snu-aidas/HVMap-dataset huggingface dataset:
Methodology for customer relationship management

Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers. This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM. This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement. 2005 Elsevier Inc. All rights reserved.
Music Education and Its Impact on Students with Special Needs

Part of the Curriculum and Instruction Commons, Curriculum and Social Inquiry Commons, Disability and Equity in Education Commons, Educational Methods Commons, Elementary Education and Teaching Commons, Higher Education and Teaching Commons, Junior High, Intermediate, Middle School Education and Teaching Commons, Other Educational Administration and Supervision Commons, Other Teacher Education and Professional Development Commons, Pre-Elementary, Early Childhood, Kindergarten Teacher Education Commons, Scholarship of Teaching and Learning Commons, and the Special Education Administration Commons Survey: Let us know how this paper benefits you.
An AUC-based permutation variable importance measure for random forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction [1]. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework [2], the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning [1] or reinforcement learning [3]. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.
Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation <ce:italic>in vitro</ce:italic>

BACKGROUND
The neocortex is the most common target of subdural electrotherapy and noninvasive brain stimulation modalities, including transcranial magnetic stimulation (TMS) and transcranial current simulation (TCS). Specific neuronal elements targeted by cortical stimulation are considered to underlie therapeutic effects, but the exact cell type(s) affected by these methods remains poorly understood.


OBJECTIVE
We determined whether neuronal morphology or cell type predicted responses to subthreshold and suprathreshold uniform electric fields.


METHODS
We characterized the effects of subthreshold and suprathreshold electrical stimulation on identified cortical neurons in vitro. Uniform electric fields were applied to rat motor cortex brain slices, while recording from interneurons and pyramidal cells across cortical layers, using a whole-cell patch clamp. Neuron morphology was reconstructed after intracellular dialysis of biocytin. Based solely on volume-weighted morphology, we developed a parsimonious model of neuronal soma polarization by subthreshold electric fields.


RESULTS
We found that neuronal morphology correlated with somatic subthreshold polarization. Based on neuronal morphology, we predict layer V pyramidal neuronal soma to be individually the most sensitive to polarization by optimally oriented subthreshold fields. Suprathreshold electric field action potential threshold was shown to reflect both direct cell polarization and synaptic (network) activation. Layer V/VI neuron absolute electric field action potential thresholds were lower than layer II/III pyramidal neurons and interneurons. Compared with somatic current injection, electric fields promoted burst firing and modulated action potential firing times.


CONCLUSIONS
We present experimental data indicating that cortical neuron morphology relative to electric fields and cortical cell type are factors in determining sensitivity to sub- and supra-threshold brain stimulation.
Text Encryption with Huffman Compression

Communication between a sender and receiver needs security. It can be done in any form, like plain text or binary data. Changing the information to some unidentifiable form, can save it from assailants. For example, plain text can be coded using schemes so that a stranger cannot apprehend it. Cryptography is a subject or field which deals with the secret transmission of messages/ data between two parties. Cryptography is the practice and study of hiding information. It enables you to store delicate information and transmit it across insecure networks so that it cannot be read by anyone except the authorized recipient. Applications of cryptography include ATM cards, computer passwords, and electronic commerce etc. Messages, which are being sent between two parties, should be of small size so that they occupy less space. Data compression involves encoding of information using fewer bits than the original representation. Compression algorithms reduce the redundancy in data representation to decrease the storage required for that data. Symmetric key cryptography algorithms [6] are fast and mostly used type of encryption. Many types of data compression techniques do exist. Huffman Compression [5] is a lossless data compression technique and can be considered as the most efficient algorithm. In this paper, a combination of new Symmetric key algorithm and existing Huffman compression algorithm has been proposed. Proposed method works on text data. Algorithms have been provided in the paper itself.
A review and critique of emotional intelligence measures

Emotional intelligence measures vary widely in both their content and in their method of assessment. In particular, emotional intelligence measures tend to use either a self-report personality-based approach, an informant approach, or an ability-based assessment procedure. In this paper, the measurement and psychometric properties of four of the major emotional intelligence measures (Emotional Competence Inventory, Emotional Quotient Inventory, Multifactor Emotional Intelligence Scale, Mayer–Salovey–Caruso Emotional Intelligence Test) are reviewed, the comparability of these measures is examined, and some conclusions and suggestions for future research on emotional intelligence measures are provided. Copyright # 2005 John Wiley & Sons, Ltd.
A review of recent advances in visual speech decoding

Visual speech information plays an important role in automatic speech recognition (ASR) especially when audio is corrupted or even inaccessible. Despite the success of audio-based ASR, the problem of visual speech decoding remains widely open. This paper provides a detailed review of recent advances in this research area. In comparison with the previous survey [97] which covers the whole ASR system that uses visual speech information, we focus on the important questions asked by researchers and summarize the recent studies that attempt to answer them. In particular, there are three questions related to the extraction of visual features, concerning speaker dependency, pose variation and temporal information, respectively. Another question is about audio-visual speech fusion, considering the dynamic changes of modality reliabilities encountered in practice. In addition, the state-of-the-art on facial landmark localization is briefly introduced in this paper. Those advanced techniques can be used to improve the region-of-interest detection, but have been largely ignored when building a visual-based ASR system. We also provide details of audio-visual speech databases. Finally, we discuss the remaining challenges and offer our insights into the future research on visual speech decoding.
The Adoption Process of Cryptocurrencies Identifying Factors That Influence the Adoption of Cryptocurrencies from a Multiple Stakeholder Perspective. Identifying Factors That Influence the Adoption of Cryptocurrencies from a Multiple Stakeholder Perspective

II The adoption process of cryptocurrencies Management Summary Cryptocurrencies 1 are rapidly gaining more and more interest as a technology that is potentially groundbreaking and disruptive for the whole payments industry on a global scale. However the future of cryptocurrencies is very unclear as there are many different usage scenarios and different stakeholders have different needs. In order to be able to give a better future perspective and to determine possibilities for improvement of cryptocurrencies factors that influence adoption will have to be determined. To achieve this the following main research question was formulated: What are factors influencing the adoption of cryptocurrencies in different usage scenarios for different stakeholders? The way current payment systems work throughout the world varies widely. The added value of cryptocurrencies therefore also hugely differs per geographical area. In order to be able to give clear and concise conclusions this research is scoped towards looking at the European market and the Dutch market in particular. Based on the Diffusion of Innovations Theory, which defines characteristics necessary for an innovation in order to be successfully adopted, a conceptual model to describe the adoption of cryptocurrencies was formulated. This model was based on academic literature resulting from a literature review. The model was then validated by means of qualitative semi‐structured interviews with subject experts. To make sure that a balanced view was obtained stakeholders from a wide range of industries were interviewed: employees from the four biggest Dutch banks, the Dutch Central Bank, the three largest Dutch cryptocurrency exchanges, senior payments consultants, Payment Service Providers, a cryptography expert and the largest Dutch company that accepts cryptocurrency, Thuisbezorgd.nl. During the interviews barriers were identified that have to be overcome in order for cryptocurrencies to be adopted on a large scale. The three main pillars which are important for future mass adoption are: 1. Ease of use: at the moment there is a lack of user‐friendliness when using bitcoins. Sending and receiving bitcoin is still cumbersome and holding bitcoins is prone to many risks. Users need to be able to have more confidence in the safety of their funds. 2. Price stability: the current price volatility driven by speculation and a lack of liquidity makes that it is very risky for a user to keep his funds in cryptocurrency as the value varies wildly. This undermines the function of cryptocurrency as a store of value. 3. Governance: the current bitcoin …
The effects of Zumba training on cardiovascular and neuromuscular function in female college students.

The present study examined the effects of Zumba training (group fitness based on salsa and aerobics) on endurance, trunk strength, balance, flexibility, jumping performance and quality of life (QoL) in female college students. Thirty female participants were randomly assigned (strata: age, BMI and physical activity) to an intervention (INT, n = 15: age: 21.0 ± 2.3 years; BMI: 21.8 ± 3.0 kg/m(2); physical activity (PA): 7.6 ± 4.6 h/week) or control group (CON, n = 14: age: 21.0 ± 2.8 years; BMI: 21.0 ± 2.1 kg/m(2); PA: 7.3 ± 3.6 h/week). Instructed Zumba training was provided twice a week for 8 weeks (training attendance: 100%). QoL was assessed using the WHO-QoL-BREF questionnaire. Endurance was measured with the 6-min walking test (6MWT). Trunk flexibility was assessed with the stand-and-reach-test and lower-extremity strength with the jump-and-reach-test. The star excursion balance test (SEBT) was employed to assess dynamic balance. Trunk strength endurance was examined using the Swiss global trunk strength test in prone and lateral (left, right) positions. All testings were performed before and after the training period. We observed large statistically significant between-group effects of total QoL score (INT: +9.8%, CON: +0.4%, p < 0.001; partial eta squared [Formula: see text]), 6MWT distance (INT: +21%, CON: -2%, p < 0.001, [Formula: see text]), trunk strength endurance (prone, INT: +48%, CON: +11%, p = 0.04, [Formula: see text]; lateral-left, INT: +71%, CON: +11%, p = 0.01, [Formula: see text], lateral-right, INT: +54%, CON: +11%, p = 0.01, [Formula: see text]) and dynamic balance (all eight reaching distances of the SEBT, INT: +11-26%, CON: +1.1-3.8%, 0.001 < p < 0.04, 0.14 < [Formula: see text]) with significantly larger improvements for INT. Flexibility and jump performance were not relevantly affected (p > 0.05). Instructed Zumba training can be applied to improve well-being, aerobic fitness and neuromuscular function in female college students.
Incivility, retention and new graduate nurses: an integrated review of the literature.

AIM
To evaluate the influence of incivility on the new graduate nurse transition experience.


BACKGROUND
Incivility in the work environment is a major source of dissatisfaction and new graduate nurses are especially vulnerable. Incivility contributes to the high levels of turnover associated within the first 2 years of new graduate nurse employment.


EVALUATION
An integrated review of the literature was conducted using MEDLINE-EBSCOhost, PsycInfo and CINAHL databases. Relevant articles were reviewed for appropriateness related to inclusion/exclusion criteria and for quality using established criteria. Sixteen studies were included in the final analysis.


KEY ISSUES
Themes that emerged included workplace incivility, nurse residency programmes, mentoring through preceptors and empowerment/work environment. Findings indicated that incivility in the workplace was a significant predictor of low job satisfaction in new graduate nurses transitioning into practice.


CONCLUSIONS
While graduate nurse transition programmes are associated with improved satisfaction and retention, they appear to address incivility by acculturating new graduate nurses to the experience of incivility. There is little evidence that the culture of incivility has been addressed.


IMPLICATIONS FOR NURSING MANAGEMENT
Nurse managers have the responsibility to be aware of the prevalence of incivility, assess for its occurrence, and implement strategies which eliminate workplace incivility and tolerance for uncivil behaviours.
Explanation-Based Learning: A Problem Solving Perspective

This article outlines explanation-based learning (EBL) and its role in improving problem-solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the PRODIGY problem-solving system. PRODIGY'S EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem-solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. PRODIGY'S EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving. 'Present address: Artificial Intelligence Research Branch, NASA Ames Research Center, Sterling Federal Systems, Mail Stop 244-17, Moffett Field CA 94035. This research was sponsored in part by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, under contract number F33615-87-C-1499, monitored by the Air Force Avionics Laboratory, in part by the Office of Naval Research under contracts N00014-84-K-0345 (N91) and N00014-86-K-0678-N123, in part by NASA under contract NCC 2-463, in part by the Army Research Institute under contract MDA903-85-C-0324, under subcontract 487650-25537 through the University of California, Irvine, and in part by small contributions from private institutions. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, ONR, NASA, ARI, or the US Government. The first and fifth authors were supported by AT&T Bell Labs Ph.D. Scholarships. Table of
ISO/IEC 27000, 27001 and 27002 for Information Security Management

With the increasing significance of information technology, there is an urgent need for adequate measures of information security. Systematic information security management is one of most important initiatives for IT management. At least since reports about privacy and security breaches, fraudulent accounting practices, and attacks on IT systems appeared in public, organizations have recognized their responsibilities to safeguard physical and information assets. Security standards can be used as guideline or framework to develop and maintain an adequate information security management system (ISMS). The standards ISO/IEC 27000, 27001 and 27002 are international standards that are receiving growing recognition and adoption. They are referred to as “common language of organizations around the world” for information security [1]. With ISO/IEC 27001 companies can have their ISMS certified by a third-party organization and thus show their customers evidence of their security measures.
Physician Burnout and Well-Being: A Systematic Review and Framework for Action.

BACKGROUND
Physician burnout in the United States has reached epidemic proportions and is rising rapidly, although burnout in other occupations is stable. Its negative impact is far reaching and includes harm to the burned-out physician, as well as patients, coworkers, family members, close friends, and healthcare organizations.


OBJECTIVE
The purpose of this review is to provide an accurate, current summary of what is known about physician burnout and to develop a framework to reverse its current negative impact, decrease its prevalence, and implement effective organizational and personal interventions.


DATA SOURCES
I completed a comprehensive MEDLINE search of the medical literature from January 1, 2000, through December 28, 2016, related to medical student and physician burnout, stress, depression, suicide ideation, suicide, resiliency, wellness, and well-being. In addition, I selectively reviewed secondary articles, books addressing the relevant issues, and oral presentations at national professional meetings since 2013.


STUDY SELECTION
Healthcare organizations within the United States were studied.


RESULTS
The literature review is presented in 5 sections covering the basics of defining and measuring burnout; its impact, incidence, and causes; and interventions and remediation strategies.


CONCLUSIONS
All US medical students, physicians in training, and practicing physicians are at significant risk of burnout. Its prevalence now exceeds 50%. Burnout is the unintended net result of multiple, highly disruptive changes in society at large, the medical profession, and the healthcare system. Both individual and organizational strategies have been only partially successful in mitigating burnout and in developing resiliency and well-being among physicians. Two highly effective strategies are aligning personal and organizational values and enabling physicians to devote 20% of their work activities to the part of their medical practice that is especially meaningful to them. More research is needed.
Internet of things capability and alliance: Entrepreneurial orientation, market orientation and product and process innovation

Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation Xiaoyu Yu Bang Nguyen Yi Chen Article information: To cite this document: Xiaoyu Yu Bang Nguyen Yi Chen , (2016),"Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation", Internet Research, Vol. 26 Iss 2 pp. Permanent link to this document: http://dx.doi.org/10.1108/IntR-10-2014-0265