nils-herrmann/clean_scirepeval huggingface dataset:
Normalization of non-standard words

In addition to ordinary words and names, real text contains non-standard “words” (NSWs), including numbers, abbreviations, dates, currency amounts and acronyms. Typically, one cannot find NSWs in a dictionary, nor can one find their pronunciation by an application of ordinary “letter-to-sound” rules. Non-standard words also have a greater propensity than ordinary words to be ambiguous with respect to their interpretation or pronunciation. In many applications, it is desirable to “normalize” text by replacing the NSWs with the contextually appropriate ordinary word or sequence of words. Typical technology for text normalization involves sets of ad hoc rules tuned to handle one or two genres of text (often newspaper-style text) with the expected result that the techniques do not usually generalize well to new domains. The purpose of the work reported here is to take some initial steps towards addressing deficiencies in previous approaches to text normalization. We developed a taxonomy of NSWs on the basis of four rather distinct text types—news text, a recipes newsgroup, a hardware-product-specific newsgroup, and real-estate classified ads. We then investigated the application of several general techniques including n-gram language models, decision trees and weighted finite-state transducers to the range of NSW types, and demonstrated that a systematic treatment can lead to better results than have been obtained by the ad hoc treatments that have typically been used in the past. For abbreviation expansion in particular, we investigated both supervised and unsupervised approaches. We report results in terms of word-error rate, which is standard in speech recognition evaluations, but which has only occasionally been used as an overall measure in evaluating text normalization systems.
Top Persuader Prediction for Social Networks

Our proof is built on Perron-Frobenius theorem, a seminal work in matrix theory (Meyer 2000). By Perron-Frobenius theorem, the power iteration algorithm for predicting top K persuaders converges to a unique C and this convergence is independent of the initialization of C if the persuasion probability matrix P is nonnegative, irreducible, and aperiodic (Heath 2002). We first show that P is nonnegative. Each component of the right hand side of Equation (10) is positive except nD $ 0; thus, persuasion probability pij estimated with Equation (10) is positive, for all i, j = 1, 2, ..., n and i ... j. Because all diagonal elements of P are equal to zero and all non-diagonal elements of P are positive persuasion probabilities, P is nonnegative.
Sentiment analysis of movie reviews using lexicon approach

Sentiment analysis is a language processing task which is used to find out opinion expressed in online reviews to categorize it into different classes like positive, negative or neutral. The paper aims to summarize the movie reviews at aspect level so that user can easily find out which aspects of movie are liked and disliked by user. Before finding aspect and its respective opinion of movie, proposed system performs subjectivity analysis. Subjectivity analysis is one of the important and useful tasks in sentiment analysis. Online reviews may consist of both objective and subjective sentences. Among these, objective sentences consist of only factual information and no sentiments or opinion. Hence subjective sentences are considered for further processing i.e. to find feature-opinion pair and to find summery at aspect level. In this paper, two different methods are implemented for finding subjectivity of sentences and then rule based system is used to find feature-opinion pair and finally the orientation of extracted opinion is revealed using two different method. Initially the proposed system uses SentiWordNet approach to find out orientation of extracted opinion and then it uses the method which is based on lexicon consisting list of positive and negative words.
Instance Segmentation by Deep Coloring

We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation. This reduction allows to train feed-forward non-recurrent deep instance segmentation systems in an end-to-end fashion using architectures that have been proposed for semantic segmentation. Our approach proceeds by introducing a fixed number of labels (colors) and then dynamically assigning object instances to those labels during training (coloring). A standard semantic segmentation objective is then used to train a network that can color previously unseen images. At test time, individual object instances can be recovered from the output of the trained convolutional network using simple connected component analysis. In the experimental validation, the coloring approach is shown to be capable of solving diverse instance segmentation tasks arising in autonomous driving (the Cityscapes benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and high-throughput microscopy image analysis. The source code is publicly available: https://github.com/kulikovv/DeepColoring.
TRUST ENHANCED TECHNOLOGY ACCEPTANCE MODEL-CONSUMER ACCEPTANCE OF MOBILE PAYMENT SOLUTIONS Tentative evidence

This study aims to explore and model the central consumer perceptions that affect the decision of whether to use mobile payment systems. In particular, we study whether the Technology Acceptance Model (TAM) describing user acceptance of technology offers comprehensive explanation for consumer decisions related to adoption of mobile payments. This study is based on two rounds of focus group interviews, the total number of subjects interviewed being 61. Our analysis suggests that the Technology Acceptance Model (TAM) provides a good basis to explain use of mobile payment solutions, yet, our data proposes that a new construct, trust, should be included into the model to augment the present descriptors in explaining consumer adoption decisions in the mobile payment context.
Self-esteem of persons with cerebral palsy: from adolescence to adulthood.

A longitudinal study of self-esteem in 22 adolescents with cerebral palsy is reported. The subjects were matched with nondisabled adolescents by age, sex, IQ, and school. Seven years later, 39 of the 44 subjects (mean age = 22.8 years) completed the Tennessee Self-Concept Scale (Roid & Fitts, 1988), the Social Support Inventory (McCubbin, Patterson, Rossman, & Cooke, 1982), and a demographic questionnaire with some open-ended questions. As adolescents, the girls with cerebral palsy scored significantly lower than the other groups on physical, social, and personal self-esteem; however, as adults, these subjects were no longer significantly different from the other groups. Male subjects with cerebral palsy had self-esteem scores similar to those of the nondisabled groups in both adolescence and adulthood. Demographic information is summarized. The factors that the subjects identified as leading to changes in self-esteem were relationships and experiences. The low self-esteem scores indicate that psychosocial occupational therapy intervention with adolescent girls with cerebral palsy and with some adults with cerebral palsy would be appropriate.
Teaching and Learning Vocabulary : Bringing Research to Practice

Teaching and Learning  Bringing Research to Practice edited by Hiebert and Kamil is a collection of twelve chapters dealing with vocabulary instruction for a variety of student populations such as young children, English language learners, and young adolescents . The book's core chapters are contributions from presenters at the 2003 Pacific Resources for Education and Learning (PREL) forum, which focused on vocabulary . To illustrate the relationship between vocabulary and literacy, all authors in the volume describe work showing that vocabulary experiences support conceptual learning and comprehension of text. The main feature of this book is that virtually all chapters d aw implications for vocabulary instruction from research .
An Engagement-Based Customer Lifetime Value System for E-commerce

A comprehensive understanding of individual customer value is crucial to any successful customer relationship management strategy. It is also the key to building products for long-term value returns. Modeling customer lifetime value (CLTV) can be fraught with technical difficulties, however, due to both the noisy nature of user-level behavior and the potentially large customer base. Here we describe a new CLTV system that solves these problems. This was built at Groupon, a large global e-commerce company, where confronting the unique challenges of local commerce means quickly iterating on new products and the optimal inventory to appeal to a wide and diverse audience. Given current purchaser frequency we need a faster way to determine the health of individual customers, and given finite resources we need to know where to focus our energy. Our CLTV system predicts future value on an individual user basis with a random forest model which includes features that account for nearly all aspects of each customer's relationship with our platform. This feature set includes those quantifying engagement via email and our mobile app, which give us the ability to predict changes in value far more quickly than models based solely on purchase behavior. We further model different customer types, such as one-time buyers and power users, separately so as to allow for different feature weights and to enhance the interpretability of our results. Additionally, we developed an economical scoring framework wherein we re-score a user when any trigger events occur and apply a decay function otherwise, to enable frequent scoring of a large customer base with a complex model. This system is deployed, predicting the value of hundreds of millions of users on a daily cadence, and is actively being used across our products and business initiatives.
Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images

Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS is often a prerequisite for other imaging analytics, methodological simplicity and generality are key factors in usability. Along those lines, we present a bottomup deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape. We incorporate the deeply supervised learning framework, but enhance it with a simple, yet effective, progressive multi-path scheme, which more reliably merges outputs from different network stages. The result is a deep model able to produce finer detailed masks, which we call progressive holistically-nested networks (P-HNNs). Using extensive cross-validation, our method is tested on a multi-institutional dataset comprising 929 CT scans (848 publicly available), of pathological lungs, reporting mean dice scores of 0.985 and demonstrating significant qualitative and quantitative improvements over state-of-the art approaches.
Convolutional Neural Networks for human activity recognition using mobile sensors

A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.
Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2Dsupervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn. P. Henderson School of Informatics, University of Edinburgh, Scotland E-mail: paul@pmh47.net V. Ferrari Google Research, Zürich, Switzerland E-mail: vittoferrari@google.com
Deep learning for healthcare decision making with EMRs

Computer aid technology is widely applied in decision-making and outcome assessment of healthcare delivery, in which modeling knowledge and expert experience is technically important. However, the conventional rule-based models are incapable of capturing the underlying knowledge because they are incapable of simulating the complexity of human brains and highly rely on feature representation of problem domains. Thus we attempt to apply a deep model to overcome this weakness. The deep model can simulate the thinking procedure of human and combine feature representation and learning in a unified model. A modified version of convolutional deep belief networks is used as an effective training method for large-scale data sets. Then it is tested by two instances: a dataset on hypertension retrieved from a HIS system, and a dataset on Chinese medical diagnosis and treatment prescription from a manual converted electronic medical record (EMR) database. The experimental results indicate that the proposed deep model is able to reveal previously unknown concepts and performs much better than the conventional shallow models.
Enterococcus faecalis: its role in root canal treatment failure and current concepts in retreatment.

Enterococcus faecalis is a microorganism commonly detected in asymptomatic, persistent endodontic infections. Its prevalence in such infections ranges from 24% to 77%. This finding can be explained by various survival and virulence factors possessed by E. faecalis, including its ability to compete with other microorganisms, invade dentinal tubules, and resist nutritional deprivation. Use of good aseptic technique, increased apical preparation sizes, and inclusion of 2% chlorhexidine in combination with sodium hypochlorite are currently the most effective methods to combat E. faecalis within the root canal systems of teeth. In the changing face of dental care, continued research on E. faecalis and its elimination from the dental apparatus may well define the future of the endodontic specialty.
Design & implementation of a line following robot for irrigation based application

The agriculture sector is the backbone of an economy which provides the basic ingredients to mankind and raw materials for industrialization. With the increasing number of the population over the world, the demand for agricultural products is also increased. In order to increase the production rate, irrigation technique should be more efficient. The irrigation techniques used till date are not in satisfactory level, especially in a developing country like Bangladesh. This paper has proposed a line follower robot for irrigation based application which may be considered as a cost-effective solution by minimizing water loss as well as an efficient system for irrigation purposes. This proposed system does not require an operator to accomplish its task. This gardening robot is completely portable and is equipped with a microcontroller, an on-board water reservoir, and an attached water pump. The area to be watered by the robot can be any field with plants, placed in a predefined path. It is capable of comparing movable objects and stationary plants to minimize water loss and finally watering them autonomously without any human intervention. The designed robot was tested and it performed nicely.
Teaching Children to Become Fluent and Automatic Readers.

The purpose of the study was to examine the effects of two instructional approaches designed to improve the reading fluency of 2nd-grade children. The first approach was based on Stahl and Heubach's (2005) fluency-oriented reading instruction (FORI) and involved the scaffolded, repeated reading of grade-level texts over the course of each week. The second was a wide-reading approach that also involved scaffolded instruction. hut that incorporated the reading of 3 different grade-level texts each week and provicled significantly less opportunity for repetition. By the end of the school year. FORI and wide-reading approaches showed similar benefits for standardized measures of word reading efficiency and reading comprehension skills compared to control approachcs. although the benefits of the wide-reading approach emerged earlier and included oral text reading fluency skill. Thus, we conclude that fluency instruction that emphasizes extensive oral reading of grade-level text using scaffolded approaches is effective for promoting reading development in young learners.
Knowledge Graph Completion with Adaptive Sparse Transfer Matrix

We model knowledge graphs for their completion by encoding each entity and relation into a numerical space. All previous work including Trans(E, H, R, and D) ignore the heterogeneity (some relations link many entity pairs and others do not) and the imbalance (the number of head entities and that of tail entities in a relation could be different) of knowledge graphs. In this paper, we propose a novel approach TranSparse to deal with the two issues. In TranSparse, transfer matrices are replaced by adaptive sparse matrices, whose sparse degrees are determined by the number of entities (or entity pairs) linked by relations. In experiments, we design structured and unstructured sparse patterns for transfer matrices and analyze their advantages and disadvantages. We evaluate our approach on triplet classification and link prediction tasks. Experimental results show that TranSparse outperforms Trans(E, H, R, and D) significantly, and achieves state-ofthe-art performance.
Ultrasonic Sensing for Water Flow Meters and Heat Meters

Ultrasonic flow meters are gaining wide usage in commercial, industrial and medical applications. Major benefits of utilizing this type of flowmeter are higher accuracy, low maintenance (no moving parts), noninvasive flow measurement, and the ability to regularly diagnose health of the meter. This application note is intended as an introduction to ultrasonic time-of-flight (TOF) flow sensing using the TDC1000 ultrasonic analog-front-end (AFE) and the TDC7200 picosecond accurate stopwatch. Information regarding a typical off-the-shelf ultrasonic flow sensor is provided, along with related equations for calculation of flow velocity and flow rate. Included in the appendix is a summary of standards for water meters and a list of low cost sensors suitable for this application space. Topic ........................................................................................................................... Page
Neural Sentiment Classification with User and Product Attention

Document-level sentiment classification aims to predict user’s overall sentiment in a document about a product. However, most of existing methods only focus on local text information and ignore the global user preference and product characteristics. Even though some works take such information into account, they usually suffer from high model complexity and only consider wordlevel preference rather than semantic levels. To address this issue, we propose a hierarchical neural network to incorporate global user and product information into sentiment classification. Our model first builds a hierarchical LSTM model to generate sentence and document representations. Afterwards, user and product information is considered via attentions over different semantic levels due to its ability of capturing crucial semantic components. The experimental results show that our model achieves significant and consistent improvements compared to all state-of-theart methods. The source code of this paper can be obtained from https://github. com/thunlp/NSC.
Emerging trends in informal sector recycling in developing and transition countries.

Optimistic estimates suggest that only 30-70% of waste generated in cities of developing countries is collected for disposal. As a result, uncollected waste is often disposed of into open dumps, along the streets or into water bodies. Quite often, this practice induces environmental degradation and public health risks. Notwithstanding, such practices also make waste materials readily available for itinerant waste pickers. These 'scavengers' as they are called, therefore perceive waste as a resource, for income generation. Literature suggests that Informal Sector Recycling (ISR) activity can bring other benefits such as, economic growth, litter control and resources conservation. This paper critically reviews trends in ISR activities in selected developing and transition countries. ISR often survives in very hostile social and physical environments largely because of negative Government and public attitude. Rather than being stigmatised, the sector should be recognised as an important element for achievement of sustainable waste management in developing countries. One solution to this problem could be the integration of ISR into the formal waste management system. To achieve ISR integration, this paper highlights six crucial aspects from literature: social acceptance, political will, mobilisation of cooperatives, partnerships with private enterprises, management and technical skills, as well as legal protection measures. It is important to note that not every country will have the wherewithal to achieve social inclusion and so the level of integration must be 'flexible'. In addition, the structure of the ISR should not be based on a 'universal' model but should instead take into account local contexts and conditions.