Reviewerly/exHarmony huggingface dataset:
Optimized Machine Learning Model with Modified Particle Swarm Optimization for Data Classification

Metaheuristic search algorithms (MSAs) receive increasing popularity in recent year due to its excellent capability of solving complex real-world optimization problems without depending on gradient information. Particle swarm optimization (PSO), as one of MSAs, is widely used in optimization task due to its simple framework and quick convergence speed toward global optimum. However, conventional PSO suffers from premature convergence and quick diversity loss of population when the population is poorly initialized due to its random characteristics. In this paper, a new variant of PSO namely PSO with multi-chaotic scheme (PSOMCS) is introduced to train artificial neural network (ANN) by optimizing its neuron weights, biases and selection of suitable activation function based on the datasets obtained from UCI machine learning repository. Initial population generated using multi-chaotic system and oppositional-based learning ensure broader search space coverage, enabling PSOMCS to solve complex optimization problems effectively. Classification performances of ANN trained with PSOMCS are compared with other existing PSO variants. Based on simulation results, ANN optimized by PSOMCS outperformed its competitors in terms of classification performance for both training and testing datasets.
Tracing Method of False News Based on Python Web Crawler Technology

At this stage, false news is rampant in the new media environment. Due to the wide dissemination channels of false news, the large amount of information and data, and the difficult governance of false news in the news communication industry, this paper proposes a false news Traceability Method Based on Python web crawler technology, builds a false news traceability management mechanism, and combines Python web crawler technology to build a false news traceability evaluation system to achieve the goal of false news traceability, Finally, through experiments, it is proved that the method of tracing the source of false news based on Python web crawler technology has high practicability and accuracy in practical application, and fully meets the research requirements.
Research on False Public Opinion Recognition Method of Social Network Based on Fuzzy Cluster Analysis

In order to solve the problem that the classification of network pseudo public opinion events is too subjective, a social network pseudo public opinion recognition method based on fuzzy cluster analysis is proposed. Combined with the principle of fuzzy cluster analysis, a new index system for identifying false and emotional events is established. On this basis, the relevant index data of network false and emotional events are collected, and the classical fuzzy cluster analysis algorithm is used to cluster and analyze the network false and emotional events, so as to obtain different types of network false and emotional event sets, analyze and summarize the characteristics of all kinds of false and emotional events, and finally confirm through experiments, The social network false public opinion identification method based on fuzzy cluster analysis has high practicability, provides a new method for the identification and classification of network false and sentiment, and provides a reference for relevant departments to accurately control all kinds of false and sentiment by using network big data.
Digital Ark of Knowledge

Abstract The general problem of creating and using encyclopedic knowledge in the digital civilization is considered. The creation and use of a digital encyclopedia are an example of intellectual activities of a collective expanded personality. The basic principles concerning the creation and functioning the “Digital ark of knowledge” at Lomonosov Moscow State University are described in the case of mathematical sciences. The arising problems and their solutions are discussed.
A Bibliometric Analysis of Phishing in the Big Data Era: High Focus on Algorithms and Low Focus on People

The phishing attacks, based on social engineering to persuade potential victims to provide valuable information, have significantly increased in the pandemic Covid-19 era, characterised by ubiquitous big data technologies. This paper aims to assess the theoretical and empirical research on phishing emails and big data that has been done to identify trends and recommend new areas for research. Using the VOSviewer program, the search results from the Web of Science (WoS) database were extracted. A mapping technique, using VoS Viewer, was used to examine articles on big data and phishing emails. The findings show that most of the field's research is carried out in nations in Asia and the United States of America and that the number of publications in this area is increasing exponentially. However, it is evident that researchers predominately concentrate on technical fields like computer science. Even though they are used in relatively small quantities, machine learning techniques, particularly artificial neural networks, are associated with most of the phishing publications that have been studied. Six clusters correspond to the main phishing domains: Phishing target or victim, Phishing channel, Big data analytics, Big data machine learning, Phishing attacker, and External phishing protection. The results indicate that real-time data collection and the development of effective algorithms are new approaches to combating phishing assaults. However, research outside of the technical domains is scarce.
RapiTest: Continuous Black-Box Testing of RESTful Web APIs

When it comes to web services, RESTful web APIs have become the de facto standard since 2000. Those APIs expose back-end data, so it is crucial that they are robust, secure, and reliable to keep sensitive data protected. Although existing tools for automating APIs test case generation have shown significant potential, they are limited in their applicability since they focus solely on random inputs through fuzzing. Using only API specifications, it is impractical to describe personalized and specific test case workflows. This paper introduces RapiTest, an open-source continuous black-box testing application for RESTful web APIs. It takes advantage of the API specification to automatically generate tests, but also makes use of a new DSL named Test Specification Language (TSL), to create rich test cases. The RapiTest web application allows the setup of several predefined verifications, regarding security and correctness of the responses, while running the tests at regular intervals, such as every 24 hours. In this way, the API can be monitored continuously to ensure it is running correctly.
Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study

Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers.The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework.We analyzed data from a nationally representative survey of US adults about their COVID-19-related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates.An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12).Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters' friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety.
Optical plasma boundary detection and its reconstruction on EAST tokamak

Abstract Plasma boundary detection and reconstruction are important not only for plasma operation but also for plasma facing materials. Traditional methods, for example, EFIT code, which is constrained by electromagnetic measurement, and is very challenging for detecting the plasma boundary in long-pulse burning plasma devices such as ITER. A novel algorithm for the reconstruction of the plasma boundary using one visible camera has been developed on experimental advanced superconducting tokamak (EAST) for fusion reactors. A U-Net convolutional neural network was used to identify the plasma boundary and the pixel coordinates of the boundary points were fitted with EFIT via the XGBoost model. This algorithm can transform the boundary from the image plane to the poloidal plane of the Tokamak based on machine learning without traditional spatial calibration, and then the reconstruction of the plasma configuration shall be realized based on a monocular visible light camera. The reconstruction accuracy of this algorithm is relatively high. The average error on the test set was only 7.36 mm (&lt;1 cm) and satisfied the accuracy requirements of control for EAST tokamak. This result can contribute to the development of the plasma boundary reconstruction and operation based on one visible camera.
Textual emotion detection utilizing a transfer learning approach

Many attempts have been made to overcome the challenges of automating textual emotion detection using different traditional deep learning models such as LSTM, GRU, and BiLSTM. But the problem with these models is that they need large datasets, massive computing resources, and a lot of time to train. Also, they are prone to forgetting and cannot perform well when applied to small datasets. In this paper, we aim to demonstrate the capability of transfer learning techniques to capture the better contextual meaning of the text and as a result better detection of the emotion represented in the text, even without a large amount of data and training time. To do this, we conduct an experiment utilizing a pre-trained model called EmotionalBERT, which is based on bidirectional encoder representations from transformers (BERT), and we compare its performance to RNN-based models on two benchmark datasets, with a focus on the amount of training data and how it affects the models’ performance.
Accountancy for E-Business Enterprises Based on Cyber Security

E-businesses (EBEs) may commit legal offenses due to perpetrating cybercrime while doing the commercial activity. According to the findings, various obstacles might deter cybercrime throughout accounting. The study examined the present laws for accounting policy elements and determined those aspects that should be included in the administrative document for e-business enterprise accounting policies. E-businesses must avoid cyber-crime (CC), which has a detrimental influence on the company's brand and diminishes client loyalty to ensure their success. According to the study's findings, the use of information and control functions of accounting can help prevent cyber-crime in the bookkeeping system by increasing the content of individual internal rules. The authors intended to make online payments for EBE-CC as safe, easy, and fast as possible. However, the internet is known for making its users feel anonymous. E-commerce (EC) transactions are vulnerable to cybercrime, resulting in considerable money and personal information losses.
Learning programs with magic values

Abstract A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice. Learning programs with magic values is difficult for existing program synthesis approaches. To overcome this limitation, we introduce an inductive logic programming approach to efficiently learn programs with magic values. Our experiments on diverse domains, including program synthesis, drug design, and game playing, show that our approach can (1) outperform existing approaches in terms of predictive accuracies and learning times, (2) learn magic values from infinite domains, such as the value of pi , and (3) scale to domains with millions of constant symbols.
Improving Transfer Learning for Movie Trailer Genre Classification using a Dual Image and Video Transformer

In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we present an extensive evaluation of the transferability of ConvNet and Transformer models pretrained on ImageNet and Kinetics to Trailers12k, a new manually-curated movie trailer dataset composed of 12,000 videos labeled with 10 different genres and associated metadata. We analyze different aspects that can influence transferability, such as frame rate, input video extension, and spatio-temporal modeling. In order to reduce the spatio-temporal structure gap between ImageNet/Kinetics and Trailers12k, we propose Dual Image and Video Transformer Architecture (DIViTA), which performs shot detection so as to segment the trailer into highly correlated clips, providing a more cohesive input for pretrained backbones and improving transferability (a 1.83% increase for ImageNet and 3.75% for Kinetics). Our results demonstrate that representations learned on either ImageNet or Kinetics are comparatively transferable to Trailers12k. Moreover, both datasets provide complementary information that can be combined to improve classification performance (a 2.91% gain compared to the top single pretraining). Interestingly, using lightweight ConvNets as pretrained backbones resulted in only a 3.46% drop in classification performance compared with the top Transformer while requiring only 11.82% of its parameters and 0.81% of its FLOPS.
Neural group recommendation based on a probabilistic semantic aggregation

Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked recommendation list, a virtual user, or a multi-hot input vector encoding. This paper proposes an innovative strategy where aggregation is made in the multi-hot vector that feeds the neural network model. The aggregation provides a probabilistic semantic, and the resulting input vectors feed a model that is able to conveniently generalize the group recommendation from the individual predictions. Furthermore, using the proposed architecture, group recommendations can be obtained by simply feedforwarding the pre-trained model with individual ratings; that is, without the need to obtain datasets containing group of user information, and without the need of running two separate trainings (individual and group). This approach also avoids maintaining two different models to support both individual and group learning. Experiments have tested the proposed architecture using three representative collaborative filtering datasets and a series of baselines; results show suitable accuracy improvements compared to the state of the art.
Can Cryptocurrency Be a Payment Method in a Developing Economy?

This study sought to understand the factors driving the consumer adoption of a cryptocurrency, in particular Bitcoin, as an electronic payment (e-payment) system for electronic commerce (e-commerce) transactions within a developing economy such as South Africa. The advent of e-commerce has led to increased online transactions facilitated by e-payment systems, which can fall prey to opportunistic hackers. Cryptocurrencies have been pegged as a solution to this security issue. However, little is currently known around consumer propensity to use a cryptocurrency as an e-payment option, particularly within a developing economy. The investigated factors that could influence user adoption were based on literature and tested on a South African representative sample of 814 respondents. Of the factors identified from literature, the study found that “perceived usefulness and perceived ease of use,” “self-efficacy,” “awareness,” “trust,” and “security” have the most significant influence on South African consumers adopting a cryptocurrency as an e-payment system.
C3I-SynFace: A synthetic head pose and facial depth dataset using seed virtual human models.

This article presents C3I-SynFace: a large-scale synthetic human face dataset with corresponding ground truth annotations of head pose and face depth generated using the iClone 7 Character Creator "Realistic Human 100" toolkit with variations in ethnicity, gender, race, age, and clothing. The data is generated from 15 female and 15 male synthetic 3D human models extracted from iClone software in FBX format. Five facial expressions - neutral, angry, sad, happy, and scared are added to the face models to add further variations. With the help of these models, an open-source data generation pipeline in Python is proposed to import these models into the 3D computer graphics tool Blender and render the facial images along with the ground truth annotations of head pose and face depth in raw format. The datasets contain more than 100k ground truth samples with their annotations. With the help of virtual human models, the proposed framework can generate extensive synthetic facial datasets (e.g., head pose or face depths datasets) with a high degree of control over facial and environmental variations such as pose, illumination, and background. Such large datasets can be used for the improved and targeted training of deep neural networks.
Dense light field reconstruction based on epipolar focus spectrum

Existing light field (LF) representations, such as epipolar plane image (EPI) and sub-aperture images, do not consider the structural characteristics across the views, so they usually require additional disparity and spatial structure cues for follow-up tasks. Besides, they have difficulties dealing with occlusions or large disparity scenes. To this end, this paper proposes a novel Epipolar Focus Spectrum (EFS) representation by rearranging the EPI spectrum. Different from the classical EPI representation where an EPI line corresponds to a specific depth, there is a one-to-one mapping from the EFS line to the view. By exploring the EFS sampling task, the analytical function is derived for constructing a non-aliasing EFS. To demonstrate its effectiveness, we develop a trainable EFS-based pipeline for light field reconstruction, where a dense light field can be reconstructed by compensating the missing EFS lines given a sparse light field, yielding promising results with cross-view consistency, especially in the presence of severe occlusion and large disparity. Experimental results on both synthetic and real-world datasets demonstrate the validity and superiority of the proposed method over SOTA methods.
Similarity matrix enhanced collaborative filtering for e-government recommendation

Personalized e-government recommendation aims to predict the user’s next action in a near future based on his or her historical behavior data. Similarity measures and distances are widely used for powering personalized recommendations. The classical similarity-based models in the recommender systems are known as K-Nearest Neighbor Collaborative Filtering (KNN-based CF). Most prior work adopts a new similarity formula to obtain the k-nearest neighbors for the user and/or item. This is a good try but falls short of solving the problem of expensive computation and introducing the order of users’ interaction sequences. In this paper, we propose SACF, a similarity matrix enhanced the e-government recommendation model that takes advantage of pairwise learning to introduce the order of uses’ interaction sequences and quickly obtain the similarity matrix using matrix factorization. In other words, the similarity matrix can be presented as the production of two low-rank feature matrices. Therefore, it is possible to effectively estimate the similarity between two users even with few co-rated items. Experimental results on a real-world e-government dataset show that the proposed approach improves the performance significantly compared with other counterparts.
Category-aware optimal transport for incomplete data classification

Classification with incomplete data is practically challenging since missing data hinders classification. The two-stage strategy of filling missing values first and then classifying complete data degenerates performance due to the separate optimization of classification and imputation. While efforts have been made to classify incomplete data directly without imputation, the benefits of imputation on classification have been sacrificed. To this end, this paper proposes a Category-Aware Optimal Transport Neural Network (CAOT-NN) for incomplete data classification. Specifically, we design a category-aware optimal transport method conditioned on intra-category data distribution, which explicitly employs the category information of each sample and fills in discriminative values for the downstream classification module. Moreover, we reconstruct the observed values during the imputation process when performing classification learning, thus implicitly utilizing the category information of the classifier for missing values estimation. Extensive experiments conducted on 16 real-world and 30 synthetic datasets from the UCI repository demonstrate the superiority of CAOT-NN against existing state-of-the-art methods. Also, the visualization analysis reveals that CAOT-NN enables a tighter intra-category distribution of the imputed data, implying that the missing mechanism may be utilized as a data augmentation strategy to boost accuracy.