snu-aidas/HVMap-dataset huggingface dataset:
Key drivers of internet banking services use

Purpose – The purpose of this paper is to analyse the determinants of internet banking use, paying special attention to the role of product involvement, perceived risk and trust. Design/methodology/approach – The impact of trust, perceived risks, product involvement and TAM beliefs (ease of use and usefulness) on internet banking adoption is tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users and the data are collected through an internet survey. Risk is measured as a formative construct. Findings – Data analysis shows that TAM beliefs and perceived risks (security, privacy, performance and social) have a direct influence on e-banking adoption. Trust appears as a key variable that reduces perceived risk. Involvement plays an important role in increasing perceived ease of use. Practical implications – This research provides banks with knowledge of what aspects to highlight in their communications strategies to increase their internet banking services adoption rates. The research findings show managers that web contents and design are key tools to increase internet banking services adoption. Practical recommendations to increase web usefulness and trust, and guidelines to reduce perceived risk dimensions are also provided. Originality/value – Despite the importance of trust issues and risk perceptions for internet banking adoption, only limited work has been done to identify trust and risk dimensions in an online banking context. We have evaluated the impact of each risk dimension instead of treating risk as a whole. Furthermore, risk has been measured as a formative construct because there is no reason to expect that risk dimensions in online financial services are correlated.
Active Contours without Edges for Vector-Valued Images

In this paper, we propose an active contour algorithm for object detection in vectorvalued images (such as RGB or multispectral). The model is an extension of the scalar Chan–Vese algorithm to the vector-valued case [1]. The model minimizes a Mumford–Shah functional over the length of the contour, plus the sum of the fitting error over each component of the vector-valued image. Like the Chan–Vese model, our vector-valued model can detect edges both with or without gradient. We show examples where our model detects vector-valued objects which are undetectable in any scalar representation. For instance, objects with different missing parts in different channels are completely detected (such as occlusion). Also, in color images, objects which are invisible in each channel or in intensity can be detected by our algorithm. Finally, the model is robust with respect to noise, requiring no a priori denoising step. C © 2000 Academic Press
Integrated pathology and radiology learning for a musculoskeletal system module: an example of interdisciplinary integrated form

Introduction
Many curricula integrate radiology with anatomy courses but none of these curricula adopt integration of pathology with radiology as interdisciplinary form at the undergraduate level. The aim of the current study was to identify the outcome of interdisciplinary integrated course of pathology and radiology in musculoskeletal system module (MSK).


Methods
A comparative interventional study was conducted on 60 students representing a whole class of the third year of level V. MSK and gastrointestinal module (GIT) were selected as study and control module, respectively, as being adopted for the same level/allocated hours, enriched with many subject areas for both fields, and availability of learning resources for both. A planned interdisciplinary integrated course for MSK pathology and radiology was implemented in the pathology lab. The subject area was selected and taught for both fields in consecutive ways by pathology and radiology experts. After teaching, gross/histopathologic specimens and radiology imaging/reports were distributed over benches and the students investigated the same. Conversely, in GIT control module, both fields were delivered separately, and no interdisciplinary form of integration occurred. Students' scores for both fields were filtered from the objective structured practical exam, quiz, and final exam. Students' marks and satisfaction were subjected to multiple comparisons using independent student's t-test. SPSS version 17 was used.


Results
Significances were obtained between total marks of students for both modules and between radiology courses for both with P=0.0152 and 0.0199, respectively. Number of students who achieved >80% in MSK was 20 and 26 compared to 15 and 17 in GIT for pathology and radiology, respectively. Student satisfaction was high for interdisciplinary integration in MSK with significant difference obtained between MSK and GIT.


Conclusion
The integration of both fields augments student performance for both. This experience must encourage curriculum committee to globalize it over all other modules.
REVIEW OF FATIGUE DETECTION AND PREDICTION TECHNOLOGIES September 2000 Prepared

This report reviews existing fatigue detection and prediction technologies. Data regarding the different technologies available were collected from a wide variety of worldwide sources. The first half of this report summarises the current state of research and development of the technologies and summarises the status of the technologies with respect to the key issues of sensitivity, reliability, validity and acceptability. The second half evaluates the role of the technologies in transportation, and comments on the place of the technologies vis-a-vis other enforcement and regulatory frameworks, especially in Australia and New Zealand. The report authors conclude that the hardware technologies should never be used as the company fatigue management system. Hardware technologies only have the potential to be a last ditch safety device. Nevertheless, the output of hardware technologies could usefully feed into company fatigue management systems to provide real time risk assessment. However, hardware technology output should never be the only input into a management system. Other inputs should at least come from validated software technologies, mutual assessment of fitness for duty and other risk assessments of work load, schedules and rosters. Purpose: For information: to provide an understanding of the place of fatigue detection and prediction technologies in the management of fatigue in drivers of heavy vehicles.
"Eczema coxsackium" and unusual cutaneous findings in an enterovirus outbreak.

OBJECTIVE
To characterize the atypical cutaneous presentations in the coxsackievirus A6 (CVA6)-associated North American enterovirus outbreak of 2011-2012.


METHODS
We performed a retrospective case series of pediatric patients who presented with atypical cases of hand, foot, and mouth disease (HFMD) from July 2011 to June 2012 at 7 academic pediatric dermatology centers. Patients were included if they tested positive for CVA6 or if they met clinical criteria for atypical HFMD (an enanthem or exanthem characteristic of HFMD with unusual morphology or extent of cutaneous findings). We collected demographic, epidemiologic, and clinical data including history of skin conditions, morphology and extent of exanthem, systemic symptoms, and diagnostic test results.


RESULTS
Eighty patients were included in this study (median age 1.5 years, range 4 months-16 years). Seventeen patients were CVA6-positive, and 63 met clinical inclusion criteria. Ninety-nine percent of patients exhibited a vesiculobullous and erosive eruption; 61% of patients had rash involving >10% body surface area. The exanthem had a perioral, extremity, and truncal distribution in addition to involving classic HFMD areas such as palms, soles, and buttocks. In 55% of patients, the eruption was accentuated in areas of eczematous dermatitis, termed "eczema coxsackium." Other morphologies included Gianotti-Crosti-like (37%), petechial/purpuric (17%) eruptions, and delayed onychomadesis and palm and sole desquamation. There were no patients with serious systemic complications.


CONCLUSIONS
The CVA6-associated enterovirus outbreak was responsible for an exanthem potentially more widespread, severe, and varied than classic HFMD that could be confused with bullous impetigo, eczema herpeticum, vasculitis, and primary immunobullous disease.
Three-axis NC milling simulation based on adaptive triangular mesh

0360-8352/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.cie.2010.06.016 * Corresponding author. Tel.: +86 21 67791413; fax E-mail address: mainpowers@gmail.com (J. Mao). NC milling simulation has become an important step in computer aided manufacturing (CAM). To achieve real-time simulation, the total number of polygons has to be reduced, which results in poor image quality. This paper presents an adaptive triangular mesh algorithm to reduce the number of polygons while image quality remains high. Binary tree is used to represent the milling surface, and the optimization of the mesh is performed dynamically in the process of simulation. In this algorithm, the resolution of triangles is automatically updated according to local surface flatness, thus greatly reducing the number of triangles at planar regions. By doing this, real-time and high quality of visual presentation is insured and the translation, rotation and zooming operations are still applicable. When machining precision is evaluated, or overcut, undercut and interference are inspected, full resolution model stored in memory is automatically loaded to ensure the accuracy and correctness of these inspections. Finally, an example is presented to illustrate the validity of proposed algorithm. 2010 Elsevier Ltd. All rights reserved.
Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain
Extreme learning machine: Theory and applications

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. r 2006 Elsevier B.V. All rights reserved.
Training of Airport Security Screeners

scientifically based training system that is effective and efficient and allows achieving an excellent level of detection performance. Object recognition is a very complex process but essentially it means to compare visual information to object representations stored in visual memory. The ability to recognize an object class depends on whether itself or similar instance has been stored previously in visual memory. In other words, you can only recognize what you have learned. This explains why training is so important. Identifying the threat items in Fig. 1a and 1b is difficult without training because the objects are depicted in a view that is rather unusual in everyday life. Detecting a bomb such as in Fig. 1c is difficult for untrained people because usually we do not encounter bombs in everyday life. Therefore, a good training system must contain many forbidden objects in many viewpoints in order to train screeners to detect them reliably. Indeed, several studies from our lab and many others worldwide have found that object recognition is often dependent on viewpoint. Moreover, there are numerous studies from neuroscience suggesting that objects are stored in a view-based format in the brain. As you can see in Fig. 2 the hammer, dirk, grenade and gun, which are visible in the bags of Fig. 1a and 1b are indeed much easier to recognize if they are shown in a view that is more often encountered in real life. Because you never know how terrorists place their threat items in a bag, airport security screeners should be trained to detect prohibited items from all kinds of different viewpoints. In a close collaboration with Zurich State Police, Airport division we have Current x-ray machines provide high resolution images, many image processing features and even automatic explosive detection. But the machine is only one half of the whole system. The last and most important decision is always taken by the human operator. In fact, the best and most expensive equipment is of limited use, if a screener finally fails to recognize a threat in the x-ray image. This is of special importance because according to several aviation security experts the human operator is currently the weakest link in airport security. This matter is being realized more and more and several authorities as well as airports are planning to increase investments into a very important element of aviation security: Effective and efficient training of screeners. Indeed, …
Big Data Management Assessed Coursework Two Big Data vs Semantic Web F 21 BD
Macro process of knowledge management for continuous innovation

Purpose The purpose of this research is to understand the mechanisms of knowledge management (KM) for innovation and provide an approach for enterprises to leverage KM activities into continuous innovation. Design/methodology/approach – By reviewing the literature from multidisciplinary fields, the concepts of knowledge, KM and innovation are investigated. The physical, human and technological perspectives of KM are distinguished with the identification of two core activities for innovation: knowledge creation and knowledge usage. Then an essential requirement for continuous innovation –an internalization phase is defined. The systems thinking and human-centred perspectives are adopted for providing a comprehensive understanding about the mechanisms of KM for innovation. Findings – A networking process of continuous innovation based on KM is proposed by incorporating the phase of internalization. According to the three perspectives of KM, three sources of organizational knowledge assets in innovation are identified. Then based on the two core activities of innovation, a meta-model and a macro process of KM are proposed to model the mechanisms of KM for continuous innovation. Then, in order to operationalize the KM mechanisms, a hierarchical model is constructed by integrating three sources of knowledge assets, the meta-model and the macro process into the process of continuous innovation. This model decomposes the complex relationships between knowledge and innovation into four layers. Practical implications – According to the lessons learned about KM practices in previous research, the three perspectives of KM should collaborate with each other for successful implementation of KM projects for innovation; and the hierarchical model provides a suitable architecture to implement systems of KM for innovation. Originality/value – The meta-model and macro process of KM explain how the next generation of KM can help the value creation and support the continuous innovation from the systems thinking perspective. The hierarchical model illustrates the complicate knowledge dynamics in the process of continuous innovation.
Automatic Image Alignment and Stitching of Medical Images with Seam Blending

This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts. Keywords—Histogram Matching, Image Alignment, Image Stitching, Medical Imaging.
An Efficient End-to-End Neural Model for Handwritten Text Recognition

Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs [10] have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss [18], an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.
Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation

¿Ontology matching¿ is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our ¿structural matching method via upper ontology¿ uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our ¿nonstructural matching method¿ via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The ¿mixed method¿ that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.
The international personality item pool and the future of public-domain personality measures

Seven experts on personality measurement here discuss the viability of public-domain personality measures, focusing on the International Personality Item Pool (IPIP) as a prototype. Since its inception in 1996, the use of items and scales from the IPIP has increased dramatically. Items from the IPIP have been translated from English into more than 25 other languages. Currently over 80 publications using IPIP scales are listed at the IPIP Web site (http://ipip.ori.org), and the rate of IPIPrelated publications has been increasing rapidly. The growing popularity of the IPIP can be attributed to Wve factors: (1) It is cost free; (2) its items can be obtained instantaneously via the Internet; (3) it includes over 2000 items, all easily available for inspection; (4) scoring keys for IPIP scales are This article represents a synthesis of contributions to the presidential symposium, The International Personality Item Pool and the Future of Public-Domain Personality Measures (L.R. Goldberg, Chair) at the sixth annual meeting of the Association for Research in Personality, New Orleans, January 20, 2005. Authorship order is based on the order of participation in the symposium. The IPIP project has been continually supported by Grant MH049227 from the National Institute of Mental Health, U.S. Public Health Service. J.A. Johnson’s research was supported by the DuBois Educational Foundation. The authors thank Paul T. Costa Jr., Samuel D. Gosling, Leonard G. Rorer, Richard Reed, and Krista Trobst for their helpful suggestions. * Corresponding author. Fax: +1 814 375 4784. E-mail address: j5j@psu.edu (J.A. Johnson). 0092-6566/$ see front matter  2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jrp.2005.08.007 L.R. Goldberg et al. / Journal of Research in Personality 40 (2006) 84–96 85 provided; and (5) its items can be presented in any order, interspersed with other items, reworded, translated into other languages, and administered on the World Wide Web without asking permission of anyone. The unrestricted availability of the IPIP raises concerns about possible misuse by unqualiWed persons, and the freedom of researchers to use the IPIP in idiosyncratic ways raises the possibility of fragmentation rather than scientiWc uniWcation in personality research.  2005 Elsevier Inc. All rights reserved.
Topological Value Iteration Algorithm for Markov Decision Processes

Value Iteration is an inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Among them, LAO*, LRTDP and HDP are state-of-theart ones. All of these use reachability analysis and heuristics to avoid some unnecessary backups. However, none of these approaches fully exploit the graphical features of the MDPs or use these features to yield the best backup sequence of the state space. We introduce an algorithm named Topological Value Iteration (TVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs and backing up states based on topological sequences. We prove that the backup sequence TVI applies is optimal. Our experimental results show that TVI outperforms VI, LAO*, LRTDP and HDP on our benchmark MDPs.
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin-images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. This research was performed at Carnegie Mellon University and was supported by the US Department of Energy under contract DE-AC21-92MC29104. 1