Reviewerly/exHarmony huggingface dataset:
ANALISIS IMPLEMENTASI MANAJEMEN PENGETAHUANDI PERPUSTAKAAN ISI SURAKARTA

Analysis of Knowledge Management Implementation in ISI Surakarta's Hospital. This study aimsto determine the level of implementation of knowledge management by librarians at ISI Surakarta Library. Thisresearch is quantitative descriptive. The population in this study were all librarians in the ISI SurakartaLibrary. This study is a population study because the population is only 12 people. The data collection methoduses a questionnaire. To find out more about the conditions in the field also used non-participatory observation,and documentation. Measurement of data in this study using a Likert scale. Test the validity of using Pearson'sproduct moment formula and reliability testing using Cronbach's alpha formula. Based on the results of dataanalysis that the implementation of knowledge management carried out by librarians at the ISI SurakartaLibrary, obtained a grand mean value of 4.129. This value is in the scale range of 3.40 - 4.20 which shows thatthe implementation of knowledge management carried out by librarians at ISI Surakarta Library is at a highlevel. The highest score is on the Human indicator, especially the statement I was given the opportunity toexpress my opinion at the meeting which scored 4.66 (very high). While those who get the lowest score are inthe Learning indicator especially in the statement Institutions do not hesitate to try to solve problems with newmethods and There
Many-to-Many Voice Conversion using Conditional Cycle-Consistent Adversarial Networks

Voice conversion (VC) refers to transforming the speaker characteristics of an utterance without altering its linguistic contents. Many works on voice conversion require to have parallel training data that is highly expensive to acquire. Recently, the cycle-consistent adversarial network (CycleGAN), which does not require parallel training data, has been applied to voice conversion, showing the state-of-the-art performance. The CycleGAN based voice conversion, however, can be used only for a pair of speakers, i.e., one-to-one voice conversion between two speakers. In this paper, we extend the CycleGAN by conditioning the network on speakers. As a result, the proposed method can perform many-to-many voice conversion among multiple speakers using a single generative adversarial network (GAN). Compared to building multiple CycleGANs for each pair of speakers, the proposed method reduces the computational and spatial cost significantly without compromising the sound quality of the converted voice. Experimental results using the VCC2018 corpus confirm the efficiency of the proposed method.
MIND MAPPING TO ENHANCE STUDENTS’ WRITING PERFORMANCE

English is a popular language currently,  it is being taught according to the needs of students. . This situation has contributed to the development of English for Specific Purposes (ESP) (Hutchinson & Waters, 1987). Due to the fact that ESP students have underlying conference repertoire, their language skills are affected by them. Among these skills, writing is considered difficult to get success due to the difficulties faced especially by the EFL students in ESP class. This research is formulated to investigate how mind mapping can enhance students’ writing performance in ESP class. This research implements Collaborative Action Research (CAR). It is done in the form of cycles. It also comprises four steps; planning, acting, developing, and reflecting which are adapted from Mertler’s model (2012). Based on the data analyzed, it could be concluded that mind mapping could be implemented to enhance students’ performance in writing procedure text and solve the problem to generate and organize their ideas before writing. it also brought positive attitude in teaching and learning..
Efficient mining of design patterns for advanced modeling and optimization of content management system-based web applications

Content Management Systems (CMSs) play an increasingly important role in the evolution of the World Wide Web, since almost half of the websites today use some form of CMS as their main development platform. CMSs provide development teams with standardized software platforms that significantly facilitate and speed up Web development, while maintaining high quality and low-cost implementation without requiring extensive programming expertise. Due to their flexibility and ease of use, CMSs are nowadays constantly gaining in popularity, and undoubtedly, they are among the most prominent platforms used by developers for building Web applications. The goal of this work 1 is to study and address challenging problems in the area of the widely used category of Web applications developed on top of CMSs, the so-called CMS-based Web applications, contributing novel models, methodologies and tools to optimize the quality of both the CMS-based Web development process and the final products.
A Novel Ensemble Clustering Approach with Internal Weighting Strategy

Due to its strong robustness and high accuracy, the ensemble clustering has been extensively applied in many fields. Despite the significant achievements, there is a limitation need to be overcome. Most of the existing ensemble clustering methods commonly value all component clusterings equally regardless of their different quality. Therefore, low-quality component clusterings are very destructive for them. Although many modified measures have been adopted to weight each component clustering base on their quality, yet these measures have a tendency to treat each cluster as an unit and ignore the diversity of clusters belonging to same basis clustering. How to take into count the diversity of clusters and the internal structure of component clusterings to improve the consensus performance is a problem that need to be solved. In this paper, a novel ensemble clustering method is proposed to solve this problem. Firstly, We adopt five clustering methods to generate diverse component clusterings. Secondly, in order to build candidate component clustering pool, we propose a new method to discard low-quality component clusterings. Thirdly, we utilize a advanced method to evaluate the unreliability of clusters with respect to candidate component clusterings. Finally, we further utilize a accepted weighting strategy and a consensus function to obtain consensus clustering. We have conducted extensive experiments to demonstrate the superiority of the proposed approach.
Object-based loss function in segmented neural networks

This paper proposes Object-based Loss Function in Segmented Neural Networks. Traditional Segmented Neural Network(SNN) are based on Pixel-based Back Propagation(PBP). Since the pixel ratios of the images occupied by different sizes of objects are not the same, the weight of the small objects in the segmentation is small, which means using PBP may greatly affects the accuracy of the detection when there are a large number of small objects. Considering this defect of PBP, we propose a Object-based Back Propagation(OBP) loss function weight design, that is, the back propagation weights of different objects are not equal, which is inversely proportional to the area occupied by the object. Segmented Neural Networks data set test.
Aplikasi Supervisi Kebersihan Ruangan Menggunakan QR Code Disekolah Dasar Negeri 1 Cipunagara

Cleanliness is free from dirty words including dust, Cleanliness also allows someone to be comfortable in the room. Cleanliness is a must in every outdoor environment. Maintaining cleanliness from the environment is easy because the principle of cleanliness is based on self-awareness, therefore cleanliness must be done early. In general, in elementary schools have talked about cleanliness, even each student has their own schedule. Not all students do pickets properly, therefore there are schools that employ cleaning workers to do checks in each class. The staff will clean again if there is a dirty class. This classroom hygiene supervision application will help the officers in carrying out the assignments, because this application will record some important points which can then be a report if one of the class experiences problems such as broken glass or broken table, it certainly becomes strong evidence to report to the teacher or the principal. This application was built with a web-based and API system so that the data input by the officer is connected directly with the principal, this application uses QRCode for staff validation so that the officer does not need to enter the web URL and responsive design so that it can be run comfortably on the smartphone. Keyword: Cleanliness, Web, QRCode, API, Responsive
Preposition-drop in Greek: EPP-driven pseudo-incorporation

Abstract The paper investigates preposition (and determiner) drop in Greek. A close look at the properties of preposition-drop reveals that despite the lack of a definite determiner, the place nominal that participates in preposition-drop is interpreted as familiar. This familiarity presupposition of the place nominal is achieved via movement of NP-to-SpecDP for the satisfaction of a D-feature bundle on D. Further, the lack of strict adjacency between the verb and the NP provides evidence that Greek preposition-drop aligns more closely with pseudo-incorporation. Here, pseudo-incorporation is triggered for the satisfaction of an EPP/LOC feature available on the verb and satisfied via PP-preposing.
Exploring the Experiences of University Students Participating in the General Physical Education Class with Flip Learning

Purpose: The purpose of this study is to explore the experience and change of students participating in the general physical education class with flip learning.<BR> Method: The research was explored using qualitative research methods T. he study participants were seven college students who participated in the general physical class in the second semester of 2018. The data were collected through participation observation, in-depth interviews, student reports, daily papers, and related papers. The results of the study are as follows.<BR> Results: First, the general physical education class with flip learning strengthened self-directed learning ability of students, and it was effective in increasing importance of health and human network. Second, the general physical education class with flip learning has shaped students to have the idea of exercise for health, the positive effect of exercise, and exercise habits. Third, the problems of the general physical education class with flip learning appeared as presentation students’ lack of responsibiitly, indifference to other people’s announcements, expedient online lecture courses, and intentional questions to get extra points.<BR> Conclusion: The results of this study show that the general physical eduaction class needs to be activated and expanded at universities.
In Memory of Dr. Elbert A. Walker

This paper, written in memory of Dr. Elbert A. Walker, is a reflection of the remarkable time spent together as a colleague and as a close friend spanning over three decades. His generosity, kindness, and mindfullness, had a transformational effect on all those close to him and will forever remain etched in memory. He leaves behind a legacy of optimism and hope that anything is possible if one tries hard enough, and anything can be learned if one is persistent and relentless in the pursuit of truth. For engineers who are more focused on the reality and have the desire to make things work, he provided the motivation to perceive pure mathematics as a foundation for success. Mathematics speaks the truth and is a collection of logical ideas and facts. But reality has a way to distort the truth. He instilled a deep sense of optimism in all those around him to think things through. Optimism is a virtue, he often remarked. His seminal contributions to Type II fuzzy systems is a driving force in our pursuit of building human-like decision-making systems.
Linear Data Structures

In the previous chapter, we looked at an overview of data structures and algorithms. From this chapter forward, we will explore the types of data structures there are, beginning with the simple linear data structures. It is important to understand linear data structures because they get you into the mind-set of what a data structure is rather intuitively. If you have experience with data structures, you’ll like how simply the topic is presented. If this is your first time learning data structures, you’ll marvel at how simple they are to understand. This is the introduction I wish I had to the topic. Let’s get started with linear data structures!
Adding Probabilistic Certainty to Improve Performance of Convolutional Neural Networks

Convolutional Neural Networks (CNN) are successfully being used for different computer vision tasks, from labeling cancerous cells in medical images to identify traffic signals in self-driving cars. Supervised CNN classify raw input data according to the patterns learned from an input training set. This set is typically obtained by manually labeling the image which can lead to uncertainties in the data. The level of expertise of the professionals labeling the training set sometimes varies widely or some of the images used may not be clear and are difficult to label. This leads to data sets with pictures labeled differently by different experts or uncertainty in the experts opinions. These kind of errors on the training set do happen more frequently when the CNN task is to classify numerous labels with similar characteristics. For example, when labeling damages on civil infrastructures after an earthquake, there are more than two hundred different labels with some of them similar to each other and the experts labeling the sets frequently disagree on which one to use. In this paper, we use probabilistic analysis to evaluate both the likelihood of the labels in the training set (produced by the CNN) and the likelihood’s uncertainty. The uncertainty in the likelihood is represented by a probability density and represents a spreading (as it were) of the CNN’s likelihood estimate over a range of values dictated by the uncertainty in the truth set.
드론 기반 공동주택 외관 상시 안전점검 프로세스 제안

Purpose: Recently, it began to recognize that the deterioration of buildings have an impact on the safety of residents due to performance degradation. Accordingly, quality control and safety inspection are carried out for apartment buildings. However, the existing safety inspection process used for this is inefficient for safety, time and cost. Therefore, it is expected that this research will reduce the burden on the safety, cost and time of the inspector and tenant by increasing efficiency of defects and safety inspection and proposing a drone-based defect inspection process that can be proactive. Furthermore, it will improve the safety and quality of life of residents. Method: This study proposed a process model using BPMN. BPMN is a standardized notation of business process modeling that has been developed to address a number of problems caused by the absence of visually defined work processes that can be referred to as standards for businesses and institutions. Result: The field applicability of the process proposed in this study has been verified, and both advantages and disadvantages have been identified. Key advantages can be summarized as a reduction in safety inspection time, use of small numbers of personnel and equipment, and prevention of safety accidents. Based on this study, it is deemed that an integrated safety inspection process will be needed in the future, which will be connected from defect identification to assessment of apartment buildings.
Incorporating Instance Correlations in Distantly Supervised Relation Extraction

Distantly-supervised relation extraction has proven to be effective to find relational facts from texts. However, the existing approaches treat the instances in the same bag independently and ignore the semantic structural information. In this paper, we propose a graph convolution network (GCN) model with an attention mechanism to improve relation extraction. For each bag, the model first builds a graph through the dependency tree of each instance in this bag. In this way, the correlations between instances are built through their common words. The learned node (word) embeddings which encode the bag information are then fed into the sentence encoder, i.e., text CNN to obtain better representations of sentences. Besides, an instance-level attention mechanism is introduced to select valid instances and learn the textual relation embedding. Finally, the learned embedding is used to train our relation classifier. Experiments on two benchmark datasets demonstrate that our model significantly outperforms the compared baselines.
Application and Designing of Wireless Mouse using Object Tracking

Paper aim towards building a system that can eliminate the need of mouse, touchpads and other touch devices like smartphones to control all of them. Controlling the functions of devices include the positioning of the cursor and clicking at the desired position to perform a specific task. All other features like scrolling, double-click, zooming, multiple touches, etc. fall in these categories that can be added to this system on an extension. The positioning of the cursor can be done using object tracking in computer vision. The ability to click can be provided with the help of sensors. Sensors are the good choice as some of them generate voltage signal whenever they experience a change in their respecting sensing medium. Arduino can further detect this voltage signal in order to assist the program for clicking purpose. Such a system can prove very useful in complex medical surgery, defense mission, and can enhance the growing experience of Virtual Reality games if used for entertainment purpose.
RDAP "Ask Me Anything" Townhall webinar "What is different about Clinical Data?"

This webinar was presented to the RDAP community on January 29, 2020 @ 1 pm EST. The goal of the webinar was to raise awareness of the special requirements of clinical data for data management....
Deep-ultraviolet microscopy for label-free hematological analysis

Clinical hematological practice often relies on analysis of the peripheral blood based on microscopic evaluation of blood smears and complete blood count (CBC). Accurate examination of blood cell abnormalities using such methods necessitates complex, time-consuming, and expensive sample preparation as well as instruments which require a many reagents and intensive maintenance. Further, hematology analysis is performed at healthcare centers by trained personnel which significantly limits monitoring frequency for patients with severe conditions and can compromise the treatment outcome. Therefore, a portable, easy-to-use, and inexpensive hematology analysis device can potentially improve quality of life for patients with blood diseases and allow point-of-care monitoring and diagnosis. In this work, we demonstrate label-free blood cell assessment based on deep-ultraviolet (UV) microscopy. Our approach provides quantitative endogenous molecular information from live cells and enables assessment and differentiation of blood cell types based on their molecular and structural signatures. We show the ability of our method by performing classification of polymorphonuclear leukocyte (PMNL) subtypes based on features extracted from deep-UV images. In addition, we demonstrate a pseudo-colorization scheme which accurately mimics the colors produced by standard Giemsa staining and enable visual examination of blood smears. The results of our work paves the way for development of a low-cost and easy-to-use hematological analysis device that can be used for point-of-care applications.
Automatic Modulation Classification Using Induced Class Hierarchies and Deep Learning

In this work, we contribute to the emerging field of deep learning (DL) methods of automatic modulation classification (AMC) for cognitive radios. Traditional AMC methods rely on expert-based knowledge of the wireless channel and incoming signals. This method suffers from a lack of generalizability to real-world channels that may be severely impaired or unknown. DL does not require a priori or expert-based knowledge and has seen success in other fields such as image processing and natural language processing. In recent years, DL has been explored as an alternative to traditional methods; however, currently proposed DL AMC methods suffer from high training times due to the many layers used to improve classification accuracy. We propose the use of induced class hierarchies to decompose the AMC task into subcomponents, while still maintaining deep architectures for improved classification accuracy. A publicly available, synthetic radio data set is used, which models severe channel impairments under a range of various signal-to-noise (SNR) levels. Three hierarchical convolutional neural network (h-CNN) architectures are developed: a single-level, baseline model; a two-level hierarchical model, termed model A; and a three-level hierarchical model, termed model B. Model A achieves a 4% improvement in classification accuracy over the baseline model while model B maintains comparable accuracy. Moreover, the training times of both models are reduced, with 50% improvement with model A and 28.6% improvement with model B, from the baseline model.
Superpixel Image Classification with Graph Attention Networks

This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect neighboring superpixels. Our experiments suggest that Graph Attention Networks (GATs), which combine graph convolutions with self-attention mechanisms, outperforms other GNN models. Although raw image classifiers perform better than GATs due to information loss during the RAG generation, our methodology opens an interesting avenue of research on deep learning beyond rectangular-gridded images, such as 360-degree field of view panoramas. Traditional convolutional kernels of current state-of-the-art methods cannot handle panoramas, whereas the adapted superpixel algorithms and the resulting region adjacency graphs can naturally feed a GNN, without topology issues.
Research on Influence of Content Diversity on Full-Reference Image Quality Assessment

With the development of image quality assessment (IQA), the full-reference (FR) IQA algorithms are becoming more and more mature. This paper analyzes the performance of five FR natural scene (NS) IQA algorithms and three FR screen content (SC) IQA algorithms, and makes analysis on the eight IQA algorithms in terms of prediction performance and design principle. Experiments show, (1) the performance of perceptual similarity based IQA method proposed by Gu et al. is better; (2) the multi-scale technology plays an important role in improving the performance of algorithms. Finally, summarized and expounded the development direction of image quality evaluation.
Implementing Machine Learning for Face Recognition based Attendance Monitoring System

It is well known that marking attendance of the students is an obligatory part in academia. Conventional method of marking the attendance is being followed by various institutions and Universities with lots of manual interventions. To reduce time consumption and human effort, the use of an automatic process of marking attendance based on image processing can be enforced. Authors have proposed a smart attendance monitoring system through face detection and recognition techniques based on their facial features. A set of images of the students are previously fed to the system against which the live images of the students are compared and attendance would be recorded based on facial characteristics. The proposed approach uses CNN algorithm for training the images and LBPH visual descriptor for image classification. This model will be capable of providing higher degree of accuracy compared to already existing literature work. Authors have compared their experimental results with the existing approaches and found satisfactory.