snu-aidas/HVMap-dataset huggingface dataset:
An Analysis of Mobile Banking User Behavior Using Customer Segmentation

This study proposed an integrated data mining and customer behavior scoring model to manage existing mobile banking users in an Iranian bank. This segmentation model was developed to identify groups of customers based on transaction history, recency, frequency, monetary background. It classified mobile banking users into six groups. This study demonstrated that identifying customers by a behavioral scoring facilitates marketing strategy assignment. Then the bank can develop its marketing actions. Thus, the bank can attract more customers, maintain its customers, and keep high customers' satisfaction. Keywords—Data mining; mobile data, mobile banking; customer segmentation Introduction Online banking is increasingly common. Financial institutions deliver online services via various electronic channels, subsequently diminishing the importance of conventional branch networks. The newly emerging channels of online banking and rapidly increasing penetration rates of mobile phones motivate this study (C. S. Chen, 2013). The internet has had a significant impact on financial institutions, allowing consumers to access many bank facilities 24 hours a day, while allowing banks to significantly cut their costs. Research has shown that online banking is the cheapest delivery channel for many banking services (KoenigLewis, Palmer, & Moll, 2010; Robinson, 2000). A number of studies have identified advantages to bank customers, including cost and time savings as well as spatial independence benefits (Koenig-Lewis et al., 2010). According to Gartner’s prediction of leading trends of 2012 in mobile applications, mobile commerce (m-commerce) remains the most important one. Gartner further forecasts that mobile devices will replace PCs as the main device to access the internet. As for the third quarter of 2012, IPSOS indicated that “The era of Multi-Screen has come, and smartphones account for the purchasing behavior of 65% of mobile device users.” According to that report, 66 percent of the smartphone holders in Taiwan access the internet via a smartphone at least once daily; approximately 57 percent of the customers perform mobile searches; and 40 percent of the customers shop via mobile phones (IPSOS, 2012). These statistics reflect vigorous growth in the scale of m-commerce. However, mobile banking remains in its infancy, and international adoption rates demonstrate the strong potential of m-commerce (FRB, 2012). Therefore, data mining for mobile banking is of priority concern for further developing mobile banking services (MBSs) (C.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [5], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [13].
Evaluation of object-oriented design patterns in game development

The use of object-oriented design patterns in game development is being evaluated in this paper. Games’ quick evolution, demands great flexibility, code reusability and low maintenance costs. Games usually differentiate between versions, in respect of changes of the same type (additional animation, terrains etc). Consequently, the application of design patterns in them can be beneficial regarding maintainability. In order to investigate the benefits of using design patterns, a qualitative and a quantitative evaluation of open source projects is being performed. For the quantitative evaluation, the projects are being analyzed by reverse engineering techniques and software metrics are calculated. 2006 Elsevier B.V. All rights reserved.
Brief mindfulness meditation training alters psychological and neuroendocrine responses to social evaluative stress

OBJECTIVE
To test whether a brief mindfulness meditation training intervention buffers self-reported psychological and neuroendocrine responses to the Trier Social Stress Test (TSST) in young adult volunteers. A second objective evaluates whether pre-existing levels of dispositional mindfulness moderate the effects of brief mindfulness meditation training on stress reactivity.


METHODS
Sixty-six (N=66) participants were randomly assigned to either a brief 3-day (25-min per day) mindfulness meditation training or an analytic cognitive training control program. All participants completed a standardized laboratory social-evaluative stress challenge task (the TSST) following the third mindfulness meditation or cognitive training session. Measures of psychological (stress perceptions) and biological (salivary cortisol, blood pressure) stress reactivity were collected during the social evaluative stress-challenge session.


RESULTS
Brief mindfulness meditation training reduced self-reported psychological stress reactivity but increased salivary cortisol reactivity to the TSST, relative to the cognitive training comparison program. Participants who were low in pre-existing levels of dispositional mindfulness and then received mindfulness meditation training had the greatest cortisol reactivity to the TSST. No significant main or interactive effects were observed for systolic or diastolic blood pressure reactivity to the TSST.


CONCLUSIONS
The present study provides an initial indication that brief mindfulness meditation training buffers self-reported psychological stress reactivity, but also increases cortisol reactivity to social evaluative stress. This pattern may indicate that initially brief mindfulness meditation training fosters greater active coping efforts, resulting in reduced psychological stress appraisals and greater cortisol reactivity during social evaluative stressors.
The development of initial trust in an online company by new customers

Lack of trust in online companies is a primary reason why many web users do not shop online. This study proposes a model to explain how new customers of a web-based company develop initial trust in the company after their first visit. The model is empirically tested using a questionnaire-based field study. The results indicate that perceived company reputation and willingness to customize products and services can significantly affect initial trust. Perceived web site usefulness, ease of use, and security control are also significant antecedents of initial trust. Finally, we found no support for the hypothesized effect of individual customer trust propensity on initial trust. # 2003 Elsevier B.V. All rights reserved.
Advanced Wireless LAN Technologies: IEEE 802.11AC and Beyond

In 2013, global mobile data traffic grew 81% and it is projected to increase 11-fold between 2013 and 2018. Further, it is predicted that by 2018, over two-thirds of the world's mobile traffic will be video and more than half of all traffic from wireless connected devices will be offloaded to Wi-Fi networks and femto-cells [1]. Consequently, wireless LANs need major upgrades to improve both throughput and efficiency. IEEE 802.11ac is an amendment to the 802.11 standard that was just ratified by IEEE 802.11. Promising up to gigabit data rates, many Wi-Fi products are being built based on this specification. In addition to technologies that improve throughput, IEEE 802.11ax is investigating and evaluating advanced wireless technologies that enable more efficient utilization of the existing spectrum.
An Efficient Algorithm for Local Distance Metric Learning

Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, such distance metrics attempt to keep all of the data points in each class close together while ensuring that data points from different classes are separated. However, particularly when classes exhibit multimodal data distributions, these goals conflict and thus cannot be simultaneously satisfied. This paper proposes a Local Distance Metric (LDM) that aims to optimize local compactness and local separability. We present an efficient algorithm that employs eigenvector analysis and bound optimization to learn the LDM from training data in a probabilistic framework. We demonstrate that LDM achieves significant improvements in both classification and retrieval accuracy compared to global distance learning and kernel-based KNN. Introduction Distance metric learning has played a significant role in both statistical classification and information retrieval. For instance, previous studies (Goldberger et al. 2005; Weinberger, Blitzer, & Saul 2006) have shown that appropriate distance metrics can significantly improve the classification accuracy of the K Nearest Neighbor (KNN) algorithm. In multimedia information retrieval, several papers (He et al. 2003; 2004; Muller, Pun, & Squire 2004) have shown that appropriate distance metrics, learned either from labeled or unlabeled data, usually result in substantial improvements in retrieval accuracy compared to the standard Euclidean distance. Most of the work in distance metrics learning can be organized into the following two categories: • Unsupervised distance metric learning, or manifold learning. The main idea is to learn a underlying low-dimensional manifold where geometric relationships (e.g., distance) between most of the observed data points are preserved. Popular algorithms in this category include ISOMAP (Tenenbaum, de Silva, & Langford 2000), Local Linear Embedding (Saul & Roweis 2003), and the Laplacian Eigenmap (Belkin & Niyogi 2003). Copyright c © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. −5 0 5 10 −8 −6 −4 −2 0 2 4 6 8 10 12 Class A Class B (a) −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 −0.25 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 Class A Class B
Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition

• Fusion between end-to-end system and language embeddings shows better efficiency than between end-to-end system such as MFCC and FBANK • Spectrograms achieve slightly better results than MFCCs Motivation • One of the challenges of processing real-world spoken content, such as media broadcasts, is the potential presence of different dialects of a language in the material. • Dialect identification (DID) can be a useful capability to identify which dialect is being spoken during a recording. • The Arabic Multi-Genre Broadcast (MGB) Challenge tasks have provided a valuable resource for researchers interested in processing multi-dialectal Arabic speech. • Investigation of end-to-end DID approach with dataset augmentation for acoustic feature and language embeddings for linguistic feature
“I am active”: effects of a program to promote active aging

BACKGROUND
Active aging involves a general lifestyle strategy that allows preservation of both physical and mental health during the aging process. "I am Active" is a program designed to promote active aging by increased physical activity, healthy nutritional habits, and cognitive functioning. The purpose of this study was to assess the effectiveness of this program.


METHODS
Sixty-four healthy adults aged 60 years or older were recruited from senior centers and randomly allocated to an experimental group (n=31) or a control group (n=33). Baseline, post-test, and 6-month follow-up assessments were performed after the theoretical-practical intervention. Effect sizes were calculated.


RESULTS
At the conclusion of the program, the experimental group showed significant improvement compared with the control group in the following domains: physical activity (falls risk, balance, flexibility, self-efficacy), nutrition (self-efficacy and nutritional status), cognitive performance (processing speed and self-efficacy), and quality of life (general, health and functionality, social and economic status). Although some declines were reported, improvements at follow-up remained in self-efficacy for physical activity, self-efficacy for nutrition, and processing speed, and participants had better nutritional status and quality of life overall.


CONCLUSION
Our findings show that this program promotes improvements in domains of active aging, mainly in self-efficacy beliefs as well as in quality of life in healthy elders.
Biomedical Signal and Image Processing Spring 2008

Introduction In this chapter we will examine how we can generalize the idea of transforming a time series into an alternative representation, such as the Fourier (frequency) domain, to facilitate systematic methods of either removing (filtering) or adding (interpolating) data. In particular, we will examine the techniques of Principal Component Analysis (PCA) using Singular Value Decomposition (SVD), and Independent Component Analysis (ICA). Both of these techniques utilize a representation of the data in a statistical domain rather than a time or frequency domain. That is, the data are projected onto a new set of axes that fulfill some statistical criterion, which implies independence, rather than a set of axes that represent discrete frequencies such as with the Fourier transform, where the independence is assumed. Another important difference between these statistical techniques and Fourier-based techniques is that the Fourier components onto which a data segment is projected are fixed, whereas PCA-or ICA-based transformations depend on the structure of the data being analyzed. The axes onto which the data are projected are therefore discovered. If the structure of the data (or rather the statistics of the underlying sources) changes over time, then the axes onto which the data are projected will change too 1. Any projection onto another set of axes (or into another space) is essentially a method for separating the data out into separate components or sources which will hopefully allow us to see important structure more clearly in a particular projection. That is, the direction of projection increases the signal-to-noise ratio (SNR) for a particular signal source. For example, by calculating the power spectrum of a segment of data, we hope to see peaks at certain frequencies. The power (amplitude squared) along certain frequency vectors is therefore high, meaning we have a strong component in the signal at that frequency. By discarding the projections that correspond to the unwanted sources (such as the noise or artifact sources) and inverting the transformation, we effectively perform a filtering of the recorded observation. This is true for both ICA and PCA as well as Fourier-based techniques. However, one important difference between these techniques is that Fourier techniques assume that the projections onto each frequency component are independent of the other frequency components. In PCA and ICA we attempt to find a set of axes which are independent of one another in some sense. We assume there are a set of independent 1 …
Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate

Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus. Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.
Nurse Managers’ Emotional Intelligence and Effective Leadership: A Review of the Current Evidence

Background
Emotional Intelligence has made a significant contribution to effective leadership, becoming one of the key characteristics of leaders.Objective: The aim of the present study was to review qualitative and quantitative studies concerning Emotional Intelligence of nurse leaders and the evidence-based composition of their results.


Method
A search was performed in the electronic databases (Pubmed, Scopus and CINAHL) for articles, which were published in the period 2000-2017 in English or Greek. Eleven studies met the inclusion criteria, of which 10 were quantitative and one was qualitative.


Results
The results suggested that Emotional Intelligence is a useful tool for nurse leaders and contributes decisively to the achievement of effective management in healthcare.


Conclusion
It is necessary for nurses to improve their social and emotional skills because of the particular nature of the nursing profession, which places the healthy or weak person at its center.
Sensors and systems for fruit detection and localization: A review

This paper reviews the research and development of machine vision systems for fruit detection and localization for robotic harvesting and/or crop-load estimation of specialty tree crops including apples, pears, and citrus. Variable lighting condition, occlusions, and clustering are some of the important issues needed to be addressed for accurate detection and localization of fruit in orchard environment. To address these issues, various techniques have been investigated using different types of sensors and their combinations as well as with different image processing techniques. This paper summarizes various techniques and their advantages and disadvantages in detecting fruit in plant or tree canopies. The paper also summarizes the sensors and systems developed and used by researchers to localize fruit as well as the potential and limitations of those systems. Finally, major challenges for the successful application of machine vision system for robotic fruit harvesting and crop-load estimation, and potential future directions for research and development are discussed. 2015 Elsevier B.V. All rights reserved.
Effect of different surface pre-treatments and luting materials on shear bond strength to PEEK.

OBJECTIVES
To assess the bonding potential of a universal composite resin cement and an adhesive/composite system to differently pre-treated PEEK surfaces.


METHODS
One hundred and fifty PEEK disks were embedded in epoxy resin, polished (P4000 grit) and treated as follows (n=30/group): (A) no treatment, (B) acid etching with sulfuric acid (98%) for 1 min, (C) sandblasting for 10s with 50 microm alumina, (D) sandblasting for 10s with 110 microm alumina and (E) silica coating using the Rocatec system (3M ESPE). Polished and sandblasted (50 microm alumina) cp titanium (grade 4) served as a control. Acrylic hollow cylinders were either luted with a universal composite resin cement (RelyX Unicem) or an unfilled resin (Heliobond) and a hybrid composite (Tetric) to the specimens. Bond strength was measured in a shear test and failure modes were assessed. Statistic analysis was performed with one-way ANOVA followed by a post hoc Scheffé test and unpaired t-tests.


RESULTS
With the universal composite resin cement, no bond could be established on any PEEK surfaces, except specimens etched with sulfuric acid (19.0+/-3.4MPa). Shear bond strength to titanium was significantly lower (8.7+/-2.8MPa, p<0.05). Applying the adhesive/composite system, shear bond strength values on pre-treated PEEK ranged from 11.5+/-3.2MPa (silica coating) to 18.2+/-5.4MPa (acid etched) with no statistically significant differences (p>0.05). No bond was obtained on the polished surface.


SIGNIFICANCE
Bonding to PEEK is possible when using a bonding system. No adherence can be achieved with the tested universal composite resin cement except on an etched surface. The results strongly encourage further research in PEEK application in dentistry.
The Fastest Pedestrian Detector in the West

Figure 1: In many applications, detection speed is as important as accuracy. (a) A standard pipeline for performing modern multiscale detection is to create a densely sampled image pyramid, compute features at each scale, and finally perform sliding window classification (with a fixed scale model). Although effective; the creation of the feature pyramid can dominate the cost of detection, leading to slow multiscale detection. (b) Viola and Jones [6] utilized shift and scale invariant features, allowing a trained detector to be placed at any location and scale without relying on an image pyramid. Constructing such a classifier pyramid results in fast multiscale detection; unfortunately, most features are not scale invariant, including gradient histograms, significantly limiting the generality of this scheme. (c) We propose a fast method for approximating features at multiple scales using a sparsely sampled image pyramid with a step size of an entire octave and within each octave we use a classifier pyramid. The proposed approach achieves nearly the same accuracy as using densely sampled image pyramids, with nearly the same speed as using a classifier pyramid applied to an image at a single scale.