nils-herrmann/clean_scirepeval huggingface dataset:
A Systematic Literature Review on Intertemporal Choice in Software Engineering - Protocol and Results

When making choices in software projects, engineers and other stakeholders engage in decision making that involves uncertain future outcomes. Research in psychology, behavioral economics and neuroscience has questioned many of the classical assumptions of how such decisions are made. This literature review aims to characterize the assumptions that underpin the study of these decisions in Software Engineering. We identify empirical research on this subject and analyze how the role of time has been characterized in the study of decision making in SE. The literature review aims to support the development of descriptive frameworks for empirical studies of intertemporal decision making in practice.
Visually Aligned Word Embeddings for Improving Zero-shot Learning

Zero-shot learning (ZSL) highly depends on a good semantic embedding to connect the seen and unseen classes. Recently, distributed word embeddings (DWE) pre-trained from large text corpus have become a popular choice to draw such a connection. Compared with human defined attributes, DWEs are more scalable and easier to obtain. However, they are designed to reflect semantic similarity rather than visual similarity and thus using them in ZSL often leads to inferior performance. To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE). Thus the neighbourhood structure of VAWEs becomes similar to that in the visual domain. Note that in this work we do not design a ZSL method that projects the visual features and semantic embeddings onto a shared space but just impose a requirement on the structure of the mapped word embeddings. This strategy allows the learned VAWE to generalize to various ZSL methods and visual features. As evaluated via four state-of-the-art ZSL methods on four benchmark datasets, the VAWE exhibit consistent performance improvement.
Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation

This paper presents a first attempt towards finding an abstractive compression generation system for a set of related sentences which jointly models sentence fusion and paraphrasing using continuous vector representations. Our paraphrastic fusion system improves the informativity and the grammaticality of the generated sentences. Our system can be applied to various real world applications such as text simplification, microblog, opinion and newswire summarization. We conduct our experiments on human generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Our experiments demonstrate that our method brings significant improvements over the state of the art systems across different metrics.
Java Treeview - extensible visualization of microarray data

Open source software encourages innovation by allowing users to extend the functionality of existing applications. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability as a platform for further development. Java Treeview is an open-source, cross-platform rewrite that handles very large datasets well, and supports extensions to the file format that allow the results of additional analysis to be visualized and compared. The combination of a general file format and open source makes Java Treeview an attractive choice for solving a class of visualization problems. An applet version is also available that can be used on any website with no special server-side setup.
Predict Anchor Links across Social Networks via an Embedding Approach

Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent we can address the anchor link prediction problem, if only structural information of networks is available. Most existing methods, unsupervised or supervised, directly work on networks themselves rather than on their intrinsic structural regularities, and thus their effectiveness is sensitive to the high dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods.
Essential oil and aromatic plants as feed additives in non-ruminant nutrition: a review

This paper summarizes the current knowledge regarding the possible modes of action and nutritional factors involved in the use of essential oils (EOs) for swine and poultry. EOs have recently attracted increased interest as feed additives to be fed to swine and poultry, possibly replacing the use of antibiotic growth promoters which have been prohibited in the European Union since 2006. In general, EOs enhance the production of digestive secretions and nutrient absorption, reduce pathogenic stress in the gut, exert antioxidant properties and reinforce the animal’s immune status, which help to explain the enhanced performance observed in swine and poultry. However, the mechanisms involved in causing this growth promotion are far from being elucidated, since data on the complex gut ecosystem, gut function, in vivo oxidative status and immune system are still lacking. In addition, limited information is available regarding the interaction between EOs and feed ingredients or other feed additives (especially pro- or prebiotics and organic acids). This knowledge may help feed formulators to better utilize EOs when they formulate diets for poultry and swine.
The effect of Gamified mHealth App on Exercise Motivation and Physical Activity

In this study, we propose a research model to assess the effect of a mobile health (mHealth) app on exercise motivation and physical activity of individuals based on the design and self-determination theory. The research model is formulated from the perspective of motivation affordance and gamification. We will discuss how the use of specific gamified features of the mHealth app can trigger/afford corresponding users’ exercise motivations, which further enhance users’ participation in physical activity. We propose two hypotheses to test the research model using a field experiment. We adopt a 3-phase longitudinal approach to collect data in three different time zones, in consistence with approach commonly adopted in psychology and physical activity research, so as to reduce the common method bias in testing the two hypotheses.
Combined treatment with botulinum toxin and hyaluronic acid to correct
unsightly lateral-chin depression*

With aging, anatomical changes are observed in the face. In the lower third, these changes are expressed as ptosis of the angle of the mouth, lip enhancement groove mentalis; decrease in concavity between the jaw and neck and very noticeable platysmal banding. The repeated contraction of muscles of the lateral-chin together with the band platysmal side form what are called a marionette groove. Treating the whole lateral-chin area can result in a more harmonious aspect of the face when compared with treatment of a marionette groove in isolation. In this paper we describe combined treatment of the lateral chin area using botulinum toxin and fillers.
Evaluation of the seismic performance of a code-conforming reinforced-concrete frame building — from seismic hazard to collapse safety and economic losses

A state-of-the-art seismic performance assessment is illustrated through application to a reinforcedconcrete moment-frame building designed per current (2003) building code provisions. Performance is quantified in terms of economic losses and collapse safety. The assessment includes site-specific seismic hazard analyses, nonlinear dynamic structural response simulations to collapse, damage analyses, and loss estimation. When selecting ground motion records for nonlinear dynamic analyses that are consistent with a target hazard level expressed in terms of a response spectral value at the building’s fundamental period, it is important to consider the response spectral shape, especially when considering higher hazard levels. This was done through the parameter commonly denoted by . Neglecting these effects during record selection is shown to lead to a factor of 5–10 overestimation of mean annual collapse rate. Structural response simulations, which properly account for uncertainties in ground motions and structural modelling, indicate a 2–7% probability of collapse for buildings subjected to motions scaled to a hazard level equivalent to a 2% probability of exceedance in 50 years. The probabilities of component damage and the means and coefficients of variation of the repair costs are calculated using fragility functions and repair-cost probability distributions. The calculated expected annual losses for various building design variants range from 0.6 to 1.1% of the replacement value, where the smaller losses are for above-code design variants and the larger losses are for buildings designed with minimum-code compliance. Sensitivity studies highlight the impact of key modelling assumptions on the accurate calculation of damage and the associated repair costs.
A survey on Automatic Analysis of Statistical Properties of Rheumatoid Arthritis using Digital Image Processing

Rheumatoid arthritis destroys joints of the body like erosion in bones which intern may cause deformity and characterized by inflammation of the tissue around the joints as well as in other organs of the body. At the beginning of this disease mainly the joints of hand and wrist are affected making hand radiograph analysis very important. Lately manual JSW measurement in hand X-ray digital radiograph of Arthritis patients were in use but it has disadvantages like inaccuracy, inter-reader variability. The reproducible quantification of the progression of joint space narrowing and the erosive bone destructions caused by RA is crucial during treatment and in imaging biomarkers in clinical trials. Current manual scoring methods exhibit interreader variability, even after intensive training, and thus, impede the efficient monitoring of the disease. Hand radiograph analysis is difficult for radiologist as there are 14 number of hand joints. To avoid observer dependency, computer-aided analysis is required. Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification with statistical properties for RA patients has not been widely investigated. The automated analysis of statistical properties helps to reduce need of skilled personnel.
A Three-stage Deep Learning Model for Accurate Retinal Vessel Segmentation.

Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both types of vessels simultaneously by using a unified pixelwise loss which treats all vessel pixels with equal importance. Due to the highly imbalanced ratio between thick vessels and thin vessels (namely the majority of vessel pixels belong to thick vessels), the pixel-wise loss would be dominantly guided by thick vessels and relatively little influence comes from thin vessels, often leading to low segmentation accuracy for thin vessels. To address the imbalance problem, in this paper, we explore to segment thick vessels and thin vessels separately by proposing a three-stage deep learning model. The vessel segmentation task is divided into three stages, namely thick vessel segmentation, thin vessel segmentation and vessel fusion. As better discriminative features could be learned for separate segmentation of thick vessels and thin vessels, this process minimizes the negative influence caused by their highly imbalanced ratio. The final vessel fusion stage refines the results by further identifying non-vessel pixels and improving the overall vessel thickness consistency. The experiments on public datasets DRIVE, STARE and CHASE DB1 clearly demonstrate that the proposed threestage deep learning model outperforms the current state-of-theart vessel segmentation methods.
Biofilm removal with a dental water jet.

The objective of this study was to evaluate the effect of a dental water jet on plaque biofilm removal using scanning electron microscopy (SEM).



Eight teeth with advanced aggressive periodontal disease were extracted. Ten thin slices were cut from four teeth. Two slices were used as the control. Eight were inoculated with saliva and incubated for 4 days. Four slices were treated using a standard jet tip, and four slices were treated using an orthodontic jet tip. The remaining four teeth were treated with the orthodontic jet tip but were not inoculated with saliva to grow new plaque biofilm. All experimental teeth were treated using a dental water jet for 3 seconds on medium pressure.



The standard jet tip removed 99.99% of the salivary (ex vivo) biofilm, and the orthodontic jet tip removed 99.84% of the salivary biofilm. Observation of the remaining four teeth by the naked eye indicated that the orthodontic jet tip removed significant amounts of calcified (in vivo) plaque biofilm. This was confirmed by SEM evaluations.



The Waterpik dental water jet (Water Pik, Inc, Fort Collins, CO) can remove both ex vivo and in vivo plaque biofilm significantly.
Pharmacokinetic Interactions of Herbs with Cytochrome P450 and P-Glycoprotein

The concurrent use of drugs and herbal products is becoming increasingly prevalent over the last decade. Several herbal products have been known to modulate cytochrome P450 (CYP) enzymes and P-glycoprotein (P-gp) which are recognized as representative drug metabolizing enzymes and drug transporter, respectively. Thus, a summary of knowledge on the modulation of CYP and P-gp by commonly used herbs can provide robust fundamentals for optimizing CYP and/or P-gp substrate drug-based therapy. Herein, we review ten popular medicinal and/or dietary herbs as perpetrators of CYP- and P-gp-mediated pharmacokinetic herb-drug interactions. The main focus is placed on previous works on the ability of herbal extracts and their phytochemicals to modulate the expression and function of CYP and P-gp in several in vitro and in vivo animal and human systems.
Movie Rating Prediction

The Internet Movie Database (IMDB) is one of the largest online resources for general movie information combined with a forum in which users can rate movies. We investigate the extent to which a movie’s average user rating can be predicted after learning the relationship between the rating and a movie’s various attributes from a training set. Two methods are evaluated: kernel regression and model trees. Modifications to standard algorithms for training these two regressors lead to better prediction accuracy on this data set.
Family sources of educational gender inequality in rural china: A critical assessment.

In this paper, we investigate the gender gap in education in rural northwest China. We first discuss parental perceptions of abilities and appropriate roles for girls and boys; parental concerns about old-age support; and parental perceptions of different labor market outcomes for girls' and boys' education. We then investigate gender disparities in investments in children, children's performance at school, and children's subsequent attainment. We analyze a survey of 9-12-year-old children and their families conducted in rural Gansu Province in the year 2000, along with follow-up information about subsequent educational attainment collected 7 years later. We complement our main analysis with two illustrative case studies of rural families drawn from 11 months of fieldwork conducted in rural Gansu between 2003 and 2005 by the second author.In 2000, most mothers expressed egalitarian views about girls' and boys' rights and abilities, in the abstract. However, the vast majority of mothers still expected to rely on sons for old-age support, and nearly one in five mothers interviewed agreed with the traditional saying, "Sending girls to school is useless since they will get married and leave home." Compared to boys, girls faced somewhat lower (though still very high) maternal educational expectations and a greater likelihood of being called on for household chores than boys. However, there was little evidence of a gender gap in economic investments in education. Girls rivaled or outperformed boys in academic performance and engagement. Seven years later, boys had attained just about a third of a year more schooling than girls-a quite modest advantage that could not be fully explained by early parental attitudes and investments, or student performance or engagement. Fieldwork confirmed that parents of sons and daughters tended to have high aspirations for their children. Parents sometimes viewed boys as having greater aptitude, but tended to view girls as having more dedication-an attribute parents perceived as being critical for educational success. Findings suggest that at least in Gansu, rural parental educational attitudes and practices toward boys and girls are more complicated and less uniformly negative for girls than commonly portrayed.
Electronic personal maternity records: Both web and smartphone services

This study develops an antenatal care information system to assist women during pregnancy. We designed and implemented the system as both a web-based service and a multi-platform application for smartphones and tablets. The proposed system has three novel features: (1) web-based maternity records, which contains concise explanations of various antenatal screening and diagnostic tests; (2) self-care journals, which allow pregnant women to keep track of their gestational weight gains, blood pressure, fetal movements, and contractions; and (3) health education, which automatically presents detailed information on antenatal care and other pregnancy-related knowledge according to the women's gestational age. A survey was conducted among pregnant women to evaluate the usability and acceptance of the proposed system. In order to prove that the antenatal care was effective, clinical outcomes should be provided and the results are focused on a usability evaluation.
Using global mapping to create more accurate document-level maps of research fields

We describe two general approaches to creating document-level maps of science. To create a local map one defines and directly maps a sample of data, such as all literature published in a set of information science journals. To create a global map of a research field one maps ‘all of science’ and then locates a literature sample within that full context. We provide a deductive argument that global mapping should create more accurate partitions of a research field than local mapping, followed by practical reasons why this may not be so. The field of information science is then mapped at the document level using both local and global methods to provide a case illustration of the differences between the methods. Textual coherence is used to assess the accuracies of both maps. We find that document clusters in the global map have significantly higher coherence than those in the local map, and that the global map provides unique insights into the field of information science that cannot be discerned from the local map. Specifically, we show that information science and computer science have a large interface and that computer science is the more progressive discipline at that interface. We also show that research communities in temporally linked threads have a much higher coherence than isolated communities, and that this feature can be used to predict which threads will persist into a subsequent year. Methods that could increase the accuracy of both local and global maps in the future are also discussed.
Survey on Chatbot Design Techniques in Speech Conversation Systems

Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prizewinning Chatbots. AIML; Chatbot; Loebner Prize; NLP; NLTK; SQL; Turing Test
A Sarcasm Extraction Method Based on Patterns of Evaluation Expressions

Sarcasm presents a negative meaning with positive expressions and is a non-literalistic expression. Sarcasm detection is an important task because it contributes directly to the improvement of the accuracy of sentiment analysis tasks. In this study, we propose a extraction method of sarcastic sentences in product reviews. First, we analyze sarcastic sentences in product reviews and classify the sentences into 8 classes by focusing on evaluation expressions. Next, we generate classification rules for each class and use them to extract sarcastic sentences. Our method consists of three stage, judgment processes based on rules for 8 classes, boosting rules and rejection rules. In the experiment, we compare our method with a baseline based on a simple rule. The experimental result shows the effectiveness of our method.
Subspace Clustering of Categorical and Numerical Data With an Unknown Number of Clusters

In clustering analysis, data attributes may have different contributions to the detection of various clusters. To solve this problem, the subspace clustering technique has been developed, which aims at grouping the data objects into clusters based on the subsets of attributes rather than the entire data space. However, the most existing subspace clustering methods are only applicable to either numerical or categorical data, but not both. This paper, therefore, studies the soft subspace clustering of data with both of the numerical and categorical attributes (also simply called mixed data for short). Specifically, an attribute-weighted clustering model based on the definition of object-cluster similarity is presented. Accordingly, a unified weighting scheme for the numerical and categorical attributes is proposed, which quantifies the attribute-to-cluster contribution by taking into account both of intercluster difference and intracluster similarity. Moreover, a rival penalized competitive learning mechanism is further introduced into the proposed soft subspace clustering algorithm so that the subspace cluster structure as well as the most appropriate number of clusters can be learned simultaneously in a single learning paradigm. In addition, an initialization-oriented method is also presented, which can effectively improve the stability and accuracy of  $k$ -means-type clustering methods on numerical, categorical, and mixed data. The experimental results on different benchmark data sets show the efficacy of the proposed approach.