Reviewerly/exHarmony huggingface dataset:
Enlightenment Activities on Science and Technology Using Mechanical Engineering Heritage

It is very meaningful attempt which is reproducing the historically valuable mechanical engineering heritages by movies or 3DCG animations etc. It is also meaningful to enlighten the mechanical engineering heritages to the society by means of those contents. I have developed CG animation contents that reproduces the operation of several types of machinery that have been certified as a mechanical engineering heritage by the Japan Society of Mechanical Engineers. This time, I demonstrated the CG animation of one of the mechanical engineering heritage No. 1 " The steam engines and hauling machinery at the Kosuge Ship Repair Dock" at the exhibition of Fukuoka City Science Museum and used it to enlighten the steam engine. In this report, we will examine the effect of enlightenment education by conducting questionnaires and oral surveys to the participants of the event.
Data

In this chapter, I discuss some findings from my Data Personas study that relate to the Australian participants’ imaginaries concerning the futures of personal data. The methods adopted for this study brought together the cultural probe of the ‘data persona’ with a customized online forum for engaging the responses of the participants. Using this approach surfaced a range of intriguing insights into the sociotechnical imaginaries held by the participants in relation to the futures of personal data. The participants’ speculations about the futures of their personal data referred to ever-more detailed datafication and dataveillance. However, most participants resisted the notion that their future data personas would incorporate every aspect of their lives and thoughts. While the participants could readily identify a multitude of ways in which they are datafied as part of their everyday lives, few had directly experienced disadvantage or discrimination from data profiling. They were therefore more likely to imagine futures in which better data profiling would offer benefits rather than harms. In these participants’ accounts, therefore, was evidence both of their engagement with publicly circulating sociotechnical imaginaries about the futures of personal data and their resistance to accepting these imaginaries unquestioningly, drawing on their personal experiences of being data subjects.
Angular: Directives

Angular helps build views that are declarative and use HTML markup. Angular helps reuse most HTML elements and features. It helps extend functionality, making it a powerful framework.
The Teaching of English Vocabulary to Young Learners in Beginner II Class at the Star English Course

The aim of this study is to describe the teaching of English vocabulary to young learners in Beginner II class at the star English Pare. The research describes the tutor’s preparation that consists of syllabus, lesson plan, material, media, and technique; the tutor’s implementation that consists of: pre-teaching activity, main teaching activity, and post-teaching activity; and the assessment. The research subjects are: the director at The Star English Course, the tutor at The Star English Course, and the students of Beginner II class at The Star English Course. The research was analyzed by using descriptive qualitative method. The data were collected from observation, interview and documentation. In preparing for teaching English vocabulary to young learners at The Star English Course, the tutor has both syllabus and lesson plan. Moreover, the tutor is obligated to use media and game in delivering the material. It is really effective because children need an interesting media and fun activity in learning. During the pre-teaching activity, the tutor started the class by a warmer to prepare the students and help students to focus on the upcoming lessons. When teaching the main lesson, the tutor carried out the activity interactively and motivated the students to participate actively through the media and game. The post-teaching activity was done by making conclusions, reviewing, and giving assignments. The tutor’s assessment in teaching English vocabulary to young learners in beginner class at The Star English Course Pare is given in each meeting and there is one meeting in the end of the month for evaluation by giving final assessment that consist of material learned that month.
Structural refinement of manually created Bayesian network for prostate cancer diagnosis

In general, the structure of a Bayesian network can be learnt from the available data. In some domains like medicine, Bayesian network can be manually created by domain experts and statistical methods can be applied to refine the structure based on the data. As the data is continuously getting evolved in many real-world applications, refinement of the expert network structure is unavoidable. Existing techniques refine the manually constructed Bayesian network either by verifying the relation of a node with the remaining nodes in the network (expert Bayes) or by examining a node only with its parents (MDL principle). In this work, we propose an algorithm that verifies relation of a node only with its non-descendant nodes that are identified with Markov assumption. The proposed algorithm performs small changes to the original network and proves that a smaller number of operations are required to find the best network structure. Maximum likelihood estimation (MLE) is considered as a scoring function to calculate score for each candidate structure and selects the network with the highest score. Manually created Bayesian network has been collected for the widespread disease prostate cancer and proposed algorithm refines the network structure.
NLRB Invites Briefs Regarding Workplace Profanity

In an era when unwelcome workplace comments and conduct are under increasingly intense scrutiny, the National Labor Relations Board has held onto a 40‐year‐old standard for determining whether profanity and similar offensive comments are protected under federal labor law. The standard may soon be changed, based on indications from the NLRB.
Effect of introduction of marketing framework on reply to questions relevant to engineering ethics

Approximately 400 students who belonged to Faculty of Science and Technology of Oita University answered the questions in which the students were assumed to be engaged in the enterprise of manufacturing and selling Stirling engines in 19 century. The questions treated the case defective product had been sold and the problems caused by the benefits of advantages of a device. The questions were asked before and after an introduction of marketing framework. The questions required that a consideration of ethics was reflected in design specification or a business plan. In the question asking problems caused by the benefits of advantages of a device, many students could not collect information and could not consider the situation assumed in the questions. After an introduction of marketing framework, a part of students considered such ethical problem. However, many students did not take into consideration even after the introduction of marketing framework. Teaching materials must be improved.
Accuracy of Prediction by Machine Learning Algorithms

Over the past few decades, Machine Learning (ML) has evolved from the endeavor of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational approaches, to an independent research discipline that has not only provided the necessary base for statistical-computational principles of learning procedures, but also has developed various algorithms that are regularly used for text interpretation, design recognition, and a many other commercial purposes and has led to a separate research interest in data mining to identify hidden regularities or irregularities in social data that growing by second. This paper efforts on explaining the concept and evolution of Machine Learning, some of the popular Machine Learning algorithms and try to compare three most popular algorithms based on some basic notions. Sentiment140 dataset was used and performance of each algorithm in terms of training time, prediction time and accuracy of prediction have been documented and compared.
Application of Financial Big Data Analysis Based on Computer Data Mining Technology

In today's information technology era, computers are still a necessity of modern society. Computer data mining technology is a necessary means in the big data environment. Nowadays, in our daily life and work, Massive data and information need to be processed, especially for the financial industry, all kinds of data need to be processed by financial practitioners. How to process the data quickly and grasp the key information more quickly and accurately is the concern of many financial elites. The idea of applying computer data mining technology to financial big data has aroused the interest of many relevant people. On this basis, this paper selects a famous commercial bank as the research object to explore the influence of computer data mining technology on financial big data analysis. And from the experimental data, the data processing method optimized by computer data mining technology is more efficient, and the time spent on data processing is greatly reduced, even relatively less than 60%, up to 69%. In addition, 61% of the employees are satisfied with the optimization method, much more than half of them.
A study on line-of-sight measurement in combination with a 9-axis motion sensor

Humans perceive external environmental information using the five senses of sight, hearing, smell, taste, and touch. Since we perceive most of the information about the external environment based on visual information, measuring the line of sight is expected to clarify how humans relay visual information to their motor control system. However, it is not possible to evaluate the gaze position quantitatively unless the absolute coordinates of the line of sight are obtained. Therefore, in this study, we propose a new sensor fusion to estimate the absolute coordinates of the line of sight. The absolute coordinates of the line of sight were estimated by a Kalman filter using a portable 9-axis motion sensor and a gaze measurement system. We evaluated the accuracy for the absolute coordinates of the line of sight estimation in the experiment. This analytical method is anticipated for use in estimating the absolute coordinates of the line of sight in sports and healthcare applications.
Image Training and Coordination

The importance of image training is emphasized in sports. This is because sport players need to perceive changing environment and situation holistically and exactly to make an appropriate strategic decision. In engineering, movement is discussed on the basis of cardinal scale, so mathematical approaches are possible and the idea of control is dominant. But in sports, what is important is to find the goal and to approach there by trial and error. We need to prioritize our goals and decisions by trial and error so that ordinal scale approach is necessary. But even if we search for better ways to get to the goal by trial and error, our efforts will be wasted, unless we have an appropriate performance indicator. This paper proposes Mahalanobis Distance-Pattern approach, which combines ordinal scale Mahalnobis Distanace and pattern for performance indicator. MDP can make the most of our instinct, so that no matter what elements datasets are composed of and how large the dimensions are we can improve our perceiving ability and get access to the goal definitely step by step.
Mixed teaching design and application in teaching Chinese as a foreign language from the perspective of distance learning

At present, blended teaching mode has become one of the important teaching methods, but this teaching mode is still a very strange concept in language teaching. It perfectly integrates classroom teaching and offline teaching, and teachers can carry out teaching activities in a more flexible and convenient way. This paper discusses the application of blended teaching design in TCFL, and gives some countermeasures on how to strengthen TCFL in the new era.
Creativity of Zen Tao Graphic Design Based on Artificial Intelligence Technology

If Chinese graphic design wants to form a design with Chinese national characteristics, it is necessary to integrate Zen culture into the design. In this paper, the main research is based on artificial intelligence technology Zen design creative analysis. Because of the randomness when the platform selects the target node for the task when the task is concurrent established through kubernetes' basic scheduling algorithm, the number of each task is not uniquely determined in multiple tests, so this paper selects a group of test data that appears more frequently in the test to show. Using the difference between colors as the auxiliary of graphic design can emphasize the key point, weaken the secondary key point, highlight the subject to be expressed, so as to catch the eye of the viewer. Usually, in the process of using contrast color, the color system should not be too much, so as not to make the viewer have a sense of visual clutter. In graphic design, creative thinking mostly comes from the guidance of visual thinking. When the designer conceives the design content, most of them will mobilize the previous visual experience in the brain to list the various symbolic meanings that the visual elements can express, and determine the final design elements through heavy screening. When the concurrency number is 10, 50, 100, 500, the actual response time is less than the expected time. The results show that the artificial intelligence technology has a certain role in promoting the plane design of Zen.
Modeling Human Prediction of Motion of Mobile Robot

Autonomous mobile robots are essential for us in recent years. Living with robots become more common, and we humans and robots are required to respect each other. For smooth interaction between humans and robots, human predictability for robot motions desired to be improved as if human can easily predict another human’s motions. We model human prediction for autonomous mobile robot motions, focusing on acceleration. We hypothesize that a human predicts the acceleration of a robot based on observed mass under the motor driving force as a constant latent variable. In the hypothesis, we propose inverse relationship between the acceleration and observed mass. In addition, we formalized surprise from unexpected motions based mathematical model that surprise increases when prediction error gets bigger, and prediction uncertainty decrease the effect of prediction error on surprise. We also investigated a bias toward perceived acceleration, which called expectation effect. We conducted an experiment using 3D animation with a mobile robot model. The result supports the prediction model and these hypotheses.
Basic Introduction to PyGame

PyGame is a free framework for Python that provides modules designed to write video games. It is built on top of the Simple DirectMedia Layer Library (SDL) that provides easy access to sound and visual elements.
Image Steganography

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of stenographic techniques some are more complex than others and all of them have respective strong and weak points. Different applications have different requirements of the steganography technique used. For example, some applications may require absolute invisibility of the secret information, while others require a larger secret message to be hidden. This project intends to give an overview of image steganography, its uses and techniques. It also supports steganography in Audio files. For a more secure approach, the project encrypts the message using secret key and then sends it to the receiver. The receiver then decrypts the message to get the original one.
Legal Regimes on Cyber Crimes and Electronic Evidence in Nigeria and Africa

Today, we live in a changing world that has passed through many ages and stages of development. From the stone Age to Information and Communications Technology Age, also known as the 'Digital Age', 'Computer Age', 'Internet Age' or 'Electronic Age'. Unfortunately, criminals have taken advantage of the multiple uses and benefits of technology to advance and perpetrate crimes, particularly, cyber crimes.The objective of this paper is to underscore the fact that fertile grounds exist for the perpetration of cyber crimes in Nigeria; and to explain the why and how the provisions of sections 84, 34(1(b)) and 258(1) of the Evidence Act apply to the Cyber crimes Act, 2015.
Two-stream fusion edge detection network

This thesis introduces a method to combine static and dynamic features in a convolutional neural network (CNN) to produce a motion and object boundary prediction map. This approach provides the CNN with dynamic and static cues and information, thus improving its predictions. The spatial stream of the CNN learns to compute an object boundary prediction map from a single RGB frame, while the temporal stream learns to compute a motion boundary prediction map from the corresponding optical ow map. The streams are then combined through an encoder-decoder architecture, where the decoder learns to fuse the features from both streams to obtain a task specific output. The proposed method yields state-of-the-art results on a motion boundaries benchmark, and systematic improvements in object boundaries benchmarks over methods that solely rely on static features extracted from a single RGB frame.
Location Selection Evaluation of Advanced Electric Substation Technical Transformation Project Based on BPNN

Electric power industry is very important of the development of national economy. With the rapid development of the advanced manufacturing process of the electricity industry, the electricity consumption of the industry has increased significantly. Therefore, the electric substation is very important of the electric supply of the power grid, which undertakes the important task of transmission and transformation, and is also an important part of the power grid advanced developing. Selecting a reasonable construction site of the substation can greatly reduce the cost, ensure the stability of the daily operation of the power grid advanced production, and bring great convenience to the advanced manufacturing producing. With the rapid development of the information age, the amount of power data information is also increasing explosively. In the context of big data, artificial intelligence technologies such as BPNN and machine learning gradually play a greater role. This paper constructs a comprehensive evaluation system for substation location, including transportation conditions, engineering geological conditions, construction conditions and construction costs of substations. Based on BPNN and matter-element extension model, the location selection of substations is evaluated. Firstly, this paper introduces the basic theory of matter-element extension model, then constructs the evaluation index system of substation location, and finally employ the BPNN to get the weight of the indexes. Finally, an example is given to prove the method effectiveness.
Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.
Computer Mathematical Statistics and UGC-Based Survey of on the Current Status of Medical Treatment by Machine Learning Algorithm

This article uses user-generated content(UGC) data crawled from social media(Zhihu) to obtain users' true opinions on the current status of “difficult and expensive medical treatment” in China,hoping to help the government prescribe the right medicine and effectively solve people's livelihood problems.This article adopts a"machine-human hybrid method",using natural language processing(NLP) technology and K-Means clustering algorithm to improve efficiency by reducing sentence redundancy,and finally manual review to obtain users’ opinions.A total of two different experiment processes are designed, and the methods for extracting key sentences are different.By comparing the results of the two experiment processes,the general experiment process most suiTable for such problems is found.Finally,through the analysis of the seven types of keywords and corresponding sentences extracted by the machine learning algorithm,the reasons why Chinese citizens think"difficult and expensive medical treatment"are given.The main influencing factors that form the Chinese citizens' perception of"difficult and expensive medical treatment in Chinese society"are described as a whole,which is divided into four categories and seven sub-categories.