Reviewerly/exHarmony huggingface dataset:
Perancangan Augmented Reality (AR) Sebagai Media Promosi Objek Wisata Berbasis Android

Tourism is one of the most important things for an area. Interesting and crowded tourism shows the progress of the area. One way to promote tourism is by media promotion. There are many ways that can be used for promotion, one of which is by utilizing information technology based on Augmented Reality (AR). AR is a technology that displays 3D virtual objects into a real environment and builds visualization of 3D images in tourist brochures to be more informative. AR helps users get clearer information about the attractions they want to visit in Langsa city. The use of AR on Android makes the system more accessible to users. Based on trials, the AR system was successfully implemented on the Android system. In addition, camera trials were also carried out based on distance, tilt angle, and lighting when detecting tourist attraction markers. The result is that the camera successfully detects the target image (marker) and generates a 3D animation of a tourist attraction. However, the best results in detecting markers are distances above 15cm at an angle with cloudy sunlight lighting criteria.
Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

The extraction of high-quality keywords and summarising documents at a high level has become more difficult in current research due to technological advancements and the exponential expansion of textual data and digital sources.Extracting high-quality keywords and summarising the documents at a highlevel need to use features for the keyphrase extraction, becoming more popular.A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques.In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification.The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step.The pre-processed data is subsequently used during the data processing step.The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step.The proposed system is then tested and evaluated using benchmark datasets collected from various sources.To demonstrate our proposed approach's effectiveness, merits, and significance, we compared it with other proposed techniques.The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains.
Luminance Level of Histogram-Based Scene-Change Detection for Frame Rate Up-Conversion

Scene change detection is an essential process of frame rate up-conversion (FRUC). The performance of FRUC highly dependents on the accuracy of scene change detection. This paper proposes a new scene-change detection method that uses analysis of luminance level of the histograms for FRUC. The histogram luminance level refers to the statistical average luminance value obtained from the generated histograms for each region. Existing histogram-based scene change methods calculate the difference between optimal threshold values using an automatic thresholding technique or extract the difference between the histogram shape to detect the scene change. The automatic thresholding method uses iterative operations—the difference between the histogram shape is simply a method of calculating the luminance difference for the current and previous frames. Thus, it requires many computational resources and incorrectly detects a scene change because calculating the histogram shape cannot reflect regional image characteristics. The proposed method addresses these problems using histogram luminance levels for each region in the given frames. It calculates the level differences between the previous and current frames to detect the initial scene change regions. Moreover, the proposed method refines the initial scene change regions by analyzing the distribution of surrounding detected regions and uses refinement to enhance scene-change detection accuracy. In the experimental results, the proposed method increased the average F1 score to 0.4816 (a 122.51% improvement) compared with the benchmark methods. The average computation time per pixel of the proposed method also decreased to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.5323 \,\,\mu \text{s}$ </tex-math></inline-formula> (a 87.06% reduction) compared with the benchmark methods.
A Image Copyright Protection Method Using Zero-Watermark by Blockchain and IPFS

Behind the popularity of multimedia technology, the dispute over image copyright is getting worse. In the digital watermark prevention technology for copyright infringement, watermark technology is considered to be an important technology to overcome data protection problems and verify the relationship between data ownership. Among the many digital watermarking technologies, zero watermarking technology has been favored in recent years. However, the existing zero watermark technology in the implementation process often needs a trusted third party to store watermarks, which may make the data too central, data storage security is low and copyright registration costs are too high, which creates a rare problem. The decentivization and information cannot be tampered of blockchain technology’s nature find new methods for image copyright protection. This paper studies the role of zero watermark algorithm in the image copyright and its complete storage and certification scheme, proposes a zero watermark image protection framework based on blockchain, and builds a system according to the framework. Combined with blockchain and zero watermarking technology, the framework uses inter IPFS (Inter Planetary File System) to solve the problem of blockchain efficient storage and sharing of large files. In addition, the application of user copyright information, image image query and image trading in the system are realized based on smart contracts, which solves the problem of lack of trusted third parties. Experiments show that the scheme is feasible and robust to various attacks.
A method of sequentially generating a set of components of a multidimensional random variable using a nonparametric pattern recognition algorithm

We study in which way a priori information on the independence of random variables affects the approximation accuracy of a nonparametric estimate of the Rosenblatt–Parzen probability density. A new technique for generating sets of independent components of a multidimensional random variable is proposed. The methodology is based on testing the hypotheses of the independence of combinations of the multidimensional random variable components using a two-alternative nonparametric kernel algorithm for pattern recognition corresponding to the maximum likelihood criterion. Classes correspond to the domains of definition of the probability densities of sets of independent and dependent components of the multidimensional random variable. Nonparametric statistics of the kernel type are used to estimate the probability densities. The choice of the bandwidths of the kernel estimates of the probability densities is made from the condition of the minimum root-mean-square criterion. The sequential procedure for generating a set of independent components begins with the analysis of paired combinations of components of a multidimensional random variable. For each pair of components, the probability of an error in recognizing classes corresponding to the assumptions of independence and dependence of the considered components is estimated. A pair of components with the maximum difference between these errors is determined. If the errors obtained do not differ significantly, then there are no independent components in the considered multivariate random variable. If there is a significant difference in the probability estimates of class recognition errors, a pair of independent components is established. These components are included in a three-component set of a multidimensional random variable. The analysis of their combinations is carried out in the same way, following the above-described procedure. The process of generating the set of independent components is stopped when no reliable difference occurs any more between the probabilities of errors in recognizing situations belonging to the accepted classes. In this case, the previous set of independent components is the desired result. In contrast to the traditional methodology based on the Pearson criterion, the proposed approach allows us to bypass a problem of the decomposition of the range of values of random variables into multidimensional intervals. The method of generating a set of independent components of a multidimensional random variable is illustrated by the results of the analysis of spectral features of remote sensing data of forest tracts using space imagery from the Landsat-8 satellite.
DecoupledPoseNet: Cascade Decoupled Pose Learning for Unsupervised Camera Ego-Motion Estimation

Although many impressive works on learning-based camera ego-motion estimation methods have been proposed recently, most of them promote the accuracy of camera pose estimation by various sequential learning with loop closure optimization, while neglecting the improvement of PoseNet itself. In this paper, we focus on the coupling of rotation and translation in ego-motion estimation, and design a cascade decoupling structure to separately learn the rotation and translation of camera relative motion between adjacent frames. Meanwhile, a rigid-aware unsupervised learning framework with iterative pose refinement scheme is proposed for camera ego-motion estimation. It can disambiguate rigid motion and deformations in dynamic scenarios by jointly learning of optical flow, stereo disparity and camera pose. Validated with evaluation experiments on the public available datasets, our method is superior to the state-of-the-art unsupervised methods, and can achieve comparable results with the supervised ones.
Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.
Augmenting Immersive Telepresence Experience with a Virtual Body

We propose augmenting immersive telepresence by adding a virtual body, representing the user's own arm motions, as realized through a head-mounted display and a 360-degree camera. Previous research has shown the effectiveness of having a virtual body in simulated environments; however, research on whether seeing one's own virtual arms increases presence or preference for the user in an immersive telepresence setup is limited. We conducted a study where a host introduced a research lab while participants wore a head-mounted display which allowed them to be telepresent at the host's physical location via a 360-degree camera, either with or without a virtual body. We first conducted a pilot study of 20 participants, followed by a pre-registered 62 participant confirmatory study. Whereas the pilot study showed greater presence and preference when the virtual body was present, the confirmatory study failed to replicate these results, with only behavioral measures suggesting an increase in presence. After analyzing the qualitative data and modeling interactions, we suspect that the quality and style of the virtual arms, and the contrast between animation and video, led to individual differences in reactions to the virtual body which subsequently moderated feelings of presence.
Entanglement of Local Hidden States

Steering criteria are conditions whose violation excludes the possibility of describing the observed measurement statistics with local hidden state (LHS) models. When the available data do not allow to exclude arbitrary LHS models, it may still be possible to exclude LHS models with a specific separability structure. Here, we derive experimentally feasible criteria that put quantitative bounds on the multipartite entanglement of LHS. Our results reveal that separable states may contain hidden entanglement that can be unlocked by measurements on another system, even if no steering between the two systems is possible.
Verification Strategies for Feature-Oriented Software Product Lines

Highly-customizable software systems in form of software product lines are becoming increasingly relevant for safety-critical systems, in which the correctness of software is a major concern. To ensure the correct behavior of a software product line, each product can be verified in isolation—however, this strategy quickly becomes infeasible for a large number of products.
Modular domain-to-domain translation network

Domain-to-domain translation methods map images from a source domain to corresponding images from a target domain. The two domains contain images from the same classes, but these images look different. Recent approaches use generative adversarial networks in various configurations and architectures to perform the translation. By using GANs, they inevitably inherit their problems like training instability and mode collapse. We propose a novel approach to the problem that does not use a GAN. Instead, it relies on an hierarchical architecture that encapsulates information of the target domain by using individually trained networks. This hierarchical architecture is then trained as one unified deep network. Using this approach, we show that images from the original domain are translated to the target domain both for the case when there is a one-to-one correspondence between the images of the two domains and for the case that such correspondence information is absent. We visualize and evaluate the translation from one information domain to the other and discuss the proposed model’s relation to the conditional generative adversarial networks. We further argue that deep learning can benefit from the proposed hierarchical architecture.
Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification

Class imbalance is a phenomenon of asymmetry that degrades the performance of traditional classification algorithms such as the Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Various modifications of SVM and ELM have been proposed to handle the class imbalance problem, which focus on different aspects to resolve the class imbalance. The Universum Support Vector Machine (USVM) incorporates the prior information in the classification model by adding Universum data to the training data to handle the class imbalance problem. Various other modifications of SVM have been proposed which use Universum data in the classification model generation. Moreover, the existing ELM-based classification models intended to handle class imbalance do not consider the prior information about the data distribution for training. An ELM-based classification model creates two symmetry planes, one for each class. The Universum-based ELM classification model tries to create a third plane between the two symmetric planes using Universum data. This paper proposes a novel hybrid framework called Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to handle the classification of binary class-imbalanced problems. The proposed RKWELM-UFS combines the Universum learning method with a Reduced-Kernelized Weighted Extreme Learning Machine (RKWELM) for the first time to inherit the advantages of both techniques. To generate efficient Universum samples in the feature space, this work uses the kernel trick. The performance of the proposed method is evaluated using 44 benchmark binary class-imbalanced datasets. The proposed method is compared with 10 state-of-the-art classifiers using AUC and G-mean. The statistical t-test and Wilcoxon signed-rank test are used to quantify the performance enhancement of the proposed RKWELM-UFS compared to other evaluated classifiers.
A Novel Technique to Detect and Track Multiple Objects in Dynamic Video Surveillance Systems

Video surveillance is one of the important state of the art systems to be utilized in order to monitor different areas of modern society surveillance like the general public surveillance system, city traffic monitoring system, and forest monitoring system.Hence, surveillance systems have become especially relevant in the digital era.The needs of the video surveillance systems and its video analytics have become inevitable due to an increase in crimes and unethical behavior.Thus enabling the tracking of individuals object in video surveillance is an essential part of modern society.With the advent of video surveillance, performance measures for such surveillance also need to be improved to keep up with the ever increasing crime rates.So far, many methodologies relating to video surveillance have been introduced ranging from single object detection with a single or multiple cameras to multiple object detection using single or multiple cameras.Despite this, performance benchmarks and metrics need further improvements.While mechanisms exist for single or multiple object detection and prediction on videos or images, none can meet the criteria of detection and tracking of multiple objects in static as well as dynamic environments.Thus, real-world multiple object detection and prediction systems need to be introduced that are both accurate as well as fast and can also be adopted in static and dynamic environments.This paper introduces the Densely Feature selection Convolutional neural Network -Hyper Parameter tuning (DFCN-HP) and it is a hybrid protocol with faster prediction time and high accuracy levels.The proposed system has successfully tracked multiple objects from multiple channels and is a combination of dense block, feature selection, background subtraction and Bayesian methods.The results of the experiment conducted demonstrated an accuracy of 98% and 1.11 prediction time and these results have also been compared with existing methods such as Kalman Filtering (KF) and Deep Neural Network (DNN).
Perceived Elementary Grid (PEG) proposal for dyadic coping evaluation in the infertile couple during assisted reproductive treatments

IMR Press is a leading publisher of open access peer-reviewed biomedical and life sciences journals. We aim to facilitate the dissemination of high-quality research in the area of biomedical science. With a long tradition and wide readership, IMR Press is dedicated to making positive contributions to academics, corporate libraries as well as to readers and authors. All the editors in IMR Press will provide the best service for researchers, allowing them to have a easy and smooth publication experience and helping maximize the impact and visibility of their research.
Floquet vortex states induced by light carrying an orbital angular momentum

We propose a scheme to create an electronic Floquet vortex state by irradiating a two-dimensional semiconductor with a laser light carrying nonzero orbital angular momentum. We analytically and numerically study the properties of the Floquet vortex states, with methods analogous to the ones previously applied to the analysis of superconducting vortex states. We show that such Floquet vortex states are similar to superconducting vortex states, and they exhibit a wide range of tunability. To illustrate the potential utility of such tunability, we show how such states could be used for quantum state engineering.
Youtube Trending Videos: Boosting Machine Learning Results Using Exploratory Data Analysis

Abstract With the emergence of social media and the Internet, bulks of data are generated every day in different fields. However, filtering out useful information, which can be turned into knowledge, requires significant effort. This can be achieved using various Big Data Technologies, statistics, data mining and a variety of machine learning algorithms that corporates and businesses are using to improve their performance every day for a long time now. In this paper, we analyze YouTube trending video data that analyses the target that grabs the user’s attention over a relatively short time. We present our analysis by measuring, mining, analyzing and comparing key aspects of time-series YouTube data with respect to its view and audience response statistics from 40 000 trending YouTube videos collected over 205 days. We have performed an exploratory data analysis (EDA) on all of it's aspect to get data insights and used statistics to find similarities between them to understand viewing pattern of different video categories. We also compare and observe the variation of activity over time with the nature of the event that affects the quality of our analysis. We make the viewership pattern clear belonging to each category separately by direct analysis. The results and findings can help us predict the category of the video that could probably be trending next in the upcoming days and help individuals get boosted views and performance when they upload video as per the predictions and analysis.
Contextual Gradient Scaling for Few-Shot Learning

Model-agnostic meta-learning (MAML) is a well-known optimization-based meta-learning algorithm that works well in various computer vision tasks, e.g., few-shot classification. MAML is to learn an initialization so that a model can adapt to a new task in a few steps. However, since the gradient norm of a classifier (head) is much bigger than those of backbone layers, the model focuses on learning the decision boundary of the classifier with similar representations. Furthermore, gradient norms of high-level layers are small than those of the other layers. So, the backbone of MAML usually learns task-generic features, which results in deteriorated adaptation performance in the inner-loop. To resolve or mitigate this problem, we propose contextual gradient scaling (CxGrad), which scales gradient norms of the backbone to facilitate learning task-specific knowledge in the inner-loop. Since the scaling factors are generated from task-conditioned parameters, gradient norms of the backbone can be scaled in a task-wise fashion. Experimental results show that CxGrad effectively encourages the backbone to learn task-specific knowledge in the inner-loop and improves the performance of MAML up to a significant margin in both same- and cross-domain few-shot classification.