nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Type and Token Identification of Idiomatic Expressions

Idiomatic expressions are plentiful in everyday language, yet they remain mysterious, as it is not clear exactly how people learn and understand them. They are of special interest to linguists, psycholinguists, and lexicographers, mainly because of their syntactic and semantic idiosyncrasies as well as their unclear lexical status. Despite a great deal of research on the properties of idioms in the linguistics literature, there is not much agreement on which properties are characteristic of these expressions. Because of their peculiarities, idiomatic expressions have mostly been overlooked by researchers in computational linguistics. In this article, we look into the usefulness of some of the identified linguistic properties of idioms for their automatic recognition. Specifically, we develop statistical measures that each model a specific property of idiomatic expressions by looking at their actual usage patterns in text. We use these statistical measures in a type-based classification task where we automatically separate idiomatic expressions (expressions with a possible idiomatic interpretation) from similar-on-the-surface literal phrases (for which no idiomatic interpretation is possible). In addition, we use some of the measures in a token identification task where we distinguish idiomatic and literal usages of potentially idiomatic expressions in context.
The Cauchy-Schwarz divergence for assessing situational information gain

In this paper, we consider the evaluation of information divergence and information gain as they apply to a hybrid random variable (i.e. a random variable which has both discrete and continuous elements) for multi-target tracking problems. In particular, we develop a closed-form solution for the Cauchy-Schwarz information divergence under the assumption that the continuous element of the random variable may be represented by a Gaussian mixture distribution and present the associated relationships for evaluating the Cauchy-Schwarz information gain. The developed information gain relationships are applied to a 0-1 target tracking problem common to space object tracking to determine the sensitivities to the information gain due to probability of detection, prior probability of object existence, and measurement noise.
Snow water equivalent determination technique using narrow acoustic beam

Snow packs generally consists of several layers of snow, which have different physical properties like tortuosity, porosity, density etc. Snow can be categorized into different types having different properties like granular, fluffy, dry or wet. One of the properties of snow is the amount of water that results from melting of snow-pack. The amount of water in snow is known as Snow Water Equivalent (SWE). SWE is useful in many areas like estimation of water availability from snow melt, assessment of snow-pack density etc. The information of density is used to estimate the strength of snow-pack. The knowledge of strength can be useful for the prediction of avalanche prone areas. There are several techniques available for the measurement of SWE. The techniques for SWE measurement are generally invasive and time consuming in nature. Researchers have developed noninvasive acoustic techniques for SWE measurement. But lot of assumptions are taken in these SWE measurement techniques, which results in inaccurate estimation of SWE. A new noninvasive acoustic technique based on use of very directional sound source and associated signal processing method is presented, in this paper. This new acoustic technique does not require calculation of reflection coefficients for SWE estimation.
Comparison of Test Specifications and Measured Field Data

Based on a paper presented at the 76th Shock & Vibration Symposium, October 30-November 4, Destin, FL, 2005. It is not always obvious how to compare a given test specification with measured field data. This article presents a systematic method based on the shock response spectrum and the fatigue damage spectrum. A random-on-random test specification for tracked vehicles, AECTP 400, is used as an example.
Consumer Decision-making Processes in Mobile Viral Marketing Campaigns

The high penetration of cell phones in today's global environment offers a wide range of promising mobile marketing activities, including mobile viral marketing campaigns. However, the success of these campaigns, which remains unexplored, depends on the consumers' willingness to actively forward the advertisements that they receive to acquaintances, e.g., to make mobile referrals. Therefore, it is important to identify and understand the factors that influence consumer referral behavior via mobile devices. The authors analyze a three-stage model of consumer referral behavior via mobile devices in a field study of a firm-created mobile viral marketing campaign. The findings suggest that consumers who place high importance on the purposive value and entertainment value of a message are likely to enter the interest and referral stages. Accounting for consumers' egocentric social networks, we find that tie strength has a negative influence on the reading and decision to refer stages and that degree centrality has no influence on the decision-making process.
Microcontroller based Closed Loop Speed Control of DC Motor using PWM Technique

Speed control of DC motor is very important especially in the fields including industrial applications, robotics and so on. So, speed control of DC motors at different load conditions is a prominent field of research in order to achieve a robust system. This paper presents the design and implementation of microcontroller based speed control of DC motors which emphasize the design of PWM control using ICs like SG3524, TL494 in the field of motor speed control. For PWM generation microchip PIC16F877A microcontroller is used. The feedback loop is created by using ADC which is available in the microcontroller IC. This ADC reads the voltage level of the motor and correspondingly the voltage level of the motor can be maintained at a fixed level. A driver circuit is used to drive the motor. Hence, a closed loop motor speed control circuit is designed and the total amount of power delivered to the motor is varied depending on load conditions. In this technique, the regulation of motor&apos;s speed is realized by changing the voltage of the motor which is adjusted by the duty ratio of PWM.
Word Image Matching Using Dynamic Time Warping

Libraries and other institutions are interested in providing access to scanned versions of their large collections of handwritten historical manuscripts on electronic media. Convenient access to a collection requires an index, which is manually created at great labour and expense. Since current handwriting recognizers do not perform well on historical documents, a technique called word spotting has been developed: clusters with occurrences of the same word in a collection are established using image matching. By annotating “interesting” clusters, an index can be built automatically. We present an algorithm for matching handwritten words in noisy historical documents. The segmented word images are preprocessed to create sets of 1-dimensional features, which are then compared using dynamic time warping. We present experimental results on two different data sets from the George Washington collection. Our experiments show that this algorithm performs better and is faster than competing matching techniques.
Group Sparsity Residual Constraint for Image Denoising

Group sparsity or nonlocal image representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input image, that is, the similar patches collected only from degraded input, which makes the quality of image denoising largely depend on the input itself. In this paper we propose a new prior model for image denoising, called group sparsity residual constraint (GSRC). Different from the most existing NSS prior-based denoising methods, two kinds of NSS prior (i.e., NSS priors of noisy input image and pre-filtered image) are simultaneously used for image denoising. In particular, to boost the performance of group sparse-based image denoising, the group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual. To reduce the residual, we first obtain a good estimation of the group sparse coefficients of the original image by pre-filtering and then the group sparse coefficients of noisy input image are used to approximate the estimation. To improve the accuracy of the nonlocal similar patches selection, an adaptive patch search scheme is proposed. Moreover, to fuse these two NSS priors better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC model. Experimental results have demonstrated that the proposed GSRC modeling outperforms many state-of-the-art denoising methods in terms of the objective and the perceptual qualities.
RankExplorer: Visualization of Ranking Changes in Large Time Series Data

For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.
Memory: A Contribution to Experimental Psychology

certain conditions the employment of a similar range of thought, even if the former does not come before the mind directly either in its methods or in its results. the boundless domain of the effect of accumulated experience belongs here. this effect results from the frequent conscious occurrence of any condition or process, and consists in facilitating the occurrence and progress of similar processes. this effect is not fettered by the condition that the factors constituting the experience shall return in toto to consciousness. this may incidentally be the case with a part of them; it must not happen to a too great extent and with too great clearness, otherwise the course of the present process will immediately be disturbed. Most of these experiences remain concealed from consciousness and yet produce an effect which is significant and which authenticates their previous existence.
An Efficient Surface Intersection Algorithm Based on Lower-Dimensional Formulation

We present an efficient algorithm to compute the intersection of algebraic and NURBS surfaces. Our approach is based on combining the marching methods with the algbraic formulation. In particular, we propose and matrix computations. We present algorithms to compute a start point on each component of the intersection curve (both open and closed components), detect the presence of singularities, and find all the curve branches near the singularity. We also suggest methods to compute the step size during tracing to prevent component jumping. The algorithm runs an order of magnitude faster than previously published robust algorithms. The complexity of the algorithm is output sensitive.
Parametric study of Vivaldi antenna

A parametric study of a wide-band Vivaldi antenna is presented. Four models were simulated using a finite element method design and analysis package Ansoft HFSS v 10.1. The simulated return loss and realized gain of each model for a frequency range of 12 to 20GHz is studied. The location of the phase centre, represented as the distance d (in cm) from the bottom of the antenna, with respect to which the phase of the respective far field copolar patterns (for a scan angle θ of 0 to 60°) in the E and H-planes, constrains to a specified maximum tolerable phase difference Δφ is calculated.
Accumulation of energy reserves in algae: From cell cycles to biotechnological applications.

Starch and lipids are key components of algal cells and responsible for buffering variable supplies of energy and carbon that are vital for cell growth and reproduction, particularly DNA replication, nuclear and cellular division. The basic characteristics of energy reserves, their ultrastructure and localization inside the cell, regulation of their synthesis in relation to cell cycle phases, and their control by external factors, including light intensity, temperature, and carbon dioxide are described. Over the last two decades, research in this field has been boosted by possible biotechnological applications of algae for the production of biofuels from energy conserving compounds (bioethanol from starch and biodiesel from lipids). Recent findings on mechanisms that lead to an accumulation of exceptionally high levels of starch and lipids in algae will be summarized in this review. Macroelement (N, S, P) limitation, or depletion in mineral medium, as the most widely used approaches for enhancing both starch and lipid accumulation, are reviewed in detail. Potential biotechnological strategies for the economically viable overproduction of lipid and starch, such as a two-step procedure exploiting the effects of nutrient limitation and depletion, as well as the means and rationale for selecting appropriate strains, are discussed.
Pattern Recognition Receptors and Inflammation

Infection of cells by microorganisms activates the inflammatory response. The initial sensing of infection is mediated by innate pattern recognition receptors (PRRs), which include Toll-like receptors, RIG-I-like receptors, NOD-like receptors, and C-type lectin receptors. The intracellular signaling cascades triggered by these PRRs lead to transcriptional expression of inflammatory mediators that coordinate the elimination of pathogens and infected cells. However, aberrant activation of this system leads to immunodeficiency, septic shock, or induction of autoimmunity. In this Review, we discuss the role of PRRs, their signaling pathways, and how they control inflammatory responses.
Maximal strength training: the impact of eccentric overload.

The search for the most potent strength training intervention is continuous. Maximal strength training (MST) yields large improvements in force generating capacity (FGC), largely attributed to efferent neural drive enhancement. However, it remains elusive whether eccentric overload, prior to the concentric phase, may augment training-induced neuromuscular adaptations. A total of 53 23±3(SD)-year-old untrained males were randomized to either a non-training control group (CG) or one of two training groups performing leg press strength training with linear progression, 3x per week for eight weeks. The first training group carried out MST with 4x4 repetitions at ~90% one-repetition maximum (1RM) in both action phases. The second group performed MST with an augmented eccentric load of 150% 1RM (eMST). Measurements were taken of 1RM and rate of force development (RFD), countermovement jump (CMJ) performance, and evoked potentials recordings (V-wave (V) and H-reflex (H) normalized to M-wave (M) in m. soleus). 1RM increased from 133±16kg to 157±23kg and 123±18kg to 149±22kg, and CMJ by 2.3±3.6cm and 2.2±3.7cm for MST and eMST, respectively (all p<0.05). Early, late, and maximal RFD increased in both groups (634-1501·s-1 (MST); 644-2111N·s-1 (eMST); p<0.05). These functional improvements were accompanied by increased V/M-ratio (MST:0.34±0.11 to 0.42±14; eMST:0.36±0.14 to 0.43±13; p<0.05). Resting H/M-ratio remained unchanged. Training-induced improvements did not differ. All increases, except for CMJ, were different from the CG. MST is an enterprise for large gains in FGC and functional performance. Eccentric overload did not induce additional improvements, suggesting firing frequency and motor unit recruitment during MST may be maximal.
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons

While sentiment and emotion analysis has received a considerable amount of research attention, the notion of understanding and detecting the intensity of emotions is relatively less explored. This paper describes a system developed for predicting emotion intensity in tweets. Given a Twitter message, CrystalFeel uses features derived from parts-of-speech, ngrams, word embedding, and multiple affective lexicons including Opinion Lexicon, SentiStrength, AFFIN, NRC Emotion & Hash Emotion, and our in-house developed EI Lexicons to predict the degree of the intensity associated with fear, anger, sadness, and joy in the tweet. We found that including the affective lexicons-based features allowed the system to obtain strong prediction performance, while revealing interesting emotion word-level and message-level associations. On gold test data, CrystalFeel obtained Pearson correlations of .717 on average emotion intensity and of .816 on sentiment intensity.
SMT-Based Nonlinear PDDL+ Planning

PDDL+ planning involves reasoning about mixed discretecontinuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems. Using this encoding, we apply a Satisfiability Modulo Theories (SMT) solver to find plans. We show that it is important to use a novel planning-specific heuristic for variable selection for SMT solving, which is inspired by recent advances in planning as SAT. We show the promising performance of the resulting solver on challenging nonlinear problems.
An accurate infrared hand geometry and vein pattern based authentication system

In this paper, hand dorsal images acquired under infrared light are used to design an accurate personal authentication system. Each of the image is segmented into palm dorsal and fingers which are subsequently used to extract palm dorsal veins and infrared hand geometry features respectively. A new quality estimation algorithm is proposed to estimate the quality of palm dorsal which assigns low values to the pixels containing hair or skin texture. Palm dorsal is enhanced using filtering. For vein extraction, information provided by the enhanced image and the vein quality is consolidated using a variational approach. The proposed vein extraction can handle the issues of hair, skin texture and variable width veins so as to extract the genuine veins accurately. Several post processing techniques are introduced in this paper for accurate feature extraction of infrared hand geometry features. Matching scores are obtained by matching palm dorsal veins and infrared hand geometry features. These are eventually fused for authentication. For performance evaluation, a database of 1500 hand images acquired from 300 different hands is created. Experimental results demonstrate the superiority of the proposed system over existing
Human Library: Understanding Experience Sharing for Community Knowledge Building

The human library is an event intended to engage members of the community in sharing and learning from each other's experiences, and is growing in popularity internationally. Human libraries fall within the larger scope of community knowledge sharing but have received little study and remain largely unsupported by technology. In this study, we examine how community libraries organize and host these events. We present how libraries have attempted to utilize technologies and leverage community support to enable human library events. Our findings reveal inconsistencies in the purpose of human library events, as well as technology applications that are not sufficient to support fully collaborative community knowledge building. We highlight opportunities for increased community participation and technological innovation and also suggest a broader consideration of computer-supported collaborative work in the context of human libraries and experience sharing.
Making time for mindfulness

Digital mental wellbeing interventions are increasingly being used by the general public as well as within clinical treatment. Among these, mindfulness and meditation programs delivered through mobile device applications are gaining popularity. However, little is known about how people use and experience such applications and what are the enabling factors and barriers to effective use. To address this gap, the study reported here sought to understand how users adopt and experience a popular mobile-based mindfulness intervention.



A qualitative semi-structured interview study was carried out with 16 participants aged 25-38 (M=32.5) using the commercially popular mindfulness application Headspace for 30-40days. All participants were employed and living in a large UK city. The study design and interview schedule were informed by an autoethnography carried out by the first author for thirty days before the main study began. Results were interpreted in terms of the Reasoned Action Approach to understand behaviour change.



The core concern of users was fitting the application into their busy lives. Use was also influenced by patterns in daily routines, on-going reflections about the consequences of using the app, perceived self-efficacy, emotion and mood states, personal relationships and social norms. Enabling factors for use included positive attitudes towards mindfulness and use of the app, realistic expectations and positive social influences. Barriers to use were found to be busy lifestyles, lack of routine, strong negative emotions and negative perceptions of mindfulness.



Mobile wellbeing interventions should be designed with consideration of people's beliefs, affective states and lifestyles, and should be flexible to meet the needs of different users. Designers should incorporate features in the design of applications that manage expectations about use and that support users to fit app use into a busy lifestyle. The Reasoned Action Approach was found to be a useful theory to inform future research and design of persuasive mental wellbeing technologies.