nils-herrmann/clean_scirepeval huggingface dataset:
Topic-to-Essay Generation with Neural Networks

We focus on essay generation, which is a challenging task that generates a paragraph-level text with multiple topics. Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training and evaluation resources. To address this, we develop a multi-topic-aware long short-term memory (MTA-LSTM) network. In this model, we maintain a novel multi-topic coverage vector, which learns the weight of of each topic and is sequentially updated during the decoding process. Afterwards this vector is fed to an attention model to guide the generator. Moreover, we automatically construct two paragraph-level Chinese essay corpora, 305,000 essay paragraphs and 55,000 question-and-answer pairs. Empirical results show that our approach obtains much better BLEU-2 score compared to various baselines. Furthermore, human judgment shows that MTA-LSTM has the ability to generate essays that are not only coherent but also closely related to the input topics.
Helping effort in a dominance hierarchy

In many cooperatively breeding species, group members form a dominance hierarchy or queue to inherit the position of breeder. Models aimed at understanding individual variation in helping behavior, however, rarely take into account the effect of dominance rank on expected future reproductive success and thus the potential direct fitness costs of helping. Here we develop a kin-selection model of helping behavior in multimember groups in which only the highest ranking individual breeds. Each group member can invest in the dominant’s offspring at a cost to its own survivorship. The model predicts that lower ranked subordinates, who have a smaller probability of inheriting the group, should work harder than higher ranked subordinates. This prediction holds regardless of whether the intrinsic mortality rate of subordinates increases or decreases with rank. The prediction does not necessarily hold, however, where the costs of helping are higher for lower ranked individuals: a situation that may be common in vertebrates. The model makes two further testable predictions: that the helping effort of an individual of given rank should be lower in larger groups, and the reproductive success of dominants should be greater where group members are more closely related. Empirical evidence for these predictions is discussed. We argue that the effects of rank on stable helping effort may explain why attempts to correlate individual helping effort with relatedness in cooperatively breeding species have met with limited success.
Exploring Think-Alouds in Usability Testing: An International Survey

The study explored think-aloud methods usage within usability testing by examining the following questions: How, and why is the think-aloud method used? What is the gap between theory and practice? Where does this gap occur?  The review informed the survey design. Usability research based on field studies and empirical tests indicates that variations in think-aloud procedures may reduce test reliability. The guidance offered on think-aloud procedures within a number of handbooks on usability testing is also mixed. This indicates potential variability in practice, but how much and for what reasons is unknown.  An exploratory, qualitative survey was conducted using a web-based questionnaire (during November-December 2010). Usability evaluators were sought via emails (sent to personal contacts, usability companies, conference attendees, and special interest groups) to be cascaded to the international community. As a result we received 207 full responses. Descriptive statistics and thematic coding were used to analyze the data sets.  Respondents found the concurrent technique particularly suited usability testing as it was fast, easy for users to relate to, and requires limited resources. Divergent practice was reported in terms of think-aloud instructions, practice, interventions, and the use of demonstrations. A range of interventions was used to better understand participant actions and verbalizations, however, respondents were aware of potential threats to test reliability, and took steps to reduce this impact.  The reliability considerations underpinning the classic think-aloud approach are pragmatically balanced against the need to capture useful data in the time available. A limitation of the study is the focus on the concurrent method; other methods were explored but the differences in application were not considered. Future work is needed to explore the impact of divergent use of think-aloud instructions, practice tasks, and the use of demonstrations on test reliability.
Chinese Clinical Entity Recognition via Attention-Based CNN-LSTM-CRF

Chinese clinical entity recognition is a fundamental task of Chinese clinical natural language processing, which has attracted plenty of attention. In this paper, we propose a novel neural network, called attention-based CNN-LSTM-CRF, for this task. The neural network employs a CNN (convolutional neural network) layer to capture local context information of words of interest, a LSTM (long-short term memory) layer to obtain global information of each sentence, an attention layer to select relevant words, and a CRF layer to predict a label sequence for an input sentence. In order to evaluate the performance of the proposed method, we compare it with other two state-of-the-art methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. Experimental results show that the proposed neural network outperforms CRF and LSTM-CRF.
Online Learning: A Comprehensive Survey

Online learning represents a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time. The goal of online learning is to maximize the accuracy/correctness for the sequence of predictions/decisions made by the online learner given the knowledge of correct answers to previous prediction/learning tasks and possibly additional information. This is in contrast to traditional batch or offline machine learning methods that are often designed to learn a model from the entire training data set at once. Online learning has become a promising technique for learning from continuous streams of data in many real-world applications. This survey aims to provide a comprehensive survey of the online machine learning literature through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the types of learning tasks and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) online supervised learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) online unsupervised learning where no feedback is available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.
Stable isotope labeling by amino acids in cell culture (SILAC).

Quantitative proteomics has become a pivotal tool that has been applied to the investigation of many different biological processes such diverse as the detection of biomarkers in tissue samples, the regulation of cell signaling, and the characterization of protein interactions. Stable isotope labeling techniques have facilitated the precise quantitation of changes in protein abundance by mass spectrometry. Among different choices, Stable Isotope Labeling by Amino acids in Cell culture (SILAC) is an easy and reliable method for unbiased comparative proteomic experiments, which has been employed to study post-translational modifications such as protein phosphorylation and methylation, to characterize signaling pathways and to determine specific protein interactions. Here we describe detailed procedures for SILAC experiments in mammalian and yeast cells.
Hyponym/Hypernym Detection in Science and Technology Thesauri from Bibliographic Datasets

Thesauri for science and technology information are increasingly used in bibliometrics and scientometrics. However, the manual construction and maintenance of thesauri is costly and time consuming, thus, methods for semi-automatic construction and maintenance are being actively studied. We propose a method that expands an existing thesaurus with specified terms extracted from the abstracts of articles. Specifically, we assign the terms to specified subcategories by clustering a word vector space, then determine the hyponyms and hypernyms based on their relations with terms in the sub-categories. The word vectors are constructed from 177,000 IEEE articles archived from 2012 to 2014 in the Scopus dataset. In experiments, the terms were correctly classified into the Japan Science and Technology thesaurus with 70.8% precision and 75.4% recall. In future, we will develop a semiautomatic thesaurus maintenance system that recommends new terms in their proper relative positions.
Depression, stress and anxiety in medical students: A cross-sectional comparison between students from different semesters.

To compare the prevalence of anxiety, depression, and stress in medical students from all semesters of a Brazilian medical school and assess their respective associated factors.



A cross-sectional study of students from the twelve semesters of a Brazilian medical school was carried out. Students filled out a questionnaire including sociodemographics, religiosity (DUREL - Duke Religion Index), and mental health (DASS-21 - Depression, Anxiety, and Stress Scale). The students were compared for mental health variables (Chi-squared/ANOVA). Linear regression models were employed to assess factors associated with DASS-21 scores.



761 (75.4%) students answered the questionnaire; 34.6% reported depressive symptomatology, 37.2% showed anxiety symptoms, and 47.1% stress symptoms. Significant differences were found for: anxiety - ANOVA: [F = 2.536, p=0.004] between first and tenth (p=0.048) and first and eleventh (p=0.025) semesters; depression - ANOVA: [F = 2.410, p=0.006] between first and second semesters (p=0.045); and stress - ANOVA: [F = 2.968, p=0.001] between seventh and twelfth (p=0.044), tenth and twelfth (p=0.011), and eleventh and twelfth (p=0.001) semesters. The following factors were associated with (a) stress: female gender, anxiety, and depression; (b) depression: female gender, intrinsic religiosity, anxiety, and stress; and (c) anxiety: course semester, depression, and stress.



Our findings revealed high levels of depression, anxiety, and stress symptoms in medical students, with marked differences among course semesters. Gender and religiosity appeared to influence the mental health of the medical students.
FPGA implementation for stereo matching algorithm

To reach a real-time stereo vision in embedded systems, we propose in this paper, the adaptation and optimization of the well-known Disparity Space Image (DSI) on a single FPGA(Field programmable gate Arrays) that is designed for high efficiency when realized in hardware. An initial disparity map was calculated using the DSI structure and then a median filter was applied to smooth the disparity map. Many methods reported in the literature are mainly restricted to implement the SAD algorithm (Sum of Absolute Differences) on an FPGA. An evaluation of our method is done by comparing the obtained results of our method with a very fast and well-known sum of absolute differences algorithm using hardware-based implementations.
Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems.

Clinical trial investigators often record a great deal of baseline data on each patient at randomization. When reporting the trial's findings such baseline data can be used for (i) subgroup analyses which explore whether there is evidence that the treatment difference depends on certain patient characteristics, (ii) covariate-adjusted analyses which aim to refine the analysis of the overall treatment difference by taking account of the fact that some baseline characteristics are related to outcome and may be unbalanced between treatment groups, and (iii) baseline comparisons which compare the baseline characteristics of patients in each treatment group for any possible (unlucky) differences. This paper examines how these issues are currently tackled in the medical journals, based on a recent survey of 50 trial reports in four major journals. The statistical ramifications are explored, major problems are highlighted and recommendations for future practice are proposed. Key issues include: the overuse and overinterpretation of subgroup analyses; the underuse of appropriate statistical tests for interaction; inconsistencies in the use of covariate-adjustment; the lack of clear guidelines on covariate selection; the overuse of baseline comparisons in some studies; the misuses of significance tests for baseline comparability, and the need for trials to have a predefined statistical analysis plan for all these uses of baseline data.
Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart Textile Technology Acceptance Model

Smart interactive textiles are the next frontier of ubiquitous computing and may serve as novel and accepted interfaces that go beyond conventional human-computer interaction. Apart from the technical perspective, it is important to understand if and under which conditions people adopt these technologies and which factors constitute perceived barriers of technology acceptance. In this work we examine people’s attitudes towards smart textiles and their relationship to the intention to use these products in their home environment. This article provides a precise modeling of younger and older people in regard to expertise in interacting with technology, their desire to control and automate functions in their home environment, and the evaluation of a smart cushion for controlling the home environment, using the Smart Textile Technology Acceptance Model that is derived from the Unified Theory of Acceptance and Use of Technology 2 model. This model was applied for a specific smart textile product and the evaluation was focused on user-diversity, attitudes, and age. The article concludes with open research questions and guidelines for practitioners to leverage the benefits of smart textile user interfaces.
A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

We consider the isolated spelling error correction problem as a specific subproblem of the more general string-to-string translation problem. In this context, we investigate four general string-to-string transformation models that have been suggested in recent years and apply them within the spelling error correction paradigm. In particular, we investigate how a simple ‘k-best decoding plus dictionary lookup’ strategy performs in this context and find that such an approach can significantly outdo baselines such as edit distance, weighted edit distance, and the noisy channel Brill and Moore model to spelling error correction. We also consider elementary combination techniques for our models such as language model weighted majority voting and center string combination. Finally, we consider real-world OCR post-correction for a dataset sampled from medieval Latin texts.
ustainable energy systems : Role of optimization modeling techniques in power eneration and supply — A review

Electricity is conceivably the most multipurpose energy carrier in modern global economy, and therefore primarily linked to human and economic development. Energy sector reform is critical to sustainable energy development and includes reviewing and reforming subsidies, establishing credible regulatory frameworks, developing policy environments through regulatory interventions, and creating marketbased approaches. Energy security has recently become an important policy driver and privatization of the electricity sector has secured energy supply and provided cheaper energy services in some countries in the short term, but has led to contrary effects elsewhere due to increasing competition, resulting in deferred investments in plant and infrastructure due to longer-term uncertainties. On the other hand global dependence on fossil fuels has led to the release of over 1100 GtCO2 into the atmosphere since the mid-19th century. Currently, energy-related GHG emissions, mainly from fossil fuel combustion for heat supply, electricity generation and transport, account for around 70% of total emissions including carbon dioxide, methane and some traces of nitrous oxide. This multitude of aspects play a role in societal debate in comparing electricity generating and supply options, such as cost, GHG emissions, radiological and toxicological exposure, occupational health and safety, employment, domestic energy security, and social impressions. Energy systems engineering provides a methodological scientific framework to arrive at realistic integrated solutions to complex energy problems, by adopting a holistic, systems-based approach, especially at decision making and planning stage. Modeling and optimization found widespread applications in the study of physical and chemical systems, production planning and scheduling systems, location and transportation problems, resource allocation in financial systems, and engineering design. This article reviews the literature on power and supply sector developments and analyzes the role of modeling and optimization in this sector as well as the future prospective of optimization modeling as a tool for sustainable energy systems.
Stress Test Evaluation for Natural Language Inference

Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed “stress tests” that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area.
Tightness – looseness : A new framework to understand consumer behavior

In February of 2011, U.S. electronics and entertainment retailer Best Buy closed all nine of its branded stores in China after only five years in the market. Although analysts had believed that China held promise for large growth opportunities, Best Buy ultimately captured less than 1% of the market and struggled to compete against local rival companies Gome and Suning, each of which had more than 1000 branded stores in the country (Waldmeir, Strauss, & Birchall, 2011). According to research conducted by China Market Research Group, a strategic market intelligence firm based in Shanghai, Best Buy's failure ultimately resulted from a lack of understanding of Chinese consumer norms for smaller, conveniently located stores (Rein, 2011). Similarly, a Financial Times article explained that the company's store strategy of dividing up items by category rather than leading brands was “at odds with local habits” (Waldmeir et al., 2011). Failures of this magnitude illustrate the critical need for cross-cultural research in consumer behavior (CB). Once primarily a Western enterprise, the field has begun to go global (Maheswaran & Shavitt, 2000; Torelli & Rodas, 2016). In just the last two decades, CB research has examined how culture influences brand loyalty (Kim, Forsythe, Gu, & Jae Moon, 2002; Lam, 2007; Luo, Zhang, & Liu, 2015; Ogba & Tan, 2009; Palumbo & Herbig, 2000; Subramaniam, Al Mamun, Permarupan, & Zainol, 2014;  2009), brand extensions (Buil, de Chernatony, & Hem, 2009; Grønhaug, Hem, & Lines, 2002; Han & Schmitt, 1997; Monga & John, 2007; Ng, 2010;
Dual-Polarized Microstrip Patch Planar Array Antennas With Improved Port-to-Port Isolation

A method of preventing the degradation in the isolation between the orthogonal polarization ports caused by beamforming network routing in combined edge/aperture fed dual-polarized microstrip-patch planar array antennas is described. The simulated and measured performance of such planar arrays is demonstrated. Measured port isolations of 50 dB at center frequency, and more than 40 dB over a 4% bandwidth, are achieved. In addition, insight into the physical reasons for the improved port-to-port isolation levels, of the proposed element geometry and beamforming network layout, is obtained through prudent use of the electromagnetic modelling.
Analysis of the Effective Factors on Online Purchase Intention through Theory of Planned Behavior

As the rapid growth of the World Wide Web, electronic commerce has become a new way for businesses on online markets. Du to its facilities, many people are willing to shop online. Although business-to-consumer e-commerce has created new businesses opportunities, questions about consumer shopping motivations toward online shopping versus conventional shopping continue to persist. The purpose of this study is to investigate factors which affect online purchase intention. In this regard, providing a conceptual framework, the effect of some factors including subjective norms, behavioral control, attitude, friends role (social influence) and perceived credibility on online purchase intention are investigated. This is a descriptivesurvival study. Data collection is cross-field due to using questionnaire as well as library resources. Statistical population includes Isfahan University’s students. Using Cochran formula, sample volume is equal to 260 people. Descriptive and inferential statistics and structural equations are used to analyze data and test hypothesis. The results of the research suggest that all mentioned factors, i.e. subjective norms, behavioral control, attitude, social influence, and credibility affect online shopping directly. We hope that the findings of this research have potential benefits for online retailers, and all those looking to make more virtual sales, also it could reveal researchers in future research.
Integration of multiple speech segmentation cues: a hierarchical framework.

A central question in psycholinguistic research is how listeners isolate words from connected speech despite the paucity of clear word-boundary cues in the signal. A large body of empirical evidence indicates that word segmentation is promoted by both lexical (knowledge-derived) and sublexical (signal-derived) cues. However, an account of how these cues operate in combination or in conflict is lacking. The present study fills this gap by assessing speech segmentation when cues are systematically pitted against each other. The results demonstrate that listeners do not assign the same power to all segmentation cues; rather, cues are hierarchically integrated, with descending weights allocated to lexical, segmental, and prosodic cues. Lower level cues drive segmentation when the interpretive conditions are altered by a lack of contextual and lexical information or by white noise. Taken together, the results call for an integrated, hierarchical, and signal-contingent approach to speech segmentation.