Reviewerly/exHarmony huggingface dataset:
A tone tutor for training student musicians

We present a real time tool for guitar students to practice the production of good tone. Empirically, according to an informal survey of guitar teachers, warm tones are generally preferable to bright tones. Our tool quantifies the tone by determining the fundamental frequency of the note and examining the harmonics. A note with many strong harmonics well above the fundamental sounds bright while a note with only harmonics near the fundamental sounds warm. We have built a smartphone app that calculates the harmonic average of an incoming signal and presents the resulting measurement in real time. This app can be used by a music student during solo practice sessions between lessons to develop good tone on their instrument.
@Hatch_2116 We run a training @scsu for ...

@Hatch_2116 We run a training @scsu for #nhv teachers on geographic information systems. Happy to share the curriculum and activities. Kind of teachers do, then
A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety.

Use of cooperative information, distributed by road-side units, offers large potential for intelligent vehicles (IVs), As vehicle automation progresses and cooperative perception is used to fill the blind spots of onboard sensors, the question of reliability of the data becomes increasingly important in safety considerations (SOTIF, Safety of the Intended Functionality). This paper addresses the problem to estimate the reliability of cooperative information for in-vehicle use. We propose a novel method to infer the reliability of received data based on the theory of Subjective Logic (SL). Using SL, we fuse multiple information sources, which individually only provide mild cues of the reliability, into a holistic estimate, which is statistically sound through an end-to-end modeling within the theory of SL. Using the proposed scheme for probabilistic SL-based fusion, IVs are able to separate faulty from correct data samples with a large margin of safety. Real world experiments show the applicability and effectiveness of our approach.
LibGuides: Orientation: Module 3

This tutorial and online quiz is created by the Thurgood Marshall Library to orient new students to the library's services and resources.
New Nonlinear Machine Learning Algorithms With Applications to Biomedical Data Science

Recent advances in machine learning have spawned innovation and prosperity in various fields. In machine learning models, nonlinearity facilitates more flexibility and ability to better fit the data. However, the improved model flexibility is often accompanied by challenges such as overfitting, higher computational complexity, and less interpretability. Thus, it is an important problem of how to design new feasible nonlinear machine learning models to address the above different challenges posed by various data scales, and bringing new discoveries in both theory and applications. In this thesis, we propose several newly designed nonlinear machine learning algorithms, such as additive models and deep learning methods, to address these challenges and validate the new models via the emerging biomedical applications.First, we introduce new interpretable additive models for regression and classification and address the overfitting problem of nonlinear models in small and medium scale data. we derive the model convergence rate under mild conditions in the hypothesis space and uncover new potential biomarkers in Alzheimer's disease study. Second, we propose a deep generative adversarial network to analyze the temporal correlation structure in longitudinal data and achieve state-of-the-art performance in Alzheimer's early diagnosis. Meanwhile, we design a new interpretable neural network model to improve the interpretability of the results of deep learning methods. Further, to tackle the insufficient labeled data in large-scale data analysis, we design a novel semi-supervised deep learning model and validate the performance in the application of gene expression inference.
DATA MINING DALAM PENGELOMPOKAN NILAI IQ SISWA

Education is one aspect of life that should be owned by every human being. The most crucial times for education is during school. During school, students will start from kindergarten until finally graduating at the high school level. One aspect that is highly considered during school is the level of intelligence. Intelligence levels are often associated with IQ levels. According to research, a person's IQ level becomes a benchmark on the level of intelligence of that person. The level of intelligence can affect the lives of students in receiving lessons so well that the school should properly classify students. Grouping students based on IQ level aims that students can receive more appropriate and effective learning methods so that students can receive lessons well. At Yos Sudarso Highschool at Batam, there are a lot of students who enroll and grouping students will take a lot of time and is not efficient if done manually. Grouping students can be done quickly and precisely by utilizing data mining. One method in data mining is clustering by using K-means. The stages of this method begin with random selection K, K here is the number of clusters that you want to form. Then set K values ​​randomly, while the value becomes the center of the cluster or commonly referred to as centroid, mean or "means". Calculating the distance of each existing data on each centroid using the Euclidian formula to find the closest distance from each data with the centroid after the manual calculation is run on the data mining software, RapidMiner. The use of the K-means method for grouping is one of the right methods when viewed from the variables to be used, namely the value of the student's IQ level. This ¬K-means method will form clusters that classify students based on IQ levels. By applying data mining clustering with the K-means method it is hoped that it can help the school in classifying students appropriately so as to facilitate the school in ensuring students get the right learning method.
Pelatihan pemanfaatan aplikasi microsoft office dalam pembelajaran sebagai upaya peningkatan profesionalisme bagi guru PAUD di PCA Mantrijeron

The 4.0 Industrial revolution affects the rapid development of technology and affects all areas of life, one of which is the field of education. As a dynamic field, education always needs renewal to keep pace with the development of one example of knowledge about IT. This community is aimed to provide knowledge and insight, and skills to the teachers about the use of Microsoft Office, namely Ms Words and MS Excel for the process of administration or learning. Through this training teachers can also improve the competence of professionals, especially in the use of technology in the learning process. The main target of the training is kindergarten teachers under the auspices of PCA Mantrijeron. The methods of implementing activities include lectures, discussions, and practices. The material of devotion consists of three major materials namely the use of Ms words and MS Excel, and the practice of developing progress reports with MS Excel. The outcome of this dedication activity is established cooperation, knowledge and skills enhancement of teachers in the use of MS Office in the process of administration and learning. The obstacle experienced by participants is the limitation of time teachers to participate in training and lack of knowledge of teachers about IT, so that the material provided is still very basic, especially the introduction of tools in the application.
Project technologies in a university’s media educational strategy in developing a probabilistic style of thinking for future mathematics teachers

The relevance of this study is determined by the scientific and socio-practical significance of the training of future mathematics teachers. Nowadays, they should not only be professionals in their sphere but also have a high level of media education. Media projects, carried out by students within the framework of media training and integrated into the disciplines of the methodological module, have contributed to the development of knowledge and skills related to the media space. As a result, a probabilistic style of thinking (PST) has appeared that corresponds to the challenges of the time and the scientific paradigm. The research problem consists of developing theoretical and methodological principles and integrative technological mechanisms for the PST of future mathematics teachers. The results of the study experimentally confirm the significance of media projects implementation in educational disciplines for the training of future mathematics teachers. This study contributes to the development of a PST. This study was funded by RFBR in accordance with the research project № 18-313-20002, entitled “Theoretical and methodical ensuring of the fractal formation and development of a probabilistic style of thinking in the global informatization of education (in the example of teaching mathematics).”
Homomorphic Multiplication for X25519 and X448

In some contexts it is useful for holders of the private and publicparts of an elliptic curve key pair to be able to independently applyan updates to those values, such that the resulting updated public keycorresponds to the updated private key. Such updates arestraightforward for older elliptic curves, but for X25519 and X448,the clamping prescribed for scalars requires some additionalprocessing. This document defines a multiplication procedure that canbe used to update X25519 and X448 key pairs. This algorithm can failto produce a result, but only with negligible probability. Failurescan be detected by the holder of the private key.
Infrastructure-supported Perception and Track-level Fusion using Edge Computing.

Data from infrastructure sensors can significantly improve the field of view for intelligent vehicles (IV), both in terms of range and completeness. In the MEC-View project, we investigate how automated driving (AD) can benefit from incorporating such data in the perception processing chain. On the infrastructure side, a central computational node, called MEC-Server, is connected to a base station and receives objects from multiple roadside sensors. Those are used to create a fused environmental model, which is distributed to vehicles close by via a managed cellular network. To use tracks received from the MEC-Server in IV perception, we propose a hybrid vehicular perception system that is able to fuse both local onboard sensor data as well as estimations by the MEC-Server. For this, we discuss multiple approaches to track-level fusion and data association, including their application in our perception system. Using careful interface design, we are able to avoid many non-linearities and are able to minimize the amount of approximations involved. For evaluation, we present a experimental setup for track-level fusion schemes that is based on virtually augmented real-world measurements and facilitates targeted adaptation of influencing variables while ensuring real-world applicability. A comparison of different fusion schemes provides insights into their relative performance and shows directions for real-world applicability.
Exploring the Data Processing Practices of Cloud ERP - A Case Study

Although cloud Enterprise Resource Planning (ERP) systems are becoming popular, there are still firms that are yet to migrate to the cloud because of data confidentiality and data security concerns. Firms that plan to adopt cloud ERP systems are expected to first understand cloud ERP's data processing practices. This study proposes a case study method with a systematic content analysis of cloud ERP providers' data processing agreements. The study will explore the data disclosure, data security, data sub-processing, and data retention and deletion practices of cloud ERP providers. In addition, this study investigates the linguistic characteristics of cloud ERP providers' data processing agreements, including readability, litigious language, and uncertainty language. The findings of this study can help organizations to have a better understanding of the data process practices of cloud ERP providers, as well as the shared responsibilities between cloud ERP providers and users in maintaining data security.
Classification of Abandoned and Unattended Objects, Identification of Their Owner with Threat Assessment for Visual Surveillance

Terrorism is on an ever-increasing rise and is one of the major threats the world is facing today. Terrorist attacks mostly take place in crowded areas such as railway stations and airports. They involve the use of explosives which are placed inside suspicious abandoned objects like bags, suitcases, etc. In this paper, we are proposing a model that can classify abandoned and unattended objects separately and backtrack to identify the owner as well as find the last known location of the owner in a social environment using visual surveillance feed in real time for rapid alert and action.
Fast Two-cycle Level Set Tracking with Interactive Superpixel Segmentation

In this paper, we propose a fast two-cycle level set algorithm of contour tracking for foreground objects based on superpixel segmentation. Contour tracking method includes three stages: superpixel segmentation, interactive feature extraction and foreground tracking. In the first stage, we preprocess the image by the method of superpixel segmentation. In the second phase, the work of defining and building the feature pool is based on the complexity of the image. Finally, on the basis of the first two stages, the fast two-cycle level set method can be used to track the contour of the target. The experimental results show the effectiveness of the proposed method.
Finding HTML and SQL Injection Vulnerabilities

In this chapter, we will learn what HTML injection and SQL injection are. We will also learn how we can prevent them. HTML injection and SQL injection are different. Therefore, we will learn them separately. In the first half of this chapter we will talk about HTML injection, and in the second part we will talk about SQL injection.
“Reaching 10,000 steps a day”, really?

By focusing on the case of walking, this article brings empirical insights into the conversation on self-tracking practices. While some perceive these forms of personal self-measurement by means of connected bracelets and watches or smartphone applications as new sources of self-knowledge, others consider them to be new ways of governing behaviours. The article situates the production of walking self-measurement in the socio-history of pedometers and shows how digital devices now support a constant evaluation of physical activity rather than sessions dedicated specifically to walking, thus integrating a public health concern – fighting physical inactivity – at the very core of daily life. However, by delving into twenty ordinary self-trackers’ ways of producing, reading and analysing traces of walking, the survey shows that beyond their appropriation of a single cognitive framework (steps, rather than kilometres or duration, as a counting unit; the 10,000 step standard), users adjust their self-measurement practices in a personal way. Their adoption of pedometers over time and their reasoning for establishing the figures that they use are far too varied to align their daily walking activity with the health objective embedded in most tools.
Entity Alignment Algorithm Based on Dual-Attention and Incremental Learning Mechanism

With the development of artificial intelligence and big data technology, large-scale general knowledge map construction is becoming increasingly important. One of the most efficient methods is undoubtedly the integration of existing knowledge maps, and entity alignment is the key in the process of knowledge map fusion. The merits of the entity alignment algorithm directly affect the efficiency and accuracy of the knowledge map fusion. However, there are some problems with the current Chinese knowledge map entity alignment algorithm, such as its low accuracy, difficulty in generating solid vectors, and difficulty in obtaining a priori alignment data. In this paper, the entity alignment algorithm is understood to be a neural network binary classification model, and we propose an entity alignment algorithm based on the dual-attention mechanism. The algorithm improves the entity vector training process, proposes a dual-attention mechanism, and applies an incremental learning mechanism. The experiments show that the improvements proposed in this paper effectively improve the classification accuracy of the algorithm, and the overall effect of the algorithm is better than that of the existing physical alignment algorithm.
Unsupervised Initialization of Archetypal Analysis and Proportional Membership Fuzzy Clustering

This paper further investigates and compares a method for fuzzy clustering which retrieves pure individual types from data, known as the fuzzy clustering with proportional membership (FCPM), with the FurthestSum Archetypal Analysis algorithm (FS-AA). The Anomalous Pattern (AP) initialization algorithm, an algorithm that sequentially extracts clusters one by one in a manner similar to principal component analysis, is shown to outperform the FurthestSum not only by improving the convergence of FCPM and AA algorithms but also to be able to model the number of clusters to extract from data. A study comparing nine information-theoretic validity indices and the soft ARI has shown that the soft Normalized Mutual Information max ( $$NMI_{sM}$$ ) and the Adjusted Mutual Information (AMI) indices are more adequate to access the quality of FCPM and AA partitions than soft internal validity indices. The experimental study was conducted exploring a collection of 99 synthetic data sets generated from a proper data generator, the FCPM-DG, covering various dimensionalities as well as 18 benchmark data sets from machine learning.
유아교육 관련 신문기사 시대별 경향 분석(2001.1.1.∼2016.12.31. C 일보기사를 중심으로)

The purpose of this study was to identify social interest and tendency on early childhood education by time by analysing early childhood education articles that were published in C Daily News, a major daily newspaper, from January 2001 to December 2016. Document research method was applied to this s...
Design Approach Of Ar Projection Mapping Onto Physical Sculpture

This paper visualizes method and processes of a projection mapping exhibition based ceramic sculpture installation entitle Forgotten Forest at the Old Court House from July 6 until July 15, 2018. The installation was presented during the Rainforest Fringe Festival 2018 in Kuching, Sarawak. The study aimed to develop a method to exhibit animation in an art installation form within a given gallery space. Forgotten Forest involved the creation of ceramic sculpture, digitisation of the sculpture model using 3D software and animation production in a form of a projection mapping
The Relation among Psychological Contract Violation on Job Consciousness, Organizational Citizenship Behavior and Turnover Intention of Elementary School Sports Instructors

Purpose: The purpose of this study is to examine and analyze the relationship among psychological contract violation on job consciousness, organizational citizenship behavior, and turnover intention of elementary school sports instructors.BR Method: Through convenience sampling method, selected 306 survey questionnaires from elementary school sports instructors were used for final analysis. With the collected data, descriptive analysis, factor analysis, correlation analysis, and structural equation model analysis were performed by SPSS WIN 18.0 & AMOS 18.0.BR Results: The results of this study are as follows. 1) The psychologic contract violation had negative influences on job consciousness of elementary school sports instructors. 2) The psychological contract violation had negative influences on organizational citizenship behavior of elementary school sports instructors. 3) The psychological contract violation had negative influences on turnover intention of elementary school sports instructors. 4) The job consciousness had positive influences on organizational citizenship behavior of elementary school sports instructors. 5) The job consciousness had negative influences on turnover intention of elementary school sports instructors. 6) The organizational citizenship behavior had negative influences on turnover intention of elementary school sports instructors.BR Conclusion: In summary, this study finds that minimizing psychological contract violations improve job consciousness and organizational citizenship behavior whereas decrease turnover intentions.
FINITE STATE AUTOMATA DAN LOGIKA FUZZY DALAM PEMILIHAN PAKET PELAMINAN DI KOTA BATAM

Marriage is something that is highly desirable for every potential partner in life. Marriage is considered a sacred activity so it needs to be well planned and mature. The growing development of a business in Batam city makes this business mushrooming because it is definitely needed by every couple who will get married. The availability of a wedding package helps each couple to choose an existing package, both in terms of price, type of tent to be used, number of clothes, and wedding photographer services. In this study, researchers conducted a determination of the problem in the form of a wedding package selection, looking for literature in accordance with the problems in the form of books and journals, analysis with fuzzy logic with the Mamdani method to complete the exact calculations so that prospective brides who will choose the aids package can choose the package that in accordance with the wishes and budget provided. The use of fuzzy logic and the Mamdani method will be assisted by Finite State Automata in making the rule design of fuzzy logic. Output targets achieved in the form of analysis of the determination of the package in the form of fuzzy logic calculations, system design from the analysis stage, retrieval of real data from each aisle in the city of Batam, and the target in the first year in the form of a determination package relevant to every wedding in the city of Batam.
Realization of a 3D Gearbox Model Through Marker-Based Augmented Reality for Edutainment Applications

Augmented reality (AR) has been proven as a valuable human-computer interface (HCI) for many real-time applications in domains such as education and entertainment. In this paper, we have shown that the modelling of a 3-D gearbox model can be enhanced with marker-based augmented reality (AR). The marker-based augmented reality (AR) visual representation of an automobile’s gearbox can improve the understanding of how each moving component interact with each other.
LibGuides: Images: Finding and Using Digital Images: Copyright and Permissions

This guide will lead you through the process of identifying image collections (searching for images in licensed and Emory-built image collections and finding images on the open web) and then using them (understanding permissions and citing images correctl