Reviewerly/exHarmony huggingface dataset:
Visualization in Public Spaces

The articles in this special section focus on scientific visualization and information technology. Visual communication can provide a method to make information known or understandable to the general public. Visual communication in public spaces, such as museums, science centers, and mainstream media, is gaining momentum driven by the success of interactive visualization in exploration of scientific data. We are now seeing different installations and specific tools to convey knowledge through interactive and exploratory visual communication in a wide variety of settings. For this Special Issue, we solicited papers describing work based on visualization in public spaces from both a technology and methodology perspective.
AI BASED E-ASSESSMENT SYSTEM:

We have seen that a number of students apply for various examinations which may be institutional, non-institutionalor even competitive. The competitive exams mostly have objective or multiple-choice questions(mcqs). Theautomation of scoring of subjective or descriptive answers is a need considered nowadays. Our objective is to designa system that can evaluate subjective answers as good as a human being. The paper presents an approach which focuseson the inference process required for development of such system. The assessment is based on the similarity measuresbetween the answers. Based on the accuracy and importance of the features, weights are assigned to those featureswhich help in calculating the result. Our system is able to assess brief answers efficiently. It aims at faster and flexibleevaluation. Implementing such a system would be a step forward in the educational field.
Examining the Relationship between Cybersecurity and Scaling Value for New Companies

IntroductionThe professional literature that examines the relationship between cybersecurity and company value is scant and underdeveloped. Moreover, we were not able to find a single article published in an academic journal that examined the relationship between cybersecurity and the growth of new companies in initial stages of development.Increasingly, the professional
Embedded Tagging and Radar Map Shape Analysis for Assessing Student Outcomes

Computer-based assessment has been shown to offer many benefits on the outcomes of student performance [1].The computational strengths of computer-based platforms allow for more indepth collection and analysis of data from students of today than the many years previous.This access to data and performance outcomes allow us to learn more about the individual student instantly and to use this feedback to tailor adapted teaching and learning experiences on the fly.Here a locally developed computer-based testing platform is used to administer, assess and collect stealth identification tagged data from quizzes, homework, and exams of undergraduate students in sophomore-level engineering course.The information gathered from this platform is represented using an accessible radar plot format then analyzed using a novel method, based on the shape of the radar plot, to develop a greater understanding of the individual strengths and deficiencies of students.Finally, to establish appropriate context for this data it is correlated to a common student success metric and analyzed for potential trends.
Demonstration of Multi-Channel Feedback Control for On-Chip Microring Weight Banks

We demonstrate a multi-channel feedback control for microring weight banks and achieve a record-high accuracy and precision. With the simplified procedures, the feedback control becomes more practical for configuring large-scale photonic networks.
The Development of the Assurance Relevance Interest Assessment Satisfaction (Arias) Collaborative Jigsaw Collaborative Learning Model in Writing News Text Class 8 Junior High School State 06 Medan

This study aims to determine (1) the process of developing a collaborative Jigsaw Cooperative Jigsaw Cooperative Assurance Relevance Interest Assessment (ARIAS) learning model in writing news texts (2) the results of the validation of the collaborative jigsaw cooperative Assurance Relevance Interest Assessment Satisfaction learning model (3) ) the effectiveness of learning outcomes that apply the collaborative Assurance Relevance Interest Assessment Satisfaction (ARIAS) learning model in jigsaw Cooperative in writing news texts by students of class VIII SMP Negeri 06 Medan. The test subjects consisted of material experts, design experts, Indonesian language study teachers, and VIII grade students of SMP Negeri 06 Medan. The results of this study indicate that (1) The results of the questionnaire needs that have been distributed can result that the respondent has never used a learning model that collaborates with other models, inspires confidence and is in accordance with student interests and students' assumption that news text material is too difficult and students feel confused organizing ideas when writing begins and find it difficult to arrange the first sentence in writing. (2) validation of material experts includes and validation of learning design experts. Validation in learning design get an average score of 89.02 gets very good criteria and validation results by material experts get an average score of 83.47 gets very good criteria (3) Test the effectiveness of learning products in the form of developing learning models of assurance relevance interest assessment satisfaction (ARIAS) Collaborative Jigsaw Cooperative of respondents 32 students consisting of 2 teachers of Indonesian Language and Literature studies who received an average of 80, 15% in the "very good" category. Students after using the development of the learning model Assurance Relevance Interest Assessment Satisfaction (ARIAS) collaborative jigsaw have improved learning outcomes compared to before using the development of the learning model of collaborative jigsaw cooperative assurance relevance interest assessment satisfaction (ARIAS). Thus the learning model Assurance Relevance Interest Assessment Satisfaction (ARIAS) collaborative cooperative jigsaw is more motivating, arouse students' self-confidence, independent and arises a sense of responsibility.
Density Peak Clustering Algorithm Considering Topological Features

The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.
Dummy-Based Location Anonymization for Controlling Observable User Preferences

In this paper, we propose a location anonymization method which can control the degree of preserving user preferences on visiting locations, where the user's preferences are defined as a series of categories of the visiting locations. Based on the user's requirement, this method mixes dummies that move by following the user's preferences and that do not. The extensive simulation results show that our method enables controlling the degree of preserving user preferences as well as protects the location privacy.
A Deconvolutional Bottom-up Deep Network for multi-person pose estimation

Due to the trade off between model complexity and estimation accuracy, current human pose estimators either are of low accuracy or requires long running time. Such dilemma is especially severe in real time multi-person pose estimation. To address this issue, we design a deep network of reduced parameter size and high estimation accuracy, via introducing deconvolution layers instead of widely used fully-connected configuration. Specifically, our model consists of two successive parts: Detection network and matching network. The former outputs keypoint heatmap and person locations, and then the latter produces the final pose estimation using multiple deconvolutional layers. Benefiting from the simple structure and explicit utilization of previously neglected spatial structure in heatmap, the matching network is of specially high efficiency and at high precision. Experiments on the challenging COCO dataset demonstrate our method can almost cut off the running parameters of matching network, while achieving higher accuracy than existing methods.
An Efficient Method for Automatic Recognizing Text Fields on Identification Card

The problem of optical character and handwriting recognition has been interested by researchers in long time ago. It has obtained great results in theory as well as practical applications. However, the accuracy of identification is still limited, especially in the case of low-quality input images. In this article, we propose an efficient method to recognize information fields for identification in ID card using Convolutional Neural Network (CNN) and Long Short-Term Memory networks (LSTM). The proposed method was trained in a large, various quality dataset including over three thousands ID card image samples. The implementation achieved better results compare to previous studies with the precision, recall and f-measure from over 95 up to over 99% out of all information fields to be recognized.
Using nonlinear dimensionality reduction techniques in big data analysis

In recent years, the huge development in the measure of data has been noted. This becomes a first step of the big data. Big data can be defined as high volume, velocity and variety of data that require a new high-performance processing. The reduction of dimensions is one of the most important methods that are necessary in the field of big data analysis. Ideally, the reduced representation should have a dimensionality that corresponds to the intrinsic dimensionality of the data. There are two important procedures, the first one is dimensions reduction, and the second one is putting the data into its model and then estimating it. In this paper, we use two techniques of nonlinear dimensional reduction. The first one includes kernel principal components analysis (KernelPCA) along with modified kernel principal components analysis smooth (KernelPCAS) and the second one is neural network. The mean square error (MSE) was used to demonstrate the effectiveness of nonlinear reduction methods in the analysis of big data as a resourceful tool for this purpose.
Radio Galaxy Morphology Simulation via Residual Conditional Variational Autoencoder

We propose a radio galaxy morphology simulation approach by using a conditional variational autoencoder composed of residual convolutional blocks namely ResCVAE. It estimates the distribution of the existed morphology-labeled radio galaxy images by mapping them to a latent low-dimension space, and simulates new images according to the obtained distribution. From designed experiments we find that the pixelwise cross-entropy (PCE) loss, as a component of the optimization objective for the ResCVAE, trains the network parameters to obtain lower reconstruction loss and more accurate simulation performance than the mean squared error (MSE) loss function under the same training settings. In addition, the strategies of applying condition and residual convolutional blocks are also evaluated and compared to the other networks, which suggests a domination of our proposed approach.
THE IMPLEMENTATION OF SQ3R TECHNIQUE IN TEACHING READING COMPREHENSION

This study aims to describe the implementation of SQ3R in teaching reading comprehension and to identify students' responses to the implementation of SQ3R. This study uses a qualitative case study conducted at one of the state vocational school in Purwakarta. It involves 4 students. Data were collected through observation and interviews. The data from 2 instruments were analyzed qualitatively. The results of this study indicate that the implementation of SQ3R helps students to understand English words. According to Apandi (2011), SQ3R helps the students to understand English words. The students also responded positively to the implementation of SQ3R. Therefore, SQ3R is recommended to be implemented in teaching reading comprehension.
Temporal Action Detection with Fused Two-Stream 3D Residual Neural Networks and Bi-Directional LSTM

This work presents an architecture for localizing interesting target events within long sequences of untrimmed videos. Mainly, we focus on finding temporal boundaries of target visual actions and bypassing irrelevant events of other actions. Both the appearance and motion information are crucial for discriminating between different actions. Based on this, we propose a trainable fused two-stream 3D Convolution neural network framework, integrated with a bi-directional Long Short-Term Memory sequence model (2-stream 3DCNN+ LSTM) for learning. The two stream CNN enables us to model features from both RGB and optical flow short video-clips of resolution $\delta=16$ frames, extracted from the long input video sequence. This framework produces a sequence of class probability scores at each video-clip. Simple low-cost mean, average and max filters are used to localize and classify each relevant action instance and to label the whole video. Such architecture utilized the power of (1) two streams CNN architecture, (2) the spatiotemporal processing of 3D convolution network for capturing spatial and motion patterns, (3) temporal orderings and long-range dependencies of the sequence model for obtaining robust classifications at each time step. We evaluate our framework using THUMOS'15 dataset, attaining 98.9% accuracy and 35.8 % mAP in the video level classification and relevant action detection tasks, respectively.
THE EFFECTIVENESS BETWEEN DISCOVERY LEARNING AND WORD WALL METHODS IN IMPROVING VOCABULARY AT SEVENTH GRADE STUDENTS

Some of the teacher is having a problem when using discovery learning as a method to improve the vocabulary of a student. Based on the objective of this research, the writer conduct wordwall method on SMP mutiara 1 Bandung that will help on improving student vocabulary, and hopefully it can be applied simultaneously to increase interest in learning especially in English course in SMP Mutiara 1 Bandung. Based on the results of research that has been done by the writer, there are very helpful results in developing the method of Word Wall as a teaching material capable of improving the quality of learning and also can improve students vocabulary. From the test results of normality and homogeneity that the data used is homogeneous and distributed normally, so that the process of the research can be continued as it should be. Keywords: Vocabulary, Discovery Learning, Wordwall
Speech segmentation in different perspectives

This special issue of JoSS is one of a series of initiatives undertaken by the Lab of Empirical and Experimental Linguistic Studies (LEEL) at the Federal University of Minas Gerais (UFMG) together with several international partners on the important topic of speech segmentation, primarily segmentation applied to spontaneous speech.
Graph Convolutional Topic Model for Data Streams.

Learning hidden topics in data streams has been paid a great deal of attention by researchers with a lot of proposed methods, but exploiting prior knowledge in general and a knowledge graph in particular has not been taken into adequate consideration in these methods. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g.Word2vec) is very valuable and useful to help topic models work better, especially on short texts. However, previous work often ignores this resource, or it can only utilize prior knowledge of a vector form in a simple way. In this paper, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance between the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both human graph knowledge(Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than the state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. The implementation of GCTM is available at this https URL.
SAR Analysis for Real Video Super- Resolution Improvement

This paper presents a simultaneous autoregressive (SAR) analysis method to describe the unknown signal-to-noise ratio (SNR) and the texture feature of low-quality real video frames when the ground-truth images are not available. The real video images degraded by the factors, such as electronic noise, oversaturated pixels, motion blur, and compression artifacts, often result in poor motion registration estimation, which makes the performance of the existing video super-resolution (VSR) algorithms lower than expected. It is hard to estimate the SNR of the low-quality real frames without any prior knowledge. To solve this problem, we made a connection between SAR hyperparameters and the SNR of real images. The relationship expression of them was given in this paper. Using the proposed method, well-registered low-quality real video frames can be selected to decrease the root mean squared error (RMSE) of motion estimation of video frames for VSR reconstruction improvement. The anomalous low-quality frame images whose SAR hyperparameters values are inconsistent with others will be considered for removal. Synthetic experiments were designed to illustrate how the SAR hyperparameters values vary with the variation of synthetic parameters. In order to better illustrate the effectiveness of the proposed method, real low-quality videos captured under different conditions were tested under VSR reconstruction experiments. The VSR reconstruction results show that the results obtained using SAR prior analysis have sharper edges and fewer ringing artifacts than the original results. It indicates that the proposed method is helpful to obtain better results of motion registration estimation for low-quality real video images.
Controlling Image-Stylization Techniques using Eye Tracking

With the spread of smart phones capable of taking high-resolution photos and the development of high-speed mobile data infrastructure, digital visual media is becoming one of the most important forms of modern communication. With this development, however, also comes a devaluation of images as a media form with the focus becoming the frequency at which visual content is generated instead of the quality of the content. In this work, an interactive system using image-abstraction techniques and an eye tracking sensor is presented, which allows users to experience diverting and dynamic artworks that react to their eye movement. The underlying modular architecture enables a variety of different interaction techniques that share common design principles, making the interface as intuitive as possible. The resulting experience allows users to experience a game-like interaction in which they aim for a reward, the artwork, while being held under constraints, e.g., not blinking. The co nscious eye movements that are required by some interaction techniques hint an interesting, possible future extension for this work into the field of relaxation exercises and concentration training.
An Analysis to Identify the Importance of Visual Face Features and Ranking

A person can remember thousands of faces. The face can be remembered based on their different shape size and color. The difference in facial structure and color makes people different from others and easy to remember. The face is a key feature to identify a person. When somebody interacts with an unknown person they remember them with the highlighted features of their face. However, sometime later memory fades. i.e. when some crime happens, the eye witness looks the criminal for a few seconds. Some of the features of his face are remembered by the eye witness. These features are used by the sketch artist to generate the sketch and help for the identification of the suspect. Sketches contain shape and texture information about the face. Color information lags in the sketches. To identify the importance of color information, we have performed a survey. Eye color and skin color along with the twelve other face features are used in conducting this survey. The 3690 responses from 314 persons are collected and analyzed. This survey helps the research community to identify the importance of facial features and ranking them
Stereo Vision Based Speed Estimation for Autonomous Driving

Human errors are considered to be one of the most common reasons for road accidents. Autonomous driving technology has the potential to reduce the number of accidents to a large extent by eliminating this error. Estimating the speed of vehicles is a capability of utmost importance for a self driving vehicle for accident free autonomous navigation. Hence speed estimation is an area which has been extensively researched in the recent years. This paper presents a novel, stereo vision based algorithm for the speed estimation problem. This algorithm involves detecting various obstacles on the road for two consecutive frames using YOLO followed by matching the features of the detected objects using Scale Invariant Feature Transform (SIFT). This algorithm also leverages the MC CNN architecture for finding the disparity map of the frames. The algorithm developed is applied on the KITTI dataset. Once calculated, the position and speed of the obstacles can then be fed to the path planning and control sub system for the decision making process. The estimated speed values are validated using the three dimensional object location data in the camera coordinates available in the KITTI dataset.