Reviewerly/exHarmony huggingface dataset:
Metode Hierarchical Density-Based Spatial Clustering of Application with Noise (HDBSCAN) Pada Wilayah Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara

The underdeveloped areas are generally the districts which are relatively underdeveloped compared to other regions on a national scale. Determination of underdeveloped villages is often done in order to determine the distribution of government assistance so that assistance can be distributed appropriately. The identification is based on facilities, infrastructure, access, social, population and economy provided in the Village Potential data (PODES). The concept of grouping based on regional or spatial is done to find out certain characteristics in an area. HDBSCAN is a grouping concept with a parameter called Mpts. The purpose of this study is to know the number of clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method. The Mpts parameters that is used in this study is from 2 to 6. Based on the results of the analysis, the clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method, were 3 clusters. Cluster 0 consists of 19 villages / urban areas , cluster 1 consists of 4 villages / urban areas and cluster 2 consists of 61 villages / urban areas. Based on the analysis, villages / urban areas included in cluster 1 could be the main target of the government in providing assistance and development of regional facilities / infrastructure.
Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine

The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaffine, which formulates the NER as an MRC task. The approach of the machine reading comprehension framework is to introduce prior knowledge, the query about entities. The biaffine mechanism scores pair start and end tokens in a sentence so that the model is able to predict named entities accurately. The proposed method outperforms from the electronic medical record dataset, called CCKS2017 data, and the TCM dataset. We also remove components to evaluate the contribution of individual components of our model. Experiments on two datasets demonstrate the effectiveness of our model.
Chapter 3. Components

Chapter 3 of Library Technology Reports (vol. 57, no. 6), “Components,” explains that metadata application profiles (MAPs) have four components: the application, entities, properties, values. When an application requires a MAP, entities are the things described. Arguably the main purpose of a MAP is to identify properties describing entities. Properties have values also described by a MAP. MAP components are familiar to metadata professionals, but MAP contents have changed, largely due to the increasing adoption of linked-data practices.
Use of vocabulary translation strategies: A semantic translation analysis

Translation is the process of transferring source language text messages into the target language. The practical objective of the message transfer process is to assist the reader of the target language text in understanding the message intended by the original author of the source language text. There are many types of translations available, but semantic translation is considered the type of translation that is the most accurate in conveying meaning. Semantic translation tries to divert as closely as possible the semantic and syntactic structures of the target language with the exact same contextual meaning in the source language text, as well as word meanings and sentence meanings from the perspective of the source text context. Semantic translation is found to be the most flexible and flexible translation.
Jaringan Syaraf Tiruan Menggunakan Metode Backpropagation Dalam Prediksi Persediaan Bahan Baku (Studi Kasus : PT. Bintang Toba Lestari)

PT. Bintang Toba Lestari is a spring bed manufacturer located in the city of Medan. In providing spring bed raw materials for spring bed production, PT. Bintang Toba Lestari usually estimates based on inventory notebooks so that the results of predictions made often miss estimates because there are no proper calculations to make predictions. This can be detrimental to the company because of the embedded capital. Embedded capital should be used for the company's regular expenses. Therefore, a system is needed that can help PT. Bintang Toba Lestari to be able to predict spring bed raw materials so that there is no embedded capital that causes the company's losses. This study uses neural networks to predict the supply of spring bed raw materials and uses backpropagation methods to process spring bed raw material inventory data so as to get the right forecasting results. With the existence of artificial neural networks using the backpropagation method, it can help PT. Bintang Toba Lestari in predicting spring bed raw material supplies . Keywords: Neural Networks, Backpropagation, Predictions, Supplies, Raw Materials .
EvaScrum: An assessment instrument to support the diagnosis of Scrum – results of two case studies

The purpose of the process evaluation is to obtain relevant information in a qualitative and quantitative way about the current state in which a process is to support processes of evolution and continuous improvement. Scrum is one of the most used agile approaches, however, there some aspects that can hinder its implementation, e.g., lack of detail of artefacts and meetings that raises, including the timetables, application of the approach, among others. In this sense, and in order to facilitate the success in the implementation of Scrum, this paper presents EvaScrum, an assessment instrument that provides the opportunity to professionals and consultants to assess and diagnose the degree of implementation of Scrum through questions, metrics, a spreadsheet and a Web application. EvaScrum is based on Mr. Scrum; a reference model which provides a clear and complete set of process elements based on Scrum and EvaScrumTOOL, a web tool to manage the assessments. This paper presents the detailed analysis of two case studies in software development enterprises where EvaScrum was applied. The results obtained have allowed case study enterprises to identify improvement opportunities to carry out in the processes and projects where Scrum is being applied, all this, in a practical, useful and suitable manner which allow these types of enterprises, to assess and diagnose their implementations of Scrum with feasible resources both time and economically.
One Method for RC5 Algorithm’s Cryptographic Strength Improving

Encryption of electronic data requires the use of crypto resistant functions. Especially relevant is the increase of the stability of block algorithms, in particular, RC5, which is part of various open cryptographic libraries. The article presents a method for improving the crypto stability of the classical RC5 algorithm with the help of nonlinear bit offset functions without increasing RC5 computational complexity. The modeling of the cryptographic system implemented based on RC5 crypto-algorithm modification is carried out in the article and obtained time and quality characteristics of the modified algorithm. The reliability of the cryptosystem obtained based on the improved RC5 algorithm is verified by the absence of statistical correlation between the input message blocks and the output blocks, as well as the absence of collisions, in which it is possible to obtain the same sequence of bits at the output with different messages at the input.
Mathematical model of software development project team composition optimization with fuzzy initial data

Increasingly frequent changes in demand for products, reducing product life cycles, changes in the business environment during a crisis, the innovative nature of projects, the complexity of forecasting external and internal conditions, the impact of the human factor lead to increased uncertainty and inability to plan team activities with a given degree of accuracy. In this regard, the subject matter of the article is the task of creating an adaptive project team that can work effectively in the mentioned above conditions. This task is especially relevant for the sphere of software development. The sphere is dynamic and characterized by frequent changes in product requirements, technologies, working conditions, and restrictions on project implementation. Agile approaches are used to manage such projects, which can help the team respond to uncertainties and frequent changes. To date, there are many agile approaches to project management, but the issue of selecting team members in such approaches is insufficiently covered. Therefore, this work formalizes the task of deciding on the selection of software development team members, considering the uncertainty and subjectivity of the information that affects the selection of candidates for the team. The task of the work is to create a decision-making model based on the use of the mathematical apparatus of fuzzy sets and methods of operations research. Such a model should allow considering the uncertainty of estimates of project requirements and the level of competence of team candidates. The result is a mathematical model of a two-criterion constrained optimization problem. The first objective function is aimed at finding a team composition that maximizes the maximum competencies of its members. The second criterion is aimed at forming a team with the maximum sum of competencies for all indicators, considering the weight of each indicator. The first constraint assumes that at least one team member meets the competency requirements expressed by a specific indicator. Additionally, it is required that the available time fund of the team members allows the project to be completed on time. It considers the limitation on the salary of the team. Conclusions. Solving the problem in accordance with the proposed mathematical model will allow making a team as readily as possible to meet the existing and new requirements for the project staff. The last circumstance is especially important when implementing a software development projects.
LibGuides: Music Studio Specific Resources: Clarinet

These resources are highlights from our collection and are all available right now at CWU Brooks Library! Arranged by studio.
Design and Implementation of Android-Based Public Transport Trayek using Cloud Computing Infrastructure

Transportation Route System is a system that was built to improve and to meet the needs of public transport users with technology at the present time. the purpose of making the transportation route system is to facilitate the public so that it can create facilities when searching for the desired transportation. The transportation route system uses SDLC (system Development Life Cycle) technology in building it so that the system is made more neat and complex. Google maps technology becomes the main thing on the transportation route system because Google Maps will show the route when the transportation is running. Keyword : Transportation, SDLC, Google Maps.
Learning where to learn: Gradient sparsity in meta and continual learning

Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight changes results in low generalization error. We show that this form of meta-learning can be improved by letting the learning algorithm decide which weights to change, i.e., by learning where to learn. We find that patterned sparsity emerges from this process, with the pattern of sparsity varying on a problem-by-problem basis. This selective sparsity results in better generalization and less interference in a range of few-shot and continual learning problems. Moreover, we find that sparse learning also emerges in a more expressive model where learning rates are meta-learned. Our results shed light on an ongoing debate on whether meta-learning can discover adaptable features and suggest that learning by sparse gradient descent is a powerful inductive bias for meta-learning systems.
Cardless Multi-Banking ATM System Services using Face Recognition and OTP

Currently systems are protected using many firewalls, IDS and security software. The existing system can be easily compromised by any tool used by an attacker. The aim of the system is to provide effective authentication from unauthorized users by providing three tier authentications which are login credentials, face Recognition scanner and OTP. The proposed methodology provides more control over data stored in the system by restricting the access to specific users for specific files with limited privileges and for a limited time period on the basis of secret key authentication using symmetric as well as asymmetric mechanisms. The integrity and confidentiality of data is fully guaranteed by not only encrypting the data using a secret key but also to the access permission and limited file information.
In the Realm of Block Chain – Cryptocurrency

This paper aimed to open eyes for the many functions of the blockchain technology which was the underlying program of the cryptocurrency. The cryptocurrency itself could not be that magical if not based on the blockchain technology. Recent developments had revealed how this technology could completely change the future. The boundaries of today’s nations would be replaced by another kind of kingdom resided in the realm of the cryptocurrency and its blockchain technology. The paper also discussed the effort of Indonesian government to step into this area.
Internet of Things

IoT comes into play when innovation meets science. IoT applications have transformed traditional human practices into revolutionary buildouts. It has lent the modern world a previously unseen phenomenon where inanimate objects can assume the brain and partake in human endeavors. It has provided intelligent solutions to various fields like production, transportation, communication, retail, energy, agriculture, healthcare, security, and many more. IoT has leveraged progress by enhancing human activities through time, money, and labor-saving solutions. The technology, like any other invention, is also not free of flaws. Despite rendering a wealth of benefits, the technology has its downsides too. Since it is a framework of a huge number of interconnected devices, it is quite prone to risks. The IoT web uses and shares large data so it has developed privacy and security issues. The need, therefore, is to use this technology wisely. Solutions needed to be worked out to make it a more secure and reliable source for the progress and development of the human race.
Check De-duplication on Dependable Data Outsourcing with Three Error Detecting Techniques on Cloud Computing

Recently, cloud computing is usually a la mode. Cloud services that provide knowledge outsourcing on a cloud, numbers of users access these services to store an over-sized quantity of data on the cloud. Several existing systems have limitations, i.e., loss of availableness, loss, and corruption of data loss of security, and merchandiser lock-in. Existing DEPSKY System beats the limitations; in any case, it comes up short on a mistake identification instrument and accompanies high computing prices. To beat this downside, I propose a singular economical de-duplication on dependable encrypted knowledge outsourcing on cloud with quick recovery. My main goal is to urge obviate perennial files on the cloud; therefore, whenever a user uploads any file 1st checking de-duplication, subsequently, the three error detection ways verify files shadows area unit hacked or not efficiently; finally quick recovery technique recovers files quicker than existing ways. My novel satisfies all elementary security needs further as I perform above existing schemes.
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules

We study acquisition functions for active learning (AL) for text classification. The Expected Loss Reduction (ELR) method focuses on a Bayesian estimate of the reduction in classification error, recently updated with Mean Objective Cost of Uncertainty (MOCU). We convert the ELR framework to estimate the increase in (strictly proper) scores like log probability or negative mean square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS). We also prove convergence results borrowing techniques used with MOCU. In order to allow better experimentation with the new acquisition functions, we develop a complementary batch AL algorithm, which encourages diversity in the vector of expected changes in scores for unlabelled data. To allow high performance text classifiers, we combine ensembling and dynamic validation set construction on pretrained language models. Extensive experimental evaluation then explores how these different acquisition functions perform. The results show that the use of mean square error and log probability with BEMPS yields robust acquisition functions, which consistently outperform the others tested.
One Step Measurements of hippocampal Pure Volumes from MRI Data Using an Ensemble Model of 3-D Convolutional Neural Network

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer s disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy
Assorted Techniques for Defining Image Descriptors to Augment Content Based Classification Accuracy

Image data has turned out to be a significant means of expression with the advancements of digital image processing technologies. Image capturing devices has now transformed to commodities due to smart integration with cell phones and other useful devices. Huge amount of images are getting accumulated daily in gigantic databases which requires categorization for prompt retrieval in real time. Content based image classification (CBIC) thus gained it's popularity in classifying images to their corresponding categories. Feature extraction techniques are the foundation of CBIC to represent the image data in the form of feature vectors. This work has implemented three different feature extraction techniques from spatial domain, transform domain and deep learning domain. The three different feature vectors feature vector are contrasted to investigate the robustness of descriptor definition for content based image classification
DEVELOPING INDEPENDENT AND CREATIVE ACTIVITY OF STUDENTS

In modern conditions, the educational process is required to focus on the development, socialization of the individual and the development of independent, critical, creative thinking skills. One of the most pressing issues today is the training of highly qualified specialists in accordance with consumer requirements, the formation of knowledge, skills and abilities of students in their chosen specialties, independent learning and practical activities.That is why researchers and advanced educators are looking for ways, technologies and methods to develop a creative approach to learning activities in students. One of the most advanced pedagogical technologies that has a positive effect today is the use of factors in the development of independent and creative research activities of students in the educational process.
Research on the Construction of Excellent Classes for College Students in China in the New Era

As a basic unit for college students' education in China, class is a campus management method with Chinese characteristics, in which students grow both academically and physically.The rapid development of global economy fueled by the Internet stimulates mounting demands for comprehensive and innovative talents by the international community.Consequently, to meet the needs of the society, Chinese universities are propelling cultivation of comprehensive and innovative talents, exploring training mechanisms such as general enrollment, credit system and collegiate system.At present, college students are identifying their personal development goals according to social demands and their personal characteristics, and generating diversified demands for higher education.However, the traditional class model suffered a great failure and faced multiple problems, including poorly organized classes, team-spirit-absent students, negative guiding effect and bad style of study.To address the existing drawbacks, based on K. Lewin's group dynamics, this paper analyzes the characteristics of four excellent classes in Chinese universities by means of literature research and multi-case study, providing reference for future construction of excellent classes.
Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data.

The central dogma of handwritten character recognition remains inextricably linked to optical character recognition methods for print media. Alongside their reliance on proprietary data and lack of open-access software, the applicability of these optical character recognition methods to handwritten characters from low-quality documents (e.g., that are damaged) remains unknown. In this paper, we compare and contrast the performance of state-of-the-art optical character recognition tools for print and learning models engineered with state-of-the-art machine learning toolkits trained on handwritten inputs. Using Tesseract OCR as a baseline, we build, optimize, and evaluate three types of convolutional neural networks that are trained on the AL-ALLand AL-PUBdatasets, a collection of images of handwritten ancient Greek characters that were labeled by volunteers through the Ancient Lives online citizen science project. We find our best-performing machine learning model to be 92.57% accurate compared to Tesseract OCR’s 11.15%. Following our analysis, we present a brief examination of our models’ shortcomings, introduce the publicly-available AL-PUBdataset, and, describe Theia, a web-based tool that democratizes our machine learning models for public use. We conclude by discussing the promise of our findings for advancing research at the intersection of machine learning, manuscript transcription, and the digital humanities.