Reviewerly/exHarmony huggingface dataset:
Identifikasi Jenis-Jenis Burung Lovebird Menggunakan Pengolahan Citra Digital Dengan Metode K-Means Clustering

Identification process type of bird lovebird, usually determined by parameters characteristic feather color of birds lovebird. Parameters to define the type of bird lovebird usually done manually by using the sense of sight. This way has a weakness that is, it takes a long time and results in an invalid identification. Based on that weakness, the researcher designed an identification system type of lovebird birds by using the Matlab programming language. Identify the type of lovebird bird conducted by researchers, namely color analysis of lovebird bird feathers. Image data were taken in the form of bird samples, lovebird josan, albino, and lutino by using a DSLR camera and identification algorithm K-means Clustering. The system is divided into two stages, namely the training stage and the testing phase. The training stage uses 30 bird images recognized according to type resulting in an accuracy level of 100%. While The testing phase uses 24 bird images, 22 images of lovebird birds identified according to their type, and two recognizable lovebirds images but not according to its kind resulting in an accuracy level of 91,67%.
Artistic Creativity As A Basis For A Future Professor-Musician’s Socio-Communicative Competence Development

The ongoing problem of higher education pedagogy is the study of the essence and conditions of the socio-communicative competence development of a modern professor. The socio-communicative competence of a professor-musician, manifested in professional and pedagogical activities, is presented in the article as a multi-level personal reality, the structure of which comprises cognitive, emotional and creative components. The essence of the socio-communicative competence of a professor-musician is considered in the context of artistic creativity, which becomes the platform of professional activity and the basis of his/her development. As a cultural phenomenon, musical art is the art of intoned meaning, a sign of a certain era, culture, history, nation, a powerful factor of social interaction. On the other hand, any musical composition contains subjective reality, reflects the individual character of its author, his/her psychology, subjective experience. Authors highlight the social and communicative aspects of musical art, which, by creating a special sounding reality, musical being, determine the position of a person in the modern world, his or her social needs. The latter, then, is the basis of artistic creativity, on which the naturally-managed process of training a future professor-musician at the pedagogical university is based. The article presents the forms of development of the socio-communicative competence of a professor-musician at university, which, if properly organized, arrange a naturally-managed process at the substantive, methodological and technological levels.
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions. This covariate shift is caused, in part, due to unobserved features in the datasets. The objective, then, is to find the best mixture distribution over the training datasets (with only observed features) such that training a learning algorithm using this mixture has the best validation performance. Our proposed algorithm, ${\sf Mix\&Match}$, combines stochastic gradient descent (SGD) with optimistic tree search and model re-use (evolving partially trained models with samples from different mixture distributions) over the space of mixtures, for this task. We prove simple regret guarantees for our algorithm with respect to recovering the optimal mixture, given a total budget of SGD evaluations. Finally, we validate our algorithm on two real-world datasets.
Cascade Mask R-CNN and Keypoint Detection used in Floorplan Parsing

Parsing floorplans have been a problem in automatic document analysis for long and have up until recent years been approached with algorithmic methods. With the rise of convolutional neural network ...
Arabic Sentiment Analysis using Deep Learning for COVID-19 Twitter Data

Novel coronavirus, (COVID-19) first noticed in December 2019, and became a world pandemic affecting not only the health sector, but economic, social and psychological wellbeing as well Individuals are using social media platforms to communicate feelings and sentiments on this pandemic This article aims at analyzing and visualizing the influence of coronavirus (COVID-19) using machine learning and deep learning methods to quantify the sentiment shared publicly corelated with the actual number of cases reported over time On the analysis of 10 Million Arabic tweets, results show that deep learning techniques using an ensemble model outperformed machine learning using SVM with an accuracy of 90% and 77% respectively It also outperformed the individual deep learning models
Research guides: VPMC01H3 Exploring Community Music: Journals and Databases for Community Music

A guide aggregating resources you will need to successfully complete your assignments for VPMC01H3 A page listing journals and databases that are relevant to the study of community music
Decision-Based Marginal Diffusion for Salt-and-Pepper Noise Removal in Color Images

Salt-and-pepper noise suppression for vector-valued images usually employs vector median filtering, total variation L1 model, diffusion methods and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and are suitable only for images with low intensity noise. In this paper, a new method, as an important preprocessing step in cyber-physical systems, is presented to suppress salt-and-pepper noise that can overcomes this limitation. This method first detects the corrupted pixels and then restores them using channel-wise anisotropic diffusion. The means is twofold. On the one hand, the marginal approach is used to perform noise suppression separately in each channel because the contaminative pixel components are of independent distribution. On the other hand, a decision-based anisotropic diffusion method is applied to each channel to restores them. The anisotropic diffusion is an energy-dissipating process with time, and dependent on geometric analysis of shape of the energy surface. Simulation results indicate that the proposed method for impulsive noise removal achieves the state-of-the-arts results.
Pengembangan Sistem Informasi Geografis Tentang Sebaran Penduduk Miskin Di Kabupaten Probolinggo

The implementation of this geographic information system development aims to present the distribution area of the poor in Probolinggo Regency in three dimensions in the form of a digital map. So that the information displayed is easy to analyze and can be identified the distribution of the population in a visual form. The method of developing this application uses the Software Development Life Cycle method, which consists of Planning, Analysis, Design, and Implementation. This application was created using QuantumGIS, P-mapper, and MapServer with the PHP programming language. This Geographical Information System can display the mapping of poor areas based on four levels of poverty, namely moderate poverty, near poverty, poor, and very poor. Thus, this application is very useful for the Regional Development Planning Agency of Probolinggo Regency to understand the meaning of the data that has been displayed visually on a map by looking at areas that have been grouped with the same poverty level.
Analisis Metode Blender Render Dan Cycle Render (Studi Kasus: Simulasi Menghadapi Bencana Alam Gempa Bumi 3D)

Blender is an open source application that is used to build and create 3-dimensional animations. In Blender rendering, there are 2 commonly used techniques, namely Blender Render and Cycles Render. In previous studies, this rendering analysis has been done by comparing the time and amount of ram on 3D objects that have been rendered by using cycles rendering and blenders rendering. The results of the study indicate that the more 3D objects rendered will require a short time due to the amount of ram that is used a lot. Therefore, this study will use a 3D video simulation to deal with earthquakes where 3D objects and scenes contained in a 3D simulation of earthquake will be given a different composition to obtain comparative material. The results of the analysis can provide 3D Architectural Designer and 3D Modeling Specialist solutions as well as areas of work related to the 3D Rendering and Simulation process to face earthquakes can be used as information for the public in the face of earthquakes.
Bibliogallery on Kittarskaya Street as an innovative model for the library development

The experience of the Bibliogallery on Kittarskaya Street is discussed. The author examines the most successful projects accomplished at the Bibliogallery that contributed to the Library advance.
LibGuides: Library Resources: Bored at Home

This guide provides a starting place to get help while working in an online environment. It let's you decide what kind of help you need.
Decoding Variants of Reed-Muller Codes over Finite Grids

In a recent article, Kim and Kopparty (2017) gave a deterministic algorithm for the unique decoding problem for polynomials of bounded total degree over a general grid S 1 × … × S m . We show that their algorithm can be adapted to solve the unique decoding problem for the general family of Downset codes . Here, a downset code is specified by a family D of monomials closed under taking factors: The corresponding code is the space of evaluations of all polynomials that can be written as linear combinations of monomials from D .
USE OF TECHNOLOGY IN MUSUEUMS, SAMPLE APPLICATIONS

Museums are institutions that provide the protection of cultural heritage and introducing their findings to visitors, while amining for education, training and entertainment. Our age is now an age of technology. Everyone livesinseperable with technology and uses technology in almost every area in their daily life. Howevertechnology has started to gain importance in education and entertainment field, apart from the daily needs of people. Therefore, from touch screens to augmented reality, museums have been resorting to many interactive and instructive technology types in order to provide more information and a better experience for their visitors
PENGARUH KUALITAS SISTEM, INFORMASI DAN LAYANAN SIAKAD ONLINE TERHADAP KEPUASAN MAHASISWA (Studi Pada Mahasiswa AKPINDO Jakarta)

AbstractThe development of information systems cannot be separated from the rapid progress of information technology and the internet. At present, this has changed the paradigm in busi­ness processes and policy making. Higher education institutions are no exception starting to use internet-based information systems as an alternative in the process of its activities. However, only few research has discussed the effect of online academic systems on student satisfaction. This study aims to analyse the effect of system quality, information quality and online service quality siakad on student satisfaction. Data were obtained by survey method of 255 students of the Indonesian Tourism Academy (AKPINDO) Jakarta with multiple linear regression methods. The results of the study revealed that the quality of academic information systems had no effect on student satisfaction, while information quality and service quality significantly affected student satisfaction. Simultaneously the quality of the system, information and services affect the satisfaction of students.
Kirkwood Library: Synchronous & Asynchronous Virtual Instruction Research: Overall effects of pandemic

Information resources that address the question of the effect on student retention of synchronous virtual instruction, and the impact of various student support services and resources on same.
All Guides: Open Access of Scholarly Publications Policy: Currently Available OA Resources

Collection development policy for resources that freely available. Practices implemented by CL.
City announces exciting new Summer Events Season

Residents can look forward to 12 free events at locations across the City of Wanneroo from December to March.
Marketing of Library and Information Products and Services During Covid -19 Pandemic: A Study

The Social networking sites play a major role in promoting library services and products to its user community In this Covid-19 Pandemic and lockdown period, physical communication was locked and online web communication technology proved a powerful tool to bridge the gap between the library and users The users were unable to reach the libraries during this pandemic period The librarians used social networking tools to provide information during this pandemic period The libraries provided a variety of services to its user community The purpose of this paper is to show how effectively the libraries used Social Networking Sites for marketing & promoting the library and information products & services to their users during this pandemic and lockdown period A structured online questionnaire was used for data collection The targeted population was academic librarians working in colleges in Goa, India From the study, it was found that Social networking sites play a major role in marketing and promoting library and information products and services during this pandemic period Maximum librarians are using the Library website, Facebook and WhatsApp tool for marketing and promoting the library services and products to reach their users © 2020 All Rights Reserved
Redesigning job analysis to improve the performance of education personnel at Universitas Jenderal Achmad Yani

This study aimed to redesign problems related to job analysis that occurs at the University of General Achmad Yani, where the workload was still not optimal. The cause of the many unclear job analyses that exist in the institution is that all sections perform a variety of additional work that is not related to their duties and obligations. This research was intended to redesign the position analysis at the managerial level of the University of General Achmad Yani. Mix methods were employed in this research, the first approach used was a qualitative approach based on case studies. There were three stages of analysis, namely organizing various related data, grouping data based on categories, themes and answer patterns, then testing the assumptions or problems that exist in the data. The quantitative approach used was explanatory research through a quantitative approach, where responses were measured using a Likert scale. The sample of this study was 71 employees at General Achmad Yani University chosen by proportional stratified random sampling. The results of this study showed that all the hypotheses were proven.
Deep Learning Techniques for Human Abnormal Activity Recognition using Video Feature Extraction and Analysis: A Systematic Review

In recent times, deep learning techniques has shown its ability to apply in any field includingspeech recognition, image/video processing, natural language processing, and many more real-life problemssolving. On the other side Human activity recognition (HAR) has become a popular topic in research due toits broad application for scientists and engineers. The researchers started working on the new ideas byintegrating the two emerging areas to solve HAR problems with the implementation of deep learning. Thepresent paper has reviewed some of the literatures related to existing HAR systems that are used torecognize the human activities with prominence on detecting abnormal behaviors. First, a review isconducted to understand how the deep features can be extracted and examined acquired from the videoframes. Second, a review is conducted to understand the process of analysis to understand the humanactivities. Third, a review is conducted to understand how the existing HAR models or systems arecontributing to the efficient recognition of human activities in real-time scenarios. Forth, the complexitieswhile implementing the HAR systems are represented as challenges and mentioned finally the future scopefor analysis of input videos for abnormalities detection in human behaviour. The review of literature is topermit the researchers to learn new techniques of deep learning while identifying the activities by enhancingthe systems performance accuracy.
Analisa Pengolahan Kulit Imitasi Sebagai Material Embellishment

ABSTRACT Imitation leather or artificial material is made to replace the main material which is genuine leather made from animal skin. Usually applied to fashion products or hard accessories such as wallets, belts, bags, hats and others. Judging from its characteristics, imitation leather has many advantages. Not much processing of imitation leather as a decorative or embellishment element. With this problem, it seems that imitation leather has the potential to be processed into embellishment. In this study the authors used several methods, namely literature study, observation methods and material exploration methods to find out the techniques that could be processed optimally. Keyword: Synthetic Leather, Embellsihment, Exploration
APPLICATIONS OF DEEP LEARNING IN TEXT CLASSIFICATION FOR HIGHLY MULTICLASS DATA

Text classification using deep learning is rarely applied to tasks with more than ten target classes. This thesis investigates if deep learning can be successfully applied to a task with over 1000 ...
Modeling Inference Time of Deep Neural Networks on Memory-constrained Systems

Deep neural networks have revolutionized multiple fields within computer science. It is important to have a comprehensive understanding of the memory requirements and performance of deep networks on low-resource systems. While there have been efforts to this end, the effects of severe memory limits and heavy swapping are understudied. We have profiled multiple deep networks under varying memory restrictions and on different hardware. Using this data, we develop two modeling approaches to predict the execution time of a network based on a description of its layers and the available memory. The first modeling approach is based on engineering predictive features through a theoretical analysis of the computations required to execute a layer. The second approach uses a LASSO regression to select predictive features from an expanded set of predictors. Both approaches achieve a mean absolute percentage error of 5% on log-transformed data, but suffer degraded performance on transformation of predictions back to regular space.
Building a Medical Recommendation System : A case study on digitalizing evidence-based radiology

In this thesis, we show how a text-based Recommendation Systems can greatly benefit from neural statistical language models, more particularly BERT. We evaluate the framework on a digital and colla ...
PENERAPAN MODEL PEMBELAJARAN GENERATIF UNTUK MENINGKATKAN AKTIVITAS BELAJAR MATEMATIKA PESERTA DIDIK KELAS VIII SMP NEGERI 11 KOTA BENGKULU

The aim of this research was to know how to manage the class with applying generative learning model improving student learning activity in mathematics Grade VIII Junior High School Number 11 Bengkulu City. The design of this research was classroom action research with four steps were: planning, acting, observing and reflecting. The subject of this research was 32 students of Grade VIII B Junior High School Number 11 Bengkulu City on academic year 2018/2019. Data collection was done by using student learning activity observation sheet. Class management with applying generative learning model improved student learning activity by: the exploration phase trained student to listen explaining, pay attention and express opinion; the focus phase trained student to team up, discuss, drawing and writing; the challenge phase trained student to solve the problem and active on class discussing; and the application phase trained student to do exercises. The result of average score in sudent learning activity improved from first cycle to third cycle: 19,50; 23,75; 28,38.
LibGuides: *Visitors' guide to the GTU library*: Where to begin?

The GTU library offers a wide range of services to the community. Learn more about getting a card and using the GTU library here!
Deep Inverse Q-learning with Constraints

Popular Maximum Entropy Inverse Reinforcement Learning approaches require the computation of expected state visitation frequencies for the optimal policy under an estimate of the reward function. This usually requires intermediate value estimation in the inner loop of the algorithm, slowing down convergence considerably. In this work, we introduce a novel class of algorithms that only needs to solve the MDP underlying the demonstrated behavior once to recover the expert policy. This is possible through a formulation that exploits a probabilistic behavior assumption for the demonstrations within the structure of Q-learning. We propose Inverse Action-value Iteration which is able to fully recover an underlying reward of an external agent in closed-form analytically. We further provide an accompanying class of sampling-based variants which do not depend on a model of the environment. We show how to extend this class of algorithms to continuous state-spaces via function approximation and how to estimate a corresponding action-value function, leading to a policy as close as possible to the policy of the external agent, while optionally satisfying a list of predefined hard constraints. We evaluate the resulting algorithms called Inverse Action-value Iteration, Inverse Q-learning and Deep Inverse Q-learning on the Objectworld benchmark, showing a speedup of up to several orders of magnitude compared to (Deep) Max-Entropy algorithms. We further apply Deep Constrained Inverse Q-learning on the task of learning autonomous lane-changes in the open-source simulator SUMO achieving competent driving after training on data corresponding to 30 minutes of demonstrations.
Making CNNs for video parsing accessible: event extraction from DOTA2 gameplay video using transfer, zero-shot, and network pruning

The ability to extract sequences of game events for high-resolution e-sport games has traditionally required access to the game's engine. This serves as a barrier to groups who don't possess this access. It is possible to apply deep learning to derive these logs from gameplay video, but it requires computational power that serves as an additional barrier. These groups would benefit from access to these logs, such as small e-sport tournament organizers who could better visualize gameplay to inform both audience and commentators. In this paper we present a combined solution to reduce the required computational resources and time to apply a convolutional neural network (CNN) to extract events from e-sport gameplay videos. This solution consists of techniques to train a CNN faster and methods to execute predictions more quickly. This expands the types of machines capable of training and running these models, which in turn extends access to extracting game logs with this approach. We evaluate the approaches in the domain of DOTA2, one of the most popular e-sports. Our results demonstrate our approach outperforms standard backpropagation baselines.