Reviewerly/exHarmony huggingface dataset:
Blockchains Contribution in the technology of Healthcare Information System

The aim of this paper is to propose a Healthcare systems model based on the combination of Blockchain, Big Data, and IoT technologies. A literature review was carried out to highlight the benefits of the combination of these technologies in terms of decentralization, security, privacy, and data integrity taking into account the constraints required by a medical system as a sensitive data area. Meanwhile, a list of the principal categories of participants in the healthcare system was drawn up. In addition, the current solutions in the healthcare system were analyzed. Finally, the structure and the implementation of our healthcare systems model were detailed based on the combination of the trending trio (Blockchain, Big Data, IoT), and their efficiency via a robustness analysis.
Visualization and Clustering of Text Retrieval

In a fast transforming world where all objects will be generating data, dealing with large data collections has been a major concern for data scientists. Major challenges faced by those scientists are among others the difficulty to represent these data in a better way and therefore to communicate hidden information from these data to the users. Accordingly, many data analysis and data visualization techniques have been proposed. Moreover, depending on the nature of data to visualize and the type of information to communicate, a certain number of data processing techniques should be considered. In this work, we analyze and visualize a sample data of TREC-6 from the TREC (Text Retrieval Conference) collections. TREC document collections comprise full text from newspapers articles and US government records. They are primarily dedicated to researchers in Information Retrieval (IR) systems and Natural Language Processing for the development of their works. First, documents are parsed and words extracted to build a corpus in a form of a matrix. Then, Principal Component Analysis is applied to the corpus matrix to reduce the dimension from  to 2. Eventually, the unsupervised K-means algorithm is used to discriminate data into clusters that are interactively visualized thanks to the popular visualization tools such as Pie Chart, Stacked Bar Chart and Scatter Chart. The diversity of the nature of information contained in TREC-6 can be observed thanks to the most frequent words of each cluster that appear on the Bar Chart upon clicking on the Pie Chart of the corresponding cluster.
COOPERATIVE ASSESSMENT WRITING PRACTICES AT AN ACEHNESE PRIVATE UNIVERSITY: HELPING OR TROUBLING?

A good writing skill could be a benchmark of someone’s good ability in English. This study aims find the way how the practice of cooperative assessment can diminish errors and mistakes in students’ writing and to see students’ view towards cooperative assessment method in writing class. Qualitative approach by using case study method was selected, and the data were taken by means of observation, document analysis and interview towards the fourth semester students of Serambi Mekah University Banda Aceh, Indonesia in 2018/2019 academic year. The findings shown that there were three techniques of cooperative assessment that may lessen errors and mistakes in students’ writing, namely; peer review, lecturers’ feedback and classroom reviewing activity. In students’ view, cooperative assessment has some benefits (improving grammatical awareness, improving students’ vocabularies, and improving the structure of students’ writing) as well as drawbacks (peer errors in editing, and time consuming). Therefore, it is recommended that students make their errors and mistakes diary notes and lecturers are advised to start making grammar errors and mistakes checklists based on the learning objectives for students’ peer reviewing practice.
Execution Time Analysis of Electrical Network Tracing in Relational and Graph Databases

In today’s society, we handle a lot of connected data. Examples are companies like Facebook and Amazon, that handle connected data in different ways. Geographic Information Systems and Network Info ...
Welcome and Introduction to SPIE Conference 11601

Welcome and Introduction to SPIE Medical Imaging conference 11601: Imaging Informatics for Healthcare, Research, and Applications
The Role of Knowledge in Determining Identity of Long-Tail Entities

Identifying entities in text is an important step of semantic analysis. Some entity mentions comprise a name or description, but many include no information that identifies them in the system’s knowledge resources, which means that their identity cannot be established through traditional disambiguation. Consequently, such NIL (not in lexicon) entities have received little attention in entity linking systems and tasks so far. However, given the non-redundancy of knowledge on NIL entities, their lack of frequency priors, their potentially extreme ambiguity, and their numerousness, they constitute an important class of long-tail entities and pose a great challenge for state-of-the-art systems. In this paper, we describe a method for imputing identifying knowledge to NILs from generalized characteristics. We enrich the locally extracted information with profile models that rely on background knowledge in Wikidata. We describe and implement two profiling machines using state-of-the-art neural models. We evaluate their intrinsic behavior and their impact on the task of determining the identity of NIL entities.
Colorimetrical evaluation of color normalization methods for H&amp;E-stained images

Color normalization is one of the pre-processing steps employed by many deep learning-based algorithms used for aiding pathology diagnoses with whole-slide images. Due to variability in tissue type, specimen preparation, staining protocol, and scanner performance, whole-slide images acquired from different sources may exhibit pronounced color variability that hinders algorithms from executing effectively. In the literature, numerous methods have been proposed to colornormalize hematoxylin and eosin (H&amp;E)-stained images. However, the objective of color normalization has not been colorimetrically defined or evaluated beyond visual comparison. In this study, a quantitative metric, color normality, was defined to evaluate the degree of color similarity between images involved in a color normalization process. The pixelwise spectral data of eight H&amp;E-stained tissue slides were optically measured as the ground truth to test the Reinhard, Macenko, and Vahadane methods. Principal component analysis was conducted on the spectral data to derive a new color normalization method as the reference. Experiment results show that the H&amp;E color gamut needs to be expressed with three components, but the widely used Macenko and Vahadane methods compressed the three-dimensional color gamut volume into a two-dimensional surface and reduced color gamut volumes by 40% or more. None of the color normalization methods could achieve a color normality of greater than 0.6174 when the image was not self-normalized.
Control optimization for parametric Hamiltonians by pulse reconstruction

Abstract Optimal control techniques provide a means to tailor the control pulses required to generate customized quantum gates, which helps to improve the resilience of quantum simulations to gate errors and device noise. However, the significant amount of (classical) computation required to generate customized gates can quickly undermine the effectiveness of this approach, especially when pulse optimization needs to be iterated. We propose a method to reduce the computational time required to generate the control pulse for a Hamiltonian that is parametrically dependent on a time-varying quantity. We use simple interpolation schemes to accurately reconstruct the control pulses from a set of pulses obtained in advance for a discrete set of predetermined parameter values. We obtain a reconstruction with very high fidelity and a significant reduction in computational effort. We report the results of the application of the proposed method to device-level quantum simulations of the unitary (real) time evolution of two interacting neutrons based on superconducting qubits.
Linear Codes over the Family of Finite Rings A_t

In this paper, we study the linear codes over the family of finite rings A t . We define a Gray map Ω, which is an isometry map and linear. Furthermore, we study some properties of MDS codes over A t and some examples.
Industry 4.0 and University Performace Based on Prism Theory

This article aims to evaluate the performance of higher education based on the Higher Education National Standard (SN) which is implemented with the performance of Prisma. Prisma performance appraisal is able to assess the performance of all entities from various stakeholders including for universities in Indonesia. Every Prism performance appraisal goes through a satisfaction orientation, strategy, process, capability and contribution. Prism is very suitable for organizational assessment, especially in the era of the Industrial Revolution 4.0 because every aspect will always be related and affect performance achievement. Prism-based higher education performance studies by compiling Higher Education Standards can be used to add references as a measure of the level of satisfaction of stakeholders and can improve PT performance internally
Quality assessment of multiply and singly distorted stereoscopic images via adaptive construction of cyclopean views

A challenging problem confronted when designing a blind/no-reference (NR) stereoscopic image quality assessment (SIQA) algorithm is to simulate the quality assessment (QA) behavior of the human visual system (HVS) during binocular vision. An effective way to solve this problem is to estimate the quality of the merged single view created in the human brain which is also referred to as the cyclopean image. However, due to the difficulty in modeling the binocular fusion and rivalry properties of the HVS, obtaining effective cyclopean images for QA is non-trivial, and consequently previous NR SIQA algorithms either require the MOS/DMOS values of the distorted 3D images for training or ignore the quality analysis of the merged cyclopean view. In this paper, we focus on (1) constructing accurate and appropriate cyclopean views for QA of stereoscopic images by adaptively analyzing the distortion information of two monocular views, and (2) training NR SIQA models without requiring the assistance of the MOS/DMOS values in existing databases. Accordingly, we present an effective opinion-unaware SIQA algorithm called MUSIQUE-3D, which blindly assesses the quality of multiply and singly distorted stereoscopic images by analyzing quality degradations of both monocular and cyclopean views. The monocular view quality is estimated by an extended version of the MUSIQUE algorithm, and the cyclopean view quality is computed from the distortion parameter values predicted by a two-layer classification-regression model trained on a large 3D image dataset. Tests on various 3D image databases demonstrate the superiority of our method as compared with other state-of-the-art SIQA algorithms.
Learning Rubost Features with Refined Spatial Interactions of Inter-part for Person Re-IDentification

The combination of the part features and attention mechanism has been made remarkable progress in Person Re-IDentification (REID), but part-based approaches are still ignoring the relations of inter-part and missing discriminative local feature representations. The Refined Spatial Interactions of Inter-part for Person Re-IDentification (RSIPR) is proposed to solve above problems. (RSIPR) consists of two main components: 1) Self Filter Attention (SFA) mechanism is employed to extract discriminatory representations from the pedestrian image adaptively. 2) Refined Spatial Interactions (RSI) is utilized to refine the interactions of inter-part and avoid ignoring the global perception due to the pedestrain is cropped into patches directly. Extensive experiments have been implemented to validate the superiority of the (RSIPR), and it has been demonstrated that the (RSIPR) achieves the state-of-the-art method without re-ranking [1] algorithm.
Predicting expertise among novice programmers with prior knowledge on programming tasks

The studies on program comprehension have seen developments over the years from the cognitive science perspective. As eye-tracking technology has proven to analyze visual attention and gaze-performance, it has then been largely used in the program comprehension studies to help understand the underlying cognitive processes among the participants. In this research work, we conducted an experiment using common fundamental programming questions on 66 undergraduate computer science students to study the gaze-behavior among the high and low-performing participants on programming comprehension. We aim to better understand the differences in the time taken by the individuals in terms of their performance with existing prior knowledge and use machine learning to predict their expertise. Findings from this study suggest that mental schemas do play a role as the high performers demonstrated less time taken to attempt the questions than the low performers and machine learning algorithms were able to successfully predict their expertise. The conclusions drawn are supported by eye-tracking metrics across individual- and group-levels.
Study on Preprocessing Method Suitable for Korean Aspect Extraction based on Unsupervised Learning : For Childcare Products Reviews

In aspect-based sentiment analysis, aspect extraction is a key task and various methods are used. However most of the aspect extraction studies target English reviews, and studies on Korean reviews are insufficient. So in this study, we proposed five preprocessing methods that fit the characteristics of Korean reviews and used Word2Vec and Attention models to derive meaningful results of aspect extraction based on unsupervised learning. As a result of the evaluation of aspect extraction performance, the method of spacing correction, morpheme analysis, and removing unnecessary POS by domain were superior to other methods. In addition, we have confirmed that Word2Vec is the most suitable embedding method for Korean aspect extraction using Attention–based Aspect Extraction compared to GloVe and FastText. We expect that the proposed preprocessing and embedding method will be useful for research on the aspect extraction of Korean reviews in the future.
Full Fledged Administration of Computer Institute

The full fledge administration of a computer institute is basically an android application as a medium of sharing data through students with the help of teachers. The application implemented for the ease of teacher to access any student’s data from their institution and for ease of the students to get any information about intuition through one application. The app can be compatible of any android device that is the reason anyone can download app and he/she can get detailed information about courses as well environment and some feedbacks given by students.
Ethics and Adaptive Touch Interfaces

chapter Share on Ethics and Adaptive Touch Interfaces Author: David McMenemy Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 407–408https://doi.org/10.1145/3447404.3447427Online:23 February 2021Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Uncertain Out-of-Domain Generalization

We study a worst-case scenario in generalization: Out-of-domain generalization from a single source. The goal is to learn a robust model from a single source and expect it to generalize over many unknown distributions. This challenging problem has been seldom investigated while existing solutions suffer from various limitations. In this paper, we propose a new solution. The key idea is to augment the source capacity in both input and label spaces, while the augmentation is guided by uncertainty assessment. To the best of our knowledge, this is the first work to (1) access the generalization uncertainty from a single source and (2) leverage it to guide both input and label augmentation for robust generalization. The model training and deployment are effectively organized in a Bayesian meta-learning framework. We conduct extensive comparisons and ablation study to validate our approach. The results prove our superior performance in a wide scope of tasks including image classification, text classification, and speech recognition.
Property Rights and Economic Freedom

The purpose of this paper is to establish a positive correlation between property rights and economic freedom. It seeks to demonstrate that property rights lead to economic freedom. From a purely theoretical perspective, it has been assumed that greater access to property rights leads to economic freedom, consequently to a sustainable economic growth. To establish this correlation in the case of Africa, we applied the use of statistical tools to substantiate the validity of our economic theory. We mainly used a simple linear regression to ascertain our hypothesis.
LibGuides at USA: PSC 490: Native American Sovereignty: Citation Mining

This guide is in support of the PSC 490 Special Topics course, Native American Sovereignty.
Formación docente en didáctica universitaria en la pandemia: entre la reflexión pedagógica y la instrumentalización

This article describes the training experience deployed with the teaching staff of different academic areas and venues from the University of Costa Rica, through the course of University Didactics in the first semester of 2020 in the context of the health pandemic. The course was aimed at promoting a reflexive and critical approach to the teaching practice in order to deepen the understanding of their role as a trainer, and be able to construct, implement and evaluate different learning scenarios relevant to academic, disciplinary, professional and personal context according to the socio-historical moment and the needs of the students. From this point of view, the training process that are developed in conjunction with the university teachers allowed access to the pedagogical and didactic notions as referent for the questioning of the traditional structures of the teaching practices, the construction and the development of the didactic strategies relevant to the educational goal, the nature of the contents and characteristics of the student body. As a result of this process, a total of 122 university professors who participated carried out innovative experiences of a virtual learning space. Most of the teaching staff valued the experience as a positive and satisfactory, as well as the student body. Among the learned lessons we can highlight: the transfer of the teaching perspective focused on the contents towards the student, and the major challenge in the generation strategies was to mediate those procedural contents that because of its nature required a field practice.
Variational saliency maps for explaining model's behavior

Saliency maps have been widely used to explain the behavior of an image classifier. We introduce a new interpretability method which considers a saliency map as a random variable and aims to calculate the posterior distribution over the saliency map. The likelihood function is designed to measure the distance between the classifier's predictive probability of an image and that of locally perturbed image. For the prior distribution, we make attributions of adjacent pixels have a positive correlation. We use a variational approximation, and show that the approximate posterior is effective in explaining the classifier's behavior. It also has benefits of providing uncertainty over the explanation, giving auxiliary information to experts on how much the explanation is trustworthy.
Learning Task Decomposition with Order-Memory Policy Network

Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better generalization. To simulate this process, we introduce Ordered Memory Policy Network (OMPN) to discover task decomposition by imitation learning from demonstration. OMPN has an explicit inductive bias to model a hierarchy of sub-tasks. Experiments on Craft world and Dial demonstrate that our model can more accurately recover the task boundaries with behavior cloning under both unsupervised and weakly supervised setting than previous methods. OMPN can also be directly applied to partially observable environments and still achieve high performance. Our visualization further confirms the intuition that OMPN can learn to expand the memory at higher levels when one subtask is close to completion.
STRUKTUR, MAJAS, DAN KONJUNGSI TEKS CERITA FANTASI KARYA SISWA KELAS VII SMP NEGERI 5 RAMBAH HILIR, KABUPATEN ROKAN HULU, PROVINSI RIAU

ABSTRACT The purpose of this research is to describe these following two things. First, to describe the generic structure of fantasy story texts by seventh grade students in SMP Negeri 5 Rambah Hilir, Rokan Hulu Regency, Riau Province. Second, to describe the language features of fantasy story texts by seventh grade students in SMP Negeri 5 Rambah Hilir, Rokan Hulu Regency, Riau Province. The method that is used is descriptive qualitative method. This research was conducted at SMP Negeri 5 Rambah Hilir, Rokan Hulu Regency, Riau Province. The data of the research are fantasy story texts collected in many sources such as the documents of 30 text stories written by the students. The instrument of this research is the researcher himself. Data were analyzed by describing, analyzing, and discussing the data toward the theory. Based on the results of the study, there are two things that can be concluded. First, the seventh grade students in SMP Negeri 5 Rambah Hilir, Rokan Hulu regency, Riau province have used the third generic structure of fantasy story in writing. The generic structures of of fantasy story text are orientation, complication and resolution. It is proven by 30 fantasy story texts that have been analyzed, there are 28 fantasy story texts that used generic structure correctly. Second, there are 16 types of figurative languages were found in writing fantasy story and 82% accuracies in using conjunctions by the seventh grade students in SMP Negeri 5 Rambah Hilir, Rokan Hulu Regency, Riau Province. Kata kunci: Struktur Teks, Diksi Teks, Konjungsi Teks
Scanning system for monitoring the electrization of metallized polymer films during their laser perforation

The article explores scanning system for monitoring the electrization of metallized polymer films based on jet- droplet optical measuring systems (JDOMS) for monitoring the electrization of products such as metallized polymer films during their laser perforation. The article shows that the main reason for the electrization of films is the removal of matter by a laser pulse with the formation in the microvolume of the plasma, a gaseous ionizing substance and its partial deposition on the edge of the perforated hole in the absence of contact with the metallization of the film. The JDOMS operating mode with the formation of a monodisperse coherent controlled charged droplet flow and video recording of the trajectory of charged droplets along the centers of perforated holes near the film is described, based on the measurements of which electrization is controlled. The maximum charges of the perforated holes of the film and moving drops are determined. The displacements from their interaction are calculated. The requirements to the means of video recording and processing of the obtained stroboscopic micrographs are determined.