Reviewerly/exHarmony huggingface dataset:
Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.
A 3-LEVEL MULTIFACTOR AUTHENTICATION SCHEME FOR CLOUD COMPUTING

The objective of this paper is to propose a secure, user friendly and economical multi-level authentication scheme that uses multiple factors for gaining access to resource on insecure platforms and for financial transactions.The proposed study is based on a premise that when multiple levels and multiple factors are incorporated in an authentication scheme it not only becomes difficult to break but also resistant to different forms of attacks.This work purposes a scheme where authentication process is carried out in three levels using multiple factors and is called 3L-MFA.The scheme also uses Out of Band (OOB) authentication as one of the factors that offers credible security against man--the-middle (MIM) attack.The first level uses username in password based on double encryption.Second level uses OTP verification based on Out of Band (OOB) authentication using email id and mobile number.Third level involves user's interaction on graphical screen in terms of predetermined number of clicks on images, buttons and selection of predetermined number of menu items.The security of proposed system depends upon double encryption using SHA-1 and AES-128-CBC, out of band authentication using OTP and user interaction on a graphical screen that uses probability combination of various numbers.
Bayes Risk Post-Pruning in Decision Tree to Overcome Overfitting Problem on Customer Churn Classification

Classification is the process of assigning a set of data into an existing class.Decision tree is claimed to be faster and produces better accuracy compared to another classifier.However, it has some drawbacks in which the classifier is susceptible to overfitting.This problem can be avoided by post-pruning that trimming the small influence subtree in conducting the classification to improve model performance in predicting data.This paper proposes a Post-Pruning method by applying Bayes Risk, in which the risk estimation of each parent node compared with its leaf.This method is applied to two datasets of customer churn classification from the Kaggle site and IBM Datasets with three different sizes for training dataset (60%, 70%, and 80%).For the result, Bayes Risk Post-Pruning can improve decision tree performance and the larger the size of the training dataset was associated with higher accuracy, precision, and recall of the model.
Integrating M-Learning in Teaching ECG Reading and Arrhythmia Management for Undergraduate Nursing Students

&lt;p class="0abstract"&gt;Use of mobile devices among nursing students is recently a widespread approach. Integrating mobile devices in education can improve the quality of nursing education by engaging students in learning experience. The present study aims at investigating theeffect of using Mobile Learning (M-learning) on students’ academicachievements and satisfaction. Therefore, quasi-experimental design was used. The study included 104 students who were enrolled in a critical care course at the faculty of Nursing. During the study, the participant were divided into control and experimental groups. The experimental group 52 students were able to use a developed mobile application, which includes learning materials and multimedia resources, dedicated to critical care course. On the other hand, control group 52 students were able to explore the classical learning materials form the course. The study included students’ attitudes, students’ satisfaction, and students’ academic achievements. Differences between the two groups were considered significant as the result was p &amp;lt; 0.05. The obtained results showed significance difference between pre-post quizzes achievements between the experimental and control group (p&amp;lt;0.000). Additionally, 76% of the students were satisfied with using M-learning. The study revealed that there was improvement in the student academic achievements and satisfaction when integrating mobile learning into nursing education&lt;span&gt;.&lt;/span&gt;&lt;/p&gt;
A Deep Adversarial Approach based on Multisensor Fusion for Remaining Useful Lifeprognostics

Multi-sensor systems are proliferating the asset management industry and by proxy, the structural health management community.Asset managers are beginning to require a prognostics and health management system to predict and assess maintenance decisions.These systems handle big machinery data and multi-sensor fusion and integrate remaining useful life prognostic capabilities.We introduce a deep adversarial learning approach to damage prognostics.A non-Markovian variational inference-based model incorporating an adversarial training algorithm framework was developed.The proposed framework was applied to a public multi-sensor data set of turbofan engines to demonstrate its ability to predict remaining useful life.We find that using the deep adversarial based approach results in higher performing remaining useful life predictions.
Recognition of Colors through Use of a Humanoid Nao Robot in Therapies for Children with Down Syndrome in a Smart City

Down syndrome is the most frequent cause of mental disability, presenting similar characteristics among people with this syndrome, among them the scarce short-term memory capacity, fatiguing attention, language delay, among others.In Mexico only 3% of Down children receive education, so in this section we propose the use of a Humanoid robot for future application in therapies for children with Down syndrome.We propose how to improve your ability to work with colors and shapes in a group of children with Down syndrome.
Distance learning in higher education: current trends

The article is dedicated to the current issues of using distance-learning technologies in higher education in Ukraine and the world.The major stages and reasons for the rapid growth in the world of universities providing distance learning under the influence of rapid development of information and communication technologies and the promotion of educational innovations, including MOOC (Massive Open Online Courses) and other, are discussed in detail such as electronic learning process management systems.The author examines and analyzes the main four steps that preceded and facilitated the emergence of distance learning, namely: communication through postal communications, communication via radio and television, communication via computer and media, communication through mass-based online courses.The article also looks at the current state and prospects of further development of distance education in Ukraine.The author points out, among other things, the basic normative provisions that determine the basic principles of creation, organization, and implementation of modern distance learning in Ukraine.In an article the basic principles of distance learning are formulated, the organizational and methodological conditions and the principles of its introduction into the educational process are substantiated; its main advantages and disadvantages are highlighted.This article specifically describes the importance of distance education for the implementation and implementation of the concept of Lifelong Learning.An important project of introducing distance (blended) learning at the National University of Kyiv-Mohyla Academy, in particular, the DistEdu platform.The article discusses the ways of engaging lecturers and students to work with this educational online platform.The article identifies directions for further development of distance education in Ukraine and outlines the potential of DistEdu-like projects in promoting distance education in the Ukrainian educational environment and the educational community.
Cross-Modal Person Search: A Coarse-to-Fine Framework using Bi-Directional Text-Image Matching

Searching person images from a gallery based on natural language descriptions remains to be a challenging and under-explored cross-modal retrieval problem. To improve the accuracy off an image-based retrieval task, e.g., person re-identification (Person Re-Id), re-ranking is known to be an effective post-processing tool. In this paper, we extend re-ranking from uni-modal retrieval to cross-modal retrieval for the first time, and develop a bi-directional coarse-to-fine framework (BCF) for cross-modal person search. Built on a recent state-of-the-art Person Re-Id model, BCF exploits first text-to-image and then image-to-text relevance, in a two-stage refinement fashion. BCF ranks competitively against a strong baseline on the newly-introduced WIDER Person Search dataset, boosting validation set performance by 9.01%(top-1)/3.87%(mAP) for val1 and 6.60%(top-1)/3.49%(mAP) for val2, respectively. With a high score, our solution ranks competitively in the ICCV 2019 WIDER Person Search by Language Challenge.
Semi-supervised Deep Learning for Network Anomaly Detection

Deep learning promotes the fields of image processing, machine translation and natural language processing etc. It also can be used in network anomaly detection. In practice, it is not hard to obtain normal instances. However, it is always difficult to label anomalous instances. Semi-supervised learning can be utilized to resolve this problem. In this paper, we make a comprehensive study of semi-supervised deep learning techniques for network anomaly detection. Three kinds of deep learning techniques including GAN (Generative Adversarial networks), Auto-encoder and LSTM (Long Short-Term Memory) are studied on the latest network traffic dataset of CICIDS2017. Five deep architectures based on semi-supervised learning are designed, including BiGAN, regular GAN, WGAN, Auto-encoder and LSTM. Seven schemes of semi-supervised deep learning for anomaly detection are proposed according to different functions of anomaly score. Grid search is utilized to find the threshold of anomaly detection. Two traditional schemes of machine learning are also adopted to compare performance. There are altogether nine schemes of anomaly detection for CICIDS2017. From results of the experiment for network anomaly detection, it can be found that Auto-encoder outperforms LSTM and the three kinds of GAN. BiGAN and LSTM are both better than WGAN and regular GAN. All the seven schemes of semi-supervised deep learning for anomaly detection outperform the two traditional schemes. The work and results in this paper are meaningful on the application of semi-supervised deep learning for network anomaly detection.
Codeword Selection for Concurrent Transmissions in UAV Networks: A Machine Learning Approach

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.
QoE-Driven Resource Allocation Optimized for Delay-Sensitive VR Video Uploading over Cellular Network

Uploading Virtual Reality (VR) video over cellular networks is expected to boom in near future, as general consumers could generate high-quality VR videos with portable 360-degree cameras and are willing to share with others. Con-sequently, concerns of uplink bandwidth and delay arose for current popular technology of tile-based VR video streaming, which requires high quality video to transcode into multiple representations for further adaptive streaming. Motivated by this, we proposed a novel scheme for uplink delivery of tile-based VR video over cellular network, in which encoding bit rate of each tile is determined by uplink resource allocation (RA), and quality of content (QoC) contribution of each tile and channel quality of user equipments (UEs) are jointly considered during RA. Moreover, the RA problem is formulated as a frequency and time dependent non-deterministic polynomial(NP)-hard problem, which can be effectively solved by our proposed approximate convex algorithm. Simulation results show that the proposed algorithm can achieve higher utility, that is higher total quality of experience (QoE) for viewers.
Generalized Adversarial Training in Riemannian Space

Adversarial examples, referred to as augmented data points generated by imperceptible perturbations of input samples, have recently drawn much attention. Well-crafted adversarial examples may even mislead state-of-the-art deep neural network (DNN) models to make wrong predictions easily. To alleviate this problem, many studies have focused on investigating how adversarial examples can be generated and/or effectively handled. All existing works tackle this problem in the Euclidean space. In this paper, we extend the learning of adversarial examples to the more general Riemannian space over DNNs. The proposed work is important in that (1) it is a generalized learning methodology since Riemmanian space will be degraded to the Euclidean space in a special case; (2) it is the first work to tackle the adversarial example problem tractably through the perspective of Riemannian geometry; (3) from the perspective of geometry, our method leads to the steepest direction of the loss function, by considering the second order information of the loss function. We also provide a theoretical study showing that our proposed method can truly find the descent direction for the loss function, with a comparable computational time against traditional adversarial methods. Finally, the proposed framework demonstrates superior performance over traditional counterpart methods, using benchmark data including MNIST, CIFAR-10 and SVHN.
Concept Drift Detection Based on Kolmogorov–Smirnov Test

With the advancement of information society, a large amount of data, which is in the form of stream, has been produced in many fields. As a result of its extensive application in the fields of sensor networks, banking and telecommunications, data stream mining is obtaining more attention. One of the most challenging steps to learn from data stream is to react to concept drift, as most of the existing data stream algorithms only deal with abrupt or gradual concept drifts. The existing work of detecting concept drift is mostly based on the changing of error rate of single window, making it difficult to be universally applied to different types of concept drifts. A method of detecting concept drift is proposed in this paper based on Kolmogorov–Smirnov test (K–S test).
A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering

Problems tackled by researchers and data scientists in aviation and air traffic management (ATM) require manipulating large amounts of data representing trajectories, flight parameters and geographical descriptions of the airspace they fly through. The traffic library for the Python programming language defines an interface to usual processing and data analysis methods to be applied on aircraft trajectories and airspaces. This paper presents how traffic accesses different sources of data, leverages processing methods to clean, filter, clip or resample trajectories, and compares trajectory clustering methods on a sample dataset of trajectories above Switzerland.
Geometrical optimization for a photovoltaic installation equipped with flat reflectors based on plane of array estimations

In order to design, manage and optimize the performance of a photovoltaic (PV) installation and establish a precise power production estimation, irradiance on the plane of array (POA) in relation with the geometrical characteristics of the PV modules installation occupies a high importance. This study focuses on the development of an estimation model of the POA irradiance for a photovoltaic installation equipped with flat reflectors. The model includes solar irradiance components (global, direct and diffuse), geometrical parameters and geographical characteristics of the PV installation. Experimental validations have been performed with measurements taken at the SIRTA Observatory (48.7°N, 2.2°E) in Palaiseau, France, for the period starting from June 2017 to June 2018. Results show mean absolute errors (relative to the mean) of 6% and 7% for an installation without and with planar reflector. Finally, we present several geometrical optimization strategies of the PV-reflector installation relying on two major variables: the reflector's length ( LR ) compared to the length of the PV module ( L PV ) and the tilt angle adjustment frequency (monthly, seasonally, fixed) of the system (for both PV and the reflectors). The objective of such optimization is to discuss about a reasonable configuration to achieve a maximum POA irradiance. Results show that the length of the mirrors highly affects the efficiency and performances of the PV-Reflector system and the annual gain increased from 8.5% to 28.7% when going from L R = L PV /2 to L R = 2 × L PV compared to a monthly-optimized installation without mirrors. As for the adjustment frequency, we show that a monthly-varied architecture is the most advantageous option with a 28.2 and 31.6% increasing in annual gain compared to a seasonal varied or fixed ones, respectively.
Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic

Wireless networks nowadays are ubiquitous in our daily life. Due to the open channel nature, wireless networks are vulnerable to eavesdropping attacks. Though wireless conversation can be encrypted against eavesdropping, attackers can still infer a user's activities via traffic analysis on encrypted data. Nevertheless, previous inference methods usually have the limitation that they can only achieve a relatively high accuracy in a specific domain. In this paper, we propose a smart spying strategy that can infer a user's activities of multiple domains with a higher accuracy. We also develop a prototype tool on top of this strategy to conduct experiments. The evaluation results show our strategy works effectively in activity inference on encrypted data, with an accuracy rate as high as 99.17%.
FADBM: Frequency-Aware Dummy-Based Method in Long-Term Location Privacy Protection

With the rapid usage of location-based services (LBSs), protection of location privacy has become a significant concern. Existing dummy-based methods mainly consider generating dummies in continuous queries, which neglects the fact that the user would launch queries in a frequent region(e.g., home), resulting in privacy disclosure in a long-term period (i.e., more than 30 days). To solve this problem, we propose a method which generates dummy frequent regions, and make dummies locate in these dummy regions as far as possible. Compared with other methods, evaluation based on real-world dataset shows that the proposed method can reduce the ratio of recognized dummies and restored trajectory in a long-term situation.
Low-level separation axioms from the viewpoint of computational topology

The present paper studies certain low-level separation axioms of a topological space, denoted by A(X), induced by a geometric AC-complex X. After proving that whereas A(X) is an Alexandroff space satisfying the semi-T1 2 -separation axiom, we observe that it does neither satisfy the pre T1 2 -separation axiom nor is a Hausdorff space. These are main motivations of the present work. Although not every A(X) is a semi-T1 space, after proceeding with an edge to edge tiling (or a face to face crystallization) of Rn, n ? N, denoted by T(Rn) as an AC complex, we prove that A(T(Rn)) is a semi-T1 space. Furthermore, we prove that A(En), induced by an nD Cartesian AC complex Cn = (En,N,dim), is also a semi-T1 space, n ? N. The paper deals with AC-complexes with the locally finite (LF-, for brevity) property, which can be used in the fields of pure and applied mathematics as well as digital topology, computational topology, and digital geometry.
Performance Analysis of Distance Measures in K-Nearest Neighbor

K-Nearest Neighbor (KNN) has important parameters that affect the performance of the KNN.The parameter is the k value and distance matrix.In KNN, the distance between two points is determined by the calculation of the distance matrix.In this paper we will analyze and compare the performance KNN using the distance function.The distance are Braycurtis, Canberra and Euclidean Distance.This study uses Confusion Matrix for evaluation of accuracy, sensitivity and specificity.The results showed that the Braycurtis distance had better performance than Canberra Distance and Euclidean Distance with accuracy values of 96%, sensitivity of 96.8% and specificity of 98.2%.
3DSAL: An Efficient 3D-CNN Architecture for Video Saliency Prediction

In this paper, we propose a novel 3D CNN architecture that enables us to train an effective video saliency prediction model.The model is designed to capture important motion information using multiple adjacent frames.Our model performs a cubic convolution on a set of consecutive frames to extract spatio-temporal features.This enables us to predict the saliency map for any given frame using past frames.We comprehensively investigate the performance of our model with respect to state-of-the-art video saliency models.Experimental results on three large-scale datasets, DHF1K, UCF-SPORTS and DAVIS, demonstrate the competitiveness of our approach.