Reviewerly/exHarmony huggingface dataset:
Automatic picking method of microseismic signal first arrival time based on empirical wavelet transform

Abstract For problems in the process of microseismic signal acquisition, there is a large number of interference signals, which leads to that the first arrival time of signal is difficult to pick up.A picking method combines empirical wavelet transform (EWT) and signal first arrival algorithm is proposed to pick up the first arrival time of microseismic signal.Firstly,the component signals are obtained by the EWT method, which is used to decompose the microseismic signals with the characteristics of adaptive and conquering modal aliasing. Then the correlation coefficient and variance contribution rate of each component signals are calculated, and the component signals within the threshold range are selected for reconstruction to reduce the interference of noise signals. Finally, the modified energy ratio (MER) and fractal-dimension (FD) are used to pick up the first arrival time for the reconstructed denoising signal. According to the experimental verification, the accuracy of signal first arrival time picking combined with the MER and FD method based on EWT is 0.59% and 0.36% respectively. But in terms of efficiency, the former is obviously higher than the latter. The experimental and analytical results show that the model is highly accurate in picking up first arrival time of the effective signals and has a strong applicability to the signals of different data quantities.
Tracking visual engagement in mixed reality : a framework for analysing interaction with digital models

Due to technological advances, affordable mixed reality devices, including both virtualreality and augmented reality, are beginning to be used to support participatory planning to better inform stakeholders of design interventions. Stakeholder participation and perception studies often utilize both qualitative interviews and quantitative techniques to gauge how a model can effectively communicate design intentions. In this paper, we examine how mixed reality enables new methods of investigating how users interact with digital models, enhancing existing techniques. We introduce a non-invasive perspective tracking system, to track how a participant explores a 3D model of a landscape intervention through the perspective of their mixed reality device. Our software provides a supplementary technique to better analyse user interaction patterns with digital models, allowing a data-driven approach to support participation studies and enable novel analytical approaches. We introduce the concept of naturally salient perspectives and show that tracking how a user frames a model during free exploration can provide new quantitative insights to support traditional qualitative participation methods.
Climate Data You Can Trust

Creating, curating, and developing the repository of climate data that underlies the U.S. National Climate Assessments requires the ongoing efforts of hundreds of experts.
Ambient Lighting Generation for Flash Images with Guided Conditional Adversarial Networks

To cope with the challenges that low light conditions produce in images, photographers tend to use the light provided by the camera flash to get better illumination. Nevertheless, harsh shadows and non-uniform illumination can arise from using a camera flash, especially in low light conditions. Previous studies have focused on normalizing the lighting on flash images; however, to the best of our knowledge, no prior studies have examined the sideways shadows removal, reconstruction of overexposed areas, and the generation of synthetic ambient shadows or natural tone of scene objects. To provide more natural illumination on flash images and ensure high-frequency details, we propose a generative adversarial network in a guided conditional mode. We show that this approach not only generates natural illumination but also attenuates harsh shadows, simultaneously generating synthetic ambient shadows. Our approach achieves promising results on a custom FAID dataset, outperforming our baseline studies. We also analyze the components of our proposal and how they affect the overall performance and discuss the opportunities for future work.
A Metaphorical Debugger Model to support deaf and hearing impaired in Java programming learning

This innovative practice full paper presents a Metaphorical Debugger Model (JAD) to support deaf and hearing impaired in the process of learning how to program in Java. In Brazil, 9.7 million people are deaf or hearing impaired (DHI). DHI people usually face several barriers to get proper education. During a series of Java classes conducted by our team, we verified that these difficulties were also related to the use of software that is not tailored for their impairment. We also saw that DHI students encountered many problems while developing and evolving their codes using the traditional tools, for instance, visual signs to perform tasks, can be difficult to understand, in addition, the high degree of abstraction required for the programming logic, can be an accessibility barrier for them. Thus, we decided to propose JAD aiming to provide accessible debugging for DHI. The JAD uses the concept of metaphorical interfaces, adopting appropriate symbols and signs borrowed from traffic to aid in the process of code debugging and evolution. In this paper, we present some user studies with JAD. The results suggest that Java programmers, DHI e non-DHI, had similar performance in task related to software evolution when JAD was used.
Sentiment Analysis of Sindhi Tweets Dataset using Supervised Machine Learning Techniques

Social websites are major opinion sharing platform now a days with millions of people using them each day. Twitter is one of the top social media platforms on internet. With the introduction of multiple languages over social media, individuals are usually sharing their opinion in their native language. Individual and organizations are interested in people's opinion on internet for their future strategies for business and products. In this paper, we have performed sentiment analysis of Sindhi language using supervised machine learning techniques. The dataset we developed is tweets in Sindhi language. Automated approach using predefined lexicon is used to give positive and negative polarity to tweets so we have two classes. Pre-processing involves removal of non-Sindhi words, extra white spaces, removal of punctuations, after that tokenization of text is performed. After data cleaning and labelling, supervised machine learning techniques were applied on the Sindhi tweets dataset. We applied Support Vector Machine (SVM), Naive Bayes, Decision Tree and K-nearest neighbour. Results showed that Decision Tree and K-nearest neighbour gave the best accuracy on Sindhi tweets dataset followed by SVM.
Analysis of Cooperative Learning in Management (Bi-Lingual) Class—Based on Rain Classroom

Rain classroom is a kind of mixed teaching tool, which is based on MOOC platform of Tsinghua University. It effectively combines teachers course ware making tool PowerPoint with students favorite WeChat App. As a new teaching mode, cooperative learning teaching mode is a new exploration in Management (Bi-lingual) teaching. The model is student-centered rather than teacher-led. It emphasizes both the independent study of students and the interaction between individual students and student groups. Meanwhile, it teaches students to get along with others, dares to show themselves, and learn modestly. Based on the research methods such as classroom practice, questionnaire, interview, and combining the characteristics of Management (Bi-lingual) translation course and the teaching requirements of this course, this paper conducts an investigation and analysis on the teaching effect. According to the result of this survey, it is proved that the Management (Bi-lingual) class based on cooperative learning is feasible. It can provide strong theoretical support for innovating the cooperative teaching mode of Management (Bi-lingual) Class, so that more teachers can grasp the key points of this mode and students of this major can achieve better development.
Continual Graph Learning.

Graph Neural Networks (GNNs) have recently received significant research attention due to their prominent performance on a variety of graph-related learning tasks. Most of the existing works focus on either static or dynamic graph settings, addressing a particular task, e.g., node/graph classification, link prediction. In this work, we investigate the question: can GNNs be applied to continuously learning a sequence of tasks? Towards that, we explore the Continual Graph Learning (CGL) paradigm and we present the Experience Replay based framework ER-GNN for CGL to address the catastrophic forgetting problem in existing GNNs. ER-GNN stores knowledge from previous tasks as experiences and replays them when learning new tasks to mitigate the forgetting issue. We propose three experience node selection strategies: mean of features, coverage maximization and influence maximization, to guide the process of selecting experience nodes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of ER-GNN and shed light on the incremental (non-Euclidean) graph structure learning.
Comparison of Digital Image Analysis and Conventional Microscopy in Evaluating Erythrocyte Morphology in Peripheral Blood Smears

Background and Objectives. The use of conventional microscopy still forms the basis for the morphologic evaluation of erythrocytes despite widespread use of automated tests in the hematology laboratory. This requires a considerable length of time and expertise, and have the potential of becoming a source of errors and delay in reporting. Advances in image processing and machine learning in recent years have shown acceptable performance characteristics and have promising applications in the diagnostic laboratory. Use of these newly-developed technologies can address the stated problems and provide an alternative approach in the microscopic analysis of erythrocytes. Methodology. This prospective validation study compared digital image analysis using a machine-learning based image recognition algorithm with conventional microscopy performed by a trained microscopist, which served as the reference standard. Random deidentified anticoagulated peripheral blood samples submitted to the hematology laboratory were assessed. Results. A total of 956 erythrocytes were evaluated after image processing using support vector machine and routine microscopy as classifiers of erythrocytes into three categories: size, central pallor, and shape. The tested software was able to achieve a strong level of agreement compared to conventional microscopy, having kappa values ranging from 0.81 to 0.86. Accuracy for size, central pallor and shape were 89.88%, 93.72% and 87.89%, respectively. Conclusion. The validated image recognition software is an acceptable diagnostic test in determining erythrocyte morphology in peripheral blood smears. Its integration can potentially minimize hands-on time and improve the diagnostic laboratory workflow. Registration. Philippine Health Research Registry (PHRR) ID: PHRR191211-002348; University of the Philippines Manila Research Ethics Board (UPMREB): 2019-356-01.
Self-adaptive Parameters Optimization for Incremental Classification in Big Data Using Swarm Intelligence

Nowadays, big data is one of the most technical challenges confront researchers and companies. The main challenge lies in the fact that big data sources usually formed in a continuous data stream. Thus, many previous researches present incremental data mining approaches to deal with the challenges of the data streams by adapting traditional machine learning algorithms. Artificial Neural Network (ANN) is a common technique in this field. The main challenge is how to optimize the neural network parameters to deal with a huge data arrive over time. These parameters, which are vital for the performance of a neural network, are called hyperparameters. Most earlier optimization approaches have dealt with big data containers instead of big data streams or handled big data streams with time consumed. This paper proposes an incremental learning process for ANN hyperparameters optimization over data stream by utilizing Grasshopper Algorithm (GOA) as a swarm intelligence technique. GOA is utilized to make a balance between exploration and exploitation to find the best set of ANN hyperparameters suitable for data stream. The experimental results illustrate that the proposed optimization model yields better accuracy results with appropriate CPU time.
Visualization, Prediction, and Causal Inference: Applications in Healthcare

Author(s): Barter, Rebecca Louise | Advisor(s): Yu, Bin | Abstract: The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare.Part I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare.Part II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare.Part III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a survival-benefit-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare.
Beliefs of psychology students about the use of information and communication technologies

espanolSe presenta un estudio sobre creencias en estudiantes de psicologia de seis universidades chilenas, sobre tecnologias de la informacion y la comunicacion (TIC); se consideran metas en las creencias, valoradas como medios o como fines. Se empleo un estudio cualitativo, en el cual participaron 76 estudiantes de psicologia de instituciones publicas y privadas. A fin de configurar las creencias, se aplico una matriz ideografica que registro opiniones sobre la relacion entre informatica y psicologia, con una justificacion de esa razon. El analisis consistio en una primera etapa de construccion de categorias mediante codificacion abierta; en la segunda etapa se realizo el levantamiento de la estructura de las metas, a traves de analisis de menciones de vinculos entre categorias, que corresponderia al modelo de creencias respecto a las TIC. Los resultados evidencian siete categorias, entre las que se destaca, como base para el uso de TIC, la denominada “Comodidad y rapidez”, asi como una meta de finalidad denominada “Apoyo en tareas psicologicas” que es, ademas, la que se valora de forma mas abstracta. Se concluye que los estudiantes poseen una representacion instrumental de las TIC solo como medio de apoyo a las actividades psicologicas, la cual se acepta y valora socialmente, de manera que esto obstaculiza su inclusion en la psicologia como area de desarrollo e investigacion. EnglishA study about the beliefs of Psychology students from six Chilean universities in relation to Information and Communication Technologies (ICT) is presented; goals on beliefs, valued as media or aims are considered. A qualitative study was employed, where 76 Psychology students were part of, from public and private institutions. In order to set the beliefs, an ideographic matrix was used, which registered opinions about the relation between informatics and psychology, with a cause for that reason. The analysis consisted of a first stage of construction of categories, through open coding; on the second stage the raising of the structure of goals was carried out, through the analysis of the reference of links amongst categories, which would correspond to the model of beliefs regarding ICT. The results show seven categories, highlighting as the basis for the ICT use, the entitled Comfort and speed, as well as a purpose goal entitled Support on psychological tasks, which also is, the most abstractly valued. It is concluded that the students possess an instrumental representation of ICT, just as support media to the psychological activities, which is accepted and socially valued, obstructing its inclusion on psychology as a development and investigation field.
A Novel Approach to Conditional Random Field-based Named Entity Recognition using Persian Specific Features

Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance in the Persian language if they are trained with good features. To get good performance in Conditional Random Field-based Persian Named Entity Recognition, a several syntactic features based on dependency grammar along with some morphological and language-independent features have been designed in order to extract suitable features for the learning phase. In this implementation, designed features have been applied to Conditional Random Field to build our model. To evaluate our system, the Persian syntactic dependency Treebank with about 30,000 sentences, prepared in NOOR Islamic science computer research center, has been implemented. This Treebank has Named-Entity tags, such as Person, Organization and location. The result of this study showed that our approach achieved 86.86% precision, 80.29% recall and 83.44% F-measure which are relatively higher than those values reported for other Persian NER methods.
A TRANSVERSAL VIRTUAL REMOTE LABORATORY FOR TEACHING IN STEM DISCIPLINES USING ROBOTIC PLATFORMS

D. Garcia-Costa, A. Suarez, P.A. Martinez, J. Martos, R. Fayos, E. Lopez-IñestaUniversity of Valencia (SPAIN)
Web Enhanced Course, Solusi Bagi Peserta Didik dalam Pemahaman Materi Perkuliahan

Cognitive abilities in students have an important role for children's success in learning because most of the activities in learning are always associated with problems of remembering and thinking. Cognitive abilities are intended so that children are able to explore the world around them through their five senses, which are collaborated with accurate analysis so that with the understanding of the knowledge they get, children can carry on their lives. In supporting the implementation of learning activities with cognitive abilities so as to produce a maximum level of understanding of lecture material, it is necessary to have an appropriate digital-based learning media so that it can collaborate between technology and cognitive abilities. One of the digital-based learning media that can be applied in supporting students' cognitive abilities is to use Web Enhanced Course (WEC). The method used in this study uses descriptive methods and linear regression. Data obtained from the results of questionnaires that have been filled out by students using simple linear regression analysis techniques. The population used in this study were students of the Faculty of Economics, Universitas Negeri Malang . The population used to measure the level of student understanding of lecture material using the Web Enhanced Course amounted to 284 students, while the sampling technique used was proportional random sampling to obtain a sample of 74 students. While the results of this study can be concluded that the use of digital learning media Web Enhanced Course has an influence on improving understanding of lecture material by students as measured using cognitive abilities. DOI: https:// dx.doi.org/10.17977/ um014v13i12020p061
Exploring Methods for Linked Data Model Evaluation in Practice

Ontology development and data modeling are core components of any linked data project. Through our own experiments building a linked data ontology for our collections, we wondered: how are our peers in the linked data community evaluating their ontologies? Are participants engaging in ontology evaluation? What methodologies and evaluation criteria are they using? Are they documenting and sharing their processes? In this paper, we present findings from a survey conducted in the fall of 2018, aimed at professionals from libraries, archives, and museums (LAM) who were part of the data modeling team on linked data projects. The purpose of this survey was to better understand the reality of ontology evaluation in the context of a linked data project. We found that our colleagues were engaging in data modeling as part of linked data projects in a variety of different tasks and roles. There was some ambiguity with respect to evaluation, possibly in part due to the iterative nature of the modeling process. Evaluation is engaged iteratively and informally through use cases, competency questions, and testing of the data in the application. On the whole, not being shared widely outside of a project. The identified barriers to evaluating their models included: lack of knowledge, resources, and documentation.
Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation

The hippocampus plays an important role in the memory and cognition abilities of humans. Precise three-dimensional (3D) segmentation of the hippocampus from magnetic resonance imaging scans is of great importance in the diagnosis of neurological diseases. Conventional automatic segmentation methods poorly achieve satisfactory performance because of the irregular shape and small volume of the hippocampus. We propose a novel two-stage segmentation method, which includes a localization stage and a segmentation stage, to handle the task of the 3D segmentation of the hippocampus. In the localization stage, a novel strategy for localizing multi-size candidate regions was developed to improve the sample balance for the 3D segmentation task. In the segmentation stage, a method which fuses the multi-size candidate regions was proposed to improve the accuracy in predicting the hippocampal boundary, after which we aggregated the segmentation results from three orthogonal views to further improve the performance. Quantitative evaluation was performed on the Alzheimer's Disease Neuroimaging Initiative dataset. The experimental results achieved Dice similarity coefficients of 92.48 ± 0.61% and 92.90 ± 0.51% for the left and right hippocampus, respectively, outperforming state-of-the-art studies in hippocampus segmentation tasks.