Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Financial Factor Mining Based on Gene Expression Programming

Financial factor mining has important theoretical significance and practical value for further improving the investor's rate of return, but it has always been manually mining. Automatic mining of investment factors by genetic algorithm can better realize the preliminary work of finding factors, understanding factors, and applying factors. This paper proposes a method based on gene expression programming (GEP) to mine the relationship between data and establish the predictive model, and the optimization goal is the return rate after the next 5 trading days. The empirical research on the financial factor mining through genetic optimization method has achieved certain results and the experimental results have been analyzed.
Perceptual Quality Assessment of DIBR Synthesized Views Using Saliency Based Deep Features

In recent years, Depth-Image-Based-Rendering (DIBR) synthesized views have gained popularity due to their numerous visual media applications. Consequently, the research in their quality assessment (QA) has also gained momentum. In this work, we propose an efficient metric to estimate the perceptual quality of DIBR synthesized views via the extraction of Deep-features. These Deep-features are extracted from a pretrained CNN model. Generally, in DIBR synthesized views, geometric distortions arise near the objects due to occlusion, and the human visual system is quite sensitive towards these objects. On the other end, saliency maps are efficiently able to highlight perceptually important objects. With this intuition, instead of extracting deep features directly from DIBR synthesized views, we obtain the refined feature vector from their corresponding saliency maps. Also, most of the pixels with geometric distortions have a nearly similar impact on the perceptual quality of 3D synthesized views. Considering this, we propose to fuse the feature maps using the cosine similarity measure based upon the deviation of one feature vector from another. It may also be emphasized that no training is performed in the proposed algorithm, and all the features are extracted from the pre-trained vanilla VGG-16 architecture. The proposed metric, when applied to the standard database, results in PLCC of 0.762 and SRCC equal to 0.7513, which is better than the existing state-of-the-art QA metrics.
Research Trend of Green Finance in China Based on Co-word cluster Analysis

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
FIUS: Fixed partitioning undersampling method

In the medical field, data techniques for prediction and finding patterns of prevalent diseases are of increasing interest. Classification is one of the methods used to provide insight into predicting the future onset of type 2 diabetes of those at high risk of progression from pre-diabetes to diabetes. When applying classification techniques to real-world datasets, imbalanced class distribution has been one of the most significant limitations that leads to patients’ misclassification. In this paper, we propose a novel balancing method to improve the prediction performance of type 2 diabetes mellitus in imbalanced electronic medical records (EMR). A novel undersampling method is proposed by utilizing a fixed partitioning distribution scheme in a regular grid. The proposed approach retains valuable information when balancing methods are applied to datasets. The best AUC of 80% compared to other classifiers was obtained from the logistic regression (LR) classifier for EMR by applying our proposed undersampling method to balance the data. The new method improved the performance of the LR classifier compared to existing undersampling methods used in the balancing stage. The results demonstrate the effectiveness and high performance of the proposed method for predicting diabetes in a Canadian imbalanced dataset. Our methodology can be used in other areas to overcome the limitations of imbalanced class distributions.
Research and Application Scenario of Risk Early Warning System for Large Power Grid Considering Intensive Transmission Channel Disaster

By analyzing the application status of intensive transmission channel monitoring, this paper put forward the bottleneck and key issues of integrated supervision in this field. Through the requirement analysis of risk early warning of large power grid, this paper has proposed the technical difficulties of the external disasters of intensive transmission channels and data sources , analyzed the data status and data characteristics of the main external environment affecting intensive transmission channels, such as meteorology, typhoon, wildfire, icing, lightning, etc., and designed and implemented the multi-source data sharing forms and specific technical route based on the whole network. According to the characteristics of risk early warning data sources of large power grid considering intensive transmission channel disasters, this paper has designed the technical architecture of data specification, put forward key technologies including standardization of data modeling, structuring of data storage, global data sharing, and service-oriented data usage.This paper has proposed approaches of relational model building and risk assessment between external disasters and key equipment in intensive transmission channels, and finally looked into the future of the application of network-wide multi-source data fusion technology in ensuring the safe and stable operation of large power grids.
Face Models: How Good Does My Data Need To Be?

Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? This paper takes a step toward answering this question. A variety of common input data types such as 2D landmarks and 3D scans are constructed from an existing high quality dataset. A morphable face model is then used to reconstruct 3D faces. By comparing to ground truth, an analysis of the relative error between different data types is obtained.
Sensor-Based Smart Glove for Rehabilitation of Paralysis Patients

This work aims at representing the design of a system which helps the robotic arm controlling through a sensory glove which can be used for the purpose of rehabilitation for the disabled. The smart glove consists of the controller component that features sensors, which include the Inertial Measurement Units (IMUs) and a sensor board (MPU6050). We have only tested the smart glove with MPU6050IMU, which is a sensory IMU board. We have also used Encoders for the measurement of the rotational movement of the hand. The result showed that the sensor glove successfully showed the finger and the wrist flexion movements. However, the glove requires further changes due to a few drawbacks which includes accuracy. Nonetheless, an improved glove design could even help us increase its applications like the manipulation of the robotic hand to mimic the actual hand movements, or for gesture recognition for the disabled or as a diagnostic tool for finger movements.
Unsupervised Learning: Deep Generative Model

Variational autoencoders (VAEs), proposed by Kingma and Welling in 2013, allow us to design complex generative models of data, which can then be trained to generate fictional images such as celebrity faces and high-resolution digital artworks.
Amazon Product Brand Analysis Framework Using Apache Spark on Real-Time Consumer’s Perception

There is enormous growth of Internet users of today’s data science world. Consumers usually prefer buying and finding items online through leading sites like Amazon in the busy hi-tech schedule. They usually read other customer’s feedback for product brand and compare it accordingly based on different criteria. Brand building and positioning involve concerted efforts of several moving parts to establish a market image that moves the company forward. In this paper, we introduce a framework for cohort analysis which highlights activities of people over a period of time. Generally, marketer uses month wise analysis for prediction of product brand sale performance-based customers review. The outcome of the analysis can be used to check the patterns or changes in customer behavior and views toward the product and why there is a change by comparing result. Apache Spark is a flexible in-memory framework for distributed computing and can also handle structured data with Hive. Apache Spark has access to HDFS and can run on both existing Hadoop clusters and new ones. It can handle much more complex data structures than MapReduce, especially compared to MapReduce.
APPS FOR THE PROMOTION OF MENTAL HEALTH IN THE CONTEXT OF THE COVID-19 PANDEMIC

The objective was to identify scientific evidence on the development of mobile apps built to promote mental health in the context of the COVID-19 pandemic. It is an integrative review of the scientific literature carried out in the Medline, Web of Science and Lilacs databases, and in the Cochrane library with the following guiding question: what is the scientific evidence on the development of mobile apps built to promote mental health in the context of the COVID-19 pandemic? A total of 194 studies were found, of which 181 were excluded for not addressing the theme and nine for not answering the guiding question, in this search scenario, four articles were identified. The findings demonstrate the existence of apps in the mental health area addressing the themes of depression, anxiety, psychological stress, burnout syndrome, suicidal ideation and psychological suffering, as well as the absence of scientific publications on the apps already available on digital platforms
Combining feature interaction and channel attention for Click-Through Rate Prediction

Click-through rate prediction is in considerable demand and has been extensively studied because it allows advertisers and advertising platforms to target consumers for particular marketing contents. In this paper, we propose a novel model that can learn the interaction between various dimensions. Besides, our model can also strengthen the weight of important interactions and weaken the weight of unimportant relationships. Furthermore, our model can learn not only the low-order interaction between features but also the high-order interaction between features, which improves the interaction ability of features. We performed extensive experiments using two open datasets of Avazu and Criteo, which proved that our model is better than other state-of-the-art models.
Web-Based Gamification Loaded E-Learning Information System For Junior High Schools

Currently teaching and learning activities are carried out using E-learning facilities, E-learning is a means of teaching and learning activities from school to college. E-learning itself is now one of the standard learning methods in several schools and gamification is needed to support e-learning so that users are interested in its use. Gamification itself implements in-game features into a non-game context such as points, badges, and leaderboard. The method used in this research is the waterfall method which this method plans a system from upstream to downstream. In terms of appearance, this website looks attractive in for its users. This system is easy to use, so it is easier for students to do learning even though they are at home. The result of this research is that students can learn anywhere using available devices.
아시아 선교사의 소진과 회복탄력성에 대한 근거이론적 접근

This study analyzes the process from burnout to resilience for missionaries who do missionary work in Asia’s creative approach nations. The research question is “How about the burnout and resilience of missionaries working in Asia’s creative approach nations?” For this study, from May to September 2020, twenty-three research participants were interviewed in depth to collect data and analyzed. There were 691 final concepts, and 126 subcategories were created, which were classified into 25 categories. The process from burnout to resilience was analyzed, and in connection with this, 9 stages of identity were derived. This study is meaningful in providing basic data for establishing member care policies to support missionaries by theorizing identity changes of missionaries.
Appendix: Matrix Equations

The Lyapunov equation and the algebraic Riccati equation are treated in depth. The Lyapunov equation arises as the equation for the asymptotic covariance matrix of the state of a stationary Gaussian system. The algebraic Riccati equation arises in the Kalman filter, in stochastic control, and in stochastic realization of a Gaussian system. Results for both equations are provided on: the existence of a solution, uniqueness with respect to conditions, a description of the set of all solutions if applicable, particular properties of solutions, and on the computation of solutions.
An Improved Echo State Network Model for Spatial-Temporal Energy Consumption Prediction in Public Buildings

With the continuous development of the global economy and the acceleration of urbanization, the annual energy consumption of buildings also occupies a considerable scale. In order to achieve energy saving and emission reduction in buildings, reasonable energy management for buildings is an important tool to achieve the goal of energy saving and emission reduction. In this paper, an improved Echo State Network method is used to predict building energy consumption. This improved echo state network can not only handle energy consumption data of a single building, but also combine multiple spatially correlated building energy consumption data to further improve the accuracy of energy prediction. The results show that the accuracy of the new model proposed in this paper for building energy consumption prediction is better than that of the classical ESN model and other classical machine learning models, and the model is well suited for end-to-end prediction tasks for multiple buildings. Combined with the clustering algorithm, it can also achieve acceleration for end-to-end prediction tasks.
Chess AI with Different Behavioral Tendencies and Its Application

research-article Chess AI with Different Behavioral Tendencies and Its Application Share on Authors: Yuanqi Hu Jiangnan University, China Jiangnan University, ChinaView Profile , Ruimin Lv Jiangnan University, China Jiangnan University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 265Pages 1–5https://doi.org/10.1145/3469213.3470698Online:18 August 2021Publication History 0citation26DownloadsMetricsTotal Citations0Total Downloads26Last 12 Months26Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Blockiness Distortion Measurement for Intra-Coded Video Frames by Using an Efficient NR Algorithm

Lossy coding schemes are used in video encoding tools to fulfill the constraints of power and bandwidth. These coding schemes produce spatial and temporal distortions which affect the quality of the received video at user end. Blockiness is the most important and annoying distortion during compression. Measuring the video quality objectively enables dynamic monitoring and evaluation of emerging digital applications. In this work, Radon transform approach is used to propose a novel No-Reference (NR) blockiness metric. The proposed metric evaluates directional projection values along the block boundaries in both the horizontal and vertical directions and also takes into account the pixels near to the block boundaries. The difference of the directional projection values gives an estimate of blocking distortions. The proposed NR metric is simple, computationally efficient, and has a strong correlation with subjective data as proved by results.
Analysis of Algorithms in Medical Image Processing

Biomedical imaging has gained tremendous attention over the years by aiming to attain better visualization of the abnormalities of structures or disorders in the images of the organs of the body. The motive behind the ensuing procedures of image processing in the medical domain is to improvise the decision-making in the clinical diagnosis and to resolve the issues faced by surgeons to operate the organ by providing the higher dimensional view of internal parts of that organ. The image processing techniques include various steps, including image acquisition, image reconstruction enhancement and analysis. This paper will summarize the imaging (medical) processing techniques used in healthcare informatics along with the challenges faced by the image construal with diverse algorithms, such as image segmentation by edge detection method, k-means etc. The interpretation formed by the physicians after visualizing medical images will improve remote healthcare and help in the treatment of patients in a better manner. The challenges faced by researchers while using a high-technology medical probe to generate better visualization promote their interest in the medical field domain.
Measuring Software Change Impact Using Requirement Dependencies

Tracking software changes and their impact are important tasks when developing and deploying software. Software developers armed with the knowledge of which changes have the highest impact on the existing software can selectively deploy changes to minimize any negative impacts on the deployment. Software developers can use requirements change impact measures to assist with how to implement the changes to software code. One challenge when identifying change impact is quantifying the effect of the impact. In this research, we approach the problem of identifying and quantifying the impact of changes by applying IR techniques along with data mining. We apply TF-IDF and LSI to help determine which changes have a high potential of modifying existing requirements. We also analyze similarities among requirements changes and similarities among requirements baselines. In both cases, our threshold for identifying similarity is between 75% - 95%. We use these transitive relationships in conjunction with the change to baseline relationships to derive more insight about the inter-relationships of the baseline requirements and changes to those requirements. We bucket these relationships according to their impact scores into buckets of low, medium, and high impact using per dataset analysis of tertiles.
On the Strategy of Combination Methods of Multimedia Technology and College English Teaching

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Application of Computer Technology in English Course of Network English in Higher Vocational Schools

Abstract With the progress of computer technology, computer has changed people’s way of life, and computer has penetrated into all aspects of people’s life, which has brought great convenience to people’s life. At the same time, computer technology has been widely used in English education classroom. The combination of computer technology and English classroom has greatly improved the efficiency of English classroom. At the same time, improve students’ interest in learning and autonomous learning ability, students’ foreign language communication ability also played a great help.
Boosting Semi-Supervised Anomaly Detection via Contrasting Synthetic Images

In this paper we propose to tackle the problem of semi-supervised anomaly detection, which aims to learn the outlier detector from the training set composed of only inliers. Built upon the recent advances of introducing contrastive learning to achieve the state-of-the-art of anomaly detection, we propose a simple but effective extension to further boost the performance via integrating the contrastive learning and the generative model of inliers into a unified framework. On one hand, the contrastive learning amongst the real samples and synthetic ones produced by the generative model improves the representation learning; on the other hand, the generative model learning is also benefited from the contrastive learning. We conduct extensive experiments to demonstrate the efficacy of our proposed method to advance anomaly detection, its superiority against several baselines, and the contribution of our model designs.
Feature based CT image registration of liver cancer

Computer-aided diagnostic systems (CADS) assist radiologists in classifying liver cancer using computed tomography (CT) images. To enhance diagnosis performance, image sequences are recorded at various time points in a single/multi-view format. Mutual information (MI) is a widely used medical image registration metric with a high rate of success, but it can result in misregistration due to a lack of spatial details. To address this issue and to establish anatomical correspondence between multi-phase CT images of the liver, a features-based technique is developed in this article. The proposed model uses fixed and moving images as inputs, with both images having the same dimensions. The registered images are the two images that differ in terms of their combinations/colors. In the output registered images, the tumor in the liver portion has classes with viewpoints. There is an appropriate way to view the tumor, and the output registered images should permit concluding that the registered image of the delayed phases, with a longer delay time, contains the most region portion within the output registered image. The detected and matched values are greater than the values of the feature outcomes. Having a large tumor provides valuable information in the presenting form for discussing the variation of the various phases and delayed testing results. And this will aid the radiologist in making an accurate diagnosis.
The average search probabilities of discrete-time quantum walks

We study the average probability that a discrete-time quantum walk finds a marked vertex on a graph. We first show that, for a regular graph, the spectrum of the transition matrix is determined by the weighted adjacency matrix of an augmented graph. We then consider the average search probability on a distance regular graph, and find a formula in terms of the adjacency matrix of its vertex-deleted subgraph. In particular, for any family of the average search probability approaches 1/4 as the valency goes to infinity. We also present a more relaxed criterion, in terms of the intersection array, for this limit to be approached by distance regular graphs.