Reviewerly/exHarmony huggingface dataset:
APPLICATION OF EXPERT SYSTEMS OR DECISION-MAKING SYSTEMS IN THE FIELD OF EDUCATION

Expert system (ES) is a branch of artificial intelligence (AI) that is used to manage different problems by making use of interactive computer-based decision-making process. It uses both factual information and heuristics to resolve the complicated decision-making issues in a specific domain. The architecture of the expert system was analyzed and found that it includes several parts such as user interface, knowledge base, working memory, inference engine, explanation system, system engineer, and knowledge engineer, user, and expert system shell in which each part of the architecture of an expert system is based on different functionary that helps it to make an adequate decision by analyzing complex situations. The research aims to analyze the application of expert systems or decision-making systems in the field of education and found that it is used for different purposes such as assessing teacher performance, providing guidance to the students regarding their career, and providing quality learning to students with disabilities. It is also used to help the students to make rightful career decisions and become efficient professionals after completing their studies.
Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

In the process of UAV line inspection, there may be raindrops on the camera lens. Raindrops have a serious impact on the details of the image, reducing the identification of the target transmission equipment in the image, reducing the accuracy of the target detection algorithm, and hindering the practicability of UAV line inspection technology in cyber-physical energy systems. In this paper, the principle of raindrop image formation is studied, and a method of raindrop removal based on generation countermeasure network is proposed. In this method, the attention recurrent network is used to generate the raindrop attention map, and the context code decoder is used to generate the raindrop image. The experimental results show that the proposed method can remove the raindrops in the image and repair the background image of raindrop coverage area and can generate a higher quality raindrop removal image than the traditional method.
Abnormal Traffic Detection Based on Generative Adversarial Network and Feature Optimization Selection

Complex and multidimensional network traffic features have potential redundancy.When traditional detection methods are used for training samples, the detection accuracy of the supervised classification model is affected due to small data samples.Therefore, a method based on generative adversarial networks (GANs) and feature optimization is proposed.First, the feature correlation and redundancy are analyzed by the potential redundancy of network traffic.The feature optimization selection method of collaborative learning automata is proposed.Second, the confrontation interactive training principle of the generative confrontation network is adapted, in which a model of the generative confrontation network is proposed to solve the problem that small training label samples.Third, the interdomain distance is minimized by using GAN and the multiple kernel variant of maximum mean discrepancy (MK-MMD).The shared features between the source domain and target domain distribution are learned by applying the information between GAN confrontation training and classification network supervision training, improving the detection accuracy.Forth, random noise data and original training label samples are mixed to form a new training set.The accuracy is further improved by adopting generative models to continuously generate samples.The final classification results are output by the 16-dimensional Softmax classifier.The method has a small loss rate when the datasets are used to train by the experimental analysis of algorithm parameters and simulation data.The model optimized by MK-MMD has strong generalization ability.The average detection accuracy rates are 91.673%(two-classification) and 91.480% (multiclassification) by comparing machine learning and other shallow neural networks, and are the highest values among the compared methods.Moreover, the effectiveness and superiority of the proposed method are verified to be the best by comparing the recall rate, false positive rate (FPR), F-measure, AUC.When the interference of other samples are mixed, the proposed method is also robust.
Stable image matching for 3D reconstruction in outdoor

Summary Stable image matching is still a pursuit goal in the communities of computer vision and graphics because of many potential applications such as 3D reconstruction, object recognition, and visual tracking. In the case of 3D reconstruction, modern structure‐from‐motion methods depend heavily on image‐matching methods for recovering geometric models. Existing methods have tried to exploit various strategies to generate correct feature correspondences for 3D reconstruction. However, there are no efficient methods that can work well in the wild. To defend this drawback, in this paper, we design a stable image‐matching method for 3D reconstruction. The method consists of feature point detection, descriptor extraction, feature matching, and matching verification. Besides, we try to choose a stable algorithm for each module of the proposed method, in which the adopted algorithms are implemented in an optimized approach for acceleration. Finally, we conduct a systematic experiment on several benchmarking datasets to assess the performance of the proposed method. Experimental results show that the proposed method has both high matching precision and fast speed.
A COPULA-BASED SUPERVISED LEARNING CLASSIFICATION FOR CONTINUOUS AND DISCRETE DATA

Despite the unreasonable feature independence assumption, the naive Bayes classifier provides a simple way but competes well with more sophisticated classifiers under zero-one loss function for assigning an observation to a class given the features observed.However, it has been proved that the naive Bayes works poorly in estimation and in classification for some cases when the features are correlated.To extend, researchers had developed many approaches to free of this primary but rarely satisfied assumption in the real world for the naive Bayes.In this paper, we propose a new classifier which is also free of the independence assumption by evaluating the dependence of features through pair copulas constructed via a graphical model called D-Vine tree.This tree structure helps to decompose the multivariate dependence into many bivariate dependencies and thus makes it possible to easily and efficiently evaluate the dependence of features even for data with high dimension and large sample size.We further extend the proposed method for features with discrete-valued entries.Experimental studies show that the proposed method performs well for both continuous and discrete cases.
The Effect of Perceived Ease of Use, Usefulness and Risk on Intention to Use the Go-Food Application in Surabaya and Sidoarjo

This study aimed to determine the effect of perceived ease of use (X1), perceived usefulness (X2) and perceived risk (X3) on the behavioral intention (Z) to use (Y) the Go-Food applications in Mr. Bre’s Geprek Chicken and other similar businesses. This research was quantitative descriptive. Purposive sampling was used and the sample included 100 respondents. An online questionnaire was used for data collection using Google Form and the data were analysed using partial least squares through SmartPLS 3 Software. Perceived usefulness, perceived risk and attitude towards had a significant effect on intention; however, perceived ease of use did not have a significant effect.&#x0D; Keywords: perceived ease of use, perceived usefulness, perceived risk, behavioral intention
Machine Learning-Based Multitarget Tracking of Motion in Sports Video

In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios.
Effectiveness of Teaching English for Specific Purposes in LMS Moodle: Lecturers' Perspective

&lt;em&gt;Learning management systems (LMS) Moodle presents a beneficial arrangement of features that support language learning in the electronic environment. The English lecturers' challenge is to obtain interaction and adaptability with their online classroom. The study is to find the effectiveness of teaching English in Moodle-based. The semi-structured interviews and open-ended questioner were employed to recognize the complete information to the English lecturer's teaching experiences based on Moodle. This study determines that the effective teaching of English Moodle-based depends on both lecturer competences in pedagogy English and lecturer experience in the Moodle system. It covers a complete curriculum with high expectations, composes applicable model content, and constructs it more obtainable, provides specific and culturally applicable instruction, maintains practical approaches to explicit learning strategies, allows learnings to use the first language, prepares vocabulary in various contexts, and develops reading comprehension, and integrates communicative competence abilities. The language learning activities of the Moodle classroom are capable of fulfilling learning practice for complete progression participants with peculiar configuration, management, adjustments, and teaching approach. The English teaching course designer should organize and manage an expected course model of course-related activity, and it applied to the utilization of the syllabus or course information page. The essential aspect of interchangeable communication significantly reshapes language teaching pedagogy in the electronic ecosystem. The effective teaching of English Moodle-based can be achieved with teaching pedagogy's full capabilities and technicality in the Moodle system&lt;/em&gt;.
Strategic Value Creation through Enterprise Blockchain

Blockchain and other distributed ledger technologies have enormous potential for creating business value but have not yet been widely adopted.Enterprise blockchain systems are recognised as solutions to existing operational problems or 'pain points' but their potential for delivering value through strategic opportunities is not well understood.Drawing from literature on strategic alliances and the resource-based view of the firm, we identify avenues through which blockchain systems can contribute to a firm's strategic capabilities and, as a result, to its sustained competitive advantage.We provide a framework for understanding how participation in blockchain solutions can enable companies to build upon existing strategic capabilities, strengthen collaborative capabilities and develop blockchain-specific capabilities.The framework can be useful to firms and service providers for incorporating strategic outcomes into the evaluation of blockchain investment opportunities.
Improving Signal's Sealed Sender

The Signal messaging service recently deployed a sealed sender feature that provides sender anonymity by cryptographically hiding a message's sender from the service provider.We demonstrate, both theoretically and empirically, that this one-sided anonymity is broken when two parties send multiple messages back and forth; that is, the promise of sealed sender does not compose over a conversation of messages.Our attack is in the family of Statistical Disclosure Attacks (SDAs), and is made particularly effective by delivery receipts that inform the sender that a message has been successfully delivered, which are enabled by default on Signal.We show using theoretical and simulationbased models that Signal could link sealed sender users in as few as 5 messages.Our attack goes beyond tracking users via network-level identifiers by working at the application layer of Signal.This make our attacks particularly effective against users that employ Tor or VPNs as anonymity protections, who would otherwise be secure against network tracing.We present a range of practical mitigation strategies that could be employed to prevent such attacks, and we prove our protocols secure using a new simulation-based security definition for one-sided anonymity over any sequence of messages.The simplest provably-secure solution uses many of the same mechanisms already employed by the (flawed) sealed-sender protocol used by Signal, which means it could be deployed with relatively small overhead costs; we estimate that the extra cryptographic cost of running our most sophisticated solution in a system with millions of users would be less than $40 per month.
Cyber Aggression between Intentions and Cyber Wellness of Students: An application of TPB Models

This exploratory study aims to broaden students' models of cyber-aggression behavior, with the Theory of Planned Behavior (TPB) by including additional constructs, namely cyber health, to predict youth cyber-aggression actions.The self-administered questionnaire was used to collect data from 291 students intentionally, aged 15 to 21 at secondary schools in the city of Padang in Indonesia, and then analyzed using covariance-based structural equation modeling (SEM).Data analysis was performed using WarpPLS software version 4.0.Structural equation modeling is used to test whether the overall model theory of planned behavior (TPB) helps predict self-reported actions by adolescents in cyber aggression.We found a strong positive relationship between adolescent attitudes towards cyber aggression and their behavioral intentions to do so, followed by perceived behavioral control variables which were also significant although relatively less important predictors of adolescent intentions for cyber aggression, while subjective norms were not significant in determining adolescent intentions for cyber aggression.Meanwhile, cyber wellness, which is predicted as a moderator variable in the development of the model, is not proven and has the same status as other exogenous variables.Overall, this study provides strong support for the theoretical utility of TPB in the research of students of cyber aggression.
Hidden Biases in Unreliable News Detection Datasets

Automatic unreliable news detection is a research problem with great potential impact.Recently, several papers have shown promising results on large-scale news datasets with models that only use the article itself without resorting to any fact-checking mechanism or retrieving any supporting evidence.In this work, we take a closer look at these datasets.While they all provide valuable resources for future research, we observe a number of problems that may lead to results that do not generalize in more realistic settings.Specifically, we show that selection bias during data collection leads to undesired artifacts in the datasets.In addition, while most systems train and predict at the level of individual articles, overlapping article sources in the training and evaluation data can provide a strong confounding factor that models can exploit.In the presence of this confounding factor, the models can achieve good performance by directly memorizing the site-label mapping instead of modeling the real task of unreliable news detection.We observed a significant drop (>10%) in accuracy for all models tested in a clean split with no train/test source overlap.Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task.We suggest future dataset creation include a simple model as a difficulty/bias probe and future model development use a clean non-overlapping site and date split. 1
Review of passive polarimetric dehazing methods

The images acquired in haze conditions are significantly degraded due to the presence of atmospheric particles. These images have low contrast, poor visibility, and some information is lost as well. These characteristics severely hinder the further processing and the applications in which the images are being used. The polarimetric dehazing methods are effective for direct imaging and enhancing the imaging quality. In addition, polarimetric dehazing methods have the capability to cope with haze and other turbid mediums. Therefore, the polarimetric dehazing methods are extensively developed and used in different applications due to their superior performance. We present in detail the principles, the implementation techniques, and the advancements in the polarimetric dehazing methods. We believe that this work is the first in-depth review on the passive polarimetric dehazing methods.
Unsupervised Graph Embedding via Adaptive Graph Learning

Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed. In this paper, two novel unsupervised graph embedding methods, unsupervised graph embedding via adaptive graph learning (BAGE) and unsupervised graph embedding via variational adaptive graph learning (VBAGE) are proposed. The proposed methods expand the application range of GAEs on graph embedding, i.e, on the general datasets without graph structure. Meanwhile, the adaptive learning mechanism can initialize the adjacency matrix without being affected by the parameter. Besides that, the latent representations are embedded with the Laplacian graph structure to preserve the topology structure of the graph in the vector space. Moreover, the adjacency matrix can be self-learned for better embedding performance when the original graph structure is incomplete. With adaptive learning, the proposed method is much more robust to the graph structure. Experimental studies on several datasets validate our design and demonstrate that our methods outperform baselines by a wide margin in node clustering, node classification, link prediction, and graph visualization tasks.
Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as the block matching and CNN (BM-CNN) method. Training the CNN is accomplished using simulated data from a fast turbulence simulation tool capable of producing a large amount of degraded imagery from declared truth images rapidly. Testing is done using independent data simulated with a different well-validated numerical wave-propagation simulator. Our proposed BM-CNN TM method is evaluated in a number of experiments using quantitative metrics. The quantitative analysis is made possible by virtue of having truth imagery from the simulations. A number of restored images are provided for subjective evaluation. We demonstrate that the BM-CNN TM method outperforms the benchmark methods in the scenarios tested.
Nature-Inspired Meta-heuristic Optimization Algorithms for Breast Cancer Diagnostic Model: A Comparative Study

The selection of features is used to obtain a subset of features by the removal of irrelevant features with no or less predictive output. Meta-heuristic algorithms are appropriate for the selection of features because feature subset representation is direct and the evaluation is easily accomplished. This paper performed a comparative study on the impact of meta-heuristic optimization algorithms on breast cancer diagnosis using Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The two feature selection algorithms were used to obtain the relevant attributes from the Wisconsin breast cancer (original) dataset. The selected attributes were passed to seven learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naïve Bayes (NB), K Nearest Neighhood (KNN), Neural Network (NN), Logistic Regression (LR), and Random Forest (RF). The diagnostic model was evaluated based on accuracy, precision, recall, and F1-measure. Experimental showed that the highest accuracy of 97.1388% was obtained in both PSO and ACO using RF classifier, the highest precision value of 0.9720 was recorded in ACO using RF classifier, the highest recall value of 0.9750 was achieved in PSO using RF classifier, the highest F1-measure value of 0.9700 was obtained in PSO using SVM, the highest kappa statistic of 0.9370 was obtained in both PSO and ACO using RF and the lowest time of 0s was taken to build a model was recorded in PSO using KNN and NB, and also in ACO using KNN. The paper concluded that the breast diagnostic model using PSO and ACO with different learning algorithms revealed that the accuracy of RF outperformed other algorithms. Also, it was shown that ACO produced better precision using RF compared with PSO and PSO gave better recall using RF compared with ACO, PSO recorded an efficient F1-measure using SVM. The best time used to build a model was obtained in PSO for KNN and NB, and ACO with KNN.Keywords— Breast cancer, Data mining, Diagnosis, Feature selection, Meta-heuristic.
Crowd Arbitration: Blockchain Dispute Resolution

Internet technology makes digital value transactions between anonymous individuals possible, but leaves unanswered the question of how to resolve disputes between unidentified parties. Blockchain dispute resolution platforms provide a response to this problem. In the social dispute resolution systems for blockchain currently in use, pseudo anonymous jurors can resolve disputes between pseudo anonymous parties. This paper presents Kleros as the most illustrative blockchain dispute resolution platform BDRP. To describe the features of the Kleros dispute resolution platform and the qualification of jurors, this research employs an online dispute resolution survey of both the jurors and stakeholders of the Kleros platform. This study raises important questions about key elements of procedural justice in resolution platforms for blockchain disputes. The research underlines the pros and cons of dispute resolution for crowdsourced blockchain and contributes to the further development of online dispute resolution systems. It tests the wisdom of the crowd as the core attribute of the resolution process in crowdsource disputes. Crowdsource mass dispute resolution, coupled with cooperative jurors and blockchain technology, could ensure greater effectiveness and fairness of the dispute resolution process, especially the adjudication of online small claims disputes.