Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Informasi Keuangan Berbasis Web Multi User Dengan UML

STIE XYZ in managing financial transactions still uses Microsoft Excel which is divided by year and type of payment. The purpose of this research is to build a systemized alternative application for managing financial transactions in accordance with financial management at STIE XYZ. Rapid Application Development (RAD) system development method was chosen because in this method a system can be developed within 180 days and the users are directly involved when developing the system so that the system is developed according to user needs. The RAD design process uses the Unified Modeling Language (UML) method. Dimension of Quality for Goods (Operations, Reliability and Durability, Conformance, Service Ability, Appearance and Perceived, and Quality) test scores&gt; 75.50, 76.50 so that it can be concluded that the product is declared successful, the results of the benefit testing are based on the variables Usability, Learnability, Efficiency and Acceptability (ULEA) produces a value&gt; 70 that is 90.65% so that it can be declared useful. Keywords: Finance, RAD, UML, ULEA
Developing Cardiac Electrophysiology Ontology: Moving Towards Data Harmonization and Integration

Introduction: Cardiac electrophysiology (EP) studies the electrical heart conduction system which is used for diagnosis and treatment of cardiac arrhythmias. In this context, a huge amount of data is generated, requiring efficient and effective access, interpretation, and data analysis from multiple sources in a unified view. To resolve this challenge, this essay presents an ontology to reconcile data heterogeneity problems in this domain.Material and Methods: The cardiac EP ontology was constructed according to the life cycle of ontology building. Structural, functional, and expert evaluation was performed to ensure its quality and usability. Results: Cardiac EP ontology was developed using protégé environment and implemented in OWL editing tool. It presented a detailed hierarchical structure of the cardiac EP domain with around 324 instances describing cardiac EP-related concepts.Conclusion: Cardiac EP ontology provides an explicit formal description of the concepts, relationships, and properties associated with cardiac electrophysiology making seamless data integration between multiple heterogeneous databases. It also is a useful framework for knowledge representation in knowledge-based systems, as well as for explicit communication between experts in the EP domain.
Pruning CNN's with Linear Filter Ensembles.

Despite the promising results of convolutional neural networks (CNNs), their application on devices with limited resources is still a big challenge; this is mainly due to the huge memory and computation requirements of the CNN. To counter the limitation imposed by the network size, we use pruning to reduce the network size and -- implicitly -- the number of floating point operations (FLOPs). Contrary to the filter norm method -- used in ``conventional`` network pruning -- based on the assumption that a smaller norm implies ``less importance'' to its associated component, we develop a novel filter importance norm that is based on the change in the empirical loss caused by the presence or removal of a component from the network architecture. Since there are too many individual possibilities for filter configuration, we repeatedly sample from these architectural components and measure the system performance in the respective state of components being active or disabled. The result is a collection of filter ensembles -- filter masks -- and associated performance values. We rank the filters based on a linear and additive model and remove the least important ones such that the drop in network accuracy is minimal. We evaluate our method on a fully connected network, as well as on the ResNet architecture trained on the CIFAR-10 dataset. Using our pruning method, we managed to remove $60\%$ of the parameters and $64\%$ of the FLOPs from the ResNet with an accuracy drop of less than $0.6\%$.
PushNet: Efficient and Adaptive Neural Message Passing.

Message passing neural networks have recently evolved into a state-of-the-art approach to representation learning on graphs. Existing methods perform synchronous message passing along all edges in multiple subsequent rounds and consequently suffer from various shortcomings: Propagation schemes are inflexible since they are restricted to $k$-hop neighborhoods and insensitive to actual demands of information propagation. Further, long-range dependencies cannot be modeled adequately and learned representations are based on correlations of fixed locality. These issues prevent existing methods from reaching their full potential in terms of prediction performance. Instead, we consider a novel asynchronous message passing approach where information is pushed only along the most relevant edges until convergence. Our proposed algorithm can equivalently be formulated as a single synchronous message passing iteration using a suitable neighborhood function, thus sharing the advantages of existing methods while addressing their central issues. The resulting neural network utilizes a node-adaptive receptive field derived from meaningful sparse node neighborhoods. In addition, by learning and combining node representations over differently sized neighborhoods, our model is able to capture correlations on multiple scales. We further propose variants of our base model with different inductive bias. Empirical results are provided for semi-supervised node classification on five real-world datasets following a rigorous evaluation protocol. We find that our models outperform competitors on all datasets in terms of accuracy with statistical significance. In some cases, our models additionally provide faster runtime.
Text Mining of Loss Data

&amp;lt;p&amp;gt;Structured datasets of loss data, i.e. data on the impact of past natural disasters, are of paramount importance for informing disaster preparedness programs and forecasting the impact of future disasters. Most of existing initiatives aim at manually building such datasets from information of goverments, humanitarian agencies and researchers. Unfortunately, the quality and completeness of such information is often insufficient, especially for small disasters and/or in areas where these organisations are not active. More often, it's local and national newspapers that report on small disasters. In this contribution, we present a series of algorithms to automatically extract structured loss data from online newspapers, even small ones that are not captured by common news aggregator (e.g. Google News). The algorithms are validated both in terms of accuracy of extraction and consistency with existing datasets; we argue that they provide a valuable tool to collect loss data in data-poor regions.&amp;lt;/p&amp;gt;
Design of the nonlinear look-up table in the chaotic pseudorandom number generator based on augmented Lorenz map

A chaos-based stream cipher using an augmented Lorenz map has been proposed. It was shown through numerical simulations that this chaotic map can generate statistically secure pseudorandom numbers, although high-speed hardware is necessary for practical use. One of the problems in digital hardware realization is implementation of the nonlinearities included in the map. In this paper, we propose a nonlinear look-up table (LUT) technique to implement the sine function considering the distribution of the argument variable. We experimentally demonstrate through field programmable gate array(FPGA) prototyping, high-speed and small-sized digital hardware implementation of the pseudorandom number generator based on the augmented Lorenz map using the proposed nonlinear LUT method.
A New Methodology on Sentiment Analysis

Today, we are living in the world where the data is present in enormous amount. There are many web applications and social networking sites where users share their thoughts in form of sentiments or opinions. Sentiment analysis provides great practical value on user’s viewpoint. Sentiment analysis is a research area which extracts the proper meaning of the user’s viewpoint that includes text analytics and classifies the polarity of the user’s opinion. Here, we are discussing some papers of sentiment analysis of data with the help of machine learning approaches and trying to propose a new algorithm by combining two feature extraction methods. Training and testing are done by neural network. Accuracy is checked by Precision, Recall, F-Score. ROC curve is plotted for checking the performance.
Origin of the Phacoid Structures within the Chia Gara Formation, Kurdistan Region, Iraq

Special structural shapes exhibit occurrence in the sequence of Chia Gara Formation in the northwestern part of the Zagros belt in Kurdistan region-Iraq. These structures name as Phacoids that have spherical to semi-spherical shape. Constructing concentric zonal features in the carbonate/shale of Chia Gara Formation. These structures are characterized by the presence of ball-like bodies within the host rocks. The diameter of phacoids ranged from 20cm to about 120cm. They are harder than the host rock. Seven outcrop sections, of Chia Gara Formation are reviewed and two of them studied in detail to determining the origin and process of their formation. The phacoids can be conceder as sedimentary key marker to recognize the Chia Gara Formation in the outcrops. Petrographically, the phacoids are mostly organic matter–rich bioclastic lime wackestone. The bitumens are present in sub-parallel lines in a calcareous groundmass. However, the host rock is generally composed of calcareous shale rich in organic matter but barren of fossils. The carbonate phacoids were forming in a competent/incompetent sequence. The mechanism of creating these carbonate phacoids structures are related to the compression stress and diagenesis processes on the carbonate/shale successions. Some phacoids directly formed from the carbonate strata after separated and rolled due to continue compression shear stress that in the final stage form the carbonate phacoid with the aids of diagenesis.
Efficient SVDD sampling with approximation guarantees for the decision boundary

Abstract Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.
Dual Information-Based Background Model For Moving Object Detection

In this article, a novel pixel based object detection framework is proposed that leverages dual type pixel-level information to construct the background model. The first type of information is initially used intensity histograms over a training set of a few initial video frames. Finally, it is formed by gathering all the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. The second type of information constitutes a set having only the discrete pixel values. Subsequently, a pixel-level periodic updating scheme is used to make the model robust and flexible enough to recognize and detect foregrounds in various critical background environments. This dual format model produces effective results over many state-of-the-art methods in a large variety of challenging real-life video sequences.
Symmetry Learning Using Non-traditional Biologically Plausible Learning Method

Most modern methods for training artificial neural networks are based on error backpropagation algorithm. However it has several drawbacks. It is not biology-plausible. It needs computing and propagating derivative of error function. It can't be directly applied to binary neurons. On the other side, algorithms based on Hebb's rule offer more biology-plausible local learning methods. But they basically unsupervised and can't be directly applied to vast amount of tasks, designed for supervised learning. There were several attempts to adapt Hebb's rule for supervised learning, but these algorithms didn't become very widespread. We propose another hybrid method, which use locally available information of neuron activity, but also utilize the information about error. In contrast to other methods, the presence of error doesn't invert the direction of weight change, but affects the learning rate. We test the proposed learning method on symmetry detection task. This task is characterized by practically infinite number of training samples, so methods should demonstrate a generalization ability to solve it. We compare the obtained results with ones, obtained in our previous work.
A Deep Learning Approach of Collaborative Filtering to Recommender System with Opinion Mining

To produce good quality recommendations for large or enterprise scale problems, a competent approach for recommender system is required. This paper presents such an approach which first generates the text score based on users’ reviews with the help of opinion mining. It then feeds ratings corresponding to the text scores to Convolutional Neural Network (CNN). CNN learns and does the dot product of user and product matrices. It is a special kind of feed forward neural network of deep learning technique to get better predictions in a product recommender system. The work done in this paper has improved accuracy and user satisfaction to great extent using CNN. It also helps e-commerce companies to increase the revenue by recommending closest products to users.
A Semi-supervised Joint Entity and Relation Extraction Model Based on Tagging Scheme and Information Gain

Joint entity and relation extraction, completing the entity recognition and relation extraction simultaneously, can better integrate the information between two tasks and reduce the errors of each task. The methods based on tagging scheme treat the joint extraction as a sequence labeling task and have achieved outstanding results. However, those tag-based methods are insufficient in making full use of the information between entities and relations. It maybe the reason why they show a relatively poor precision. In this paper, we propose a novel semi-supervised approach that combines the tagging scheme with an information gain module. The information gain module is a combination of distant supervision and attention mechanism, which is used for obtaining prior information of candidate entities and relations. We believe that it is important for the joint extraction results to make much of the links among them. Our tagging scheme adds distant supervision to tag entity words and relational words in the given sentences, and combines with attention mechanism to improve their weight. It effectively helps our bias objective function improve model performance. Experiments on public dataset show that our methods are better than most of the existing pipelined and joint learning methods.
Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols

Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision how interfaces can bring definitions of technical terms and symbols to readers when and where they need them most. We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that declutters it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols. A usability study showed that the tool helps researchers of all experience levels read papers. Furthermore, researchers were eager to have ScholarPhi's definitions available to support their everyday reading.
The Significance of Youth Political Participation and the Direction of School Education

본 연구는 2005년 이후 약 15년 만에 만 18세로 선거권 연령이 하향된 제도변화에 주목하여 청소년 정치참여 관련 활동에 참여하고 있는 청소년, 청소년 활동가, 비청소년 활동가를 대상으로 면담을 실시하고 청소년 정치참여의 의미와 학교교육 방향을 논의하였다. 연구 결과, 청소년 정치참여는 ‘현존하는 시민(being citizen)’으로서의 정체성을 천부적으로 가진 권리주체임을 자각하는 과정이었으며, 선천적ㆍ후천적 조건의 차이로 인해 사회적 논의의 장에서 소외되고 있는 청소년의 시민권 문제를 성찰하는 과정이었다. 즉 정치는 비단 국회에서 일어나는 활동만을 의미하지 않으며 삶 그 자체이기에, 사회 전체가 일상민주주의를 체험하고 학습하는 공간으로 변모해야 한다는 점을 환기시킨다. 따라서 만 18세 선거권 연령 하향 결정에 따라 촉발될 교육현장에서의 정치교육 방향은 단순히 선거 절차나 방법 등에 관한 지식을 체계적으로 정리하여 특정 교과목의 학습내용으로 제공하는 형태를 지향하지 않는다. 오히려 교육과정, 교육환경 등 교육체제 전반에 대한 체계적 설계를 요청한다. 이를 통해 청소년은 사회적 가치의 권위적 배분 과정이 민주적인지를 비판적으로 사고하고 사회불평등에 대한 책임의식을 내면화하여 ‘현존하는 시민(being citizen)’으로서 살아가기 위한 실질적 역량을 함양할 수 있을 것이다.This study has performed interviews with youth, youth activists, and non-adolescent activists who are participating in activities related to youth politics, and discussed the meaning of youth political participation and the direction of school education. Research has confirmed that youth political participation was a process of self-awareness that they were innate rights holders with their identity as being citizens . In other words, politics does not only mean activities taking place in the National Assembly, but it is life itself, so not only the school scene but also the entire society should be transformed into a space to experience and learn daily democracy. In the end, the direction of political education in the educational field, which will be triggered by the decision to lower the age of the 18-year-old electoral district, does not simply aim to systematically organize the knowledge of election procedures or methods and provide them with the learning content of a particular subject. We ask for a systematic design of the entire educational system, such as curriculum and educational environment related to the everyday lives of citizens. This will allow adolescents to develop practical capabilities to live as present citizens by critically thinking whether the authoritative distribution process of social values is democratic and internalizing a sense of responsibility for social inequality.
Multiresolution Adaptive Threshold Based Segmentation of Real-Time Vision-Based Database for Human Motion Estimation

The human motion recognition and analysis is a challenging task and hence an active area of research. Analyzing the video frames for tracking the body motion needs to take into consideration the speed of the body in motion and background changing rapidly with time. Hence to reduce the computational cost for motion estimation, data sets are usually adopted from traditional sources. The researcher discusses that in order to track the body motion the segmentation techniques should eliminate the stationary frames and reduce the processing parameters thereby partitioning the video frames into meaningful interpretations. Various segmentation techniques are discussed and analyzed to study the body motion applying the techniques to the already available CAVIAR database. To improvise upon the existing techniques, the research has proposed multiresolution adaptive threshold-based segmentation and background subtraction.
Body, Clothes, Water, and Toys: Media Towards Natural Music Expressions with Digital Sounds

In this paper, we introduce our research challenges for creating new musical instruments using everyday-life media with intimate interfaces, such as the self-body, clothes, water and stuffed toys. Various sensor technologies including image processing and general touch sensitive devices are employed to exploit these interaction media. The focus of our effort is to provide user-friendly and enjoyable experiences for new music and sound performances. Multimodality of musical instruments is explored in each attempt. The degree of controllability in the performance and the richness of expressions are also discussed for each installation.
Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

Abstract Deep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and  Self-Control Gradient Estimator

Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders (VAEs) with binary latent variables as hashing codes provide state-of-the-art performance in terms of precision for document retrieval. We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing. Instead of solving the optimization relying on existing biased gradient estimators, an unbiased low-variance gradient estimator is adopted to optimize the hashing function by evaluating the non-differentiable loss function over two correlated sets of binary hashing codes to control the variance of gradient estimates. This new semantic hashing framework achieves superior performance compared to the state-of-the-arts, as demonstrated by our comprehensive experiments.
An Academic Profile of IT Program Leadership in the U.S.

IT as an academic discipline is in its infant stages compared to CS. As a result, IT programs compete with CS and industry to recruit and retain qualified faculty. A multi-phase project is underway that will add to our understanding of IT leadership and faculty profiles and investigate a variety of questions and issues related to IT faculty and IT programs. This paper presents the first data collected as part of that project and provides a profile of IT program leadership in the U.S. Findings reveal that most department or school leaders are male, come from a CS background, and most possess a Ph.D. degree.