Reviewerly/exHarmony huggingface dataset:
Detection of Interviewer Falsification in Statistics Indonesia’s Mobile Survey

Interviewer falsification is an important issue faced by institutions conducting censuses and surveys around the world, including Statistics Indonesia.This study discusses several methods to systematically detect interviewer falsification and validation using data mining techniques so that human supervisors can take further actions.After analyzing relevant features and conducting experiments, the results showed that unsupervised classification algorithm using simple 2-means clustering achieved 70.5% accuracy, while the supervised classification using logistic regression improved the accuracy to 88.5%.A greater level of accuracy is still needed to be pursued in further research, but the current results are certainly better than the traditional method which has almost no falsification detection method at all.
Nonlinear estimation of quadcopter states using unscented Kalman filter

In recent years, using Unmanned Arial Vehicles (UAV) like quadcopter in civilian and military fields are increased dramatically. Performance and robustness are the most important specifications required for most applications. Different sensors are usually used for a quadcopter to provide the necessary measured states (attitude and position) for control. The white noise generated by physical sensors is one of the important issues that affect the quality of states measurements. The available solutions are still have limited performance for a wide range of nonlinearity. In this paper, Unscented Kalman Filter (UKF) is proposed as a robust estimator that has the ability to work efficiently with high nonlinear systems. Modified PID (PI-D) controller which has better properties than traditional PID controller is used with proposed filter in order to get better performance of quadcopter. The obtained results are compared with that of Extended Kalman Filter (EKF) and proved to be more reliable. Moreover, the results show that the proposed filter largely decreases the error generated by noise and improves the performance of quadcopter better than the EKF.
Enhanced Positioning Algorithm Using a Single Image in an LCD-Camera System by Mesh Elements’ Recalculation and Angle Error Orientation

In this article, we present a method to position the tool in a micromachine system based on a camera-LCD screen positioning system that also provides information about angular deviations of the tool axis during its running. Both position and angular deviations are obtained by reducing a matrix of LEDs in the image to a single rectangle in the conical perspective that is treated by a photogrammetry method. This method computes the coordinates and orientation of the camera with respect to the fixed screen coordinate system. The used image consists of 5 × 5 lit LEDs, which are analyzed by the algorithm to determine a rectangle with known dimensions. The coordinates of the vertices of the rectangle in space are obtained by an inverse perspective computation from the image. The method presents a good approximation of the central point of the rectangle and provides the inclination of the workpiece with respect to the LCD screen reference system of coordinates. A test of the method is designed with the assistance of a Coordinate Measurement Machine (CMM) to check the accuracy of the positioning method. The performed test delivers a good accuracy in the position measurement of the designed method. A high dispersion in the angular deviation is detected, although the orientation of the inclination is appropriate in almost every case. This is due to the small values of the angles that makes the trigonometric function approximations very erratic. This method is a good starting point for the compensation of angular deviation in vision based micromachine tools, which is the principal source of errors in these operations and represents the main volume in the cost of machine elements’ parts.
KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.
An easy line-structured light system calibration method based on homography matrix

An easy line-structured light system calibration method is proposed, which is based on the constructed light plane and homography matrix. In this method, the sequential images of the light plane and calibration target are obtained at different positions by shifting a translating target plane within the depth of camera's field, then a series of feature points would be extracted from these images to form a light plane. Then, a homography matrix, which is the mapping relationship between the light plane and the image plane of camera, can be calculated. In the experiment, the 3D data can be obtained by using this homography matrix when image coordinates of the light plane are extracted in an arbitrary image. Then the entire object can be measured by using a translation device. For the real data of calibration, the maximum residual error is less than 0.05 mm, standard deviation is less than 0.02 mm, the relative error of the measured distance between the two planes is less than 1.3%. The proposed method can make the entire calibration process easy and flexible to use.
Dynamic Control of Airflow Balance in Data Centers

Abstract Efficient cooling of data center infrastructure is an important way to reduce total energy consumption. Containment, with separation of hot and cold airflows has allowed significant increase in efficiencies. However, balancing the airflow, so that IT equipment in an aisle only receives the cooling airflow that that aisle needs is still often not done. We propose a new architecture where IT racks are clustered together with shared hot aisles ducted to a common ceiling plenum. Each aisle has an actively controlled damper used to balance the airflow to the cooling infrastructure. Using a differential air pressure sensor in each aisle and an algorithm designed to balance the flow network, we minimize the cooling airflow and maximize cooling efficiency.
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations

Recent advances in deep learning have shown promising results in many low-level vision tasks. However, solving the single-image-based view synthesis is still an open problem. In particular, the generation of new images at parallel camera views given a single input image is of great interest, as it enables 3D visualization of the 2D input scenery. We propose a novel network architecture to perform stereoscopic view synthesis at arbitrary camera positions along the X-axis, or Deep 3D Pan, with adaptive kernels equipped with globally and locally adaptive dilations. Our proposed network architecture, the monster-net, is devised with a novel t-shaped adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given. Extensive experiments were performed on the KITTI, CityScapes and our VXXLXX_STEREO indoors dataset to prove the efficacy of our method. Our monster-net significantly outperforms the state-of-the-art method, SOTA, by a large margin in all metrics of RMSE, PSNR, and SSIM. Our proposed monster-net is capable of reconstructing more reliable image structures in synthesized images with coherent geometry. Moreover, the disparity information that can be extracted from the kernel is much more reliable than that of the SOTA for the unsupervised monocular depth estimation task, confirming the effectiveness of our method.
A Stochastic Derivative Free Optimization Method with Momentum

We consider the problem of unconstrained minimization of a smooth objective function in $\mathbb{R}^d$ in setting where only function evaluations are possible. We propose and analyze stochastic zeroth-order method with heavy ball momentum. In particular, we propose, SMTP, a momentum version of the stochastic three-point method (STP) Bergou et al. (2019). We show new complexity results for non-convex, convex and strongly convex functions. We test our method on a collection of learning to continuous control tasks on several MuJoCo Todorov et al. (2012) environments with varying difficulty and compare against STP, other state-of-the-art derivative-free optimization algorithms and against policy gradient methods. SMTP significantly outperforms STP and all other methods that we considered in our numerical experiments. Our second contribution is SMTP with importance sampling which we call SMTP_IS. We provide convergence analysis of this method for non-convex, convex and strongly convex objectives.
Application of Classification Algorithm C 4.5 for Predicting Asset Maintenance

In asset management, determining maintenance actions is one of the problems faced by the company. The importance of maintenance to accelerate the production or performance of a company is now a necessity that must be run. The problem faced by Astra Daihatsu Motor is the difficulty in determining the maintenance action that must be chosen because of information delays when there are assets that are damaged, failed or failure. With the proposal using a decision tree with C4.5 algorithm can predict failures and damage that occur so that it can determine more accurate maintenance actions. Decision tree is a prediction model using tree structure or hierarchical structure. The concept of a decision tree is to transform data into decision trees and decision rules. The main benefit of using a decision tree is its ability to break down complex decision-making processes to be simpler so that decision makers will better interpret the solution of the problem. Using the decision tree method with the C4.5 algorithm can help the problems faced by Astra Daihatsu Motor in determining maintenance. This is shown from the test results of 98.20%. And it can be concluded that the application of C4.5 algorithm is able to produce asset maintenance patterns with better accuracy.
TEXT CLASSIFICATION BASED ON FUZZY RADIAL BASIS FUNCTION

Automated classification of text into predefined categories has always been considered as a vital method in thenatural language processing field. In this paper new methods based on Radial Basis Function (RBF) and Fuzzy Radial BasisFunction (FRBF) are used to solve the problem of text classification, where a set of features extracted for each sentencein the document collection these set of features introduced to FRBF and RBF to classify documents. Reuters 21578 datasetutilized for the purpose of text classification. The results showed the effectiveness of FRBF is better than RBF.
Child labour and online protection in a world of influencers

Nowadays, children grow up in a commercialized environment, where they are confronted with advertising and marketing on a daily basis. From a very young age, they already display a level of brand consciousness. Aside from being an attractive target group for advertisers – as they can purchase products or services themselves and have an important influence on their parents’ purchasing behaviour, brands are now increasingly relying on child influencers to promote goods and services for them. The child-influencer phenomenon raises important questions from a children’s rights perspective and necessitates a detailed study of the benefits and potential harms to the child. This chapter takes a novel approach by not only critiquing the advertising and marketing rules for gaps, shortcomings and failures in light of children’s rights, but also in analysing the role of the child in the constellation of influencer regulation through the lens of labour law and protection of vulnerable consumers.
Content-Based Image Retrieval: The State of the Art

Content-Based Image Retrieval (CBIR) is the solution to the image retrieval problem based on the contents of the query image. The objective of the CBIR system is to retrieve the visually similar images from the database efficiently and effectively but still, no satisfactory performance has been achieved. The performance of the CBIR system mainly depends on the feature extraction, feature selection, distance measures (similarity computation), Classification, and ranking of matched images. Feature extraction is the procedure of deriving the set of features from images for matching the visual similarity and they can be further classified based on color, texture, and shape descriptors. Performance is not up to mark when Color, Texture or Shape descriptors individually applied. Better determination of blend of Color, Texture, and/or Shape features can enhance performance in the context of precision and recall. This paper mainly concentrates on the brief review of the different state of art techniques used for CBIR along with prerequisite knowledge over this domain.
Critical Influential Factors For Software Testing-as-a-Service Adoption: Preliminary Findings From Systematic Literature Review

Software applications are currently becoming complex, dynamic, distributed, and component-based therefore, giving birth to a number of emerging challenges for the testing department. To cope with these emerging challenges employing Cloud Computing technology would be the best choice. However, moving software testing to the CC environment is not free of cost, nor it is the best possible solution for all testing problems. To guide decision maker on the adoption of CC for software testing, this study makes a significant input to the existing literature on the CC adoption in the context of software testing and can be considered the base for future research in the stated field. Specifically, this study discusses the necessary and sufficient condition for software testing in the Cloud. To guide software development organization for cloud-based testing adoption, this study identifies a list of factors using systematic literature review (SLR).
An Empirical Comparison of Machine Learning Algorithms for Predicting Breast Cancer

According to recent statistics, breast cancer is one of the most prevalent cancers among women in the world. It represents the majority of new cancer cases and cancer-related deaths. Early diagnosis is very important, as it becomes fatal unless detected and treated in early stages. With the latest advances in artificial intelligence and machine learning (ML), there is a great potential to diagnose breast cancer by using structured data. In this paper, we conduct an empirical comparison of 10 popular machine learning models for the prediction of breast cancer. We used well known Wisconsin Breast Cancer Dataset (WBCD) to train the models and employed advanced accuracy metrics for comparison. Experimental results show that all models demonstrate superior accuracy, while Support Vector Machines (SVM) had slightly better performance than other methods. Logistic Regression, K-Nearest Neighbors and Neural Networks also proved to be strong classifiers for predicting breast cancer.
Adversarial Fence Patrolling: Non-Uniform Policies for Asymmetric Environments

Robot teams are very useful in patrol tasks, where the robots are required to repeatedly visit a target area in order to detect an adversary. In this work we examine the Fence Patrol problem, in which the robots must travel back and forth along an open polyline and the adversary is aware of the robots' patrol strategy. Previous work has suggested non-deterministic patrol schemes, characterized by a uniform policy along the entire area, guaranteeing that the minimal probability of penetration detection throughout the area is maximized. We present a patrol strategy with a non-uniform policy along different points of the fence, based on the location and other properties of the point. We explore this strategy in different kinds of tracks and show that the minimal probability of penetration detection achieved by this non-uniform (variant) policy is higher than former policies. We further consider applying this model in multi-robot scenarios, exploiting robot cooperation to enhance patrol efficiency. We propose novel methods for calculating the variant values, and demonstrate their performance empirically.
Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

The Mixed-Membership Stochastic Blockmodel (MMSB) is proposed as one of the state-of-the-art Bayesian relational methods suitable for learning the complex hidden structure underlying the network data. However, the current formulation of MMSB suffers from the following two issues: (1), the prior information (e.g. entities' community structural information) can not be well embedded in the modelling; (2), community evolution can not be well described in the literature. Therefore, we propose a non-parametric fragmentation coagulation based Mixed Membership Stochastic Blockmodel (fcMMSB). Our model performs entity-based clustering to capture the community information for entities and linkage-based clustering to derive the group information for links simultaneously. Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP). By integrating the community structure with the group compatibility matrix we derive a generalized version of MMSB. An efficient Gibbs sampling scheme with Polya Gamma (PG) approach is implemented for posterior inference. We validate our model on synthetic and real world data.
Zero-day Vulnerability Inspired Hazard Assessment for Autonomous Driving Vehicles

Safety of the intended functionality (SOTIF) is expected to be the third type of road vehicle auto-driving safety in addition to information security and functional safety. SOTIF which is unknown and uncertain is caused by non-system failure or personnel misoperation. Advanced driver assistance systems (ADAS) that SOTIF cannot be accepted will be dangerous. The unknown triggering events triggered suddenly or exploited by a malicious hacker will pose a threat to vehicle safety. In the field of traditional network security, there have been many pieces of research on vulnerability assessment. This paper establishes hazard graph for SOTIF and triggering event models and proposes a safety assessment method for unknown triggering events of SOTIF and a method to enhances SOTIF inspired by the zero-day vulnerability assessment in the traditional cybersecurity field. The AEB system is used as an example to apply the proposed algorithm, and Petri net accessibility testing is used as a verification of the correctness of the algorithm in this paper.
THE INFLUENCE OF VISUALIZATION STRATEGY ON READING COMPREHENSION ABILITY

The objective of thisresearch was to know the effect of using Visualization Strategy in teaching recount text on reading comprehensionability at the second grade students of SMPN 4 Kerinci. Then, the type of the researchwas quasi experimental research. Besides, the population of the research was the second grade students of SMPN 4 Kerinci, it was 75 students. Moreover, the sample of this research consisted of 50 students from two classes. There were experiment class and control class. The analysis of data used t-test. The result of the research showedthat there was significance effect of using Visualization strategy toward students’ reading comprehension of recount text. It could be seen of hypothesis was gotten the mean of control class was 60 and the standard deviation was 102 while the experimental class was 67,8 and standard deviation was 118,16. And after doing t-test, it was found that tobserve = 2,6&gt; ttable = 1,68. It can be summarized that there was the significant effect of using visualization strategy in the students’ reading comprehension achievement of recount text.
Teams-Games-Tournament (TGT) to Teach Vocabulary of English for Specific Purpose in Increasing Students’ Self-Esteem

The objective of this study was to investigate the implementation of playing game on students’ mastery on vocabulary of ESP viewed from self-esteem in producing words. The investigation were: (1) to know whether Teams-Games-Tournament (TGT) was more effective than Grammar Translation Method (GTM) to teach vocabulary of ESP; (2) to know whether the students whose self-esteem was high had better vocabulary of ESP mastery than those who had low self-esteem; and (3) to prove whether there was an interaction between teaching methods and self-esteem to teach vocabulary of ESP.The research design used for the research was a simple factorial design 2 x 2. Simple Random sampling was used to select 48 students of two classes. Meanwhile, in determining the sample into the experimental and control class, the researcher chose the class randomly. The data were analyzed by using multifactor analysis of ANOVA 2x2 and Tuckey test.The findings of this research revealed: (1) TGT was more effective than GTM in teaching vocabulary of ESP. (2) The students who were taught by TGT had higher vocabulary mastery than those who were taught by GTM. (3) The students having high self-esteem had better ESP vocabulary than those of students with low self-esteem. It is concluded that: 1) Teams-Games-Tournament is more effective than GTM in teaching vocabulary of ESP; 2) The students having high self-esteem have better vocabulary of ESP mastery than those having low self-esteem; 3) There was interaction between teaching methods and students’ self-esteem
A study of attention deficit hyper disorder (ADHD) problem of dyslexic children

Attention deficit hyper disorder (ADHD) and dyslexia both hamper the learning ability of children in the classroom. An estimated 15.99% of dyslexic children are found in every classroom, and 5.60% of the children have ADHD. The study was undertaken to investigate the correlation between dyslexia and ADHD in school-going children. A survey method was used, and 963 students were selected through random sampling technique. The screening and diagnostic test of Dyslexia (SDTD-J) test by Dr. Khan Zeenat and S. B. Dandegaonkar was used for identifying the percentage of dyslexic children in the classroom, and James E. Gilliam test was used for identifying the percentage of ADHD children in the classroom. The findings also showed that 35.06% of dyslexic children also have ADHD problems.&#x0D; Keywords: Dyslexia, attention deficit hyper disorder.