Reviewerly/exHarmony huggingface dataset:
Fusion Analysis of Economic Data of the Medical and Health Industry Based on Blockchain Technology and Two-Way Spectral Cluster Analysis

Due to the huge potential in gene expression analysis, which is helpful for disease diagnosis, new drug development, and life science research, the two-way clustering algorithm was proposed and it was widely used in gene expression data research. In order to understand the economic data of medical and health industry, this paper analyzes the economic data of the medical and health industry in different regions of China based on blockchain technology and two-way spectral cluster analysis and makes statistics on the economic data of the medical and health industry in eastern, central, and western regions of China. This paper studies the development status of China’s medical and health industry and the factors affecting the agglomeration of medical and health service industry and analyzes them under the blockchain technology and two-way spectral cluster analysis method. The results show that the overall development trend of China’s medicine and health is from government-led to government, society, and individual sharing. After the transformation of blockchain technology and two-way spectral cluster analysis, the output value of the pharmaceutical industry increased by about 10%.
Reading Habits and their Correlation with Reading Achievement among ESL Learners in Selected Malaysian Secondary Schools

Reading is a complex process that involves cognitive, physical, intellectual, cultural, and linguistic processes. Research shows that learners who read extensively tend to accomplish a high level of academic achievement. In Malaysia, the educational goal has shifted from simply forming literate citizens to nurturing a well-educated and intellectual society. Promoting efficient reading habits can be an effective approach to realizing this goal. This study, therefore, aims to examine reading habits and ascertain their correlation with reading achievement among final-year English as a Second Language (ESL) learners in selected Malaysian secondary schools. The study utilized a cross-sectional survey in which data were gathered from a sample of 351 ESL learners using questionnaires and written tests as data-gathering instruments. The analysis was conducted by means of descriptive statistics, Pearson correlation, and simple linear regression. The findings revealed a significant relationship between ESL learners’ reading habits and their reading achievement. Additionally, ESL learners’ reading habits contribute significantly to their reading achievement. These findings highlight the significance of reading habits in determining ESL learners’ reading achievement itself. However, this study is limited to reading habits and reading achievement among students in selected secondary schools. Thus, further research may replicate this study in the context of computer-assisted or blended learning.
A Fusion Estimation Algorithm Based on Machine Learning for Nonlinear Systems

This paper is concerned with distributed estimation problem for multi-sensor nonlinear systems. Based on the approximation performance of the BP network, we uses local CKF estimations as inputs and estimations of a distributed fusion CKF weighted by matrices (MW-CKF) algorithm as the target outputs to train the network, a BP net-based MW-CKF algorithm is proposed, which realizes the fusion effect of the MW-CKF algorithm. Compared with the MW-CKF, the proposed method reduces the computational cost of calculating cross-covariance matrices, and the theoretical equivalence of the machine learning method is proved. A target tracking process in a horizontal plane simulation verifies the effectiveness of the proposed fusion algorithm.
Linear Text Transformation for Pre-Printed Document

Skew identification and correction is one of the most fundamental phases in addressing the problems related to the OCR system. One of the challenges faced by the OCR system which may result in inaccurate results is when the text is not properly identified, aligned, de-noised, etc. So to get accurate results skew detection and correction are needed. In this work, we implement two of the skew detection algorithms - Hough transform and Radon transform. Images of printed text lines that have a skew present are used as input for the proposed model, where the image undergoes character-level segmentation using the concept of the connected component. Each segmented character undergoes skew angle detection. Once the character’s skew is corrected, all the characters are concatenated into one image which forms the deskewed text image. Intext images, slant correction of texts is one of the main challenges. So to overcome this challenge, we have made use of the character level slant correction approach.
Report Based Face Detection and Recognition

Face recognition is a computer dynamic that determines the position and size of a human face in a digital image. This study suggests a system that will help you recognize students' faces, know entrance times, and create timing reports with “smart surveillance and instant reporting. An instant report that displays the admittances results in the form of a graphical report as well as a textual report.
Detecting Cancerous Cells Using Data Augmentation In Deep Cascaded Networks

Jain, Akshay Chaturvedi, Pallavi Gupta, LalitaIn this article, an approach has been introduced for detecting cancerous cells. Image processing techniques have been used, based on cancer cell area using CNNs. A very intriguing aspect of this experiment was that from a very small image dataset, a large number of images were generated using information augmentation which was then taken as the training set data. The suggested scheme detects cancer behaviors through a convolutional neural network in images of celled samples. Previously, the same attempts failed to stay away from the database dependencies, which were somewhat proportional to the number of images in datasets, so we used a method called data augmentation on smaller sets of images. The scheme preprocesses the input image by grayscale, binarization, inversion, median filtering, and flood-fill procedures. Depending on the sort of feature to be identified, the preprocessed image is then cancerous cell detected. This methodology was used for several sets of pictures, and the system was optimized with the feedback from those tests. For independent cancer cell detection with narrower datasets, the suggested technique can be efficiently used, which will greatly accelerate the study of cancer and open greater dimensions.
Non-destructive testing method of bridge pile foundation based on elastic wave reflection method

the current process of non-destructive testing of bridge pile foundations, due to the poor signal filtering ability of the detection method, the accuracy of the detection results is reduced, and the daily maintenance of the bridge is adversely affected. In order to improve the reliability of testing results, a non-destructive testing method for bridge pile foundation based on elastic wave reflection method is designed. Collect elastic wave detection data, use waveform signal separation method to realize signal preprocessing. According to the processed signal, the reflection information of the pile top after being excited is analyzed. Introduce this part of the data into the ANSYS program to determine the condition and damage location of the bridge pile foundation. So far, the design of the non-destructive testing method for bridge pile foundation based on elastic wave reflection method is completed. Constructing a case analysis link, the experimental results show that the elastic wave detection method can effectively improve the accuracy of the detection results and ensure the reliability of the detection results.
An Efficient Adaptive Weights Update Scheme for a Gaussian Mixture Filter

This paper proposes an adaptive weight update scheme of the Gaussian components for the Gaussian mixture filter in the time update stage. This method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by large uncertainty in the measurements or ambiguity in the model. The Gaussian mixture filter is improved through combination with the Cubature Kalman Filter(CKF). The Gaussian components are predicted and updated using a CKF with the results merged and weighted. A series of extensive trails were run to assess the estimation precision offered by various algorithms. The results based on the Unmanned Underwater Vehicle(UUV) lake trial data demonstrate the superiority of the proposed algorithm through better accuracy and stability compared with the conventional navigation algorithms, with are reasonable computational time to meet real-time navigation requirements.
Neighboring Information Exploitation for Anomaly Detection in Intelligent IoT

The identification of anomalies has become increasingly important for the security of sensory data gathering in the intelligent Internet of Things (iIoT). The current approaches might not be applied to the general cases of anomalies, i.e., both long-term and short-term anomalies, as well as not be suitable with real-time applications such as natural disaster monitoring and early warning systems. To address this challenge, this paper proposes a comprehensive approach, named DWT-PCA Anomaly Detection (DAD) to detect both long- and short-term anomalies. DAD bases on the combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to improve the system performance. In particular, we first utilize the DWT to extract approximation coefficients and detail coefficients from the input data which are capable to highlight long-term and short-term anomalies, respectively. We then exploit the spatial-temporal correlation of neighboring sensors by applying PCA on these coefficients to obtain a high detection accuracy. Numerical experiments based on the real dataset of Intel Berkeley Research reveal that the proposed scheme obtains higher accuracy and a lower false-positive rate on detecting three typical anomalies: drift, noise, and outlier, comparing to existing schemes.
The Application of Scaffolding Instruction to English Writing Teaching in Junior Middle School in the Context of Wisdom Education in the Era of Big Data

Today's society is the Internet age. Informatization is everywhere in life, and it is also widely used in contemporary education. Wisdom education is integrated into traditional education. Writing teaching is an important part of English teaching in junior high school, which embodies students' comprehensive language application abilities. However, the effect of English writing teaching is not satisfactory: students lack writing materials and necessary writing strategies; what's more, they don't have a good command of language knowledge. So, students' writing enthusiasm is not high. In order to improve students’ writing skill, this paper analyzes and explores the application of scaffolding instruction to English writing teaching in junior middle school, which aims to cultivate students' interest in writing and improve their writing skill through the use of scaffolding instruction. Also the author hopes this research can make a contribution to English writing teaching in junior middle school.
Principles and Semantics: Modelling Violations for Normative Reasoning

The present paper proposes a structural operational semantics and the related semantics for normative systems. The proposed approach focuses on explicitly representing in force obligations and violations as events in a temporal framework, determining the state of a normative system. In the paper we use a set of core principles, defining some of the properties required when reasoning about norms, to motivate the semantics of the approach. Finally, we show that the proposed approach is capable of reasoning about more complex legal scenarios.
Application of the clarke-wright algorithm in determining tsunami disaster evacuation route in Purworejo district

Abstract The potentials for tsunami in Indonesia are spread over many areas, one of which is Purworejo. Purworejo has a high tsunami potential because it is located on the Indo-Australian plate. In Central Java, Purworejo occupies the 3rd position in the tsunami-prone area. Therefore, a route is needed to evacuate the population to mitigate the tsunami. This research focuses on two sub-districts, namely Ngombol and Purwodadi. The problems in this paper are: (1) Which points can be used as evacuation sites (2) How to model tsunami evacuation routes with graphs (3) What are the optimal evacuation routes. The method used to find the evacuation route is the VRP with time window. The algorithm used is Clarke Wright algorithm. The optimal evacuation route is obtained if all points are evacuated with minimal time and distance. From nine final evacuation points, only 4 points that fulfill criteria for the shortest distance and can be accessed by vehicles. There are eight final routes with minimum time of 42 minutes. Each of depots B3 and C2 has 3 routes. Whereas each of depots C4 and C5 has 1 route.
Improving Load Balancing for Modern Data Centers Through Resource Equivalence Classes

Load balancing is one of the most significant concerns for data center (DC) management, and the basic method is reassigning applications from overloaded servers to underloaded servers. However, to ensure the service availability, during the reassignment of an application, some resources (i.e., transient resources) are consumed simultaneously on its initial server and its target server, which imposes a challenge for load balancing. The latest research has proposed a concept called resource equivalence class (REC: a set of resource configurations such that a latency-critical (LC) application running with any one of them can meet the QoS target). In this paper, we use the REC to improve the load balancing for a DC where multiple LC applications have already been co-located on servers with the service availability and QoS requirements. We formulate the proposed load rebalancing problem as a multi-objective constrained programming model. To solve the proposed problem, we propose to use a machine learning-based classification model to construct the RECs for applications, and we develop a local search (LS) algorithm to approximate the optimal solution. We evaluate the proposed algorithm via simulated experiments using real LC applications. To our knowledge, it is the first time to use REC for improving load balancing.
Virtual Reality in Education System

In the education process, student faces Problem with understanding due to the Complexity. So that many students Conducted on the use of VR in education and training. In today world were surrounded by a wonderful array of technologies. We have as simulated many of these technologies in to our everyday lives. We know that computer is the heart of this generation. Virtual reality technologies for supporting teaching and Learning have been an academic research topic for decades. Virtual reality sometime called virtual environment. In the five years, major process has been made and the Virtual reality technology is getting closer to being implemented in education. This paper Present an overview of the uses of virtual reality in education. The purpose of this Research is to explore the current use of virtual reality to support teaching and Learning. We present new opportunities with VR and put together the most interesting, recent virtual reality application used in education in relation to several education areas such as general, engineering and health related education. This paper presenting methods for testing and validation.
Deep Reinforcement Ant Colony Optimization for Swarm Learning

The extremely large number of possible states in the real environment limited the implementation of reinforcement learning algorithms until recent times. Hence, such algorithms were implemented only for simple tasks in tabular grid worlds. Deep learning have expanded the use of such algorithms for multidimensional and complex virtual environments of computer video games. Modern deep learning and multi-agent reinforcement learning are actively porting classical machine learning algorithms to neural network architecture. Combining these trends, we propose a new deep reinforcement learning algorithm based on the traditional ant colony optimization algorithm for solving the problem of cooperative homogeneous swarm learning. The algorithm shapes the collective behavior of a decentralized system comprising a set of independent homogeneous agents, locally interacting with each other and the environment. The algorithm also represents an alternative stigmergic approach to implementing the leading multi-agent technology of centralized learning with decentralized execution. Local and often random interactions in the process of centralized learning lead to the emergence of agent swarm collective behavior, uncontrolled by individual agents in the process of real-world operation. We study the advantages of the algorithm in an experimental virtual environment of the StarCraft Multi-Agent Challenge.
Towards Scalable Security of Real-time Applications: A Formally Certified Approach

In this paper, we present our ongoing work to develop an efficient and scalable verification method to achieve runtime security of real-time applications with strict performance requirements. The method allows to specify (functional and non-functional) behaviour of a real-time application and a set of known attacks/threats. The challenge here is to prove that the runtime application execution is at the same time (i) correct w.r.t. the functional specification and (ii) protected against the specified set of attacks, without violating any non-functional specification (e.g., real-time performance). To address the challenge, first we classify the set of attacks into computational, data integrity and communication attacks. Second, we decompose each class into its declarative properties and definitive properties. A declarative property specifies an attack as a one big-step relation between initial and final state without considering intermediate states, while a definitive property specifies an attack as a composition of many small-step relations considering all intermediate states between initial and final state. Semantically, the declarative property of an attack is equivalent to its corresponding definitive property. Based on the decomposition and the adequate specification of underlying runtime environment (e.g., compiler, processor and operating system), we prove rigorously that the application execution in a particular runtime environment is protected against declarative properties without violating runtime performance specification of the application. Furthermore, from the specification, we generate a security monitor that assures that the application execution is secure against each class of attacks at runtime without hindering real-time performance of the application.
Design of Cadets Administration System for Nusantara Cilacap Maritime Academy Based On Website

Submitting permits and registering for scholarships are activities often conducted by cadets at the Cilacap Nusantara Maritime Academy. All activities carried out are still conventional, with the cadets having to come straight to the permit and scholarship registration. To overcome this, a website-based management system was set up by the Nusantara Cilacap Maritime Academy. The system development method used is the waterfall model. The programming language used is PHP with a MySQL database and CodeIgniter framework. Evaluation methods are using BlackBox testing for the system and eight golden rules for the user interface. The result of this research is the development of a website-based management system for the Nusantara Cilacap Maritime Academy. This research concludes that the Cilacap Nusantara Maritime Academy's administrative system can increase the efficiency of time for filing permits and writing letters.
Programmer Experience: – An HCI Prespective

User experience (abbreviated as UX) is how a user feels when interacting with a particular system. Not only does it include user’s direct interactions with the system, but also how it helps the user in the overall satisfactory completion of a particular task. Software programmers are also users of a specific systems, and several types of artifacts related to software development lifecycle. We performed a systematic mapping about Programmer eXperience (PX). In this paper we considered the programmers as a type of users of an IDE and different programming and system related artifacts. After studying different research articles about programmer experiences related to different programming languages, tools and environments, we analyzed that most of the currently available literature focuses on PX from a Computer Software Engineering perspective, also it has been felt that there is an increasing interest in the PX topic, but still the main concept is not clearly defined and understood. Thus, we have tried to focus our main research work on programmer’s experience from a Human-Computer-Interaction (HCI) perspective. Most of current literature and research work focuses on the usability and PX aspects focusing on four topics: (i) programming languages, (ii) programmers’ interaction with integrated development environment, (iii) application programming interfaces, and (iv) programmers’ behavior. This represents a research opportunity for this systematic review and others that can be performed.
Regulation, Tax, and Cryptocurrency Pricing

This paper finds that jurisdictional gaps in cryptocurrency regulation, including regulatory uncertainty and the receptiveness of the legal standing, taxation, and antiâ€“money laundering laws, have incremental explanatory power for the cross-jurisdiction price disparity of the same cryptocurrency. Comparatively, the association is more pronounced for jurisdictions with tighter capital controls and for crypto exchanges that predominantly serve domestic markets. Utilizing staggered adoptions of cryptocurrency policies, we identify the influence of crypto regulation on pricing using both the difference-in-differences design and the regulatory event study methodology. Furthermore, the incremental explanatory power derives from both the demand and supply sides.
Robust 3D Authentication on Point Clouds with Random Sampling and Sequential Dependency

This study integrates random sampling and sequential dependency to propose a robust 3D authentication algorithm on point clouds. First, the random sampling technique generates lots of virtual points distributed within the bounding volume of the input point cloud. For be robust against the point removal attacks, the proposed algorithm generates the authentication code for the processing point from the spatial relationship between the previous processed point and its corresponding virtual points. Finally, parts of the authentication code are embedded by adjusting the length between the processing point to the center of its corresponding virtual points. The feasibility is demonstrated by extensive experiments with high embedding capacity and low visual distortion. The robustness can be also effectively raised.
Overview of Roassal

This chapter begins the second part of the book, which focuses on the core of Roassal. This chapter includes an overview of Roassal and its main components. This chapter goes one step further than the previous chapters by listing the essential components of Roassal.