nils-herrmann/clean_scirepeval huggingface dataset:
Opinion Mining on YouTube

This paper defines a systematic approach to Opinion Mining (OM) on YouTube comments by (i) modeling classifiers for predicting the opinion polarity and the type of comment and (ii) proposing robust shallow syntactic structures for improving model adaptability. We rely on the tree kernel technology to automatically extract and learn features with better generalization power than bag-of-words. An extensive empirical evaluation on our manually annotated YouTube comments corpus shows a high classification accuracy and highlights the benefits of structural models in a cross-domain setting.
The new threat of digital marketing.

Because of their avid use of new media and their increased spending power, children and teens have become primary targets of a new "media and marketing ecosystem." The digital marketplace is undergoing rapid innovation as new technologies and software applications continue to reshape the media landscape and user behaviors. The advertising industry, in many instances led by food and beverage marketers, is purposefully exploiting the special relationship that youth have with new media, as online marketing campaigns create unprecedented intimacies between adolescents and the brands and products that now literally surround them.
Bioactive Principles from Medicinal Plants

Plants are widely used in many indigenous systems of medicine for therapeutic purposes and are increasingly becoming popular in modern society as alternatives to synthetic medicines. Bioactive principles are derived from the products of plant primary metabolites, which are associated with the process of photosynthesis. The present review highlighted the chemical diversity and medicinal potentials of bioactive principles as well inherent toxicity concerns associated with the use of these plant products, which are of relevance to the clinician, pharmacist or toxicologist. Plant materials are composed of vast array of bioactive principles of which their isolation, identification and characterization for analytical evaluation requires expertise with cutting edge analytical protocols and instrumentations. Bioactive principles are responsible for the therapeutic activities of medicinal plants and provide unlimited opportunities for new drug leads because of their unmatched availability and chemical diversity. For the most part, the beneficial or toxic outcomes of standardized plant extracts depend on the chemical peculiarities of the containing bioactive principles.
ADHD and academic performance: why does ADHD impact on academic performance and what can be done to support ADHD children in the classroom?

This paper reviews the relationship between attention deficit hyperactivity disorder (ADHD) and academic performance. First, the relationship at different developmental stages is examined, focusing on pre-schoolers, children, adolescents and adults. Second, the review examines the factors underpinning the relationship between ADHD and academic underperformance: the literature suggests that it is the symptoms of ADHD and underlying cognitive deficits not co-morbid conduct problems that are at the root of academic impairment. The review concludes with an overview of the literature examining strategies that are directed towards remediating the academic impairment of individuals with ADHD.
Judging Borrowers By The Company They Keep : Social Networks and Adverse Selection in Online Peer-to-Peer Lending

We study the online market for peer-to-peer (P2P) lending, in which individuals bid on unsecured microloans sought by other individual borrowers. Using a large sample of consummated and failed listings from the largest online P2P lending marketplace Prosper.com, we test whether social networks lead to better lending outcomes, focusing on the distinction between the structural and relational aspects of networks. While the structural aspects have limited to no significance, the relational aspects are consistently significant predictors of lending outcomes, with a striking gradation based on the verifiability and visibility of a borrower’s social capital. Stronger and more verifiable relational network measures are associated with a higher likelihood of a loan being funded, a lower risk of default, and lower interest rates. We discuss the implications of our findings for financial disintermediation and the design of decentralized electronic lending markets.  October 2009 ∗Decision, Operations and Information Technologies Department, **Finance Department. All the authors are at Robert H. Smith School of Business, University of Maryland, College Park, MD 20742. Mingfeng Lin can be reached at mingfeng@rhsmith.umd.edu. Prabhala can be reached at prabhala@rhsmith.umd.edu. Viswanathan can be reached at sviswana@rhsmith.umd.edu. The authors thank Ethan Cohen-Cole, Sanjiv Das, Jerry Hoberg, Dalida Kadyrzhanova, Nikunj Kapadia, De Liu, Vojislav Maksimovic, Gordon Phillips, Kislaya Prasad, Galit Shmueli, Kelly Shue, and seminar participants at Carnegie Mellon University, University of Utah, the 2008 Summer Doctoral Program of the Oxford Internet Institute, the 2008 INFORMS Annual Conference, the Workshop on Information Systems and Economics (Paris), and Western Finance Association for their valuable comments and suggestions. Mingfeng Lin also thanks to the Ewing Marion Kauffman Foundation for the 2009 Dissertation Fellowship Award, and to the Economic Club of Washington D.C. (2008) for their generous financial support. We also thank Prosper.com for making the data for the study available. The contents of this publication are the sole responsibility of the authors. Judging Borrowers By The Company They Keep: Social Networks and Adverse Selection in Online Peer-to-Peer Lending
A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage

Social networking sites (SNS) are quickly becoming one of the most popular tools for social interaction and information exchange. Previous research has shown a relationship between users’ personality and SNS use. Using a general population sample (N=300), this study furthers such investigations by examining the personality correlates (Neuroticism, Extraversion, Openness-to-Experience, Agreeableness, Conscientiousness, Sociability and Need-for-Cognition) of social and informational use of the two largest SNS: Facebook and Twitter. Age and Gender were also examined. Results showed that personality was related to online socialising and information seeking/exchange, though not as influential as some previous research has suggested. In addition, a preference for Facebbok or Twitter was associated with differences in personality. The results reveal differential relationships between personality and Facebook and Twitter usage.
Crowdfunding : An Industrial Organization Perspective ∗

The first objective of the paper is to identifiy a number of issues related to crowdfunding that are worth studying from an industrial organization (IO) perspective. To this end, we first propose a definition of crowdfunding; next, on the basis on a previous empirical study, we isolate what we believe are the main features of crowdfunding; finally, we point to a number of strands of the literature that could be used to study the various features of crowdfunding. The second objective of the paper is to propose some preliminary efforts towards the modelization of crowdfunding. In a first model, we associate crowdfunding with pre-ordering and price discrimination, and we study the conditions under which crowdfunding is preferred to traditional forms of external funding. In a second model, we see crowdfunding as a way to make a product better known by the consumers and we give some theoretical underpinning for the empirical finding that non-profit organizations tend to be more successful in using crowdfunding.
Parallel Lattice Boltzmann Methods for CFD Applications

The lattice Boltzmann method (LBM) has evolved to a promising alternative to the well-established methods based on finite elements/volumes for computational fluid dynamics simulations. Ease of implementation, extensibility, and computational efficiency are the major reasons for LBM’s growing field of application and increasing popularity. In this paper we give a brief introduction to the involved theory and equations for LBM, present various techniques to increase the single-CPU performance, outline the parallelization of a standard LBM implementation, and show performance results. In order to demonstrate the straightforward extensibility of LBM, we then focus on an application in material science involving fluid flows with free surfaces. We discuss the required extensions to handle this complex scenario, and the impact on the parallelization technique.
Morton Filters: Faster, Space-Efficient Cuckoo Filters via Biasing, Compression, and Decoupled Logical Sparsity

Approximate set membership data structures (ASMDSs) are ubiquitous in computing. They trade a tunable, often small, error rate ( ) for large space savings. The canonical ASMDS is the Bloom filter, which supports lookups and insertions but not deletions in its simplest form. Cuckoo filters (CFs), a recently proposed class of ASMDSs, add deletion support and often use fewer bits per item for equal . This work introduces the Morton filter (MF), a novel ASMDS that introduces several key improvements to CFs. Like CFs, MFs support lookups, insertions, and deletions, but improve their respective throughputs by 1.3× to 2.5×, 0.9× to 15.5×, and 1.3× to 1.6×. MFs achieve these improvements by (1) introducing a compressed format that permits a logically sparse filter to be stored compactly in memory, (2) leveraging succinct embedded metadata to prune unnecessary memory accesses, and (3) heavily biasing insertions to use a single hash function. With these optimizations, lookups, insertions, and deletions often only require accessing a single hardware cache line from the filter. These improvements are not at a loss in space efficiency, as MFs typically use comparable to slightly less space than CFs for the same .  Alex D. Breslow and Nuwan S. Jayasena.
Data mining techniques for Fraud Detection

Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually evolved and applied to many business fields. Fraud detection involves monitoring the behaviour of populations of users in order to estimate, detect, or avoid undesirable behaviour. Undesirable behaviour is a broad term including misbehaviour; fraud intrusion, and account defaulting. This paper present the concept of data mining and current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection. The goal of this paper is to provide a comprehensive review of data mining and different techniques to detect frauds.
From UI Design Image to GUI Skeleton: A Neural Machine Translator to Bootstrap Mobile GUI Implementation

A GUI skeleton is the starting point for implementing a UI design image. To obtain a GUI skeleton from a UI design image, developers have to visually understand UI elements and their spatial layout in the image, and then translate this understanding into proper GUI components and their compositions. Automating this visual understanding and translation would be beneficial for bootstraping mobile GUI implementation, but it is a challenging task due to the diversity of UI designs and the complexity of GUI skeletons to generate. Existing tools are rigid as they depend on heuristically-designed visual understanding and GUI generation rules. In this paper, we present a neural machine translator that combines recent advances in computer vision and machine translation for translating a UI design image into a GUI skeleton. Our translator learns to extract visual features in UI images, encode these features' spatial layouts, and generate GUI skeletons in a unified neural network framework, without requiring manual rule development. For training our translator, we develop an automated GUI exploration method to automatically collect large-scale UI data from real-world applications. We carry out extensive experiments to evaluate the accuracy, generality and usefulness of our approach.
3-D nFPGA: A Reconfigurable Architecture for 3-D CMOS/Nanomaterial Hybrid Digital Circuits

In this paper, we introduce a novel reconfigurable architecture, named 3D field-programmable gate array (3D nFPGA), which utilizes 3D integration techniques and new nanoscale materials synergistically. The proposed architecture is based on CMOS nanohybrid techniques that incorporate nanomaterials such as carbon nanotube bundles and nanowire crossbars into CMOS fabrication process. This architecture also has built-in features for fault tolerance and heat alleviation. Using unique features of FPGAs and a novel 3D stacking method enabled by the application of nanomaterials, 3D nFPGA obtains a 4x footprint reduction comparing to the traditional CMOS-based 2D FPGAs. With a customized design automation flow, we evaluate the performance and power of 3D nFPGA driven by the 20 largest MCNC benchmarks. Results demonstrate that 3D nFPGA is able to provide a performance gain of 2.6 x with a small power overhead comparing to the traditional 2D FPGA architecture.
Adaptive Learning Algorithms for Transferable Visual Recognition

Adaptive Learning Algorithms for Transferable Visual Recognition by Judith Ho↵man Doctor of Philosophy in Engineering – Electrical Engineering and Computer Sciences University of California, Berkeley Professor Trevor Darrell, Chair Understanding visual scenes is a crucial piece in many artificial intelligence applications ranging from autonomous vehicles and household robotic navigation to automatic image captioning for the blind. Reliably extracting high-level semantic information from the visual world in real-time is key to solving these critical tasks safely and correctly. Existing approaches based on specialized recognition models are prohibitively expensive or intractable due to limitations in dataset collection and annotation. By facilitating learned information sharing between recognition models these applications can be solved; multiple tasks can regularize one another, redundant information can be reused, and the learning of novel tasks is both faster and easier. In this thesis, I present algorithms for transferring learned information between visual data sources and across visual tasks all with limited human supervision. I will both formally and empirically analyze the adaptation of visual models within the classical domain adaptation setting and extend the use of adaptive algorithms to facilitate information transfer between visual tasks and across image modalities. Most visual recognition systems learn concepts directly from a large collection of manually annotated images/videos. A model which detects pedestrians requires a human to manually go through thousands or millions of images and indicate all instances of pedestrians. However, this model is susceptible to biases in the labeled data and often fails to generalize to new scenarios a detector trained in Palo Alto may have degraded performance in Rome, or a detector trained in sunny weather may fail in the snow. Rather than require human supervision for each new task or scenario, this work draws on deep learning, transformation learning, and convex-concave optimization to produce novel optimization frameworks which transfer information from the large curated databases to real world scenarios.
TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation

Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years. It is typically being modeled as a sequence labeling problem but contains intrinsic and sufficient differences than text parsing or speech processing. In this paper, we introduce a novel hybrid temporal convolutional and recurrent network (TricorNet), which has an encoder-decoder architecture: the encoder consists of a hierarchy of temporal convolutional kernels that capture the local motion changes of different actions; the decoder is a hierarchy of recurrent neural networks that are able to learn and memorize long-term action dependencies after the encoding stage. Our model is simple but extremely effective in terms of video sequence labeling. The experimental results on three public action segmentation datasets have shown that the proposed model achieves superior performance over the state of the art.
A prospective study of the factors affecting outcomes of nonsurgical root canal treatment: part 1: periapical health.

To investigate the probability of and factors influencing periapical status of teeth following primary (1°RCTx) or secondary (2°RCTx) root canal treatment.



This prospective study involved annual clinical and radiographic follow-up of 1°RCTx (1170 roots, 702 teeth and 534 patients) or 2°RCTx (1314 roots, 750 teeth and 559 patients) carried out by Endodontic postgraduate students for 2-4 (50%) years. Pre-, intra- and postoperative data were collected prospectively on customized forms. The proportion of roots with complete periapical healing was estimated, and prognostic factors were investigated using multiple logistic regression models. Clustering effects within patients were adjusted in all models using robust standard error.



proportion of roots with complete periapical healing after 1°RCTx (83%; 95% CI: 81%, 85%) or 2°RCTx (80%; 95% CI: 78%, 82%) were similar. Eleven prognostic factors were identified. The conditions that were found to improve periapical healing significantly were: the preoperative absence of a periapical lesion (P = 0.003); in presence of a periapical lesion, the smaller its size (P ≤ 0.001), the better the treatment prognosis; the absence of a preoperative sinus tract (P = 0.001); achievement of patency at the canal terminus (P = 0.001); extension of canal cleaning as close as possible to its apical terminus (P = 0.001); the use of ethylene-diamine-tetra-acetic acid (EDTA) solution as a penultimate wash followed by final rinse with NaOCl solution in 2°RCTx cases (P = 0.002); abstaining from using 2% chlorexidine as an adjunct irrigant to NaOCl solution (P = 0.01); absence of tooth/root perforation (P = 0.06); absence of interappointment flare-up (pain or swelling) (P =0.002); absence of root-filling extrusion (P ≤ 0.001); and presence of a satisfactory coronal restoration (P ≤ 0.001).



Success based on periapical health associated with roots following 1°RCTx (83%) or 2°RCTx (80%) was similar, with 10 factors having a common effect on both, whilst the 11th factor 'EDTA as an additional irrigant' had different effects on the two treatments.
Mental Accounting and Consumer Choice

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“ Turn on , Tune in , Drop out ” : Anticipating student dropouts in Massive Open Online Courses

In this paper, we explore student dropout behavior in Massive Open Online Courses(MOOC). We use as a case study a recent Coursera class from which we develop a survival model that allows us to measure the influence of factors extracted from that data on student dropout rate. Specifically we explore factors related to student behavior and social positioning within discussion forums using standard social network analytic techniques. The analysis reveals several significant predictors of dropout.
Why People Think Computers Can't

Most people think computers will never be able to think. That is, really think. Not now or ever. To be sure, most people also agree that computers can do many things that a person would have to be thinking to do. Then how could a machine seem to think but not actually think? Well, setting aside the question of what thinking actually is, I think that most of us would answer that by saying that in these cases, what the computer is doing is merely a superficial imitation of human intelligence. It has been designed to obey certain simple commands, and then it has been provided with programs composed of those commands. Because of this, the computer has to obey those commands, but without any idea of what's happening.
Parent-child relationships and offspring's positive mental wellbeing from adolescence to early older age.

We examined parent-child relationship quality and positive mental well-being using Medical Research Council National Survey of Health and Development data. Well-being was measured at ages 13-15 (teacher-rated happiness), 36 (life satisfaction), 43 (satisfaction with home and family life) and 60-64 years (Diener Satisfaction With Life scale and Warwick Edinburgh Mental Well-being scale). The Parental Bonding Instrument captured perceived care and control from the father and mother to age 16, recalled by study members at age 43. Greater well-being was seen for offspring with higher combined parental care and lower combined parental psychological control (p < 0.05 at all ages). Controlling for maternal care and paternal and maternal behavioural and psychological control, childhood social class, parental separation, mother's neuroticism and study member's personality, higher well-being was consistently related to paternal care. This suggests that both mother-child and father-child relationships may have short and long-term consequences for positive mental well-being.
An Automated Text Categorization Framework based on Hyperparameter Optimization

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely μTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of μTC along with an extensive experimental comparison with relevant state-of-the-art methods. μTC was compared on 30 different datasets. Regarding accuracy, μTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing. ∗CONACyT Consejo Nacional de Ciencia y Tecnoloǵıa, Dirección de Cátedras, Insurgentes Sur 1582, Crédito Constructor 03940, Ciudad de México, México. †INFOTEC Centro de Investigación e Innovación en Tecnoloǵıas de la Información y Comunicación, Circuito Tecnopolo Sur No 112, Fracc. Tecnopolo Pocitos II, Aguascalientes 20313, México. ‡Centro de Investigación en Geograf́ıa y Geomática “Ing. Jorge L. Tamayo”, A.C. Circuito Tecnopolo Norte No. 117,  Aguascalientes, Ags, México. 1 ar X iv :1 70 4. 01 97 5v 2 [ cs .C L ] 1 4 Se p 20 17