nils-herrmann/clean_scirepeval huggingface dataset:
Closing the Gap How an Adaptive Behavioral Based Program on a Tablet Can Help Low Performing Children Catch Up in Math : a Randomized Placebo Controlled Study

Hassler Hallstedt, M. 2018. Closing the Gap.  Proficiency with basic arithmetic, also known as math fact (i.e., 3+8, 12-3), is considered to be a critical early math skill. Intervention research in mathematics have demonstrated that math fact deficits among students with low math performance can be improved with additional targeted, nontechnological interventions (i.e., small-group tutoring). The aim of the present thesis was to investigate the effect, using a randomized placebo controlled design, of addititional adaptive, behavioral based, math training on a tablet on low performing second graders. The first study (study I), investigated if arithmetic skills could be assessed in a reliable and valid way on tablet. The examination showed that arithmetic scales could be transferred from paper-based tests to tablet with comparable psychometric properties, although not for a pictorial scale, and that separate norms are needed for tablet. Study II demonstrated that training on a tablet, for on average 19 hours across 20 weeks, improved basic arithmetic skills after training in the math conditions compared to control/ placebo conditions. The effects were medium sized at post assessment. There was a fadeout of effects at 6 months follow-up, where small effects were shown, and the effects decreased further at 12 months follow-up. Children with lower non-verbal IQ seemed to gain significantly more at followups than children with higher non-verbal IQ. The study found no additional effects of combining working memory training and math training. Study III, using a machine learning analysis, found that children demonstrating a positive response at 6 months follow-up were characterized by having completed 90 % or more of the math program at the default level, in combination with having a fairly favorable socioeconomic background. In summation, this work demonstrates how an adaptive behavioral based program on a tablet can help low performing children improve critical early math skills.
Multilevel Sensor Fusion With Deep Learning

In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors. This approach is designed to efficiently and automatically balance the tradeoff between early and late fusion (i.e., between the fusion of low-level versus high-level information). More specifically, at each level of abstraction—the different levels of deep networks—unimodal representations of the data are fed to a central neural network which combines them into a common embedding. In addition, a multiobjective regularization is also introduced, helping to both optimize the central network and the unimodal networks. Experiments on four multimodal datasets not only show the state-of-the-art performance but also demonstrate that CentralNet can actually choose the best possible fusion strategy for a given problem.
Smart network solutions in an amoeboid organism.

We present evidence that the giant amoeboid organism, the true slime mold, constructs a network appropriate for maximizing nutrient uptake. The body of the plasmodium of Physarum polycephalum contains a network of tubular elements by means of which nutrients and chemical signals circulate through the organism. When food pellets were presented at different points on the plasmodium it accumulated at each pellet with a few tubes connecting the plasmodial concentrations. The geometry of the network depended on the positions of the food sources. Statistical analysis showed that the network geometry met the multiple requirements of a smart network: short total length of tubes, close connections among all the branches (a small number of transit food-sites between any two food-sites) and tolerance of accidental disconnection of the tubes. These findings indicate that the plasmodium can achieve a better solution to the problem of network configuration than is provided by the shortest connection of Steiner's minimum tree.
Financial credit risk assessment: a recent review

The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area.
Causal models and learning from data: integrating causal modeling and statistical estimation.

The practice of epidemiology requires asking causal questions. Formal frameworks for causal inference developed over the past decades have the potential to improve the rigor of this process. However, the appropriate role for formal causal thinking in applied epidemiology remains a matter of debate. We argue that a formal causal framework can help in designing a statistical analysis that comes as close as possible to answering the motivating causal question, while making clear what assumptions are required to endow the resulting estimates with a causal interpretation. A systematic approach for the integration of causal modeling with statistical estimation is presented. We highlight some common points of confusion that occur when causal modeling techniques are applied in practice and provide a broad overview on the types of questions that a causal framework can help to address. Our aims are to argue for the utility of formal causal thinking, to clarify what causal models can and cannot do, and to provide an accessible introduction to the flexible and powerful tools provided by causal models.
Data Mining in the Dark : Darknet Intelligence Automation

Open-source intelligence offers value in information security decision making through knowledge of threats and malicious activities that potentially impact business. Open-source intelligence using the internet is common, however, using the darknet is less common for the typical cybersecurity analyst. The challenges to using the darknet for open-source intelligence includes using specialized collection, processing, and analysis tools. While researchers share techniques, there are few publicly shared tools; therefore, this paper explores an open-source intelligence automation toolset that scans across the darknet connecting, collecting, processing, and analyzing. It describes and shares the tools and processes to build a secure darknet connection, and then how to collect, process, store, and analyze data. Providing tools and processes serves as an on-ramp for cybersecurity intelligence analysts to search for threats. Future studies may refine, expand, and deepen this paper's toolset framework.   Mining in the Dark 2 Nafziger, Brian
Suggestions for presenting the results of data analyses

We give suggestions for the presentation of research results from frequentist, information-theoretic, and Bayesian analysis paradigms, followed by several general suggestions. The information-theoretic and Bayesian methods offer alternative approaches to data analysis and inference compared to traditionally used methods. Guidance is lacking on the presentation of results under these alternative procedures and on nontesting aspects of classical frequentist methods of statistical analysis. Null hypothesis testing has come under intense criticism. We recommend less reporting of the results of statistical tests of null hypotheses in cases where the null is surely false anyway, or where the null hypothesis is of little interest to science or management.
Modeling Scientific Influence for Research Trending Topic Prediction

With the growing volume of publications in the Computer Science (CS) discipline, tracking the research evolution and predicting the future research trending topics are of great importance for researchers to keep up with the rapid progress of research. Within a research area, there are many top conferences that publish the latest research results. These conferences mutually influence each other and jointly promote the development of the research area. To predict the trending topics of mutually influenced conferences, we propose a correlated neural influence model, which has the ability to capture the sequential properties of research evolution in each individual conference and discover the dependencies among different conferences simultaneously. The experiments conducted on a scientific dataset including conferences in artificial intelligence and data mining show that our model consistently outperforms the other state-of-the-art methods. We also demonstrate the interpretability and predictability of the proposed model by providing its answers to two questions of concern, i.e., what the next rising trending topics are and for each conference who the most influential peer is.
A Model for Understanding How Virtual Reality Aids Complex Conceptual Learning

Designers and evaluators of immersive virtual reality systems have many ideas concerning how virtual reality can facilitate learning. However, we have little information concerning which of virtual reality's features provide the most leverage for enhancing understanding or how to customize those affordances for different learning environments. In part, this reflects the truly complex nature of learning. Features of a learning environment do not act in isolation; other factors such as the concepts or skills to be learned, individual characteristics, the learning experience, and the interaction experience all play a role in shaping the learning process and its outcomes. Through Project Science Space, we have been trying to identify, use, and evaluate immersive virtual reality's affordances as a means to facilitate the mastery of complex, abstract concepts. In doing so, we are beginning to understand the interplay between virtual reality's features and other important factors in shaping the learning process and learning outcomes for this type of material. In this paper, we present a general model that describes how we think these factors work together and discuss some of the lessons we are learning about virtual reality's affordances in the context of this model for complex conceptual learning.
A Statistical View of Deep Learning

I’ve taken to writing this series of posts on a statistical view of deep learning with two principal motivations in mind. The first was as a personal exercise to make concrete and to test the limits of the way that I think about and use deep learning in my every day work. The second, was to highlight important statistical connections and implications of deep learning that I have not seen made in the popular courses, reviews and books on deep learning, but which are extremely important to keep in mind. This document forms a collection of these essays originally posted at blog.shakirm.com.
Theories Used in Information Systems Research: Identifying Theory Networks in Leading IS Journals

Though use of theory is critical in Information Systems (IS) research, the theoretical foundations of IS research have been understudied. Using Social Network Analysis, we analyze theory usage in IS research published in MIS Quarterly and Information Systems Research from 1998 to 2006. We find Technology Acceptance Model, ResourceBased View and Game Theory to be the three most frequently used theories. While strong dominance is found in research focusing on Information Technology (IT) for individuals, organizations and markets, no theoretical dominance is found in IT for groups and IS development. Psychology, Economics and Sociology are disciplines IS researchers most frequently leverage for theories. Psychology contributes several theories representing a large fraction of the long tail of theories. Our analysis suggests that IS consists of a few distinctive clusters of research instead of a single core. Our results provide insights on theoretical foundations of IS and suggest research opportunities for scholars.
Sentiment analysis using convolutional neural network with fastText embeddings

Convolution Neural Networks (CNNs) are famous for their great performance in Computer Vision experiments achieving state-of-art results. However, recent works have shown that CNNs can perform well for Natural Language Processing. The whole idea consists of gathering the embeddings as an image. This paper presents the usage of the recently released Facebook fastText word embeddings as representation of word to perform the task of sentiment analysis. The interest in this work comes from the advent of social media and technological advances, which have been flooding the Internet with opinions. The results show that the proposed aproach outperforms the baseline models and it has similar performance to the state-of-art models.
Biological degradation of plastics: a comprehensive review.

Lack of degradability and the closing of landfill sites as well as growing water and land pollution problems have led to concern about plastics. With the excessive use of plastics and increasing pressure being placed on capacities available for plastic waste disposal, the need for biodegradable plastics and biodegradation of plastic wastes has assumed increasing importance in the last few years. Awareness of the waste problem and its impact on the environment has awakened new interest in the area of degradable polymers. The interest in environmental issues is growing and there are increasing demands to develop material which do not burden the environment significantly. Biodegradation is necessary for water-soluble or water-immiscible polymers because they eventually enter streams which can neither be recycled nor incinerated. It is important to consider the microbial degradation of natural and synthetic polymers in order to understand what is necessary for biodegradation and the mechanisms involved. This requires understanding of the interactions between materials and microorganisms and the biochemical changes involved. Widespread studies on the biodegradation of plastics have been carried out in order to overcome the environmental problems associated with synthetic plastic waste. This paper reviews the current research on the biodegradation of biodegradable and also the conventional synthetic plastics and also use of various techniques for the analysis of degradation in vitro.
Classifying deformities of the columella base in rhinoplasty.

Although much has been published with regard to the columella assessed on the frontal and lateral views, a paucity of literature exists regarding the basal view of the columella. The objective of this study was to evaluate the spectrum of columella deformities and devise a working classification system based on underlying anatomy.



A retrospective study was performed of 100 consecutive patients who presented for primary rhinoplasty. The preoperative basal view photographs for each patient were reviewed to determine whether they possessed ideal columellar aesthetics. Patients who had deformity of their columella were further scrutinized to determine the most likely underlying cause of the subsequent abnormality.



Of the 100 patient photographs assessed, only 16 (16 percent) were found to display ideal norms of the columella. The remaining 84 of 100 patients (84 percent) had some form of aesthetic abnormality and were further classified based on the most likely underlying cause. Type 1 deformities (caudal septum and/or spine) constituted 18 percent (18 of 100); type 2 (medial crura), 12 percent (12 of 100); type 3 (soft tissue), 6 percent (six of 100); and type 4 (combination), 48 percent (48 of 100).



Deformities may be classified according to the underlying cause, with combined deformity being the most common. Use of the herein discussed classification scheme will allow surgeons to approach this region in a comprehensive manner. Furthermore, use of such a system allows for a more standardized approach for surgical treatment.
Students’ approaches to groupwork in a blended course, associations with perceptions of the online environment and academic achievement – when is learning engaged?

University teachers provided first year Arts students with hundreds of cinematic images online to analyse as a key part of their predominantly face-to-face undergraduate course. This qualitative study investigates the extent to which the groups engaged in learning involving their analysis of the images and how this was related to their perception of the ICT-mediated environment. Interviews and questionnaires completed by students revealed that the extent of engaged learning was related to the quality of the approach to groupwork reported by the students, the quality of their approach to the analysis of the images and their perceptions of key aspects of the online environment which provided the images. The findings have implications for the design and approach to teaching best suited for students involved in groupwork and the use of ICT resources provided to promote engaged experiences of learning.
Management of Stahl ’ s ear : a case report

Stahl’s ear deformity was first described in the 19th century and it consists of a rare auricular deformity characterized by hypoplasia of the antihelix crus with enlargement of its base and a third crus of the antihelix connected to posterior portion of helix crus, which deforms the posterolateral wall of the external auditory canal. Reconstructive surgery is the definitive treatment, however, because of the diversity of clinical presentations, no standard technique exist for all cases. The method described in this report is another treatment option and entails the resection of the third crus and reconstruction of superior crus of the antihelix.
Laser earlobe revision.

Earlobe tears and disfigurement often result from prolonged pierced earring use and trauma. They are a common cosmetic complaint for which surgical reconstruction has often been advocated.



A series of 10 patients with earlobe tears or disfigurement treated using straight-line closure, carbon dioxide (CO2 ) laser ablation, or both are described. A succinct literature review of torn earlobe repair is provided.



Successful repair with excellent cosmesis of torn and disfigured earlobes was obtained after straight-line surgical closure, CO2 laser ablation, or both.



A minimally invasive earlobe repair technique that involves concomitant surgical closure and CO2 laser skin vaporization produces excellent cosmetic results for torn or disfigured earlobes.
PDGM: Percolation-based directed graph matching in social networks

Linking multiple accounts owned by the same user across different online social networks (OSNs) is an important issue in social networks, known as identity reconciliation. Graph matching is one of popular techniques to solve this problem by identifying a map that matches a set of vertices across different OSNs. Among them, percolation-based graph matching (PGM) has been explored to identify entities belonging to a same user across two different networks based on a set of initial pre-matched seed nodes and graph structural information. However, existing PGM algorithms have been applied in only undirected networks while many OSNs are represented by directional relationships (e.g., followers or followees in Twitter or Facebook). For PGM to be applicable in real world OSNs represented by directed networks with a small set of overlapping vertices, we propose a percolation-based directed graph matching algorithm, namely PDGM, by considering the following two key features: (1) similarity of two nodes based on directional relationships (i.e., outgoing edges vs. incoming edges); and (2) celebrity penalty such as penalty given for nodes with a high in-degree. Through the extensive simulation experiments, our results show that the proposed PDGM outperforms the baseline PGM counterpart that does not consider either directional relationships or celebrity penalty.
An Efficient Autocalibration Method for Triaxial Accelerometer

This paper investigates the autocalibration of microelectromechanical systems (MEMS) triaxial accelerometer (TA) based on experimental design (DoE). First, for a special 6-parameter second-degree model, a six-point experimental scheme is proposed, and its G-optimality has been proven based on optimal DoE. Then, a new linearization approach is introduced, by which the TA model for autocalibration can be simplified as the expected second-degree form so that the proposed optimal experimental scheme can be applied. To reliably estimate the model parameter, a convergence-guaranteed iterative algorithm is also proposed, which can significantly reduce the bias caused by linearization. Thereafter, the effectiveness and robustness of the proposed approach have been demonstrated by simulation. Finally, the proposed calibration method has been experimentally verified using two typical types of MEMS TA, and desired experimental results effectively demonstrate the efficiency and accuracy of the proposed calibration approach.
High-Performance Transformerless Online UPS

In this paper, a high-performance single-phase transformerless online uninterruptible power supply (UPS) is proposed. The proposed UPS is composed of a four-leg-type converter, which operates as a rectifier, a battery charger/discharger, and an inverter. The rectifier has the capability of power-factor collection and regulates a constant dc-link voltage. The battery charger/discharger eliminates the need for the transformer and the increase of the number of battery and supplies the power demanded by the load to the dc-link capacitor in the event of the input-power failure or abrupt decrease of the input voltage. The inverter provides a regulated sinusoidal output voltage to the load and limits the output current under an impulsive load. The control of the dc-link voltage enhances the transient response of the output voltage and the utilization of the input power. By utilizing the battery charger/discharger, the overall efficiency of the system is improved, and the size, weight, and cost of the system are significantly reduced. Experimental results obtained with a 3-kVA prototype show a normal efficiency of over 95.6% and an input power factor of over 99.7%.