nils-herrmann/clean_scirepeval huggingface dataset:
The Visual Analogue Scale for Rating, Ranking and Paired-Comparison (VAS-RRP): A new technique for psychological measurement

Traditionally, the visual analogue scale (VAS) has been proposed to overcome the limitations of ordinal measures from Likert-type scales. However, the function of VASs to overcome the limitations of response styles to Likert-type scales has not yet been addressed. Previous research using ranking and paired comparisons to compensate for the response styles of Likert-type scales has suffered from limitations, such as that the total score of ipsative measures is a constant that cannot be analyzed by means of many common statistical techniques. In this study we propose a new scale, called the Visual Analogue Scale for Rating, Ranking, and Paired-Comparison (VAS-RRP), which can be used to collect rating, ranking, and paired-comparison data simultaneously, while avoiding the limitations of each of these data collection methods. The characteristics, use, and analytic method of VAS-RRPs, as well as how they overcome the disadvantages of Likert-type scales, ranking, and VASs, are discussed. On the basis of analyses of simulated and empirical data, this study showed that VAS-RRPs improved reliability, response style bias, and parameter recovery. Finally, we have also designed a VAS-RRP Generator for researchers' construction and administration of their own VAS-RRPs.
Facial Expression Recognition Using Enhanced Deep 3 D Convolutional Neural Networks

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.
Comprehending conventional and novel metaphors: An ERP study

The neural mechanisms underlying the processing of conventional and novel conceptual metaphorical sentences were examined with event-related potentials (ERPs). Conventional metaphors were created based on the Contemporary Theory of Metaphor and were operationally defined as familiar and readily interpretable. Novel metaphors were unfamiliar and harder to interpret. Using a sensicality judgment task, we compared ERPs elicited by the same target word when it was used to end anomalous, novel metaphorical, conventional metaphorical and literal sentences. Amplitudes of the N400 ERP component (320-440 ms) were more negative for anomalous sentences, novel metaphors, and conventional metaphors compared with literal sentences. Within a later window (440-560 ms), ERPs associated with conventional metaphors converged to the same level as literal sentences while the novel metaphors stayed anomalous throughout. The reported results were compatible with models assuming an initial stage for metaphor mappings from one concept to another and that these mappings are cognitively taxing.
Materials for Enabling Hands-On Robotics and STEM Education

We describe our approach to enabling hands-on experiential robotics for all ages through the introduction of a robot programming workbook and robot test-bed. We describe the vision and motivation for the project, and then the details of the robot hardware, software, and the accompanying workbook and textbook materials. Together, these resources are aimed at providing free, detailed, and readily accessible materials to K-12 and university educators and students for direct immersion in hands-on robotics.  It has long been recognized that experiential, hands-on education provides superior motivation for learning new material, by providing real-world meaning to the otherwise abstract knowledge. Robotics has been shown to be a superb tool for hands-on learning, not only of robotics itself, but of general topics in science, technology, engineering, and math (STEM). Given the current shortage of student interest in STEM topics, and an undersupply of a trained workforce in those areas, increasing attention has been paid to developing innovative tools for improved teaching of STEM, including through robotics. A broad spectrum of avenues for pursuing robotics at the pre-university level exists; some of the most visible are FIRST (2) LEGO Mindstorms (5), and Botball (1). Increasingly, high schools across the country are providing elective robotics courses as well as after-school programs. Gradually, middle schools are starting to get involved as well. However, significant barriers still exist to making robotics accessible to pre-university students and teachers. In this paper, we describe our vision for making robotics accessible to all pre-university K-12 ages, as well as the materials and tools we are developing toward making that vision possible.
Graph Visualization and Navigation in Information Visualization: A Survey

ÐThis is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, state-transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective. Index TermsÐInformation visualization, graph visualization, graph drawing, navigation, focus+context, fish-eye, clustering.
Alibaba-Venus at ActivityNet Challenge 2018-Task C Trimmed Event Recognition ( Moments in Time )

In this paper, we present a solution to Moments in Time (MIT)  Challenge. Current methods for trimmed video recognition often utilize inflated 3D (I3D)  to capture spatial-temporal features. First, we explore off-the-shelf structures like non-local , I3D, TRN [4] and their variants. After a plenty of experiments, we find that for MIT, a strong 2D convolution backbone following temporal relation network performs better than I3D network. We then add attention module based on TRN to learn a weight for each relation so that the model can capture the important moment better. We also design uniform sampling over videos and relation restriction policy to further enhance testing performance.
Impact of 12h shift patterns in nursing: a scoping review.

To provide a comprehensive scoping review of evidence of the impact and effectiveness of 12h shifts in the international nursing literature, supplemented by a review of evidence in other, non-nursing related industries.



A search of the academic literature was undertaken in electronic databases (AMED, MEDLINE, CINAHL, PsychInfo, Scopus, HMIC, the Cochrane Library, Business Source Premier, Econ Lit, ASSIA and Social Policy and Practice).



A total of 158 potentially relevant nursing research papers and reviews were published between 1973 and 2014. Two reviewers independently reviewed the articles, leaving 85 primary research studies and 10 review papers in the nursing field to be included in the scoping review. Thirty-one relevant primary research papers and reviews were also identified in the non-nursing related industries literature.



Research into 12h nursing shifts fell within five broad themes: 'risks to patients', 'patient experience', 'risks to staff', 'staff experience' and 'impact on the organisation of work'. There was inconclusive evidence of the effects of 12h shift patterns in all five themes, with some studies demonstrating positive impacts and others negative or no impacts. This also mirrors the evidence in other, non-nursing related industries. The quality of research reviewed is generally weak and most studies focus on the risks, experience and work/life balance for staff, with few addressing the impact on patient outcomes and experience of care or work productivity.



There is insufficient evidence to justify the widespread implementation or withdrawal of 12h shifts in nursing. It is not clearly understood where there are real benefits and where there are real and unacceptable risks to patients and staff. More research focusing on the impact of 12h nursing shifts on patient safety and experience of care and on the long term impact on staff and work organisation is required.
The Biomechanics of Jumping

The main task for any level of coach is to construct a training program that will ensure continual progression of an athlete whilst avoiding injury. This is a particularly challenging task with athletes who have had several training years behind them. According to the principles of training, to ensure adaptation, overload in the form of manipulating frequency, volume and intensity must be applied. Furthermore, training exercises must be specific to the target task to ensure a carry over effect. Biomechanics is a sports science sub-discipline which is able to quantify the potential effect of training exercises, rather than leaving it to the coaches "gut feel".
The AGI Containment Problem

There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem – the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed.
Blockchain in government: Benefits and implications of distributed ledger technology for information sharing

Blockchain refers to a range of general purpose technologies to exchange information and transact digital assets in distributed networks. The core question addressed in this paper is whether blockchain technology will lead to innovation and transformation of governmental processes. To address this question we present a critical assessment of the often exaggerated benefits of blockchain technology found in the literature and discuss their implications for governmental organizations and processes. We plea for a shift from a technology-driven to needdriven approach in which blockchain applications are customized to ensure a fit with requirements of administrative processes and in which the administrative processes are changed to benefit from the technology. Having sound governance models are found to be a condition for realizing benefits. Based on a critical assessment we offer directions for further research into the potential benefits of BC applications in e-government and the role of governance of BC architectures and applications to comply with societal needs and public values.
Watching Subtitled Films Can Help Learning Foreign Languages

Watching English-spoken films with subtitles is becoming increasingly popular throughout the world. One reason for this trend is the assumption that perceptual learning of the sounds of a foreign language, English, will improve perception skills in non-English speakers. Yet, solid proof for this is scarce. In order to test the potential learning effects derived from watching subtitled media, a group of intermediate Spanish students of English as a foreign language watched a 1h-long episode of a TV drama in its original English version, with English, Spanish or no subtitles overlaid. Before and after the viewing, participants took a listening and vocabulary test to evaluate their speech perception and vocabulary acquisition in English, plus a final plot comprehension test. The results of the listening skills tests revealed that after watching the English subtitled version, participants improved these skills significantly more than after watching the Spanish subtitled or no-subtitles versions. The vocabulary test showed no reliable differences between subtitled conditions. Finally, as one could expect, plot comprehension was best under native, Spanish subtitles. These learning effects with just 1 hour exposure might have major implications with longer exposure times.
Computational Higher Type Theory I: Abstract Cubical Realizability

The goal of this work is to develop a computation-based account of higher-dimensional type theory for which canonicity at observable types is true by construction. Types are considered as descriptions of the computational behavior of terms, rather than as formal syntax to which meaning is attached separately. Types are structured as collections of terms of each finite dimension. At dimension zero the terms of a type are its ordinary members; at higher dimension terms are lines between terms of the next lower dimension. The terms of each dimension satisfy coherence conditions ensuring that the terms may be seen as abstract cubes. Each line is to be interpreted as an identification of two cubes in that it provides evidence for their exchangeability in all contexts. It is required that there be sufficiently many lines that this interpretation is tenable. For example, lines must be reversible and closed under concatenation, so that the identifications present the structure of a pre-groupoid. Moreover, there must be further lines witnessing the unit, inverse, and associativity laws of concatention, the structure of an ∞-groupoid. In this paper we give a “meaning explanation” of a computational higher type theory in the style of Martin-Löf and of Constable and Allen, et al.  Such an explanation starts with a dimension-stratified collection of terms endowed with a deterministic operational semantics defining what it means to evaluate closed terms of any dimension to canonical form. The dimension of a term is the finite set of dimension names it contains; these dimension names may be thought of as variables ranging over an abstract interval, in which case terms may be thought of as tracing out lines in a type. The end points, 0 and 1, of the interval may be substituted to obtain the end points of such lines. Dimension names may be substituted for one another without restriction, allowing dimensions to be renamed, identified, or duplicated. The semantics of types is given by specifying, at each dimension, when canonical elements are equal, when general elements are equal, and when these
Repurchase intention in B2C e-commerce - A relationship quality perspective

Information systems professionals must pay attention to online customer retention. Drawing on the relationship marketing literature, we formulated and tested a model to explain B2C user repurchase intention from the perspective of relationship quality. The model was empirically tested through a survey conducted in Northern Ireland. Results showed that online relationship quality and perceived website usability positively impacted customer repurchase intention. Moreover, online relationship quality was positively influenced by perceived vendor expertise in order fulfillment, perceived vendor reputation, and perceived website usability, whereas distrust in vendor behavior negatively influenced online relationship quality. Implications of these findings are discussed.  This work was partially supported by Strategic Research Grant at City University of Hong Kong, China (No. CityU 7002521), and the National Nature Science Foundation of China (No. 70773008).  Corresponding author at: P7722, City University of Hong Kong, Hong Kong, China. Tel.: +852 27887492; fax: +852 34420370. E-mail address: ylfang@cityu.edu.hk (Y. Fang).
Studies on the diversity of the distinct phylogenetic lineage encompassing Glomus claroideum and Glomus etunicatum

Morphological and molecular characters were analysed to investigate diversity within isolates of the Glomus claroideum/Glomus etunicatum species group in the genus Glomus. The inter- and intra-isolate sequence diversity of the large subunit (LSU) rRNA gene D2 region of eight isolates of G. claroideum and G. etunicatum was studied using PCR-single strand conformational polymorphism (SSCP)-sequencing. In addition, two isolates recently obtained from Southern China were included in the analysis to allow for a wider geographic screening. Single spore DNA isolation confirmed the magnitude of gene diversity found in multispore DNA extractions. An apparent overlap of spore morphological characters was found between G. claroideum and G. etunicatum in some isolates. Analysis of the sequence frequencies in all G. etunicatum and G. claroideum isolates (ten) showed that four LSU D2 sequences, representing 32.1% of the clones analysed for multispore extraction (564) were found to be common to both species, and those sequences were the most abundant in four of the ten isolates analysed. The frequency of these sequences ranged between 23.2% and 87.5% of the clones analysed in each isolate. The implications for the use of phenotypic characters to define species in arbuscular mycorrhizal fungi are discussed. The current position of G. claroideum/G.etunicatum in the taxonomy of the Glomeromycota is also discussed.
Faster Kernel Ridge Regression Using Sketching and Preconditioning

Kernel Ridge Regression (KRR) is a simple yet powerful technique for non-parametric regression whose computation amounts to solving a linear system. This system is usually dense and highly illconditioned. In addition, the dimensions of the matrix are the same as the number of data points, so direct methods are unrealistic for large-scale datasets. In this paper, we propose a preconditioning technique for accelerating the solution of the aforementioned linear system. The preconditioner is based on random feature maps, such as random Fourier features, which have recently emerged as a powerful technique for speeding up and scaling the training of kernel-based methods, such as kernel ridge regression, by resorting to approximations. However, random feature maps only provide crude approximations to the kernel function, so delivering state-of-the-art results by directly solving the approximated system requires the number of random features to be very large. We show that random feature maps can be much more effective in forming preconditioners, since under certain conditions a not-too-large number of random features is sufficient to yield an effective preconditioner. We empirically evaluate our method and show it is highly effective for datasets of up to one million training examples.
A Survey of Data Mining Techniques for Social Network Analysis

Social network has gained remarkable attention in t he last decade. Accessing social network sites such as Twitter, Fac ebook LinkedIn and Google+ through the internet and the web 2.0 technologies h a become more affordable. People are becoming more interested in and relying o social network for information, news and opinion of other users on div erse subject matters. The heavy reliance on social network sites causes them to gen erate massive data characterised by three computational issues namely; size, noise a nd dynamism. These issues often make social network data very complex to analyse ma nually, resulting in the pertinent use of computational means of analysing t hem. Data mining provides a wide range of techniques for detecting useful knowl edge from massive datasets like trends, patterns and rules [44]. Data mining techni ques are used for information retrieval, statistical modelling and machine learni ng. These techniques employ data pre-processing, data analysis, and data interpretat ion processes in the course of data analysis. This survey discusses different dat a mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are liste d in the Table.1 including the tools employed as well as names of their authors.
Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies

In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al.  has showed that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for seeded classes. The Exploratory learning  method was proposed to solve this problem; however it is limited to the flat classification task. This paper builds such exploratory learning methods for hierarchical classification tasks.
 We experimented with subsets of the NELL [8] ontology and text, and HTML table datasets derived from the ClueWeb09 corpus. Our method (OptDAC-ExploreEM) outperforms the existing Exploratory EM method, and its naive extension (DAC-ExploreEM), in terms of seed class F1 on average by 10% and 7% respectively.
Evaluating Compositionality in Sentence Embeddings

An important challenge for human-like AI is compositional semantics. Recent research has attempted to address this by using deep neural networks to learn vector space embeddings of sentences, which then serve as input to other tasks. We present a new dataset for one such task, “natural language inference” (NLI), that cannot be solved using only word-level knowledge and requires some compositionality. We find that the performance of state of the art sentence embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We analyze the decision rules learned by InferSent and find that they are consistent with simple heuristics that are ecologically valid in its training dataset. Further, we find that augmenting training with our dataset improves test performance on our dataset without loss of performance on the original training dataset. This highlights the importance of structured datasets in better understanding and improving AI systems.
Direct Optimization through arg max for Discrete Variational Auto-Encoder

Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. In this work we reparameterize discrete variational auto-encoders using the Gumbel-Max perturbation model that represents the Gibbs distribution using the arg max of randomly perturbed encoder. We subsequently apply the direct loss minimization technique to propagate gradients through the reparameterized arg max. The resulting gradient is estimated by the difference of the encoder gradients that are evaluated in two arg max predictions.
A High Efficiency ZCS-ZVS Buck Converter with Coupled Inductor

A novel topology for a soft-switching buck dc– dc converter with a coupled inductor is proposed. The soft-switching buck converter has advantages over the traditional hardswitching converters. The most significant advantage is that it offers a lower switching loss. This converter operates under a zero-current switching condition at turn on and a zero-voltage switching condition at turn off. It presents the circuit configuration with a least components for realizing soft switching. Because of soft switching, the proposed converter can attain a high efficiency under heavy load conditions. Likewise, a high efficiency is also attained under light load conditions, which is significantly different from other soft switching buck converters