Reviewerly/exHarmony huggingface dataset:
Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes

Turner syndrome adheres serious health-related complications with a tendency to affect various organs during different stages of life which includes hypertension, infertility, and retarded growth. The proper diagnosis of TS requires an expensive test named karyotype test which is not easily available in remote health care units in the countryside. Therefore, we proposed to use facial images to detect TS to pursue a higher accuracy of recognition. The proposed scheme achieved the accuracy of 91.3% with mixed feature extraction schemes using thirty principle components selected with criteria that retained 95% of the information from the turner dataset. Moreover, this research is the first that uses facial features to accurately diagnose TS patients and has the capability to help doctors to establish a cost-effective TS prognosis process in remote health care units that lack required health care facilities.
Weighted PCL over Product Valuation Monoids

We introduce a weighted propositional configuration logic over a product valuation monoid. Our logic is intended to serve as a specification language for software architectures with quantitative features such as the average of all interactions’ costs of the architecture and the maximum cost among all costs occurring most frequently within a specific number of components in an architecture. We provide formulas of our logic which describe well-known architectures equipped with quantitative characteristics. Moreover, we prove an efficient construction of a full normal form which leads to decidability of equivalence of formulas in this logic.
Intermediate node selection for Scatter-Gather VM migration in cloud data center

Virtual machine migration is used in cloud computing for server consolidation, system maintenance, opportunistic power savings, and load balancing. Scatter-Gather VM live migration is a new type of virtual machine migration. It is proposed to decouple the source quickly and reduce the time to evict the state of a migrating virtual machine from the host. In Scatter-Gather live migration, the memory pages of the VM are transferred not just to the destination but also to several pre-selected intermediaries. This open problem of selection of the intermediate nodes is not addressed in the literature. In this paper, we propose to define the problem of intermediate node selection in Scatter-Gather migration and prove that it is NP-complete. The problem is mathematically modelled as an integer programming problem based on two optimality criteria – minimizing eviction time and minimizing energy. Two heuristic algorithms – maximum-decrease-in-eviction-time and least-increase-in-energy are proposed to solve the problem and their performance is analyzed with respect to three parameters – eviction time, energy and total migration.
Discriminator Feature-Based Inference by Recycling the Discriminator of GANs

Generative adversarial networks (GANs)successfully generate high quality data by learning amapping from a latent vector to the data. Various studies assert that the latent space of a GAN is semanticallymeaningful and can be utilized for advanced data analysis and manipulation. To analyze the real data in thelatent space of a GAN, it is necessary to build an inference mapping from the data to the latent vector. Thispaper proposes an effective algorithm to accurately infer the latent vector by utilizing GAN discriminator features. Our primary goal is to increase inference mappingaccuracy with minimal training overhead. Furthermore,using the proposed algorithm, we suggest a conditionalimage generation algorithm, namely a spatially conditioned GAN. Extensive evaluations confirmed that theproposed inference algorithm achieved more semantically accurate inference mapping than existing methodsand can be successfully applied to advanced conditionalimage generation tasks.
An Efficient Query Scheme for Privacy-Preserving Lightweight Bitcoin Client with Intel SGX

In Bitcoin, lightweight clients outsource most of storage and computation tasks to full nodes in order to run on resource-limited devices. In the interaction with the full node, the lightweight client leaks considerable information about which address or transaction is relevant to it. The existing schemes to solve this problem do not support efficient yet privacy-preserving transaction search due to the fact that the blockchain is inherently inefficient for transaction query and proposed schemes perform transaction search in a block-by-block manner. Therefore, we propose an efficient transaction query scheme for the privacy-preserving lightweight client with the Intel SGX enclave running on the full node. Our main idea is to leverage the secure enclave to serve transaction-query requests from lightweight clients. However, the usage of secure enclave alone does not achieve our goals. Our scheme reorganizes the blockchain and leverages prefix tree to increase transaction-search efficiency. Due to limited capacity, the secure enclave stores reorganized blockchain data in the untrusted full node. Thus, our scheme integrates prefix tree and oblivious searching technologies to simultaneously support efficient transaction search and protect access pattern of externally stored blockchain data for the secure enclave. Security analysis and performance evaluation show that our scheme provides efficient transaction search and verification functionalities for lightweight Bitcoin clients in a privacy-preserving way.
Go-in-Circles: A Directed Walking Algorithm for VR Navigation Based on One-Step-Ahead Strategy

In virtual reality (VR), how to map large-scale virtual spaces to small tracking area is a challenging problem. In this paper, we propose a novel steering algorithm for the redirected walking (RDW) technique to steer users away from the boundaries of the tracking area while reducing perceptible discrepancies. Inspired by the motion illusion that when a person is blindfolded, she goes in circles while thinking she is walking in a straight and infinitely long path, we map the path composed of straight-line segments in virtual space to a path composed of arcs in real space, and use one-step-ahead strategy and a buffer zone to reduce the number of user-boundary collisions. To verify the effectiveness of our algorithm, we conduct two experiments based on simulated in large-scale virtual spaces with a relatively small tracking area. The results indicate that our algorithm can effectively reduce collisions and perceptual distortion, and show the potential to address the challenge of simultaneously redirecting more than one user.
Discrete Curvelet Transform Algorithm for Image Compression System

A medical image has dominated the compression techniques since it requires to have a smaller size of the image and should maintain the relevant diagnostic information.Reducing the size of the image, directly give impact to the transmission speed.This paper presents an approach towards compression algorithm development using discrete curvelet transform.Curvelet functions signify functions which have discontinuities along straight lines where wavelet functions are not properly worked.The curvelet transform algorithm were synthesized using MATLAB and various medical modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound are used as a sample's medical images.The results obtained clearly identify that discrete curvelet transform algorithm gives a good quality for the reconstructed images.
Camera-based On-Road Detections for the Visually Impaired

Herein, we research the possibilities of assisting visually impaired pedestrians moving in traffic situations by using camera-based detection of relevant objects in their immediate surroundings.Therefore, we use and adapt algorithms from the field of driver assistance.We present a road background segmentation based on watersheds, whose results are used as input for the presented crosswalk and lane detection algorithms.The crosswalk detection is based on the application of two 1D mean filters and the lane detection on local computations of the EDF (Edge Distribution Function).In our evaluation, the described algorithms achieved good hit rates of 99.87% (road segmentation), 98.64% (crosswalk detection), and 97.89% (lane detection).
IoT and Blockchain in the Development of Smart Cities

With the advent and proliferation of the internet, the fourth industrial revolution is in full swing.As a result, different technologies have the potential to impact the course of human development.In other words, worldwide populations are moving towards growing urban centers and as a result, smart cities are emerging as the integration of human activities and technologies.These smart cities are built on top of different technologies such as blockchain and the Internet of Things (IoT).Consequently, the applications of these technologies in current and future smart cities will not only change the nature of human interaction and governance but also how business is conducted.This paper proposes an experimental study (qualitative and quantitative) that will determine the impact of blockchain and IoT technologies on the development of smart cities.It aims to derive insight from questions such as how current business models are preparing themselves for this disruption, the challenges they will face, and the potential contributions the two technologies will have on business development.The study's outcomes will provide the rationale for why businesses should start paying attention to these technologies and start on an early adoption plan that will slowly transform their business models as smart cities mature.
Penerapan Metode K-Medoids Untuk Pengelompokkan Kondisi Jalan Di Kota Semarang

Road conditions in Indonesia specifically in the city of Semarang are more often damaged during the rainy season. If needed it will not be repaired users can increase fuel usage and vehicle maintenance until it passes through traffic safety. One of the solutions to these problems is to provide up-to-date information on the surface of the road so that the department can repair roads quickly. This study discusses the grouping of road data using the k-medoids method. Road condition data collection uses an accelerometer sensor that supports data according to mobile devices. From the data used, the number of 638 data was successfully grouped into 3 clusters which could indicate the level of frequency of roads that were good, lightly damaged and heavily damaged
Control System for a Tensile-Testing Device Using Low-Cost Hardware and Open-Source Software

The aim of this study is to verify whether a Raspberry Pi 3 B+ can be utilized as a low-cost device for controlling a tensile-testing device used for material research purposes. A list of requirements based on already-available hardware was drawn up, which the new control system had to fulfil. To connect all the necessary equipment, a connection board was constructed, and some additional hardware was acquired for the system to be able to perform all the necessary tasks. The whole controlling system was also put in a small enclosure to make it portable. The control-system software was written in C++ using the Pigpio library. The developed system was then tested, and the results were compared to a commercially available Instron 8802 device. A comparison of the results shows that the upgraded equipment can produce comparable results to commercially available devices and is sufficiently accurate to be applied for research purposes for the characterization of soft tissues and other materials.
Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models

The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.
Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering algorithms perform poorly on this kind of data. In this paper, a novel hybrid feature selection technique is proposed, which can reduce drastically the number of features with an acceptable loss of prediction accuracy. The proposed approach operates in multiple stages, starting by removing irrelevant features with a low discrimination power, and then eliminating the ones with low variation range. Afterward, among each set of features with high cross-correlation, a single feature that is strongly correlated with the output is kept. Finally, a Genetic Algorithm with a customized cost function is provided to select a small subset of the remainder of features. To show the effectiveness of the proposed approach, we investigated two challenging case studies with sample set sizes of about 100 and the number of features larger than 1000. The experimental results look promising as they showed a percentage decrease of more than 99% in the number of features, with a prediction accuracy of more than 92%.
A Spectral Hazy Image Database

We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing. SHIA (Spectral Hazy Image database for Assessment) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free (ground-truth) images, taken in the visible and the near infrared ranges every 10 nm starting from 450 to 1000 nm. The number of images that form SHIA is 1540 with a size of $$1312\,\times \,1082$$ pixels. All images are captured under the same illumination conditions. Three of the well-known dehazing image methods based on different approaches were adjusted and applied on the spectral foggy images. This study confirms once again a strong dependency between dehazing methods and fog densities. It urges the design of spectral-based image dehazing able to handle simultaneously the accurate estimation of the parameters of the visibility degradation model and the limitation of artifacts and post-dehazing noise. The database can be downloaded freely at http://chic.u-bourgogne.fr .
An Improved Sampling-Based Approach for Spacecraft Proximity Operation Path Planning in Near-Circular Orbit

This paper proposes an improved sampling-based approach for spacecraft proximity operation path planning under Clohessy-Wiltshire-Hill dynamics. The proposed approach is based on a modified version of the FMT* (Fast Marching Tree) algorithm with safety strategy which is divided into three parts: (1) incorporating relative ellipse to simplify the sampling state space and avoid collision with target; (2) combining internal/external ellipse-based collision detection algorithms for hovering obstacle and non-coplanar ellipse obstacle; (3) using rotating hyperplane method to handle the coplanar ellipse obstacle and uncertainty obstacle. By referring the safety strategy to simplify the state space before FMT* algorithm, the approach can reduce the complexity of path planning, especially the resampling and collision avoidance detection cyclic process in FMT*, thereby improve the planning efficiency. Two simulated scenarios, a coplanar path planning with/without coplanar ellipse obstacle, are developed to illustrate the approach. As a result, the proposed approach appears to be potential for spacecraft proximity real-time path planning as well as other complex space mission path planning generalized to different dynamics and environments, such as On-Orbit Service, and enabling a real-time computation of low-cost trajectory.
StopGuessing: Using Guessed Passwords to Thwart Online Password Guessing

Practitioners who are seeking to defend against online password-guessing attacks usually find a shortage of techniques. We present an open source system to defend against password-guessing attacks using information ignored previously: the guessed passwords themselves. We examine passwords to detect and are more forgiving of login failures caused by users mistyping their passwords.
Wide Hidden Expansion Layer for Deep Convolutional Neural Networks

Non-linearity is an essential factor contributing to the success of deep convolutional neural networks. Increasing the non-linearity in the network will enhance the network's learning capability, attributing to better performance. We present a novel Wide Hidden Expansion (WHE) layer that can significantly increase (by an order of magnitude) the number of activation functions in the network, with very little increase of computational complexity and memory consumption. It can be flexibly embedded with different network architectures to boost the performance of the original networks. The WHE layer is composed of a wide hidden layer, in which each channel only connects with two input channels and one output channel. Before connecting to the output channel, each intermediate channel in the WHE layer is followed by one activation function. In this manner, the number of activation functions can grow along with the number of channels in the hidden layer. We apply the WHE layer to ResNet, WideResNet, SENet, and MobileNet architectures and evaluate on ImageNet, CIFAR-100, and Tiny ImageNet dataset. On the ImageNet dataset, models with the WHE layer can achieve up to 2.01% higher Top-1 accuracy than baseline models, with less than 4% computation increase and less than 2% more parameters. On CIFAR-100 and Tiny ImageNet, when applying the WHE layer to ResNet models, it demonstrates consistent improvement in the accuracy of the networks. Applying the WHE layer to ResNet backbone of the CenterNet object detection model can also boost its performance on COCO and Pascal VOC datasets.
BLOCKCHAIN TECHNOLOGY AND SYSTEMIC RISK

This paper examines the effect of financial institutions management information system migration to blockchain technology on systemic risk. Our study examines a sample of 40 financial institutions around the world. The aim is to identify to what extent the migration of financial company management information system to blockchain system contribute to minimize systemic risk measures through regressions on panel data. We have reached the empirical evidence which indicates that the change we mentioned earlier affect immediately the systemic risk level. Companies that have adopted the blockchain technology recorded a significant reduction in systemic risk level. In addition, financial institutions that have adopted this latter system recorded a significant decrease in long-run marginal expected shortfall and systemic risk index. Keywords: Blockchain; Systemic Risk, Financial institutions JEL Classifications : G21, G32 DOI: https://doi.org/10.32479/ijefi.9069
WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for improvement. Random walks based methods are currently popular methods to learn network embedding; however, they are random and limited by the length of sampled walks, and have difficulty capturing network structural information. Some recent researches proposed using meta paths to express the sample relationship in HNs. Another popular graph learning model, the graph convolutional network (GCN) is known to be capable of better exploitation of network topology, but the current design of GCN is intended for homogenous networks. This paper proposes a novel combination of meta-graph and graph convolution, the meta-graph based graph convolutional networks (MGCN). To fully capture the complex long semantic information, MGCN utilizes different meta-graphs in HNs. As different meta-graphs express different semantic relationships, MGCN learns the weights of different meta-graphs to make up for the loss of semantics when applying GCN. In addition, we improve the current convolution design by adding node self-significance. To validate our model in learning feature representation, we present comprehensive experiments on four real-world datasets and two representation tasks: classification and link prediction. WMGCN’s representations can improve accuracy scores by up to around 10% in comparison to other popular representation learning models. What’s more, WMGCN’feature learning outperforms other popular baselines. The experimental results clearly show our model is superior over other state-of-the-art representation learning algorithms.
Recursive Shrinkage Covariance Learning in Adaptive Importance Sampling

The estimation of covariance matrices has been a central problem in a variety of disciplines, including quantitative finance, genomics, and signal processing. In Bayesian statistical inference, the efficiency of Monte Carlo methods, such as adaptive importance sampling (AIS), can be improved significantly if the distribution used to draw samples has a similar covariance structure to the posterior distribution of interest. Unfortunately, it is generally difficult to learn covariance matrices in high-dimensional settings due to the large number of samples needed for its appropriate estimation. This problem is intensified in the importance sampling context, where the usual weighted covariance estimators do not yield full rank estimates in most practical settings due to the weight degeneracy problem. In this work, we propose an AIS algorithm that robustly learns the covariance structure of the target distribution. The new method is based on applying shrinkage in a recursive manner, where the learned covariance matrix is constructed iteratively using a sequence of biased weighted covariance estimators. Simulation results indicate that the proposed method outperforms other state-of-the-art AIS methods, especially in the case where the number of samples drawn per iteration is relatively small.
Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique

Graphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component.