Reviewerly/exHarmony huggingface dataset:
Reliability Analysis of Swarm Self-security Intelligence System Based on Fault Tree and Monte Carlo Simulation

Group autonomous security intelligent system is an improvement of the traditional security system. By establishing group autonomous security intelligent system, traditional security system fault tree model, using the Monte Carlo method to simulate the fault tree model, finally through the fault tree simulation calculation, matlab group autonomous security intelligent system and traditional security system reliability curve, inefficiency curve, the average fault-free working time (MTBF) and the importance of the components. The results show that the group autonomous security intelligent system lasts longer and the average fault-free working time is longer than the traditional security systems.
Research and Application of English Learning Games Based on VR technology

English learning games, as a combination of games and education and teaching, can not only reflect the diversification of education forms, but also enhance the immersion and interaction of teaching situations. Combined with the emerging VR technology, this paper takes the mobile VR English learning game as an example by using Storm Mirror. By skillfully integrating the English teaching content into the puzzle game, this paper focuses on the game scene architecture and functional module design, and fully applies virtual reality technology in the development of English learning games. Virtual reality technology is not only a better solved the teaching content and knowledge visualization, missing immersive and interactive teaching process, the problem of teaching mode is monotonous, and through the virtual reality technology to enhance the game experience, improve the students' interest in learning, and make students truly happy in school learning, and to lay the foundation to improve students' English core accomplishment.
Skincare Recommender System Using Neural Collaborative Filtering with Implicit Rating

Skincare products are essential cosmetics for women, especially in this modern era. Many e-commerce services provide a variety of skincare products in their catalogs. One problem with purchasing skincare products online is that users cannot try the product and depend on other customers' rating reviews. However, rating reviews on a scale of 1 to 5 are considered insufficient to represent product quality, and users need to read review texts written by other users to get more specific information about the quality of the product. This paper investigated NCF (Neural Collaborative Filtering) for skincare recommender systems. Instead of using explicit rating as usually used on standard recommender systems, we adapted the sentiment score as a rating which, in our experiments, proved can improve the classifier's performance. We collected 180,104 rows of data with 11 data attributes and 1,339 skincare products to evaluate our proposed method. Experiments on the dataset show that the proposed NCF with explicit ratings achieved an RMSE of 0.8033, and the NCF with implicit ratings achieved an RMSE of 0.4931.
Machine Learning-based Multilabel Toxic Comment Classification

The emergence of social media marked the beginning of a revolution not just in the realm of digitalization but also in that of communication. In spite of the fact that social media platforms make it possible for anyone located anywhere in the world to express their viewpoints and interact with a large audience, social media has also evolved into a venue for cruel behaviour, offensive language, cyberbullying, personal assaults, and the use of profane language. To tackle this challenge, we are detecting the toxicity level in the Jigsaw dataset by Google, which consists of six different classes, including toxic, severe_toxic, obscene, threat, insult, and identity_hate. This is a multilabel classification in which one comment can fall under more than one class. We study the impact of Multinomial NB, Logistic Regression, and Support Vector Machine with TF-IDF on identifying toxicity in text. These models were trained using the training data and after training were tested on the test data provided in the dataset. Experimental results show that Logistic Regression trumps the other models in terms of accuracy and hamming loss.
Student Detection and People Counting System Based on Apriori Algorithm

Due to the continuous progress of machine vision and image processing science and technology and the rise of deep learning and artificial intelligence technology in recent years, the development of digital computing science and technology methods has been greatly promoted. The purpose of this paper is to design a student detection and people counting system based on the Apriori algorithm. Four algorithms are proposed to optimize and improve Apriori algorithm, namely sampling-based method, partition-based method and hashing method, and ways to reduce the number of transactions. In order to improve the training speed and test results of the algorithm, the loss function of the Apriori algorithm is improved, and the classification loss related to the detection task, the return loss of the detection frame, the return loss of the face landmark, and the return loss of the useless point are retained to eliminate the face density in the work. etc., optimize the multi-task loss function. Finally, three recorded videos are tested using four improved algorithms. The test results show that the hash-based method has a correct rate of 100% and a false positive rate of 0. It is the most optimized and improved algorithm among the four Apriori Efficient methods. The partition-based method has a correct rate of 85.71% and a false positive rate of 14.29%. The improvement results are minimal.
Construction of Scientific Talents Training Evaluation System Based on Improved Forest Random Algorithm

The cultivation of scientific talents is an urgent need to achieve national prosperity, national rejuvenation and people's happiness. At present, it has received extensive attention and thinking from all walks of life, and has become the mainstream value orientation for the purpose of talent cultivation in higher education in my country. It fully reflects the needs of the times and social responsibility to crack this issue. This paper first uses the demand dimension model to analyze the differences between the perspectives of teachers and students in talent training in colleges and universities, which is conducive to the unification of training models and structures. This paper establishes a random forest classification architecture based on the C4.5 algorithm, calculates the information gain rate of different statistical features, selects the statistical feature with prominent information gain rate as the root node to construct multiple decision trees, and composes the generated multiple trees into a random forest. And adopt the simple majority voting mechanism to obtain the classification results. The cultivation of scientific talents is a social system project, involving many complex historical reasons and social factors. It is necessary to pay attention to the institutional mechanisms and cultural traditions at the macro level of the country and society, and to analyze the university spirit and training mode at the meso level of colleges and universities. The final results of the research show that when the maximum tree depth on the dataset is 3dm, the accuracy rates of ID3 algorithm, RF algorithm and DPRF algorithm are 84.56%, 80.66% and 86.89%, respectively, and the improved forest random DPRF algorithm has the highest accuracy. It has certain feasibility in the construction of scientific personnel training evaluation system.
Improvement of blockchain-based multi-layer location data sharing scheme for Internet of Things

With the continuous development of the Internet of Things, user location data sharing has attracted increasing attention in various fields. However, current state-of-the-art data sharing system rely on centralized servers. This can create a single point of failure, with deleted data being unrecoverable. In addition, when performing data integrity verification, present schemes are only applicable to static scenarios. Dynamic update scenarios will incur enormous computational and communication costs. In this paper, we design a multi-layer location sharing scheme based on blockchain, which solves the single point of failure problem well. Our proposed scheme can also be adapted to dynamic scenarios. We utilize an accumulator to make our scheme more efficient in data updating and verification. The scheme also has a constant communication cost. Finally, we adopt a formal security analysis based on Real-Or-Random (ROR), and evaluate the performance of our scheme. The experimental results show that our scheme in comparison with existing methods, finding it to be superior in terms of computational cost, communication cost, safety, and efficiency.
Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions; For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target.
Implementation of Intelligent Chatbot in Student Portal: A Systematic Literature Review

The student portal gives users access to the channels of information and resources that they require for their academic pursuits. This includes access to the library services, information about their courses, and academic-related materials. On the other hand, this progress is not without its problems, as most of these services have a finite lifespan while the team still supports them. The student portal should build an artificial intelligence (AI) agent, like a chatbot, to provide 24/7 support for its users. This Chatbot must have a straight-forward user interface or GUI with excellent efficiency; hence it must be a simple system that users with minimal technical understanding can operate. It is intended that by acting as a customer support representative, a chatbot can enhance the user experience on student portal. This paper presents the findings from systematic literature review on previous works of intelligent chatbot implementation in student portal. Out of six main journal search engines and 100 articles found related to this study, only 21 articles are taken into consideration for further analysis. The results from existing studies on chatbot are tagged to nine categories based on content, and the findings are elaborated in terms of the implementation challenges of chatbot for student portal.
Using Logistic Regression Approach to Predicating Breast Cancer DATASET

The aim of this study is to predicate breast cancer by using three approaches: Multilayer perceptron, multiple linear regression, and logistic regression, then make a comparison between the performance of these approaches to decide the best approach to analyses the Wisconsin breast cancer dataset. In this study, we used the Wisconsin breast cancer dataset (699 instances and 11 attributes). Three approaches (multilayer perceptron, multiple linear regression, and logistic regression) were applied to the dataset to predicate the Wisconsin breast cancer. Performance was calculated for each approach and a comparison was made between the three approaches to see which is better. From the performance comparison between multi-layer perceptron, multiple linear regression, and logistic regression, the Wisconsin breast cancer dataset is best to analyses using multi-layer perceptron, with 100% compared to logistic regression 97.6% and multiple linear regression 84.4%. The computerized and especially logistic regression model could be useful to the predicate for many fields of science. For this reason, it’s used abundantly in the medical field specially to build a model to predicate breast cancer. The conclusion from this experiment showed that not all the independent variables have the coefficient effect with the dependent variable. However, the logistic regression approach is the best approach to analyses the Wisconsin breast cancer dataset rather than the multi-layer perceptron and multiple linear regression approach.
Constructions of binary codes with two distances

In this paper, we consider the problem of determining the exact value of A2(n,{d1,d2}) defined as the maximal cardinality of a binary code of length n with two possible distances d1 and d2. We prove that if d2>2d1, one has A2(n,{d1,d2})≤n+1. A similar bound holds for codes with d1≢d2(mod2):A2(n,{d1,d2})≤{n+1 for d1 even,n+2 for d1 odd. Furthermore, we settle two conjectures left open by earlier authors that imply the following exact values:A2(n,{2,d})={(n2)+1 for d=4 and n≥6,n for 4<d<n−1,n+1 for d=n−1, and provide combinatorial constructions that improve on the lower bounds on A2(n,{d1,d2}) known so far. Finally, we prove the general upper boundA2(n,{d1,d2})≤(n+1)(n+2)2.
Cooperative Enhancement of Ship Targets in SAR Images Based on Density Features

In this paper, we propose a new method to improve the target-to-clutter ratios (TCRs) of SAR image chips cropped from a large SAR image from the perspective of cooperative enhancement (CE) with the density features. First, the superpixel-based saliency map of each SAR image chip is independently obtained. Then, we innovatively design the superpixel-histogram density (SHD) feature of salience maps to accumulate target cues from all the SAR image chips containing ship targets, where the weight of cues from each SAR image chip is calculated in accordance with its self-quality. The SHD feature exploits the dependence of all the SAR image chips, i.e., sparse ship targets and non-sparse sea clutter. Finally, we fuse the salience maps and SHD features of SAR image chips. In this way, the ship target therein is enhanced and the sea clutter is relatively suppressed from the CE viewpoint. Experimental results on Gaofen-3 SAR images are reported to demonstrate that the newly proposed method provides higher TCRs than the state-of-the-art with better target enhancement performance.
Adaptive Refining-Aggregation-Separation Framework for Unsupervised Domain Adaptation Semantic Segmentation

Unsupervised domain adaptation has attracted widespread attention as a promising method to solve the labeling difficulties of semantic segmentation tasks. It trains a segmentation network for unlabeled real target images using easily available labeled virtual source images. To improve performance, clustering is used to obtain domain-invariant feature representations. However, most clustering-based methods indiscriminately cluster all features mapped by category from both domains, causing the centroid shift and affecting the generation of discriminative features. We propose a novel clustering-based method that uses an adaptive refining-aggregation-separation framework, which learns the discriminative features by designing different adaptive schemes for different domains and features. The clustering does not require any tunable thresholds. To estimate more accurate domain-invariant centroids, we design different ways to guide the adaptive refinement of different domain features. A critic is proposed to directly evaluate the confidence of target features to solve the absence of target labels. We introduce a domain-balanced aggregation loss and two adaptive separation losses for distance and similarity respectively, which can discriminate clustering features by combining the refinement strategy to improve segmentation performance. Experimental results on GTA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\rightarrow $ </tex-math></inline-formula> Cityscapes and SYNTHIA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> Cityscapes benchmarks show that our method outperforms existing state-of-the-art methods.
Load Balancing Mechanism for Edge-CloudBased Priorities Containers

Considering edge devices have limited resources, it's critical to keep track of their current resource usage and device resource allocation algorithms that send containers to edge and cloud nodes according to their priority.To minimize the strain on edge devices and enable the running of mission-critical applications, edge containers may need to be transferred to a cloud platform.In this paper, we suggested a mechanism for prioritizing container balance between the edge and cloud while attempting to assign delay-sensitive containers to edge nodes.We assess the performance of Docker container management systems on resource-constrained computers and offer ways for reducing administration and migration overhead based on the workload type, to bring load balance to the systems.The proposed algorithm gives flexibility to get the best possible ways to achieve load balancing.The Tensorflow's object discovery API was used, accessed with Flask, Python's micro-web framework.Docker container management technology was used in the implementation of this application.
Impact of Organizational Learning Capabilities on Service Quality of Islamic Banks Operating in Jordan

The major aim of the study was to examine the impact of Impact of organizational learning capabilities on service quality. Therefore, it focused on Islamic banks operating in Jordan. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a purposive sample of managers via email. This study was conducted using structural equation modeling (SEM) to test hypotheses. The results show that interaction with external environment has the highest positive impact relationship on service quality. Upon study findings, the researcher recommends managers and decision makers to invest the human element continuously by involving them in the decision-making process to acquire administrative skills along with technical skills they already have.
TPOT on Increasing the Performance of Credit Card Application Approval Classification

Credit approval or credit card approval with the help of machine learning classification has become a hot topic in recent years. However, credit card approval classifications from previous papers have limited performance. Therefore, we propose evaluating tree-based pipeline optimization (TPOT) as the classification model creation automation for credit card approval in this study. The data used is 690, namely the credit card approval dataset sourced from Kaggle, which we obtained from the University of California learning machine and is publicly available. Then we compare the TPOT result model with the credit card approval classification from the related paper as a benchmark, namely naïve Bayes, support vector machine (SVM), and decision tree. The test results show that the TPOT model of credit card approval classification has higher accuracy than the benchmark model of the related research. The results of the TPOT have accuracy = 0.89. While the accuracy of naïve Bayes, SVM, and decision tree are 0.823, 0.852, and 0.796, respectively. Then we also see that the TPOT model results can produce a receiver operating curve (ROC) with the highest area under curve (AUC) value compared to the benchmark method, which is 0.940. AUC naive Bayes method, SVM, and decision tree are 0.897, 0.918, and 0.796, respectively.
Reconstruction-based unsupervised drift detection over multivariate streaming data

In real-world applications, data streams are gener-ated all the time. Real-time data processing of complex multi-variate data becomes essential for many downstream analysis tasks. However, real-world data is not bound to be of the same distribution - the environment where it is recorded could be rapidly evolving, making it a challenge to apply a stationary model throughout the whole flow of data. Moreover, labels are often expensive to acquire or delayed in such scenarios. This paper considers the severe problem in an unsupervised setting, where we detect the distributional drifts in the input data stream without considering the data labels or specific classifiers. We propose AECDD (Autoencoder-based Concept Drift Detec-tor), a reconstruction-based unsupervised drift detection model using an Autoencoder to track changes in data distribution. More specifically, instead of detecting drifts by tracking the classifi-cation error change as in many existing approaches, we track the reconstruction error of the Autoencoder in an unsupervised manner. Our empirical evaluation shows that AECDD captures the drifts well in multivariate data streams. Finally, we also demonstrate the drift in the reconstruction error space by an intuitive visualization.
An Integrated Cloud and Blockchain Enabled Platforms for Biomedical Research

In the current pandemic scenario, healthcare data tends to be an important asset among organizations. The major challenge is to handle the data effectively while maintaining the privacy and security of the data. In a real-world, context healthcare data proves to be heterogeneous. Hence, managing such significance to big data has ardently laid numerous challenges among researchers and scientists around the globe. Cloud environment and blockchain technology can be discussed as usable platforms which can deliver a comprehensive centralized data privacy system. In the current approach study, we have integrated both technologies to provide usability in medical systems. Further, we have also proposed and implemented a blockchain application with an integrated cloud-based environment regarding heterogeneous medical databases. The study is proposed in 2 phases to maintain the privacy and the accessibility of the data. The double-spending problem is also presented, as mentioned above, using Blockchain’s consensus process. Each network node independently verifies the validity of individual transactions and entire blocks. As a result, there is no need to put faith in a single entity or other nodes. As a result, third parties are no longer required for network actions or blockchain management.