Reviewerly/exHarmony huggingface dataset:
Supplemental Material: Geophysical extent of the Wyoming Province, western USA: Insights into ancient subduction and craton stability

&lt;p&gt;(1) A published compilation of U-Pb zircon, monazite, titanite and baddeleyite age determinations of igneous and metamorphic rocks from the WP and adjacent areas. This compilation is provided as both an Excel spreadsheet (B36417_SuppMat1.xls) and shapefile (B36417_SuppMat1.zip). (2) A compilation of published magnetotelluric studies over known suture zones (B36417_SuppMat2.xls). (3) A report and spreadsheet (B36417_SuppMat3.xls) documenting a newly reported U-Pb zircon age on gneiss sample 04SP-QF from the Wind River Range, Wyoming. (4) Figures summarizing the data fit of the final 3-D resistivity model. &lt;/p&gt;
User Experience Evaluation for Automation Tools: An Industrial Experience

Evaluating the User Experience in some contexts is challenging, especially in automation applications, due to specific situations and requirements. This paper presents an experience of applying the UX evaluation method for an automation tool in the Android software industry to assist software engineers in identifying the UX problems faced by users. The work applies heuristic evaluation, survey, and user interview methods to find the UX problems, understand the respective reasons, validate the given information, and finally assess the UX. The evaluation identified critical problems related to error messages, system response to errors, and proper feedback about what software is doing. The found problems and discussions contributed to developing new UX evaluation methodologies.
CHARACTERIZING AND PREDICTING EARLY REVIEWERS FOR EFFECTIVE PRODUCT MARKETING FROM E-COMMERCE WEBSITES

Before making a purchase, consumers might acquire useful information from online reviews. Early design reviews have a massive effect on a product's saleability. In this study, we investigate the features of early reviewers depending on their reviews on Amazon and eBay, two of the most prominent e-commerce sites.We split product lifetime into 3 categories: early, majority, and laggards. A user who has posted a review early in the process is called as an early reviewer. We quantitatively define early reviewers based on their rating behaviours, the helpfulness scores received from others, and the link among their evaluations and product popularity. We discovered that (1) early reviewers make greater insightful comments and (2) early reviewers give a greater overall rating score. According to our analysis of product reviews, early reviewers' rating and acquired usefulness score are likely to affect product popularity. Our proposed technique outperforms a range of alternative baselines in numerous tests on 2 autonomous e-commerce datasets.
Gesture controlled interaction using hand pose model

Direct use of the hand as an input device is a smart method for providing natural human-computer interaction (HCI). Hand pose estimation is an attractive topic for research in recent years. It has been widely used in virtual reality. The domain of computer vision-based human hand three-dimensional shape and hand pose estimation has fascinated momentous attention recently due to its key role in various applications, such as natural human-computer interactions. Hand pose estimation is difficult due to some challenges. First, we need to detect the human hand which is very changeable. Second, the high degree of freedom leads to difficulties in pose estimation. In this paper, we aim to build a hand pose estimation system that can correctly detect a human hand and estimate its pose which can be useful in the areas like industrial automation, sign language recognition etc. We integrated a hand pose model with a game named U3D with 6 different gestures. As the object detection methods perform poorly in the palm detection tasks we used 21 hand joints and increased accuracy to approximately 95.63%. We used the Blender 3D computer graphics software toolset for creating animation, visual effects, motion graphics, and interactive unity 3D games.
Perceiving Complex Water Resource Systems from the Perspective of Emergence and Information

A challenge to managing water resources is characterizing the scale-dependent heterogeneity created by the interactions among hydrological, ecological and anthropological processes.It is often difficult to collect sufficient empirical data over the range of scales required to construct mathematical models that facilitate robust bottom-up descriptions or predictions.An alternative is identifying emergent properties of complex systems, whose components self-organize into novel structures or processes via their collective interactions with each other and the environment.A new level of organization and complexity emerges that cannot be predicted from or attributed to the components alone.Emergence offers a number of perspectives from which to
AN INVESTIGATION OF THE INFLUENCES OF TRAINING DATA PROPERTIES ON AERIAL IMAGE SEGMENTATION

Abstract. While numerous studies are being conducted to improve neural network performance for image segmentation, studies on the impact of training data in terms of data quality, bias and labeling noise are comparatively scarce. When opening state of the art algorithms to a large and varying dataset, they do not achieve the same results as under optimal and controlled conditions due to a mismatch of the data used for training and the data that is to be predicted. This paper presents an approach to show the influences of diverging image properties such as scale, contrast, brightness and saturation between the training data of a model and data that is to be predicted. For this purpose, a U-Net is trained to segment buildings in aerial images. It was found that while changes in brightness have a strong effect on precision, recall and F-score, a change in saturation does not have too much or even positive effect on segmentation. In general, however, it can be said that any differences between training and prediction data have a negative effect on segmentation results.
SPIE Computer-Aided Diagnosis conference anniversary review

The SPIE Computer-Aided Diagnosis conference has been held for 16 consecutive years at the annual SPIE Medical Imaging symposium. The conference remains vibrant, with a core group of submitters as well as new submitters and attendees each year. Recent developments include a marked shift in submissions relating to the artificial intelligence revolution in medical image analysis. This review describes the topics and trends observed in research presented at the Computer-Aided Diagnosis conference as part of the 50th-anniversary celebration of SPIE Medical Imaging.
The Effects of Achievement-Focused Motivation on Lifelong Learning Tendencies of Nursing Students

BACKGROUND/AIMS: Nurses need to have lifelong learning to be able to follow up the most current information and practices in the area of healthcare, acquire the information, skills and qualifications required for the safe conduct of nursing practices, to continue to offer good-quality healthcare services and to improve health services.The aim of this study was to determine the effects of achievement-focused motivation on the lifelong learning tendencies of nursing students. MATERIALS AND METHODS:This descriptive and cross-sectional study was performed with the participation of 264 nursing students.The data were collected using a Personal Information Form, the Achievement-Focused Motivation Scale and the Lifelong Learning Tendency Scale.Descriptive statistics, Independent-Samples t-test, One-Way ANOVA and Pearson's Correlation Analysis were utilized for the evaluation of the data collected. RESULTS:The mean of scores obtained by the participants from the Achievement-Focused Motivation Scale and the Lifelong Learning Tendency Scale were respectively 143.24±24.99 and 70.28±11.94.Upon assessment of the correlation between the means of the scores obtained by the participants from the Achievement-Focused Motivation Scale and the Lifelong Learning Tendency Scale, it was found that there was a statistically significant relationship between the participants' scores in the Achievement-Focused Motivation Scale and its sub-scales and their scores in the Lifelong Learning Tendency Scale (p<0.01). CONCLUSION:It was ascertained that the nursing students' lifelong learning tendencies developed as their achievement-focused motivation levels went up.
Weakly-supervised segmentation of referring expressions

Visual grounding localizes regions (boxes or segments) in the image corresponding to given referring expressions. In this work we address image segmentation from referring expressions, a problem that has so far only been addressed in a fully-supervised setting. A fully-supervised setup, however, requires pixel-wise supervision and is hard to scale given the expense of manual annotation. We therefore introduce a new task of weakly-supervised image segmentation from referring expressions and propose Text grounded semantic SEGgmentation (TSEG) that learns segmentation masks directly from image-level referring expressions without pixel-level annotations. Our transformer-based method computes patch-text similarities and guides the classification objective during training with a new multi-label patch assignment mechanism. The resulting visual grounding model segments image regions corresponding to given natural language expressions. Our approach TSEG demonstrates promising results for weakly-supervised referring expression segmentation on the challenging PhraseCut and RefCOCO datasets. TSEG also shows competitive performance when evaluated in a zero-shot setting for semantic segmentation on Pascal VOC.
LEARNING FROM THE PAST: CROWD-DRIVEN ACTIVE TRANSFER LEARNING FOR SEMANTIC SEGMENTATION OF MULTI-TEMPORAL 3D POINT CLOUDS

Abstract. The main bottleneck of machine learning systems, such as convolutional neural networks, is the availability of labeled training data. Hence, much effort (and thus cost) is caused by setting up proper training data sets. However, models trained on specific data sets often perform unsatisfactorily when used to derive predictions for another (yet related) data set. We aim to overcome this problem by employing active learning to iteratively adapt an existing classifier to another domain. Precisely, we are concerned with semantic segmentation of 3D point clouds of multiple epochs. We first establish a Random Forest classifier for the first epoch of our data set and adapt it for successful prediction to two more temporally disjoint point clouds of the same but extended area. The point clouds, which are part of the newly introduced Hessigheim 3D benchmark data set, incorporate different characteristics with respect to the acquisition date and sensor configuration. We demonstrate that our workflow for domain adaptation is designed in such a way that it i) offers the possibility to greatly reduce labeling effort compared to a passive learning baseline or to an active learning baseline trained from scratch, if the domain gap is small enough and ii) at least does not cause more expenses (compared to a newly initialized active learning loop), if the domain gap is severe. The latter is especially beneficial in scenarios where the similarity of two different domains is hard to assess.
Object-aware Policy Network in Deep Recommender Systems

Abstract Deep learning has been successfully applied in the recommender system. Low-dimensional dense embedding is typically used to represent the feature of users and items. To optimize the model, some models propose to dynamically search the embedding size based on the popularity of different users and items. However, these models ignore the interaction between the user and the item which will hinder the optimization of the features in embedding. In this paper, we propose Object-aware Policy Network (OPN) and introduces an object-aware method that is used for optimizing the features in embedding. We evaluate our model on the two real-world benchmark datasets. With less than 10% increased time consumption in all experiments, the results show that our proposed model is able to improve the performance of binary classification task by a margin of 0.30 and multiclass classification task by a margin of 0.35 compared to the best accuracies achieced by baselines on different datasests.
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias

We study the dynamics and implicit bias of gradient flow (GF) on univariate ReLU neural networks with a single hidden layer in a binary classification setting. We show that when the labels are determined by the sign of a target network with $r$ neurons, with high probability over the initialization of the network and the sampling of the dataset, GF converges in direction (suitably defined) to a network achieving perfect training accuracy and having at most $\mathcal{O}(r)$ linear regions, implying a generalization bound. Unlike many other results in the literature, under an additional assumption on the distribution of the data, our result holds even for mild over-parameterization, where the width is $\tilde{\mathcal{O}}(r)$ and independent of the sample size.
Pengembangan Aplikasi SOLID-Calculator untuk Pengukuran Kualitas Desain Diagram Kelas

Today's development of information technology makes the competition for software products increasingly tight. Developers should release immediately and gain the trust of users. One crucial strategy that needs to be done is improving the software's quality. The application of software engineering principles in every development process is how to deliver quality software. The SOLID principles are the standard for developers to build well-designed software. Using this principle will make the software easier to understand, more flexible, easy to maintain, and testable. Implementation of the SOLID Principles can be started from the design of the class diagram. In this research, a software development called SOLID-Calculator was carried out to speed up measuring the quality of class diagrams. When developers can quickly find out the level of design quality, the faster errors can be detected and corrected. It is believed that the product will soon enter the market share and even excel in user ratings.
Ensiklopedia Digital Varietas Ubi Jalar Berdasarkan Klasifikasi Citra Daun Menggunakan KNearest Neighbor

&lt;p class="Abstract"&gt;Sweet potato is a source of carbohydrates which is an alternative food in order to accelerate food diversification. This is due to the high productivity of sweet potato so it is very profitable to cultivate. Sweet potato has many varieties, one of the differences is observed based on leaf shape which has four kinds of leaf shape, namely cordate, lobed, triangular and almost divided. The problem that often occurs is that many varieties have similarities, causing difficulties in distinguishing sweet potato varieties, especially for novice farmers. To overcome this problem, the researchers created a digital encyclopedia of sweet potato varieties based on leaf shape using computer vision. The parameters used are area, perimeter, metric, length, diameter, ASM, IDM, entropy, contrast and correlation at angles of 0°, 45°, 90° and 135°. The amount of data used is 256 training data and 40 testing data. The K-Nearest Neighbor method is able to classify sweet potato leaf images for digital encyclopedias with an accuracy of 95% with variations in the values of K = 23 and K = 25.&lt;/p&gt;
ODD HARMONIOUS LABELING ON SOME STRING GRAPH CLASSES

A graph with the labeling properties of odd harmonic is called an odd harmonious graph. The purpose of this research was to get labeling properties of odd harmonic on the class of string graphs. The research used was a qualitative research method. The result of the research was that the definition and construction of a string graph, the union of a string graph, and the multiple string graph are obtained. Furthermore, it has been proved that a string graph, the union of a string graph, and the multiple string graph is an odd harmonious graph.
Analysis of Changes in Atmospheric CO2 Emissions Using Prophet Facebook

CO2 emissions have been an environmental issue for decades. The trigger for the increasing concentration of CO2 in the atmosphere is the growth of industries related to burning fossil fuels for coal, natural gas, and petroleum. For nearly a century, several attempts have been made to suppress the rapid growth of CO2 . This study uses daily atmospheric CO2 levels observed in Mauna Loa laboratories. The method used is a Prophet that can handle seasonality and mark the change points. Almost 20% of data was missing value, which was then imputed using spline interpolation. Based on the analysis results, CO2 levels have an upward trend throughout the year and seasonality. There is no point of change in the last ten years that shows a decrease in CO2 levels. Using forward chaining cross-validation evaluation and error measurement, the prophet model can follow the pattern of CO2 levels well. The average RMSE value is less than 2.0, with an MAPE value bellow 0.5%.
Sistem Informasi Pelayanan Jemaat Gereja Berbasis Website Menggunakan Analisis PIECES

The church is a spiritual institution for Christians. In the church there are various kinds of service activities, such as catechism services, baptism services, wedding blessing services, and social service services. In general, the Church in processing congregational service data, catechism data, baptismal data, marriage data is still recorded in the books, so that data can be lost or damaged, and it is less effective in searching data, because it takes a long time. In providing information such as worship agendas, church activities, and other routine church activities, it is still manual, namely through congregation bulletins. The purpose of this research is to build a website-based church service system model to support the implementation of various church services more effectively and efficiently. This study uses the PIECES analysis method to check and analyze the advantages and disadvantages that exist in the system. This system is designed using UML, to make it easier to design a website-based system. This church service information system provides features of church service activities and more complete information about the church that can be accessed more practically anytime and anywhere.
Rainfall Prediction Model Using Machine Learning Techniques

Abstract: Predicting rainfall is one of the most difficult aspects of weather forecasting. Accurate and timely rainfall forecasting can be extremely useful in preparing for ongoing building projects, transportation activities, agricultural jobs, aviation operations, and flood situations, among other things. By finding hidden patterns from available elements of past meteorological data, machine learning techniques may accurately predict rainfall. This study adds to the body of knowledge by giving a comprehensive examination and assessment of the most recent Machine Learning algorithms for rainfall prediction. This study looked at publications that were published between 2013 and 2017 and were found in reputable internet search libraries. This study will aid academics in analysing recent rainfall prediction work with a focus on data mining approaches, as well as providing a baseline for future directions and comparisons. Keywords: Rainfall prediction, Rainfall, Machine learning techniques.
Formation of Information and Mathematical Competence of Higher Education Applicants in the General Course of Physics

The article deals with the process of formation of information and mathematical competence of the higher education of engineering specialties in the general course of physics with the use of the Simscape visual programming package. During the study, the theoretical analysis of scientific literature was carried out; systematized and generalized scientific data for the study of the essence and structure of information and mathematical competence of higher education applicants; the experience of using innovative forms of study in the general course of physics is studied; the proposed approach to the educational process was introduced; Summarized and systematized results. The methodological basis of the study is to examine the process of formation of information and mathematical competences when studying physics using the MatLab software package. The solution of the problem in the study of oscillatory processes in the mechanical system with bound oscillations of two material points with MatLab Simscape is given. The components of the process of formation of information and mathematical competence of future engineers in the general course of physics are highlighted: motivational, creative, operational and technological, activity. These components include an increase in motivation, awakening of interest in the study of physics, mathematics, informatics and desires to self-realize; ability to show creativity, readiness for critical thinking and creative comprehension of the use of knowledge, skills and skills; ability to solve physics problems, selection of techniques and methods for solving non-standard tasks, the use of mathematical and information tools when solving problems, knowledge of integration links of objects of general realization; solving professional tasks with the use of mathematical and information technologies. The main tasks during the study of the general course of physics in the process of formation of information and mathematical competence are: a positive attitude to the study of physical and mathematical disciplines and the use of knowledge gained in future professional activities; ability to correctly choose information and mathematical tools for solving applied tasks; ability in accordance with the set task to interpret and use the information received; ability to organize self-education and self-development. It has been established that this approach allows you to form an information and mathematical competence of higher education for higher education in the general course of physics, promotes the development of critical and algorithmic thinking and creative comprehension of the use of higher education acquisitions, skills and skills in the process of solving physical problems.
Sales Digital Cashier Application Development Using Website (Case Study : Gogo Bakery)

Cash register technology can simplify and optimize payment transaction management. The activity of buying and selling goods cannot be separated from sales transactions and the sales transaction process cannot be separated from the cashier. GOGO Bakery is a trading shop engaged in the sale of bread. At this store, the transaction process, data processing, and report generation have not been managed computerized, until now this store still has not provided satisfactory and ineffective service, because the payment process at the cashier is not optimal and also the provision of product and price information is not listed properly, which causes consumers to not know the prices and the latest products in the store before buying. In stores, there are usually a lot of item data, transaction data, and others that are impossible to memorize. for that,
Alternatives to a nonhomogeneous partial differential equation quantum algorithm

Recently J. M. Arrazola et al. [Phys. Rev. A 100, 032306 (2019)] proposed a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form $Aψ(\textbf{r})=f(\textbf{r})$. Its nonhomogeneous solution is obtained by inverting the operator $A$ along with the preparation and measurement of special ancillary modes. In this work we suggest modifications in its structure to reduce the costs of preparing the initial ancillary states and improve the precision of the algorithm for a specific set of inputs. These achievements enable easier experimental implementation of the quantum algorithm based on nowadays technology.