Reviewerly/exHarmony huggingface dataset:
Utilizing ASCII Codes Redundancy for Data Compression

When information is organized in documents and saved into computers its contents are stored using traditional ASCII codes. The occupied space by those ASCII codes is one challenge in the field of information technology. This is why many different data compression techniques have been developed and others are still in progress to meet this challenge. This paper looks at the ASCII codes from another view as inherited redundant codes. This inherited redundancy forms spatial redundancy that can be utilized to compress data documents. This compression is based on eliminating the repetition of the same ASCII code for the same character along its occurrences in a document. The proposed technique is based on the idea of assigning a position code for each element in the document. The position code is then connected with the original ASCII code for that element by reserving a memory area to keep track of the position codes of all bytes' patterns participating in the original document. The technique is then evaluated and assessed using analytical analysis and MATLAB work. The obtained results are then discussed to show the capability of the proposed method.
Technologies of distributed data stream processing based on big data

In the big data environment, data stream processing requires high real-time performance, and data calculation requires persistence and high reliability. Distributed Data Stream Processing System (DDSPS) can solve the problem of data stream processing in the big data environment. In addition to the advantages of scalability and fault tolerance of distributed systems, it also has high real-time processing capabilities. This article introduces three open source distributed streaming data processing systems, and compares and analyzes these three streaming frameworks. The research content can provide technical reference for the theoretical research and application technology development of data stream processing in the big data environment.
MOOC Courses as a Tool for the Development of Digital Competencies of Teachers

The aim of the paper is to highlight the importance of MOOC courses as a support and complement in teacher education. Online courses in the MOOC format meet the conditions to offer a possible solution to the urgent need for undergraduate and in-service teacher training in area of the digital competence that teachers have. It is necessary to develop their students’ digital literacy. However, there is almost no evidence of the effectiveness and efficiency of MOOCs for developing digital competences. The paper focuses on social science teachers who lag behind others in the use of digital technologies. It is important to create MOOC courses for the development of digital competencies and for the use of digital technologies in teaching.
Semi-supervised Time Series Anomaly Detection Model Based on LSTM Autoencoder

Nowadays, time series data is more and more likely to appear in various real-world systems, such as power plants, medical care, etc. In these systems, time series anomaly detection is necessary, which involves predictive maintenance, intrusion detection, anti-fraud, cloud platform monitoring and management, etc. Generally, the anomaly detection of time series is regarded as an unsupervised learning problem. However, in a real scenario, in addition to a large set of unlabeled data, there is usually a small set of available labeled data, such as normal or abnormal data sets labeled by experts. Only a few methods use labeled data, and the existing semi-supervised algorithms are not yet suitable for the field of time series anomaly detection. In this work, we propose a semi-supervised time series anomaly detection model based on LSTM autoencoder. We improve the loss function of the LSTM autoencoder so that it can be affected by unlabeled data and labeled data at the same time, and learn the distribution of unlabeled data and labeled data at the same time by minimizing the loss function. In a large number of experiments on the Yahoo! Webscope S5 and NAB data sets, we compared the performance of the unsupervised model and the semi-supervised model of the same network framework to prove that the performance of the semi-supervised model is improved compared to the unsupervised model.
Cartoon Your Life

The software transforms real-world photos into multi-style cartoons based on generative adversarial network (GAN). The web page of the source code is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which is the implementation of our TVCG paper “GAN-based Multi-Style Photo Cartoonization” [1]. This code is also an improved version of our preliminary work on CVPR 2018 [2], which has drawn considerable attentions in image style translation area and so far has been cited 124 times in Google Scholar. A variant of these code have been deployed in Huawei Ascend chip processor, and incorporated into Atlas 200 DK Developer Kit and Atlas 300 Inference Server. Averagely 27k users per month can use our code to generate their own cartoons in Huawei Ascend community.
Mining and Analyzing Occupational Characteristics from Job Postings

Hiring/recruitment is key to an organization’s ability to position itself for success by attracting the right talent. Similarly, job search enables workers to connect to the right jobs in the right organizations. To assist in the hiring and job search processes, many technology solutions such as interest inventories, job recommendation models, job boards, and career pathway planning tools have been developed. However, solutions for preparing job postings are lacking. Job postings/ads play an essential role in hiring the right talent since they signal to the jobseeker the knowledge, skills, abilities, and other occupation-related characteristics (KSAOs) needed for a job. If the job ad does not convey the correct occupational characteristics, it is less likely that a well-qualified candidate will apply. Therefore, we present an interactive job ad visualization tool that analyzes the text in a job ad and matches phrases in it to a large occupational taxonomy of KSAOs. We combine O*NET, an occupational taxonomy, with natural language processing to perform semantic similarity matching between KSAOs for an occupation and ad text, and thereby assist jobseekers in their search process and recruiters in preparing job ads.
SPbLA: The Library of GPGPU-Powered Sparse Boolean Linear Algebra Operations

Sparse matrices are widely applicable in data analysis while the theory of matrix processing is well-established. There are a wide range of algorithms for basic operations such as matrix-matrix and matrix-vector multiplication, factorization, etc. To facilitate data analysis, GraphBLAS API provides a set of building blocks and allows for reducing algorithms to sparse linear algebra operations. While GPGPU utilization for high-performance linear algebra is common, the high complexity of GPGPU programming makes the implementation of GraphBLAS API on GPGPU challenging. In this work, we present a GPGPU library of sparse operations for an important case — Boolean algebra. The library is based on modern algorithms for sparse matrix processing. We provide a Python wrapper for the library to simplify its use in applied solutions. Our evaluation shows that operations specialized for Boolean matrices can be up to 5 times faster and consume up to 4 times less memory than generic, not the Boolean optimized, operations from modern libraries. We hope that our results help to move the development of a GPGPU version of GraphBLAS API forward.
Data Visualisation for Remote Area Electrification Decision-Making

Energy poverty is a key challenge in many developing countries. Given that energy poverty contributes to a variety of social issues, the governments of these countries have aimed to bring electricity to as many remote areas as possible. However, this presents challenges because of the significant uncertainties arising from the geographic, social, demographic, economic and political characteristics that must be considered. These characteristics must be clearly communicated in such a way that supports the decision-making process. Using Indonesia as a case study, this research interviews experts in the field and seeks to cohesively integrate various visualisation techniques that may facilitate informed decision-making regarding remote area electrification.
Application of slack variable-optimized SVM on piano teaching reform

Abstract The data mining task of the classification algorithm is mainly to classify the data and classify them into each known category. As a classification algorithm, SVM has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition. Based on the advantages of the SVM algorithm, this paper optimizes the slack variables, establishes a teaching quality evaluation model, and takes 500 subjects of the questionnaire survey as data samples by comparing with a variety of classification methods to evluate, and the results show that the accuracy of the SVM model is 92.70%, which proves the effectiveness of the research model.
Secure Cloud Services Using Quantum Neural Network

The latest technology, namely, cloud computing has a lot of advantages.Kumar, Surya Bhushan Mandal, Ranjan Kumar Mukherjee, Kuntal Dwivedi, Rajiv Kumar But this technology is still in its infancy. Attacks on cloud storage servers are frequently observed, out of which Denial of Service (DoS) and Distributed Denial of Service (DDoS) are main types. It is observed that, these attacks, either can make the cloud storage servers extremely slow to response or simply crash them. In this endeavor, intelligent defender for cloud storage servers has been proposed to identify and resolve such attack. In this manuscript, the proposed algorithm can identify attacks and protect cloud storage servers. Experimental Results have been proposed to exhibit the effectiveness of the proposed approach.
Validation of Architecture Effectiveness for the Continuous Monitoring of File Integrity Stored in the Cloud Using Blockchain and Smart Contracts

The management practicality and economy offered by the various technological solutions based on cloud computing have attracted many organizations, which have chosen to migrate services to the cloud, despite the numerous challenges arising from this migration. Cloud storage services are emerging as a relevant solution to meet the legal requirements of maintaining custody of electronic documents for long periods. However, the possibility of losses and the consequent financial damage require the permanent monitoring of this information. In a previous work named “Monitoring File Integrity Using Blockchain and Smart Contracts”, the authors proposed an architecture based on blockchain, smart contract, and computational trust technologies that allows the periodic monitoring of the integrity of files stored in the cloud. However, the experiments carried out in the initial studies that validated the architecture included only small- and medium-sized files. As such, this paper presents a validation of the architecture to determine its effectiveness and efficiency when storing large files for long periods. The article provides an improved and detailed description of the proposed processes, followed by a security analysis of the architecture. The results of both the validation experiments and the implemented defense mechanism analysis confirm the security and the efficiency of the architecture in identifying corrupted files, regardless of file size and storage time.
Analysis of Cosmetology Teachers’ Perception of Untact Online Cosmetology Courses and their Barriers and Effectiveness

This study attempted to suggest basic data needed to design 'untact' online courses and develop an operating plan for teachers in cosmetology high schools, and the results found the following: The perception of online courses revealed a positive correlation in 'interest,' 'efficiency,' and 'communication' with statistical significance.As perception was higher, online courses' effectiveness ('interest,' 'efficiency,' and 'communication') increased.In terms of barriers to online courses, as 'instructor' and 'contents' barriers increased, and 'interest' decreased.When 'instructor,' 'contents' and 'learner management' were high, the efficiency dropped.Furthermore, as 'instructor ' and 'environmental' factors increased, 'communication' decreased.This study was performed among cosmetology teachers nationwide.Therefore, further studies should be conducted on the perception of online courses against more people, including cosmetology-related high school students.
Improving Support Vector Classification Efficiency with Principal Component Analysis

In this paper, a combination of principal component analysis (PCA) technique and a support vector classification (SVC) technique to improve the speed of creating a data classifier is proposed. The dimensionality reduction using PCA technique is applied on a set of data before the process of finding support vectors to see if they can be used together to reduce calculation time when performing the binary classification on two linear separable data clusters. The proposed method is tested on several randomly generated data sets and compared two important metrics, time and accuracy, with traditional support vector classification technique. The results of the proposed method show speed improvement in some cases, comparing to using only the original SVC technique. The values obtained from the accuracy metric show that results from the proposed method does not suffer from the reduction of features in the test data sets.
Instance Sequence Queries for Video Instance Segmentation with Transformers

Existing methods for video instance segmentation (VIS) mostly rely on two strategies: (1) building a sophisticated post-processing to associate frame level segmentation results and (2) modeling a video clip as a 3D spatial-temporal volume with a limit of resolution and length due to memory constraints. In this work, we propose a frame-to-frame method built upon transformers. We use a set of queries, called instance sequence queries (ISQs), to drive the transformer decoder and produce results at each frame. Each query represents one instance in a video clip. By extending the bipartite matching loss to two frames, our training procedure enables the decoder to adjust the ISQs during inference. The consistency of instances is preserved by the corresponding order between query slots and network outputs. As a result, there is no need for complex data association. On TITAN Xp GPU, our method achieves a competitive 34.4% mAP at 33.5 FPS with ResNet-50 and 35.5% mAP at 26.6 FPS with ResNet-101 on the Youtube-VIS dataset.
Performance Evaluation of Adversarial Learning for Anomaly Detection using Mixture Models

With the rapid advancement in wireless technology and with the introduction of next-generation 5G-wireless networks, a huge amount of data is generated and transmitted across computer networks worldwide. As a result, we have seen a continuous increase in new network threats and anomalies that creates a significant research challenge to handle the vulnerability along with data integrity, confidentiality, and reliability. More precisely, wireless networks are considered to be highly vulnerable to advanced persistent threat (APT) actors. In this paper, we evaluate an adversarial mechanism for anomaly detection using different statistical mixture models. Indeed, to prevent attacks like data poisoning and evasion from affecting the performance of anomaly detection systems, adversarial learning has proven to be very effective in many research studies. The performance of the mechanism, in which several recently proposed mixture models are integrated, was evaluated using both NSL-KDD and UNSW-NB15 data sets, in terms of accuracy, Detection Rates (DRs), False Positive Rates (FPRs), and computational time.
Graph-DPP: Sampling Diverse Neighboring Nodes via Determinantal Point Process

Recently, various graph neural network based approaches have been proposed to learn graph feature representations. However, there exists a long-term outstanding issue, i.e., over-smoothing problem. That is, when convoluting deeper neighboring nodes, the feature difference gradually vanishes. To address this issue, this paper proposes a novel determinantal point process based sampling strategy, called Graph-DPP, to sample diverse neighboring nodes for convolution. The target of diversified sampling is to maximize the relevance between sampled nodes and target node and simultaneously minimize the similarity between any two sampled nodes. To this end, we first adapt the Hawkes process to calculate feature similarity between any two neighboring nodes. Then, their structural similarity value is calculated. Both feature and structural similarity are used to generate the positive semi-definite similarity matrix for the later sampling. To the best of our knowledge, this is among the first attempts to integrate determinantal point process technique with graph neural network model. To evaluate the model performance, the proposed Graph-DPP strategy is respectively combined with GCN, GAT and GraphSAGE, and then are performed on three datasets. Experimental results show that the proposed Graph-DPP sampling strategy could achieve the state-of-the-art model performance.
Real-Time Bangladeshi Currency Recognition Using Faster R-CNN Approach for Visually Impaired People

In this research work, we represent a new method for recognition of Bangladeshi money using Raspberry Pi 4 and the faster R-CNN approach. Here we represent a microprocessor-based smart blind glass where one of the important features is Bangladeshi money recognition. Money is a very important element for all the people in the world including blind people. They cannot recognize the banknotes without the help of others. Sometimes they cheat by wrong people in various money issues. So, they suffer a lot in their day-to-day life. Through this paper, we introduce a smart blind glass that can recognize money and provide audio information to the blind man's ears. In this recognition, we introduced a new method faster R-CNN approach which is widely used for object and face recognition. By using a faster R-CNN model in money dataset training, we found about average 97.8% real-time accuracy. Our recognition accuracy was high compared with other state-of-the-art research work.
The Application of Computer Aided Technology in Interior Design

In recent years, the development of computer information technology has been very rapid, and more and more advanced computer information technologies have gradually been applied in the education system, and have effectively promoted the development of education. Among them, computer-aided design software provides good conditions for the teaching of interior design majors. Computer-aided design software has significant characteristics and effectively changes the traditional interior design mode. This article analyzes the application of computer-aided design software in interior design.
Image Steganography Scheme Using Dilated Convolutional Network

Nowadays, Convolutional Neural Networks (CNN) have allowed us to solve many problems, difficult to solve with classical methods, in different fields of applications. The use of this technique in the field of modern steganography has improved the performance of steganographic schemes in terms of concealability and invisibility. In this article, we propose a system based on CNN in order to hide a color image in another color image of the same size. The proposed system is based on Auto-Encoder network and U-net architecture. The network is subdivided into two sub-networks, the first is for the concealment of the image secret by the sender, the second is for its extraction by the receiver. The network is end to end trained to ensure the integrity of the concealment and extraction process. The tests were performed on challenging images dataset publicly available, such as ImageNet, LFW, PASCAL-VOC12. The results show that the proposed steganographic scheme can hide a color image in another one of the same sizes, i.e. a capacity of 24 bpp, with acceptable PSNR and SSIM values compared to other previous work.
Novel Python-based “ all-regressor model ” application for photovoltaic plant-specific yield estimation and systematic analysis

The deployment of photovoltaic technology in global electricity generation has reduced the dependence on fossil fuels and the adverse effect of global warming climate changes. However, the variability of PV output due to its reliance on meteorological parameters hinders the total maximization of the technology. In this paper, the interdependence of meteorological parameters on accurate estimation of the specific yield of the PV system is examined using machine learning algorithms. The analysis uses 2 years of meteorological data in a 10-min interval. The meteorological parameters used as input are global solar radiation (GR), wind speed (WS), wind direction (WD), the standard deviation of wind direction (WSD), air temperature (AT), and relative humidity (RH), while the output variable is the PV specific yield (PVSY). This study uses a Python-based “all regressor” machine learning algorithm, to analyze their accuracy for the estimation analysis at different input feature space. The accuracy of the models and algorithms is analyzed using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R2). The best performance in the two feature space was retrieved for AT, and GR with MSE and MAE values of 0.113 and 0.252, respectively. The best performance in the three feature space was retrieved for WSD, AT, and GR with MSE and MAE values of 0.109 and 0.255, respectively. The results show that the most accurate PVSY estimation was for the four input feature space with input variables of WS, AT, GR, and RH. The K-nearest neighbor (KNN) proved to be the most appropriate algorithm for this feature space with an RMSE and MAE score of 0.101 and 0.266, respectively. The study also shows that the most significant meteorological inputs in PV yield estimation are air temperature and global solar radiation. The study also noted that a large feature space adds uncertainties to the training process. Finally, the paper concludes that the sunshine-based parameters are key inputs for high-quality PV yield estimation regardless of the number of input combinations. It is also concluded that, in this study, the most accurate regression algorithms for estimation are the Least Absolute Shrinkage and Selection Operator (LASSO), KNN, Elastic Net (EN), Decision Tree regressor (DT), and Extra Tree regression (ET).
Dynamic Computing Resources Allocation for Multiple Deep Learning Tasks

To improve the speed of deep learning tasks, the models of Deep Neural Network (DNN) could be trained in a computational node where the parameters for the models are refined by multiple GPUs in the node parallelly. A number of previous works have studied parallel learning focus on the improvement of one single task. In such a situation, the whole hardware resources can be fully utilized for that task. However, in a real system, it is usual to have several learning tasks running in the same node. So in this paper, we propose an adaptive allocation of computing resources for multiple learning tasks, with the knowledge of current learning phase for each task. In experiments we train VGG-16 models using CIFAR-10 dataset on a node with multiple GPUs, and the results show that with adaptive adjustment for computing resources, the training time can achieve a decrease of 4.70% with unchanged per task batch size. Besides, the experiment that mixing up ResNet-50 and VGG-16 models shows that this computing resources adjustment is effective for different models.