Miracle-dz/UniFAR-FTUs huggingface dataset:
Aprendizado de máquina para análise de recaída para depressão em pacientes com transtorno bipolar

A recaida para depressao em pacientes com Transtorno Afetivo Bipolar (TAB) atinge taxas de 70% de recorrencia nos 4 primeiros anos de tratamento e pode causar uma drastica reducao na qualidade de vida e levar ate o suicidio. O TAB e uma desordem do humor caracterizada por episodios recorrentes de depressao ou mania. Para estudar o transtorno e encontrar tratamentos mais eficientes, o Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) foi criado pela Escola de Medicina de Harvard. O STEP-BD e um conjunto de dados composto por informacoes de 4.360 pacientes com TAB, o qual pode ser considerado atualmente uma das mais completas bases de dados em termos de escopo. Varios estudos foram desenvolvidos para descobrir tratamentos mais eficientes para prevenir recaidas. Porem, a maioria destes estudos usaram apenas metodos classicos de estatistica, principalmente com o objetivo de medir a sua correlacao com atributos especificos. Este trabalho apresenta uma analise do uso de algoritmos de aprendizado de maquina para encontrar padroes relacionados a recaida para depressao no TAB com o uso de dados longitudinais providos pelo STEP-BD. Estes dados longitudinais incluem 148 atributos coletados em um total de 50.987 visitas de pacientes espalhadas ao longo de semanas durante anos. Assim, diversos experimentos foram conduzidos neste trabalho e os resultados mostram que os algoritmos obtiveram desempenho limitado. Foi possivel perceber que atributos relacionados ao estado de humor de depressao e mania, coletados pelo STEP-BD, nao podem ser usados propriamente para predizer recaida para depressao antes de sua ocorrencia, sendo apropriados apenas para uso como um indicador que o paciente ja se encontra no estado de depressao.
Institutional Investors and Bank Governance: An International Analysis of Bank Earnings Management

Despite the growing importance of institutional investors in global capital markets and the link between bank earnings management and financial crash risk, little is known about institutional investors’ role in mitigating bank earnings management. We conduct the first international analysis of this issue using a broad sample of banks and institutional investors. We find that institutional ownership is more negatively related to earnings management in countries with more-stringent bank disclosure requirements or when ownership is held by domestic rather than foreign institutional investors. Institutional ownership is also more negatively related to earnings management in countries with weaker investor protection or when ownership is held by institutional blockholders. Our results have important implications for policies regarding institutional investors’ ownership engagement with banks.
Empirical techniques and algorithms to develop a resilient non-supervised touch-based authentication system

Touch dynamics (or touch based authentication) refers to a behavioral biometric for touchscreen devices wherein a user is authenticated based on his/her executed touch gestures. This work addresses two research topics. We first present a series of empirical techniques to detect habituation in the user’s touch profile, its detrimental effect on authentication accuracy and strategies to overcome these effects. Habituation here refers to changes in the user’s profile and/or noise within it due to the user’s familiarization with the device and software application. With respect to habituation, we show that habituation causes the user’s touch profile to evolve significantly and irrevocably over time even after the user is familiar with the device and software application. This phenomenon considerably degrades classifier accuracy. We demonstrate techniques that lower the error rate to 3.68% and sets the benchmark in this field for a realistic test setup. Finally, we quantify the benefits of vote-based reclassification of predicted class labels and show that this technique is vital for achieving high accuracy in realistic touch-based authentication systems. In the second half, we implement the first ever non-supervised classification algorithm in touch based continual authentication. This scheme incorporates clustering into the traditional supervised algorithm. We reduce the mis-classification rate by fusing supervised random forest algorithm and non-supervised clustering (either Bayesian learning or simple rule of combinations). Fusing with Bayesian clustering reduced the mis-classification rate by 50% while fusing with simple rule of combination reduced the mis-classification rate by as much as 59.5% averaged over all the users.
Executive compensation in less regulated markets: the impact of debt monitoring

This paper shows that in the lightly regulated Alternative Investment Market (AIM) voluntary corporate board structures might not reduce agency costs between shareholder and executive directors. In this less regulated market, we find that the extent of debt affects executive pay. In addition, the theoretical determinants of executive pay affect CEO and other executives’ pay and incentives differently in this market. We find no evidence that debt levels affect CEO pay in a matched sample of Main Market firms. Our results suggest that debtholders could be better monitors of executive directors’ actions, in comparison to voluntary governance committees in less regulated markets.
Preliminary Observations and Assessment of Çanakkale-Ayvacık Earthquake Activity

Biga Yarimadasi’nin guneybati ucunda Ayvacik ve yakin cevresinde 14 Ocak 2017 tarihinde Mw=4.6 ile baslayan 6 Şubat 2017 Mw=5.5, (Mw=5.5, Kandilli Rasathanesi ve Deprem Arastirma Enstitusu – KRDAE -, Mw=5.3, AFAD, Mw=5.4, COMU) ile devam eden yogun deprem aktivitesini izlemek uzere bir proje baslatilmistir. Bolge aktif bir depremselligi icermekte ve Kuzeybati Ege bolgesinin onemli tektonik unsurlarindan bir kismini barindirmaktadir. Deprem aktivitesinin devam etmesi nedeniyle, bolgede ulusal aglara (KRDAE ve AFAD) bagli sabit istasyonlara ek olarak, 10 adet surekli formda kayit alan 3-bilesen gecici istasyon en az 1 yil sureyle gozlem yapmak amaci ile kurulmustur. Bu istasyonlarin birbirlerine uzakligi 6 ila 12 km arasinda degismektedir. Bolgedeki mikrodeprem aktivitesinin yogunlugu ve surekliligi yaninda bolgenin jeotermal sahalari da kapsamasi, bolgenin sismotektonik karakterinin incelenmesini ve detayli olarak calisilmasi gerekliligini on plana cikarmaktadir. Calisma alaninda ulusal kurumlara ait sabit istasyonlar olmakla beraber istasyon yogunlugu bu detaylarin ortaya cikarilmasinda yetersiz kalacaktir. Bu nedenle gerek ulusal sismik aga gerekse gecici istasyonlara ait veri bolgenin tektonik niteliginin anlasilmasina katkilar saglayacaktir. Ilksel sonuclar, ana sok ve episantr dagilimlarina gore yakin uzakliklarda kurulan gecici istasyonlarin sabit aglar ile karsilastirildiginda ozellikle mikrodepremlerin tespit edilmesi ve yerlerinin belirlenmesinde onemli iyilestirmeler sagladigini gostermektedir. Bunun yaninda ve orta buyuklukteki deprem verisine ait kaynak mekanizmasi cozumlerinde, agirlikli olarak KB-GD yonlu normal atim bileseni baskin faylanma gozlenmektedir. Calisma basta Ayvacik ve yakin cevresi olmak uzere Biga Yarimadasi guneybati kesiminde meydana gelen depremlerin zaman ve uzaysal dagilimlari, frekans icerikleri ve kaynak mekanizmalarini dagilimi yaninda yerel tomografi ile ayrintili hiz modelini cikarmaya yoneliktir.
Formulaic sequences in spoken ENL, ESL and EFL

In this pilot study we set out to compare formulaic sequences of the type of 3-grams in ENL (spoken British English), EFL (English spoken by advanced German learners of English) and ESL (spoken Indian English). The study shows that, for the overall number of types and tokens, there are no significant differences between ENL and ESL, but there are significantly fewer 3-grams in EFL vs. ENL. A comparison of the common core (i.e. the 3-grams all three variants have in common) reveals that these common-core 3-grams are significantly more frequently used in ESL and EFL-variants compared to ENL. A functional analysis shows differences in the distribution of the 3-grams across the variants. A study of the variant-specific 3-grams reveals less variability in EFL vs. ENL but a higher number and variability of both types and tokens in ESL.
Experimental Comparisons of Semi-Supervised Dimensional Reduction Methods

Semi-Supervised learning is one of the hottest research topics in the technological community,which has been developed from the original semi-supervised classification and semi-supervised clustering to the semi-supervised regression and semi-supervised dimensionality reduction,etc.At present,there have been several excellent surveys on semi-supervised classification: Semi-Supervised clustering and semi-supervised regression,e.g.Zhu's semi-supervised learning literature survey.Dimensionality reduction is one of the key issues in machine learning,pattern recognition,and other related fields.Recently,a lot of research has been done to integrate the idea of semi-supervised learning into dimensionality reduction,i.e.semi-supervised dimensionality reduction.In this paper,the current semi-supervised dimensionality reduction methods are reviewed,and their performances are evaluated through extensive experiments on a large number of benchmark datasets,from which some empirical insights can be obtained.
Passive Imaging with Ambient Noise

Waves generated by opportunistic or ambient noise sources and recorded by passive sensor arrays can be used to image the medium through which they travel. Spectacular results have been obtained in seismic interferometry, which open up new perspectives in acoustics, electromagnetics, and optics. The authors present, for the first time in book form, a self-contained and unified account of correlation-based and ambient noise imaging. In order to facilitate understanding of the core material, they also address a number of related topics in conventional sensor array imaging, wave propagation in random media, and high-frequency asymptotics for wave propagation. Taking a multidisciplinary approach, the book uses mathematical tools from probability, partial differential equations and asymptotic analysis, combined with the physics of wave propagation and modelling of imaging modalities. Suitable for applied mathematicians and geophysicists, it is also accessible to graduate students in applied mathematics, physics, and engineering.
Behavioral Biases Influencing the Decision Making of Portfolio Managers of Capital Securities and Traders in Morocco

Behavioral finance is a new discipline that has emerged to explain the anomalies in the financial markets (ex: the succession of speculative bubbles and stock market crashes). It is based on two assumptions namely the irrationality of investors and limit arbitration. Behavioral finance is based on prospect theory and the psychological biases. In this paper we will present first the behavioral finance and its state of research. Secondly, we will present the results of the survey that was conducted with a sample composed of managers of equity securities portfolios exercising in the management companies in Morocco and Moroccan traders and which deals with psychological biase that affect their decisions the most. The results showed that capital portfolio managers and Moroccan traders are influenced by psychological biases in their decision-making.
Performance of Modern Fault Passage Indicator Concept in Compensated MV-Networks

This paper introduces a novel concept for fault passage indication applicable in secondary distribution level in compensated MV-networks. A special emphasis is put to dependable and secure detection of all kind of earth faults encountered in practical networks with overhead lines and underground cables. With the proposed solution requirements for sensitivity and selectivity are fulfilled not only in continuous solid and resistive earth faults, but also during restriking faults. The operation of the fault passage indication is based on the latest evolution of the neutral admittance based detection principle utilizing multifrequency admittance measurement. The performance of the proposed novel fault indication concept has been validated by a field test in a 20kV distribution network. The results show that the multifrequency admittance criterion provides very good overall performance and has thus a high potential to become a standard and widely used functionality in fault passage indication applications.
An overview of content-based image retrieval techniques

Content based image retrieval (CBIR) depends on several factors, such as, feature extraction method (the usage of appropriate features in CBIR), similarity measurement method and mathematical transform (chosen to calculate effective features), feedback usage and etc. All these factors are important in CBIR to enhance image retrieval accuracy and effort. An efficient retrieval mechanism can be achieved by improving some of its influencing factors. For this purpose, this paper provides a brief review of the factors that have an impact (positive or negative) on the CBIR. The usage of low-level image features such as shape, texture and color are assembling information from an image for recuperation. In this paper, various spectral methods of texture features extraction are discussed. In addition, all the current methods of demonstrating image texture features in the modern literature have been investigated for the purpose to achieve the research’s aim (to discover the most adequate features in CBIR that support image retrieval quality and retrieve the relevant images to the query image ). This paper also addresses the shortcomings of one spectral approach and the solutions provided by another approach for finding the most effective approach in texture feature representation.
A Discussion of Semi-Supervised Learning and Transduction

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard SchA¶lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in TA? bingen. SchA¶lkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational B iology (2004), all published by The MIT Press.
Factors influencing behavioural intention to use mobile banking among retail banking clients

Mobile banking enables clients to bank virtually without restrictions to time and place. Despite wide adoption of mobile banking around the world, use of mobile banking is still considered low. Philippines has the lowest digital banking penetration across Asian countries although the country known for the title of “SMS-intensive country in the world”. Factor analysis, partial least squares structural equation modelling (PLS-SEM) and ANOVA with bootstrapping were used to test research hypotheses. Findings from 212 Universal bank retail branch clients show that perceived ease of use, perceived usefulness, social influence and trust propensity have significant positive influence on behavioral intention to use mobile banking. Results also show that respondents with matured age (22 years and above), male, at least Bachelor Degree graduate and receiving a monthly income higher than Php 10,001 has higher behavioral intention. This study is relevant for proposing marketing strategies to increase adoption of mobile banking and contribute for further advancement on technology acceptance literature.
The Acquisition of Case, Number and Gender in Croatian

The analysis of Croatian data based on the child corpus of one Croatian child, Antonija, has provided sufficient evidence for obtaining answers to questions raised at the beginning of this paper. Morphological complexity proves to be a trigger for early acquisition. The first oppositions appear already at age 1 ; 6 while the first grammatical markers appear in the one-word stage. Such course of morphological development differs significantly form the data in other and/or less morphologically complex languages. Child behaves from the very beginning as an ? efficient? learner being confronted with morphological richness in target language. The child balances between acquiring vocabulary and morphology. After acquiring the critical vocabulary mass, the seesaw flips to the other side and the acquisition of morphology follows in a high speed.
Mediating Role of Sustainability Capability in Determining Sustainable Supply Chain Management in Tourism Industry of Thailand

The current study empirically investigates the factors of sustainable supply chain performance in tourism industry of Thailand. Globally, competition has increased due to involvement of enhanced quality and sustainable practices in business operations including effective supply chain management as it has become one of the prime concerns of firms as it plays vital role in achievement of goals and competitive advantages. The study investigate the relationship between customer awareness as it is one of the crucial factor which needs to be addressed by firms primarily in order to operate in turbulent and highly competitive environment. The customer awareness positively influence SSCM and sustainability capability, similarly competitiveness is investigated with sustainability capability and SSCM, moreover, mediation role was examined between customer awareness, competitiveness and SSCM by sustainability capability. The current study is conducted on tourism industry of Thailand, to determine the effect and influence of customer awareness and competitiveness on sustainability capability and SSCM. The study is unique in determining the mediating role of sustainability capability between independent variable and dependent variable. The analysis of the study suggest that customer awareness influence SSCM, similarly competitiveness also effect SSCM and sustainability capability, moreover mediating effect was also reported and demonstrated in analysis phase of the study by shrinking the effect of independent variables on dependent variables.The study is relevant and investigated the factors related to the development of SSCM and provides guidance to firms to achieve sustainable and continuous improvements in business operations and supply chain of involved stakeholders of Thai tourism industry.
The Effects of Repeated Oral Reading and Timed Reading on L2 Oral Reading Fluency.

The effects of repeated oral reading and timed reading on L2 oral reading fluency were examined among Japanese university students (N = 50) over 12 weeks. Three quasi-experimental groups were used in the study. Group 1 practiced two types of reading: Repeated oral reading with chunking practice and timed reading. Group 2 did timed reading only. Group 3 served as the comparison group. The participants were rated on their oral reading fluency of a short passage before and after the treatment period in terms of prosody, accuracy, and speed. The results showed that both treatment groups made statistically significant within-subjects oral reading fluency gains. Between-subjects tests indicated that Group 1 outperformed both Group 2 and the comparison group by the end of the treatment. Additionally, there were no statistically significant differences found between Group 2 and the comparison group. Finally, it was shown that Group 1 made the most improvements in terms of the rhythmic aspects of their oral reading production, Group 2 gained the most in terms of speed, and Group 3 made negligible gains. These results provide empirical evidence of the benefits of repeated oral reading and timed reading on the development of L2 oral reading fluency.
Designing personalized prompts for a virtual assistant to support elderly care home residents

In this paper, we present the first results of the [email protected] research project which aims to develop an artificially intelligent virtual assistant (VA) to engage and help older adults with Alzheimer's disease (AD) to complete activities of daily living (ADL) more independently. In order to define the most appropriate prompting style for each user profile, we performed 12 semi structured qualitative interviews with dyads of elderly care home residents and their family caregiver. During these interviews, we presented the virtual assistant and the different 'static' prompts to support people in the activity of hand washing. We gathered as much feedback and suggestions as possible coming directly from the end users about how to improve the provided prompts and thus, increase acceptability. The results are presented around three extracted themes: a) comments on current design of the virtual assistant, b) perceived usefulness, user adoption and c) suggestions for improvements. These should guide in the future developers of assistive technology to support elderly care home residents.
Resampling Techniques for Real Estate Appraisals: Testing the Bootstrap Approach

Applied to real estate markets analysis, the resampling methods aim to contribute to the knowledge growth of real estate market dynamics, overcoming the issues related to the data scarcity and operational limits of traditional statistical theory. Among resampling methods, the Bootstrap technique appears to be the most suitable for the interpretation of real estate phenomena. In this study, for residential properties located in Cosenza (Calabria Region, Italy), a Bootstrap approach has been used in order to determine the marginal prices of the real estate characteristics detected, comparing the results with those obtainable with a traditional Multiple Regression Analysis.