Reviewerly/exHarmony huggingface dataset:
Prediction of Hospital Status of COVID-19 Patients from E-Health Records

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Embedded in these big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 610 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 2.5 years since COVID-19 has declared as a pandemic. As some of these cases require hospitalization. it is important to estimate the demand in hospitalization. Moreover, different levels of hospitalization may require different types of resources (e.g., hospital beds, medical staff). For example, patients admitted into the intensive care unit (ICU) may require assisted ventilation. Hence, in this paper, we present models to make predictions based on e-health records. Specifically, our binary model predicts whether a patient require hospitalization, whereas our multi-class model predicts what level of hospitalization (e.g., regular ward, semi-ICU, ICU) is required by the patient. Our models uses few-shot learning (and may use multi-task learning) with autoencoders (comprising encoders and decoders) and a predictor. Evaluation results on real-life e-health records show the practicality of our models in predicting hospital statuses of COVID-19 cases and the benefits of these models towards effective allocation of resources (e.g., hospital facilities, staff).
Research and Implementation of Intelligent Financial Audit System Based on Deep Learning

With the increasing popularity of computer technology and network technology, database technology has been widely used in all walks of life. The intellectualization of audit work takes big data and artificial intelligence as the foundation and core, and adopts the form of audit template to fix the data acquisition technology, data sorting technology, data analysis technology, data mining technology and audit judgment technology with strong universality and high maturity, so as to form an audit software with audit template as the core. Traditional audit methods are difficult to find all kinds of financial problems hidden in massive data. According to the requirements of audit informatization and intelligent improvement, an intelligent audit framework is designed and a financial audit platform is developed based on data mining technology and deep learning algorithm. This paper expounds how to apply the innovative in-depth learning method to the audit process to solve the key problems in the project audit process, such as data collection and sorting, multi-source heterogeneous data feature extraction, project anomaly identification and project anomaly analysis. Based on the interdisciplinary thinking and the comprehensive use of data mining technology, fault diagnosis theory based on expert knowledge and financial audit theory, this paper develops an intelligent financial audit system which can deal with a variety of data types and automatically find audit clues.
Seemingly Unrelated Regressions

Therefore, the estimates of the variance–covariance matrix of OLS from the system of two equations differs from OLS on each equation separately by a scalar, namely s2 rather than $$\mathrm {s}_{\mathrm {i}}^{2}$$ for i = 1, 2.
Creating a credit card anti-fraud prediction model using TensorFlow and Machine Learning

People’s desire for credit cards is increasing because of the rapid rise and development of e-commerce. There is no doubting that the advancement of e-commerce has made life and work more easier for individuals. But, at the same time, this system has some flaws, which are occasionally used to get fraudulent benefits. In the real world, both fraudulent and legitimate transactions occur, and traditional pattern matching algorithms are typically ineffective in detecting fraud. This article combines tensor flow and machine learning to process historical credit card transaction data, perform machine learning, develop a credit card anti-fraud prediction model, and forecast consumer credit card theft in advance. The recall rate and accuracy rate of the model are obtained, which is conducive to building an efficient and accurate credit card fraud detection mechanism.
Long-Term Power Demand Intelligent Forecast of Shanghai Based on ACO-ELM Model

With the goal of carbon peak, carbon neutralization and dual control of energy consumption, higher requirements for regional energy and power consumption have been put forward. In order to grasp the regional power demand more accurately, this paper proposes an extreme learning machine power demand prediction method based on ant colony optimization. Firstly, by analyzing the particularity of Shanghai's economic and social development, the influences of regional GDP, population change, energy consumption and unit energy consumption on power demand are fully considered. Secondly, correlation degree analysis was used to screen the influencing factors, and the influential factors with high correlation with power demand were selected to construct the model input feature set. Finally, the power demand prediction model of extreme learning machine is established, and the ant colony algorithm is used to optimize the model parameters. Example analysis shows that the proposed model has high prediction accuracy and can provide reliable reference for power production departments.
Graph Topology Noise Aware Learning by Feature Clustering and Pseudo-labels Generator

Graph Convolutional Networks (GCNs) and their variants have achieved impressive performance in a wide range of graph-based tasks. For graph data, both feature information and structural information play a crucial role. Most GCNs update the node representation by aggregating the information from neighbors. However, the structural information may contain noise, which may mislead the downstream tasks. Hence, a new graph topology optimization method for the semi-supervised node classification tasks, GTNACP is proposed to improve the quality of structural information. The core idea of our method is to filter the structural information to be optimized by comparing the difference between the clustering results of the input data and the pseudo-label values obtained from pre-training. Due to the introduction of pseudo-labels with noise, instead of fully confiding in the generated labeled set, we design new loss functions as measurements of their confidence. In this way, GTNACP can alleviate the impact of incorrect pseudo-labels. Moreover, we experimentally find that deleting or adding edges directly by error can irreversibly degrade the performance. In order to alleviate such negative impact, GTNACP adopts an edge modification method based on node similarity and clustering performance. Our experiments verify that GTNACP can be easily combined with traditional GCNs and outperform baseline models in various semi-supervised node classification tasks, and to some extent, can effectively mitigate over-smoothing.
Research on Dance Movement Evaluation Method Based on Deep Learning Posture Estimation

This study proposes a quantitative evaluation method of monocular video dance movements based on deep learning, and provides a scientific and quantitative dance movement evaluation system to help students and dance teachers quickly identify and judge their dance movement standards. In this paper, the neural network-based 3D human pose estimation method is used to realize the identification and collection of key points of video human pose and skeleton. Aiming at the problem that it is difficult for users to ensure the time consistency with the reference video in the actual video acquisition process, a video frame alignment method based on ant colony algorithm is proposed. Aiming at the problem of large coordinate displacement caused by the tester's height, fat and thinness, and different video shooting angles, the evaluation and analysis method of moving human body posture based on similarity matching between feature planes is used to solve the problem. The scores of the input movements, the user video and the benchmark video are output at the same time, and provided to dance learners, so that the students' “non-standard” dance movements can be identified in a timely and effective manner, and the evaluation results are fed back to the students in a friendly way, so that Students can correct dance movements in time to achieve better training results.
Design and Implementation of Computer Room Monitoring System Based on Wireless Sensor Network

This paper is a real-time monitoring and early warning system for the software and hardware operation status of the data center computer room by using the Internet of things and big data technology. Through the network security management cloud platform, it can realize the real-time viewing of the operation status and security early warning function through multiple terminals under unattended status, the collected log data is used for website operation environment security analysis and prediction to ensure network security operation and maintenance.
The Implementation of Android Application for Online Learning Using Blended Learning System at Sharia and Law Faculty UIN Sunan Gunung Djati Bandung

The online learning system during the Covid-19 pandemic is the best choice in the learning process, however, the quality of learning and knowledge transfer remains the top priority. An android-based blended learning system is used at the Faculty of Sharia and Law which presents the features needed in the online teaching and learning process by combining face-to-face learning using an e-learning system and a two-way communication system, so that maximum quality of learning can be achieved.
Crime Analysis and Prediction using Machine Learning Algorithms

Analysis of crime is an organized approach in identifying and assessing criminal patterns and trends. Crime and infringement are hazards to law and are expected to be in order. Accuracy in crime foresee tendency can provide assistance to increase the modern security by algorithmically. We have to visit many places regularly for our daily activities in our everyday lives. This paper has been done to predict which state has the highest crime rate using the machine learning techniques namely K-means, Naive Bayes, and Linear Regression. This study will provide assistance with most agreeable techniques of machine learning classification for the prediction of crime rates and its analysis. The algorithm which shows the best among these three in terms of efficiency and accuracy as considering the best algorithm for crime prediction and its analysis. This will recognize crime prone area and help public to become aware of the crime zones and find their safe route to the destination.
Doppler Navigation for small Aircraft over Sea

The standard aircraft navigation system consists of closely hybridizing an inertial measurement unit (IMU) and a GNSS receiver. GNSS receivers are inexpensive but the signal may be unavailable or unusable, while a low-drift IMU is expensive but provides autonomous navigation. A well-known alternative over land is to merge the measurements from a low-cost IMU with at least three relative groundspeed measurements from a Doppler radar. But this solution gives a biased speed over the sea because its clutter does not provide a zero Doppler reference due to the wind. The paper describes a method to correct this bias by learning a Doppler clutter model offline from a database consisting of a number of significant features extracted from Range-Velocity radar maps.
Real-Time Human Action Recognition Using Deep Learning

The future of computer vision lies in deep learning to develop machines to solve our human problems. One of the most important areas of research is smart video surveillance. This feature is related to the study and recognition of movements, and it's used in many fields, like security, sports, medicine, and a whole lot of new applications. The study and analysis of human activity is very important to improve because it is a very sensitive field, like in security, the human needs a machine's help a lot; and in recent years, developers have adopted many advanced algorithms to discover the type of movements humans preform, and the results differ from one to another. The most important part of human activity recognition is real time, so one can detect any issue, like a medical problem, in time. In this regard, the authors will use methods of deep learning to reach a good result of recognition of the nature of human action in real time clips.
A Hybrid Recommendation Model for Social Network Services Using Twitter Data

With the continuous popularization of social networking sites, e.g., Twitter and Facebook, people’s requirements for the quality of social network services are rising rapidly. Traditional personalized recommendation models rely on independent algorithms, i.e., streaming ranking algorithms, content-based algorithms, and collaborative filtering analysis. However, the recommendation performance of these models for different users is not stable, resulting in poor user experience on such web services. To tackle this issue, we propose a novel recommendation model integrating multiple recommendation algorithms, which can make corresponding recommendations according to the characteristics of users. The comparative analysis with Twitter data shows that the proposed model performs better than the traditional models.
Gradient-Rebalanced Uncertainty Minimization for Cross-Site Adaptation of Medical Image Segmentation

Automatically adapting image segmentation across data sites benefits to reduce the data annotation burden in medical image analysis. Due to variations in image collection procedures, there usually exists moderate domain gap between medical image datasets from different sites. Increasing the prediction certainty is beneficial for gradually reducing the category-wise domain shift. However, uncertainty minimization naturally leads to bias towards major classes since the target object usually occupies a small portion of pixels in the input image. In this paper, we propose a gradient-rebalanced uncertainty minimization scheme which is capable of eliminating the learning bias. First, the foreground pixels and background pixels are reweighted according to the total gradient amplitude of every class. Furthermore, we devise a feature-level adaptation scheme to reduce the overall domain gap between source and target datasets, based on feature norm regularization and adversarial learning. Experiments on CT pancreas segmentation and MRI prostate segmentation validate that, our method outperforms existing cross-site adaptation algorithms by around 3% on the DICE similarity coefficient.
Empathy as a factor of development creative individuality of students

The purpose of the study is to offer a point of view on solving the problem of developing the creative individuality of students. The article considers the ways of revealing the empathic abilities of students through the visual arts. The article is a description of the process of analysis and interpretation of works of easel painting by students. Special attention is paid to the description of the manifestation of emotional states of the individual in the process of studying works of art. The authors propose to consider creative individuality as a property of a person, reflecting the ability to appreciate, interpret and create an artistic image with various visual materials, characterized by the diversity of the idea, process and product, determined by the aesthetic attitude of the individual to the surrounding world. As a result, the specificity and role of classes on the analysis of easel paintings in the development of students ' creative individuality are revealed, the choice of methods and techniques that contribute to the development of individual perception of fine art is justified.
Detecting Anomalies in Quick Access Recorder Data

For the civil aviation industry, QAR provides a gold mine of data for a wide range of applications. The data quality is an essential precondition for its usage. Thus, to detect the abnormalities or outliers is an important procedure for its further utilization. In this study, we adopted four approaches to detect the abnormalities, i.e. Pauta criterion, boxplot, K-means algorithm and LOF algorithms. Results show that these approaches perform differently for various parameters in a QAR data set. The Pauta criterion seems to be not working well for most of the QAR parameters. In contrast, the K-means clustering and LOF algorithms generally work well with the selected QAR parameters.
Text Classification Models and Topic Models: An Overall Picture and a Case Study in Vietnamese

Document classifiers are supervised learning models in which documents are assigned labels based on models that are trained on labeled datasets. The accuracy of a classifier depends on the size and quality of training datasets, which are costly and time-consuming to construct. Besides, a suitable word representation method may improve the quality of the text classifier. In this paper, we study the effect of different word representation methods on 16 classification models trained on a labeled dataset. Then, we experiment with the ability to discover latent topics using 6 topic models. Based on experimental results using combination of classification models and topic models, we propose a method to label datasets for training classification models using topic models and classification models. Although we perform experiments on a Vietnamese document dataset, our approach may apply to any datasets and does not require any labeled datasets for bootstrapping.
A Computationally Efficient Card-Based Majority Voting Protocol with Fewer Cards in the Private Model

Card-based cryptography realizes secure multiparty computation using physical cards. In 2018, Watanabe et al. proposed a card-based three-input majority voting protocol using three cards. In a card-based cryptographic protocol with n-bit inputs, it is known that a protocol using shuffles requires at least 2n cards. In contrast, as Watanabe et al.'s protocol, a protocol using private permutations can be constructed with fewer cards than the lower bounds above. Moreover, an n-input protocol using private permutations would not even require n cards in principle since a private permutation depending on an input can represent the input without using additional cards. However, there are only a few protocols with fewer than n cards. Recently, Abe et al. extended Watanabe et al.'s protocol and proposed an n-input majority voting protocol with n cards and n + ⌊n/2⌋ + 1 private permutations. This paper proposes an n-input majority voting protocol with ⌈n/2⌉ + 1 cards and 2n-1 private permutations, which is also obtained by extending Watanabe et al.'s protocol. Compared with Abe et al.'s protocol, although the number of private permutations increases by about n/2, the number of cards is reduced by about n/2. In addition, unlike Abe et al.'s protocol, our protocol includes Watanabe et al.'s protocol as a special case where n=3.
High Quality Audio Adversarial Examples Without Using Psychoacoustics

In the automatic speech recognition (ASR) domain, most, if not all, current audio AEs are generated by applying perturbations to input audio. Adversaries either constrain norm of the perturbations or hide perturbations below the hearing threshold based on psychoacoustics. These two approaches have their respective problems: norm-constrained perturbations will introduce noticeable noise while hiding perturbations below the hearing threshold can be prevented by deliberately removing inaudible components from audio. In this paper, we present a novel method of generating targeted audio AEs. The perceptual quality of our audio AEs are significantly better compared to audio AEs generated by applying norm-constrained perturbations. Furthermore, unlike approaches that rely on psychoacoustics to hide perturbations below the hearing threshold, we show that our audio AEs can still be successfully generated even when inaudible components are removed from audio.
Two-step wireless link quality prediction using multi-camera images

Given the recent advances in wireless communication technology, all things are being connected to networks, and it is expected that various new and novel applications will be created. The diversification in applications will yield various requirements for wireless communication. We believe that autonomous robot services will be popular after COVID, and the management, monitoring, and operation of the mobility robots requires highly reliable wireless links. In this paper, we propose a wireless link quality prediction system that uses camera images. The proposal generates a prediction model for each camera and combines the output of the models using weights calculated by the outputs of reliability models. By providing separate prediction models for each camera, the prediction system easily handles new additional cameras and drops the cameras found to have low reliability. Indoor experiments show that the proposed prediction scheme outperforms the prediction method that uses all camera images as input features without regard to their reliability.
Reliable Data Communication Model for Fog Computing

To maintain data privacy and control who has access to what in the cloud, attribute-based encryption might be utilized. Attribute security is violated when apparent qualities are introduced to the encrypted message to assist people to identify necessary details in vast systems. To offer an effective attribute-based access control with an authorized search strategy, this research expands the anonymous key-policy attribute-based encryption (AKP-ABE) to provide fine-grained data retrieval while safeguarding attribute privacy (EACAS). In EACAS, data users may generate the trapdoor using the secret key supplied by data owners and conduct searches based on access restrictions to get the relevant data. Cryptographic protocols and trapdoor generation use a synthetic property devoid of syntactic significance to provide an attribute-based search on the exported encoded information in the fog. Data owners may implement granular access control on their outsourced data by establishing the search criteria that will be used by data consumers to locate relevant content based on protected attributes. We show that compared to the state-of-the-art methods, EACAS requires less time and space to process and store data.
Explanation-based Graph Neural Networks for Graph Classification

Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant influence on the whole graph, providing accurate explanations for predictions of GNN. We noted that explanation components could help improve classification performance as unique representations of each class. Therefore, we suggest the GNN performance can be further improved by using explanation components. In this paper, we propose an Explanation-Based Graph Neural Networks (EBGNN) that utilizes contrastive learning at the instance level, by applying explanation components. In EBGNN, the explanation components ensure similarity for instances within the same class, and promote separability for instances in different classes. Finally, we conducted an evaluation on five benchmark datasets (MUTAG, IMDB-BINARY, PROTEINS, NCI1, and DD). Our experiment showed a significant increase in graph classification performance compared to state-of-the-art methods.