Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Knowledge Sharing Communities Using Team Expertise Access Maps

It is difficult for organizations to effectively manage personal knowledge so it can be mobilized, shared, and rewarded to benefit the organization. The difficulties are compounded in large organizations where people with potentially valuable knowledge are unknown to one another and dispersed geographically. Issues that are potentially amenable to management include identification, indexing and codification of the knowledge held in people's heads, and the cultural issues of discovery, mutual trust and sharing at the personal level. A large engineering and project management organization ("EPMO") has prototyped a methodology to graphically codify, index and map staff knowledge using mind mapping technologies. Not only does the methodology provide a graphical structure making it easy for staff to determine who is likely to posses the kind of knowledge they need to find, but interview process is an important facilitator to precondition the knowledge bearers for sharing, and the content of the resulting maps tends to present the knowledge bearers in a more humanized way.
ZU Scholars ZU Scholars

There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and fraud detection system to resolve this limitation. The system leverages three deep learning models: autoencoder (AE), variational autoencoder (VAE)
It's Testing Time! Patterns for Testing Software

The Error Simulator allows for simulating errors during the runtime of a component. This is especially useful, when testing a component in an environment with multiple threads or processes. If resources are shared among threads and processes, race conditions can lead to unexpected behavior during runtime. In some cases, a particular scenario cannot be tested by just having a test driver invoking methods in the component to be tested. Here the Error Simulator comes into play.
DEPARTEMENT TOEGEPASTE ECONOMISCHE WETENSCHAPPEN

- This paper presents a set of metrics and pseudo-metrics for the measurement of conceptual distances in M.E. R.O. DE. business models. The measures are developed and validated using measure and measurement theory. It is argued that this metrics set constitutes a strong formal basis for the further assessment and prediction of relevant internal and external attributes of object-oriented specifica tions.
Ozone formation sensitivity study using machine learning 1 coupled with the reactivity of VOC species

The formation of ground-level ozone (O 3 ) is dependent on both atmospheric chemical 11 processes and meteorological factors. In this study, a random forest (RF) model coupled 12 with the reactivity of volatile organic compound (VOC) species was used to investigate 13 the O 3 formation sensitivity in Beijing, China, from 2014 to 2016, and evaluate the 14 relative importance (RI) of chemical and meteorological factors to O 3 formation. The 15 results showed that the O 3 prediction performance using concentrations of 16 measured/initial VOC species ( R 2 = 0.82/0.81) was better than that using total VOCs 17 (TVOCs) concentrations ( R 2 = 0.77). Meanwhile, the RIs of initial VOC species 18 correlated well with their O 3 formation potentials (OFPs), which indicate that the model 19 results can be partially explained by the maximum incremental reactivity (MIR) method. 20 O 3 formation presented a negative response to nitrogen oxides (NO x ) and relative 21 humidity (RH), and a positive response to temperature (T), solar radiation (SR) and 22 VOCs. The O 3 isopleth calculated by the RF model were generally comparable with 23 those calculated by the box model. O 3 formation shifted from a VOC-limited regime to 24 a transition regime from 2014 to 2016. This study demonstrates that the RF model 25 coupled with the initial concentrations of VOC species could provide an accurate, 26 flexible, and computationally efficient approach for O 3 sensitivity analysis.
Household Access to Microcredit and Children's Food Security in Rural Malawi: A Gender Perspective

Using data from the 1995 Malawi Financial Markets and Food Security Survey, this study seeks to discover if women's relative control over household resources or intra-household bargaining power in rural Malawi, gauged by their access to microcredit, plays a role in children's food security, measured by anthropometric nutritional Z-scores. Access to microcredit is assessed in a novel way as self-reported credit limits at microcredit organizations. Since credit limits, that is, the maximum sums that might be borrowed, hinge upon supply-side factors such as the availability of credit programs and the financial resources of lenders, it is plausible they are more exogenous than demand driven loan uptake or participation in microcredit organizations, the common ways of gauging access to microcredit. It is indicated that whereas the access to microcredit of adult female household members improves 0-6 year old girls', though not boys', long-term nutrition as measured by height-for-age, the access to microcredit of male members has no such salutary effect on either girls' or boys' nutritional status. This may be interpreted as evidence of a positive relation between women's relative control over household resources and young girls' food security. That women's access to microcredit improves young girls' long-term nutrition may be explained in part by the subsidiary finding that it raises household expenditure on food.
A Logic for Hybrid Rules ∗

In the ongoing discussion about rule extensions for Ontology languages on the Semantic Web a recurring issue is how to combine ﬁrst-order classical logic with nonmono-tonic rule languages. Whereas several modular approaches to deﬁne a combined semantics for such hybrid knowledge bases focus mainly on decidability issues, we tackle the matter from a more general point of view. In this paper we show how Quantiﬁed Equilibrium Logic (QEL) can function as a uniﬁed framework that embraces classical logic as well as disjunctive logic programs under the (open) answer set semantics. In the proposed variant of QEL we relax the unique names assumption from earlier versions. Moreover, we show that this framework elegantly captures several modular approaches to nonmonotonic semantics for hybrid knowledge bases.
Collaboration Recommendation of Finance Research Based on Multi-feature Fusion

: [ Objective ] Research collaboration builds an important social network system. This paper proposes a new recommendation model for research collaboration in finance, aiming to promote the scientific collaboration and improve research productivity. [ Methods ] First, we established the scientific collaboration networks at individuals, institutions and regions levels. Then, we established a recommendation model based on network neighbors and paths. Finally, we conducted empirical study to examine the model at three levels. [ Results ] A total of 68 905 articles published from 2000 to 2014 on finance were analyzed to construct their research collaboration networks. The AUC values of the proposed model at individual, institutional and regional levels were 84.25%, 87.34%, and 91.84%, respectively, which were higher than those of the traditional algorithms. [ Limitations ] The training and testing sets were only classified by time. More segmentation methods were needed to optimize the new model. [ Conclusions ] This study helps researchers find collaboration opportunities, and provides new directions for studies on scientific collaboration networks.
Example-based Dynamic Skinning in Real Time

In this paper we present an approach to enrich skeleton-driven animations with physically-based secondary deformation in real time. To achieve this goal, we propose a novel, surface-based deformable model that can interactively emulate the dynamics of both lowand high-frequency volumetric effects. Given a surface mesh and a few sample sequences of its physical behavior, a set of motion parameters of the material are learned during an off-line preprocessing step. The deformable model is then applicable to any given skeleton-driven animation of the surface mesh. Additionally, our dynamic skinning technique can be entirely implemented on GPUs and executed with great efficiency. Thus, with minimal changes to the conventional graphics pipeline, our approach can drastically enhance the visual experience of skeleton-driven animations by adding secondary deformation in real time.
Smoking behaviour and expectations among Auckland adolescents.

AIM To compare the relationships of age level, gender, smoking status and ethnicity with Form Two and Form Six students' beliefs about smoking and their familial smoking behaviour. METHODS A stratified sample of 3041 Form Two and Form Six students from schools in the Auckland region was surveyed. Smoking prevalence, frequency, parental smoking and smoking inside the home were assessed using a 60-item questionnaire. RESULTS Analyses revealed several important differences across and within the Form Two and Form Six data relating to ever smoking, daily smoking and expectations of smoking in the future. Specifically, Form Six students were more likely to be ever smokers, daily smokers and have higher expectations of smoking in the future (p <0.001). Asian students reported a consistently lower propensity to smoke and hold expectations of smoking in the future compared with other ethnic groups. Students from higher-decile-rank schools were more likely to be ever smokers, daily smokers and have positive smoking expectations for the future; (p <0.001, p <0.01, p <0.001). Significant differences in familial smoking acceptability were observed across all measures. CONCLUSIONS The critical age differences were: (1) higher rates of ever smoking; (2) higher rates of daily smoking; and (3) higher levels of smoking expectations in Form Six compared with Form Two. Evidence from this study suggests that by Form Two a proportion of adolescents have already experimented with tobacco and some are daily smokers. Future research should continue to monitor the beliefs and behaviour of young adolescents and older teenagers who are at critical stages for the initiation and establishment of smoking behaviour. In addition, New Zealand's tobacco-control agenda should continue to support adult-focused programmes that reduce the acceptability and visibility of smoking.
Asymmetric Reinforcement Learning: Computational and Neural Bases of Positive Life Orientation Computational and Neural Bases of Positive Life Orientation

While forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is supposed to support the optimism bias. Whether learning bias is specific to " high-level " abstract belief update or a particular expression of a more general " low-level " reinforcement learning process is unknown. Here we report evidence in favor of the second hypothesis. In a simple instrumental learning task, participants incorporated worse-than-expected outcomes at a lower rate compared to better-than-expected ones. This asymmetry was correlated across subjects with standard measure of dispositional optimism. Multimodal imaging indicated that inter-individual variability in the expression of asymmetric update relies on the dorsomedial prefrontal cortex at both morphological and functional levels. Our results constitute a new step in the understanding of the genesis of optimism bias at the neurocomputational level. Introduction "It is the peculiar and perpetual error of the human understanding to be more moved and excited by affirmatives than negatives; whereas it ought properly to hold itself indifferently disposed towards both alike" (p. 36) a. People typically underestimate the likelihood of negative events and overestimate the likelihood of positive events. This cognitive trait in (healthy) humans is known as the optimism bias and has been repeatedly evidenced in many different guises and populations (Shepperd et al., 2013, 2015; Weinstein, 1980) such as students projecting their salary after graduation (Shepperd et al., 1996), women estimating their risk of getting breast cancer (Waters et al., 2011) or heavy smokers assessing their risk of premature mortality (Schoenbaum, 1997). One mechanism hypothesized to underlie this phenomenon is an asymmetry in belief updating, colloquially referred to as " the good news / bad news effect " (Eil and Rao, 2011; Sharot et al., 2011). Indeed, preferentially revising one's beliefs when provided with favorable compared to unfavorable information constitutes a learning bias which could, in principle, generates and sustains an overestimation of the likelihood of desired events and a concomitant underestimation of the likelihood of undesired events (optimism bias) (Sharot and Garrett, 2016).
Mean field equilibria of dynamic auctions with learning: A dynamic revenue equivalence theorem

Auctions are observed as a market mechanism in a wide range of economic transactions involving repeated interactions among the market participants: sponsored search markets run by Google and Yahoo! , online marketplaces such as eBay and Amazon, crowdsourcing, etc. In many of these markets, the participants typically have incomplete information; for example, the participants may not know the quality of the good being auctioned or their value for the good. In such settings, repeated interactions among the participants give rise to extremely complex bidder behavior due to the presence of learning among the participants.
Toward Widely-Available and Usable Multimodal Conversational Interfaces

Multimodal conversational interfaces, which allow humans to interact with a computer using a combination of spoken natural language and a graphical interface, offer the potential to transform the manlier by which humans communicate with computers. While researchers have developed myriad such interfaces, none have made the transition out of the laboratory and into the hands of a significant number of users. This thesis makes progress toward overcoming two intertwined barriers preventing more widespread adoption: availability and usability. Toward addressing the problem of availability, this thesis introduces a new platform for building multimodal interfaces that makes it easy to deploy them to users via the World Wide Web. One consequence of this work is City Browser the first, multimodal conversational interface made publicly available to anyone with a web browser and a microphone. City Browser serves as a proof-of-concept that significant amounts of usage data can be collected in this way, allowing a glimpse of how users interact with such interfaces outside of a laboratory environment. City Browser, in turn, has served as the primary platform for deploying and evaluating three new strategies aimed at improving usability. The most pressing usability challenge for conversational interfaces is their limited ability to accurately transcribe and understand spoken natural language. The three strategies developed in this thesis context-sensitive language modeling, response confidence scoring, and user behavior shaping - each attack the problem from a different angle, but they are linked in that each critically integrates information from the conversational context. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Gamma programming paradigm and heterogeneous computing

The Gamma programming paradigm is based on the multiset data structure. In this model a succession of chemical reactions consume the elements of the multiset or multiset of tuples, and produce new elements according to specific rules. This paper describes how the Gamma paradigm can be integrated with the entity-relationship (ER) model to structure parallel and distributed programs. Such an integration can permit querying as well as application programming in a seamless fashion based on the notion of transactions and provides for heterogeneous processing. We also describe how to design a scheduler for the Gamma paradigm and how this paradigm is useful in heterogeneous programming.
The Cambridge Handbook of Multimedia Learning

During the past 10 years, the field of multimedia learning has emerged as a coherent discipline with an accumulated research base that has never been synthesized and organized in a handbook. The Cambridge Handbook of Multimedia Learning constitutes the world's first handbook devoted to comprehensive coverage of research and theory in the field of multimedia learning. Multimedia learning is defined as learning from words (e.g., spoken or printed text) and pictures (e.g. illustrations, photos, maps, graphs, animation, or video). The focus of this handbook is on how people learn from words and pictures in computer-based environments. Multimedia environments include online instructional presentations, interactive lessons, e-courses, simulation games, virtual reality, and computer-supported in-class presentations. The Cambridge Handbook of Multimedia Learning seeks to establish what works (that is, to ground research in cognitive theory), and to consider when and where it works (that is, to explore the implications of research for practice). (http://books.google.fr/books? id=duWx8fxkkk0C&printsec=frontcover&hl=fr#v=onepage&q&f=false)
Molecular ecological analysis of methanogenic communities in terrestrial and submarine permafrost deposits of Siberian Laptev Sea area

Despite general concern that the massive deposits of methane stored under permafrost underground and undersea could be released into the atmosphere due to rising temperatures attributed to global climate change, little is known about the methanogenic microorganisms in permafrost sediments, their role in methane emissions, and their phylogeny. The aim of this thesis was to increase knowledge of uncultivated methanogenic microorganisms in submarine and terrestrial permafrost deposits, their community composition, the role they play with regard to methane emissions, and their phylogeny. It is assumed that methanogenic communities in warmer submarine permafrost may serve as a model to anticipate the response of methanogenic communities in colder terrestrial permafrost to rising temperatures. The compositions of methanogenic communities were examined in terrestrial and submarine permafrost sediment samples. The submarine permafrost studied in this research was 10°C warmer than the terrestrial permafrost. By polymerase chain reaction (PCR), DNA was extracted from each of the samples and analyzed by molecular microbiological methods such as PCR-DGGE, RT-PCR, and cloning. Furthermore, these samples were used for in vitro experiment and FISH. The submarine permafrost analysis of the isotope composition of CH4 suggested a relationship between methane content and in situ active methanogenesis. Furthermore, active methanogenesis was proven using 13C-isotope measurements of methane in submarine permafrost sediment with a high TOC value and a high methane concentration. In the molecular-microbiological studies uncultivated lines of Methanosarcina, Methanomicrobiales, Methanobacteriacea and the Groups 1.3 and Marine Benthic from Crenarchaeota were found in all submarine and terrestrial permafrost samples. Methanosarcina was the dominant group of the Archaea in all submarine and terrestrial permafrost samples. The archaeal community composition, in particular, the methanogenic community composition showed diversity with changes in temperatures. Furthermore, cell count of methanogens in submarine permafrost was 10 times higher than in terrestrial permafrost. In vitro experiments showed that methanogens adapt quickly and well to higher temperatures. If temperatures rise due to climate change, an increase in methanogenic activity can be expected as long as organic material is sufficiently available and qualitatively adequate.
Fractionation of Heavy Metals in Mine Tailings Amended with Composted Manure

ABSTRACT This study evaluated the effect of composted cow manure (CCM) on the chemical fractionation and retention degree of heavy metals (HMs) in mine tailings from Zimapán, México. In a greenhouse experiment, mine tailings from three deposits were incubated for 3 months; experimental units were placed in a PVC container, where increasing doses of CCM were applied. HM pseudo-total concentrations, HM extractions with ethylenediaminetetraacetic acid (EDTA, 0.05 M), and a sequential chemical extraction (SCE) were carried out. The HM concentrations were determined by atomic absorption spectrophotometry. The pseudo-total concentrations of Pb, Cu, Cd, and Ni found were up to 1506, 206, 27, and 23 mg kg−1, respectively; extractable Pb was up to 42%; 21% for Cu; 51% for Cd; and 16% of Ni of the pseudo-total concentrations of each metal. Treatment with 12% of CCM in mine tailing decreased EDTA-extractable HM concentrations, while the SCE revealed a decrease in exchangeable fraction and an increase in the organic fraction of HM. A positive correlation between CCM application and organic fractions of HMs was found, although the highest increasements were recorded in the organic fraction.
Dog Breed Identification

This project uses computer vision and machine learning techniques to predict dog breeds from images. First, we identify dog facial keypoints for each image using a convolutional neural network. These keypoints are then used to extract features via SIFT descriptors and color histograms. We then compare a variety of classification algorithms, which use these features to predict the breed of the dog shown in the image. Our best classifier is an SVM with a linear kernel and it predicts the correct dog breed on its first guess 52% of the time; 90% of the time the correct dog breed is in the top 10 predictions.
Credit Card Duplicity Reduction System Using Classification Metrics

: Recognizing cheats in Credit card exchanges is maybe a standout amongst the best test beds for computational knowledge calculations. Actually, this issue includes a few significant difficulties, to be specific: idea float (clients' propensities develop, and fraudsters change their methodologies after some time), class lopsidedness (real exchanges far dwarf fakes), and confirmation inactivity (just a little arrangement of exchanges are opportune checked by investigators). most of learning calculations that have been proposed for misrepresentation recognition depend on suspicions that barely hold in a genuine “Fraud Detection System” (FDS). Advanced classification methods provide the ability to detect fraudulent transactions without disturbance of legitimate transactions and unnecessarily spent resources on fraud forensics for financial institutions. However, it is significant that Credit Card organizations can perceive fake exchanges so clients are not charged for things that they didn't buy the point of this undertaking is to distinguish whether a Credit Card exchange is false or not utilizing Logistic Regression, Neural Network and Decision tree models and to look at the exhibitions of these models. Our dataset is PCA transformed to protect client privacy and is also highly imbalanced. We aim to identify potential for further feature reduction and feature engineering and overcome the data imbalance to improve model accuracy.