Reviewerly/exHarmony huggingface dataset:
Chapter 4. Human Beginnings and Music

Abstract In this survey I begin with speculative account of the beginnings of early music, song and dance in Ice Age Caves some 40,000+ years BP, drawing from archeology and Chauvet Cave in France. I trace early instrument finds—flutes along with tuning systems derived from modern measurements. With comments about music in the Renaissance and then synthesizers during present times, I consider how music technologies change along with changes in audience, embodiment and listening. This long survey shows that each era has different musics, sounds, embodiments, and audiences. Since today's electronic musics are very different from earlier musics, predictions about future development are difficult to make. However, musics are pervasive with electronically produced music now taking up more and more time for everyday listeners.
Research on Information Security Investment in Healthcare Institutions Under Medical Treatment Combinations

The construction of medical treatment combinations (MTCs) is to promote the subsidence of high-quality medical resources but brings third-party risk propagation. This paper develops two models to optimize information security decisions under two types of MTCs. Based on theoretical and numerical analyses, influences of the participation conditions of hospitals and propagation probability on the optimal security decisions and total expected costs are investigated. We find that the security decision under the loose MTC (L-MTC) differs significantly from that under the tight MTC (T-MTC). We also find that the selection of MTCs can be influenced by the participation conditions of hospitals. When the potential loss is large and the potential loss coefficient of the partner hospital is small, T-MTC prevails in information security cost. However, an increase in the propagation probability can extend the trend of the cost advantage under the T-MTC. In contrast, although the area of the cost advantage under the L-MTC is relatively large, the core hospital with the moderate potential loss and the partner hospital with a large potential loss coefficient are prone to form L-MTC from the perspective of their interests.
Movie Recommendation System Using Latent Factor

Deep neural networks have been shown to be very successful for tasks such as speech recognition, computer vision, and natural language processing. However, there has been relatively less research on using deep neural networks for recommender systems. In this work, we attempt to develop techniques based on neural networks to solve the key problem in a recommendation collaborative filtering. A movie recommendation system can help you choose what to watch by suggesting movies based on your preferences. It can take into account what you have watched in the past, what you like, and what you don't like, to give you better movie recommendations. We propose a new model, Movie Recommendation System using Latent Factor (MRSuLF), which is capable of directly modeling the user-item interaction matrix by using low-rank matrix factorization. Experiments on several implicit feedback datasets demonstrate the effectiveness of the proposed model. Our model achieves state-of-the-art performance on the MovieLense datasets. The system is evaluated on a dataset of 100,000 ratings. The results show that the system is able to recommend movies that are highly rated by users.
The NERO Video Game

In the NeuroEvolving Robotic Operatives (NERO) game, the player trains a team of virtual robots for combat against other players' teams. The virtual robots learn in real time through interacting with the player. Since NERO was originally released in June, 2005, it has been downloaded over 50,000 times, appeared on Slashdot, and won several honors. A significant update was released in November, 2005, including improved performance, new battle mode options, and faster learning. The virtual robots learn using the real-time NeuroEvolution of Augmenting Topologies (rt-NEAT) method, which can evolve increasingly complex artificial agents in NERO adapt in real time as they interact with the player in the new, updated game. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
Web content analysis and design trends of Agricultural Universities’ Library Website in Gujarat State, India: A study

The library is one of the fastest-growing sectors in the field of the modern education system. The advancement of information technology leads libraries and resources center to adopt the IT environment to reach and build the targeted users. The content is widely used in our day-to-day life, now in the digital era, web content is widely spread over the website. The Library field also transforms its content into web content. The present study is to assess the web content and design trend of agricultural university library websites in the state of Gujarat. A checklist was designed for the data collection and for evaluating library websites based on referred literature. The study tries to evaluate the content of the agricultural university library of Gujarat state in response to basic information, physical and electronic resources, E-learning platform, Services, Value-based services, and web 2.0 services. The library web pages/websites must be maintained regularly and updated. This will make the demand for the library web pages among the users.
Interfaces

We learned in Chapter 13 that classes contain methods and fields. We also read in Chapter 12 on methods that
Matching and Localizing: A Simple yet Effective Framework for Human-Centric Spatio-Temporal Video Grounding

Human-Centric Spatio-Temporal Video Grounding (HC-STVG) is a recently emerging task that aims to localize the spatio-temporal locations of the target person depicted in a natural language query. To tackle this task, we propose a simple yet effective two-stage framework, which is based on a Matching and Localizing paradigm. Under our framework, we decompose HC-STVG into two stages. In the first stage, we conduct cross-modal matching between the query and candidate moments to determine the temporal boundaries. Specifically, we develop an Augmented 2D Temporal Adjacent Network (Aug-2D-TAN) as our temporal matching module. In this module, we improve 2D-TAN [7] from two aspects: 1), A Temporal-Aware Context Aggregation module (TACA) to jointly aggregate the past contexts in forward direction and the future contexts in backward direction, which helps to learn more discriminative moment representations for cross-modal matching. 2), A Random Concatenation Augmentation (RCA) mechanism to combat overfitting and reduce the risk of unreasonably learning query-independent saliency prior, which is mistakenly provided by the training videos that only contain a single salient event. In the second stage, we utilize the pretrained MDETR [4] model to associate the language query with meaningful bounding boxes. Then, we conduct a query-based denoising procedure on the language-aware bounding boxes to obtain the frame-wise prediction for spatial localization. Experiments show that our simple yet effective framework can achieve a promising performance for the challenging HC-STVG task.
GANs for Automatic Generation of Data Plots

In the last years, online learning saw a surge in relevance, along with interest in the automation of the learning process. Variety and amount of practice exercises are of particular importance, since they limit the amount of practice a student can have. In this paper, we propose the use of generative adversarial networks (GANs) to produce scatter plots, for students’ training on data profiling tasks in data-driven courses. Our results show that progressively grown GANs (ProGANs) generate scatter plots with little tuning and display an adequate level of randomness, diversity, and quality. These properties show promise in allowing for diversity of exercises created from the generated plots.
GROUPING OF SEWING TOOL ASSISTANCE RECIPIENTS USING K-MEANS CLUSTERING ANALYSIS

Aid programs for underprivileged communities need continuous data collection in overcoming welfare problems, what has happened so far is by providing direct assistance to very poor families in every village in Indonesia. Related to this, the problem that has occurred so far is that the direct assistance program is not right on target, because many deserving families should not receive the assistance. The data obtained from the village government is not accurate, it is found that data is considered invalid. This study aimed to determine the distribution of sewing equipment recipients in the best cluster, for sewing equipment recipients in Greged Village, Cirebon Regency. As one way to improve data accuracy, a computational method or model is needed in the form of a data mining algorithm using the k-means clustering method to generate priority groups among hundreds of citizens or the poor. The stages start from data collection, training data, and testing data that consider several criteria from household information, economic conditions, housing conditions, and the number of household members in Greged Village, Cirebon Regency. The results of the tests carried out using 155 data with the best level of accuracy were in the K3 cluster with the Davies Bouldin Index’s value o: - 0.584. With the K-Means method, it is very appropriate to determine the recipient of the sewing equipment program in Greged Village.
Development of Mower for Irregular Ground

Farmer has been aging rapidly in Japan and therefore the workload reduction has been required. One of hard labor is mower. We then have been developing a mower for irregular ground and working by convenient operation for everybody. In this paper, the structure and performance of this device we have been developing are described.
Deep Learning based Human Detection in Privacy-Preserved Surveillance Videos

Visual surveillance systems have been improving rapidly over the recent past, becoming more capable and pervasive with incorporation of artificial intelligence.At the same time such surveillance systems are exposing the public to new privacy and security threats.There have been an increasing number of reports of blatant abuse of surveillance technologies.To counteract this, data privacy regulations (e.g.GDPR in Europe) have provided guidelines for data collection and data processing.However, there is still a need for a private and secure method of model training for advanced machine learning and deep learning algorithms.To this end, in this paper we propose a privacy-preserved method for visual surveillance.We first develop a dataset of privacy preserved videos.The data in these videos is masked using Gaussian Mixture Model (GMM) and selective encryption.We then train high-performance object detection models on the generated dataset.The proposed method utilizes state-of-art object detection deep learning models (viz.YOLOv4 and YOLOv5) to perform human/object detection in masked videos.The results are encouraging, and are pointers to the viability of the use of modern day deep learning models for object detection in privacy-preserved videos.
Enhanced Feature Fusion and Multiple Receptive Fields Object Detection

CenterNet is a widely used single-stage anchor-free object detector. It only uses single feature map to detect all size objects, and does not effectively use different levels of feature maps. We present an enhanced feature fusion and multi receptive field object detector, named EM-CenterNet. Our detector first fuses different levels of feature maps, and then enhances feature fusion through semantic information transfer path. Besides, we design another key component, which is composed of continuous several dilated convolutions and shortcut connections, so that our detector can cover all object’s scales. We compare the EM-CenterNet method with the baseline on the Pascal VOC and COCO datasets. Experiments show that our method increases the AP by 12.2% on the Pascal VOC dataset, and increases the AP by 5.9% on the COCO dataset.
AN ANALYSIS OF THE IMPACT OF MULTIPLAYER ONLINE BATTLE ARENA (MOBA) GAMES TOWARD LISTENING COMPREHENSION

The user of online games in this world has increased rapidly year by year. The last data has shown that there are 699 million active users around the world. Indonesia is become one of the most country who consumed the online game. This research aims to determine, how online games are played by the students, the causes of the students play the online games and the effect of the online game or MOBA game on the studentsâ€™ listening comprehension as the main goal. This research used Mixed-method. The data is used questionnaire, listening test, interview, and documentation. 62 students have taken for the sample. Based on the research that has been done, the researcher can conclude that the online game gives the impact to the listening comprehension of the students at SMKN 6, Samarinda. It proved by the mean score pre-test and post-test in experimental group was 69.67 that increased into 84.03 after treatment. Therefore, the display data of sample T-test has shown that value sig. (2-talled) is 0.00 &lt; 0.05 with the R square 0.76. It means there is significant influence of playing online game on the studentâ€™s listening skill of 76%.
Proposal of interaction recognition method using human skeleton and object information

To understand human-centric images and videos, detecting human-object interactions is an important probrem. Motion recognition based on skeleton information has been studied, but it is not possible to accurately estimate target object using only skeleton information. Therefore, we propose a method for detecting human-object interactions using the skeleton information in conjuction with the object recognition information. We define object-specific interaction by this method. Our hypothesis is that by training human-object interactions using the same recognition method as a human, sufficient recognition accuracy can be obtained even with a small dataset. Since we use only coordinates for training, robust recognition is possible in various situations.
Fuzzy Models for a Two-Wheeled Self-Balancing Robot

A self-balancing robot is a robot that is primarily capable to maintain a stable tilt angle. These kinds of robots have the advantages of having small size, footprint, being easy to carry and practical. The system is mainly unstable by nature, which need control strategies to maintain is equilibrium. The main goal of this paper is the development of fuzzy models for a two-wheeled self-balancing robot, previously built and tested experimentally. The fuzzy model input is the control signal applied to the stepper motor, i.e., the number of steps, and the fuzzy model output is the tilt angle, namely the robot angle with respect to the vertical equilibrium point. An incremental online algorithm is applied in order to obtain evolving fuzzy models. The system usually uses a microcontroller, an accelerometer, and a gyroscope module to obtain the tilt angle, and stepper motors play the role of actuators. The developed fuzzy models are validated against experimental input-output data measured from the real robot.
A comprehensive survey of fraud detection methods in credit card based on data mining techniques

Credit cards play a vital role in the financial transactions where it becomes inescapable portion for family, industry and worldwide actions. Although utilizing credit card offers enormous advantages when utilized carefully and liability, fraudulent activities can cause significant credit and financial harm. Several approaches have been suggested to counter the growth of credit card fraudulence, such as k-nearest neighbor, Random Forest, Neural Network, logistic regression, decision tree, hidden Markov model, naïve bayes, Support Vector Machine, or those represent the hybrid approaches etc. The techniques for fraudulence discovery track the user's actions and warn the user when detrimental incident happens. Fraudulence is inhuman behavior and there are an infinite number of various ways of fraud. Thus, based on previous research, various approaches and strategies will be investigated and addressed. In this paper we will learn how to tackle fraud and how to trust the hospital, organizations, and businesses as well insurance companies against fraudulence by Fraud Detection Systems (FDSs) and how to encounter them via Fraud Prevention Systems (FPSs). This survey presents a comprehensive reviewing for the fraudulence matter in credit card, fraud types and challenges that obstruct the performance of FDSs.
Introduction

This book intends to provide a continuous assessment of the crisis in governance in Africa. As it is, there are huge deficits in the capacity of African states to harness vast human and material resources to promote good governance. This manifests in pervasive corruption, collapsed service delivery, collapsed state-owned enterprises, eroded social trust, capital flight, escalating levels of poverty and wars, human insecurity, and stunted growth. The public sector is the pulse of service delivery because the entire governance system revolves around the sourcing of materials and services, mostly from the private sector, in order to achieve its public policy intents. The procurement process, therefore, ordinarily ought to yield positive economic outcomes and an efficiency-driven system in favour of the government itself and its service recipients. However, this more often than not is not the case. Despite its enormous wealth, the African continent is in an economic quagmire, a dilemma that requires multi-facet research activities. This is the motivation for this book.
Neural Networks—Introduction

In Chap. 3 we introduced linear regression, a linear model that learns a set of weights to apply to an input, in hopes of finding the model with the lowest error. However, linear models are limited in the behaviors that they can learn. In this chapter we learn about Neural Networks, a class of model that are capable of learning any possible function. We first introduce why Neural Networks are useful, then discuss how they are set up, trained, and used.
University Students’s Perception About Extracurricular English Camp In Improving English Skills

This study aims at describing students’ perception about English Camp for the first-year student of English Education Study Program at Universitas Brawijaya on enhancing English proficiency through the three domains of learning. English Camp is innovation from English Education Study Program that begin from 2016. But, the perception of students who had joined English Camp are still unknown. The type of this study is descriptive, using quantitative approach. Data were gained from student answer on questionnaire that they had fulfilled. The sampling technique used in this study was accidental sampling. Subjects in this study were 40 students from a total of English Education student batch of 2018 at Universitas Brawijaya. This study using questionnaire that had been validated by the expert as the instrument. The questionnaire uses Likert scale where there are two forms of statements, positive and negative statements. The results show that almost all of respondent give positive perception that English Camp can enhance their English proficiency through the three domains of learning: cognitive, affective and psychomotor. Almost all of student show their agreement based on the 10 statements through questionnaire the researcher provide. The conclusion of this study is that almost all students give positive perception. It refers to the three domains of learning by Bloom. From the three domains, it shows a positive perception in each domain. This indicates that students get enhanced in English proficiency by following English Camp.
STEM in the Classroom (the Role of Technology and Gamification as Part of the Educational Process)

STEM is an education acronym that stands for Science, Technology, Engineering and Mathematics, but STEM education extends beyond these technical fields and is an integral part of our daily lives. In recent years, the importance of STEM learning in the classroom to prepare today's students for their future realization is of great significance, but it will grow more and more and it is important to introduce it at an early school age. The purpose of this article is to show how technology and gamification can be successfully integrated into the learning process of students from the primary stage of education to develop skills they will need in the future and in their lives outside the classroom. As a result of technological progress and expanding the boundaries of artificial intelligence, technological resources such as programmable toys are becoming smarter and more accessible to users, making them a powerful tool in the hands of the modern teacher. With their help, in combination with the game approach in teaching topics from the field of STEM sciences, children of this age very quickly, subconsciously and without significant effort develop their creativity, logical and algorithmic thinking. The possibilities that this combination gives us are limitless and allow students, in a fun and understandable way, to reach new depths of the world that surrounds them, stimulate their curiosity and interest in the field of STEM sciences and make them more prepared about the unknowns of the future. This is an effective way to inspire children to innovate into adulthood.
Environment Setup

The only tool that you need in order to develop applications or customizations using Salesforce DX is the Salesforce CLI, a command-line interface for executing Salesforce DX commands. Having said that, to improve productivity, an integrated development environment (IDE) is preferred. There are several IDEs to choose from, including Illuminated Cloud (a plug-in for the IntelliJ IDEA IDE), The Welkin Suite, and others. In this book, we are going to standardize on using Visual Studio Code with the Salesforce Extensions for Visual Studio Code; the extension pack is published by Salesforce, and both the extension pack and Visual Studio Code are free.
Study on the Evaluation Model for the Automatic Analysis of Teaching Mode

For modern education, information-based teaching has become a trend in practice. However, we currently lack an existing automated system that can evaluate the quality and effectiveness of an information-based teaching model. Thus, this paper proposes an improved S-T analysis which is taken as the evaluation model. As the experiment results show a higher efficiency from the improved model in comparison to the traditional method when using the manual data set, this paper also provides a new proposal for automatic analysis of the teaching model.
Protected Functions: User Space Privileged Function Calls

The operating system’s traditional design controls and manages all system resources, which comes at the cost of performance and scalability overhead. The scalability overhead results from the kernel’s internal metadata structures and locks primarily designed for sequential access. Additionally, implementing software services and resource management requires compliance with the strict kernel abstractions and programming paradigms that can result in semantic bugs. Although plausible, decoupling from the strict kernel control path and code stack comes at the penalty of losing a higher trust entity to enforce protection separation and protection of user code and data. This paper offers a hardware-assisted method to run confined user-space functions at a higher privilege level. Our method allows the implementation of fined-grained user-level services and protocols without modifying the operating system’s protection scheme. This is done by introducing two high-level instructions to the x86 ISA. Our simulation shows that user-level functions that leverage our instructions run in the same order as standard function calls, while the real benefit lies in the flexibility and ability to decouple the protected code from the kernel limitations.