Reviewerly/exHarmony huggingface dataset:
Study on Application of Virtual Reality in Eye Disease Prevention

Abstract Virtual reality with computer technologies as the core and in combination with related sciences can generate a digital environment highly approximate to the real environment in aspects of visual sense, auditory sense and touch sense, etc., and characterized by high immersion and interactivity. It has been widely used in the medical field, but rarely used in disease prevention, especially in science popularization and prevention of eye diseases. This paper focuses on the study on the application of VR in eye disease prevention and presents a mobile phone App. It utilizes virtual reality as the presentation form, augmented reality as the connection tool, Unity3D as the development engine, and Oculus as the device carrier for further display and experience. It uses shader to add special effects for the camera lens in its simulation of eye disease's symptoms, which allows common people to experience the world seen by the patient of eye disease through the mobile phone camera, understand their inconveniences in daily life, and thus offer them more respects through understanding. At the same time, it can allow people to know the hazards of these diseases, and pay more attention to eye health, thus reaching the result of eye disease prevention.
Study on estimating quantum discord by neural network with prior knowledge

Machine learning has achieved success in many areas because of its powerful fitting ability, so we hope it can help us to solve some significant physical quantitative problems, such as quantum correlation. In this research, we will use neural networks to predict the value of quantum discord. Quantum discord is a measure of quantum correlation which is defined as the difference between quantum mutual information and classical correlation for a bipartite system. Since the definition contains an optimization term, it makes analytically solving hard. For some special cases and small systems, such as two-qubit systems and some X-states, the explicit solutions have been calculated. However, for general cases, we still know very little. Therefore, we study the feasibility of estimating quantum discord by machine learning method on two-qubit systems. In order to get an interpretable and high-performance model, we modify the ordinary neural network by introducing some prior knowledge which comes from the analysis about quantum discord. Our results show that prior knowledge actually improves the performance of neural network.
Estimation of Time Voting in Elections Using Artificial Neural Network

Since the first election policy was enacted simultaneously, it does not mean that it does not have potential problems, instead it causes other problems, which require extra time and energy in doing recapitulation. Simultaneous elections consist of presidential elections, DPR elections, Provincial DPRDs, City / Regency DPRDs, DPD, the more they are elected, the more influential is the time of voting and the time of vote recapitulation. The longer the voting time is done by the voters, the longer the recapitulation time. The longer time of recapitulation results in the fatigue of KPPS members which triggers inaccurate work and prone to manipulation and fraud so that it can damage the quality of elections. This study aims to determine the estimated time needed for voting for ballots in elections using the Multilayer Perceptron Artificial Neural Network (ANN) approach. The resulting time estimate is based on the time of the voter in the voting booth. The results of this study indicate that ANN with the Multilayer Perceptron Algorithm can calculate the estimated time required for ballot balloting by producing the best combination of learning parameters with 4 hidden neurons, learning rate 0.001, and 2000 epoch iterations resulting in an RMSE value of 108,015 seconds.
The Extra-Institutional Logics of an Emerging Market Field: Values and Practice in the Bitcoin Experience

Mature fields of market activity are characterized by clear norms, entrenched meanings, and rigid power hierarchies that stabilize logics of action. However, how do logics of action emerge in a nascent field of market activity that lacks these types of institutions? To answer this question, I draw on theories of institutions and markets to examine the experiences of 23 adopters of the cryptocurrency Bitcoin during 2015 to 2016. Utilizing an iterative combination of correspondence analysis (CA) techniques and narrative analysis, I show how Bitcoin adoption was motivated by competing value frameworks that corresponded to different user types in the system. This resulted in the emergence novel “extra-institutional logics” that supported the growth of Bitcoin as a whole despite being centered on different visions for the future of the system. These logics provide a unique glimpse into how Bitcoin was able to survive during a period of notable instability preceding a historic market bubble. This article provides support for dynamic theories of markets and demonstrates how logics of action are tethered to the characteristics of the fields in which they emerge.
Quantification and Visualization for Difference of Fuzzy Clustering Results

This paper proposes a method to obtain the quantitative difference of fuzzy clustering results and to visualize it in lower dimensional space. Since the result of fuzzy clustering is shown as the degree of belongingness of objects to exploratory obtained clusters, the obtained clusters do not have "explicit order". Therefore, if we obtain multiple results from multiple datasets individually, then we cannot compare the results, since each obtained "order" of clusters for each dataset may differ from each other. However, there is a need to compare the results. For example, if we observe multiple datasets in which each dataset consists of objects and variables and such a dataset is observed for a multiple number of subjects (or times), and if we obtain the clustering result at each subject (or time), then we usually would like to know the difference of the clustering results over the subjects (or times). The proposed method in this paper enables the capturing of the difference based on the mathematical comparability of the obtained clusters over the different datasets corresponding to different subjects (or times) and visualize the difference of clustering results over the subjects (or times). Numerical examples are shown for an improved understanding of the proposed method.
Modelling of Railway Signalling System Requirements by Controlled Natural Languages: A Case Study

The railway sector has been a source of inspiration for generations of researchers challenged to develop models and tools to analyze safety and reliability. Threats were coming mainly from within, due to occasionally faults in hardware components. With the advent of smart trains, the railway industry is venturing into cybersecurity and the railway sector will become more and more compelled to protect assets from threats against information & communication technology. We discuss this revolution at large, while speculating that instruments developed for security requirements engineering can then come in support of in the railway sector. And we explore the use of one of them: the Controlled Natural Language for Data Sharing Agreement (CNL4DSA). We use it to formalize a few exemplifying signal management system requirements. Since CNL4DSA enables the automatic generation of enforceable access control policies, our exercise is preparatory to implementing the security-by design principle in railway signalling management engineering.
A Human Action Recognition Model Inspired by Multiple Scale Temporal Segments Model Fusion

Deep convolutional networks have made progress in many tasks in vision, such as image classification, target detection, target segmentation, etc., two-stream ConvNets, C2D+LSTM method and 3DCNN method, successfully introduces the advantages of convolutional neural networks in still image tasks into the field of video action recognition Most of the existing long video recognition methods use fixed sampling as input, which will lose important information of sampling interval. This paper aims to design a spatiotemporal segment with different scales. The structure of the network model is used to learn the video-level descriptions of different kinds of actions. Through this multi-model design, the purpose of optimizing the action classification results with different information distribution is achieved. At the same time, we improved the network structure and optical flow extraction method, designed the network structure using dense block and adopted the TV-Net method to extract the optical flow representation, which also improved the overall model effect. We designed the multi-scale two-stream ConvNets model fusion, the algorithm achieved a satisfactory 95.7% accuracy on the UCF101 data set.
Effect of barrier layer on moisture absorption of thin carbon-fiber-reinforced plastic mirror substrates

Carbon-fiber-reinforced plastic (CFRP) has a higher strength-to-weight ratio and forming flexibility than metals, making it suitable for fabricating lightweight x-ray mirrors. However, CFRP has the disadvantages of print-through and deformation due to moisture absorption, which have prevented its use in optical mirrors. To expand the application of CFRP, we studied the formation of a moisture barrier layer on CFRP substrates. We formed a flattening layer a few micrometers thick on a CFRP substrate, following which we coated the substrate with SiO<sub>x</sub> as a moisture barrier. The effect of moisture absorption was then evaluated using accelerated aging tests. We found that the diffusivity of the CFRP substrate at 60°C and a relative humidity of 100% was ∼2 × 10<sup> − 6</sup> mm<sup>2</sup> h<sup> − 1</sup>, which is 1/500th that of the barrier-less substrate. In the tests, the moisture absorption rate increased after ∼800 h. As we observed cracks on the flattening layer after 600 h, the rate increase could be associated with these cracks. Considering the damage to the barrier layer, we propose a modified model for the time profile, which is congruent with the observed time profile of the moisture content.
M-Health Android Application Using Firebase, Google APIs and Supervised Machine Learning

With the increasing use of mobile technology across the globe, android applications are of significant use, including the health sector. For efficient quick results while looking up for medical help in case of accidents or medicines and blood requirement emergencies, one can use such mobile-health application. The proposed paper focuses on making advantageous use of Android and Machine Learning to provide an integrated platform for delivering health-related information on major healthcare units such as hospitals, pharmacies and blood banks. These facilities are provided through the developed app in which the data is customized according to the factors viz. user’s location, their distance to nearby health units obtained through Google Maps API, availability of required resources such as number of beds in hospitals and the ratings which are predicted by applying Multinomial Naive Bayes technique on the customers’ reviews. The list of the health units, their reviews and count of beds in a hospital, medicines in pharmacies or blood types in blood banks are all stored in the Firebase.
Automatic Design of Fuzzy Controller for Rotary Inverted Pendulum with Success-History Adaptive Genetic Algorithm

In this paper an adaptive genetic algorithm for fuzzy controller design is proposed. The problem of rotary inverted pendulum stabilization and swing-up sequence is considered. The genetic algorithm is used to determine the structure of the fuzzy rule base, which is then used to control the pendulum. The experiments on a model show that the success-history adaptive genetic algorithm is capable of automatic search for an optimal rule base with no prior expert knowledge about the system dynamics.
Bipolar Argumentation Frameworks, Modal Logic and Semantic Paradoxes

Bipolar Argumentation Frameworks (BAF) are a natural extension of Dung’s Argumentation Frameworks (AF) where a relation of support between arguments is added to the standard attack relation. Despite their interest, BAF present several difficulties and their semantics are quite complex. This paper provides a definition of semantic concepts for BAF in terms of fixpoints of the functions of neutrality and defense, thus preserving most of the fundamental properties of Dung’s AF. From this angle it becomes easy to show that propositional dynamic logic provides an adequate language to talk about BAF. Finally, we illustrate how this framework allows to encode the structure of the referential discourse involved in semantic paradoxes such as the Liar. It turns out that such paradoxes can be seen as BAF without a stable extension.
Assessing detection performance of night vision VIS/LWIR-fusion

Thermal imagers (TI) and low light imagers (LLI; e.g. Night Vision Goggles, NVG) are today’s technologies for nighttime applications. Both possess their own advantages and disadvantages. LLI typically is ineffective in dark areas whereas TI operates also in complete darkness. On the other hand, TI often does not provide enough scene details, hindering situational awareness. Hence, it is nearby to combine the systems for common use. Today such combined systems are available as so-called Fusion Goggles: a LWIR bolometer optically fused to an image intensifier based NVG. Future development will probably replace the NVG by solid-state night vision technologies enabling sophisticated image fusion. Performance assessment and modeling of fused systems is an open problem. Main reason is the strong scene and task dependency of fusion algorithms. The idea followed here is to divide the task in detection and situational awareness ones and analyze them separately. An experimental common line of sight system consisting of a LWIR-bolometer and a low light CMOS camera was set-up to do so. Its first use was detection performance assessment. A data collection of a human at different distances and positions was analyzed in a perception experiment with the two original bands and eight different fusion methods. Twenty observers participated in the experiment. Their average detection probability clearly depends on the imagery. Although the resolution in LWIR is three times worse than the visual one, the achieved detection performance is much better. This transforms in the fused imagery also, with all fusion algorithms giving better performance than the visual one. However, all fusion algorithms to a different degree decrease observer performance compared to LWIR alone. This result is in good agreement with a Graph-Based Visual Saliency image analysis. Thus, it seems possible to assess fusion performance for the detection task by saliency calculations.
Extended Stepping Theories of Active Logic: Declarative Semantics

Active Logic is a conceptual system with reasoning formalism that allows for correlation of their results with specific points in time and that has tolerance to inconsistencies. Currently, tolerance to inconsistencies (paraconsistency) in Active Logic systems is theoretically justified in the works of the authors of this paper and is attributed to the so-called formalisms of stepping theories, which integrate the principles of Active Logic and Logic Programming. More specifically, the argumentation semantics of so-called formalisms of stepping theories with two kinds of negation has been proved to be paraconsistent. This formalism has more expressive power than the other formalisms of stepping theories and to a greater extent satisfies the principles of Logic Programming. This case study proposes the declarative semantics for formalisms of stepping theories and represents its equivalency with respect to the argumentation semantics of this type of formalism. This, in turn, means that the proposed declarative semantics is also paraconsistent, and logical inconsistencies existing in these theories do not result in their destruction.
A Characteristic Analysis for Quality Competitiveness Excellent Company

Quality management has become an pervasive philosophy in most sectors of business. Specific movements such as statistical quality control, quality circle, total quality management, and quality management system have become embedded in business organizations. Only the companies with competitive edge can survive in the competition in global market. KSA(Korean Standards Association) established in 1962 has launched all kinds of quality education, quality standard certification service for business, and KNQA(Korean National Quality Award) system. This article considers quality competitiveness excellent company award among KNQA. We performed a statistical analysis of audit data for quality competitiveness excellent company for three years, from 2015 to 2017. By using ANOVA and two sample t-tests, the average scores of 13 evaluation fields were significantly different depending on company size and type. We proposed ways to improve the current hall of fame system. We discovered that the average scores of 13 evaluation fields in the audit data according to years and hall of fame status were not significantly different. We also showed linear relationships among 13 evaluation fields by correlation analysis and obtained an estimated linear regression equation : Business Performance, which is a comprehensive index, as a dependent variable was significantly related to Customer Focus and Product Liability as regressor variables among 13 evaluation fields by regression analysis.
molic: An R package for multivariate outlier detection in contingency tables

Outlier detection is an important task in statistical analyses.An outlier is a case-specific unit since it may be interpreted as natural extreme noise in some applications, whereas in other applications it may be the most interesting observation.The molic package has been written to facilitate the novel outlier detection method in high-dimensional contingency tables (Lindskou, Eriksen, & Tvedebrink, 2019).In other words, the method works for data sets in which all variables are categorical, implying that they can only take on a finite set of values (also called levels).
ALIKAH- A Clickbait and Fake News Detection System using Natural Language Processing

Nowadays, social media is a very important thing in our daily lives. People can't even think about a second without social media. Because of their busy life, people depend more on social media for information, thus increasing its popularity. Social media can be considered as a two-sided coin having its own advantage and disadvantage. These media help people to connect with their family or friends around the world. But the other side of the social media has many disadvantages. It can be considered as the cause of many problems in our society. One such major issue is the fake news. People were unable to distinguish the true and fake news and also about the credibility of the news and the news provider. They blindly believe the news without knowing the truth and they share the news with others. As a result, the fake news spread faster than the true news. By this, many people and organizations get affected. So, in a world of increasing fake news, a fake news detection system is an essential thing. This project deals with fake news. The system is a Webapp named ALIKAH- a clickbait and fake news detection system. It is just like social media network where the news providers can provide the news. This system distinguishes the fake and true news among the news provided in the Alikah system. There are three modules in this system-the admin, news providers and the users. Admin manages and monitors the system and its functionalities. News providers can provide the news to this system after getting permission from the admin and the user can view, like, comment, report and subscribe to the news and the news provider. Neural network is used as the classifier. This system detects the fake news by checking the credibility of the news provider, monitoring the comments and also by checking the relation of the heading and content of the news provided. It also helps to detect the fake news spreading on other social media like Facebook, by using its heading and content. This system definitely will be a beneficiary to the people and organizations which get affected by the news and also help to find the providers of these news.
Sequential Monte Carlo for Hierarchical Bayes with Large Datasets

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Generalized STAR (1;1) Model with Outlier - Case Study of Begal in Medan, North Sumatera

Abstract This study was aimed to compare generalized space time autoregressive (GSTAR) (1;1) model with its modification. The modification of GSTAR (1;1) model was done by adding the outlier factor which gained by outlier detection procedure. A case study was done towards the amount of monthly crime activity- begal at seven police sectors in Medan, North Sumatera. By using three steps of Box-Jenkins, Autoregressive (1) model was applied to each location and resulted West Medan as fifth location with additive outlier (AO) in the third data of time which weighted -57.5247. The comparison of model was done by calculating the root mean square of error (RMSE) in each model. Since GSTAR (1:1) model has larger RMSE (8.1728) rather than its modification (7.9700), then GSTAR (1;1) model with outlier is the best model in this case study. This invention shows that the model of space time with outlier model is useable to find precise result rather than space time model only.
A Deep Regression Model for Seed Localization in Prostate Brachytherapy

Post-implant dosimetry (PID) is an essential step of prostate brachytherapy that utilizes CT to image the prostate and allow the location and dose distribution of the radioactive seeds to be directly related to the actual prostate. However, it a very challenging task to identify these seeds in CT images due to the severe metal artifacts and high-overlapped appearance when multiple seeds are clustered together. In this paper, we propose an automatic and efficient algorithm based on a 3D deep fully convolutional network for identifying implanted seeds in CT images. Our method models the seed localization task as a supervised regression problem that projects the input CT image to a map where each element represents the probability that the corresponding input voxel belongs to a seed. This deep regression model significantly suppresses image artifacts and makes the post-processing much easier and more controllable. The proposed method is validated on a large clinical database with 7820 seeds in 100 patients, in which 5534 seeds from 70 patients were used for model training and validation. Our method correctly detected 2150 of 2286 (94.1%) seeds in the 30 testing patients, yielding 16% improvement as compared to a widely-used commercial seed finder software (VariSeed, Varian, Palo Alto, CA).
State of the Art Determination of Risk Management in the Implantation Process of Computing Systems

Risk management is a vital step for software development projects to be successful. A systematic mapping study (SMS) to systematize the empirical evidence of methodologies, methods and standards that deal with risk management in software development projects is presented. Subsequently, a characteristics-based analysis (DESMET method) is conducted to evidence the degree of compliance of the analyzed methodologies regarding the risks for the computing systems implantation process. Finally, conclusions and future works are presented.