Reviewerly/exHarmony huggingface dataset:
Unsupervised Feature Selection Methodology for Clustering in High Dimensionality Datasets

Feature selection is an important research area that seeks to eliminate unwanted features from datasets. Many feature selection methods are suggested in the literature, but the evaluation of the best set of features is usually performed using supervised metrics, where labels are required. In this work we propose a methodology that tries to aid data specialists to answer simple but important questions, such as: (1) do current feature selection methods give similar results? (2) is there is a consistently better method ? (3) how to select the m-best features? (4) as the methods are not parameter-free, how to choose the best parameters in the unsupervised scenario? and (5) given different options of selection, could we get better results if we fusion the results of the methods? If yes, how can we combine the results? We analyze these issues and propose a methodology that, based on some unsupervised methods, will make feature selection using strategies that turn the execution of the process fully automatic and unsupervised, in high-dimensional datasets. After, we evaluate the obtained results, when we see that they are better than those obtained by using the selection methods at standard configurations. In the end, we also list some further improvements that can be made in future works.
Characterized Organic Pollutants and Their Health Effects in Sampled Groundwater Around Ilorin Metropolis

The study was aimed at determining the possible volatile organic compounds present in groundwater from handdug wells within and around filling stations across Ilorin metropolis as well as to ascertain the possible sources of the contaminants and their health effects.Water samples were obtained from twenty-six (26) wells that were found within the scope of stations above the age of 15 years and functioning with underground storage tank capacities of over 33,000 L each for different petroleum products stored above the water level.Laboratory analysis to determine volatile organic compounds and their concentrations were carried out using the gas chromatography-mass spectrophotometer (GC-MS) after prior extraction of hydrocarbon from the water samples by the Liquid-Liquid Extraction (LLE) method.The result revealed a total of fifty-three (53) VOCs across samples where nonanal, dodecane, methyl palmitate, heptanophenone, 13, hexyloxacyclotridec -10 -en -2 -one, cyclohexane, octyl, decahydro-4, 4, 8, 9, 10 -pentamethyl naphthalene, (z) -3 -heptene, were the most frequently occurring compounds, which could be traced to anthropogenic activities involving the use of paints, detergents, stain removers, leaking underground storage tanks, piped networks containing petroleum products as the possible sources of release into the environmental media.Related health impacts from exposure to these contaminants includes brain damage, cancer, tumours, anaemia, central and peripheral nervous system breakdown, liver, bones, autism, Skin, eye, and nose irritation, headache, dizziness, narcosis, and death at high levels of exposure.The study concluded that groundwater sources at close proximity to filling stations are susceptible to contamination through activities in the stations and such water resource should be treated before consumption and use to avoid negative health effects.
Factors affecting knowledge sharing behavior of lecturers: The case of public universities

Studying knowledge sharing behavior of lecturers at public universities in Ho Chi Minh City is an urgent issue.In the development and vastness of knowledge treasure, knowledge sharing actually becomes a way to reduce the difficulties and waste of time to learn, acquire the knowledge, thereby, helping public universities build a team of lecturers who have good knowledge and meet the job requirements for their development.Therefore, the author conducts a study to bring out the factors affecting activities of knowledge sharing behavior of lecturers at public universities in Ho Chi Minh City now.Based on the data collected, we use Cronbach's Alpha, EFA and run regression model for knowing the impact levels of each independent variable on dependent variable of the knowledge sharing behavior of lecturers.Based on the findings, some recommendations are given for improving the knowledge sharing behavior of lecturers at public universities in Ho Chi Minh City.
An Efficient 3D CNN for Action/Object Segmentation in Video.

Convolutional Neural Network (CNN) based image segmentation has made great progress in recent years. However, video object segmentation remains a challenging task due to its high computational complexity. Most of the previous methods employ a two-stream CNN framework to handle spatial and motion features separately. In this paper, we propose an end-to-end encoder-decoder style 3D CNN to aggregate spatial and temporal information simultaneously for video object segmentation. To efficiently process video, we propose 3D separable convolution for the pyramid pooling module and decoder, which dramatically reduces the number of operations while maintaining the performance. Moreover, we also extend our framework to video action segmentation by adding an extra classifier to predict the action label for actors in videos. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for action and object segmentation compared to the state-of-the-art.
SubRank: Subgraph Embeddings via a Subgraph Proximity Measure

Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.
Current researches and trends of crowd counting in the field of deep learning

Crowd counting is a branch of computer vision, which has important practical significance. This paper summarizes the current situation and development trend of crowd counting based on computer vision. Firstly introducing the methods of crowd counting with shallow learning, and then presenting the research status and trend of deep learning in crowd counting. Then, the performance evaluation of crowd counting method is introduced, and several benchmark datasets for crowd counting test and assessment are provided. Finally, the focus of research at the current stage is affirmed, and the prospect is made.
Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Hand posture estimation attracts researchers because of its many applications. Hand posture recognition systems simulate the hand postures by using mathematical algorithms. Convolutional neural networks have provided the best results in the hand posture recognition so far. In this paper, we propose a new method to estimate the hand skeletal posture by using deep convolutional neural networks. To simplify the proposed method and to be more functional, the depth factor is ignored. So only the simple color images of hands are used as inputs of the system. The proposed method is evaluated by using two datasets with high-diversity named Mixamo and RWTH, which include 43,986 and 1160 color images, respectively, where 74% of these images are selected as a training set and, 26% of the rest images are selected as the evaluation set. The experiments show that the proposed method provides better results in both hand posture recognition and detection of sign languages compared to state-of-the-art methods.
SENTIMENT ANALYSIS ON TWITTER BY USING MAXIMUM ENTROPY AND SUPPORT VECTOR MACHINE METHOD

With the advancement of social media and its growth, there is a lot of data that can be presented for research in social mining. Twitter is a microblogging that can be used. In this event, a lot of companies used the data on Twitter to analyze the satisfaction of their customer about product quality. On the other hand, a lot of users use social media to express their daily emotions. The case can be developed into a research study that can be used both to improve product quality, as well as to analyze the opinion on certain events. The research is often called sentiment analysis or opinion mining. While The previous research does a particularly useful feature for sentiment analysis, but it is still a lack of performance. Furthermore, they used Support Vector Machine as a classification method. On the other hand, most researchers found another classification method, which is considered more efficient such as Maximum Entropy. So, this research used two types of a dataset, the general opinion data, and the airline's opinion data. For feature extraction, we employ four feature extraction, such as pragmatic, lexical-grams, pos-grams, and sentiment lexical. For the classification, we use both of Support Vector Machine and Maximum Entropy to find the best result. In the end, the best result is performed by Maximum Entropy with 85,8% accuracy on general opinion data, and 92,6% accuracy on airlines opinion data.
An Automatic Defect Detection Method for Gas Insulated Switchgear

According to the characteristics of gas insulated switchgear (GIS) equipment fault detection and internal structure, a GIS pipe inspection robot with visual equipment was designed. Based on inspection images, a novel method of defect detection was proposed for small target detection inside the GIS. Firstly, image enhancement is applied for image acquired in low light based on Retinex-Net. Then, small target detection algorithm is implemented using improved YOLOv3 for foreign object detection inside GIS. In order to improve detection speed, the number of parameters of YOLOv3 is reduced by model pruning operation, which holding the accuracy of the model. The experimental results demonstrate that the mAP (Mean Average Precision) reaches 0.86, and the proposed algorithm is capable of real-time detection as faster than 15 frames per second (fps).
Psychological readiness for personality change and tolerance for the modern world ambiguity of civil engineering institution`s students (SHEI PSACEA)

Problem statement. In modern social conditions ambiguity is the typical situation of professional activity of specialists of different spheres, and tolerance is acknowledged to be one of the key competences of a person in the modern world. That is why the study of psychological prerequisites of behavior of a person who lives and works in the modern situations of ambiguity is of great significance. The article is devoted to the research of psychological readiness for the personality changes and tolerance for the modern world ambiguity of the civil engineering institution’s students (SHEI “Prydniprovska State Academy of Civil Engineering and Architecture”). The phenomenon of ambiguity tolerance as a social and psychological mindset with emotional, cognitive and behavioral components has been considered. The purpose of the article – the presentation of results of the empiric research of psychological readiness of students of SHEI PSACEA for the personality changes and tolerance to the ambiguity of the modern world. Conclusions. A comparative analysis of psychological readiness forpersonality change and ambiguity toleranceof the students of different specialities of SHEI “Prydniprovska State Academy of Civil Engineering and Architecture” has been carried out. It has been empirically proven that the students of Architectural specialities demonstrate the lowest test indicators of ambiguity tolerancecompared with the students of the other specialities. The students of Civil Engineering and Economic specialities have demonstrated the highest level of tolerance. The students of all specialities of SHEI PSACEA have had the highest indicators of such components of readiness forpersonality change as resourcefulness, confidence, passion, optimism.
Comparing solar data from NWP models for Brazilian territory

Brazil is experiencing continuous growth in the solar energy market over the last years, and it is expected that solar power plants will have a significant contribution to the Brazilian energy matrix soon. The solar irradiance variability is an important issue due to its impact on electricity distribution systems. However, solar energy variability has not been comprehensively evaluated in Brazil. The study aimed to assess the capacity of two numerical models of mesoscale in simulating the solar radiation conditions in the Northeastern region of Brazil. For this, a case study was conducted comparing the surface solar irradiation data acquired in 41 automated weather stations operating in the study area and managed by the Brazilian Institute of Meteorology with estimates provided by two weather mesoscale models BRAMS and WRF. The study indicated that the difference between the model's uncertainties is small (typically less than 5%) when evaluating the entire region of the Brazilian Northeast. However, local comparisons may show more notable discrepancies reflecting uncertainties in the estimates produced by the models.
A Sentiment Lexicon-based Analysis for Food and Beverage Industry Reviews. The Greek Language Paradigm

The purpose of this research is to implement a methodology to detect and quantify customers' opinions which referred to the Food and Beverage (F&B) sector using the Greek language.Due to the large and continuously opinionative data produced by the evaluations of the customers' reviews, the F&B companies, and/or other stakeholders face difficulties to extract all the necessary data and to proceed to further analysis.As far as the Greek market is concerned, the F&B sector is one of the most dynamic sectors.Delivery or take away food or coffee is very common, with the vast majority of consumers to order from aggregators' platforms (online digital markets).In this study, 8,950 customers' reviews are extracted from 690 companies selected randomly from a total of 6,795 companies covering the most popular capitals of Greece and presented in the most used common e-platform.The mining of customers' reviews covers a month period during the year of 2018 and the evaluated functions are the quality of food, the customer service, the image of the company, the pricing, and the quantity of food.As it appears, the sentiment analysis in an aspect-level using the lexicon-based technique should approach methodologically the problem by identifying not only the relevant information but also the particular expressions and phrases the evaluators use over the Internet.The extracted keywords and phrases from the customers' reviews are used to form the corresponding dictionaries of the functions and to proceed in the sentiment classification.The method is tested in an annotated dataset of 2,000 customers' reviews and, overall, the findings are expected to contribute towards the design and implementation issues of a sentiment lexicon particularly devoted to the Greek F&B industry.
Exploring the Potential of Augmented Reality Teaching Aid for Vocational Subject

The implication on technology in the education sector, especially as a learning aid is significant to encounter the obstacles faced in Industrial Revolution 4.0 era. In the effort of solving this issue, researchers have developed a learning aid for Fundamental of Pneumatic System subject by implementing Augmented Reality technology. The progression in this technology is highly related to the rapid growth of communication technology in this era. Fundamental of Pneumatic System is a subject that is abstract and complex, with the need of high cognitive ability to understand the working principles, besides having multifunctional components. In order to ease the deliberation of information from educators, and to increase the understanding of students in the fundamentals of pneumatic, the 3D model using the latest AR technology will then enhance the learning and teaching process to be more interactive and effective. The objective of this research is to develop learning aid which uses AR technology by using ADDIE model. The creation of learning aid is to enhance the learning and teaching process especially for the fundamental of pneumatic system subject. The content and the efficiency of the learning aid were analyzed by the compilation of feedbacks through designed surveys, given to a small group of pre-service teachers. In a nutshell, ADDIE model is successfully used to develop the teaching aid and the result of this findings show that the teaching aid is suitable to be used in class, especially to enhance the teaching and learning process.
Usability Testing to Measure Real Perception Generation Level in Introduction of Bengkulu University Building Based on Virtual Tour with 360° Object Modelling

The development of photography technology creates a 360 ° camera that carries an ultra-wide twin1-lens folded optical lens (a small double lens with a folding design).This technology can produce spherical panoramic images.Spherical panoramas are borderless and seamless image objects used to create virtual tours.Bengkulu University, with the broadest environment in Southeast Asia, has more than 30 buildings with several floors and rooms.It's making it challenging to find the location of the building or event for incoming guests.The purpose of this study is to analyze the level of generation of a real perception of the introduction of the Bengkulu University building by a 360 0 virtual tour application.Usability testing is intended to analyze the level of perception generation, to introduce the Bengkulu University building to the guest.This research was carried out by collecting 360 0 photos for each building and room, then built a webbased virtual tour application that can display the location and directions to go to the building or office, then information on each building or outdoors will be displayed according to the shape of the object model 360 0 .Users can run a virtual tour application using a smartphone and VR-box to get a real perception like being in the building or room.Usability testing is done by making a questionnaire based on three components of usability testing (effectiveness, efficiency, and satisfaction), to obtain the level of perception generation from buildings with 3600 object modelling.Usability testing of the 360 0 Virtual Tour of University Bengkulu was carried out with a total of 100 respondents.The results were obtained by generating of usability testing were 94% (Effectiveness 96.1%, Efficiency 96.6%, and Satisfaction 89.2%).
Genre/Form Access in Library Catalogs: A Survey on the Current State of LCGFT Usage

This study provides analysis of a large online survey that was distributed to the cataloging community in 2018. The survey aimed to answer a number of important research questions to gain a general sense of the current state of Library of Congress Genre/Form Terms (LCGFT) usage. Findings include an overall broad acceptance of LCGFT, suggesting that the LCGFT project has been successfully embraced as a new controlled vocabulary; however, the adoption of the vocabulary remains uneven, especially between different types of institutions and different areas of the LCGFT vocabulary. Additionally, training points to a much-needed area for improvement as the survey found that the vast majority of non-users of LCGFT had never received vocabulary training. Survey results also suggest that retrospective LCGFT application, particularly using automated means, presents forthcoming challenges for librarians and library IT staff. Despite these limitations and challenges, survey results make it clear that LCGFT has become a widely accepted part of the bibliographic universe that helps to make genre and form information explicitly accessible to library users.
Statistical Inference via Data Science: A Modern Dive Into R and the Tidyverse

The monograph belongs to the The R series, and it can serve as a convenient way for learning data science and statistics simultaneously with the R language. The textbook consists of four parts, ele...
Exploring the Effects of Using Game on Students’ Vocabulary Mastery: A Case Study in Instructional Material and Media Development Class

The fundamental issue is how to teach vocabulary as effectively as possible, since vocabulary became the most important factor in learning English.Vocabulary should not be learnt separately or by memorization without understanding.This aimed to explore the effectiveness of using game in Instructional material and media development (IMALD) classroom.This study adopts a qualitative methodology.Vocabulary test and observation were also done to support this research.The result of this study indicates that there is improvement on student vocabulary mastery.Student motivation and involvement during teaching learning process was also increasing.The results of the tests showed that most of students get high scores when translating English vocabulary.The implementation of games and other supporting fun activities succeeded in helping students to learn and understand new vocabulary.Overall, using games during teaching English was successful in improving students' vocabulary mastery.Implications of the study suggested that teachers should apply games in teaching vocabulary in order to provide students an effective learning.
A DIJKSTRA ALGORITHM IMPLEMENTATION IN DETERMINING SHORTEST ROUTE TO MOSQUE IN RESIDENTIAL CITRA INDAH CITY

The application of artificial intelligence (Artificial Intelligence) for problem-solving in the field of computer science has experienced rapid development from year to year as the development of artificial intelligence itself. Problems involving searching (searching) is one example of the use of artificial intelligence that is quite popular to solve various kinds of problems. In daily activities, the use of roads is always an unavoidable activity, so determining the shortest path from one point to another becomes a problem that is often encountered. This is also felt by residents who live in a large enough housing. Sometimes to be able to reach the destination they are often confused in deciding which way to go to get the shortest distance to the destination. Citra Indah City Housing is a residential area in the Jonggol District area, Bogor Regency, developed by the Ciputra group. Within the Vignolia Hill Cluster, there is a mosque located on the northwest corner of the Vignolia Hill cluster or at the western end of the AH.17 block. A large number of blocks raise problems regarding the shortest route that can be taken by residents to get to the mosque. So, the purpose of this research is to determine the shortest path taken by citizens to get to the mosque. The method used is to apply the Djikstra algorithm which is able to produce the shortest route for residents to get to the mosque.
Visual Relationship Detection with Contextual Information

Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predictions of the relationships.But real-world image relationships often determined by the surrounding objects and other contextual information.In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.The core of the framework is a relationship inference network, which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the image.Experimental results on Stanford VRD and Visual Genome demonstrate that the proposed method achieves a good performance both in efficiency and accuracy.Finally, we demonstrate the value of visual relationship on two computer vision tasks: image retrieval and scene graph generation.