nils-herrmann/clean_scirepeval huggingface dataset:
A Mobile Game-based English Vocabulary Practice System Based on Portfolio Analysis

English learning has become a vital educational strategy in many non-English-speaking countries. Vocabulary is a critical element for language learners. Therefore, developing sufficient vocabulary knowledge enables effective communication. However, learning a foreign language is difficult and stressful. In addition, memorizing English vocabulary is often considered boring, and learners may lack motivation for learning activities. To increase English learning motivation and interest, this study constructed a mobile game-based English vocabulary practice system that entails selecting words according to textbook passages, a difficulty ratio, and learning portfolios. The learning activities involved in the system transform vocabulary learning from tedious memorization to game-based learning, thereby enhancing learners’ vocabulary memory and their familiarity with curriculum-related vocabulary through various multimedia. An experiment was conducted by dividing students into three groups that involved different vocabulary review methods. All students had similar English proficiency levels, and the course content and instructor were the same for all groups. This study statistically analyzed whether learners become familiar with vocabulary after playing the game. According to the analysis results, students who used the proposed system exhibited higher learning interest, attention, and learning effectiveness, as well as a sense of accomplishment and triumph, compared with other students.
Maternal cfDNA screening for Down syndrome--a cost sensitivity analysis.

This study aimed to determine the principal factors contributing to the cost of avoiding a birth with Down syndrome by using cell-free DNA (cfDNA) to replace conventional screening.



A range of unit costs were assigned to each item in the screening process. Detection rates were estimated by meta-analysis and modeling. The marginal cost associated with the detection of additional cases using cfDNA was estimated from the difference in average costs divided by the difference in detection.



The main factor was the unit cost of cfDNA testing. For example, replacing a combined test costing $150 with 3% false-positive rate and invasive testing at $1000, by cfDNA tests at $2000, $1500, $1000, and $500, the marginal cost is $8.0, $5.8, $3.6, and $1.4m, respectively. Costs were lower when replacing a quadruple test and higher for a 5% false-positive rate, but the relative importance of cfDNA unit cost was unchanged. A contingent policy whereby 10% to 20% women were selected for cfDNA testing by conventional screening was considerably more cost-efficient. Costs were sensitive to cfDNA uptake.



Universal cfDNA screening for Down syndrome will only become affordable by public health purchasers if costs fall substantially. Until this happens, the contingent use of cfDNA is recommended.
Smart Grid Technologies: Communication Technologies and Standards

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.
Time Series ANN Approach for Weather Forecasting

Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting.
Macro Tree Transducers

Macro tree transducers are a combination of top-down tree transducers and macro grammars. They serve as a model for syntax-directed semantics in which context information can be handled. In this paper the formal model of macro tree transducers is studied by investigating typical automata theoretical topics like composition, decomposition, domains, and ranges of the induced translation classes. The extension with regular look-ahead is considered. 0 1985 Academic Press, Inc.
Stranger on the internet: Online self-disclosure and the role of visual anonymity

This paper deals with the phenomenon of online self-disclosure. Two qualitative data analyses of YouTube videos were conducted. The studies revealed emerging forms of self-disclosure online, which are not necessarily bound to conditions of visual anonymity. This finding puts previous research results into question, which stress the strong correlation between self-disclosure and visual anonymity. The results of both qualitative studies showed that people also tend to disclose information in (visually) non-anonymous settings. The paper concludes by presenting a revised model of online self-disclosure and describing enhancing factors for self-disclosing behaviour on the internet based on the latest research results.
Skyfire: Data-Driven Seed Generation for Fuzzing

Programs that take highly-structured files as inputs normally process inputs in stages: syntax parsing, semantic checking, and application execution. Deep bugs are often hidden in the application execution stage, and it is non-trivial to automatically generate test inputs to trigger them. Mutation-based fuzzing generates test inputs by modifying well-formed seed inputs randomly or heuristically. Most inputs are rejected at the early syntax parsing stage. Differently, generation-based fuzzing generates inputs from a specification (e.g., grammar). They can quickly carry the fuzzing beyond the syntax parsing stage. However, most inputs fail to pass the semantic checking (e.g., violating semantic rules), which restricts their capability of discovering deep bugs. In this paper, we propose a novel data-driven seed generation approach, named Skyfire, which leverages the knowledge in the vast amount of existing samples to generate well-distributed seed inputs for fuzzing programs that process highly-structured inputs. Skyfire takes as inputs a corpus and a grammar, and consists of two steps. The first step of Skyfire learns a probabilistic context-sensitive grammar (PCSG) to specify both syntax features and semantic rules, and then the second step leverages the learned PCSG to generate seed inputs. We fed the collected samples and the inputs generated by Skyfire as seeds of AFL to fuzz several open-source XSLT and XML engines (i.e., Sablotron, libxslt, and libxml2). The results have demonstrated that Skyfire can generate well-distributed inputs and thus significantly improve the code coverage (i.e., 20% for line coverage and 15% for function coverage on average) and the bug-finding capability of fuzzers. We also used the inputs generated by Skyfire to fuzz the closed-source JavaScript and rendering engine of Internet Explorer 11. Altogether, we discovered 19 new memory corruption bugs (among which there are 16 new vulnerabilities and received 33.5k USD bug bounty rewards) and 32 denial-of-service bugs.
Organo-Lead Halide Perovskite Induced Electroactive β-Phase in Porous PVDF Films: An Excellent Material for Photoactive Piezoelectric Energy Harvester and Photodetector.

Methylammonium lead iodide (CH3NH3PbI3) (MAPI)-embedded β-phase comprising porous poly(vinylidene fluoride) (PVDF) composite (MPC) films turns to an excellent material for energy harvester and photodetector (PD). MAPI enables to nucleate up to ∼91% of electroactive phase in PVDF to make it suitable for piezoelectric-based mechanical energy harvesters (PEHs), sensors, and actuators. The piezoelectric energy generation from PEH made with MPC film has been demonstrated under a simple human finger touch motion. In addition, the feasibility of photosensitive properties of MPC films are manifested under the illumination of nonmonochromatic light, which also promises the application as organic photodetectors. Furthermore, fast rising time and instant increase in the current under light illumination have been observed in an MPC-based photodetector (PD), which indicates of its potential utility in efficient photoactive device. Owing to the photoresponsive and electroactive nature of MPC films, a new class of stand-alone self-powered flexible photoactive piezoelectric energy harvester (PPEH) has been fabricated. The simultaneous mechanical energy-harvesting and visible light detection capability of the PPEH is promising in piezo-phototronics technology.
Research on Selection of the Third-Party Logistics Service Providers

At present, the supply chain is the last field, which can make enterprise reducing cost and improving flexibility. How to construct the supply chain successfully has received much attention and logistics outsourcing has become common among large manufacturers. The information age and globalization are forcing companies to place a premium upon collaboration as a new source of competitive advantage, so companies are facing significant challenges to evaluate and select appropriate third-party logistics (3PL) providers. This paper will choose a set of indicators, construct a framework for performance measures and introduce an evaluation method which proceeds as follow: (1) choosing principal components applying principal components analysis; (2) ranking the 3PL providers applying grey relational analysis.
The role of nutrition labels and advertising claims in altering consumers' evaluation and choice

Despite policy efforts, consumers' well-informed healthful choice is a challenge. Due to increasing number of benefit claims advertising taste or health front of pack (FOP), consumers face the dilemma to trade taste for health. To understand the mechanisms underlying food evaluation, this study investigates the health-pleasure trade-off and its effect on consumers' choice. 240 EU consumers took part in a taste experiment, after being presented with the product FOP. Half of the products carried a nutrition label FOP, respectively, reduced fat for potato chips, reduced sugar for cereal bars. Further, one third of the products carried health benefit claim, one third taste benefit claim, and one third no additional claim FOP. Attention to information and its effect on experienced taste, health perception and the buying intention were measured. The results show that the message displayed FOP altered consumers evaluation and choice. The effectiveness of the FOP message further depended on consumers' health motivation and the healthfulness perception of carrier products. The outcomes are summarized in a framework of health-pleasure trade-off. Current findings call for the establishment of standards to avoid the use of misleading information FOP.
Hierarchical Metric Learning for Fine Grained Image Classification

This paper deals with the problem of fine-grained image classification and introduces the notion of hierarchical metric learning for the same. It is indeed challenging to categorize fine-grained image classes merely in terms of a single level classifier given the subtle inter-class visual differences. In order to tackle this problem, we propose a two stage framework where i) the image categories are represented hierarchically in terms of a binary tree structure where different subset of classes are present in the non-leaf nodes of the tree. This is accomplished in an automatic fashion considering the available training data in the visual domain, and ii) a (non-leaf) node specific metric learning is further deployed for the categories constituting a given node, thus enforcing better separation between both of its children. Subsequently, we construct (non-leaf) node specific binary classifiers in the learned metric spaces on which testing is henceforth carried out by following the outcomes of the classifiers sequence from root to leaf nodes of the tree. By separately focusing on the semantically similar classes at different levels of the hierarchy, it is expected that the classifiers in the learned metric spaces possess better discriminative capabilities than considering all the classes at a single go. Experimental results obtained on two challenging datasets (Oxford Flowers and Leeds Butterfly) establish the superiority of the proposed framework in comparison to the standard single metric learning based methods convincingly.
Portfolio Choices with Orthogonal Bandit Learning

The investigation and development of new methods from diverse perspectives to shed light on portfolio choice problems has never stagnated in financial research. Recently, multi-armed bandits have drawn intensive attention in various machine learning applications in online settings. The tradeoff between exploration and exploitation to maximize rewards in bandit algorithms naturally establishes a connection to portfolio choice problems. In this paper, we present a bandit algorithm for conducting online portfolio choices by effectually exploiting correlations among multiple arms. Through constructing orthogonal portfolios from multiple assets and integrating with the upper confidence bound bandit framework, we derive the optimal portfolio strategy that represents the combination of passive and active investments according to a risk-adjusted reward function. Compared with oft-quoted trading strategies in finance and machine learning fields across representative real-world market datasets, the proposed algorithm demonstrates superiority in both risk-adjusted return and cumulative wealth.
Planar MS-CPS bypass balun for CPS-fed textile antennas

This paper presents the design, numerical analysis and measurements of a planar bypass balun that provides 1:4 impedance transformations between the unbalanced microstrip (MS) and balanced coplanar strip line (CPS). This type of balun is suitable for operation with small antennas fed with balanced a (parallel wire) transmission line, i.e. wire, planar dipoles and loop antennas. The balun has been applied to textile CPS-fed loop antennas, designed for operations below 1GHz. The performance of a loop antenna with the balun is described, as well as an idea of incorporating rigid circuits with flexible textile structures.
Generalized parallel CRC computation on FPGA

The cyclic redundancy check (CRC) is a popular error detection code (EDC) used in many digital transmission and storage protocols. Most existing digit-serial hardware CRC computation architectures are based on one of the two well-known bit-serial CRC linear feedback shift register (LFSR) architectures. In this paper, we present and investigate a generalized CRC formulation that incorporates negative degree terms. Through software simulations, we identify useful formulations that result in reduced time and/or area complexity CRC circuits compared to the existing non-retimed approaches. Implementation results on an Altera field-programmable gate array (FPGA) device are reported. We conclude that the proposed approach is most effective when the digit size is greater than the generator polynomial degree.
Reference-Dependent Preferences and Labor Supply : The Case of New York City Taxi Drivers

Increased attention has been paid in recent years to deviations from the standard neoclassical model of consumer behavior. A substantial segment of this work focuses on reference-dependent preferences where there is a change in the shape of the utility function at some base (reference) level of income or consumption. These models have strong predictions for how responses to changes in prices are affected by the actual level of consumption or income relative to the reference level. A difficulty in bringing this class of models to the data is that the reference level of income or consumption is seldom observed, and we have only weak information on what determines the reference point. There are important reasons to understand how these considerations affect estimation of labor supply elasticities. Evaluation of much government policy regarding tax and transfer programs depends on having reliable estimates of the sensitivity of labor supply to wage rates and income levels. To the extent that individuals’ levels of labor supply are the result of optimization with reference-dependent preferences, the usual estimates of wage and income elasticities are likely to be misleading. In this study, I develop an empirical model of daily labor supply that incorporates referencedependent preferences but does not require that the reference level of income be observed or defined in advance. I apply this model to data on the daily labor supply of New York City taxi drivers by allowing taxi drivers to have a reference level of daily income. The estimates suggest that, while there may be a reference level of income on a given day such that there is a discrete increase in the probability of stopping when that income level is reached, the reference level varies substantially day to day for a particular driver. Additionally, most shifts end before the reference income level is reached. Essentially, the data show more smoothness in the relationship between income and the continuation and stopping probabilities than seems consistent with an important role for reference-dependent preferences.
Hybrid force/velocity control for physical human-robot collaboration tasks

During human-robot collaboration tasks, we may physically touch the robot at a generic location and engage an intentional exchange of forces while realizing coordinated motion of the common contact point. In order to control the relative motion and the exchanged contact forces, the latter need to be estimated without using any local force sensing device. Building upon our recent works, we generalize the classical hybrid force/velocity control design to this situation, handling complementary quantities along the directions of a suitable contact task frame in a dynamically decoupled way. The contact force is estimated online using our residual method together with an external sensor to localize the contact point, and the time-varying contact task frame is obtained analytically from this estimate. Experimental results are presented for a KUKA LWR4 robot using a Kinect sensor.
CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering

In just 3 years CRISPR genome editing has transformed biology, and its popularity and potency continue to grow. New CRISPR effectors and rules for locating optimum targets continue to be reported, highlighting the need for computational CRISPR targeting tools to compile these rules and facilitate target selection and design. CHOPCHOP is one of the most widely used web tools for CRISPR- and TALEN-based genome editing. Its overarching principle is to provide an intuitive and powerful tool that can serve both novice and experienced users. In this major update we introduce tools for the next generation of CRISPR advances, including Cpf1 and Cas9 nickases. We support a number of new features that improve the targeting power, usability and efficiency of CHOPCHOP. To increase targeting range and specificity we provide support for custom length sgRNAs, and we evaluate the sequence composition of the whole sgRNA and its surrounding region using models compiled from multiple large-scale studies. These and other new features, coupled with an updated interface for increased usability and support for a continually growing list of organisms, maintain CHOPCHOP as one of the leading tools for CRISPR genome editing. CHOPCHOP v2 can be found at http://chopchop.cbu.uib.no.
The prevalence of secondary dentinal lesions in cheek teeth from horses with clinical signs of pulpitis compared to controls.

With the advent of detailed oral examination in horses using dental mirrors and rigid endoscopy, secondary dentinal lesions are observed more frequently. More information regarding the association of secondary dentinal defects with apical dental disease would improve the sensitivity of oral examination as a diagnostic aid for pulpitis.



To assess prevalence and severity of secondary dentinal defects observed on examination of occlusal surfaces of cheek teeth (CT) from horses showing clinical signs of pulpitis compared to asymptomatic controls.



Records from all cases of equine CT exodontia at the University of Bristol over a 4 year period were examined. Case selection criteria included the presence of clinical signs of pulpitis, an intact extracted tooth and availability of a complete history and follow up. Cases where coronal fracture or periodontal pocketing featured were excluded. CT from cadavers with no history of dental disease served as normal controls. Triadan positions and eruption ages of control teeth were matched with those of teeth extracted from cases. CT from selected cases and control teeth were examined occlusally. Secondary dentinal defects were identified and graded. Prevalence of occlusal lesions in CT with pulpitis and controls was compared.



From the records of 120 horses where exodontia was performed, 40 cases matched selection criteria. Twenty-three mandibular and 21 maxillary CT were extracted from cases. The controls consisted of 60 mandibular and 60 maxillary CT from 7 cadaver skulls. Secondary dentinal defects were significantly over-represented in CT extracted from cases of pulpitis (P < 0.001). Of diseased mandibular CT, 56.5% had defects compared to none of the controls. Of diseased maxillary CT, 57% had defects compared with 1.6% of controls. Multiple defective secondary dentinal areas and severe lesions were more prevalent in diseased mandibular CT compared with diseased maxillary CT.  Careful examination of occlusal secondary dentine is an essential component in investigation of suspected pulpitis in equine CT.
Moderate structural realism about space-time

This paper sets out a moderate version of metaphysical structural realism that stands in contrast to both the epistemic structural realism of Worrall and the—radical—ontic structural realism of French and Ladyman. According to moderate structural realism, objects and relations (structure) are on the same ontological footing, with the objects being characterized only by the relations in which they stand. We show how this position fares well as regards philosophical arguments, avoiding the objections against the other two versions of structural realism. In particular, we set out how this position can be applied to space-time, providing for a convincing understanding of space-time points in the standard tensor formulation of general relativity as well as in the fibre bundle formulation.