nils-herrmann/clean_scirepeval huggingface dataset:
A Compact UWB Three-Way Power Divider

A three-way power divider with ultra wideband behavior is presented. It has a compact size with an overall dimension of 20 mm times 30 mm. The proposed divider utilizes broadside coupling via multilayer microstrip/slot transitions of elliptical shape. The simulated and measured results show that the proposed device has 4.77 plusmn 1 dB insertion loss, better than 17 dB return loss, and better than 15 dB isolation across the frequency band 3.1 to 10.6 GHz.
Using Non-Local Features to Improve Named Entity Recognition Recall

Named Entity Recognition (NER) is always limited by its lower recall resulting from the asymmetric data distribution where the NONE class dominates the entity classes. This paper presents an approach that exploits non-local information to improve the NER recall. Several kinds of non-local features encoding entity token occurrence, entity boundary and entity class are explored under Conditional Random Fields (CRFs) framework. Experiments on SIGHAN 2006 MSRA (CityU) corpus indicate that non-local features can effectively enhance the recall of the state-of-the-art NER systems. Incorporating the non-local features into the NER systems using local features alone, our best system achieves a 23.56% (25.26%) relative error reduction on the recall and 17.10% (11.36%) relative error reduction on the F1 score; the improved F1 score 89.38% (90.09%) is significantly superior to the best NER system with F1 of 86.51% (89.03%) participated in the closed track.
EmotionLines: An Emotion Corpus of Multi-Party Conversations

Feeling emotion is a critical characteristic to distinguish people from machines. Among all the multi-modal resources for emotion detection, textual datasets are those containing the least additional information in addition to semantics, and hence are adopted widely for testing the developed systems. However, most of the textual emotional datasets consist of emotion labels of only individual words, sentences or documents, which makes it challenging to discuss the contextual flow of emotions. In this paper, we introduce EmotionLines, the first dataset with emotions labeling on all utterances in each dialogue only based on their textual content. Dialogues in EmotionLines are collected from Friends TV scripts and private Facebook messenger dialogues. Then one of seven emotions, six Ekman’s basic emotions plus the neutral emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245 utterances from 2,000 dialogues are labeled in EmotionLines. We also provide several strong baselines for emotion detection models on EmotionLines in this paper.
The Effects of Background Music on Primary School Pupils ’ Task Performance

Research on the effects of background music has a long history. Early work was not embedded within a theoretical framework, was often poorly conceptualised and produced equivocal  ndings. This paper reports two studies exploring the effects of music, perceived to be calming and relaxing, on performance in arithmetic and on a memory task in children aged 10–12. The calming music led to better performance on both tasks when compared with a no-music condition. Music perceived as arousing, aggressive and unpleasant disrupted performance on the memory task and led to a lower level of reported altruistic behaviour by the children. This suggests that the effects of music on task performance are mediated by arousal and mood rather than affecting cognition directly. The  ndings are discussed in relation to possible practical applications in the primary school and the home.
Broadband Spatially Combined Amplifier Array Using Tapered Slot Transitions in Waveguide

Most reported spatially combined or quasioptical amplifier arrays exhibit resonant narrowband performance (<10%) and have not addressed thermal management issues. We report a waveguide-based spatial combining scheme using broadband tapered-slot transitions, capable of realizing full waveguide band coverage (40% fractional bandwidth) with good thermal properties. An X-band prototype using eight mediumpower GaAs monolithic microwave integrated circuits (MMIC’s) produced an output power of 2.4 W and 9-dB power gain at 1-dB compression, with a combining efficiency of 68% and < 1-dB gain variation over the full waveguide band (8–12 GHz).
Diagnosis of Breast Cancer using Decision Tree Models and SVM

Breast cancer represents the second important cause of cancer deaths in women today and it is the most common type of cancer in women. Disease diagnosis is one of the applications where data mining tools are proving successful results. Data mining with decision trees is popular and effective data mining classification approach. Decision trees have the ability to generate understandable classification rules, which are very efficient tool for transfer knowledge to physicians and medical specialists. In fundamental truth, they provide trails to find rules that could be evaluated for separating the input samples into one of several groups without having to state the functional relationship directly. The objective of this paper is to examine the performance of recent invented decision tree modeling algorithms and compared with one that achieved by radial basis function kernel support vector machine (RBF-SVM) on the diagnosis of breast cancer using cytological proven tumor dataset. Four models have been evaluated in decision tree: Chi-squared Automatic Interaction Detection (CHAID), Classification and Regression tree (C&R), Quick Unbiased Efficient Statistical Tree (QUEST), and Ross Quinlan new decision tree model C5. 0. The objective is to classify a tumor as either benign or malignant based on cell descriptions compound by microscopic examination using decision tree models. The proposed algorithm imputes the missing values with C&R tree. Then, the performances of the five models are measured by three statistical measures; classification
Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers

Since the early 1960's, researchers have built a number of programming languages and environments with the intention of making programming accessible to a larger number of people. This article presents a taxonomy of languages and environments designed to make programming more accessible to novice programmers of all ages. The systems are organized by their primary goal, either to teach programming or to use programming to empower their users, and then, by each system's authors' approach, to making learning to program easier for novice programmers. The article explains all categories in the taxonomy, provides a brief description of the systems in each category, and suggests some avenues for future work in novice programming environments and languages.
A Novel Band-Reject Element for Pseudoelliptic Bandstop Filters

A novel band-reject element for the design of inline waveguide pseudoelliptic band-reject filters is introduced. The element consists of an offset partial-height post in a rectangular waveguide in which the dominant TE10 mode is propagating. The location of the attenuation pole is primarily determined by the height of the post that generates it. The element allows the implementation of weak, as well as strong coupling coefficients that are encountered in asymmetric band-reject responses with broad stopbands. The coupling strength is controlled by the offset of the post with respect to the center of the main waveguide. The posts are separated by uniform sections of the main waveguide. An equivalent low-pass circuit based on the extracted pole technique is first used in a preliminary design. An improved equivalent low-pass circuit that includes a more accurate equivalent circuit of the band-reject element is then introduced. A synthesis method of the enhanced network is also presented. Filters based on the introduced element are designed, fabricated, and tested. Good agreement between measured and simulated results is achieved
Demand for Differentiated Durable Products : The Case of the U . S . Computer Printer Market ∗

This paper develops an estimation technique for analyzing the impact of technological change on the dynamics of consumer demand in a differentiated durable products industry. The paper presents a dynamic model of consumer demand for differentiated durable products that explicitly accounts for consumers’ expectations of future product quality and consumers’ outflow from the market, arising endogenously from their purchase decisions. The timing of consumers’ purchases is formalized as an optimal stopping problem. A solution to that problem defines the hazard rate of product adoptions, while the nested discrete choice model determines the alternativespecific purchase probabilities. Integrating individual decisions over the population distribution generates rich dynamics of aggregate and product level sales. The empirical part of the paper takes the model to data on the U.S. computer printer market. The estimates support the hypothesis of consumers’ forward-looking behavior, allowing for better demand forecasts and improved measures of welfare gains from introducing new products. ∗I would like to thank Patrick Bayer, John Rust, Christopher Timmins, and especially my advisors, Steven Berry, Ariel Pakes and Martin Pesendorfer for valuable advice and general encouragement. I have greatly benefitted from discussions with Eugene Choo, Philip Haile, Jerry Hausman, Günter Hitsch, Nickolay Moshkin, Katja Seim and Nadia Soboleva. Seminar participants at Harvard and Yale provided many helpful suggestions. I am indebted to Mark Bates of PC Data, Inc. for providing me with the data without which this research would not be possible. I am grateful to Susan Olmsted for her help with administrative issues. All errors are my own. 3563, fax (203) 432-5779.
20nm DRAM: A new beginning of another revolution

For the first time, 20nm DRAM has been developed and fabricated successfully without extreme ultraviolet (EUV) lithography using the honeycomb structure (HCS) and the air-spacer technology. The cell capacitance (Cs) can be increased by 21% at the same cell size using a novel low-cost HCS technology with one argon fluoride immersion (ArF-i) lithography layer. The parasitic bit-line (BL) capacitance is reduced by 34% using an air-spacer technology whose breakdown voltage is 30% better than that of conventional technology.
Female online shoppers: Examining the mediating roles of e-satisfaction and e-trust on e-loyalty development

Female online shoppers: examining the mediating roles of e-satisfaction and e-trust on e-loyalty development Shihyu Chou Chi-Wen Chen Jiun-You Lin Article information: To cite this document: Shihyu Chou Chi-Wen Chen Jiun-You Lin , (2015),"Female online shoppers: examining the mediating roles of e-satisfaction and e-trust on e-loyalty development", Internet Research,
Configuration and tip-over stability analysis for stair-climbing of a new-style wheelchair robot

In this paper, a wheelchair robot equipped with new-style variable-geometry-tracked mechanism is proposed. This new-style mechanism can adapt to convex terrain and turn to concave geometry by active control of track tension, based on which the terrain adaptability of the wheelchair robot is improved. Aiming at climbing stairs, the transformation rule of robot configuration is presented, the description of passenger's attitude and action is established. Following that, the tip-over stability analysis and simulation are performed with Force-Angle stability measure, and the variation of the tip-over stability margin of the robot under different conditions of passenger's attitude and action during stair-climbing is obtained. The analysis and simulation results provide a valid reference to the wheelchair robot's potential application.
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research

Natural language processing (NLP) is a part of the artificial intelligence domain focused on communication between humans and computers. NLP attempts to address the inherent problem that while human communications are often ambiguous and imprecise, computers require unambiguous and precise messages to enable understanding. The accounting, auditing and finance domains frequently put forth textual documents intended to communicate a wide variety of messages, including, but not limited to, corporate financial performance, management’s assessment of current and future firm performance, analysts’ assessments of firm performance, domain standards and regulations as well as evidence of compliance with relevant standards and regulations. NLP applications have been used to mine these documents to obtain insights, make inferences and to create additional methodologies and artefacts to advance knowledge in accounting, auditing and finance. This paper synthesizes the extant literature in NLP in accounting, auditing and finance to establish the state of current knowledge and to identify paths for future research.
A Probabilistic Covariate Shift Assumption for Domain Adaptation

The aim of domain adaptation algorithms is to establish a learner, trained on labeled data from a source domain, that can classify samples from a target domain, in which few or no labeled data are available for training. Covariate shift, a primary assumption in several works on domain adaptation, assumes that the labeling functions of source and target domains are identical. We present a domain adaptation algorithm that assumes a relaxed version of covariate shift where the assumption that the labeling functions of the source and target domains are identical holds with a certain probability. Assuming a source deterministic large margin binary classifier, the farther a target instance is from the source decision boundary, the higher the probability that covariate shift holds. In this context, given a target unlabeled sample and no target labeled data, we develop a domain adaptation algorithm that bases its labeling decisions both on the source learner and on the similarities between the target unlabeled instances. The source labeling function decisions associated with probabilistic covariate shift, along with the target similarities are concurrently expressed on a similarity graph. We evaluate our proposed algorithm on a benchmark sentiment analysis (and domain adaptation) dataset, where state-of-the-art adaptation results are achieved. We also derive a lower bound on the performance of the algorithm.
Cardiotocographic Diagnosis of Fetal Health based on Multiclass Morphologic Pattern Predictions using Deep Learning Classification

Medical complications of pregnancy and pregnancy-related deaths continue to remain a major global challenge today. Internationally, about 830 maternal deaths occur every day due to pregnancy-related or childbirth-related complications. In fact, almost 99% of all maternal deaths occur in developing countries. In this research, an alternative and enhanced artificial intelligence approach is proposed for cardiotocographic diagnosis of fetal assessment based on multiclass morphologic pattern predictions, including 10 target classes with imbalanced samples, using deep learning classification models. The developed model is used to distinguish and classify the presence or absence of multiclass morphologic patterns for outcome predictions of complications during pregnancy. The testing results showed that the developed deep neural network model achieved an accuracy of 88.02%, a recall of 84.30%, a precision of 85.01%, and an F-score of 0.8508 in average. Thus, the developed model can provide highly accurate and consistent diagnoses for fetal assessment regarding complications during pregnancy, thereby preventing and/or reducing fetal mortality rate as well as maternal mortality rate during and following pregnancy and childbirth, especially in lowresource settings and developing countries. Activation function; deep learning; deep neural network; dropout; ensemble learning; multiclass; regularization; cardiotocography; complications during pregnancy; fetal heart rate
Determinants of women’s satisfaction with maternal health care: a review of literature from developing countries

Developing countries account for 99 percent of maternal deaths annually. While increasing service availability and maintaining acceptable quality standards, it is important to assess maternal satisfaction with care in order to make it more responsive and culturally acceptable, ultimately leading to enhanced utilization and improved outcomes. At a time when global efforts to reduce maternal mortality have been stepped up, maternal satisfaction and its determinants also need to be addressed by developing country governments. This review seeks to identify determinants of women's satisfaction with maternity care in developing countries.



The review followed the methodology of systematic reviews. Public health and social science databases were searched. English articles covering antenatal, intrapartum or postpartum care, for either home or institutional deliveries, reporting maternal satisfaction from developing countries (World Bank list) were included, with no year limit. Out of 154 shortlisted abstracts, 54 were included and 100 excluded. Studies were extracted onto structured formats and analyzed using the narrative synthesis approach.



Determinants of maternal satisfaction covered all dimensions of care across structure, process and outcome. Structural elements included good physical environment, cleanliness, and availability of adequate human resources, medicines and supplies. Process determinants included interpersonal behavior, privacy, promptness, cognitive care, perceived provider competency and emotional support. Outcome related determinants were health status of the mother and newborn. Access, cost, socio-economic status and reproductive history also influenced perceived maternal satisfaction. Process of care dominated the determinants of maternal satisfaction in developing countries. Interpersonal behavior was the most widely reported determinant, with the largest body of evidence generated around provider behavior in terms of courtesy and non-abuse. Other aspects of interpersonal behavior included therapeutic communication, staff confidence and competence and encouragement to laboring women.



Quality improvement efforts in developing countries could focus on strengthening the process of care. Special attention is needed to improve interpersonal behavior, as evidence from the review points to the importance women attach to being treated respectfully, irrespective of socio-cultural or economic context. Further research on maternal satisfaction is required on home deliveries and relative strength of various determinants in influencing maternal satisfaction.
Dual-Band Circularly Polarized Dual-Slot Antenna With a Dielectric Cover

A new circularly-polarized (CP) dual-slot antenna fed by a single L-probe is proposed for dual-band applications. The antenna consists of a zonal slot and an annular slot, both of them being cut onto a square conducting cavity with a protective dielectric cover. It is observed that the resonance frequencies of the lower and upper bands are controlled by the zonal and annular slots, respectively, making designs of the dual-band antenna very easy. To demonstrate the idea, the proposed antenna was designed at 2.4/5.8 GHz for two IMS bands. The reflection coefficient, axial ratio, radiation pattern and gain of the dual-band antenna were studied, and reasonable agreement between the simulated and measured results was obtained. A parametric study was carried out to characterize the proposed antenna.
Approximation Algorithms for Offline Risk-averse Combinatorial Optimization

We consider generic optimization problems that can be formu lated as minimizing the cost of a feasible solution w T x over a combinatorial feasible set F ⊂ {0, 1}. For these problems we describe a framework of risk-averse stochastic problems where the cost vector W has independent random components, unknown at the time of so lution. A natural and important objective that incorporates risk in this stochastic setting is to look for a feasible solution whose stochastic cost has a small tail or a small convex combi nation of mean and standard deviation. Our models can be equivalently reformulated as nonconvex programs for whi ch no efficient algorithms are known. In this paper, we make progress on these hard problems. Our results are several efficient general-purpose approxim ation schemes. They use as a black-box (exact or approximate) the solution to the underlying deterministic pr oblem and thus immediately apply to arbitrary combinatoria l problems. For example, from an available δ-approximation algorithm to the linear problem, we constru ct aδ(1 + ǫ)approximation algorithm for the stochastic problem, which invokes the linear algorithm only a logarithmic number of times in the problem input (and polynomial in 1 ǫ ), for any desired accuracy level ǫ > 0. The algorithms are based on a geometric analysis of the curvature and approximabilit y of he nonlinear level sets of the objective functions.
The effects of website design on purchase intention in online shopping: the mediating role of trust and the moderating role of culture

Lack of trust in online transactions has been cited as the main reason for the abhorrence of online shopping. We have tested the mediating role of trust in online transactions to provide empirical evidence that trust in the online store represents the generic mechanism through which the focal independent variables of website design are able to positively influence purchase intention and reduce the perceived risk. We have further demonstrated the moderating effect of the individual’s culture in e-commerce and thereby offered insights into the relative importance of website design factors contributing to trust for customers of different cultural values.
The Epistemology of a Rule-Based Expert System - A Framework for Explanation

Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, MYCIN, indicates that the representation has serious limitations: people other than the original rule authors find it difflcuit to modify the rule set, and the rules a r e unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in MYCIN’s original rule representation: the view that expert knowledge can be encoded as a uniform, weakly-structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines MYCIN’s rules from the perspective Of a teacher trylng to Justify them and to convey a problem-soivlng approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses. This design knowledge, consisting of structural and strategic concepts which lie outside the representation, is shown to be procedurally embedded In the rules. Moreover, because the data/hypothesis associations are themselves a proceduralized form of underlying disease models, they can only be supported by appealing to this deeper level of knowledge. Making explicit this structural, strategic and support knowledge enhances the ability to understand and modify the system.