Reviewerly/exHarmony huggingface dataset:
GRU-DF: A Temporal Model with Dynamic Imputation for Missing Target Values in Longitudinal Patient Data

Temporal models are desirable in studying progressive diseases because the data are typically collected at regular time intervals. However, such clinical data often contain many missing entries, including those from the target variable that we are interested in predicting. Standard imputation techniques (e.g., linear interpolation) are inappropriate in treating missing target observations because they approximate the missing entries before the onset of model training and, thus, would inevitably lead to training a self-fulfilling model. The absence of target observations is particularly problematic for time series data where their availability at each time step is indispensable in building a temporal model. We propose a novel approach that incorporates the missing target value imputation into the training process of the Gated Recurrent Unit (GRU) model. We evaluate our new model in our motivating domain of predicting disease progression of multiple sclerosis patients using a real-world dataset of 508 subjects. The goal is to forecast patients' disability levels based on data collected in six-month intervals. Our model demonstrates a 27.9% performance gain over a GRU model with a standard forward-fill treatment for the missing target observations. Additionally, our model displays a 21.6% advantage over a non-temporal approach for our machine learning task.
Q-CAM: Queue Monitoring System using Camera

Development and design of mobile queue counter monitoring system is an alternative way for people who visit service premises to monitor queue number through phone without having to be at the place to wait for their turn. A long waiting line and waiting time are the reasons why this project had been developed. Many related systems had been introduced by different companies but due the high cost of installation and maintenance, some service companies had difficulties to implement the system. However, the proposed mobile queue counter monitoring system called Q-CAM is an add-on device which will be installed with the existing system and uses only ESP32 camera and FTDI Programmer as hardware development. A smartphone application is developed by using MIT App Inventor to ease the customer to monitor queue number on the app. In this work, the hardware designs of the propose device is described. Furthermore, the steps to develop the smartphone app is demonstrated. Preliminary experimental results show that the average time required to display a captured queue number image on the app were 5 seconds (during strong Wi-Fi signal) and 10.8 seconds (during weak Wi-Fi signal).
The Estimation of Hourly Solar Radiation on tilted Surfaces using Artificial Neural Network: A Case Study of Surabaya

Abstract This study aims to generate an accurate model for estimating the radiation of solar panels on different inclination angles. The output of this model is useful for determining the optimal installation angle of the solar panel either on land or on the ships. Furthermore, the amount of the hourly direct and diffuse radiation on the horizontal surface is estimated using Artificial Neural Networks (ANN), which were trained with the monthly radiation data of Surabaya from 2018 to 2019. Subsequently, the radiation on the inclined surface is estimated using a mathematical model. Also, the ANN accuracy was validated with a regression value higher than 99% for either direct or diffuse radiation estimate. A full-year evaluation based on the proposed model suggests an inclination angle of 25° for the solar panel installed in Surabaya. Meanwhile, the evaluation gives different angles for each month with the advantage compared with the fixed angle installation.
Optimized Machine Learning Algorithm using Hybrid Proximal Method with Spectral Gradient Techniques

Deep learning models are widely implemented in various machines to perform complicated tasks. Therefore, a significant amount of research effort focuses on improving the implementation of such models. One of the key bottlenecks in model implementation is the lengthy and inefficient model training process. A large amount of literature was published to improve the training process. In this paper, the Spectral Proximal (SP) optimization method is studied and presented. The SP method is an optimization algorithm that combines the Multiple Damping Gradient (MDG) method with a sparsity optimizer to improve training efficiency in machine learning. The MDG algorithm utilizes a damping matrix to correct errors in the descent direction. On top of that, the sparsity optimizer eliminates insignificant elements in the solution to reduce unnecessary computation. We conducted a training experiment to evaluate the SP method against the Adam method. In the experiment, both methods are used to train You Only Look Once version 3 (YOLOv3) model with an object detection dataset, known as YYMNIST dataset. The dataset utilized selected images from the Modified National Institute of Standards and Technology (MNIST) dataset. From the experiment, SP method displays a higher convergence rate and achieved a slightly higher mean Average Precision (mAP) than Adam method. However, SP method requires slightly longer training time due to higher computational requirements.
Yeni Bir Güvenlik Katmanı Ekleyerek Mobil Hizmet Kullanıcısı Kimliğinin Güvenliğini Sağlama

Today, various common identity systems (e.g.Facebook Login, Google Connect, Apple ID) are used to improve operational efficiency for service providers and provide an easier authentication method in web or mobile services for users.Almost all common identity systems focus on delivering seamless user experience while proving user identity securely to the service provider.In particular, the use of common identity systems with a high security level is becoming a more important requirement on smartphones.In this context, MNOs (Mobile Network Operators) are considered as an important actor in providing common identity services, as they have strong GSM capabilities.Currently, it is possible to see many identity management solutions -based on OpenID Connect and Mobile Connect standards-from MNOs which are used for authentication in mobile applications of service providers.However, existing solutions generally does not provide very high level of assurance in the asserted digital identity.With advancements in value-added mobile services and increasing security requirements; there is a need for common identity systems that provide higher levels of assurance (i.e., particularly LoA4), strong authentication and non-repudiation services for service providers and users.This study presents the development and implementation of a multi-factor authentication method for mobile services based on Mobile Connect and OpenID Connect standards.The designed model includes the usage of three identity -knowledge, ownership, biometric-factors of user in order to access sensitive mobile services on the smartphone.The system development and testing studies were systematically presented based on the functional requirements.The realization and deployment of the proposed model by MNOs could play an important role in the development of mobile services that require a high level of assurance in the future.
Syntax-aware Natural Language Inference with Graph Matching Networks

The task of entailment judgment aims to determine whether a hypothesis is true (entailment), false (contradiction), or undetermined (neutral) given a premise. While previous methods strike successful in several benchmarks and even exceed the human baseline, recent researches show that it remains arguable if the methods learn the statistical bias in the datasets. In this paper, we propose the syntax-aware Natural Language Inference (SynNLI) model, which utilizes graph matching networks to obtain syntax-guided contextualized representation while aligning the premise and the hypothesis accordingly. We show that the proposed method outperforms multiple baseline models on MNLI develop set, and visualize the model internal behavior.
A Distinguisher for RNGs with LFSR Post-processing

Random number generator (RNG) is a fundamental element in modern cryptography. If the quality of the outputs generated by RNGs is not as well as expected, the cryptographic applications which use the random number service are vulnerable to security threats. In reality, the entropy source of RNGs could be impressible by the changes of environmental factors, resulting in defects in the generated data, such as poor statistical properties. Thus, RNG is generally designed with a preset post-processing module to improve the quality of the output sequences. Linear feedback shift register (LFSR) is one of the frequently used methods for post-processing thanks to the characteristic of simplicity and no reduction in output throughput. However, we point out that even if the statistical properties of the outputs of the entropy source are extremely poor, the sequences processed by LFSR can still pass the statistical test. This undoubtedly increases the security risks in the usage of RNGs. In this work, we propose a distinguisher for the RNGs with LFSR post-processing for the first time. The distinguisher can be used to detect the RNGs with LFSR post processing, and we theoretically prove the sequences before processing can be recovered. On this basis, we design a new statistical test via combining the distinguisher with the Frequency Test in the NIST test suite. The experimental results show that if the sequence is biased before being processed by LFSR, our proposed method can detect it, but the NIST SP 800-22 Test Suite cannot.
Design and Implementation of the Cisco Webex System Based on Self-service Sign-up Using G Suite for Rapid Deployment

Following the spread of the new coronavirus (COVID-19), there is a growing requirement for online class at universities and other educational institutions. Information Media Center (IMC) at Tokyo University of Agriculture and Technology (TUAT), provides Google G Suite for Education, which can be used by all faculty members and students of our university. G Suite provides a service called Google Meet, which can be used for remote meetings. However, since Meet is somewhat inferior in terms of functionality as a web conferencing system, it is expected to have some barriers for the smooth class management by faculty members. Therefore, IMC considered that Cisco Webex Training could be a choice for effective online class use. First of all we discuss the functionality of Cisco Webex Training in this paper. A product version of some web conferencing systems such as Webex can be easily implemented for a large-scale environment by working together with a university’s integrated authentication system. However, in order to do this, it is necessary to renovate the existing authentication system. It also coincides with the end of the fiscal year in Japan’s budget period. In this situation, online classes are also an important issue that needs to be implemented quickly for COVID-19 measures. From these situations, renovations and cost incurrence for coordination should be kept to a minimum. Therefore, IMC starts preparations for a small-start implementation of the system. First, we built a system that allows users to open an account for the Webex system by signing up, and then users can start the service by self-service when they need it. This system consists of a combination of the Webex system and the various services included in G Suite, and is ready for use when both services are available. In this paper, we also provide a brief description of the process of building the system and the details of the system. By proposing an introduction method that is different from the linking the authentication system normally used to deploy the Webex system, we can propose a new use case for the university community.
Photon entanglement through linear optics networks with birefringent crystals

We develop a linear optics network for the generation of photonic entangled states via designing a quantum circuit consisting of optical elements, i.e., beam splitters and birefringent crystals. To achieve the purpose, at first we introduce non-entangled single-photon states with their Gaussian spectral amplitude functions as the inputs of the circuit. Then, we show that the outcome of the circuit is an entangled Gaussian photonic state characterized with its covariance matrix. The quantum optical Gaussian states constitute an important class of robust quantum states which are manipulatable by the existing technologies. Meanwhile, we investigate the generation of biphoton entangled states, in detail. Also, we evaluate the concurrence (as a measure of entanglement) and also the probability density function (PDF) corresponding to biphoton states. In the continuation, we study other possible applications of such quantum circuits. We demonstrate that how one can estimate the position of outcome, i.e., the probability of finding entangled photons in a confidence ellipsoid. Our numerical results show that the entanglement of biphoton states strongly depends on their correlation matrix. As an outstanding feature, the PDF of the output state of the circuit provides an elegant criterion to identify the entangled photonic states from their separable counterparts. The designed quantum circuit and the obtained results may be implemented in the development of quantum information and communication protocols with continuous variables, besides their practical importance in realizing more complicated quantum networks.
Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Computer vision is widely used in manufacturing, sports, medical diagnosis, and other fields. In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse the key issues that affect the overall expression of the error action sequences, and then, we propose a motion energy model based on the direct motion energy decomposition of the video clips of human error actions in the 3 Deron action sequence space through the filter group. The method can avoid preprocessing operations such as target localization and segmentation; then, we use MET features and combine with SVM to test the human body error database and compare the experimental results obtained by using different feature reduction and classification methods, and the results show that the method has the obvious comparative advantage in the recognition rate and is suitable for other dynamic scenes.
VR/Technologies for Rehabilitation

Early on, Virtual Reality (VR) was linked to communication—even as ‘a combination of the television and telephone wrapped delicately around the senses’ [1]. VR Technologies are increasingly being adopted in the areas of (re)habilitation and therapeutic intervention healthcare treatments. VR applications that supplement traditional intervention are in human physical, cognitive, and psychological functioning medical treatment programs. Extended Reality (XR), incorporating applications beyond solely VR, with Augmented Reality (AR) and Mixed Reality (MR) applications are also being introduced. This chapter introduces via micro-review, four chapters under the theme VR/technologies for rehabilitation. It follows on from the previous and opening part of this book where ten chapters were introduced and micro-reviewed under the theme Gaming, VR, and immersive technologies for education/training. Specifically, the chapters are titled:—‘Game-based (re)habilitation via movement tracking’ [2]; ‘Case studies of users with neurodevelopmental disabilities: Showcasing their roles in early stages of VR training development’ [3]; ‘AquAbilitation: ‘Virtual interactive space’ (VIS) with buoyancy therapeutic movement training’ [4]; and finally, ‘Interactive Multisensory VibroAcoustic therapeutic intervention (iMVATi)’ [5].
Empirically Testing Deep and Shallow Ranking Models for Click-Through Rate (CTR) prediction

Recent studies have reported that deep learning models perform excellently for reranking the top recommendation items. However, we found that it is not easy to reproduce some of these results. In particular, we found that recommendations based on a simple neighbor-based model, on average, outperform the results generated by deep learning models based on two datasets from e-commerce websites (one open dataset and one private dataset from our collaborating partner). Moreover, we performed an error analysis to investigate when the deep learning models perform better than simple models and when they do not. Our analysis is especially useful for medium- and small-sized online retailers that may have a smaller training dataset.
Dengue prediction model: A systematic review using social network analysis

Abstract Since 1968, Dengue Harmonic Fever’s incidence in Indonesia has continued to rise and has become a public health issue. Indonesia has the largest number of Dengue Harmonic Fever cases than 30 other epidemic countries worldwide. It is very important to carry out research related to dengue cases’ prediction to prevent the spread of Dengue. This literature review is intended to determine the extent of the dengue prediction approach carried out by previous researchers, and a research gap will be obtained. The algorithm used to cluster articles is a modularity algorithm, using several open-source tools to process data. The online databases used are Google Scholar and Crossref by using keywords: journal, algorithm, prediction, and Dengue. The data are taken from the expansion of 1928-2020. This study’s results are 200 articles that are suitable and divided into four clusters of important articles. Also, several important parameters were obtained in the prediction study of dengue fever, namely humidity, temperature, rainfall, and population density.
An experimental study of different machine and deep learning techniques for classification of encrypted network traffic

There is a continuous evolution in the technology industry with different types of devices being produced and connected to the internet. Multiple types of applications run on the different devices, thereby generating a complex and huge amount of traffic (i.e., Big Data) on the internet. This has made it difficult and challenging for different Internet Service Providers (ISPs) to maintain their service quality and keep their networks and services secure. It is important for service providers to have the ability to classify large and complex network traffic to help promote a better Quality of Service (QoS) and manage networks. In this paper, we utilize, apply and evaluate different machine and deep learning techniques for classification of encrypted network traffic to help in managing networks, and thereby, help in improving quality and security of network. A comparison between the different algorithms used is presented. The experiment results show that ANN+XGB, CNN+XGB, and CapsNet+XGB performed better than the LSTM+XGB and Ensemble model in the classification of encrypted network traffic with accuracies of 96%, 96%, 96%, 93%, and 95% respectively using a total of 23 statistical features. More statistical features were considered compared to other existing related works to improve the process of the classification and different hidden patterns associated with the statistical features. The results show the effectiveness of the machine and deep learning algorithms for the classififcation of encrypted network traffic into different categories.
An Improved Translation-Based Method for Knowledge Graph Representation

Traditional knowledge graph representation methods based on translation models have low-quality negative samples and false negative examples. In order to solve the shortcomings of translation models, this paper proposes an improved translation model knowledge representation model which is called TransE-KC. First, introduce the K-Means algorithm for clustering, and then randomly select 20 entities in different clusters to calculate the similarity between the replaced entities, and rank them, select the highest ranked entity, and perform Replacement; secondly, for the problem of "false negatives", this article introduces a Cuckoo Filter to filter it. The experimental results on the public data set show that, compared with the TransE model, the TransE-KC model has a greater improvement in link prediction average ranking and triplet classification accuracy.
Anomaly Detection Monitoring System for Healthcare

Most developing countries suffer from inadequate health care facilities and a lack of medical practitioners as most of them emigrate to developed countries. The outbreak of the COVID-19 pandemic has left these countries more vulnerable to facing the worse outcome of the pandemic. This necessitates the need for a system that continuously monitors patient status and detects how their physiological variables will change over time. As a result, it will reduce the rate of mortality and mitigate the need for medical practitioners to monitor patients continuously. In this work, we show how an autoencoder and extreme gradient boosting can be merged to forecast physiological variables of a patient and detect anomalies and their level of divergence. An accurate detection of current and future anomalies will enable remedial action to be taken by medical practitioners at the right time and possibly save lives.
Evolutionary Computation for Global Optimization – Current Trends

This article comments on the development of Evolutionary Computation (EC) in the field of global optimization. A brief overview of EC fundamentals is provided together with the discussion of issues of parameter settings and adaptation, advances in the development of theory, new ideas emerging in the EC field and growing availability of massively parallel machines.
Deep Learning Architecture for Off-Line Recognition of Handwritten Math Symbols

Real scientific challenge, handwritten math formula recognition is an attractive field of pattern recognition leading to practical applications. Hundreds of alphanumeric and math symbols need to be recognized, many are so similar in appearance that some use of context is necessary for disambiguation. Analysis of the spatial relationships between symbols is challenging. In this work, we focus on handwritten math symbols and propose to recognize them by a deep learning approach. The symbol images, used for train, validation, and test are generated from Competition on Recognition of Online Handwritten Mathematical Expressions dataset (CROHME) 2019’s online patterns of mathematical symbols. As the large dataset is crucial for the performance of the deep learning model and it is labor-intensive to obtain a large amount of labeled data in real applications, we first augmented the database. Standing on the transfer learning technique, we then tested and compared several pre-trained Convolutional Neural networks (CNNs) like VGGNet, SqueezeNet, DenseNet, and Xception network and we tuned them to better fit our data. An accurate classification of 91.88% (train), 88.82% (validation), and 83.68% (test) for 101 classes is achieved, using only off-line features of the symbols.