nils-herrmann/clean_scirepeval huggingface dataset:
Credit Risk Models I : Default Correlation in Intensity Models

This report analyzes the modelling of default intensities and probabilities in single-firm reduced-form models, and reviews the three main approaches to incorporating default dependencies within the framework of reduced models. The first approach, the conditionally independent defaults (CID), introduces credit risk dependence between firms through the dependence of the firms’ intensity processes on a common set of state variables. Contagion models extend the CID approach to account for the empirical observation of default clustering. There exist periods in which the firms’ credit risk is increased and in which the majority of the defaults take place. Finally, default dependencies can also be accounted for using copula functions. The copula approach takes as given the marginal default probabilities of the different firms and plugs them into a copula function, which provides the model with the default dependence structure. After a description of copulas, we present two different approaches of using copula functions in intensity models, and discuss the issues of the choice and calibration of the copula function. ∗This report is a revised version of the Master’s Thesis presented in partial fulfillment of the 2002-2003 MSc in Financial Mathematics at King’s College London. I thank my supervisor Lane P. Hughston and everyone at the Financial Mathematics Group at King’s College, particularly Giulia Iori and Mihail Zervos. Financial support by Banco de España is gratefully acknowledged. Any errors are the exclusive responsibility of the author. CEMFI, Casado del Alisal 5, 28014 Madrid, Spain.
Second Language Acquisition: Reconciling Theories

This article argues that previous attempts to explain SLA should not be disregarded. Instead, when they are put together, they provide a broader and deeper view of the acquisition process. There is evidence to support the claim that second language acquisition (SLA) is a complex adaptive system due to its inherent ability to adapt to different conditions present in both internal and external environments. Based on this understanding, widely discussed second language theories, including behaviorism, will be treated as explanations of parts of a whole, since each captures a different aspect of SLA. In order to justify this assumption, excerpts from some English language learning histories are provided to exemplify how learners describe their learning processes. The final claim is that SLA should be seen as a chaotic/complex system.
Indexing Query Graphs to Speedup Graph Query Processing

Subgraph/supergraph queries although central to graph analytics, are costly as they entail the NP-Complete problem of subgraph isomorphism. We present a fresh solution, the novel principle of which is to acquire and utilize knowledge from the results of previously executed queries. Our approach, iGQ, encompasses two component subindexes to identify if a new query is a subgraph/supergraph of previously executed queries and stores related key information. iGQ comes with novel query processing and index space management algorithms, including graph replacement policies. The end result is a system that leads to significant reduction in the number of required subgraph isomorphism tests and speedups in query processing time. iGQ can be incorporated into any sub/supergraph query processing method and help improve performance. In fact, it is the only contribution that can speedup significantly both subgraph and supergraph query processing. We establish the principles of iGQ and formally prove its correctness. We have implemented iGQ and have incorporated it within three popular recent state of the art index-based graph query processing solutions. We evaluated its performance using real-world and synthetic graph datasets with different characteristics, and a number of query workloads, showcasing its benefits.
STUDENT WRITING IN HIGHER EDUCATION: AN ACADEMIC LITERACIES APPROACH

This article addresses the issue of student writing in higher education. It draws on the findings of an Economic and Social Research Council funded project which examined the contrasting expectations and interpretations of academic staff and students regarding undergraduate students' written assignments. It is suggested that the implicit models that have generally been used to understand student writing do not adequately take account of the importance of issues of identity and the institutional relationships of power and authority that surround, and are embedded within, diverse student writing practices across the university. A contrasting and therefore complementary perspective is used to present debates about 'good' and `poor' student writing. The article outlines an 'academic literacies' framework which can take account of the conflicting and contested nature of writing practices, and may therefore be more valuable for understanding student writing in today's higher education than traditional models and approaches.
Isolating Sources of Disentanglement in Variational Autoencoders

We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate the β-TCVAE (Total Correlation Variational Autoencoder) algorithm, a refinement and plug-in replacement of the β-VAE for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the model is trained using our framework.
Monolingual Machine Translation for Paraphrase Generation

We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pairs automatically extracted from clustered news articles available on the World Wide Web. Alignment Error Rate (AER) is measured to gauge the quality of the resulting corpus. A monotone phrasal decoder generates contextual replacements. Human evaluation shows that this system outperforms baseline paraphrase generation techniques and, in a departure from previous work, offers better coverage and scalability than the current best-of-breed paraphrasing approaches.
The effect of "Internet of Things" on supply chain integration and performance: An organisational capability perspective

The Internet of things (IoT) is a next generation of Internet connected embedded ICT systems in a digital environment to seamlessly integrate supply chain and logistics processes. Integrating emerging IoT into the current ICT systems can be unique because of its intelligence, autonomous and pervasive applications. However, research on the IoT adoption in supply chain domain is scarce and acceptance of the IoT into the retail services in specific has been overly rhetoric. This study is drawn upon the organisational capability theory for developing an empirical model considering the effect of IoT capabilities on multiple dimensions of supply chain process integration, and in turn improves supply chain performance as well as organisational performance. Cross-sectional survey data from 227 Australian retail firms was analysed using structural equation modelling (SEM). The results indicate that IoT capability has a positive and significant effect on internal, customer-, and supplier-related process integration that in turn positively affects supply chain performance and organisational performance. Theoretically, the study contributes to a body of knowledge that integrates information systems research into supply chain integration by establishing an empirical evidence of how IoT-enabled process integration can enhance the performance at both supply chain and organisational level. Practically, the results inform the managers of the likely investment on IoT that can lead to chain’s performance outcome.
Breast cancer triage CAD for low resource countries using low-cost ultrasound and minimally-trained operators

We propose an automated breast cancer triage CAD system using machine vision on low-cost, portable ultrasound imaging devices. We demonstrate that the triage CAD software can effectively analyze images captured by minimally-trained operators and output one of three assessments - benign, probably benign (6-month follow-up recommended) and suspicious (biopsy recommended). This system opens up the possibility of offering practical, cost-effective breast cancer diagnosis for symptomatic women in economically developing countries.
DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware

With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely--on their own--be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware.
 DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly.
 For malware detection, we achieve up to 99.82% accuracy with zero false positives; for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.
Franchising reproductive health services.

Networks of franchised health establishments, providing a standardized set of services, are being implemented in developing countries. This article examines associations between franchise membership and family planning and reproductive health outcomes for both the member provider and the client.



Regression models are fitted examining associations between franchise membership and family planning and reproductive health outcomes at the service provider and client levels in three settings.



Franchising has a positive association with both general and family planning client volumes, and the number of family planning brands available. Similar associations with franchise membership are not found for reproductive health service outcomes. In some settings, client satisfaction is higher at franchised than other types of health establishments, although the association between franchise membership and client outcomes varies across the settings.



Franchise membership has apparent benefits for both the provider and the client, providing an opportunity to expand access to reproductive health services, although greater attention is needed to shift the focus from family planning to a broader reproductive health context.
Another Generalization of Wiener's Attack on RSA

A well-known attack on RSA with low secret-exponent d was given by Wiener in 1990. Wiener showed that using the equation ed − (p − 1)(q − 1)k = 1 and continued fractions, one can efficiently recover the secret-exponent d and factor N = pq from the public key (N, e) as long as d < 1 3 N 1 4 . In this paper, we present a generalization of Wiener’s attack. We show that every public exponent e that satisfies eX − (p− u)(q − v)Y = 1 with 1 ≤ Y < X < 2 1 4 N 1 4 , |u| < N 1 4 , v = [ − qu p− u ] , and all prime factors of p − u or q − v are less than 10 yields the factorization of N = pq. We show that the number of these exponents is at least N 1 2−ε.
An Integrative Approach to the Assessment of E-Commerce Quality

WebQual is a method for assessing the quality of Web sites. The method has been developed iteratively through application in various domains, including Internet bookstores and Internet auction sites. In this paper we report on the application of a new version of WebQual to Internet bookstores: Amazon, BOL, and the Internet Bookshop. WebQual draws on previous work in three areas: Web site usability, information quality, and service interaction quality to provide a rounded framework for assessing e-commerce offerings. Although WebQual is grounded in the subjective impressions of Web site users, the data collected lends itself to quantitative analysis and the production of e-commerce metrics such as the WebQual Index. The reliability of the instrument is examined and core constructs of Web site quality identified using factor analysis. The role of WebQual in assessing an organizations e-commerce capability is discussed.
Crowdsourcing a Word-Emotion Association Lexicon

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotionannotation questions, and show that asking if a term is associated with an emotion leads to markedly higher inter-annotator agreement than that obtained by asking if a term evokes an emotion.
CONSTRAINING INFORMATION FLOW

Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients. In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck. By enforcing a constraint on the mutual information between the observations and the discriminator’s internal representation, we can effectively modulate the discriminator’s accuracy and maintain useful and informative gradients. We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to significant improvements across three distinct application areas for adversarial learning algorithms. Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running. We show that our method can learn such skills directly from raw video demonstrations, substantially outperforming prior adversarial imitation learning methods. The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings. Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods.
Convolutional Encoders for Neural Machine Translation

We propose a general Convolutional Neural Network (CNN) encoder model for machine translation that fits within in the framework of Encoder-Decoder models proposed by Cho, et. al. . A CNN takes as input a sentence in the source language, performs multiple convolution and pooling operations, and uses a fully connected layer to produce a fixed-length encoding of the sentence as input to a Recurrent Neural Network decoder (using GRUs or LSTMs). The decoder, encoder, and word embeddings are jointly trained to maximize the conditional probability of the target sentence given the source sentence. Many variations on the basic model are possible and can improve the performance of the model.
Role of Hibernation File in Memory Forensics of windows 10

University, Kurukshetra-136119 azadmehla@kuk.ac.in Pankaj Sharma M.Tech Student, Department of Computer Science & Applications, Kuruksheta University, Kurukshetra-136119 pankajshastri@kuk.ac.in RajenderNath Professor, Department of Computer Science & Applications, Kuruksheta University, Kurukshetra-136119 rnath_2K3@rediffmail.com
Dendritic error backpropagation in deep cortical microcircuits

Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates the necessary synaptic modifications across different brain areas has remained a longstanding puzzle. Here, we introduce a multi-area neuronal network model in which synaptic plasticity continuously adapts the network towards a global desired output. In this model synaptic learning is driven by a local dendritic prediction error that arises from a failure to predict the top-down input given the bottom-up activities. Such errors occur at apical dendrites of pyramidal neurons where both long-range excitatory feedback and local inhibitory predictions are integrated. When local inhibition fails to match excitatory feedback an error occurs which triggers plasticity at bottom-up synapses at basal dendrites of the same pyramidal neurons. We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm. Finally, complementing this cortical circuit with a disinhibitory mechanism enables attention-like stimulus denoising and generation. Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological implementation of deep learning.
SYSTEMS CONTINUANCE : AN EXPECTATION-CONFIRMATION MODEL 1 By :

This paper examines cognitive beliefs and affect influencing one's intention to continue using (continuance) information systems (IS). Expectationconfirmation theory is adapted from the consumer behavior literature and integrated with theoretical and empirical findings from prior IS usage research to theorize a model of IS continuance. Five research hypotheses derived from this model are empirically validated using a field survey of online banking users. The results suggest that users'continuance intention is determined by their satisfaction with IS use and perceived usefulness of continued IS use. User satisfaction, in turn, is influenced by their confirmation of expectation from prior IS use and perceived usefulness. Postacceptance perceived usefulness is influenced by 'Ron Weber was the accepting senior editor for this paper. users'confirmation level. This study draws attention to the substantive differences between acceptance and continuance behaviors, theorizes and validates one of the earliest theoretical models of IS continuance, integrates confirmation and user satisfaction constructs within our current understanding of IS use, conceptualizes and creates an initial scale for measuring IS continuance, and offers an initial explanation for the acceptancediscontinuance anomaly.
Aesthetic-Driven Image Enhancement by Adversarial Learning

We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement.
Off-ramps and on-ramps: keeping talented women on the road to success.

Most professional women step off the career fast track at some point. With children to raise, elderly parents to care for, and other pulls on their time, these women are confronted with one off-ramp after another. When they feel pushed at the same time by long hours and unsatisfying work, the decision to leave becomes even easier. But woe to the woman who intends for that exit to be temporary. The on-ramps for professional women to get back on track are few and far between, the authors confirm. Their new survey research reveals for the first time the extent of the problem--what percentage of highly qualified women leave work and for how long, what obstacles they face coming back, and what price they pay for their time-outs. And what are the implications for corporate America? One thing at least seems clear: As market and economic factors align in ways guaranteed to make talent constraints and skill shortages huge issues again, employers must learn to reverse this brain drain. Like it or not, large numbers of highly qualified, committed women need to take time out of the workplace. The trick is to help them maintain connections that will allow them to reenter the workforce without being marginalized for the rest of their lives. Strategies for building such connections include creating reduced-hour jobs, providing flexibility in the workday and in the arc of a career, removing the stigma of taking time off, refusing to burn bridges, offering outlets for altruism, and nurturing women's ambition.
Gene essentiality prediction based on fractal features and machine learning.

Essential genes are required for the viability of an organism. Accurate and rapid identification of new essential genes is of substantial theoretical interest to synthetic biology and has practical applications in biomedicine. Fractals provide facilitated access to genetic structure analysis on a different scale. In this study, machine learning-based methods using solely fractal features are presented and the problem of predicting essential genes in bacterial genomes is evaluated. Six fractal features were investigated to learn the parameters of five supervised classification methods for the binary classification task. The optimal parameters of these classifiers are determined via grid-based searching technique. All the currently available identified genes from the database of essential genes were utilized to build the classifiers. The fractal features were proven to be more robust and powerful in the prediction performance. In a statistical sense, the ELM method shows superiority in predicting the essential genes. Non-parameter tests of the average AUC and ACC showed that the fractal feature is much better than other five compared features sets. Our approach is promising and convenient to identify new bacterial essential genes.