Miracle-dz/UniFAR-FTUs huggingface dataset:
High-degree noise addition method for the $k$-degree anonymization algorithm

Social network datasets are a valuable source of information for academic researches as well as business and marketing studies. Since social network datasets contain personal and sensitive information of their users, sharing the data with a third party gives rise to many privacy-preserving issues. The $k$-degree anonymization was developed to protect the users of social networks from the re-identification attack by modifying the network structure with a sequence of edge editing operations. In this paper, we introduce a novel approach for noise addition operation in the well-known $k$-degree anonymization algorithm $k$-DA. We propose the high-degree noise addition method that modifies the degree sequence anonymized by the degree anonymization procedure of $k$-DA before it is processed by the graph construction procedure of $k$-DA. Our proposed method significantly reduces the number of necessary repetitions of the graph constructing algorithm and positively affects the efficiency and runtime of the whole $k$-DA algorithm. Moreover, we show that the proposed high-degree noise addition algorithm improves $k$-DA in terms of data utility. We demonstrate its usability by running experiments on 13 different real-world social network datasets.
Impact of Dimensionality Reduction on The Accuracy of Data Classification

One of the most essential research aspects that can be used in network security and information for data and information protection from unauthorized access is the system of intrusion detection. It was necessary to build systems that have the ability to Intrusion detection and ensure that the service is provided with the appropriate speed and time. In this paper introduced enhancing the robustness of machine learning systems via dimensionality reduction system. Firstly, the proposed system is chosen for the NSL-KDD dataset analyses. The data has been manipulated to make its formatting more relevant to the classification algorithms used and the second step was applied principal component analysis algorithm to reduce the dimensions. The data were classified according to the Naïve Bayes algorithm and Decision Tree (J48) algorithms and have achieved an accuracy of 99.29% with the attributes reduced to five attributes in decision Tree (J48) algorithm and test time models on test split: 0.3 seconds and build the model: 9.03 seconds compared to the same shorthand in Naïve Bayes algorithm whereas classification accuracy was 87.89 % and time range test model on test split: 0.73 seconds and build the model: 0.19 seconds, also well it was obtained some results for the reduced data in the classification stage, which exceeded the accuracy of the results of the original data without dimension reducing.
Review of Deep Learning Application for Short-Term Household Load Forecasting

The load forecasting is important for the distribution system operation and expansion planning. The main methodologies for load forecasting using deep learning are Long Short-Term Memory (LSTM) and Convolution Neural Networks (CNN). LSTM is specialized in sequential data; on the other hand CNN is specialized in image data. The residential consumption can be treated as a time series (sequential data) and a two-dimensional (image data) dataset. Therefore, LSTM and CNN can be used to extract data characteristics from the residential consumption dataset. Thus, this paper reviews and compares the main methodologies for residential load forecasting such as CNN, LSTM, and CNN-LSTM. The mean square error (MSE) and root mean square error (RMSE) are used as metrics. The dataset is from real residential consumers in Ireland. The result shows a similar performance in training and testing. The best results are found when CNN and LSTM are used together.
Degradation Behavior and Defect Analysis for SiC Power MOSFETs Based on Low-Frequency Noise Under Repetitive Power-Cycling Stress

In this article, the degradation behavior of the commercial 1.2-kV SiC power MOSFETs was investigated under repetitive power-cycling stress, and defect analysis based on low-frequency noise (LFN) was carried out. The experimental results show that increasing power-cycling stress results in a forward shift of threshold voltage and increasing ON-resistance. Meanwhile, the drain-to-source current significantly decreases with the increase of the cycles. Furthermore, the gate–source leakage current of the device became larger and the blocking characteristics deteriorated after 10-k cycles. The negative shift of the gate-capacitance versus gate-voltage curve was analyzed. Trap characterization was performed by using the LFN method, and it was found that the trap density of the device increased 5.47 times after 10-k cycles. The physical mechanism could be attributed to electrically active trapped charges generated at the SiC/SiO2 interface during power-cycling stress. This article may be helpful for degradation detection and fault analysis of SiC power MOSFETs in the electronic system.
A Comparative Study: Blockchain Technology Utilization Benefits, Challenges and Functionalities

Blockchain technology enables users to verify, preserve, and synchronize the contents of a data sheet (a transaction ledger) replicated by multiple users. Blockchain technology has provided considerable advantages and incentives to industries in terms of enabling better services. This review aims to explore the benefits, challenges and functionalities that affect blockchain applications in different sectors. This article is constructed as a systematic literature review study. From 1976 articles, 168 final articles were selected and classified into three main dimensions, that is, benefits, challenges, and functionalities, in four different sectors: government, financial, manufacturing, and healthcare. The results were extracted and compared based on factors in three dimensions, which were categorized as benefits (informational, technological, economic, organizational, and strategic), challenges (technological, organizational, adoption, operational, and environmental and sustainability), and functionalities (point-to-point transmission, data ownership, data protection, and transaction processing). The results of this review study aim to support professionals, practitioners, and stakeholders who wish to implement and manage transformation projects related to blockchain in their sectors. Moreover, helping these possible blockchain users to understand the implied factors associated with blockchain would be beneficial for the decision-making processes of their organizations.
Analysis and Design Optimization of V-Shaped Permanent Magnet Vernier Motor for Torque Density Improvement

This paper presents an inner rotor vernier motor featuring the V-shaped permanent magnet (PM) rotor topology; the torque performance of the proposed motor is compared to that of a conventional V-Shaped interior PM motor. The volume of the proposed motor is kept 35% lower than that of the conventional motor. The equations of back electromotive force and reactance are presented, and the air gap flux densities and flux density distributions of both motors are analyzed and compared. A 2D finite element analysis is conducted at three different operating speeds in order to investigate the performance of both motors. The torque components indicate that the proposed motor achieves superior torque performance (i.e., higher average torque and lower torque ripple) at a reduced volume, for the same input current density. Moreover, the torque–current angle characteristic curves at different current densities are obtained and compared. The losses, efficiencies, and power factors of both motors are compared and discussed. Finally, multi-objective optimization of the proposed vernier motor is carried out to further enhance its performance. The results of the analysis prove that the proposed motor achieves better torque density owing to the vernier effect and superior torque performance due to the V-shaped rotor topology.
Effect of Humanoid Robot's Reaction on Human Touch to Robot

We have investigated the effect on the human feeling of a small humanoid robot's response to a human touch on the robot. Conventional studies revealed the importance of human social touch with the robot, however, few studies have examined the effect of how the robot responds to human touch on a robot. This paper focuses on the changes of human impressions to the robot when the robot responds to human touches, e.g., hitting, holding, and stroking the robot. To explore this, we conducted an experiment in which a small humanoid robot interacts with humans using a tactile sensor attached to its upper arm and responds to human touches, for example, hitting, holding, and stroking the upper arm, with body movements, voice, gaze movements, and LED lights of the eyes. We investigated whether the robot's reaction and the gaze movement contribute to the impression of the human subjects. We found that the robot's response changed the human impression by comparing the interactions without the robot's response, while the presence and absence of the eye contact by a gaze control were not effective in the human impression. Also, the result shows that people had a better impression on the condition with a robot's response than that without the robot's response.
Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

This paper proposes a new fiber Bragg grating central wavelength interrogation system by combining evolutionary algorithm and machine learning techniques integrated with an unsupervised autoencoder (AE) pre-training strategy. The proposed unsupervised AE pre-training convolution neural network (CNN) allows training of the convolutional layers independently from a regression task in order to learn a new data representation and give better generalization. It is also used to improve the system accuracy by four times without extra-labeled data. Moreover, AE is combined with a differential evolutionary (DE) algorithm to automate the human labeling task. The proposed autoencoder pre-training convolution neural network and differential evolutionary (AECNNDE) interrogation system achieve good accuracy and can speed-up the computational time by a maximum of 30 times than DE algorithm.
Neural-Based Hierarchical Approach for Detailed Dominant Forest Species Classification by Multispectral Satellite Imagery

Among different forest inventory problems, one of the most basic is defining dominant species. These data are crucial in forest management to determine forest category, and a cheaper remote sensing-based approach would be a useful supplement to field surveys. We used WorldView multispectral satellite imagery to address this problem as an image segmentation task dividing the image into regions with particular dominant species. Neural networks have recently become one of the most useful tools for this kind of problem, including incomplete or erroneous training labels. However, it is still challenging to distinguish between such similar patterns as different forest compositions. To handle this, we represented the multiclass forest classification problem as a hierarchical set of binary classification tasks, which allowed us to reach better results with both high- and medium-resolution satellite imagery. We also examined supplementary data, such as tree height, to improve the species classification results for wider tree age diversity. We conducted experiments considering six neural network architectures to find the best one for each task in the hierarchical decomposition. The proposed approach was tested on sample territories in Leningrad Oblast of Russia, for which the field-based observations were acquired and made publicly available as a single dataset. The proposed approach showed significantly better results (average F1-score 0.84) than multiclass classification (average F1-score 0.7).
The Mechanism of Activation of Ice Nuclei

Abstract Experimental evidence is presented to show that the process of activation in ice nucleation is due to a surface phase change which takes place within the layer of water adsorbed on the nucleating substrate. The outstanding activation of a wide variety of organic compounds suggests that organic residues in the atmosphere may be a source of activated ice nuclei.
An Interface-Induced Dielectric Properties Degradation in Heterogeneous Stacked Device With P(VDF-TrFE)-Based Ferroelectric Polymers

Heterogeneous stacked electronics are one of the major topics in flexible electronics applications, which require high performances. At the same time, P(VDF-TrFE)-based polymers with unique characteristics, e.g., ferroelectric and flexible properties, also bring attention to heterogeneous stacked device applications. Since the interfacial characteristic has strong effects with performance and reliability, therefore, it becomes one of the key issues in P(VDF-TrFE)-based heterogeneous stacked devices. In this article, a mechanism of an interface trap charge-induced low-k interfacial layer was proposed and examined by experimental methods. To analyze the low-k interfacial layer effects, both general-doping (few 10<sup>15</sup> cm<sup>−3</sup>) p-type and n-type silicon substrate were used. These experimental results showed that different types of silicon substrates have distinct interface properties. Then, different interface trap density (<inline-formula> <tex-math notation="LaTeX">$1.75\times 10^{12}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$4.1\times 10^{11}$ </tex-math></inline-formula> eV<sup>−1</sup> cm<sup>−2</sup>) silicon substrates were fabricated to further analyze the low-k interfacial layer. These showed the substrate with the lowest interface trap density has less low-k interfacial effect than other substrates do. Based on these experimental verifications, the mechanism of interface trap charge-induced low-k interface characteristic was proposed. As a consequence, this interface trap–charge-induced behavior is a necessary consideration for applications of heterogeneous stacked devices.
The Relation between Software Development Methodologies and Factors Affecting Software Reliability

Methodology of software development is a system of principles, as well as a set of ideas, concepts, approaches, methods and tools that determine the style of software development. The choice and use of a particular methodology of software development ultimately affects the reliability indices of the software product. This paper has analyzed the current approaches to the use of the most common methodologies of software development and identified 15 major factors that affect the quality and reliability of software. Structuring of certain factors in relation to the stages of development of the software lifecycle has been carried out and the factors of the initial stage of the life cycle have been highlighted that most influence the quality and reliability of the final product.
Development of Domain-Specific Language for Data Processing on the Internet

The active development of the world wide web leads to the need to create new effective methods of automatic processing the information presented on web resources. In order for the use of the semantic web to become widespread, the tools for operating data on the Internet should be simple and user friendly. That will allow to use them even to inexperienced users and web sites owners without engaging expensive specialists. As such a tool, the article presents a new web data operating domain-specific language (WDOL). The main functions of which are the extraction, storage, processing and display of web data. The WDOL programming code is much more expressive than the solutions implemented by general-purpose programming languages. Most of the code is information about the structure of the presentation on a web property that the user can easily obtain using the built-in features of modern browsers. Such code is easy to write, read, and modify by inexperienced users. It does not need to be modified when using different types of data stores, or using other algorithms to retrieve and parse HTML code. The task of debugging, and testing takes over the execution system.
Human Abnormality Classification Using Combined CNN-RNN Approach

With the advent of big data, Facial Expression Recognition (FER) has become a promising area in the Deep Learning domain. The facial expression reflects our mental activities and provides useful information on human behaviors. With the increasing improvement of the deep learning-based classification method, special demands for human stability measurement using facial expression have emerged. Recognizing human abnormalities such as drug addiction, autism, criminal mentality, etc., are quite challenging due to the limitation of existing FER systems. Besides, there are no existing datasets that consist of helpful images that describe the true expressions of the human face that can detect human abnormality. To achieve the best performance on human abnormality recognition we have created a Normal and Abnormal Humans Facial Expression (NAHFE) dataset. In this paper, we propose a new model by stacking the Convolutional Neural Network and Recurrent Neural Network (RNN) together. The proposed combined method consists of convolution layers followed by the recurrent network. The associated model extracts the features within facial portions of the images and the recurrent network considers the temporal dependencies which exist in the images. The proposed combined architecture has been evaluated based on the mentioned NAHFE dataset and it has achieved state-of-the-art performance to detect human abnormalities.
A Roadmap for Recognizing Engineering Vehicle from Aerial Images of UAV

Engineering vehicles on construction sites mainly include excavators, wheeled cranes and so on. If the engineering vehicle is working under or near the high-voltage power line, its bucket or boom probably enter the high-voltage breakdown range when they are lifted, which is very easy to result in accidents such as short circuit breakdown. So, it is necessary to find out the engineering vehicles working near high-voltage power line during inspection. Unmanned aerial vehicle (UAV) inspection is one of the main methods of electric power inspection at present. Lots of images are produced by UAV during the power line inspection. It will save a lot of inspection work if the engineering vehicles working near high-voltage power line can be recognized from these images. First, this paper analyzes the specific requirements of engineering vehicle recognition from aerial images of UAV power line inspection. Then, based on the research status of vehicle recognition in aerial images and other related fields at domestic and abroad, this paper comprehensively analyzes the research status of classical pattern recognition method and deep neural network method to recognize engineering vehicles in aerial images of UAV. Third, in view of the practical problems such as the low aerial image data of engineering vehicles, the roadmap of recognizing the engineering vehicles in the aerial image of UAV using the capsule network method is designed.
Determining Protein–Protein Interaction Using Support Vector Machine: A Review

Protein-Protein Interaction (PPI) is a network of protein interconnections which regulates most of the biological methods. A sound state of biota largely depends on synchronized interactions between protein molecules, and any aberrant interactions between protein molecules may lead to complications, including cervical leukemia, tuberculosis, and other neural disorders. In PPI investigation, a plethora of computational methods have been developed over the years to analyze and predict PPI conclusively; however, a majority of these techniques proved to be strenuous and expensive. Therefore, the need for faster, accurate, and critical analysis of PPI warrants the adoption of Machine Learning (ML) methods such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest Model (RFM). These classifiers are useful in PPI unfolding in terms of amino acid sequence data. The SVM classifier, in particular, is serviceable in solving a majority of complex classification problems producing robust results in a reasonable time frame. This publication summarizes some state-of-art SVM based PPI investigations and challenges incurred in the application of the SVM method.
Energy-Optimal Adaptive Cruise Control for Electric Vehicles Based on Linear and Nonlinear Model Predictive Control

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which plans the host car's speed trajectory in real time for higher energy efficiency by taking look-ahead traffic information and road conditions into consideration. After linear MPC (LMPC) is formulated in time domain, a nonlinear MPC (NMPC) formulation in space domain is proposed in this paper to overcome the drawbacks of LMPC in time domain. To cope with computational complexity of NMPC in space domain, the nonlinear equality constraints are relaxed to inequality constraints to yield a convex optimization problem. Moreover, it is proven that the relaxed optimization problem can be recast as a second-order cone programming (SOCP) problem, for which the efficient numerical optimizers exist. The performance of NMPC is evaluated in simulation, which is compared with the time domain LMPC and a theoretically optimal receding horizon dynamic programming (RH-DP) solution. Results indicate that the proposed space domain NMPC outperforms LMPC in time domain and the optimal solution of NMPC is very close to the result of RH-DP.
Fuzzy Logic-Based Controller for BESS and Load Management in a Microgrid Economic Operation

Battery energy storage systems (BESS) play an essential role in effectively managing the power of a microgrid, maintaining a balance between fluctuating power generated from the distributed energy resources and load demand. By using this system in the time-of-use rate context, it can achieve excellent savings. This paper proposes a fuzzy logic-based algorithm developed for the Battery and Load Management System to support the operation of a microgrid with photovoltaic (PV) farm generation. Also, a comparison of financial cost savings is discussed. The software MATLAB/Simulink® was used to develop and simulate the different scenarios. Results demonstrate that the use of fuzzy logic improved the system since it was able to manage the loads and allow a lower-cost operation properly.
Solving Least Squares for Linear Equations over Strongly Connected Directed Networks

In this paper, we introduce a distributed continuous-time flow on directed networks for the computation of least-squares solution to a linear algebraic equation of the form $Ax=b$ where $A$ has full column rank. It is assumed that each one of the $n$ autonomous agents only knows a subset of the partitioned matrix $[A \ \ b]$. Each agent in the network is able to send information to certain other agents called its “out-neighbors”. Neighbor relations are characterized by a directed graph $\mathbb{G}$ whose vertices correspond to the labels of agents and whose edges depict the neighbor relations. It is shown that for any such matrix $A$ and any strongly connected neighbor graph, the estimates of all agents in the proposed algorithm converge exponentially to the desired solution $({A}^{\prime}A)^{-1}A^{{\prime}}b$, even if only one of the agents across the network uses a nonzero and sufficiently small constant step-size.