Reviewerly/exHarmony huggingface dataset:
MEANING AND DIMENSIONALITY OF STATE OF COMFORT IN PATIENTS WITH CHRONIC HEMODIALYSIS KIDNEY DISEASE

ABSTRACT Objective: to understand the meaning and dimensionality of state of comfort from chronic hemodialysis patients’ perspectives. Method: this is a qualitative study conducted with 30 patients from a hemodialysis clinic from May to June 2018. A semi-structured interview with guiding questions was used. Data were analyzed according to Bardin’s content analysis method, supported by Kolcaba’s theoretical framework. Results: five subcategories for being and feeling comfortable emerged: psychological well-being; Silent environment; Good quality of care; No health changes; Reduction in the frequency/duration of hemodialysis. Regarding dimensionality to achieve comfort, there were three subcategories according to the adopted states. Regarding dimensionality, relief is achieved when a patient is disconnected from the machine, does not attend any session or does not present clinical manifestations of hemodialysis complications. In calm, visible discomforts are linked to change of routine, abandonment of work activities, financial difficulties and poor family support, and psychospiritual issues that weaken in their daily life, making them vulnerable to discomfort. In transcendence, absence of symptoms, attachment to religion, faith or spirituality and resilience to the new routine figured as indexes. States do not act individually, they are related to the experiences of contexts. Conclusion: comfort has meaning of relief from discomfort, state of having met basic human needs, mental and physical well-being, physical, mental and environmental comfort, and final state of nursing therapeutic actions, which permeate the contexts and states of relief, calm, and transcendence. These elements combine with each other to generate unique responses.
Identification of Traffic Information on Twitter Data using Topic Modeling and Entity Recognition

Tweet data on several official Twitter accounts from news portals can provide traffic information near real-time, which helps control smooth mobilization. However, the data is mixed with news on current issues, such as government policies and the pandemic situation. For this reason, a news grouping process is needed by finding word vectors through word embedding and inserting them into topic modeling to help separate traffic news from other news. We have compared two well-tested methods when processing Twitter data in various categories: Latent Dirichlet Allocation (LDA) and Non-negative matrix factorization (NMF). In previous research, it appears that the two methods still find the words that compose the topic are quite challenging to interpret. Therefore, we use Word2vec as input to compare the term frequency-inverse document frequency (TF-IDF), which is very commonly used. It is hoped that Word2vec has collected related words and, in turn, will result in a better division of topics. This study shows that the combination of LDA with word vectorization with the Word2vec model presents a coherence value of 0.56 and the term frequency-inverse document frequency (TF-IDF) of 0.57. However, the application of Word2vec to NMF gave better results than TF-IDF. TF-IDF was only able to achieve a coherence value of 0.49 while Word2vec got 0.52. Furthermore, at NMF, the word2vec model can recognize words in the form of locations successfully. When the traffic news has been separated, we applied Named Entity Recognition (NER) to detect the incident's location. We've labeled the location of 30% of the tweet data that has been grouped into training data. This method has successfully detected the location when tested on some other data.
Immersive Sound Masterclass

Immersive Sound Techniques and Virtual Worlds.This presentation offers a theoretical and practical overview of contemporary sonic themes, issues and techniques arising around immersive and interactive sound within virtual environments. The affordances offered by 360 audio technologies and techniques (e.g ambisonics) present new opportunities for sound designers, composers and their creative collaborators.The traditional sound stage – as manifest by the long established stereo standard – is greatly expanded in virtual spaces into a spherical, fully immersive sound world. This new 4-dimensional compositional space (x, y, z + time) offers a number of creative challenges and presents some intriguing questions during the production process at the point of capture, and subsequently in post-production during the mixing process (or its virtual equivalent).In our contemporary context, the ubiquitous trope of immersion operates alongside emerging principles of non-linear, interactive and adaptive sound composition. A variety of tools exist to enable the creation of such content within V.R., e.g. Fabric (Tazman Audio), Fmod (Firelight Technology), Wwise (Audiokinetic, exemplary practical examples exist and a theoretical vocabulary has developed derived from digital media, game audio and film sound on one hand, and the legacy of experimental and contemporary music and sound arts on the other.This presentation outlines a creative methodology informed by various creative practices of sound art and design (soundscape studies, acoustic ecology, game audio, film sound, sound installations and acousmatic / electroacoustic music) and explores the remediation of related historical concepts within the apparently new world of contemporary V.R.
Pedagogical System for Preschool Education in Preschool Organizations (Mtt): a Comparative Analysis with Foreign Experiences

The article describes the organization and management of the effectiveness of preschool education in developed countries on the basis of modern requirements, improving the competence of teachers, educational work in the preschool education system. Basic concepts: preschool education, activities, personality of the child, pedagogical staff, education system, educational work.
MULTI LAYER SECURITY USING ENCRYPTION AND STEGANOGRAPHY

With the high availability of internet and the huge growth of technology information security has become complex and a problem that needs urgent attendance, in this present era, security has become one of the major concerns in the IT industry, to enhance the security of information certain encryption mechanism has been created to resolve the issues. Encryption is the process of converting a message or information into code known as cipher to prevent an unauthorized person from acquiring access to the information. Another technique use in this project is Steganography which is the hiding of the existence of information so that the information can be transmitted undetected. Steganography allows the sender hide the message within a cover message [1]. The use of steganography, in this project is combined with encryption to create an extra layer of security information.This project Fibonacci series technique is use for encryption and decryption to secure the information, this technique can secure any type of file and the encryption and decryption is based on loss-less and key dependent. And the application of Audio and Image Steganography which will be used to hide the encrypted information, hence providing extra layer of security for files. These mechanisms are implemented using python.
The Application Of Cue Card Media To Improve Students' Ability At Writing Descriptive Text At The Second Grade MTs Hifzhil Quran Yayasan Islamic Centre Sumatera Utara

This research was aimed to apply of Cue Card Media to improve thestudents‘ ability at writing descriptive text at the second grade students of MtsHifzhil Qur’an Yayasan Islamic Centre Sumatera Utara. This research wasconducted by using classroom action research, which carried out through fourstep, they were planning, action, observation, and reflection. The subjects of thisresearch were the second grade student of Mts Hifzhil Qur’an Yayasan IslamicCentre Sumatera Utara in academic year 2020/2021. The subject consists of 36students. The technique or procedure of analyzing the data of this research wasapplied quantitative and qualitative approach. The quantitative data was takenfrom written test. The qualitative data were taken from observation sheets,interview sheets, diary notes, and documentations. The result of data analysisshowed that there was improving student’s ability at writing descriptive text fromeach cycle. It showed from the mean of pre-test was 56,91, the percentage of thestudents who successful was 22,22% and the students who unsuccessful was77,78% , the mean of the post-test I was 72,63, the percentage of the students whosuccessful was 55,55% and the students who unsuccessful was 44,44%, and themean of post –test II was 81,38, the percentage of the students who successful was88,11% and the students who unsuccessful was 11,11%. Based on the data, theresearcher concluded that the student’s ability at writing descriptive text had beenimproved by using Cue Card Media. The student’s respond in writing teachingwhen they were being taught by using guided writing was very good. It can beseen from student’s activity observation sheet on cycle I until cycle II in text
Effectiveness of Discovery Learning Model in Learning Writing Argumentation Discourse

Abstract. This research is motivated by the problems faced in the learning of students of the Indonesian Language and Literature Education Study Program, Makassar State University, namely: (1) students have difficulty in pouring ideas, ideas, analysis and reflection on the writing of argumentative discourse, (2) Students ability to write the argumentation argument is still low, because the learning model used is less effective. This research is an experimental research which aims to: (1) describe the results of learning to write argumentation discourse using and without using discovery learning models, and (2) knowing the effectiveness of using discovery learning models in learning to write argumentative discourse. Data were collected using an argumentation writing test and analyzed using descriptive and inferential techniques. The results showed that: (1) the average learning outcomes in the control class pretest = 74,03 and posttest = 79,82 (medium category) . Meanwhile, the average learning outcomes in the experimental pretest = 74, 26 and posttest = 87.20 (high category), and (2) discovery learning model is effectively used in learning to write argumentative discourse. The test results of independent samples t-test posttest classroom control and class experimental results obtained t count = 4.56> table = 1.59. Keywords: Effectiveness, discovery learning, argumentation discourse
Phillips Curve (Struggling Jargon): Practical Implication in Indian Context

Inflation and Unemployment are two intricately linked concepts of Economics. The renowned economist William Phillips, described an inverse relationship between rates of unemployment and corresponding rates of rises in wages resulting within an economy. Over the years many researchers have tried to test the validity of Phillips Curve over various areas. Current research tries to find out the empirical relationship between Inflation and unemployment by testing of Phillips Curve in Indian context. To test the operation of Phillips curve in current Indian economic situation the data of twenty seven years i.e. 1992 to 2018 have been taken into consideration. To check the applicability of already existing theory of inverse relationship between Unemployment and Inflation, researchers have applied Co-integration and Granger Causality Test.
CT, MRI Image Reconstruction using Deep Autoencoder with Boltzmann Machine Training Architecture

Medical images play a significant role in diagnosis and experimentation. For years, many experimentation and analysis have been made on medical images with a broad scope of achieving the desired results. Medical modalities like MRI, CT, PET, X-Ray, Ultrasound, etc. are popular in representing internal organ pictures of human bodies. Such volumetric images are stored and accessed with a huge demand in storage space. Compressing the images solves the problem of storage and transmission and also become beneficial for the telemedicine application. Since Medical images depict the human body's detailed and vital information, it needs to be compressed without distorting the details present over it. The lossless image compression approach works efficiently in representing the information present on the medical images without distorting the details. Deep learning neural networks proved to be efficient in analyzing the medical images in recent years. Deep Autoencoder attains to be achieving a higher level in dimensionality reduction with retaining the original quality of the input data. Pretraining of the architecture with the Deep Boltzmann Machines makes the algorithm more efficient to achieve a higher compression ratio with a high PSNR value. Analyzing the Deep autoencoder's performance with the other state of the art architecture proves to be efficient with low computational time.
The Italian Competition Authority and two other Regulations Authorities publish a joint investigative report on big data

On 10 February 2020, the Italian Competition Authority, the Authority for Communications Guarantees and the Authority for the Protection of Personal Data (together the “Authorities”) published a…
Optimising Own PV Consumption with PV Energy Yield Predictions from Machine Learning Algorithms and Weather Data

Weather data are evaluated in view of their influence on high-quality PV energy yield predictions based on machine learning algorithms (MLAs). Optimisation experiments evidence that the prediction quality can be increased to over 30% by incorporating specific weather parameters in the ML-training. The results will feed into a planning tool for optimising the own consumption (including in wintertime) of PV plant owners. The outcome of this study also illustrates evolving best practice in using meteorological data to produce PV energy yield predictions with specific MLAs.
Analysis and Enlightenment of Foreign Open Breast Image Datasets

With the upsurge of medical artificial intelligence,the use of computer vision technology to study medical images,which can effectively help doctors to identify and screen diseases,has become a focus of researchers.This paper summarizes the basic situation,specific information,related research,and data sharing and utilizing ways of foreign breast image datasets.This review provides inspirations for the opening of Chinese medical and health data.
LibGuides: Major Authors: Robert Frost: Find Articles

A tool to help UAS Ketchikan students locate and use resources related to Robert Frost, either found on the shelf at the Ketchikan Campus Library, or electronic books available to them.
ANALYSIS AND IMPLEMENTATION OF NODES COMMUNICATION BETWEEN INTERPLANETARY FILE SYSTEM (IPFS) IN SMART CONTRACT ETHEREUM

Abstract. At present all business activities are bound to contracts or agreements. A written contract has several weaknesses, the contract can be lost and damaged, it is not cost effective and one party can commit fraud. The solution for that is to use the smart contract Ethereum. Smart contract Ethereum is a computer protocol that functions to facilitate, verify, or enforce digital negotiations written through the program code. Smart contract works without going through a third party and has a credible transaction process so that it cannot be tracked or changed. But Blockchain technology is not suitable for storing large amounts of data and expensive costs, the authors combine IPFS technology on Ethereum Blockchain. So the Ethereum Blockchain only stores the hash of the file, then the hash of this file can be connected to the file on IPFS to access it. In this study a web-based DApp (Decentralized applications) system was built that implemented IPFS on the smart contract Ethereum. The final result of this study is a discussion of data integrity and Quality of Service (QoS) communication between IPFS nodes on the smart contract Ethereum as a reference for implementation of the company. With the results of the implementation it  was found that the data integrity possessed by IPFS was very good by fulfilling aspects of information security and having Quality of Service with average throughput values of 56.41 Kbps, 65.81 Kbps and 79.68 Kbps, for average packet loss values of 1.92%, 1.58% and 1.06%, while the average value of delay is 24.79 ms, 25.87 ms and 17.30 ms with the average value of the Quality of Service index which is 3 which meets the Satisfying category based on THIPON standards. Keywords: Blockchain, Smart Contract Ethereum, IPFS, Data Integrity, Quality of  Service (QoS)
Mobile Application-Based Search for Nearby Places of Worship by Implementing the React Native Framework

A place of worship is an important means of carrying out worship for religious people. One of the problems faced in the journey to a place of worship is the search for the fastest and right path for road users who do not know which route and road to take to arrive at a place of worship. Therefore, a mobile application-based search for places of worship was developed. The algorithm used in the routing is Floyd-Warshall, and the making of the application uses the React Native framework which can run hybrid. From the results of black box testing on systems that follow the ISO 9126-2 standard, the result is that each metric produces a number and an average of 1 which means the system functions in accordance with the needs and expectations. In usability testing, an average of more than 80% was obtained for each criterion, which means that the user agrees with the satisfaction of using the system. Then, in testing manual and system calculations, the accuracy is 100%, which means that the algorithm has been successfully applied to the system.Keywords: Places of Worship, Routes, Floyd-Warshall, React Native
Compartmentation in the Guarani Aquifer System northeastern region of the Rio Grande do Sul State

This study aims to evaluate the structural compartmentation of the Guarani Aquifer System (GAS) in the northeastern region of the Rio Grande do Sul (RS) state. For this purpose, an integrated analysis of 747 morphostructural lineaments identified in the scale 1:1,000,000 with geometry features and potentiometry of the GAS was performed. Based on this evaluation, a main set of structures, N30W and N60E oriented, responsible for the GAS compartmentation within the study area, was identified. These directions are related to the main tectonic structures that originated from the basement of the Paraná Basin, which could indicate that these identified structures are part of large fault systems in this region. The variations in the GAS potentiometry and altimetry of the top related to the compartmentation show the existence of several blocks formed under the influence of NW and NE structures located mainly in the large valleys, which sometimes work as a discharge zone of the aquifer. According to the analysis, the GAS in the northeastern region of RS seems to be divided into compartments of different sizes. This structural and geometrical framework imposed by tectonic structures influences the GAS potentiometry and flow dynamics.
Fault-Tolerant Quantum Computation Through Quantum Low-Density Parity-Check Codes

Quantum computation has shown advantages in several problems over the corresponding classical algorithms. The noisy intermediate-size quantum devices with dozens of qubits make it more promising in the past decade, as the quantum advantage was demonstrated experimentally. Before entering the era of scalable quantum computation, one has to resolve the errors in a quantum many-body system, which is inevitable due to the environment interaction during quantum control processes. The goal of quantum error correction is to reduce such errors and increase decoherence time. The most successful candidates of quantum error correction codes are topological codes, especially surface codes, which were discovered by Alexei Kitaev. The ordered phase of the 2D Ising model on a torus ensures that toric codes are fault-tolerant below a critical error probability. Other than the FT threshold, the topological codes feature efficient encoding and decoding, local measurements, but suffer from asymptotically zero code rate. To encode more logical qubits with finite resources, one can loosen the condition on locality and extend to a broader class of quantum low-density parity-check (LDPC)codes. There are many known algebraic constructions for such codes, but only a precious few of them have finite code rates and meet the fault-tolerant condition: the stabilizer weight is bounded and the distance scales at least logarithmically with the code size. In this thesis, I construct the higher-dimensional quantum hypergraph product (HQHP) codes, which generalize quantum hypergraph product (QHP) codes and toric codes in all dimensions. The HQHP codes projected into a single space gives subsystem product codes, which can then be gauge fixed to concatenated codes or homological product codes. Those include some common CSS codes, like Shor’s codes, Bacon Shor’s codes, and subsystem QHP codes. The HQHP codes can be mapped to the tensor product of chain complexes, which provides an algebraic framework to construct quantum LDPC codes with finite code rates, square root distances, FT thresholds and single-shot properties with redundant checks. Meanwhile, their rich connection to other codes are very instructive and may lead to further optimizations. Regarding the remained procedures towards fault-tolerant universal quantum computation through quantum LDPC codes, I will discuss the fault-tolerant condition for each code, including distance, stabilizer weight, decoding, fault-tolerant gates and measurement protocols.
Implementation difficulties of tutor support technologies in higher education

The article is devoted to problems of implementation in practice of the University of technology tutor support, which would allow modern students to quickly and painlessly build an individual trajectory of personal and professional development, and University – to implement the requirements of standard of the last generation. The article is based on observations of the included experiment on the implementation of tutor support, which was carried out in the framework of advanced training courses for teachers of PSNIU. During the experiment, conditions that are extremely important for ensuring the effectiveness of the implementation of tutor support technology were identified. Among them: the certainty (firmness) of the tutor's position in the implementation of a specific goal of tutor support; the importance of organizing spatial interaction during the tutorial; the formation of an adequate understanding of the content of the tutor's activity among the participants of the tutorial; recognition of the self-value of the tutor's activity.
LibGuides Home: Remote Online Library Support: Research Help

This guide will support you while you are working remotely and will direct you to Library resources and support.
LibGuides: CSUMB Makerspace: Gallery

The Library Makerspace is a collaborative work environment on the third floor of the Tanimura & Antle Library, Room 3125. During Covid-19 conditions, the Makerspace is closed to visitors.