Reviewerly/exHarmony huggingface dataset:
Fault classification via energy based features of two-dimensional image data

Automated anomaly detection is the prerequisite to minimize human errors and costs caused by manual inspection. Recently, image-based anomaly detections have gained more attention by widely adopting machine vision systems and computer-aided detections. We propose a classification method using spectral features based on 2D discrete wavelet packet transform under the hierarchical structure of wavelet energies. By capturing the self-similar and long-range dependent characteristics of 2D fractional Brownian field (fBf), wavelet packet spectra are derived to construct a linear model representing the relationship between wavelet energies and resolution levels. 2D DWPT-based energy features effectively preserve irregular oscillations in original images at high-frequency domains as well as at low-frequency domains under a pyramidal structure. In comparison with the existing 2D discrete wavelet transform method, the proposed method shows a potential in efficiently classifying normal and abnormal image data in a numerical example and a real industrial application.
Challenges for introducing artificial intelligence to improve the efficiency of a next generation assessment approach

The U.S. Army sought to develop capabilities that allow for the automated or semi-automated, with greatly reduced human involvement, creation of tests and assessments. In recognizing the potential for an assessment approach that goes beyond multiple-choice, the Army chose our team to introduce and evaluate automated capabilities to author concept mapping-based assessments. This paper describes our initial approaches toward introducing efficiencies into the authoring process for concept map-based assessments. We are developing and evaluating methods to automatically generate concept maps from a knowledge domain and convert the maps into assessments for formative and summative purposes. Our initial work has sought to overcome challenges as we introduced artificial intelligence into the authoring process. In this paper, we describe our emergent approach and the challenges we have faced in seeking efficiencies in the conversion of text to concept maps.
A Comparative Analysis Between Information Flow Control Tools for Java-written systems

Information Flow Control (IFC) tools are a common way to analyze source code with the goal to find confidentiality or integrity violations for sensitive information. Therefore, to correctly protect such information (e.g., passwords), it is important to choose the most suitable tool for each target software system. In this context, we evaluate precision, recall, and accuracy for three open-source IFC tools for Java-written systems. We also check whether these tools are useful to protect sensitive information of real systems. First, we execute these tools against test cases of the SecuriBench Micro benchmark built for this purpose. Then, we run three selected IFC tools (JOANA, PIDGIN, and Flowdroid) to assess whether they are able to detect violations for rules we define considering each real system. Our results show that JOANA and PIDGIN overcome FlowDroid regarding precision, recall, and accuracy. Furthermore, the execution of JOANA and PIDGIN allow us to find eight confidentiality and integrity violations for the target systems. We registered these violations as issues on those projects. Our results also demonstrate that JOANA is faster than PIDGIN. At last, we provide some discussion for developers on which IFC tool fits better when dealing with sensitive information in software systems.
BGSA: Broker Guided Service Allocation in Federated Cloud

• A broker based resource allocation model for Federated Cloud environment has been proposed. • A genetic algorithm for constraint based multi-objective optimization has been proposed. • Simulated results show the effectiveness of proposed model. Cloud brokering has emerged a new service paradigm because of the proliferation of a large number of cloud service providers. Cloud brokering attracts users because it aggregates and extends computing services from multiple cloud service providers. Service providers offer services with varying capabilities, technologies and pricing models which creates a heterogeneous service environment. Cloud users are also increasing day by day with varied requirements. Users prefer services from multiple service providers in order to fulfill their money and application requirements. Therefore, service allocation becomes one of most challenging task to fulfill all the requirements of users within resource constraints. Service allocation problem in multiple cloud environment has been studied as combinatorial optimization problem. Broker guided service allocation increases application resilience, provides unified billing and simplifies service management. In this paper we have proposed a broker guided service allocation (BGSA) model for Federated Cloud. BGSA employs a genetic algorithm based technique for service allocation. BGSA optimizes service allocation and derives well distributed set of non-dominated solutions. Simulation based evaluation of BGSA has been done and obtained results are compared with other two multi-objective algorithms. Observed results improve both execution time and total utilization.
A Multi-Correlation Stereo Matching Network for Disparity Estimation

Recent Convolutional Neural Networks(CNNs) based networks for disparity estimation has a better performance in both prediction accuracy and time efficiency compared with traditional hand-crafted feature based methods. However, to achieve SOTA accuracy, CNN models tend to require a large amount of memory and computing resources, especially for those networks with 3D convolution. Hence we propose a compact and effective 2D convolution based network for disparity estimation, called MCSMNet, which has three main features:1) It introduces the basic units of ShuffleNet[1] and SENet[2] to make the training of network more effective without reducing network parameters; 2)It contains Multi-Correlation so that the context information could be fused in high-level features well; 3)It uses hierarchical refinement strategy to further improve the accuracy of networks. We conduct experiments to demonstrate the effectiveness of MCSMNet on the popular datasets, Scene Flow. Experimental results show that MCSMNet achieves competitive accuracy compared with existing 3D convolution based networks.
Electronic Voting Machine (EVM) Using Infrared Detection Technique on Arduino Platform

Abstract The voting system is a set of rules that determine how people’s intentions may be manifest and how decisions can be achieved. Normally, vote papers are placed in the voting box and then will be sorted and calculated manually. But the problem is the use of manpower in calculating the vote manually may lead to error in the counting of votes and it takes a long time to complete the calculation process. For this purpose, an electronic voting machine (EVM) was introduced, where in this work it replace the conventional voting method in which the vote card will be calculated automatically. This paper focusing on implementation of embedded system which will help in the election process, where the Arduino UNO uses as a controller. This system uses a simple counting system based on the detection of black mark on the vote card using infrared (IR) sensor. Then, LCD 16x2 will display the number of votes for the respective candidate and the servo motor will activate to sort the valid and invalid vote card into 2 compartments in the voting box.
Predicting Artist Drawing Activity via Multi-camera Inputs for Co-creative Drawing

This paper presents the results of computer vision experiments in the perception of an artist drawing with analog media (pen and paper), with the aim to contribute towards a human-robot co-creative drawing system. Using data gathered from user studies with artists and illustrators, two types of CNN models were designed and evaluated. Both models use multi-camera images of the drawing surface as input. One models predicts an artist’s activity (e.g. are they drawing or not?). The other model predicts the position of the pen on the canvas. Results of different combination of input sources are presented. The overall mean accuracy is 95% (std: 7%) for predicting when the artist is present and 68% (std: 15%) for predicting when the artist is drawing. The model predicts the pen’s position on the drawing canvas with a mean squared error (in normalised units) of 0.0034 (std: 0.0099). These results contribute towards the development of an autonomous robotic system which is aware of an artist at work via camera based input. In addition, this benefits the artist with a more fluid physical to digital workflow for creative content creation.
Predicting Solar Irradiance Using Regression Model (Case Study: ITERA Solar Power Plant)

Abstract Simple regression model can be used to predict the solar irradiance which cover a certain area in certain interval of time. With combining some variables like relative humidity, temperature and wind speed, a model which describe local solar irradiance can be developed. This paper elaborates the details of recently developed simple regression model, built with python based open-source cloud computing platform. This model dedicated to predict solar irradiance in certain relatively small area. For current study, it was in ITERA Solar Power Plant, Southern Lampung, Indonesia. Generally, this model can be applied in any places with vary conditions as long as all the required parameters are met, even though can only predict the local solar irradiance in a relative small spot. According to the computational issue, a simple linear regression model can be used to estimate the solar radiation in Lampung Selatan, Indonesia. The correlation between solar radiation and the average air temperature can be approached linearly. The coefficient value may be vary, but our computational progress resulting at 0.72768 for without outliers.
Disentangled and controllable sketch creation based on disentangling the structure and color enhancement

Existing sketch-based image processing methods include sketch recognition, sketch synthesis and sketch-based image retrieval. For sketch creation, a meaningful task is proposed namely disentangled and controllable sketch creation (DCSC) based on disentangling the structure and color enhancement. Specifically, as the first subtask, sketch structure enhancement (SSE) is used to enhance a non-professional sketch (NPS) and obtain a professional sketch (PS), which is a process denoted as NPS2PS. A data set named SketchMan is first provided, consisting of NPSs and PSs with various postures in different scenes. SSE is trained as a conditional image-to-image translation problem, and there are three models: direct sketch-to-sketch (SS), grayscale guided SS and contour guided SS. Multiple IOU metrics are proposed based on Corner Point Map (CPM), Straight Line Map (SLM) and Segmented Area Map (SAM). As the second subtask, sketch color enhancement (SCE) is trained as a two-stage framework containing a topology enhancement network (TE-Net) that maps a sketch to the corresponding grayscale domain and a color injection network (CI-Net) that injects the global color feature to the AdaIN residual blocks to perform adaptive sketch colorization. The TE-Net and CI-Net disentangle the topological and color features to perform more controllable and diverse SCE results. Experimental results demonstrate that our proposed methods are effective to address the challenging and meaningful DCSC task compared with other state-of-the-art methods.
Development of Digital Interactive Module “E-MOSI” (Elektronik Modul Puisi) for Grade IV Students of Elementary School of Kemala Bhayangkari 02

This study aims to develop electronic module teaching materials for fourth-grade poetry and determine their feasibility and practicality. This research is motivated by the difficulty of understanding Indonesian subjects, especially poetry materials, and the lack of alternative teaching materials. The novelty of this teaching material is in the form of modules that can be accessed utilizing gadgets and laptops, as well as the completeness of materials supported by audio and video learning. This type of research is development research, using the research and development method, consisting of ten development steps (potentials and problems, collecting data, product design, design validation, design revision, product trial, product revision, usage trial, product revision, and mass production. Based on these ten steps, the development of the digital interactive module "E-MOSI" (Elektronik Modul Puisi) or Electronic Poetry Module resulted in feasibility by the three validators, namely 85.7% with very feasible criteria. The percentage of practicality for small group trials was 86% of teacher responses and 94% of student responses, while large group trials resulted in teacher responses of 92% and student responses of 87.8%. The test results were also calculated using the t-test, with ha results being accepted, i.e., there were differences before and after using "E-MOSI". This study concludes that the development of the digital interactive module "E-MOSI" for fourth-grade students of SD Kemala Bhayangkari 02 met the very feasibility criteria and high practicality category.
Knowledge-Based Stable Roommates Problem: A Real-World Application

Abstract The Stable Roommates problem with Ties and Incomplete lists (SRTI) is a matching problem characterized by the preferences of agents over other agents as roommates, where the preferences may have ties or be incomplete. SRTI asks for a matching that is stable and, sometimes, optimizes a domain-independent fairness criterion (e.g. Egalitarian). However, in real-world applications (e.g. assigning students as roommates at a dormitory), we usually consider a variety of domain-specific criteria depending on preferences over the habits and desires of the agents. With this motivation, we introduce a knowledge-based method to SRTI considering domain-specific knowledge and investigate its real-world application for assigning students as roommates at a university dormitory.
Information Security Using Image Steganography Based on Generative Adversarial Networks

Nowadays in the field of covert communication image steganographic algorithms had become one of the most common methodology and these algorithms are used to hide the secret information such as audio, video and image files in rich textured regions to decrease the chance of being identified. In Recent times i.e., from the year 2014 the generative adversarial networks i.e., GAN's had become the most well-known architectures in the area of image steganography. However, this Generative Adversarial based image steganographic algorithms will directly hide the secret information on the entire images taken, that means here the secret information is hidden on both foreground and background part of an image, so by doing this it will not consider the regional texture complexity into account, and also this may lead to compromise with the antidetection capability also. So, to solve this problem, this research work proposes a new image steganographic technique on the generated foreground region of an image with more textures. More specifically, it can be said that by using this generative adversarial method, the foreground region of an image is generated on the given cover image at the exact position and also the secret information is embedded into the foreground region of an image at the same time during the generation of a region. In this paper, different methods of generative adversarial networks are studied and discussed.
Self-Effectiveness of Students in the Conditions of Digitalization of Education

The article deals with the problem of the formation of self-efficacy of students of professions associated with the risk of decision-making. Self-efficacy implies the ability of a person to cope with specific and complex situations, including emergencies, and to influence the effectiveness of activities and functioning [1]. Therefore, it was decided to take an increase in the level of professional self-efficacy as the basis for solving the problems of the negative impact of the "human factor" on professional activity in conditions of a high level of risk. High self-efficacy it allows not only to take up decisions and cope with complex professional tasks, but also with various stressful situations [16], as well as to avoid risks to their own health, which is of particular importance for employees of professions associated with the risk of.
Poster: Learning Index on Content-based Pub/Sub

Content-based Pub/Sub paradigm has been widely used in many distributed applications and existing approaches suffer from high redundancy subscription index structure and low matching efficiency. To tackle this issue, in this paper, we propose a learning framework to guide the construction of an efficient in-memory subscription index, namely PMIndex, via a multi-task learning framework. The key of PMIndex is to merge redundant subscriptions into an optimal number of partitions for less memory cost and faster matching time. Our initial experimental result on a synthetic dataset demonstrates that PMindex outperforms two state-of-the-arts by faster matching time and less memory cost.
Introduction to the APRIN Case Study Collection for Engineers

The author reviews the case study collection for engineers newly published from APRIN (Association for the Promotion of Research Integrity), Japan. This educational material consists of nine chapters and is intended mainly for university students. The aim and the features of the included case studies are described.
Continuous integration and TeX with Org-Mode

The Island of T E X is a collaborative effort to provide a home to community-based T E X projects.This article discusses the Island's long-term goals and how the worldwide community can come aboard and help the organization enhance the T E X experience for everybody, from newbies to power users. The beginningOnce upon a time, a group of friends decided to create an organization as a means to systematize and concentrate our efforts in developing the T E X ecosystem.Thus, the Island of T E X, a collaborative endeavour to provide a home to communitybased open source T E X-related projects, was finally charted in the T E X world map.Island residents include Paulo Cereda, Ben Frank, Brent Longborough, Marco Daniel, Nicola Talbot, and Enrico Gregorio.So far, the Island holds four main groups of projects: Docker images, programming libraries, assorted tools, and T E X editors.We plan more groups for the near future.
A Study on the Digital Forensic Method for Decentralized Web Service

In recent years, various decentralized web services have emerged. Note, however, that websites that can be connected using decentralized web services are difficult to access through the use of general search engines. In this paper, ZeroNet, one of the decentralized web services, is analyzed to verify the information generated when used. The analyzed information is used in the digital forensic method to show an example of how to apply the method by presenting a scenario.
Future Innovation in Healthcare by Spatial Computing using ProjectDR

Spatial Computation is the next step in the continuing convergence between the digital and physical realms. It is a set of inventions and developments that can better our lives through learning the real world, acknowledging and connecting our connection to, and traveling through various locations in the world. The lack of modern, precise, and effective diagnosis limits the rehabilitation of patients, despite technical advancements in medicines. The capabilities of spatial computing are expanded in a healthcare framework during the care and treatment of the patient. In this article, our purpose is to clarify the function of ProjectDR in the field of healthcare, which enables the display of medical images, such as CT scans and MRI results, directly on the patient's body in a manner that moves as patients do.
SUBiNN: a stacked uni- and bivariate kNN sparse ensemble

Abstract Nearest Neighbor classification is an intuitive distance-based classification method. It has, however, two drawbacks: (1) it is sensitive to the number of features, and (2) it does not give information about the importance of single features or pairs of features. In stacking, a set of base-learners is combined in one overall ensemble classifier by means of a meta-learner. In this manuscript we combine univariate and bivariate nearest neighbor classifiers that are by itself easily interpretable. Furthermore, we combine these classifiers by a Lasso method that results in a sparse ensemble of nonlinear main and pairwise interaction effects. We christened the new method SUBiNN: Stacked Uni- and Bivariate Nearest Neighbors. SUBiNN overcomes the two drawbacks of simple nearest neighbor methods. In extensive simulations and using benchmark data sets, we evaluate the predictive performance of SUBiNN and compare it to other nearest neighbor ensemble methods as well as Random Forests and Support Vector Machines. Results indicate that SUBiNN often outperforms other nearest neighbor methods, that SUBiNN is well capable of identifying noise features, but that Random Forests is often, but not always, the best classifier.
한국인 학습자를 위한 HSK 동사 등급 탐구

The purpose of this study is to statistically verify that the HSK verb vocabulary grade is not suitable for Korean learners. For example, when targeting Korean learners, even if the verb vocabulary is at the same grade, there may be a difference in average scores between the vocabularies. On the contrary, although the series of the HSK verb vocabulary are different, we try to verify that the scores may be similar among the vocabulary types.First, there was a statistically significant difference in scores for grade 5 ‘Chinese characters’ and grade 5 ‘nonequivalent word’, grade 5 ‘Equivalent’ and grade 5 ‘nonequivalent word’. There was a statistically significant difference between the grade 6 ‘Chinese characters’ and grade 6 ‘nonequivalent word’. It can be seen that the HSK verb vocabulary grade is not suitable for Korean learners.Second, HSK grade 4 ‘Chinese characters’ and grade 6 ‘Chinese characters’, grade 5 ‘Chinese characters’ and grade 6 ‘Chinese characters’ had similar scores. There was a vocabulary with a similar score difference in the ‘Equivalent’ and ‘nonequivalent word’. The verb vocabulary is grade 4, grade 5, and grade 6, but the difference in grades is not significant.In this study, it was verified whether the difficulty of grade 5 vocabularies could be made similar by adjusting the series of some vocabulary types related to grade 5. As a result, the scores of the grade 5 ‘Chinese characters’, the grade 5 ‘Equivalent’, and the grade 5 ‘nonequivalent word’ were similar for the grade 5 subjects.