nils-herrmann/clean_scirepeval huggingface dataset:
Credit Scoring Methods

Despite the proliferation of banking services, lending to industry and the public still constitutes the core of the income of commercial banks and other lending institutions in developed as well as post-transition countries. From the technical perspective, the lending process in general is a relatively straightforward series of actions involving two principal parties. These activities range from the initial loan application to the successful or unsuccessful repayment of the loan. Although retail lending belongs among the most profitable investments in lenders’ asset portfolios (at least in developed countries), increases in the amounts of loans also bring increases in the number of defaulted loans, i.e. loans that either are not repaid at all or cases in which the borrower has problems with paying debts. Thus, the primary problem of any lender is to differentiate between “good” and “bad” debtors prior to granting credit. Such differentiation is possible by using a credit-scoring method. The goal of this paper is to review credit-scoring methods and elaborate on their efficiency based on the examples from the applied research. Emphasis is placed on credit scoring related to retail loans. We survey the methods which are suitable for credit scoring in the retail segment. We focus on retail loans as sharp increase in the amounts of loans for this clientele has been recorded in the last few years and another increase can be expected. This dynamic is highly relevant for post-transition countries. In the last few years, banks in the Czech and Slovak Republics have allocated a significant part of their lending to retail clientele. In 2004 alone, Czech and Slovak banks recorded 33.8% and 36.7% increases in retail loans, respectively. Hilbers et al. (2005) review trends in bank lending to the private sector, with a particular focus on Central and Eastern European countries, and find that rapid growth of private sector credit continues to be a key challenge for most of these countries. In the Czech and Slovak Republics the financial liabilities of households formed 11 % and 9 %
Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps

It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code. This process takes place both at an application’s inception and in an evolutionary context as GUI changes keep pace with evolving features. Unfortunately, this practice is challenging and time-consuming. In this paper, we present an approach that automates this process by enabling accurate prototyping of GUIs via three tasks: detection, classification, and assembly. First, logical components of a GUI are detected from a mock-up artifact using either computer vision techniques or mock-up metadata. Then, software repository mining, automated dynamic analysis, and deep convolutional neural networks are utilized to accurately classify GUI-components into domain-specific types (e.g., toggle-button). Finally, a data-driven, K-nearest-neighbors algorithm generates a suitable hierarchical GUI structure from which a prototype application can be automatically assembled. We implemented this approach for Android in a system called REDRAW. Our evaluation illustrates that REDRAW achieves an average GUI-component classification accuracy of 91% and assembles prototype applications that closely mirror target mock-ups in terms of visual affinity while exhibiting reasonable code structure. Interviews with industrial practitioners illustrate ReDraw’s potential to improve real development workflows.
THE (TEMPORAL) SEMANTICS AND (MODAL) PRAGMATICS OF THE PERFECT

The English perfect involves two fundamental components of meaning: a truth-conditional component involving temporal notions and a presupposition best expressed in terms drawn from the analysis of modality. The semantics draws much for the Extended Now theory (McCoard 1978 and others), but improves on it by showing that many aspects of the perfect's temporal contribution may be factored out into independent semantic or pragmatic principles. The pragmatic analysis unifies views of the function of the perfect as indicating either a 'result state' or the 'current relevance' of some past event. This unification is naturally stated in terms similar to those used to explain the context-dependency of modals.
Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes

We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies, including both linear and nonlinear ones. We implement the D-NTM with both continuous and discrete read and write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU controller. We provide extensive analysis of our model and compare different variations of neural Turing machines on this task. We show that our model outperforms long short-term memory and NTM variants. We provide further experimental results on the sequential MNIST, Stanford Natural Language Inference, associative recall, and copy tasks.
Applying Social Media Intelligence for Predicting and Identifying On-line Radicalization and Civil Unrest Oriented Threats

Research shows that various social media platforms on Internet such as Twitter, Tumblr (micro-blogging websites), Facebook (a popular social networking website), YouTube (largest video sharing and hosting website), Blogs and discussion forums are being misused by extremist groups for spreading their beliefs and ideologies, promoting radicalization, recruiting members and creating online virtual communities sharing a common agenda. Popular microblogging websites such as Twitter are being used as a real-time platform for information sharing and communication during planning and mobilization if civil unrest related events. Applying social media intelligence for predicting and identifying online radicalization and civil unrest oriented threats is an area that has attracted several researchers’ attention over past 10 years. There are several algorithms, techniques and tools that have been proposed in existing literature to counter and combat cyber-extremism and predicting protest related events in much advance. In this paper, we conduct a literature review of all these existing techniques and do a comprehensive analysis to understand state-of-the-art, trends and research gaps. We present a one class classification approach to collect scholarly articles targeting the topics and subtopics of our research scope. We perform characterization, classification and an in-depth meta analysis meta-anlaysis of about 100 conference and journal papers to gain a better understanding of existing literature.
On the effectiveness of isolation-based anomaly detection in cloud data centers

The high volume of monitoring information generated by large-scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource-intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large-scale environment such as clouds. Isolation-based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation-based anomaly detection method, to detect anomalies in large-scale cloud data centers. We propose a method to code time-series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time-series and to be applied on-line and in real-time.
Classification Rules for the Job Shop Scheduling Problem with Machine Breakdowns

This paper addresses the job shop scheduling problem in the presence of machine breakdowns. In this work, we propose to exploit the advantages of data mining techniques to resolve the problem. We proposed an approach to discover a set of classification rules by using historic scheduling data. Intelligent decisions are then made in real time based on this constructed rules to assign the corresponding dispatching rule in a dynamic job shop scheduling environment. A simulation study is conducted at last with the constructed rules and four other dispatching rules from literature. The experimental results verify the performance of classification rule for minimizing mean tardiness.
First-Language Acquisition in Adolescence: Evidence for a Critical Period for Verbal Language Development

It has been hypothesized that there is a critical period for first-language acquisition that extends into late childhood and possibly until puberty. The hypothesis is difficult to test directly because cases of linguistic deprivation during childhood are fortunately rare. We present here the case of E.M., a young man who has been profoundly deaf since birth and grew up in a rural area where he received no formal education and had no contact with the deaf community. At the age of 15, E.M. was fitted with hearing aids that corrected his hearing loss to 35 dB, and he began to learn verbal Spanish. We describe his language development over the 4-year period since his acquisition of hearing aids and conclude that the demonstrates severe deficits in verbal comprehension and production that support the critical period hypothesis.
Effects of combined endurance and strength training on muscle strength, power and hypertrophy in 40-67-year-old men.

Both strength and endurance training have several positive effects on aging muscle and physical performance of middle-aged and older adults, but their combination may compromise optimal adaptation. This study examined the possible interference of combined strength and endurance training on neuromuscular performance and skeletal muscle hypertrophy in previously untrained 40-67-year-old men. Maximal strength and muscle activation in the upper and lower extremities, maximal concentric power, aerobic capacity and muscle fiber size and distribution in the vastus lateralis muscle were measured before and after a 21-week training period. Ninety-six men [mean age 56 (SD 7) years] completed high-intensity strength training (S) twice a week, endurance training (E) twice a week, combined training (SE) four times per week or served as controls (C). SE and S led to similar gains in one repetition maximum strength of the lower extremities [22 (9)% and 21 (8)%, P<0.001], whereas E and C showed minor changes. Cross-sectional area of type II muscle fibers only increased in S [26 (22)%, P=0.002], while SE showed an inconsistent, non-significant change [8 (35)%, P=0.73]. Combined training may interfere with muscle hypertrophy in aging men, despite similar gains in maximal strength between the strength and the combined training groups.
Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in the Analysis of Geological Data

Attention-based deep learning model as a human-centered smart technology has become the state-of-the-art method in addressing relation extraction, while implementing natural language processing. How to effectively improve the computational performance of that model has always been a research focus in both academic and industrial communities. Generally, the structures of model would greatly affect the final results of relation extraction. In this article, a deep learning model with a novel structure is proposed. In our model, after incorporating the highway network into a bidirectional gated recurrent unit, the attention mechanism is additionally utilized in an effort to assign weights of key issues in the network structure. Here, the introduction of highway network could enable the proposed model to capture much more semantic information. Experiments on a popular benchmark data set are conducted, and the results demonstrate that the proposed model outperforms some existing relation extraction methods. Furthermore, the performance of our method is also tested in the analysis of geological data, where the relation extraction in Chinese geological field is addressed and a satisfactory display result is achieved.
Analysis of Respiratory Sounds: State of the Art

This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds.



Review of the current medical and technological literature using Pubmed and personal experience.



The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms…



The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools.
THE ROLE OF INTERNAL AND EXTERNAL SOCIAL CAPITAL IN CROWDFUNDING : EVIDENCE FROM CHINA

How does a proponent’s internal and external social capital impact crowdfunding performance? Based on the theory of social capital, we developed a research model and conducted an econometric analysis using the objective data collected from a Chinese comprehensive crowdfunding platform, ZhongchouNet. Results of an econometric analysis of a sample of 862 projects show that two types of the proponent’s external social capital have significant effect on crowdfunding performance alone. Moreover, the effect of internal and external social capital on the success of a campaign is fully moderated by the type of the project. These results suggest that, to improve crowdfunding performance, the proponents, especially proponents of for-profit projects, should make good use of the mechanisms of social capital construction existed inside the crowdfunding platform.
Content-Based Image Retrieval: Theory and Applications

Advances in data storage and image acquisition technologie s have enabled the creation of large image datasets. In this scenario, it is necess ary to develop appropriate information systems to efficiently manage these collect ions. The commonest approaches use the so-called Content-Based Image Retrieval (CBIR) systems . Basically, these systems try to retrieve images similar to a user-define d sp cification or pattern (e.g., shape sketch, image example). Their goal is to suppor t image retrieval based on contentproperties (e.g., shape, color, texture), usually encoded into feature vectors . One of the main advantages of the CBIR approach is the possibi lity of an automatic retrieval process, instead of the traditional keyword-bas ed approach, which usually requires very laborious and time-consuming previous annot ation of database images. The CBIR technology has been used in several applications su ch as fingerprint identification, biodiversity information systems, digital librar ies, crime prevention, medicine, historical research, among others. This paper aims to introduce the problems and challenges con cerned with the creation of CBIR systems, to describe the existing solutions and appl ications, and to present the state of the art of the existing research in this area.
Urdu Natural Scene Character Recognition using Convolutional Neural Networks

In this paper we investigate the challenging problem of cursive text recognition in natural scene images. In particular, we have focused on isolated Urdu character recognition in natural scenes that could not be handled by tradition Optical Character Recognition (OCR) techniques developed for Arabic and Urdu scanned documents. We also present a dataset of Urdu characters segmented from images of signboards, street scenes, shop scenes and advertisement banners containing Urdu text. A variety of deep learning techniques have been proposed by researchers for natural scene text detection and recognition. In this work, a Convolutional Neural Network (CNN) is applied as a classifier, as CNN approaches have been reported to provide high accuracy for natural scene text detection and recognition. A dataset of manually segmented characters was developed and deep learning based data augmentation techniques were applied to further increase the size of the dataset. The training is formulated using filter sizes of 3x3, 5x5 and mixed 3x3 and 5x5 with a stride value of 1 and 2. The CNN model is trained with various learning rates and state-of-the-art results are achieved.
A practical solution to the pervasive problems of p values.

In the field of psychology, the practice of p value null-hypothesis testing is as widespread as ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of the statistical peculiarities of the p value procedure. In particular, p values are based on data that were never observed, and these hypothetical data are themselves influenced by subjective intentions. Moreover, p values do not quantify statistical evidence. This article reviews these p value problems and illustrates each problem with concrete examples. The three problems are familiar to statisticians but may be new to psychologists. A practical solution to these p value problems is to adopt a model selection perspective and use the Bayesian information criterion (BIC) for statistical inference (Raftery, 1995). The BIC provides an approximation to a Bayesian hypothesis test, does not require the specification of priors, and can be easily calculated from SPSS output.
Impact of Internet Use on Loneliness and Contact with Others Among Older Adults: Cross-Sectional Analysis

Older adults are at increased risk of experiencing loneliness and depression, particularly as they move into different types of care communities. Information and communication technology (ICT) usage may help older adults to maintain contact with social ties. However, prior research is not consistent about whether ICT use increases or decreases isolation and loneliness among older adults.



The purpose of this study was to examine how Internet use affects perceived social isolation and loneliness of older adults in assisted and independent living communities. We also examined the perceptions of how Internet use affects communication and social interaction.



One wave of data from an ongoing study of ICT usage among older adults in assisted and independent living communities in Alabama was used. Regression analysis was used to determine the relationship between frequency of going online and isolation and loneliness (n=205) and perceptions of the effects of Internet use on communication and social interaction (n=60).



After controlling for the number of friends and family, physical/emotional social limitations, age, and study arm, a 1-point increase in the frequency of going online was associated with a 0.147-point decrease in loneliness scores (P=.005). Going online was not associated with perceived social isolation (P=.14). Among the measures of perception of the social effects of the Internet, each 1-point increase in the frequency of going online was associated with an increase in agreement that using the Internet had: (1) made it easier to reach people (b=0.508, P<.001), (2) contributed to the ability to stay in touch (b=0.516, P<.001), (3) made it easier to meet new people (b=0.297, P=.01, (4) increased the quantity of communication with others (b=0.306, P=.01), (5) made the respondent feel less isolated (b=0.491, P<.001), (6) helped the respondent feel more connected to friends and family (b=0.392, P=.001), and (7) increased the quality of communication with others (b=0.289, P=.01).



Using the Internet may be beneficial for decreasing loneliness and increasing social contact among older adults in assisted and independent living communities.
Bacterial resistance to antimicrobial agents and its impact on veterinary and human medicine.

Antimicrobial resistance has become a major challenge in veterinary medicine, particularly in the context of bacterial pathogens that play a role in both humans and animals.



This review serves as an update on acquired resistance mechanisms in bacterial pathogens of human and animal origin, including examples of transfer of resistant pathogens between hosts and of resistance genes between bacteria.



Acquired resistance is based on resistance-mediating mutations or on mobile resistance genes. Although mutations are transferred vertically, mobile resistance genes are also transferred horizontally (by transformation, transduction or conjugation/mobilization), contributing to the dissemination of resistance. Mobile genes specifying any of the three major resistance mechanisms - enzymatic inactivation, reduced intracellular accumulation or modification of the cellular target sites - have been found in a variety of bacteria that may be isolated from animals. Such resistance genes are associated with plasmids, transposons, gene cassettes, integrative and conjugative elements or other mobile elements. Bacteria, including zoonotic pathogens, can be exchanged between animals and humans mainly via direct contact, but also via dust, aerosols or foods. Proof of the direction of transfer of resistant bacteria can be difficult and depends on the location of resistance genes or mutations in the chromosomal DNA or on a mobile element.



The wide variety in resistance and resistance transfer mechanisms will continue to ensure the success of bacterial pathogens in the future. Our strategies to counteract resistance and preserve the efficacy of antimicrobial agents need to be equally diverse and resourceful.
Aspect Based Sentiment Analysis Survey

Sentiment analysis or Opinion mining is becoming an important task both from academics and commercial standpoint. In recent years text mining has become most promising area for research. There is an exponential growth with respect to World Wide Web, Mobile Technologies, Internet usage and business on electronic commerce applications. Because of which web opinion sources like online shopping portals, discussion forums, peer-to-peer networks, groups, blogs, micro blogs and social networking applications are extensively used to share the information, experience and opinions. In sentiment analysis, the opinion is evaluated to its positivity, negativity and neutrality with respect to the complete document or object. But this level of analysis does not provide the necessary detailed information for many applications. To obtain more fine grained analysis we need to go to Aspect Based Sentiment Analysis (ABSA). Aspect Based Sentiment analysis introduces a suite of problems which require deeper NLP capabilities and also produces a rich set of results.