Reviewerly/exHarmony huggingface dataset:
Design of Distance Education System for Ideological and Political Courses of Electronic Engineering Specialty Based on Big Data

According to the existing literature, the existing engineering ideological and political curriculum distance education system has the problems of long response time and few concurrent users, which cannot meet the development needs of the professional talents of electronic engineering. Therefore, the design and research of the long-distance education system of the ideological and political course of electronic engineering specialty dependent on big data is proposed. Based on the analysis of the demand of distance education system, the overall structure of the distance education system is designed. The design of the system business functions mainly includes login verification module design, instant communication module design, online classroom module design, video answer module design and video back-view module design, and design the server cluster load balancing strategy according to big data technology, so as to realize the operation of the remote education system of the ideological and political courses of electronic engineering. The design system is shorter than response time while compared with existing system. and the number of users is more. It is fully proved that the design system has better application performance.
Lung Cancer Diagnosis Based on Chan-Vese Active Contour and Polynomial Neural Network

Lung cancer is of the most serious and common type of cancers. It is usually diagnosed in the final stages which make it hard to treat. Now days, many techniques are to help in the detection of lung cancer, but still there is further need to develop more systems with more improvement and higher accuracy for better detection. In this research work, we introduce a computer-aided detection (CAD) system using computed tomography (CT) scans for nodule classification. The proposed system is divided into four stages that involve image pre-processing by using Gabor filter and Kuwahara filter, image segmentation by applying Chan- Vese active contouring. Feature extraction where features are computed using Discrete Wavelet Transform (DWT) at one, two and three levels of decomposition. After that, 13 features are computed from each wavelet sub-band. As a result the output features are compared and the best output is used to train Polynomial Neural Network (PNN) classification method to classify benign and malignant nodules. The result of the proposed system shows high performance in both the segmentation and the classification with accuracy of 96.66% for the classifying method.
Impact-Driven Discretization of Numerical Factors: Case of Two- and Three-Partitioning

Many real-world data sets contain a mix of various types of data, i.e., binary, numerical, and categorical; however, many data mining and machine learning (ML) algorithms work merely with discrete values, e.g., association rule mining. Therefore, the discretization process plays an essential role in data mining and ML. In state-of-the-art data mining and ML, different discretization techniques are used to convert numerical attributes into discrete attributes. However, existing discretization techniques do not reflect best the impact of the independent numerical factor onto the dependent numerical target factor. This paper proposes and compares two novel measures for order-preserving partitioning of numerical factors that we call Least Squared Ordinate-Directed Impact Measure and Least Absolute-Difference Ordinate-Directed Impact Measure. The main aim of these measures is to optimally reflect the impact of a numerical factor onto another numerical target factor. We implement the proposed measures for two-partitions and three-partitions. We evaluate the performance of the proposed measures by comparison with human-perceived cut-points. We use twelve synthetic data sets and one real-world data set for the evaluation, i.e., school teacher salaries from New Jersey (NJ). As a result, we find that the proposed measures are useful in finding the best cut-points perceived by humans.
User-Oriented Selections of Validators for Trust of Internet-of-Thing Services

Due to energy efficiency and near real-time finality settlement, permissioned blockchain is mainly used in blockchain-based Internet of Things (BIoT) services. This blockchain requires validators to reach the consensuses. Currently, the validators are chosen based on generic attributes of trusted nodes capriciously. However, service users have no mechanism to provide their requirements in assessing validators. Existing works are biased from users’ perspectives, which risks collusion attacks due to improperly selected validators. This article aims to solve this by considering users’ expectations on services. A user-oriented framework is proposed to evaluate the trust of BIoT services with the consideration of validator attributes. It provides an effective mechanism to express requirements. Specification language is adopted in our algorithm to perform compliance checking. The feasibility has been verified in a simulation. We found that the permissioned blockchain has also benefited from the proposed user-oriented selection of validators in terms of computing efficiency due to additional constraints.
HGATE: Heterogeneous Graph Attention Auto-Encoders

Graph auto-encoder is considered a framework for unsupervised learning on graph-structured data by representing graphs in a low dimensional space. It has been proved very powerful for graph analytics. In the real world, complex relationships in various entities can be represented by heterogeneous graphs that contain more abundant semantic information than homogeneous graphs. In general, graph auto-encoders based on homogeneous graphs are not applicable to heterogeneous graphs. In addition, little work has been done to evaluate the effect of different semantics on node embedding in heterogeneous graphs for unsupervised graph representation learning. In this work, we propose a novel Heterogeneous Graph Attention Auto-Encoders (HGATE) for unsupervised representation learning on heterogeneous graph-structured data. Based on the consideration of semantic information, our architecture of HGATE reconstructs not only the edges of the heterogeneous graph but also node attributes, through stacked encoder/decoder layers. Hierarchical attention is used to learn the relevance between a node and its meta-path based neighbors, and the relevance among different meta-paths. HGATE is applicable to transductive learning as well as inductive learning. Node classification and link prediction experiments on real-world heterogeneous graph datasets demonstrate the effectiveness of HGATE for both transductive and inductive tasks.
A Blockchain-Based Human-to-Infrastructure Contact Tracing Approach for COVID-19

In a post-pandemic era with personal precautions and vaccination, the emergence of COVID-19 variants with higher transmissibility and the socio-economic reopening have raised new challenges to existing human-to-human digital contact tracing systems, where privacy, efficiency, and energy-consumption issues are major concerns. In this article, we propose a novel blockchain-based human-to-infrastructure contact tracing framework for the post-pandemic era. Specifically, our approach collects and records the interaction information between persons and predeployed anchor nodes to trace the possible contacts with confirmed patients, so as to capture the indirect contacts and reduces the energy consumption of users. To address the privacy leakage and reliability issues in contact tracing, we introduce a self-sovereign identity (SSI) model-based blockchain which enables users to gain full control of their own identities and eliminate the linkage between the identity and location information in interaction records. To further preserve the privacy of confirmed patients, we introduce the private set intersection cardinality (PSI-CA) protocol to estimate the risk of infection by only counting the number of encounters between users and confirmed patients. Two self-executed smart contracts are deployed on the SSI blockchain to perform contact tracing, which guarantees the robustness of the system. The performance analysis validates the effectiveness of our approach.
Security Breaches in the Healthcare Domain: A Spatiotemporal Analysis

Over the past several years, data breaches have grown and become more expensive in the healthcare sector. Healthcare organizations are the main target of cybercriminals due to the sensitive and valuable data, such as patient demographics, SSNs, and personal treatment records. Data breaches are costly to breached organizations and affected individuals; hospitals can suffer substantial damage after the breach, while losing customer trust. Attackers often use breached data maliciously, e.g., demanding ransom or selling patient’s information on the dark web. To this end, this paper investigates data breaches incidents in the healthcare sector, including community, federal, and non-federal hospitals. Our analysis focuses on the reasoning and vulnerabilities that lead to data breaches, including the compromised information assets, geographical distribution of incidents, size of healthcare providers, the timeline discovery of incidents, and the discovery tools for external and internal incidents. We use correlation to examine the impact of several dimensions on data breaches. Among other interesting findings, our in-depth analysis and measurements revealed that the average number of data breaches in the United States is significantly higher than in the rest of the world, and the size of the health provider, accounting for factors such as the population and number of adults in a region, highly influences the level of exposure to data breaches in each state.
Neural Style Transfer Using VGG19 and Alexnet

Art is the perfect way for people to express their emotions in a way that words are unable to do. By simply looking at art, we can understand a person’s creativity and thoughts. In former times, artists spent a great deal of time creating an image of varied styles. In the current deep learning era, we are able to create images of different styles as we prefer within a short period of time. Neural style transfer is the most popular and widely used deep learning application that applies the desired style to the content image, which in turn generates an output image that is a combination of both style and the content of the original image. In this paper we have implemented the neural style transfer model with two architectures namely Vgg19 and Alexnet. This paper compares the output-styled image and the total loss obtained through VGG19 and Alexnet architectures. In addition, three different activation functions are used to compare quality and total loss of output styled images within Alexnet architectures.
UGCLink: User Identity Linkage by Modeling User Generated Contents with Knowledge Distillation

User identity linkage aims to link users with the same identities across different social networks. Recently, re- searchers model the similarities of users’ behaviors such as Point of Interests(PoIs) or User Generated Contents(UGCs) to predict the identities of users. However, it is non-trivial to solve the problem due to the following challenges: 1) PoIs are always sparse in the non-location-based social platforms, and it is impractical to measure the similarities of users solely with PoIs; 2) The similarities of hierarchical are hierarchical from the view of word, phrase, and sentence. How to model the hierarchical structure remains a key challenge; 3) The unreliable semantics of words. Two different words may refer to the same physical appearance of users, indicating that users are with the same identities.To tackle the above problems, we propose UGCLink, a knowledge distillation framework that models UGCs to predict user identities. Two main components are included in the framework, where the student network aims to model the similarities of UGCs and the teacher network guides the student network to learn better word embeddings that reveal the physical appearance of users. Besides, the teacher network, a document classification model that classifies UGCs into the categories of PoIs, is trained to guide the word embedding learning process in the student network to circumvent the unreliable semantic problem. We demonstrate that our proposed method outperforms the state- of-the-art methods by more than 11% in terms of AUC score.
A Scalable Framework for High-Performance Computing with Cloud

In cloud environment, virtual machine (VM) is provisioned depending on the configuration provided for the hardware computing resources based on user requirement. In order to provision these VMs, cloud environment has its own distributed infrastructure of several servers. On major and minor scale, infrastructure as a service is getting used as a feasible platform for the execution of high-performance computing (HPC) applications. However, few lags still exist within the futuristic traits of HPC application and current cloud scheduling-based algorithms. This paper presents a scalable framework that uses an algorithm-based approach to predict the matching underlying computing resource suitable to run high-performance computing (HPC) applications. It will behave like a decision-maker to predict the placement of containers. It is developed to facilitate a cloud computing environment to provide solution for availing the appropriate computing resources from dedicated allocated containers for HPC and monitoring of the same using Kubernetes. The framework along with proposed solution over containers is discussed and illustrated by computational algorithm with results. It will scale up or down the resources as required and will spawn or decommission the HPC clusters within minutes. We use the Kubernetes to monitor the deployed containers, and our results indicate that shared resources are having no impact on performance.
Attention mechanism enhancement algorithm based on cycle consistent generative adversarial networks for single image dehazing

This paper proposes AMEA-GAN, an attention mechanism enhancement algorithm. It is cycle consistency-based generative adversarial networks for single image dehazing, which follows the mechanism of the human retina and to a great extent guarantees the color authenticity of enhanced images. To address the color distortion and fog artifacts in real-world images caused by most image dehazing methods, we refer to the human visual neurons and use the attention mechanism of similar Horizontal cell and Amazon cell in the retina to improve the structure of the generator adversarial networks. By introducing our proposed attention mechanism, the effect of haze removal becomes more natural without leaving any artifacts, especially in the dense fog area. We also use an improved symmetrical structure of FUNIE-GAN to improve the visual color perception or the color authenticity of the enhanced image and to produce a better visual effect. Experimental results show that our proposed model generates satisfactory results, that is, the output image of AMEA-GAN bears a strong sense of reality. Compared with state-of-the-art methods, AMEA-GAN not only dehazes images taken in daytime scenes but also can enhance images taken in nighttime scenes and even optical remote sensing imagery.
Detecting spam tweets using machine learning and effective preprocessing

Nowadays, with the rapid increase in popularity of online social networks (OSNs), these platforms are realized as ideal places for spammers. Unfortunately, these spammers can easily publish malicious content, advertise phishing scams by taking advantage of OSNs. Therefore, effective identification and filtering of spam tweets will be beneficial to both OSNs and users. However, it is becoming increasingly difficult to check and eliminate spam tweets due to this great flow of posts. Motivated by these observations, in this paper we propose an approach for the detection of spam tweets using machine learning and effective preprocessing techniques. The approach proposes the advantages of the preprocessing and which of these preprocessing techniques are the most effective. To compare these techniques UtkML Twitter spam dataset is used in testing. After the most effective methods determined, the detection accuracy of the spam tweets will be better optimized by combining them. We have evaluated our solution with four different machine learning algorithms namely - Naïve Bayes Classifier, Neural Network, Logistic Regression and Support Vector Machine. With SVM Classifier, we are able to achieve an accuracy of 93.02%. Experimental results show that our approach can improve the performance of spam tweet classification effectively.
Comparative Analysis of Single-Stage YOLO Algorithms for Vehicle Detection Under Extreme Weather Conditions

Boppana, Udaya MouniEfficiencyMustapha,Aida of Jacob,KavikumarvehicleDeivanayagampillai, Nagarajan detection algorithms in Computer Vision is predominantly calculated under clear weather condition; good weather with favorable lighting. Benchmarking of top-performing algorithms still reports a relatively low performance for vehicle detection under extreme circumstances. This is because vision-based detection algorithms face challenges in dealing with low quality images with background noise, bad lighting, and weather-caused distortions. The literature has reported substantial work on restoration of images prior to object detection. Nonetheless, the measures affecting the vision based algorithms and their effectiveness as well as how much degradation of the quality of the input image reduces the detectors output are less investigated. This paper focuses primarily on single-shot detector, which is You Look Once (YOLO) algorithm along its variations to detect vehicles real-time. The comparative experiments are set to evaluate their efficiencies based on precision, mean average precision (mAP), and recall rate using distorted vehicle images from the AAU Rain Snow Dataset. Based on our experimental analysis, YOLOv3 performed better as compared to other variants of YOLO. These findings will be used as the benchmarking results for improvement of vehicle detection algorithms under extreme weather conditions.
A Tool for Organizing Key Characteristics of Virtual, Augmented, and Mixed Reality for Human–Robot Interaction Systems: Synthesizing VAM-HRI Trends and Takeaways

Frameworks have begun to emerge to categorize Virtual, Augmented, and Mixed Reality (VAM) technologies that provide immersive, intuitive interfaces to facilitate Human-Robot Interaction. These frameworks, however, fail to capture key characteristics of the growing subfield of VAM-HRI and can be difficult to consistently apply due to continuous scales. This work builds upon these prior frameworks through the creation of a Tool for Organizing Key Characteristics of VAM-HRI Systems (TOKCS). TOKCS discretizes the continuous scales used within prior works for more consistent classification and adds additional characteristics related to a robot's internal model, anchor locations, manipulability, and the system's software and hardware. To showcase the tool's capability, TOKCS is applied to the ten papers from the fourth VAM-HRI workshop and examined for key trends and takeaways. These trends highlight the expressive capability of TOKCS while also helping frame newer trends and future work recommendations for VAM-HRI research.
Static Output Feedback Fault Tolerant Control and Relaxed Stability Analysis of Positive Polynomial Fuzzy Systems

The increased demand for safety in control systems has motivated the research for fault-tolerant controllers (FTCs). This article presents the static output feedback FTC design and relaxed stability analysis for positive nonlinear systems on account of polynomial fuzzy models. A polynomial copositive Lyapunov function candidate is proposed for the stability analysis of positive polynomial fuzzy-model-based (PPFMB) control systems for the first time. This method develops the decision variable on the polynomial copositive Lyapunov function candidate to be a polynomial vector and also makes it independent of the form of the positive polynomial fuzzy model. Thus, it can be applied to a more extensive range of PPFMB control systems and potentially generates more relaxed stability analysis results. To achieve the stability analysis and control synthesis, the following challenges are overcome: 1) approximate the nonconvex terms in stability and positivity conditions into convex ones through the idea of matrix decomposition; 2) eliminate the nonconvex conditions resulting from the derivative of the polynomial Lyapunov function by employing an sum-of-squares (SOS) optimization method; 3) relax the results of stability analysis and control design by introducing the advanced membership-function-dependent technique to develop both the stability conditions and the positivity conditions. The relaxed conditions are obtained on the basis of SOS form so that the MATLAB third-party toolbox, SOSTOOLS, can be used to find feasible solutions. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed method.
A Comparative Analysis Between Hyperledger Fabric and Ethereum in Medical Sector: A Systematic Review

In a medical record management system, a complete database is used to store and obtain patient medical information. The electronic medical record has supplanted the conventional paper medical record as the primary source of healthcare information for clinical, legal, and administrative purposes. The risks of data being misused are high because the patient has no control over the data. As a consequence, we need a patient-centred approach that is fully decentralized, capable of detecting data theft, preventing data abuse, and enabling patients to exercise control over their access. The perfect approach to all issues and needs is Blockchain technology. Blockchain has the potential to have an effect on billing, record sharing, medical testing, identity theft, and financial data theft in the future. In addition, the implementation of smart contracts in healthcare could make things much simpler. When this command is used, the Blockchain can be used to create and validate records. This paper compares and contrasts the most widely used Blockchain systems. The Hyperledger framework and the Ethereum platform are the two major general-purpose blockchain frameworks in use. The primary difference between Hyperledger and Ethereum is the purpose for which they were developed. The primary goal of this paper is to find the contrast between these two blockchain platforms for medical record management systems in order to determine which blockchain platform is best for developing healthcare applications.