Reviewerly/exHarmony huggingface dataset:
Does Digital Finance Promote the Upgrading of Industrial Structure

The growth of digital finance improves SMEs’ financing conditions and business circumstances and has a crucial role in upgrading the industrial structure of China. This paper takes China’s 30 provinces from 2011 to 2019 as samples and studies the effect of digital finance on upgrading China's industrial structure from both theory and demonstrative two aspects. The results reflect that digital finance stimulus the optimization and upgrading of China's industrial structure as a whole. Furthermore, digital finance in the eastern region has a significant positive impact on the industrial structure. However, digital finance plays an inhibitory role in upgrading the central and western regions in the industrial structure.
Ensemble machine learning for personalized antihypertensive treatment

Abstract Due to its prevalence and association with cardiovascular diseases and premature death, hypertension is a major public health challenge. Proper prevention and management measures are needed to effectively reduce the pervasiveness of the condition. Current clinical guidelines for hypertension provide physicians with general suggestions for first‐line pharmacologic treatment, but do not consider patient‐specific characteristics. In this study, longitudinal electronic health record data are utilized to develop personalized predictions and prescription recommendations for hypertensive patients. We demonstrate that both binary classification and regression algorithms can be used to accurately predict a patient's future hypertensive status. We then present a prescriptive framework to determine the optimal antihypertensive treatment for a patient using their individual characteristics and clinical condition. Given the observational nature of the data, we address potential confounding through generalized propensity score evaluation and optimal matching. For patients for whom the algorithm recommendation differs from the standard of care, we demonstrate an approximate 15.87% decrease in next blood pressure score based on the predicted outcome under the recommended treatment. An interactive dashboard has been developed to be used by physicians as a clinical support tool.
Optimized FPGA Implementation and Synthesis of Image Segmentation Techniques

Image segmentation is the process of dividing the image into its constituents such as regions and objects. Edge detection algorithms detect the edge pixels, which are the edges of the image in which the pixels' intensity changes abruptly. The techniques namely Sobel, Prewitt, and Robert are the prominent gradient-based edge detection algorithms that are operated on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{x}3$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\mathrm{x}2$</tex> kernels in the X and Y directions. This is performed in two different phases - python and Verilog(hardware descriptive language) for the synthesis on Basys 3 Artix 7 FPGA. In the first phase, the input image goes through the grayscale inversion for the gradient-based edge detection (Sobel, Prewitt, and Robert) operator. Later on, it goes through an edge detection algorithm that determines the edges of the entire image. The final image is then compared based on different input parameters like PSNR, MSE, and execution time required. In the second phase, the input grayscale image is passed through the line buffers to store the input pixels for further processing of the operators. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{x}3$</tex> 3 and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\mathrm{x}2$</tex> kernels are pre-defined based on the operator for convolution and are then fed to the output buffer. The final images were then compared with the same input parameters. The proposed approach in the first phase was performed with no pre-built functions in python and the second phase was performed in Verilog. The proposed approach is expected to suggest a highperformance operator.
The Visual Listening Guide

Abstract This chapter is a systematic examination of the Visual Listening Guide, a new type of printed graphic guide to facilitate active listening and understanding of orchestral works. Created by musicologist Hannah Chan-Hartley, the Visual Listening Guide is the first of its kind to appear in a concert program book, for use during a live performance. This chapter describes the development and creation of the Guide’s distinctive design. An analysis of its elements reveals how the Guide aims to show the structure of a symphonic work, acting like a map to it, indicating important sonic landmarks and other features. It also reviews the reception of the Visual Listening Guide following its first phase of use by concert-goers from 2015 to 2020, and considers the efficacy and potential of graphic guides to communicate about Western classical music to the broad public, helping organizations foster audience engagement and appreciation.
Sorting Device Coding Print Quality Machine on Packing Box Prototype Utilizing Optical Character Recognition

Abstract The design of a system that can recognize the characters printed by the coding machine so that it can then determine the assessment of very good and bad prints, is the goal of this research. This system is useful as an indicator of an indication of a problem with the coding machine which is indicated by the number of products rejected by the system. In addition, this system is an effort to reduce printing errors so that they are sorted and do not pass to the market, thus causing misinformation to the public. This system is in the form of a moving conveyor table that has a firing area box and a reject rod as a product separator. The OCR method is programmed in a computer that is part of the system, to be able to recognize the characters printed by this coding machine. USB to TTL which functions as serial communication from the Laptop to the Microcontroller, is a complement to this tool. Testing as many as 60 times on 12 types of characters, this system has been carried out on this tool. There are six types of good characters and six types of bad characters, which have been provided. Of the 60 tests, the results were four failed tests. That is, the percentage of success of this tool is 93.33%, and the percentage of error is 6.66%.
The Metamorphosis (of RAM3S)

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.
Domain Adaptive Semantic Segmentation via Image Translation and Representation Alignment

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called Image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixel-centroid triplet loss is designed to explicitly minimize the intra-class feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 → Cityscapes and SYNTHIA → Cityscapes scenarios.
Probabilistic Evaluation of the Exploration–Exploitation Balance during the Search, Using the Swap Operator, for Nonlinear Bijective S-Boxes, Resistant to Power Attacks

During the search for S-boxes resistant to Power Attacks, the S-box space has recently been divided into Hamming Weight classes, according to its theoretical resistance to these attacks using the metric variance of the confusion coefficient. This partition allows for reducing the size of the search space. The swap operator is frequently used when searching with a random selection of items to be exchanged. In this work, the theoretical probability of changing Hamming Weight class of the S-box is calculated when the swap operator is applied randomly in a permutation. The precision of these probabilities is confirmed experimentally. Its limit and a recursive formula are theoretically proved. It is shown that this operator changes classes with high probability, which favors the exploration of the Hamming Weight class of S-boxes space but dramatically reduces the exploitation within classes. These results are generalized, showing that the probability of moving within the same class is substantially reduced by applying two swaps. Based on these results, it is proposed to modify/improve the use of the swap operator, replacing its random application with the appropriate selection of the elements to be exchanged, which allows taking control of the balance between exploration and exploitation. The calculated probabilities show that the random application of the swap operator is inappropriate during the search for nonlinear S-boxes resistant to Power Attacks since the exploration may be inappropriate when the class is resistant to Differential Power Attack. It would be more convenient to search for nonlinear S-boxes within the class. This result provides new knowledge about the influence of this operator in the balance exploration–exploitation. It constitutes a valuable tool to improve the design of future algorithms for searching S-boxes with good cryptography properties. In a probabilistic way, our main theoretical result characterizes the influence of the swap operator in the exploration–exploitation balance during the search for S-boxes resistant to Power Attacks in the Hamming Weight class space. The main practical contribution consists of proposing modifications to the swap operator to control this balance better.
Linear Algebraic Computation of Propositional Horn Abduction

Linear algebraic characterization of logic programs has been investigated to perform logical inference in large-scale knowledge bases and has gained encouraging results. In this paper, we further extend the linear algebraic characterization in abductive reasoning by exploiting the transpose of the program matrix. Then we propose an efficient exhaustive search strategy, which combines the flexibility and robustness of numerical computation with the compactness and efficiency of set operations, in order to compute solutions of abductive Horn propositional tasks. Experimental results demonstrate that our method is competitive with conflict-driven techniques and has the potential to speed up on parallel computing platforms.
Fuzzy Time Series using Chen and Lee Model to Predict COVID-19 in Aceh Indonesia

Indonesia as a development country with huge of population and has five big islands need to study further about spread of COVID-19, especially in Aceh. Aceh is surrounded by water on three sides that are the Indian Ocean to the west and north and the Strait of Malaca to the east. Aceh as one of the provinces in Indonesia severe earthquakes in December 2004. To help monitor Aceh population, thus, in this study Aceh was chosen as the province to predict of survival COVID-19 that classify to ODP, PDP, and Positive using fuzzy time series approach. Fuzzy time series in one of the methods that have been used to predict seasonal issue, such as pandemic COVID-19. By using Chen and Lee model, this approach succeeds to fit the data actual during the predict phase.
Platform-Oblivious Anti-Spam Gateway

This paper addresses a novel anti-spam gateway targeting multiple linguistic-based social platforms to expose the outlier property of their spam messages uniformly for effective detection. Instead of labeling ground truth datasets and extracting key features, which are labor-intensive and time-consuming, we start with coarsely mining seed corpora of spams and hams from the target data (aiming for spam classification), before reconstructing them as the reference. To catch each word’s rich information in the semantic and syntactic perspectives, we then leverage the natural language processing (NLP) model to embed each word into the high-dimensional vector space and use a neural network to train a spam word model. After that, each message is encoded by using the predicted spam scores from this model for all included stem words. The encoded messages are processed by the prominent outlier techniques to produce their respective scores, allowing us to rank them for making the outlier visible. Our solution is unsupervised, without relying on specifics of any platform or dataset, to be platform-oblivious. Through extensive experiments, our solution is demonstrated to expose spammers’ outlier characteristics effectively, outperform all examined unsupervised methods in almost all metrics, and may even better supervised counterparts.
High-Tech Educational Environment as a Means of Personification in Engineering English Language Training

Engineering activity is becoming more integrated, complex, and innovative. Foreign language communicative competence is one of the key competencies of future elite engineers at South Ural State University, along with profound engineering knowledge. The purpose of this article is to sum up the results of implementing advanced language training in the elite education zone of future engineers through creating a high-tech educational environment where students have constant access to connectivity. On-line courses design allows students to get access to a broad range of educational resources and tools that help build an individual path of independent study to develop their potential. The effectiveness has been revealed via an online questionnaire, testing, and project work. The use of E-learning technologies allows to implement continuous learning processes in different educational contexts and provide on-demand support to students that make learning self-directed, motivated, adaptive, and resourceful.
Evaluation of Scrum-Based Software Development Process Maturity using the SMM and AMM: A Case of Education Technology Startup

PT XYZ builds an application in Education Technology to facilitate education processes easier, a necessity, especially in this pandemic era. Like many other startups who want to deliver faster and better, PT XYZ implements Scrum in its software development process. However, there are problems in the process indicated by the discrepancies of Sprint Goal compared to the Objective Key Result (OKR) set. This research evaluates the organization's software development process maturity and recommends improvement using a combination of Scrum Maturity Model (SMM) and Agile Maturity Model (AMM) frameworks. We collected data by interviewing, filling questionnaires, and observing the software development process, documentation, and tools. The result was processed using KPA rating from Agile Maturity Model. Our research shows that the organization's maturity level is at 1, with all general goals achieving a large rating. We recommend that PT XYZ implement 14 out of 69 practices in SMM that has not implemented. We also recommend the organization start the improvement from General Goals in maturity level 2, so the organization can also improve the general maturity. The stages of the research can be used as a reference in assessing maturity and providing recommendations for other startups.
Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm

Financial supervision plays an important role in the construction of market economy, but financial data has the characteristics of being nonstationary and nonlinear and low signal-to-noise ratio, so an effective financial detection method is needed. In this paper, two machine learning algorithms, decision tree and random forest, are used to detect the company's financial data. Firstly, based on the financial data of 100 sample listed companies, this paper makes an empirical study on the fraud of financial statements of listed companies by using machine learning technology. Through the empirical analysis of logistic regression, gradient lifting decision tree, and random forest model, the preliminary results are obtained, and then the random forest model is used for secondary judgment. This paper constructs an efficient, accurate, and simple comprehensive application model of machine learning. The empirical results show that the comprehensive application model constructed in this paper has an accuracy of 96.58% in judging the abnormal financial data of listed companies. The paper puts forward an accurate and practical method for capital market participants to identify the fraud of financial statements of listed companies and has certain practical significance for investors and securities research institutions to deal with the fraud of financial statements.
A Technical Report for Visual Attention Estimation in HMD Challenge

The 360° video, also known as omnidirectional video (ODV), immersive video or spherical video, has become increasing popular and drawn great research attention. To achieve the effectiveness of 360°videos, it is quite important to understand how human perceive and interact with 360° videos so that efficient techniques for their encoding, transmission, and rendering can be developed. The Visual Attention Estimation in HMD (Head Mounted Display) is a competition to encourage contestants to design lightweight models for predicting human eye attention in 360° videos. The models need to not only achieve high accuracy but also have outstanding performance on HMD devices. We employ the approach of ATSal [1] and fine-tune the expert models with the dataset provided in this event to achieve 0.840 AUC-J, 0.476 CC, 3.206 KLC, 1.478 NSS, 0.412 SIM, currently ranked the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place in the qualification competition leaderboard.
Estimation of optimal moving target of mobile robots by using a topological map update

In this paper, we propose a methodology for estimating the optimal moving destination for the mobile robot to autonomously perform the objects delivery service in an indoor environment. The process for position estimation is to create a generalized Voronoi graph composed of nodes and links from a grid map, and then perform object recognition through an object recognition module using an RGB-D sensor to extract object information and location information. Then, the obstacle shape and distance information are calculated using the laser sensor. From the extracted object position, size, and obstacle information, the optimal candidate area in which the robot can move is estimated, and the optimal final position within the area using a hybrid technique of center of gravity and thinning is selected. This location information goes through the process of moving or generating nodes through geometric distance comparison with existing nodes on the grid map according to the rule-based node location selection method, so that the mobile robot updates the optimal node location for the final destination.
Analysis of the error distribution density convergence with its orthogonal decomposition in navigation measurements

Abstract Modern trends towards the expansion of online services lead to the need to determine the location of customers, who may also be on a moving object (vessel or aircraft, others vehicle – hereinafter the “Vehicle”). This task is of particular relevance in the fields of medicine – when organizing video conferencing for diagnosis and/or remote rehabilitation, e.g., for post-infarction and post-stroke patients using wireless devices, in education – when organizing distance learning and when taking exams online, etc. For the analysis of statistical materials of the accuracy of determining the location of a moving object, the Gaussian normal distribution is usually used. However, if the histogram of the sample has “heavier tails”, the determination of latitude and longitude’s error according to Gaussian function is not correct and requires an alternative approach. To describe the random errors of navigation measurements, mixed laws of a probability distribution of two types can be used: the first type is the generalized Cauchy distribution, the second type is the Pearson distribution, type VII. This paper has shown that it’s possible obtaining the decomposition of the error distribution density using orthogonal Hermite polynomials, without having its analytical expression. Our numerical results show that the approximation of the distribution function using the Gram-Charlier series of type A makes it possible to apply the orthogonal decomposition to describe the density of errors in navigation measurements. To compare the curves of density and its orthogonal decomposition, the density values were calculated. The research results showed that the normalized density and its orthogonal decomposition practically coincide.