Reviewerly/exHarmony huggingface dataset:
Processing and Visualization of Medical Images Using Machine Learning and Virtual Reality

The presented paper deals with automatic medical image segmentation and their visualization in virtual reality, and presents a complete pipeline that learns to extract anatomical models from medical images and prepares them to be accurately visualized on a stereoscopic head mounted display. First, we analyze methods of medical image segmentation, and develop a model based on convolutional neural networks. Using an annotated dataset of 800 image slices from cardiac magnetic resonance we train and test the segmentation network to extract the left ventricular anatomy from the images. We further develop a post-processing pipeline that allows displaying the extracted models in virtual reality even on mobile devices. This serves to achieve low computational complexity while preserving high anatomical fidelity of the extracted models. Finally, we discuss how we built both front- and backend for a virtual reality web application using A-Frame and Entity-Component-System architectural pattern.
partition: A fast and flexible framework for data reduction in R

Data are increasingly feature-rich, including many variables for each observation; in modern genomics, for example, high-resolution genetic data captures much more information than it did just a decade ago.While improved measurements contribute immensely to science, they also increase computational burden and complicate interpretability (Karczewski & Snyder, 2018).Data reduction techniques, such as principal component analysis (PCA) and K-Means clustering, are vital tools used to address these issues, particularly for noise and redundancy.However, these techniques may lead to problems in scalability, information loss, and interpretability (Malod-Dognin, Petschnigg, & Pržulj, 2018).The Partition framework is an approach to data reduction that is flexible, scalable, and interpretable, developed to address information loss while maintaining speed (Millstein et al., 2020).As opposed to other data reduction strategies, Partition only reduces data if the data reduction retains a specified amount of information.This framework is also agnostic to how partitions form; users can easily use other tools such as PCA and t-Distributed Stochastic Neighbor Embedding (t-SNE) to create a partition or summarize data within a partition subset and thus reduce data while constraining information loss.
Deep similarity learning using a siamese ResNet trained on similarity labels from disparity maps of cerebral MRA MIP pairs

Similarity measures are a critical component for iterative registration tasks. In intrinsic 2D/3D registration an initialization close to the solution is usually needed for convergence. Traditional similarity measures are limited in their applicability to perspective projection data. Deep Learning allows us to create similarity measures that encode almost arbitrary non-linear relationships like perspective projection. We apply a siamese network and a 2-channel network to the problem of comparing two perspective maximum intensity projections of medical data. We propose the use of residual units as the building block. A major challenge is to define similarity under perspective projection so that it can be used for training. Extrinsic registration gives us a gold standard of registered examples but we need a way to quantify the dissimilarity of unregistered image pairs for training. We propose the use of disparity as the basis for construction of similarity labels, i.e. labels are based on distances between projected points when viewing the same 3D point through different cameras. The final similarity labels are independent of the choice of camera parametrization and describe the visual similarity of two given views of a common 3D model. We compare our deep similarity metric to traditional similarity measures for a comparative evaluation. Our best configuration has a Pearson correlation coefficient of 0.792 and a Spearman’s rank correlation coefficient of 0.480. The best traditional method is normalized cross correlation with a 5.4% lower Spearman’s rank correlation coefficient.
On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics

This paper proposes a portable hardware token for user’s authentication; it is based on the use of keystroke dynamics to verify users biometrically. The proposed approach allows for a multifactor authentication scheme, in which a user cannot be granted access unless they provide a correct password on a hardware token and their biometric signature. The latter is extracted while the user is typing their password. This paper explains the design rationale of the proposed system and provides a comprehensive insight in the development of a hardware prototype of the same. The paper also presents a feasibility study that included a systematic analysis based on training data obtained from 32 users. Our results show that dynamic keystroke can be employed to construct a cost-efficient solution for biometric user authentication with an average error rate of 4.5%.
Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

Nowadays, the need for face recognition is no longer include images only but also videos. However, there are some challenges associated with the addition of this new technique such as how to determine the right pre-processing, feature extraction, and classification methods to obtain excellent performance. Although nowadays the k-Nearest Neighbor (k-NN) is widely used, high computational costs due to numerous features of the dataset and large amount of training data makes adequate processing difficult. Several studies have been conducted to improve the performance of k-NN using the FHC (Fast Hybrid Classification) method by optimizing the local k values. One of the disadvantages of the FHC Method is that the k value used is still in the default form. Therefore, this research proposes the use of k-NN value optimization methods in FHC, thereby, increasing its accuracy. The Fast Hybrid Classification which combines the k-means clustering with k-NN, groups the training data into several prototypes called TLDS (Two Level Data Structure). Furthermore, two classification levels are applied to label test data, with the first used to determine the n number of prototypes with the same class in the test data. The second classification using the optimized k value in the k-NN method, is employed to sharpen the accuracy, when the same number of prototypes does not reach n. The evaluation results show that this method provides 86% accuracy and time performance of 3.3 seconds.
Live Migration of Virtual Machines using Mirroring Technique

Virtualization is a key concept in empowering the "Infrastructure as-a-Service (IaaS)" of cloud-based services.Live migration of virtual machines is the process of moving virtual machine from one physical host to another without interrupting other VMs.Migration of VMs supports the improved efficiency of resource usage and dynamic resource provision capabilities.The performance metrics of live migration are down time and total migration time.In this paper, a technique to reduce the total migration time of standard memory migration using the mirroring of VMs at destination host is proposed.It mainly concentrates on the total migration time and maximum profit of providers.The proposed technique avoids the iterative transmission of memory pages from the source host to destination host.Performance results depict that the proposed algorithm can reduce 36% of the data transmitted during migration and 26% of the total migration time when compared with pre-copy migration technique.
Stimulating Unceremonious Mobile Learning: An Empirical Study of Business Students

The widespread use of mobile technology by students as well as teachers leads the educationalists and researchers to explore its effects on the learning and teaching behaviour of students and teachers. The present study attempts to combine the determinants (such as Enjoyment, self-efficacy, system quality and information, avoiding uncertainty, and usefulness and easy to use) and subsequently evaluate them to explore the integration of IT in classroom instruction and its effects on the learning achievement of students. Using experimental approach of quantitative research paradigm, it tested the variable “academic performance of business students” by obtaining the achieved scores of business students in Mid Term and Final Term examination before and after the exposure of students to m-learning. The result found in the study demonstrates a statistically significant improvement in the scores achieved by students after the exposure of students to m-learning as compared to the traditional classes. Therefore, it is suggested that with the implementation of the m-learning within the higher education institutes in Pakistan, particularly business schools, will more likely to increase students’ learning.
Emotion-Based Adaptive Learning Systems

Right from our primary school to professional academic level, the classical education system modus operandi, forces us to follow a series of predefined steps to climb the stairs of academic levels. Traditionally those predefined steps forces students to go through the beginner level to advanced level and then specialized in a specific level. The main problem was that the teaching styles and content delivery was not tailored to every learning styles and student personalities. The traditional education system is moving towards adaptive learning system where students are not bound only to one predefined set of contents. Therefore the traditional "one size fits all" approach is no longer valid as it were before. Each student has their curriculum based on their unique needs and personality. Adaptive learning may be referred as the process of creating unique learning experience for each and every learner based upon the learner's personality, interests and performance. This research presents a novel approach of adaptive learning by presenting an emotion-based adaptive learning system where the emotion and psychological traits of the learner is considered to provide learning materials that would be most appropriate at that particular instance of time. It shall demonstrate an intelligent agent based expert system using artificial intelligence and emotion detections capabilities to measure the user learning rate and find an optimum learning scheme for the latter.
Feasibility Analysis of Deep Learning-Based Reality Assessment of Human Back-View Images

Realistic personalized avatars can play an important role in social interactions in virtual reality, increasing body ownership, presence, and dominance. A simple way to obtain the texture of an avatar is to use a single front-view image of a human and to generate the hidden back-view image. The realism of the generated image is crucial in improving the overall texture quality, and subjective image quality assessment methods can play an important role in the evaluation. The subjective methods, however, require dozens of human assessors, a controlled environment, and time. This paper proposes a deep learning-based image reality assessment method, which is fully automatic and has a short testing time of nearly a quarter second per image. We train various discriminators to predict whether an image is real or generated. The trained discriminators are then used to give a mean opinion score for the reality of an image. Through experiments on human back-view images, we show that our learning-based mean opinion scores are close to their subjective counterparts in terms of the root mean square error between them.
CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices

We introduce an efficient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs. As in earlier work, we parametrize an orthogonal matrix as a product of Householder reflections. However, to overcome low parallelization capabilities of computing Householder reflections sequentially, we propose employing an accumulation scheme called the compact WY (or CWY) transform -- a compact parallelization-friendly matrix representation for the series of Householder reflections. We further develop a novel Truncated CWY (or T-CWY) approach for Stiefel manifold parametrization which has a competitive complexity and, again, yields benefits when computed on GPUs and TPUs. We prove that our CWY and T-CWY methods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent. We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction.
Review of three-dimensional environment information perception and reconstruction methods for mobile robot based on multi-sensor fusion

The technique of three-dimensional environment information perception and reconstruction is a model in which a subject equipped with a specific sensors establishes an environment and simultaneously estimates its own motion during movement without environmental prior information. This technique is widely used in application platforms such as driverless, unmanned aerial vehicle, and indoor robots. The research on mobile indoor robots has attracted wide attention as its diversity of application scenarios. In recent years, with the increasing maturity of sensor technology, a variety of sensors have been used in mobile robots to implement the function. Multi-sensor fusion to improve the quality of environment information perception and reconstruction has become the focus of research in this field. In this review, we discussed on the sensor types and hardware characteristics of multi-sensor fusion, and put forward that using cameras as the sensor core is the most widely used and best effect method. Furthermore, based on the research results in this field, multi-sensor fusion methods are divided into three levels: sensor data level, feature level and decision level. The implementation characteristics and effects of various methods based on typical technologies and representative papers are also discussed. Finally, the key issues to be solved in the multi-sensor fusion process are proposed, which points out the direction for the follow-up research work.
Dual Disparity-Based Novel View Reconstruction for Light Field Images Using Discrete Cosine Transform Filter

Compared to conventional photography which allows capturing spatial intensity only, light field imaging can capture angular and spatial information by collecting information from all directions. This massive information could be used in many applications such as depth estimation and post-capture refocusing. In addition, the emergence of consumer light field cameras has resulted in the widespread use of light field imaging. However, its limited resolution constitutes a major drawback to the use of enormous capabilities provided. In this article, we tried to alleviate the effect of this drawback by using a machine learning algorithm. Our proposed network is built upon existing reconstruction techniques that divide the process into disparity estimation and final image reconstruction. This is achieved by using two consecutive neural networks while the whole network is trained simultaneously. We propose to use a predefined convolutional network at the first stage to decrease preprocessing time, and in addition, we use dual disparity vectors to alleviate the interpolation error whereas warping input images. Our system was trained to reconstruct multi-angular resolution images fast and accurately using real light field images for training. Experimental results demonstrate that the proposed system can reconstruct high-quality images faster than the state-of-the-art techniques.
A Novel Blind and Robust Watermarking Technique of Multiple Images

The most traditional watermark techniques are used widely in information hiding technology. However, these techniques require some information of origin images to extract it from the cover image in frequency domain. This paper introduces a new blind watermark technique for embedding three watermark gray images into a color cover image based on Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The proposed technique is consists of three phases: Firstly, the three gray images are embedded into SVD components of cover color image to produce watermarked image. Secondly, the watermarked image is again embedded into the low frequency (DWTDCT) domains of one component of RGB origin cover image to produce the final resultant watermarked image. Finally, the three watermark images are blind extracted in the reverse operations without require to their SVD components. The implementation of the proposed technique is a perceptible improvement of experimental results compared with the recently watermarked techniques with respected to PSNR and Normalized Correlation (NC). This technique is also a robust to noise and intentional attacks.
Scalable Distributed Metadata Server Based on Nonblocking Transactions

Metadata performance scalability is critically important in high-performance computing when accessing many small files from millions of clients. This paper proposes a design of a scalable distributed metadata server, PPMDS, for parallel file systems using multiple key-value servers. In PPMDS, hierarchical namespace of a file system is efficiently managed by multiple servers. Multiple entries can be atomically updated using a nonblocking distributed transaction based on an algorithm of dynamic software transactional memory. This paper also proposes optimizations to further improve the metadata performance by introducing a server-side transaction processing, multiple readers, and a shared lock mode, which reduce the number of remote procedure calls and prevent unnecessary blocking. Performance evaluation shows the scalable performance up to 3 servers, and achieves 62,000 operations per second, which is 2.58x performance improvement compared to a single metadata performance.
User Acceptance of Digital 2D Animation for Educational Visualization Application: Bintang Hati Novel

Digital 2D Animation for Educational Visualization is a desktop courseware application. It digitalized the traditional short story of PT3 novel in 2D animation. The secondary school is still using the narrative novel in certain subjects such as Malay Language. Consequently, students are not interested in reading the entire page is cluttered with text. The purpose of this research is to evaluate user acceptance of Digital 2D Animation for Educational Visualization Application by doing test to 30 secondary schools students in one of the districts of Kedah. The development of this application is used to transform from conventional teaching and learning process to the digital teaching and learning process. In the digital era, teachers can communicate teaching experiences to students in a more fun way of delivering ideas with the help of animation in learning as a teaching tool aid. The results indicate that animation can be used as a teaching aid tool to improve students’ academic performance especially to developing countries like Malaysia.
Geological Survey of Western Australia: AusAEM20 - WA project

The Department of Mines, Industry Regulation and Safety’s (DMIRS) Geological Survey Division and Geoscience Australia (GA) have recently entered into a new National Collaboration Framework Agreemen...
A Load-Balancing Approach Using an Improved Simulated Annealing Algorithm

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center’s VMs. It aims at reducing the degree of a load’s imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic “simulated annealing (SA)”. We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.
EXAMINING AVAILABILITY AND FREQUENCY USE OF COMPUTER-BASED TECHNOLOGY RESOURCES AMONG STUDENTS IN NIGERIAN UNIVERSITIES

Ponte Academic JournalApr 2020, Volume 76, Issue 4 EXAMINING AVAILABILITY AND FREQUENCY USE OF COMPUTER-BASED TECHNOLOGY RESOURCES AMONG STUDENTS IN NIGERIAN UNIVERSITIESAuthor(s): Desmond W. Govender ,Aderinsola E. KayodeJ. Ponte - Apr 2020 - Volume 76 - Issue 4 doi: 10.21506/j.ponte.2020.4.26 Abstract:The integration of computer-based technology (CBT) into traditional face-to-face education has significantly affected many aspects of education systems worldwide. This article reports on the availability and frequency of use of CBT resources among undergraduate students in federal universities in South West Nigeria. Specifically, the study sought to investigatethe availability of CBT tools at different campuses and to identify those CBT applications most frequently used by students at these campuses. A descriptive survey research design using a mixed-method approach was applied. The quantitative and qualitative results were compared and contrasted to strengthen the findings. The study was conducted in six federal universities in the South West geopolitical zone of Nigeria. A total of 2,327 undergraduate students completed the questionnaire, while 18 of these students participated in the focus group interviews. The results revealed a low availability and frequency use of CBT resources at the campuses. Over 98% of students complained that they had to rely on their personal laptop, mobile phone, or a cybercafé; as well as purchase their own data for internet connections in order to complete assignments or access additional information. Factors that affected the use of CBT on campus included unstable power supply, poor network connectivity, unstable bandwidth, and lack of computer facilities on campus. Based on the findings, it was recommended that university management should ensure that all students have ready access to the internet, new software applications, and up-to-date CBT facilities, as this will encourage students to become conversant with the use of CBT from their first year. The use of CBT to supplement and augment classroom-based learning will enable students to learn at their own pace, thereby encouraging them to improve their academic performance. Download full text:Check if you have access through your login credentials or your institution Username Password
Author Matching Using String Similarities and Deep Neural Networks

Disambiguation (AND) is one of the problems in the process of classification of publication data, where the introduction of the characteristics of each author is difficult to recognize because of the frequent changes in patterns in writing author names.In the world of publications, author classification is needed to classify authors with types and fields of science based on published papers, therefore in this paper the author will classify with the aim of providing definite values whether the author is with the paper title a and author a with the paper title b is the same person or not.The method used by the author is one branch of Deep Learning, namely the Deep Neural Network DNN).DNN is used because it has high data processing acceleration capabilities with the use of GPU technology.With this method, AND data classified by the author produces a high level of accuracy with a value of 98%.