Reviewerly/exHarmony huggingface dataset:
Decentralized Identity Management Using Blockchain

Identity management is the process of setting and organizing the roles and access privileges of a user’s identity. The current identity management system is centralized and is controlled by a single entity. Users’ privacy concerns are not in their best interest. Users have very little to no control over their data. The centralized system becomes a single point of failure which is prone to attack that leads to users losing their data privacy if these centralized systems are breached. Therefore we propose a Blockchain-based decentralized Identity Management System that makes use of self-sovereign identity, decentralized identifiers, and verifiable credentials. It also gives users the ability to choose from a very large number of identity providers instead of just a select few corporations. The main advantages of the proposed solution include the elimination of the need for a central authority for identity verification and identity data management, the reduction of time spent on identity verification, the ability to share data with permission, and the ability to verify the origin of the data while sharing.
Critical Analysis of several Feature-based Opinion Mining Methodologies

Feature-based opinion mining is the process of extracting user opinions from the characteristics of an object and is used to do granular analysis by isolating particular properties of an object about which a user has expressed an opinion. In the sentiment analysis method, emotions, feelings, attitudes, and views are all regarded as significant components. With the development of technology built on the World Wide Web, a person or a group can frequently express their opinions or sentiments about internet technology with the use of reviews, blogs, and ratings. The categorization and prediction of the various types of emotions, both good and negative, is the main goal of the machine learning algorithms. When classifying opinions at the sentence level, the polarity of a document is ascribed to a particular group of words, however when classifying sentiments at the aspect level, the emphasis is on identifying the numerous components that make up a text. Finding out if the document as a whole or the topic being covered is typically positive or negative is one of the main objectives. This contribution analyses numerous feature-based opinion classification methodologies.
A SIMD Interpreter for Linear Genetic Programming

Genetic programming (GP) has been applied as an automatic programming tool to solve various kinds of problems by genetically breeding a population of computer programs using biologically inspired operations. However, it is well known as a computationally demanding approach with a significant potential of parallelization. In this paper, we emphasize parallelizing the evaluation of genetic programs on Graphics Processing Unit (GPU). We used a compact representation for genotypes. This representation is a memory-efficient method that allows efficient evaluation of programs. Our implementation clearly distinguishes between an individual's genotype and phenotype. Thus, the individuals are represented as linear entities (arrays of 32 bits integers) that are decoded and expressed just like nonlinear entities (trees).
A planning framework for QoS based composition services in IoT environment

Recently, Internet has developed into a new technology known as Internet of Things (IoT), enabling the interconnection of billions sensors, actuators, devices, as well as users, which are applied for huge data generating. Moreover, due to the numerous and heterogeneous included devices, and services delivered, the IoT service discovery, selection, and composition is a challenging task. Given that several services have the same functionality but different non-functional criteria (QoS). Thus it is interesting to create an automatic, dynamic, and optimal IoT service composition system to respond on real time to large scale of services, including QoS. Accordingly, we propose, in this paper, an approach for IoT service composition based automatic planning (AP) enhanced by genetic algorithm (GA). The objective is to produce a plan composition satisfying the optimal QoS. In addition, we employ the cloud technology to establish an optimal IoT service composition with less memory consumption, and expanded scalability of our suggested framework. The simulation results have illustrated the efficiency of our approach to resolve the composition problem in a distributed and dynamic IoT system.
Hybrid model for detection of frauds in credit cards<sup>*</sup>

The goal of this study is to decide on the correct model that can predict a credit card holder’s likelihood of missing their next payment based on their demographic characteristics and payment history. Our goal is to reduce the number of defaulters that are labelled as non-defaulters. That’s because our company can afford to have non-defaulters who do default but not non-defaulters who do not default, as it is implied that the bank has a stringent no-NPA policy (more or less). When putting machine learning algorithms into practice, supervised learning must be used since we have known upstream and downstream values and anticipate a prediction model at the conclusion. The correlation amid attributes and target sets is established in the first stage. Classification stage classifies input data into certain categories. In order to examine the performance of recommended approach, the comparison of recommended approach and other available approaches has been conducted in this work along with this achieving the 85% accuracy result.By accurately categorizing credit card delinquencies and non-defaulters, the mortgage lenders will be able to utilize the accuracy of the model to lower the high delinquent rate.
Mask Recognition through Deep Learning

A major part of computer vision is formed by Object detection. Most of the such tasks are done with efficient object detection. This paper aims to incorporate techniques for facial mask detection to achieve an accurate and efficient mask detection algorithm. The goal is to examine various deep learning algorithms to perform mask detection in this era of Covid. This paper aims on building an application based on facial mask recognition using different deep learning algorithms and compare the results to find out the most accurate algorithm.
Performance Analysis of queueing assisted Blockchain system

Researchers, engineers, and institutions have given blockchains a lot of attention in recent years. The implementation of blockchains has begun to revive many applications ranging from e-finance and e-healthcare to smart homes and the Internet of Things, social security, and logistics, among other things. In this article, a queuing-assisted blockchain system is offered as a means of better understanding the functioning and theoretical elements of blockchain technology. It is possible to utilize the suggested system to represent a diverse range of queueing-assisted blockchain-based systems in a variety of applications. This paper replicated the transaction confirmation process of a blockchain system with a miner to better understand how the blockchain system works. Factors such as unfavorable transactions in the mining process are also considered. Here we consider the M/M/1 queueing assisted blockchain system. The analytical model validation was carried out using the software packages MATHEMATICA 9 and MAPLE 18 in order to investigate the findings obtained through queueing model.
Random Pixel Selection through Image Cropping for Data Augmentation and Classification

Data augmentation is one of the strategies addressing the challenge of lack of data, which constitutes an obstacle to getting high accuracy during training Deep Neural Networks. Through this paper, we propose a new method based on the random selection of pixels by cropping some rows and columns from a given image to create new images with a reduced number of pixels. It allows the creation of images with new rows and columns which differ from the original image. This assures enriching and diversifying the original dataset while trying to capture the regions of interest and dismiss the other regions, which leads to better generalization of the training model, The accuracy of the proposed method on Deep Convolution Neural Network applied to the Kaggle Cat VS Dog dataset achieves 77.05% with 40000 samples, whereas the accuracy when applied the original dataset (with 10000 samples) was 73.65%.
Graph Sampling Aggregators Neural Network for Session-based Recommender System (GS-SBR)

In the digital world and competitive digitalization, recommendation systems (RSs) play a significant role in consumers' activities, and intelligent decision. Recently, a new recommendation system, called the Session-Based Recommendation (SBR), presents a product to the user through a session without resorting to personal information. SBRs are applied in various areas. Many recently published scientific papers on SBR are based on GNNs. In the study, We introduce (GS-SBR), a new SBR that uses the Graph Sampling Aggregator (GraphSAGE) that outperforms numerous state-of-the-art efforts.
Recognition of Group Brawl Behavior in Buses Based on Deep Learning

The environment in the bus is complex, the people are dense and the obstruction is more serious, and the risk is greater when brawl occurs, which threatens seriously the safety of public transport vehicles, therefore, it is particularly important to identify and warn the brawl behavior in time. In this paper, the object detection of people inside the carriage is mainly obtained by yolov5 object detection algorithm, and then the overlapping frame part of the video is fed into the inflated three-dimensional convolutional network for video classification. Finally, the location information of the brawler in the video was identified. Taking the inside of the bus compartment as the experimental scene, 1122 abnormal behavior video samples and 911 normal behavior video samples were collected in the field, and the accuracy rate of brag identification was 91.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , the false alarm rate was 4.9%, and the missed alarm rate was 3.3%. After testing, it is shown that this method is more suitable for the recognition of brawl behavior in the scene inside the bus.
An Exploration of Learning About Cardiovascular Disease Predictionusing Deep Learning

Artificial intelligence has advanced Technology in recent years in the form of software algorithms for use and applications in the health-monitoring system. The best choices for eliminating human error in disease diagnosis and assisting in disease prevention through early detection. Heart-related illnesses, often known as circulatory sicknesses are the foremost cause of death in universal during the past several decades and have become the most serious illness in both India and the rest of the globe. Therefore, a trustworthy, accurate, and practical system is required to identify these disorders early enough for effective therapy. Based on the required task, deep information has stood as a more correct and persuasive radio for a type of restorative questions in the way that ailment, guess, and encroachment. It is a representation instruction pattern amounting to coatings that non-linearly transform the file to disclose hierarchic companionships and structures. In this survey, we construe the benefits and troubles of administering deep Knowledge in cardiology that also relate to curing usually, while suggesting distinguishing guidance as best able for dispassionate use,for that reason Deep Knowledge modelling can be used as further research work.
Ensemble Classification for Human Activity Recognition with Feature Reduction

Recognizing human action is an efficient method for identifying, deciphering, and identifying human patterns and activities. The hardest part is to obtain data about human activities. The ability to recognize another person's behaviours is the primary difficulty that existed prior to the methodical disciplines of artificial intelligence and machine learning. The technique for recognizing human activity is put into action in a variety of steps, including pre-processing the data, extracting the attributes, and classifying the data. This study suggests a hybrid version for recognizing human behavior that combines KMC, Principal Component Analysis, and several algorithms that are combined using voting mechanism. Python is used to test the methodology that is presented, and several metrics like accuracy, precision, and recall are taken into account to study the outcomes.
Comparative Analysis of Accuracy of Supervised Learning Classifier for breast cancer classification

Breast Cancer is widely spreading disease among women all over the world. This cancer is considered as one of the deadly disease among women. Data mining algorithms play a vital role to predict the early stage cancer .The research problem is that there are lots of classifier with different level of accuracy. An approach for improving the performance and accuracy of three different classifiers, Decision Tree, Logistic Regression, and SVM, is proposed in this study. We also compare the classifier on Wisconsin breast cancer dataset. Accuracy of classifier depends on the shape of data. Imbalanced data is a big problem for the classification phase. Each classifier's efficiency is accessed in terms of confusion matrix accuracy, AUC, and ROC.There are five sections to this study. First, we'll load the dataset and applied correlation to remove some features. For scaling and to filter data, preprocessing algorithm is applied then dataset is splited into training and testing data set and then applied CART algorithms for classification purpose. Then generate some graph to visualize the data. Based on the WBC dataset using Jupyter notebook software, the goal of this paper is to discover the most accurate classifier. The results have been compared with the results of previous papers and reveal that they are extremely precise. The SVM classifier model was discovered to be the best classifier of all the types. It also removes the over fitting of data.
Design of English Teaching Resource Management System Based on Intelligent Fuzzy Decision Tree

Aiming at the problems of “strange” and “waste” of current English teaching resources, in order to further increase the value of the use of different English teaching resources, a resource management system based on improved collaborative recommendation algorithm is proposed. Teaching resource management system is a necessary tool and environment for teaching innovation and improving teaching quality under the new situation. To further enrich the important role of classroom multimedia teaching and network application in college English teaching, not only can it achieve positive results, but also open up new ways for college English teaching reform. There are more and more various teaching resource management systems, and they play an important role in teaching reform and teaching innovation. This platform has played a vital role in the process of English teaching, has broad application prospects, and is an essential part of modern English teaching. This article analyzes the English teaching resource management system based on the intelligent fuzzy decision tree from the overview of network English teaching resources and the English course resource management system under the network environment, in order to provide suggestions for further teaching reform and teaching innovation.
EDCNet: A Lightweight Object Detection Method Based on Encoding Feature Sharing for Drug Driving Detection

Object detection methods have been widely used in various scenarios including automatic driving. However, existing methods are difficult to be deployed on terminals with weak computing power. In this paper, we propose a lightweight object detection model named EDCNet based on encoding feature sharing for drug driving detection with handheld terminals. The backbone of EDCNet is a segmentation network following the typical encoding-decoding segmentation structure, which extracts the region containing testing strips. The feature merging block in the encoding module reduces the number of parameters while fully leveraging image features at each scale. And the decoding part saves memory overhead by reusing max-pooling indexes. On the basis of object segmentation, a novel method named encoding feature sharing is introduced to intercept features of objects on feature maps. The intercepted features are then input into a classification network which judges whether and which drug the tester has taken. The amount of parameters of the EDCNet is 3.34M, and the performance of EDCNet is tested on a drug driving test dataset. Experimental results show that the segmentation accuracy (IoU) of EDCNet can reach 98.21% and the classification accuracy is 99.99%. The comprehensive performance of EDCNet outperforms other existing object detection algorithms and can meet the requirements of deployment on drug driving detection terminals.
Non-classical K-nearest Neighbor Clustering Algorithm

In order to reduce the influence of too many human factors in the clustering process, a Non-classical K-nearest Neighbor fast Clustering Algorithm is proposed. The K nearest neighbor points of the classical KNN algorithm are taken from the sample set, while the K nearest neighbor points of the proposed algorithm are taken from the data set to be clustered, this is the biggest difference between the two algorithms. The core idea of the algorithm in this paper is to refer to the average nearest neighbor distance between all data points and their nearest neighbor points, introduce clustering threshold and judge the affiliation of data points. In order to improve the clustering accuracy, the minimum-maximum scale method was used to normalize the data progression before calculation. The clustering results of artificial data sets show that the clustering accuracy of this algorithm is better than that of K-means algorithm and FDP algorithm, experiments on real UCI data sets show that the clustering accuracy of this algorithm needs to be improved.
Unsupervised Anomaly Detection Based on CNN-VAE with Spectral Residual for KPIs

Current large-scale applications, such as trading systems, blockchain, social software, etc, are increasingly adopting microservice architecture, which bring challenges to manual operation and maintenance, intrusion detection. In both operations and intrusion detection, there are a common characteristic that service metrics and network traffic are normal for most of the time, but anomaly data is more important. In this paper, we propose an unsupervised anomaly detection algorithm based on convolutional neural network with Spectral Residual, which is verified experimentally and has potential application capability with 19.2% f1-score improvement compared to the Variational AutoEncoder.
Research of Data-driven Iterative Optimization Scheme for Machine Learning Models

With the development of big data technology providing massive data information for machine learning, more researchers are focusing on data methods in big data technology, by constructing better data set to improve machine learning model's performance. This research based on the data-driven and human-machine collaboration methods in big data technology, continuously build data set to optimize machine learning models. We take the growth and risk assessment model of technology companies as an example, design and implement a data-driven machine learning model pipeline. The experiments show that the pipeline can effectively and continuously optimize these machine learning models, and ensure the stability of machine learning application.
PRKP: A Parallel Randomized Iterative Algorithm for Solving Linear Systems

This paper proposes a distributed-memory parallel randomized iterative algorithm for solving linear systems, called the parallel randomized kaczmarz projection (PRKP) algorithm. The algorithm has the property of greedy sampling, alternating projection, and lazy approximation. We derive the alternating projection process from the Randomized Kaczmarz algorithm and develop the greedy sampling and lazy approximation process to improve the convergence rate and reduce the per-process communication volume. Moreover, we develop a sampling residual estimation scheme for our proposed algorithm, which greatly reduces the extra computation cost required to obtain residuals. Our experimental results show that the proposed algorithm significantly outperforms previous parallel iterative algorithms on overdetermined linear systems, and yields up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{159}\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{384}\times$</tex> speedups on average respectively compared to the leading pipelined CG and pipelined GMRES algorithms.
Efficient. Scalable and Robust Data Shuffle Service for Distributed MapReduce Computing on Cloud

Distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.
Ensemble Learning to Predict Npm Package Downloads

With the emergence of node.js, the JavaScript language has opened up the front-end and back-end full-stack development at the same time, and more and more npm modules need to be installed and downloaded. In order to help people analyze and predict the download volume data of npm modules, this paper proposes a method for predicting and analyzing the download volume of npm packages based on ensemble learning. After the first step, go to npm to download historical data; Obtain the feature distribution of the data; the third step uses the AdaBoost regression algorithm, the GradientBoosting regression algorithm, the RandomForest regression algorithm, the ExtraTree regression algorithm, and the Bagging regression algorithm as individuals to train the model; the fourth step uses the model to predict the validation set data. After a lot of testing and analysis, the final prediction effect is OK, which can meet the needs of the predictive analysis npm package.