Reviewerly/exHarmony huggingface dataset:
IMMPDAF Approach for L-Band Radar Multiple Target Tracking

The interacting multiple model (IMM) and probabilistic data association (PDA) filter (IMMPDAF), is developed to support the air surveillance of the Track While Scan (TWS) radar systems. Take advantage of both, IMM and PDA algorithms to increase the tracking efficiency of fast maneuvers targets. The known algorithm was implemented in a radar tracking application for the purpose of modernizing the `L' band radar, as a replacement for the conventional NN (Nearest Neighbor) algorithm which proved to be ineffective in situations with airborne targets in the air. In extensive experiments with complex, target maneuver and non-maneuver scenarios has been shown that proposed algorithm to have robust tracking characteristics with small position and velocity error.
Multimodal Data Representation Model for Complex Description of Observation Objects

The paper is devoted to solving the problem of the complex presentation of multimodal data about an object of observation, the characteristics of which are measured and investigated taking into account the time stamps and the relationship between the data of various modalities. This problem is relevant for many engineering tasks. In particular, the comprehensive presentation of data on a physical object is important in the technology of digital twins, which has recently become more widely used. Based on the fact that a digital twin is determined by both a behavioral model and a visual model, the paper presents a data model based on the concept of a multimodal data element. This model makes possible a comprehensive and consistent presentation of data about the object of observation. The mathematical model of the complex presentation of multimodal data is based on the concepts of an aggregate and a multi-image defined in an algebraic system of aggregates. The article analyzes the relationship between the complex presentation and processing of multimodal data, which are determined in time, using the apparatus of the algebraic system of aggregates and the consideration of aggregated data as functions of several variables. Thus, the analysis of the data of the muxel model can be performed using logical operations, ordering operations, and relations defined in the algebraic system of aggregates as well as operations and approaches defined in other mathematical concepts. Since the muxel model presents large amounts of data, it is advisable to optimize the way how data is presented. This can be achieved by data compression, modification of the data structure, or a combination of the first and second approaches. In the first approach, it is advisable to apply muxel model data compression based on the RLE method. In the second approach, it is proposed to apply data structures similar to the Sparse Voxel Octree, which is used to reduce data volume of models in voxel graphics. The practical implementation of the processing of multimodal data presented using the muxel model can be performed by means of ASAMPL programming language.
A Hardware Accelerator Based on Neural Network for Object Detection

Abstract At present, deep learning algorithms such as neural networks are widely used in all aspects of artificial intelligence. The computing performance of the CPU is low, and the power consumption of the GPU is large. This topic uses FPGA to study and implement the target detection algorithm. The proposed related technology can meet the accuracy requirements of the unmanned system for target detection, as well as the real-time requirements of the video stream. The frame rate of this design is 23.1FPS, and the mAP value is basically the same as that of CPU and GPU. The delay is 47.2% of Intel CPU, and the energy efficiency is 26% of Titan X GPU.
Pathways to Live Visuals in Dance Performances: a Quantitative Audience Study

INTRODUCTION:We present an audience study investigating the impact of different technologies to create visuals in dance performances.OBJECTIVES: We investigated four conditions: motion capture, sensors, camera image, and minimal interaction; and four variables: how much did the audience perceive a connection between the body and the visuals; the visuals as merely copying the dancer; how much distracting were the visuals; and how much did the audience enjoy the visuals.METHODS: We used a questionnaire to collect data.We analyzed it using Friedman's test, and Spearman's correlation test. RESULTS:The audience perceived a stronger connection in the camera condition, but in the same condition, visuals tend to be merely copying the dancer.We also suggest that the perceived connection has a positive correlation with enjoyment, while distraction has a negative correlation.CONCLUSION: Our results help to highlight the impact that different technology have on live visuals for dance.
Flexible Imputation of Missing Data (2nd Edition)

Occurrence of missing data can cause serious issues, including decreased sample size, biased estimates, and algorithmic problems.Therefore, proper treatment of missing data is a significant part of data analysis in statistics, especially in clinical and experimental studies.Treatment of missing data is usually included in a section of its own in most textbooks, presenting best or most convenient practices, based on the methods presented and the statistical sophistication of the audience.However, the contexts, complexity, and severity of missing data are so diverse and complicated that its treatment warrants a volume of its own.There are currently several books on missing data, ranging from practical to more theoretical.Flexible Imputation of Missing Data (2nd Edition) is an updated addition to the literature on missing data, which combines practice, theory, and applications using the R programming language.The twelve chapters of the book are grouped in four sections: the basics, advanced techniques, case studies, and extensions.Exposition in each chapter is accompanied by plenty of graphs, code, examples, and exercises.The code and the entire book are available online at the author's personal website.The programming language of the book is R and the treatment of missing data is performed by the package mice, which was created by the author.Chapter 1, introduction, provides motivation for dealing with missing data, how missing data occur (e.g. by nonresponse or attrition), current practice in the literature, categories of missing data (MCAR, MAR, MNAR), and common fixes and their advantages and drawbacks.Common practices, such as listwise and pairwise deletion methods, mean imputation, (stochastic) regression regression, last/baseline observation carried forward (LOCF and BOCF), and indicator method (popular in public health) are discussed.The largest portion of the chapter is dedicated to multiple imputation.According to the author, the emphasis of the entire book is on multiple imputation because of its efficiency and statistical properties compared to other methods.The entire Chapter 2, multiple imputation, is devoted to the method of multiple imputation (MI), starting with a historical sketch of the concept and practice of MI.
Emoji Usage and Interpersonal Relationship in Computer-Mediated Cmmunication

Based on 514 one-to-one chat corpus generated by 1028 interlocutors, this paper analyzed the way Chinese used emojis in computer-based communication (CMC) and explored whether interpersonal factors would affect people's emoji usage. The study found that people using emoji in instant messaging had formed certain customary features and preferences. People would concentrate on using a few emojis which occupied a large proportion of the total number of emojis used in daily communication. They were keen to use one type of emojis instead of multiple emojis at a time, and preferred to use emoji at the end of the turn or alone, and seldom put it at the beginning or middle of the turn. In addition, we found that different styles of emojis would affect the circumstances under which they appeared, which provided an angle for analyzing the usages and functions of different emojis. Studies also confirmed that as a key element of daily conversation, interpersonal relationships also influenced the way people used emoji in CMC. When communicating with people with higher intimacy, netizens tended to use more diverse and richer emojis; when users communicated with more alienated people, the types of emojisused were limited. In terms of the way to express information, compared with communicating with family members, close friends, classmates and colleagues, netizens were less likely to use emoji to convey information and more likely to use notional words in face of casual acquaintances, so as to avoid poor communication caused by different interpretations of emojis.
An IMPROVED PAGERANK ALGORITHM BASED ON A HYBRID APPROACH

PageRank is an algorithm that brings an order to the Internet by returning the best result to the users corresponding to a search query. The algorithm returns the result by calculating the outgoing links that a webpage has thus reflecting whether the webpage is relevant or not. However, there are still problems existing which relate to the time needed to calculate the page rank of all the webpages. The turnaround time is long as the webpages in the Internet are a lot and keep increasing. Secondly, the results returned by the algorithm are biased towards mainly old webpages resulting in newly created webpages having lower page rankings compared to old webpages even though new pages might have comparatively more relevant information. To overcome these setbacks, this research proposes an alternative hybrid algorithm based on an optimized normalization technique and content-based approach. The proposed algorithm reduces the number of iterations required to calculate the page rank hence improving efficiency by calculating the mean of all page rank values and normalising the page rank value through the use of the mean. This is complemented by calculating the valid links of web pages based on the validity of the links rather than the conventional popularity.
A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels

Feature selection has shown significant promise in improving the effectiveness of multi- label learning by constructing a reduced feature space. Previous studies typically assume that label assignment is complete or partially complete; however, missing-label and unlabeled data are commonplace and accompanying occurrences in real applications due to the high expense of manual annotation and label ambiguity. We call this “highly incomplete labels” problem. Such label incompleteness severely damages the inherent label structures and masks true label correlations. In this paper, we propose a novel structure-induced feature selection model to simultaneously identify the most discriminative features and recover the highly incomplete labels. To our best knowledge, it is the first attempt to explore the local density structure of data to capture the intricate feature-label dependency in the highly incomplete learning scenarios. Feature selection is guided by the label structure reconstruction, and highly incomplete labels are recovered via the structure transferred from feature space. In this elegant manner, the processes of selecting discriminative features and recovering incomplete labels are coupled in a unified optimization framework. Comprehensive experiments on public benchmark datasets demonstrate the superiority of the proposed approach.
Autoencoder Based Dimensionality Reduction of Feature Vectors for Object Recognition

Object recognition can be performed with high accuracy thanks to the robust feature descriptors defining the significant areas in images. However, these features suffer from high dimensional structure, in other words "curse of dimensionality" for further processes. Autoencoders (AE) are proposed in this study to solve the dimensionality reduction problem of visual features. To assess the efficacy, object recognition is performed using reduced dimensional visual features. For this purpose, dimensionalities of three well-known feature vectors, namely, HOG, SIFT and SURF, are reduced to half. Moreover, deep learning based features are also reduced. Then, reduced vectors, which are called as AE-HOG, AE-SIFT, AE-SURF and AE-DEEP are fed to object recognition task. Also, dimensionality reduction is implemented by a variant of AE, variational autoencoder (VAE) and PCA, which is the most studied unsupervised method for these features, and the results are compared. Furthermore, all experiments are repeated on noisy images. Results suggest that dimensionality reduction of these feature vectors can be accomplished successfully owing to the proposed method.
Correlation of E-Learning Courses toward Nursing Students’ Cognitive Skills

B ackground : E-learning is one of the new learning methods in the Indonesian education system. This system has been used to increase students’ understanding levels. The researcher has noticed that students had a lack of cognitive level due to the misinterpretation and mis-extrapolation of the materials. E-learning shall improve students’ cognitive level in courses. The researcher is interested in exploring the correlation between the effectiveness of online classes and nursing students’ cognitive level. Objective : This study aims to identify the relationship between the effectiveness of online courses through the e-learning system and students’ understanding level at Universitas Muhammadiyah Yogyakarta. Method : This study implemented a quantitative method with a cross-sectional design along with The Pearson product-moment correlation to measure the correlation strength. Results : The researcher found E-learning adequate to be applied to nursing students of Universitas Muhammadiyah Yogyakarta (85.8% out of total respondents, N=288). The average score of the seven components measured (productivity, quality, efficiency, flexibility, eminence, development, and satisfaction) was 68.54, while the average rating of students’ cognitive level was 53.92. Conclusion : There was a significant correlation between e-learning as an education system and nursing students’ cognitive level.
Research on the Evolution and Classification of Digital Learning Resources

With the maturity of new technologies such as big data, virtual reality and artificial intelligence, the types of digital learning resources are constantly enriched, The current hot topic of academic circles is how to systematically classify digital learning resources, and then provide guidance for exploring the construction, supply and application of different types of digital learning resources.This study cuts into the research of foreign AECT on digital learning resources and research results of domestic digital learning resources.At the same time, it combines the typical information technology in the education field, and summarizes the classification framework of digital learning resources, and uses the compass principle as a guide to summarize the digital learning resource classification framework and divide the digital learning resources.The framework divides digital learning resources into six categories: basic knowledge classes, auxiliary extension classes, tool software classes, network platform classes, virtual reality classes, and generative learning resources, and proposes a digital learning resource compass from three dimensions: discipline, acquisition mode of digital learning resources and types of digital learning resources.
EMPIRICAL OBSERVATION IN THE SEARCH OF STRATEGY FOR DEVELOPMENT OF E-LEARNING: CASE OF NATIONAL UNIVERSITIES IN UZBEKISTAN

Majority of universities in the world are implementing technology enhanced learning in order to improve education and help students to develop learning. Nowadays technology enhanced learning offers universities with all context of education and universities are trying to apply it. One such case is the TARCI (Tashkent Automobile and Road construction institute) in Uzbekistan that is subject of this project. The university’s current system is not reliable; also it is outdated in terms of its structure; and need to be replaced with e-learning system. In thisresearch study using a questionnaire as data collection techniques. This data collection was necessary as none of the interviewed individuals had experience with any e-learning systems. The analysis of the data shows positive attitude students for implementing an e-learning system. This proposed system is based on NodeJS and Elgg software and it is believed to be a full scale e-learning system that offers courses for students in TARCI. Through user requirements, the project comes up with a reliable e-learning system that will help the TARCI’s administration manage courses and monitor students’ progress. Hence therefore the collaboration between students and staff would be improved with developed e-learning system. KEYWORDS:e-learning, educational content, website, learning process, online access, teaching.
Ensemble Extreme Learning Machine Approach to Thermal Infrared Subpixel Temperature Estimation

The disadvantage of low resolution and low signal-to-noise ratio (SNR) of thermal infrared (TIR) data makes it difficult to be applied in practice, although it has been studied for decades. In contrast, visible and near infrared (VNIR) and shortwave infrared (SWIR) data have higher resolution and SNR. In this letter, an ensemble extreme learning machine (EELM) is proposed, which is based on the extreme learning machine (ELM), to fuse together VNIR, SWIR, and TIR to improve TIR resolution and SNR. The associated ELM nets are trained with correlated training data which are selected from the random forest method. The temperature estimation cube can be obtained after putting SWIR data and VNIR data into these networks. Finally, the cube structure is adjusted by an ensemble rule to obtain the final temperature estimation results. We test the EELM algorithm on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) L1B data set and compare it with other scholars' algorithms. The experimental results show that the EELM algorithm can not only improve the resolution of TIR data, but also estimate the subpixel temperature of TIR more accurately.
Aplikasi Sistem Pakar untuk Mendiagnosa Penyakit ISPA menggunakan Metode Naive Bayes Berbasis Website

ISPA (Acute Respiratory Infection) is a disease that occurs due to respiratory tract disorders that can cause a variety of diseases ranging from asymptomatic illnesses, mild to severe infections due to environmental factors. The lack of public knowledge about the symptoms, how to treat it and how to overcome it result in a fairly high mortality rate due to ISPA. The expert system to be created is intended to make it easier a person to diagnose ISPA by adopting human knowledge into a computer system so the expert system is able to solve problems like an expert does. Expert system application is made using the Naive Bayesmethod because the Naive Bayesmethod is the best classification method with high probability when used in its system calculation. With this application, people will feel like they are consulting with a doctor or an expert who handles ISPA. This application is built based on websites that use the PHP programming language, Codeigniter framework and MySQL database. There are 104 training data and 39 testing data that have been tested. From 39 tests, there are 36 test data that are suitable and there are 3 test data that are not suitable. Accuracy obtained from the test is of 92.3%.
Optimizing Genetic Algorithms for Sentiment Analysis of Apple Product Reviews Using SVM

Online reviews have the potential to provide buyers with insights about products such as quality, performance and recommendations. Website is one of the media that contains information or reviews provided by individuals, groups or organizations about an object or topic, one of which is Apple products. This study analyzes consumer sentiment reviews of Apple product users consisting of 200 reviews which will be classified into positive opinions and negative opinions using the Support Vector Machine algorithm and the application of genetic algorithms (GA) to obtain optimal accuracy values. The stages of this research are, firstly collecting a dataset, the second is preprocessing data. Third, the sentiment analysis process uses SVM and GA as optimization techniques. Fourth, do the validation process on the accuracy results obtained using the Confusion Matrix and ROC Curve. The results of this study indicate that Apple product review sentiment analysis produces the best accuracy of 70.00% and AUC 0.924 for SVM algorithm. Whereas the SVM + GA algorithm produces 85.76% accuracy and AUC 0.945, so that the accuracy value increases by 15.76% and the AUC 0.021 on the SVM model when compared before optimization with genetic algorithms (GA) is performed
A Method to Identify Geochemical Mineralization on Linear Transects

Mineral exploration in biogeochemistry is related to the detection of anomalies in soil, which is driven by many factors and thus a complex problem. Mikšová, Rieser, and Filzmoser (2019b) have introduced a method for the identification of spatial patterns with increased element concentrations in samples along a linear sampling transect. This procedure is based on fitting Generalized Additive Models (GAMs) to the concentration data, and computing a curvature measure from the pairwise log-ratios of these fits. The higher the curvature, the more likely one or both elements of the pair indicate local mineralization. This method is applied on two geochemical data sets which have been collected specifically for the purpose of mineral exploration. The aim is to test the technique for its ability to identify pathfinder elements to detect mineralized zones, and to verify whether the method can indicate which sampling material is best suited for this purpose.
Penerapan Model Pembelajaran Two Stay Two Stray dalam Meningkatkan Keaktifan Belajar Pebelajar

The study aimed to improve the learning activeness of fourth-grade students through the application of the Two Stay Two Stray learning model. This research design is qualitative research. The instrument of data collection uses observation of teacher and student activities and interviews. The results of the study indicate that: this learning model must form groups of 4 people. Then discuss with the group that has been determined. After discussing, two people from each group visited other groups to get information on the results of discussions from each group. While the task of two members who settled in their group must give the results of their discussion to other groups. The learning process using the learning model Two Stay Two Stray can improve the learning activeness of fourth-grade students of MIN 1 Probolinggo. By using this learning model, students become enthusiastic in learning and they are no longer passive because each individual has their respective duties, so this learning model is declared successful in increasing student learning activeness.