Reviewerly/exHarmony huggingface dataset:
Data-centric Cyber-attack Detection in Community Microgrids Using ML Techniques

This article proposes a data-centric strategy that emphasises data preprocessing, interpretation of machine learning models' performance, improving data quality and modifying models to deal with issues identified during the iterative loop of classification model development. The framework consists of three stages: stage-1 focuses on data collection and pre-processing, followed by data quality improvement and feature extraction in stage-2, and the final stage-3 with model hyper-parameter tuning. The concept of model interpretation is added within the framework that helps to understand the learning behaviour of machine learning (ML) models. This makes the models' performance more explainable and is known as Explainable Artificial Intelligence (XAI). For stage-1, the data is generated from a simulation of cyber-attacks in CIGRE low voltage microgrid network, which is then preprocessed. In stage-2, data is augmented using ensembled Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbour (ENN) methods, followed by feature extraction using the Boruta python package. Finally, the hyper-parameters are tuned through a Tree-structured Parzen Estimator (TPE) algorithm. A time-series transformer model is also presented for cyber-attack detection. The findings from the proposed approach demonstrate that the model's predictive performance increases with subsequent stages.
Data Generation, Testing and Evaluation of Chinese Natural Language Processing in the Cloud

With the rapid development of artificial intelligence, natural language processing, as an important branch, has also become a hot research field. A series of super large-scale pre-trained models represented by BERT and GPT have made great progress in natural language understanding and natural language generation, even some of the experimental accuracy exceed the human benchmark. However, these models will also make some mistakes and even fairness problems when they have the language ability equivalent to human beings. In order to verify whether the models can truly understand natural language, the evaluation of these models is particularly important. More methods are needed to evaluate the model. The language model-based evaluation tools often require a lot of computing resources. In this paper, we propose a method for testing and evaluation of Chinese natural language processing in cloud, generate testing data and design tests for Chinese data and test two pre-trained models. The experimental results show that our method can find defects of the model, though it has high performance on specific dataset.
Abnormal Identification of Oil monitoring Data Based on Classification-Driven SAE

In order to accurately understand the operating state of the equipment, monitor the abnormality of the oil state data in time, and effectively extract the abnormal data information in the oil monitoring data, this paper established a classification-driven SAE oil monitoring data abnormality recognition model. The nonlinear characteristics of the data predicted the state of the oil data. The label information is introduced into the collected oil monitoring data, and then the data is preprocessed. The deep features in the oil monitoring data are extracted by the stacked autoencoder (SAE). In the coding stage, the oil monitoring data training network with labels is used to realize the identification of abnormal data. The experimental results showed that: Compared with the Back Propagation Neural Network (BPNN) and the Support Vector Machine (SVM) classifier, the classification-driven stacked autoencoder had higher anomaly identification accuracy and could effectively detect abnormal data in oil monitoring data, so as to identified the abnormal monitoring of equipment status.
Ground Fault Identification and Key Feature Extraction Method for Distribution Network Based on Waveform Analysis

Because the fault current is weak and difficult to be identified, a method for ground fault identification and key feature extraction in distribution network based on waveform analysis is proposed. By analyzing the mutation characteristics and transient characteristics, waveform analysis is used as the feature extraction method, combined with the normalization processing method, to obtain the target feature components. Identify fault persistence features, extract frequency band components, and obtain a set of pulse signals through mathematical morphological transformation. The positive impulse noise and negative impulse noise fault signals extracted are suppressed by combining the opening operation and the closing operation. After analyzing the characteristic quantity of distribution network, the characteristic parameters of fault identification are determined. The volt-ampere characteristics of linear distribution network components are analyzed, and fault line identification is realized according to the characteristic components. Analyze metallic ground fault, arc ground fault, and intermittent arc ground fault waveforms, divide characteristic areas, and complete ground fault identification. The experimental results show that the current transient component fluctuation curve of this method is consistent with the actual fluctuation curve, and the maximum identification accuracy and identification time are 0.988 and 20 s respectively, experiments show that this method has high accuracy and recognition rate.
Towards Aggregated Payment Channel Networks

Payment channel networks (PCNs) have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. It provides a high-throughput solution for blockchain-based payment systems. However, such “layer-2” blockchain solutions have their own problems: payment channels require a separate deposit for each channel of two users. Thus it significantly locks funds from users into particular channels without the flexibility of moving these funds across channels. In this paper, we proposed Aggregated Payment Channel Network (APCN), in which flexible funds are used as a per-user basis instead of a per-channel basis. To prevent users from misbehaving such as double-spending, APCN includes mechanisms that make use of hardware trusted execution environments (TEEs) to control funds, balances, and payments. The distributed routing protocol in APCN also addresses the congestion problem to further improve resource utilization. Our prototype implementation and simulation results show that APCN achieves significant improvements on transaction success ratio with low routing latency, compared to even the most advanced PCN routing.
Deep Transfer Learning Methods for Classification Colorectal Cancer Based on Histology Images

Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.
Robust Design of Online Psychological Counseling System for College Students Based on Mobile Wireless Network

The traditional way of psychological counseling is to have face-to-face communication with psychological counseling teachers at a specific time and place. It is difficult to meet the development needs of information and intelligence due to the limitations of time and place. In order to solve these shortcomings of the traditional psychological counseling mode, this paper puts forward the design method of College Students' online psychological counseling system based on mobile wireless network, optimizes the system hardware structure combined with mobile wireless network technology, and improves the system software function and operation process. Finally, experiments show that the performance of College Students' online psychological counseling system based on mobile wireless network obviously meets the current research requirements, It effectively solves the problems of traditional methods and fully meets the research requirements.
Human Fall Detection Algorithm Based on YoloX-s and Lightweight OpenPose

The existing research shows that falls account for a significant proportion of safety accidents. At the same time, as many countries enter an aging society, falls have increasingly become a non-negligible safety issue affecting the lives and health of the elderly. To address the current problems of human fall detection, we propose to extract a human skeleton model based on YoloX-s in combination with Lightweight OpenPose. This model can identify human fall by the difference values of angle change’s rate between the key points of the neck and knees. The results demonstrate that the accuracy rate for fall detection is 97.92% and that for normal behavior detection is 96.46%. The computing speed of the method satisfies the need for real-time processing with satisfactory robustness.
Creating A New Supply Chain with Blockchain

Blockchain technology has brought different perspectives to industrial developments and methods. Blockchain technology is being widely used in finance, commerce, and similar fields. this technology has the potential to bring remarkable advantages in today's supply chain operations. Food supply plays a critical role in this period when confidence in the product is declining. In addition, the balance of nature is disturbed by practices that threaten soil health. With blockchain technology, the supply chain can be monitored, it can improve soil health by creating an economy that is both cost-effective and time-efficient, and it can benefit social health by improving food health. A B2C marketing platform is suggested to create transparent supply chain processes.
Quality assessment of heavy metals presented in crude oil by identification spectral lines by the LIPS - System

Petroleum is a complex mixture containing on the major and minor elements from organic and inorganic compounds and amounts of inorganic elements and metals. Compositional determination of crude oil is one of the most important topics for its identification and subsequent processing. In this study we identified the analytic lines of heavy metals (HMs) found in crude oil extracted from Iraq south field that are unique spectral by using the laser-assisted plasma (laser material processing) that led to the ionization of the sample in the hot plasma, the plasma emission spectrum was recorded by the spectrometer connected to the fiber optic and resolved spectra were used for identification of detected metals based on the principle the finger print. We are selection and determine the result optimum of the global reference line properties (highest signal intensity I, induce temperature Te) and lower skew value of analytic lines intensity when use the limited laser parameters and lower pretreatment time (drying time). After five pretreatment stages, the treatments by drying the sample at different time (1–9) day with constant temperature at 30 Co were executed. Found results are designated and compared in all cases and the atomic parameters of selected lines were taken by assisted NIST database. For control the challenge presented by the properties of the crude oil samples, as organic liquid case (crude oil). The optimum results obtained indicate that the LIBS - technique is effective and rapid technique to identify the heavy metals (HMs) presented in the crude oil. The major elements such as C, H, N, O and minor (trace) Fe, V, Ni, C <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> , Cu, Co, Ce, Mn, Mo, Ti, Ta. Sc, U, W, Hg, Pb, Sm, Y, Tm, Er, Eu, Re, Zr, Dy, La, Rh, Ru, Th, Gd, Nb and Nd metals have been identified and their parameters calculated based on analysis of the spectrum that contain all information needed to derive the elemental analysis in the crude oil.
Providing a labeled statements dataset to enhance the trans-compilation-based tools

Nowadays Mobile Applications are a necessity as everyone is depending on them in their everyday tasks. We use them for communication, entertainment, and utilities. Every day new devices are introduced to the market. The diversity in these devices resulted in many platforms like Android and iOS. These different mobile platforms used by different companies and manufacturers made it challenging for mobile developersto build an application or a system that can be deployed on different platforms. Developers will be required to go through multiple development cycles to develop the application for each of the targeted platforms. To solve these issues, cross-platform frameworks emerged as a solution that would help the developers to write one codebase and deploy it on multiple platforms at the same time. Even though cross-platform frameworks reduced the time and cost needed to develop an application, but they did not provide the native feel and performance of the native applications. It was also challenging for developers to integrate cross-platform applications with a pre-existing system and to maintain the system. Some approaches were introduced to solve these issues by allowing the developer to write one codebase and generate the native code for each platform. One of the recently presented approaches is the trans-compilation approach that depends on the parser code to recognize the source code and generate the corresponding code for it in the target language. Even though the trans-compiler-based approaches were effective in providing an application that combines the advantages of the cross-platform and native development, it faced some problems in dealing with statements that is not expressed in the language's grammar rules, eternal APIs, and APIs that require code definition, like Firebase. In this paper, our aim is to provide a solution that can be integrated with the trans-compilation approach to solve the previously mentioned issues to enhance the conversion success rate by recognizing statements and predict the corresponding ones
Flexural Behavior of Reinforced Recycled Aggregate Concrete Beams Strengthened by CFRP

The flexural behavior of concrete beams containing recycled concrete aggregate (RCA) and strengthened externally by CFRP sheets was discussed in this paper. In the experimental program, twelve concrete beams, 150×200 mm in cross-section over an effective span of 1100 mm, with a tensile reinforcement ratio of 0.9%, with four-point load tested. The beams were subdivided into two groups. The first group consists of four beams, one of them is a control beam with natural aggregate (NA), and the others with different RCA ratios (30%, 50%, and 70%), whereas the second group consists of eight beams with a 50% RCA and uses CFRP sheets to strengthen them in various techniques. The externally bonded reinforcement (EBR) method and externally bonded reinforcement on the groove (EBROG) method were both compared. Generally, the test results showed that the flexural strength of RCA beams decreased significantly from 3% to 10.5% as RCA content increased. While the utilization of CFRP sheets to strengthen specimens led to an increase in the flexural strength of RCA beams, where the rate of increase ranged from 5% to 45.6% for the EBR method and 30.5% to 40.4% for the EBROG method.
Training an open quantum classifier

Quantum machine learning (QML) aims to embed the power of quantum computation with learning theory. Quan-tum noise and finding the best recipe for encoding classical information into a quantum register could be seen as challenges to overcome for computational performance. Classification of quantum information is a subtask for QML. In this study, we adopt a dissipative route for quantum data classification and examine the developed theory on a gradient descent-based learning task. In particular, we follow repeated interactions based on open quantum dynamics where the binary decision is encoded on a steady state. Based on the analytical results, we develop a cost function for training an open quantum neuron. We demonstrate that the dissipation-driven protocol is suitable for a supervised learning scheme.
Design of Network Teaching Resource Security Integration Algorithm Based on Multi-Dimensional Association Rules

Aiming at the problem that the uneven distribution of network teaching resources leads to the long integration time, a network teaching resource integration algorithm based on multi-dimensional association rules is designed. This paper constructs a resource index model based on multi-dimensional association rules by calculating the support degree of the feature set of network teaching resources, mining network teaching resources, and preprocessing the mined resource data. According to the verification and conversion results of network teaching resources, the standard conversion format is used to obtain the teaching resources with unified format. This paper mainly uses inversion calculation tools to calculate the resource distribution center of gravity, uses analytic hierarchy process to determine the resource weight vector, calculates the difference between different resources according to the analysis matrix, and integrates the network teaching resources according to the difference. The experimental results show that the shortest integration time of the algorithm is 5s, which shows that the use of multidimensional association rules can shorten the integration time and improve the integration efficiency.
Abnormal Data Detection Method of Web Database Based on Improved K-Means Algorithm

A k-means clustering based anomaly detection method for software test data is proposed to enhance the detection ability of software test anomaly data. The distribution document model of abnormal teaching data is established based on portable multi-dimensional control software; Identifying software parameters based on semantic features and extracting feature quantities of software related information; Clustering of abnormal data by feature combination analysis according to feature distribution; Through the fusion of abnormal feature distribution, the joint detection of multi-dimensional features is completed; K-means clustering is used to obtain the optimal data combination and complete data anomaly detection. The experimental results show that the advantages of this method are good performance and accuracy.
A Meta-heuristic Approach for Exploring Rough Terrain using UAV/UGV Heterogeneous System

Due to increased exposure of the automation and robotics fields to applications such as the exploration of unknown terrain and search and rescue missions, among others, the idea of a system composed of an Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicle (UAV) is being proposed to execute these missions. In this paper, several meta-heuristic optimization techniques were implemented to plan the UAV and UGV's paths including Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Aquila Optimizer (AO) and several modified versions of these techniques, such as (AGWO), a newly developed hybrid approach between the AO and GWO. The results showed that the combination of the Variable Weight Grey Wolf Optimizer (VW-GWO) and Ant Colony Optimizer (ACO) resulted in the best performance to solve these problems.
Cardiac Magnetic Resonance Imaging Segmentation using Ensemble of 2D and 3D Deep Residual U-Net

The domain of deep learning stimulates medical image analysis, which is a catalyst of scientific research and an essential element of healthcare. Since semantic segmentation techniques empower image processing and quantitative determination in various applications, designing a dedicated solution is challenging and heavily reliant on input data characteristics and hardware constraints. Cardiac magnetic resonance imaging segmentation provides three essential heart structures, left ventricle (LV) cavity, myocardium (MYO), and right ventricle (RV) cavity. In clinical applications, manual contouring is frequently utilized to perform semantic segmentation. A fully automated cardiac magnetic resonance image (CMRI) segmentation technique is becoming more desirable as deep learning-based frameworks advance. Motivated by the power of U-Net, residual network, and deep supervision, this paper proposes a Deep Residual U-Net to achieve better LV, MYO, and RV segmentation in short-axis CMRI. The model is formed with residual connections and has a similar layout to U-Net. It provides three advantages: First, residual connections make deep network training easier. Second, the network's rich skip connections enable feature propagation, allowing for network creation with lower complexity but more remarkable performance. Third, Deep supervision facilitates the loss computation at every feature dimension except at least two, empowering gradients to be implanted more deeply in the network and improving each layer's training in Deep Residual U-Net. Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset has been used to assess the segmentation efficiency of proposed model. The proposed approach significantly outperformed all other methods, evidencing its supremacy over recent state-of-the-art U-Net-based techniques.
Four Components of Interactive Projection Mapping Design Plan: Case Study by Using Building Block Toys for Learning

Interactive projection mapping has been proven to help students learn more actively and dynamically. Unfortunately, this technology cannot be found easily in many schools or universities because there are not many instructions or plans for how to make an interactive projection mapping system. If it is available, the document only discusses the technical part of the system, even though project creation is inseparable from other components such as human resources, how to do it, and costs. If the project is successfully created, new problems may arise, namely various teaching materials, so a customized interactive projection mapping system is needed. This paper presents a distinctive design plan for interactive projection mapping with four main components that must be considered when making a project. This interactive projection mapping uses building block toys so the teaching materials can be customized based on the objects made by the toys.
RS-SVGG: An Intelligent Circulating Tumor Cell Detection Method Based on VGG

The image-based C irculating T umor C ell detection is a powerful tool for both the diagnosis and the recovery process of cancer. However, healthy cells are dominant in the captured image in most scenarios. That makes the task of recognition CTCs from healthy cells can barely be manually achieved. This paper presents RS-SVGG, a machine learning based automatic CTC detection method based on VGG network. RS-SVGG adopts Multi-scale Region Selective Search algorithm to split the original large size image into small fixed-sized suspected target CTC images. Then final CTCs are detected from these target CTC images by SVGG, which is an optimized VGG network. The proposed method was evaluated using real clinical images. The experimental results show that RS-SVGG outperforms traditional image processing methods and can achieve up to the accuracy of 97.56%, which also outperforms the detection accuracy of human.