Reviewerly/exHarmony huggingface dataset:
Defect Repairing Cost and Its Bond on Apartment Building in Relation to Lawsuit Parties

An appropriateness of defect repairing cost for apartment is being challenged in Korea. The problem is that there is no detailed prescriptions or specifications about the defect repairing cost other than calculating standard of the defect repairing bond in the applicable laws. There is also not even a proper base in establishing the standard itself. In order to specify the standard of defect repairing bond and to make it serve as a realistic system, it is necessary to discuss how much is the actual scale of the defect repairing cost and how it is reasonable to establish the calculation system of defect repairing bondin the apartment buildings. The researchers have proposed a ratio of about 1.5% to the current standard of 3% of the construction cost as per the analysis of actual data in the previous study. However, it is necessary to supplement this scale considering the facts that the scale of lawsuit for defect is huge and the recent trend of litigation for the defect repairing has changed. Therefore, this study approaches the relationship between defect repairing cost and bond at a new point of view by analyzing the existing cases again. The builder for apartment construction makes agreement with the developer, therefore the builder has responsibility of defect repairing. It also becomes an auxiliary party in the lawsuit for defect in the resulting construction. In this study, we have categorized all the parties involved in the construction and defect repairing cost. Type A is the case where the litigant has a construction capacity rank of 1-10, Type B of rank 11-30, type C refers to the construction capacity rank other than type A and Type B, and for the case where the lawsuit party is not a builder but a guarantee company, it is categorized as type D.And then, the differences in the defect repairing cost rates in each category were reviewed. This review was conducted because the builders at upper group are generally excellent in terms of technology and quality, but it is not known in detail that how much they are good in counteracting the defect. The result of the case analysis by type showed that the rate of the defect repairing cost by the builder of upper rank was relatively low. In addition, the minimum repairing cost ratio was relatively small between the types, but the maximum repair cost showed great differences between the types. On the other hand, only two cases showed excess of 3% of the defect repairing bond rate among the total of 49 cases. This big difference might have caused large defect cost bond rate in the type A and Type B. The results of this study suggest the following points. First, 47 cases out of 49 cases showed the defect repair bond rates below 3%, which speaks that the current standard is not being practiced. Second, considering the fact that average defect repair bond rate is lowest and the maximum and minimum deviation of defect repair cost rate is also the lowest in the type B, the type B offers the lowest defect repair cost bond among other types of lawsuit cases.
Poetry

These pieces of COVID-19 poetry in their pixelated form follow McLuhan’s playful use and misuse of the phonetic English language. They contribute to media ecology a continued poetic exploration into how our blurry presences in physical and digital spaces engender our landscapes at varying degrees and levels of experience. Through poetic exploration of this envirusment , I invoke the inherently insufficient and terminally playful qualities of language; both enabling and disabling our dualistic experiential accounts of proximity, relations and visceral corporeality in present-COVID. Depending on which dimension readers interpretatively focus on while reading, their own unique personal frame will guide their own unique translation.
Development of Computational Thinking Skills: An Experience With Undergraduate Students

Computational thinking is a type of analytical thinking that is developed intentionally, that is, under a “learned” approach, and the best way to learn it is through computer programming. In this paper, the experience of using block-based programming environments such as Scratch, Lightbot and the PSelnt pseudo-code tool for the understanding of computational concepts and development of computational thinking skills is reported. A quasi-experimental methodological design was used, since it did not work with a control group. A mixed exploratory investigation was carried out. 85 first-year students from the Accounting Professional School of the National University of San Agustin de Arequipa participated in the experience. The results show that in introductory programming courses of short time period, teaching with block-based programming environments compared to the use of pseudocode with the PSelnt tool, facilitates the appropriation of computational concepts and the development of computational thinking skills; thus obtaining better learning results. It is concluded that block-based programming environments favor the construction of knowledge and foster the development of creativity and algorithmic thinking.
Improve the local search using Automatic Modified Position in Improved Spider Monkey Optimization Algorithm

Spider Monkey Optimization is the new field of Swarm Intelligence. The SMO algorithms well balanced for a good exploration. Algorithm based on Spider's extraordinary behavior. Monkeys the SMO algorithm is a population-based meta-heuristic. So these articles present automatic modifying the position of the local search to improve its position. Then we say the updating algorithm called Improved Spider Monkey Optimization algorithm. Using this alternative technique we improve speed convergence. Also this algorithm tested on the problems of reference. The research paper shows proposes a productive variant of SMO that improves the Number of function. Here we have some equations to resolve these problems also we compare the result between SMO and new ISMO
Correction to: Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function

A correction to this paper has been published: https://doi.org/10.1007/s00024-021-02764-5
Evaluation of video watermarking algorithms on mobile device

Advancement of Internet services and design of image and video capturing devices along with various storage technologies has made video piracy an issue. Asserting the originality of digital data and having a copyright on the file is always a challenging task. Digital watermarking is a technique of embedding secret information known as watermarks within image or video file. This can be used for authentication and ownership verification purposes. This paper presents an analysis of mobile deployment of various video watermarking algorithms. The analysis is carried out using the quality parameters like PSNR, time of execution and power consumption. The goal of video watermarking method, implemented on a mobile phone, is to enhance security and to achieve copyright protection for the video files that are captured using a mobile phone. The video file is applied with three different watermarking methods, DCT, LSB and bitstream. These methods are compared for their performance using the parameters PSNR, power consumed and time of execution. It is observed that, the proposed bitstream method gives better performance compared to other methods when evaluated based on these parameters.
Pattern-based service composition for user satisfaction and service revenue

Service composition is an effective means of building profit-added service in a service-oriented computing environment. Most of the current research neglects characteristics of domain business as well as successful domain experiences, so optimisation performance cannot be further improved in specific service context. Firstly, the concept of service pattern is proposed to describe the knowledge extracted from previous composite solutions in domain business. Both service patterns and related advantageous services are pre-computed and organised, so that they can be reused in optimisation of service composition. Then a pattern-based bi-objective service composition method whose objective is to obtain a service solution for maximisation of user satisfaction and service revenue quickly is presented. Experimental results show the effectiveness and advantages of the proposed approach.
Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Data warehouse comprises of data collected from different probable heterogeneous resources at different time intervals with the objective of responding to user analytic queries. Big data is a field that helps in analysing and extracting information from large datasets. The unfolding Big Data incorporation inflicts multiple confronts, compromising the feasible business research practice. Heterogeneous resources, high dimensionality and massive volumes that confront Big Data prototype may prevent the effectual data and system integration processes. In this work, we plan to develop a Tobit Regressive based Gaussian Independence Bayes Map Reduce Classifier (TR-GIBMRC) method for categorizing the collected and stored data which helps the users in making decision with minimum time consumption. The TR-GIBMRC method consists of two processes. They are, Tobit Regressive Feature Selection and Gaussian Independence Bayes Map Reduce Classification. Tobit Regressive Feature Selection process is used to select relevant features from collected and stored data. Tobit statistical model, used to describe the relationship between non-negative dependent variable and an independent variable for selecting relevant features. Next, Gaussian Independence Bayes Map Reduce Classifier is used to classify the selected relevant features for decision making with lesser time consumption. Gaussian Independence Bayes Map Reduce Classifier, a probabilistic classifier segments the data by class by measuring the mean and variance of data in each class. The data point gets allocated to the class with minimal variance. This in turn helps to perform efficient data classification for accurate decision making. Experimental evaluation is carried out on the factors such as feature selection rate, classification accuracy, classification time and error rate with respect to number of features and number of data points.
Blockchain Nodes

In the previous chapter, I covered basic concepts related to blockchain and the pieces that make up an individual blockchain. I covered how blockchain technology solved the double spending problem by utilizing a P2P network, which led to the creation of a global distributed shared ledger and digital cash. The blockchain P2P network is stitched together by connecting multiple nodes, and in this chapter, you will be taking a closer look at the nodes that make up the network.
Deploying

Deploying means moving software and configuration between environments. Deployment allows software to be built in a development environment, tested in one or more test environment, and then released to one or more production environments.
The Meaning of Accelerated Motion

In classic mechanics, the force is the interaction of matter, and it is also the basic reason for accelerated motion of objects.In general relativity, the force is no longer the interaction of objects, but the result of curvature of space and time.In this paper, the significance of acceleration is elaborated according to Axioms 1 and 3. Being different from Axiom 2 in which the acceleration force of the continuum is given by the ratio of variables, the accelerated motion in Axiom 1 is carried out in units superimposed because there is only one space or time independently where space-time bending is meaningless.The accelerated motion in Axiom 3 can be considered to be described by using the equation of the linear and curvilinear process of the continuum extending to infinitely distance.Further, this linear and curvilinear process of the continuum is only a quantitative continuum in essence and thus an accelerated motion transmitting from finite to infinite quantities must be jumping by an infinitely great accelerating force.
Features Selection for Human Activity Recognition in Telerehabilitation

The article considers the problem of features selection for human activity recognition in telerehabilitation. The notion of telerehabilitation is considered. The types of physical activity that are necessary to conduct a telerehabilitation are indicated. A method for carrying it out using wearable devices is proposed. It is proposed to use a smartphone, which includes an accelerometer and a gyroscope to determine the physical activity performed by the patient. A literature review of approaches to the recognition of physical activity types is given. An experiment to select such features for the classification of physical activity that would allow for high classification accuracy with minimal computational costs, regardless of the location of the smartphone on the human body is carried out. As a means of carrying out the experiment, the MATLAB environment was used. For the experiment an open data sets has been used, which contains data from the sensors of the smartphone.
Integrating Drone Technology in STEM Education: A Case Study to Assess Teachers’ Readiness and Training Needs

[This Proceedings paper was revised and published in the 2019 issue of the journal Issues in Informing Science and Information Technology, Volume 16] Aim/Purpose: Drone technology has been increasingly used in education. This paper reports a study of assessing teachers’ readiness and training needs for using drone technology in their teaching. Background: New technology promotes new ways of practices. With the sophisticated design and the affordance to explore our world from a bird’s eye perspective, a drone has been increasingly used to support science, technology, engineering, and mathematics education. However, it also brings challenges to teachers to integrate drone technology in their teaching. It is therefore important to obtain a better understanding of various aspects of integrating drone technology in education. Methodology: A group of pre-service teachers was engaged in a case study conducted using a designed-based approach. The participants were randomly assigned into three groups. They were required to develop lesson plans with the application of drone technology in teaching. The lesson plans were subsequently analyzed using the TPCK framework to identify teachers’ readiness and training needs. Findings: The participants, to a large extent, have sufficient competence to master the skills and knowledge of drone technology and to integrate it into their teaching. However, they were required to strengthen the pedagogical knowledge, subject content knowledge, and technological content knowledge in order to maximize the potential benefits of drone technology in education. Contribution: This paper reports the level of readiness and training needs of teachers regarding the use of drone technology in their teaching. Recommendations for Practitioners: To conduct teacher training regarding the use of drone technology in education, a particular focus should be put on enhancing teachers’ pedagogical knowledge, subject content knowledge, and technological content knowledge. Recommendations for Researchers: Researcher may further explore the strategies to integrate drone technology in teaching. Impact on Society: This paper suggests the area of teacher training regarding the use of drone technology in education. The teaching and learning effectiveness could be improved. Future Research: Future research may study the safety issue and ethical issue of using a drone in education.
Impressive Approach for Documents Clustering Using Semantics Relations in Feature Extraction

The Internet or World Wide Web (WWW) is awful spread today, therefore to navigate, summarize, and organize informat ion effect ively fast and high -quality web document clustering algorith ms play an important ro le.In this area, dimensionality reduction and semantic relat ions are also of fantastic influence step in the data mining process.For computing the document similarity, it is used vector-spacemodel that represents several features present in document.In general, it cannot account for the words (noun) such as names of the people, countries and items .as features.They almost are ignored as irrelevant attributes.But some of these irrelevant terms are valuable in specific do main.Moreover traditional feature representation is not able to reflect the semantic contents of a document because of the synonym problem and polysemy problem.Motivation of these reasons, we proposed the domain ontology which represents the semantic relations of specific terms and semantic words like lexical database.It can increase the process of extraction of features in specific documents and reduce the dimensionality.As a result, the calculation of similarity measure will be more definite, and enhancing in the segmentation between clusters.In this paper, we tested the proposed method in documents clustering area with Particle Swarm Optimization (PSO) document clustering algorith m that performs a globalized search in the entire solution space.The proposed method can support the efficient clustering approach for document clustering of PSO algorith m using semantic relation in features extraction.
PyMySQL Module

The PyMySQL module provides access to a MySQL database from Python. It implements the Python DB-API v 2.0. This module is a pure Python database interface implementation meaning that it is portable across different operating systems; this is notable because some database interface modules are merely wrappers around other (native) implementations that may or may not be available on different operating systems.
MANAGING CYBER SECURITY COMPLIANCE ACROSS BUSINESS SECTORS

Cyber security is a key consideration in ensuring the privacy and safety of organizational data.This need extends beyond individual business sectors up to, and including, national security.Due to the varying regulatory requirements for cyber security, a Western PA IT firm had the need to create a cyber security management strategy that allowed them to manage compliance for their customers, which span most major industries, in such a way that is time and cost effective.The IT firm solved this problem by creating a master database that contains all necessary regulations (plus many additional details) mapped to a master set of security controls.This cyber security management strategy not only allows the IT firm to bring customer networks into compliance by finding and remediating deficiencies but also manage and maintain their compliance throughout the years.
Machine Learning Techniques for Sentiment Analysis of Indian Languages

Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.
African-American language use: Ideology and so-called obscenity

This chapter is about what has been perceived as obscene language. As author indicate below, he prefer to use the term uncensored speech in order not to prejudge the actions of the users of such speech. His wish not to prejudge is not the result of unreflexive liberal humanism; rather, it reflects one of the major conclusions presented below, namely, in many cases, rigorous analysis of form, meaning, and communicative behavior is required before one can pass judgment on the speech of members of communities other than one's own, where the term community membership is determined by age, socioeconomic class, ethnicity, gender, and other variables. It is not true that all African Americans use uncensored speech. Some do not. It is not true that all Whites use uncensored speech. Empirical research alone will tell him how many African Americans – and Whites as well as others – do or do not use uncensored speech.
Human Computer Interaction using Hand Gesture Recognition

Hand gesture recognition provides an interaction between human and Pcs.Its applications extend from therapeutic recovery to purchaser gadgets control (for example cell phone).To identify hand gestures, different methods are used.Signals a non-verbal type of correspondence gives the HCI interface.Ongoing vision-based hand motion acknowledgment is viewed as increasingly more possible for HCI with the assistance of most recent advances in the field of PC vision.This venture manages conversation of different procedures, techniques and calculations identified with the motion acknowledgment.The hand motion is the most simple and common method for correspondence.Hand motion acknowledgment has the different favorable circumstances of ready to speak with the Technology through fundamental communication via gestures.The motion will ready to diminish the utilization of most noticeable equipment gadgets which are utilized to control the exercises of the PC.
Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment.We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition.Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness.The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers.Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy.Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.