Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Various Feature Sets for Malaria-Infected Erythrocyte Detection

Devi, Salam Shuleenda Herojit Singh, Ngangbam Hussain Laskar, RabulMalaria being prevalent disease in urban areas, demands its accurate and fast diagnosis. Due to malaria infection in human being, the erythrocyte features got distorted. To diagnose these, various techniques have been developed, i.e., machine learning-based system, rapid diagnostic test, quantitative buffy coat, etc. In machine learning, the system performance depends on the feature set and classifier model. In this paper, the analysis of the importance of the feature set on malaria-infected erythrocyte classification has been performed. Further, a classifier model based on ANN-GA has been developed to classify the erythrocyte. The process consists of illumination correction, erythrocyte segmentation, feature extraction with or without feature selection techniques, and classification. Erythrocytes segmentation is done using image binarization with marker-controlled watershed segmentation. The six feature sets (morphological feature, texture and intensity feature) have been evaluated using various classifiers such as support vector machine (SVM), k-nearest neighbor (k-NN), and Naive Bayes to choose the better feature set. From the experimental results, it has been observed that the feature set $$\textit{f}_6$$ (combination of morphological, texture and intensity feature ranked with ANOVA) outperforms other feature sets. Further, erythrocyte classification has been performed using ANN-GA with $$\textit{f}_6$$ feature set. It may also conclude that the various features such as morphological feature, texture and intensity feature are equally important to detect the malaria-infected erythrocyte.
Studying the patterns and long‐run dynamics in cryptocurrency prices

Abstract This study analyses the price movements of a select sample of cryptocurrencies and examines whether they are cointegrated and predictable using machine learning algorithm and Johansen Test. The study used daily historical trading data of 76 cryptocurrencies sourced from different cryptocurrency exchanges. A sub‐sample of six cryptocurrencies were chosen for the cointegration and machine learning analysis based on their market share, attractiveness to the investors and availability of data for the full sample period. The data records starting from April 29, 2013 to February 7, 2019 were considered for the study. An error correction model was estimated to investigate both the long‐run and short‐run dynamics between the cryptocurrency prices. The evidence from the error correction model estimates shows that there is a long‐run association between the prices of crypto currencies. The machine learning algorithm involving neural networks (multilayer perception) was used to comprehend the data patterns in the cryptocurrency price series, and the results show that the model fits well in identifying and predicting the data patterns. The study also examines the possible value drivers of cryptocurrencies by estimating a linear regression with a set of covariates, which include the cryptocurrency demand and supply interaction variables and financial variables such as the NZX/S&amp;P 50 index and exchange rates. The linear model estimates confirm that cryptocurrency market fundamentals have an important impact on cryptocurrency prices; however, they do not support the prediction that financial fundamentals are the major value drivers of cryptocurrencies.
Student Data Management Information System Using the Zachman Framework

Abstract The purpose of this study is to find out student data management quickly and accurately which can produce information systems for use by schools, students, and stakeholders related to student data management. In the case of improper student data management, it can cause errors in decision making such as payments, student attendance and progress assessment to students. Method used in this study was qualitative technique and literature studies. Interview was conducted to support data analysis. The result showed that Zachman framework able to overcome the existing information system problems. With many perspectives on the Zachman architecture method in this study is expected to provide a significant solution for managing student data management in schools by preparing an integrated system in managing student data and developing data in the future
Long-term Measurement and Analysis of the Free Proxy Ecosystem

Free web proxies promise anonymity and censorship circumvention at no cost. Several websites publish lists of free proxies organized by country, anonymity level, and performance. These lists index hundreds of thousands of hosts discovered via automated tools and crowd-sourcing. A complex free proxy ecosystem has been forming over the years, of which very little is known. In this article, we shed light on this ecosystem via a distributed measurement platform that leverages both active and passive measurements. Active measurements are carried out by an infrastructure we name ProxyTorrent, which discovers free proxies, assesses their performance, and detects potential malicious activities. Passive measurements focus on proxy performance and usage in the wild, and are accomplished by means of a Chrome extension named Ciao. ProxyTorrent has been running since January 2017, monitoring up to 230K free proxies. Ciao was launched in March 2017 and has thus far served roughly 9.7K users and generated 14TB of traffic. Our analysis shows that less than 2% of the proxies announced on the Web indeed proxy traffic on behalf of users; further, only half of these proxies have decent performance and can be used reliably. Every day, around 5%--10% of the active proxies exhibit malicious behaviors, e.g., advertisement injection, TLS interception, and cryptojacking, and these proxies are also the ones providing the best performance. Through the analysis of more than 14TB of proxied traffic, we show that web browsing is the primary user activity. Geo-blocking avoidance—allegedly a popular use case for free web proxies—accounts for 30% or less of the traffic, and it mostly involves countries hosting popular geo-blocked content.
Integrating State-of-the-Art CNNs for Multi-Sensor 3D Vehicle Detection in Real Autonomous Driving Environments

This paper presents two new approaches to detect surrounding vehicles in 3D urban driving scenes and their corresponding Bird's Eye View (BEV). The proposals integrate two state-of-the-art Convolutional Neural Networks (CNNs), such as YOLOv3 and Mask-RCNN, in a framework presented by the authors in [1] for 3D vehicles detection fusing semantic image segmentation and LIDAR point cloud. Our proposals take advantage of multimodal fusion, geometrical constrains, and pre-trained modules inside our framework. The methods have been tested using the KITTI object detection benchmark and comparison is presented. Experiments show new approaches improve results with respect to the baseline and are on par with other competitive state-of-the-art proposals, being the only ones that do not apply an end-to-end learning process. In this way, they remove the need to train on a specific dataset and show a good capability of generalization to any domain, a key point for self-driving systems. Finally, we have tested our best proposal in KITTI in our driving environment, without any adaptation, obtaining results suitable for our autonomous driving application.
Texture Underfitting for Domain Adaptation

Comprehensive semantic segmentation is one of the key components for robust scene understanding and a requirement to enable autonomous driving. Driven by large scale datasets, convolutional neural networks show impressive results on this task. However, a segmentation algorithm generalizing to various scenes and conditions would require an enormously diverse dataset, making the labour intensive data acquisition and labeling process prohibitively expensive. Under the assumption of structural similarities between segmentation maps, domain adaptation promises to resolve this challenge by transferring knowledge from existing, potentially simulated datasets to new environments where no supervision exists. While the performance of this approach is contingent on the concept that neural networks learn a high level understanding of scene structure, recent work suggests that neural networks are biased towards overfitting to texture instead of learning structural and shape information. Considering the ideas underlying semantic segmentation, we employ random image stylization to augment the training dataset and propose a training procedure that facilitates texture underfitting to improve the performance of domain adaptation. In experiments with supervised as well as unsupervised methods for the task of synthetic-to-real domain adaptation, we show that our approach outperforms conventional training methods.
A Secure Network Architecture for Heterogeneous IoT Devices using Role-based Access Control

In the last few years, markets of Internet of Things (IoT) technology is emerging rapidly as the technology will become an important part of our everyday life in the next few decades. Since accessibility of IoT devices requires controlling, monitoring, and information exchange, the number of IoT devices that connect to the Internet is increasing daily. As devices could be the targets of adversaries, IoT security should be critical to protect sensitive data. Many techniques are being implemented to protect IoT devices from adversaries; nevertheless, consumers of IoT devices are still facing many security and privacy issues. Traditional security methods are unable to protect the devices owing to the limitations and constraints of the devices. This paper proposed a novel IoT security framework using role-based strategy to limit user's device manageability and to protect the IoT network. The proposed architecture will mitigate adversarial effects by improving monitoring capabilities to take corrective action and preventive action faster in the heterogeneous IoT devices network.
Multi-Vehicle Cooperative Positioning Correction Framework Based on Vehicular Blockchain

Although existing cooperative positioning methods have improved vehicular Global Positioning System (GPS) positioning accuracy to varying degrees, they still have challenges in further improving system positioning accuracy, robustness and security. In this paper, we propose a multi-vehicle (sensor-rich vehicle) cooperative positioning correction framework for improving GPS positioning accuracy of common vehicles. Furthmore, to improve system security and robustness, the connections among sensor-rich vehicles, common vehicles and roadside units are built by a blockchain architecture. And the corresponding smart contract is designed. Extensive simulation results show the accuracy, robustness and security of our proposal in terms of vehicular positioning, information transferring and sharing.
Dimensions of ‘Socio’ Vulnerabilities of Advanced Persistent Threats

Advanced Persistent Threats (APT) are highly targeted and sophisticated multi-stage attacks, utilizing zero day or near zero-day malware. Directed at internetworked computer users in the workplace, their growth and prevalence can be attributed to both socio (human) and technical (system weaknesses and inadequate cyber defenses) vulnerabilities. While many APT attacks incorporate a blend of socio-technical vulnerabilities, academic research and reported incidents largely depict the user as the prominent contributing factor that can weaken the layers of technical security in an organization. In this paper, our objective is to explore multiple dimensions of socio factors (non-technical vulnerabilities) that contribute to the success of APT attacks in organizations. Expert interviews were conducted with senior managers, working in government and private organizations in the United Arab Emirates (UAE) over a period of four years (2014 to 2017). Contrary to common belief that socio factors derive predominately from user behavior, our study revealed two new dimensions of socio vulnerabilities, namely the role of organizational management, and environmental factors which also contribute to the success of APT attacks. We show that the three dimensions postulated in this study can assist Managers and IT personnel in organizations to implement an appropriate mix of socio-technical countermeasures for APT threats.
Blockchain Technologies and Their Impact on Game-Based Education and Learning Assessment

New problems emerge as digital space becomes not only a space for teaching and learning, but also for evaluating learning success and for storing and exchanging learning credentials. The paper discusses two strains of innovative education: (1) the merging of game-based learning with the game-based assessment of learning success and (2) the use of blockchain-based technologies to enable the secure storage and exchange of educational credits and learning credentials. First, different strategies of combining learning and assessment are distinguished, and the concept of “Integrated Game-based Learning/Assessment (GBL/A)” is introduced as the most promising, but also the most challenging form of game-based assessment. As moving already sensitive data like educational credits and learning credentials to digital environments heightens the need for security and trust even further, the paper takes a closer look at blockchain-based technologies and their promise to deliver secure and transparent storage of data, while at the same time giving users control over how this data is used in a specific context. The potential of blockchain-based technologies in the educational sector is discussed and put in relation to GBL/A. And finally, this potential (and probable success) of blockchain-based technologies is pointed out as a strong argument to strive, in good time, for an “Ethics of permanent storage.”
Accelerating Motion Planning for Learned Mobile Manipulation Tasks Using Task-Guided Gibbs Sampling

We present Task-Guided Gibbs Sampling (TGGS), an approach to accelerating motion planning for mobile manipulation tasks learned from demonstrations. This method guides sampling toward configurations most likely to be useful for successful task execution while avoiding manual heuristics and preserving asymptotic optimality of the motion planner. We leverage a learned task model, which is used by the motion planner to evaluate plan cost, to also guide sampling, yielding plans with high rates of success faster than unbiased or goal-biased sampling. This is accomplished by tightly integrating sampling with a hybrid motion planner that builds separate base and arm roadmaps using Gibbs sampling. Such an approach allows the sampled arm configurations to depend on the reachable base configurations and vice-versa. We evaluate our method on two household tasks using the Fetch robot and greatly improve upon motion planners that rely on unbiased sampling or either of two goal-biased planners when using the same cost metric.
Gender Prediction by Speech Analysis

Abstract Speech is one of the important methods of communication for humans. The speech signal itself contain linguistic information that can be used to identify the speaker information such as gender, emotions and many more. There are some problems that involve in detection gender of the speaker. In forensic analysis, the police need to detect criminal profile from any evidence such as voice from any calls and while in healthcare aspect, some vocal fold pathologies can be bias to a particular gender such as vocal fold cyst can be seen particularly in female patients and the patient will have problem with their voices. Three features are extract from the speech signal which are Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding (LPC) and Linear Prediction Coding Coefficient (LPCC). While for the classification, two classifier are used which are Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The recognition rate is higher for the combination of MFCC and LPCC compared to other features. SVM classifier had outperformed KNN classifier and obtained highest recognition rate of 97.45%. Lastly a graphical user interface system is develop that will record the voice of the speakers, pre-process the signal, extract MFCC and LPCC and classify it using SVM.
C to D-Wave: A High-level C Compilation Framework for Quantum Annealers

A quantum annealer solves optimization problems by exploiting quantum effects. Problems are represented as Hamiltonian functions that define an energy landscape. The quantum-annealing hardware relaxes to a solution corresponding to the ground state of the energy landscape. Expressing arbitrary programming problems in terms of real-valued Hamiltonian-function coefficients is unintuitive and challenging. This paper addresses the difficulty of programming quantum annealers by presenting a compilation framework that compiles a subset of C code to a quantum machine instruction (QMI) to be executed on a quantum annealer. Our work is based on a modular software stack that facilitates programming D-Wave quantum annealers by successively lowering code from C to Verilog to a symbolic “quantum macro assembly language” and finally to a device-specific Hamiltonian function. We demonstrate the capabilities of our software stack on a set of problems written in C and executed on a D-Wave 2000Q quantum annealer.
Satellite-based Free-Space Quantum Key Distribution Systems using QPSK Modulation and Heterodyne Detection Receiver

This paper proposes to design a satellite-based free-space quantum key distribution (QKD) systems using quadrature phase-shift-keying (QPSK) signaling. Our proposed QKD system based on continuous variable encoding to provide the secret-key transmission, where the key information is encoded on four coherent states corresponding to QPSK modulation scheme. In addition, we use heterodyne detection receiver to improve the receiver sensitivity thus decode the signal with high fidelity. The performance of the proposed free-space QKD system is analytically investigated. More especially, the mathematical expression for quantum bit error rate (QBER) is derived taking into account the effect of physical layer impairments and parameters. The numerical results help to determine the appropriate values of system's parameters such as the threshold coefficients and the required transmitted power.
Multi-Kernel Broad Learning systems Based on Random Features:A Novel Expansion for Nonlinear Feature Nodes

The Broad Learning System has been proved to be effective and efficient. However, the associated feature nodes in the system are mainly based on linear mappings. Although such kind of features has been successful in various datasets and applications, more general features (especially for the nonlinear features) are necessary for specific applications. Motivated by the powerful capability of the kernel methods, a novel expansion of broad learning system based on multiple kernels is proposed in this paper. Firstly, the nonlinear feature mappings in the form of multiple kernels are merged into the feature nodes of broad learning system. After that, the resulted features are further enhanced through nonlinear activation functions. The experimental results on UCI datasets indicate that the proposed method outperforms the other methods.
A Novel Two-Dimensional Unwinding Decomposition for Image Signals

This paper presents a new two-dimensional (2D) signal analysis method, namely partial unwinding adaptive Fourier decomposition. Because of its efficiency and accuracy it has great ability in practice. Besides theory and algorithm we provide the application in image reconstruction. One-dimensional (1D) unwinding adaptive Fourier decomposition has been studied. It depends on Nevanlinna factorization and a maximal selection. The 1D method leads to a fast convergence nonlinear phase decomposition. However, for a multi-dimensional ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m\text{D}$ </tex-math></inline-formula> ) case, the usual factorization fails. Consequently, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m\text{D}$ </tex-math></inline-formula> signals have no genuine unwinding decomposition. This work aims to propose a 2D partial unwinding adaptive Fourier decomposition that depends on the basic mathematical operations reducing the 2D case to the 1D case. Its decomposing terms are orthogonal to each other and result in 2D-nonlinear phase decomposition. Its fast convergence gives rise to efficient image reconstruction. A number of experiments illustrate that our proposed 2D-PUAFD exhibits the best reconstruction performance among all the tested methods.
A Game Theoretic Solver for the Minimum Weighted Vertex Cover

Toward the global optimality and computation time reduction, we address the minimum weighted vertex cover (MWVC) problem by proposing a population based game theoretic optimizer (PGTO) that combines learning in games with population based optimization. A population of candidate solutions are iterated through the procedures of swarm evolution (SE), learning in games (LIG), and local search (LS). Via strict theoretic analysis, we prove that LIG converges with probability one to Nash equilibria which could be further refined by LS. Numerical simulations show that a larger population size and a proper mutation probability are more likely to provide the best performance. Comparison experiments with typical algorithms demonstrate the superiority of the presented methodology to the state of the art, both in terms of solution efficiency and computation time.
Image Spatial Domain Steganography: A study of Performance Evaluation Parameters

In recent years, Steganography is deemed as a distinctive field of study utilized for the safeguarding of data from unallowed access. Steganography is signified as the skill of concealing secret messages in obvious view within numerous media sources like images, audio, texts, video, network channel and others, for the purpose of not arousing any concern. Meanwhile steganalysis is the art of using certain attack strategies onto the the steganographic structure to disclose the discrete data. This study explains clearly the numerous evaluation factors according to image steganographic algorithms. The efficacy of a steganographic method is evaluated through three principle variables, which are; embedding payload capacity, image quality measure and security measure. The emphasis of this research on image steganography that is the most famous method in the steganographic field. Typically, the Least Significant Bit is the main technique used, that is efficient, for the embedment of the secret data. Additionally, the current research presents an extensive knowledge-based Least Significant Bit (LSB) information in numerous varying images formats. The entire metrics that are displayed in this research are explained with arithmetical equations, in addition to the discussions on several significant trends carried out at the conclusion of this study.