Reviewerly/exHarmony huggingface dataset:
Health Gains of Telephone Follow-up Nursing Intervention to Patient with Heart Disease

Aim: To identify the health gains related to nursing interventions in the telephone follow-up of adult patients with heart disease. Methodology: Systematic literature review by research in EBSCO, (CINAHL and MEDLINE) in the publication time interval between January 2012 and October 2017, using the PICO method. Elected 8 articles for analysis. Results: By the using of a telephone nursing intervention with heart disease patient’s health gains were identified related to: symptomatic control, management of the therapeutic regimen, use of health services, safety/adverse events and quality of life. Conclusions: The telephone nursing intervention contributes to optimize the follow-up to the patient, translating into health gains sensitive to nursing care. Implication to professional practice: In the contexts of nursing practice for adult patients with heart disease, telephone follow-up proves to be an intervention to be considered, translating into gains in health, promoting patient follow-up and improving training in disease management.
Sparse Restricted Boltzmann Machine Based on Data Class Entropy

The sparse of RBM is a critical technique to prevent the over-fitting of neural networks. Current competition sparse methods suffered from the fix sparse threshold. To this point, an adaptive RBM sparse method based on the complexity of data class distribution is proposed to determine the sparse threshold adaptively. In the method, a hidden-layer based entropy computing is introduced to overcome the curse of dimensionality for the estimation of distribution. Experiments on MNIST and CIFAR-10 are conducted and show that the proposed method is more effective than CaRBM and GC-RBM.
Observation Models and Least Squares Adjustment

In Chapter 4, different adjustment models are presented such as intersection and resection either in 2D or 3D using observed distances, angles, azimuths, difference in heights, or from images. All the mentioned applications are supported by numerical solved examples and MATLAB codes. At the end of the chapter, an important computational geophysics example is shown in the application of earthquake location determination.
A Computational Framework for Measuring the Facial Emotional Expressions

The purpose of this chapter is to discuss and present a computational framework for detecting and analysing facial expressions efficiently. The approach here is to identify the face and estimate regions of facial features of interest using the optical flow algorithm. Once the regions and their dynamics are computed a rule based system can be utilised for classification. Using this framework, we show how it is possible to accurately identify and classify facial expressions to match with FACS coding and to infer the underlying basic emotions in real time.
Money detector based on LDR sensor

People with visual disability unable to recognize the nominal of banknotes in conducting economic life, hence, rely on their touch and hear senses to recognize the banknotes. In this work, we investigated the LDR sensor to detect the nominal value of banknotes with voice output. It was found that the error rate 100% for IDR 2000, IDR 10000, IDR 100000, and IDR 50000, 75% error rate was for IDR 5000 and IDR 2000, and 50% error rate for IDR 1000.
Figure-ground representation in deep neural networks

Deep neural networks achieve state-of-the-art performance on many image segmentation tasks. However, the nature of the learned representations used by these networks is unclear. Biological brains solve this task very efficiently and seemingly effortlessly. Neurophysiological recordings have begun to elucidate the underlying neural mechanisms of image segmentation. In particular, it has been proposed that border ownership selectivity (BOS) is the first step in this process in the brain. BOS is a property of an orientation selective neuron to differentially respond to an object contour dependent on the location of the foreground object (figure). We explored whether deep neural networks use representations close to those of biological brains, in particular whether they explicitly represent BOS. We therefore developed a suite of in-silico experiments to test for BOS, similar to experiments that have been used to probe primate BOS. We tested two deep neural networks trained for scene segmentation tasks (DOC [1] and Mask R-CNN [2]), as well as one network trained for object recognition (ResNet-50 [3]). Units in ResNet50 predominantly showed contrast tuning. Units in Mask R-CNN responded weakly to the test stimuli. In the DOC network, we found that units in earlier layers of the network showed stronger contrast tuning, while units in deeper layers of the network showed increasing BOS. In primate brains, contrast tuning seems wide-spread in extrastriate areas while BOS is most common in intermediate area V2 where the prevalence of BOS neurons exceeds that of earlier (V1) and later (V4) areas. We also found that the DOC network, which was trained on natural images, did not generalize well to the simple stimuli typically used in experiments. This differs from findings in biological brains where responses to simple stimuli are stronger than to complex natural scenes. Our methods are general and can also be applied to other deep neural networks and tasks.
TechUp : E-Commerce Website based on Chatbot

TechUp is a site based chatbot. This chatbot can make it simple to interface with the site. The bot fathoms and talks with the customer in Simple Language. This chatbot is associated with an online business webpage. This site has an arrangement of things. The chatbot makes you settle on a decision which thing is sensible for you. Its abilities on a very basic level like an online mechanized collaborator. . The reason for this undertaking is to give a simple shopping office on the web and simple pitching office to the shippers everything being equal. The thought regarding this application is that it will assist the client with interacting with the online business motor through a canny collaborator.
Using Convolutional Neural Networks to Distinguish Different Sign Language Alphanumerics

Using Convolutional Neural Networks (CNN)’s to create Deep Learning systems that turns Sign Language into text has been a vital tool in breaking communication barriers between deaf-mute people. Conventional research on this subject concerns training networks to recognize alphanumerical gestures and produce their textual equivalents. A problem with current methods is that images are scarce, with little variation in available gestures, often skewed towards skin tones and hand sizes that makes a significant subset of gestures hard to detect. Current identification programs are only trained in a single language despite there being over two-hundred known variants so far. This presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work presents a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. As a result, a program is created that can receive finger spelling from multiple tactile languages and deduct the corresponding alphanumeric and its language which no other neural network has been able to replicate.
Active Contour based Segmentation of ROIs in Medical Images

Despite the impressive performance, Active Contour Model (ACM) is yet to be verified for automatic segmentation of Region-of-Interest (ROI) in medical images (MIs). Different modality MIs have unique properties and requirements that constitute the automatic segmentation of ROI even more challenging than that for natural images. In this paper, we investigate the performance of popular ACM based segmentation methods of ROI in multi-modality MIs. Three different ACM based segmentation methods that rely on the object's regions and edges are examined. Performances of the methods are evaluated and compared in terms of popular evaluation metrics with commonly used modalities of MIs. Our experimental results demonstrate that region-based ACM generally has the best segmentation performance and computational efficiency over the edge-based ACMs for all modalities of MI. Region-based segmentation thus can be promising for automatic segmentation of ROIs in MI-based understanding, detection and recognition applications.
Cross-gender Voice Conversion with Constant F0-Ratio and Average Background Conversion Model

This paper presents the method for spectral voice conversion using parallel training data. The proposed solution was submitted to the 2018 Voice Conversion Challenge. The method focuses on the preparation of the generative model for cross-gender voice conversion in differential-filtering framework. To improve the quality of the Gaussian mixture conversion model we introduced the usage of the averaged speaker background model pre-training step. Constant F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ratio transformation of source speech using WORLD vocoder was also proposed to improve cross-gender conversion quality. The evaluation results show that the proposed solution outperforms most of the concurrent systems submitted to the 2018 Voice Conversion Challenge, both in terms of speech quality and similarity. The system achieved 76% similarity score and 3.22 mean opinion score in cross-gender conversion task.
4차 산업시대의 네일아트 연구

With the emergence of the Era of the Fourth Industrial Revolution, various services and products converged with thefourth industrial technologies...
A Note on the Limiting Properties of the Extended Kalman Filter’s Estimates : Invited Presentation

In this paper, I focus on the estimates produced by the extended Kalman filter (EKF) and their corresponding predicted error covariance matrices. The EKF is often used in place of the standard Kalman filter when the functional relationship between consecutive states and/or the functional relationship between the states and their measurements is not linear. In these cases, the state estimates and their predicted error covariances are biased. In this paper, I mathematically show that as the nonlinear relationship between the states and the measurements approaches linearity, these biases go to 0.
Parallel Concatenated Network with Cross-layer Connections for Image Recognition

The traditional convolutional neural networks are heavy with millions of parameters and the classification accuracy is not high. To address this issue, we propose a novel model called parallel concatenated convolutional neural network with cross-layer connections. The model mainly includes parallel processing and concatenate operation. In parallel processing, the diversity of features is increased by using different sizes of convolution kernels. The parallel outputs are integrated together by concatenate operation. Meanwhile, an improved cross-layer connection structure is also added to the model. At the experimental stage, the model was tested on the Caltech-256 and Food-101 datasets, the experiment results indicate that the constructed PCNet (without cross-layer connections) increases the recognition accuracy by 2.54% and 7.31% compared to AlexNet, and the proposed RPCNet (with cross-layer connections) is improved by 6.12% and 12.28% compared to AlexNet.
Learning media for human digestive system based on augmented reality

The learning process is an activity that aims to transfer knowledges, including students in junior high school. With the existence of augmented reality-based interactive learning media that enables a more interesting learning process to make students being motivated, excited, and interested in the learning process, especially in humans who use mobile devices. The process of making this application using software Unity 3D is intended for mobile devices with the Android operating system. This medium learning application has features in the form of three dimensional models of every organ in humans and also contains the information about the selected process. Application development in this application is Luther Calculation. With this research, students are expected to be able to consolidate the existing material in three dimensional forms well. And also, reducing the students' boredom and increasing the students' learning interest.
INSTALLATION AND TESTING THE LOFAR SOFTWARE ON ACC CYFRONET CLUSTER PROMETHEUS

We present the actual status and the most important issues related to the installation of the data reduction LOFAR software on high power computer Prometheus located in ACC Cyfronet. We refer to the software itself as well its practical use cases in the context of the scientific tool and the detailed installation/testing methodology. We address most typical challenges and problems that occured during our attempts to set up the complete and ready-to-use LOFAR environment (including not only programs, but also libraries, scripts and other additional tools) on non-standard (cluster-type) computing system. The result of these works is then briefly summarized. We also discuss the issues related to LOFAR documentation, maintenance, distribution and further development. Finally, we propose some future improvements.
A study on machine learning techniques for the schema matching network problem

Abstract Schema matching is the problem of finding semantic correspondences between elements from different schemas. This is a challenging problem since disparate elements in the schemas often represent the same concept. Traditional instances of this problem involved a pair of schemas. However, recently, there has been an increasing interest in matching several related schemas at once, a problem known as schema matching networks . The goal is to identify elements from several schemas that correspond to a single concept. We propose a family of methods for schema matching networks based on machine learning, which proved to be a competitive alternative for the traditional matching problem in several domains. To overcome the issue of requiring a large amount of training data, we also propose a bootstrapping procedure to generate training data automatically. In addition, we leverage constraints that arise in network scenarios to improve the quality of this data. We also study a strategy for receiving user feedback to assert some of the matchings generated and, relying on this feedback, improve the final result’s quality. Our experiments show that our methods can outperform baselines, reaching F1-score up to 0.83.
A proposal of interaction analysis framework for multilingual discussion

Global problems, such as environmental issues, refugee crises, and food shortages, transcend borders and must be addressed at the international level. The goal of our research is to examine ways in which we can clarify common features, similarities, and differences in people's thinking to confront problems among people who speak different languages. In our study, we first designed an interactive analysis framework for analyzing the similarities and differences in thinking among different people. To evaluate the effectiveness of our proposed framework, we conducted experimental discussions combined with making a mind map about environmental issues among Chinese and Japanese-speaking participants. After the discussion activity, we analyzed the logs and mind maps by focusing on keywords and the flow of information. Our results showed that the participants had various ways of thinking about environmental problems and their solutions. We succeeded in summarizing the similarities and differences as discussion points and share them using our framework in a visible form.
SAFEPOWER

With the ever increasing industrial demand for bigger, faster and more efficient systems, a growing number of cores is integrated on a single chip. Additionally, their performance is further maximized by simultaneously executing as many processes as possible. Even in safety critical domains like railway and avionics multi-core processors are introduced under strict certification regulations.
Improved face image super-resolution with restricted patch-searching area

Recently, an example-based super-resolution (SR) method specifically for face image, which takes the correspondence of facial parts into consideration, was proposed. The method constructs a database of face-image patches with their positions from example images of normalized faces. Given a low-resolution (LR) image, the method generates an SR image by finding similar patches in the database and incorporating high-frequency component of the patches to the LR image. Since the method just divides an example image into patches, for example, 5x5 patches in five-pixels intervals, the SR process may misses the chance to have the patch of the finest position. The proposed method generates patches in one-pixel intervals to increase the chance. In order to cope with the massively increased size of the patch database, the proposed method restricts the area to search the patch candidates for each position. An experimental result shows the proposed method achieves better results than the previous in term of the peak signal-to-noise ratio (PSNR). Furthermore, the proposed method has reduced computational time by 90% compared to the previous.
Scalable Video Coding Using Accordion Discrete Wavelet Transform and Tucker Decomposition for Multimedia Applications

The digital world demands the transmission and storage of high quality video for streaming and broadcasting applications, the constraints are the network bandwidth and the memory of devices for the various multimedia and scientific applications, the video consists of spatial and temporal redundancies. The objective of any video compression algorithm is to eliminate the redundant information from the video signal during compression for effective transmission and storage. The correlation between the successive frames has not been exploited enough by the current compression algorithms. In this paper, a novel method for video compression is presented. The proposed model, applies the transformation on set of group of pictures (GOP). The high spatial correlation is achieved from the spatial and temporal redundancy of GOP by accordion representation and this helps to bypass the computationally demanding motion compensation step. The core idea of the proposed technique is to apply Tucker Decomposition (TD) on the Discrete Wavelet Transform (DWT) coefficients of the Accordion model of the GOP. We use DWT to separate the video in to different sub-images and TD to efficiently compact the energy of sub-images. The blocking artifacts will be considerably eliminated as the block size is huge. The proposed method attempts to reduce the spatial and temporal redundancies of the video signal to improve the compression ratio, computation time, and PSNR. The experimental results prove that the proposed method is efficient especially in high bit rate and with slow motion videos.
IMPAct: A Holistic Framework for Mixed Reality Robotic User Interface Classification and Design

The number of scientific publications combining robotic user interfaces and mixed reality highly increased during the 21st Century. Counting the number of yearly added publications containing the keywords “mixed reality” and “robot” listed on Google Scholar indicates exponential growth. The interdisciplinary nature of mixed reality robotic user interfaces (MRRUI) makes them very interesting and powerful, but also very challenging to design and analyze. Many single aspects have already been successfully provided with theoretical structure, but to the best of our knowledge, there is no contribution combining everything into an MRRUI taxonomy. In this article, we present the results of an extensive investigation of relevant aspects from prominent classifications and taxonomies in the scientific literature. During a card sorting experiment with professionals from the field of human–computer interaction, these aspects were clustered into named groups for providing a new structure. Further categorization of these groups into four different categories was obvious and revealed a memorable structure. Thus, this article provides a framework of objective, technical factors, which finds its application in a precise description of MRRUIs. An example shows the effective use of the proposed framework for precise system description, therefore contributing to a better understanding, design, and comparison of MRRUIs in this growing field of research.
A live migration strategy for virtual encryption card equipment

With the popularity of virtualization technologies, virtualization security issues have been appeared, which greatly hampers the development of its further application. Bringing the encryption card to the virtual environment is a good idea to enforce the security of the virtual platform. However, due to the particularity of the encryption card equipment, there is some difficult to virtualize the encryption card equipment. One of the most important difficulties is supporting Live Migration. As for the first question that we must ensure the encryption task continuity when migrating a virtual encryption card, we propose the encryption field migration for the physical encryption card, which guarantees the continuity of virtualization encryption card. For the second problem that Live Migration of virtualization encryption card must be protected from any attack, this paper designs migration protocol to ensure the security. For the last problem that the encryption card live migration should not increase the migration time of virtual machine drastically, including total migration time and downtime, we separate the migration protocol to a different stage, and complete in the different stage of virtual machine migration. By this way, we decrease the influence of virtual machine migration time. The implement and results of experiments on Kernel-based Virtual Machine indicate that the proposed scheme is feasible in function and performance.
Towards Better Effort Estimation with Cross-Project Defect Prediction Approaches

This research aims to tackle a data shift problem of software effort estimation. Cross project defect prediction approaches were found to be helpful for the same problem of software defect prediction. We examined the CPDP approaches and explored its applicability and adaptability for the problem of software effort estimation.
Contextualized MALL in Target and Non-Target Countries

Mobile-assisted language learning (MALL) is known to be conducive to contextualized language learning. However, the literature lacks an understanding of context. Nor is there a quantitative evaluation tool. The chapter's dependent contextualized variables were the following: real-world context level (one's place), real-life context level (one's life), and device mobility (place diversity). The independent variables were target/non-target country (Taiwan/Israel) and language learning orientation (dedicated/generic). In the target country, the spoken language is the studied language. Dedicated/generic language learning orientation represents the existence/non-existence of pedagogy within activities. Fifty-three Chinese L2 students involved in 296 activities and 519 events were interviewed. Using device mobility and an evaluation index developed to measure real-world/real-life context levels, MALL was found more contextualized in Taiwan only in generic activities. The findings refine our understanding of the benefits of studying in the target country. The index can be used in future studies.