals

This study aims to evaluate the adoption of Cloud Computing in Saudi university hospitals and to investigate the factors that impact the adoption. This study integrates the Technological, Organizational, Environmental (TOE) framework and the Diffusion of Innovation (DOI) theory, and adds the decision maker context to the original model. The study sample included Saudi university hospitals in Riyadh city. The data were collected using semi-structured interviews and a questionnaire. The result of this study determines the five most significant factors influencing the adoption of cloud computing in Saudi university hospitals, which are in sequence: Relative advantage, Decision-maker's innovativeness, Decision-maker's knowledge in IT, Compatibility, and Top management support. Moreover, among the four different contexts, the most important context is the Decision-maker context, followed by the Technological context, then the Organizational context, and finally the Environmental context. The findings are beneficial for hospitals to guide them to make better decisions regarding cloud computing adoption. Scholars can use this study to gain a more holistic understanding of cloud computing adoption and apply new theories in this field. cloud computing; (TOE) framework; (DOI) theory; technological innovation; IT adoption; healthcare; Saudi hospitals
Learning to Relate Images

A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. Recently, there has been increasing interest in learning to infer correspondences from data using relational, spatiotemporal, and bilinear variants of deep learning methods. These methods use multiplicative interactions between pixels or between features to represent correlation patterns across multiple images. In this paper, we review the recent work on relational feature learning, and we provide an analysis of the role that multiplicative interactions play in learning to encode relations. We also discuss how square-pooling and complex cell models can be viewed as a way to represent multiplicative interactions and thereby as a way to encode relations.
Stitching Web Tables for Improving Matching Quality

HTML tables on web pages (“web tables”) cover a wide variety of topics. Data from web tables can thus be useful for tasks such as knowledge base completion or ad hoc table extension. Before table data can be used for these tasks, the tables must be matched to the respective knowledge base or base table. The challenges of web table matching are the high heterogeneity and the small size of the tables. Though it is known that the majority of web tables are very small, the gold standards that are used to compare web table matching systems mostly consist of larger tables. In this experimental paper, we evaluate T2K Match, a web table to knowledge base matching system, and COMA, a standard schema matching tool, using a sample of web tables that is more realistic than the gold standards that were previously used. We find that both systems fail to produce correct results for many of the very small tables in the sample. As a remedy, we propose to stitch (combine) the tables from each web site into larger ones and match these enlarged tables to the knowledge base or base table afterwards. For this stitching process, we evaluate different schema matching methods in combination with holistic correspondence refinement. Limiting the stitching procedure to web tables from the same web site decreases the heterogeneity and allows us to stitch tables with very high precision. Our experiments show that applying table stitching before running the actual matching method improves the matching results by 0.38 in F1-measure for T2K Match and by 0.14 for COMA. Also, stitching the tables allows us to reduce the amount of tables in our corpus from 5 million original web tables to as few as 100,000 stitched tables.
Improvements in muscle symmetry in children with cerebral palsy after equine-assisted therapy (hippotherapy).

To evaluate the effect of hippotherapy (physical therapy utilizing the movement of a horse) on muscle activity in children with spastic cerebral palsy.



Pretest/post-test control group.



Therapeutic Riding of Tucson (TROT), Tucson, AZ.



Fifteen (15) children ranging from 4 to 12 years of age diagnosed with spastic cerebral palsy.



Children meeting inclusion criteria were randomized to either 8 minutes of hippotherapy or 8 minutes astride a stationary barrel.



Remote surface electromyography (EMG) was used to measure muscle activity of the trunk and upper legs during sitting, standing, and walking tasks before and after each intervention.



After hippotherapy, significant improvement in symmetry of muscle activity was noted in those muscle groups displaying the highest asymmetry prior to hippotherapy. No significant change was noted after sitting astride a barrel.



Eight minutes of hippotherapy, but not stationary sitting astride a barrel, resulted in improved symmetry in muscle activity in children with spastic cerebral palsy. These results suggest that the movement of the horse rather than passive stretching accounts for the measured improvements.
Distributed practice in verbal recall tasks: A review and quantitative synthesis.

The authors performed a meta-analysis of the distributed practice effect to illuminate the effects of temporal variables that have been neglected in previous reviews. This review found 839 assessments of distributed practice in 317 experiments located in 184 articles. Effects of spacing (consecutive massed presentations vs. spaced learning episodes) and lag (less spaced vs. more spaced learning episodes) were examined, as were expanding interstudy interval (ISI) effects. Analyses suggest that ISI and retention interval operate jointly to affect final-test retention; specifically, the ISI producing maximal retention increased as retention interval increased. Areas needing future research and theoretical implications are discussed.
Public opinion on automated driving : Results of an international questionnaire among 5000 respondents

This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between q = 0.80 and q = 0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.
Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives

The emergence of mobile application software (App) has explosively grown in conjunction with the worldwide use of smartphones in recent years. Among numerous categories of mobile Apps, social Apps were one of those with the greatest growth in 2013. Despite abundant research on users’ behavior intention of mobile App usage, few studies have focused on investigating key determinants of users’ continuance intention regarding social Apps. To fill this gap, we integrated customer value perspectives to explore the influential factors in the continuance intention of social App use. Moreover, users’ satisfaction and habit from both the marketing and psychology literature were also incorporated into the research model. A total of 378 valid questionnaires were collected by survey method, and structural equation modeling was employed in the subsequent data analysis. The results indicate that the continuance usage of social Apps is driven by users’ satisfaction, tight connection with others, and hedonic motivation to use the Apps. In addition, full mediation effects of satisfaction and habit were found between perceived usefulness and intention to continue use. These findings extend our understanding of users’ continuance intention in the context of social Apps. Discussion and implications are provided.
Comparative analysis of RADAR-IR sensor fusion methods for object detection

This paper presents the Radar and IR sensor fusion method for objection detection. The infrared camera parameter calibration with Levenberg-Marquardt (LM) optimization method is proposed based on the Radar ranging data represented by Cartesian coordinate compared with 6 fusion methods. The proposed method firstly performs the estimation of the intrinsic parameter matrix of infrared camera with some optical trick. Then the method searches the extrinsic parameters using the generative approach. The initial angle and translation of the extrinsic parameters are optimized by the LM method with the geometrical cost function. In the experiments, the performance of proposed method outperforms by a maximum 13 times the performance of the other baseline methods on the averaged Euclidian distance error. In future work, the angular noise of the Radar information will be improved and the proposed method will provide the effective proposals for the deep neural network.
Semi-Supervised Text Classification Using EM

For several decades, statisticians have advocated using a c ombination of labeled and unlabeled data to train classifiers by estimating parameters o f a generative model through iterative Expectation-Maximization (EM) techniques. Thi s chapter explores the effectiveness of this approach when applied to the domain of text class ification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This model is an ext remely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classifi cat on with generative models. First, despite the simplistic representation, some text do mains have a high positive correlation between generative model probability and classifica tion accuracy. In these domains, a straightforward application of EM with the naive Bayes tex t model works well. Second, some text domains do not have this correlation. Here we can ad opt a more expressive and appropriate generative model that does have a positive c orrelation. In these domains, semi-supervised learning again improves classification ac curacy. Finally, EM suffers from the problem of local maxima, especially in high dimension do mains such as text classification. We demonstrate that deterministic annealing, a varia nt of EM, can help overcome the problem of local maxima and increase classification accurac y further when the generative model is appropriate.
Neural mechanisms of ageing and cognitive decline

During the past century, treatments for the diseases of youth and middle age have helped raise life expectancy significantly. However, cognitive decline has emerged as one of the greatest health threats of old age, with nearly 50% of adults over the age of 85 afflicted with Alzheimer's disease. Developing therapeutic interventions for such conditions demands a greater understanding of the processes underlying normal and pathological brain ageing. Recent advances in the biology of ageing in model organisms, together with molecular and systems-level studies of the brain, are beginning to shed light on these mechanisms and their potential roles in cognitive decline.
Extracting news content with visual unit of web pages

The Document Object Model (DOM) provides a tree structure called DOM tree for representing with objects in HTML. Many researchers have considered using leaf nodes of DOM tree as basic objects in extracting information from web pages. However, web pages are more of information blocks which each have a consistent visual format rather than individual DOM tree nodes. And those information blocks do not necessarily have a direct map to DOM tree nodes. In this paper, we propose a visual oriented extraction method that extracts news content by visual unit (vu, for short). Visual units are identified by a top-down approach based on visual features and text features. After that, page content is extracted according to domain characteristic. In experiments, the proposed approach achieves 94.86% accuracy over 700 news web pages from 7 different news sites. The result demonstrates that our method represents a promising approach for news content extraction with visual units and domain characteristic.
Electromagnetic energy harvesting from train induced railway track vibrations

Anelectromagnetic energy harvester is designed to harness the vibrational power from railroad track deflections due to passing trains. Whereas typical existing vibration energy harvester technologies are built for low power applications of milliwatts range, the proposed harvester will be designed for higher power applications for major track-side equipment such as warning signals, switches, and health monitoring sensors, which typically require a power supply of 10 Watts or more. To achieve this goal, we implement a new patent pending motion conversion mechanism which converts irregular pulse-like bidirectional linear vibration into regulated unidirectional rotational motion. Features of the motion mechanism include bidirectional to unidirectional conversion and flywheel speed regulation, with advantages of improved reliability, efficiency, and quality of output power. It also allows production of DC power directly from bidirectional vibration without electronic diodes. Preliminary harvester prototype testing results illustrate the features and benefits of the proposed motion mechanism, showing reduction of continual system loading, regulation of generator speed, and capability for continuous DC power generation.
A social-cognitive model of trait and state levels of gratitude.

Three studies tested a new model of gratitude, which specified the generative mechanisms linking individual differences (trait gratitude) and objective situations with the amount of gratitude people experience after receiving aid (state gratitude). In Study 1, all participants (N = 253) read identical vignettes describing a situation in which they received help. People higher in trait gratitude made more positive beneficial appraisals (seeing the help as more valuable, more costly to provide, and more altruistically intended), which fully mediated the relationship between trait and state levels of gratitude. Study 2 (N = 113) replicated the findings using a daily process study in which participants reported on real events each day for up to 14 days. In Study 3, participants (N = 200) read vignettes experimentally manipulating objective situations to be either high or low in benefit. Benefit appraisals were shown to have a causal effect on state gratitude and to mediate the relationship between different prosocial situations and state gratitude. The 3 studies demonstrate the critical role of benefit appraisals in linking state gratitude with trait gratitude and the objective situation.
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator

Triangle is a C program for two-dimensional mesh generation and construction of Delaunay triangulations, constrained Delaunay triangulations, and Vorono ı̈ diagrams. Triangle is fast, memory-efficient, and robust; it computes Delaunay triangulations and constrained Delaunay triangulations exactly. Guaranteed-quality meshes (having no small angles) are generated using Ruppert’s Delaunay refinement algorithm. Features include user-specified constraints on angles and triangle areas, user-specified holes and concavities, and the economical use of exact arithmetic to improve robustness. Triangle is freely available on the Web at “http://www.cs.cmu.edu/ quake/triangle.html” and from Netlib. This paper discusses many of the key implementation decisions, including the choice of triangulation algorithms and data structures, the steps taken to create and refine a mesh, a number of issues that arise in Ruppert’s algorithm, and the use of exact arithmetic.
An Unsupervised Clustering Tool for Unstructured Data

We present an unsupervised clustering tool, Principal Direction Divisive Partitioning, which is a scal-able and versatile top-down method applicable to any set of data that can be represented as numerical vectors. A description of the basic method, a summary of the main application areas where this has been used, and some recent results on the selection of signiicant words as well as the process of updating clusters as new data arrives is discussed.
Evolving Graphs and Networks with Edge Encoding: Preliminary Report

We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge encoding, uses edge operators rather than the node operators of cellular encoding. While both cellular encoding and edge encoding can produce all possible graphs, the two encodings bias the genetic search process in different ways; each may therefore be most useful for a different set of problems. The problems for which these techniques may be used, and for which we think edge encoding may be particularly useful, include the evolution of recurrent neural networks, finite automata, and graph-based queries to symbolic knowledge bases. In this preliminary report we present a technical description of edge encoding and an initial comparison to cellular encoding. Experimental investigation of the relative merits of these encoding schemes is currently in progress.