Reviewerly/exHarmony huggingface dataset:
Motion Statistic Based Local Homography Transformation Estimation for Mismatch Removal

Accurately establishing pixel-level correspondence between images taken from same objects is an essential problem in many computer vision applications, such as 3D reconstruction, simultaneous localization and mapping (SLAM), and augmented reality (AR). Existing local feature descriptor based image matching approaches are unable to avoid mismatches which cause negative effects to the above mentioned applications. This paper proposes a motion statistic based local homography transformation estimation method for removing mismatches. The proposed method estimates local homography transformations between the grids in a pair of images and then classifies each match as correct or incorrect by checking whether it is consisting with the corresponding local homography transformation or not. Experimental results on the widely used Oxford affine image dataset show that the proposed approach finds out more potential correct matches than the existing state-of-the-art method.
SOFM for Image Compression Based on Spatial Frequency Band-Pass Filter and Vector Quantization

This chapter presents image compression based on SOFM and vector quantization (VQ). The purpose of this chapter is to show the significance of SOFM with bandpass filter in process of image compression to increase compression ratio and to enhance image compression effectiveness. Image compression by SOFM model is presented and consists of three stages: The first is band-pass filter. The result experiments used Lena.bmp, girl256.bmp, and show compression in block image 16x16 given best compression ratio with a small signal-noise ratio (SNR).
텍스트 마이닝을 활용한 주택가격 변동에 관한 연구

The real assets such as a real estate account for more than 70% of the average assets per household in Korea, and apartment transactions are the most common among all housing transactions. In this way, the apartment can have a representative in Korea's housing market. Meanwhile, the housing market is influenced by various factors such as interest rate, economic changes and government policies. One of the main factors influencing the flow of the real estate price is the news articles of the press. In this study, we analyzed the news articles, which are atypical data, using text mining to analyze whether the frequency of the words frequently mentioned in the news articles of the press related to apartment price flow has a significant effect on apartment prices. As a result of the analysis, the upturn, parcel out, sale, Gangnam keyword were exposed as the top in the news articles. Based on this, it can be seen that some words with high frequency of exposure in news articles have a statistically significant relation with apartment price flow through granger causality test, impulse response function and variance decomposition of vector autoregression (VAR) model.
A Novel Cooperative Divide-and-Conquer Neural Networks Algorithm

Dynamic modularity is one of the fundamental characteristics of the human brain. Cooperative divide and conquer strategy is a basic problem solving approach. This chapter proposes a new subnet training method for modular neural networks with the inspiration of the principle of “an expert with other capabilities.” The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical method and its advantage is theoretically discussed. Both methodology and empirical study are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical learning method, the average testing error is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated. Finally, a mind-gut frame is proposed.
Object Recognition in Hand Drawn Images Using Machine Ensembling Techniques and Smote Sampling

With the increase in the advancement of technology, the size of multimedia content generated is increasing every day. Handling and management of this data to extract the patterns result in a more optimized and efficient way is the need of the hour. In this proposed work, we have presented techniques to classify the stroke-based hand-drawn object. We have used the Quick Draw dataset which is a repository of approximately 50 million hand-drawn drawings of 345 different objects. Our research presents an approach to the classification of these drawings created using hand strokes. We are converting the given raw image data, to much more simplified and concise data and then performed oversampling on data belonging to classes with fewer instances using Synthetic Minority Over-sampling Technique (SMOTE) method to balance the distribution of each class in the dataset. Finally, we are classifying the drawings using K-Nearest Neighbor (K-NN), Random Forest Classifier (RFC), Support Vector Classifier (SVC) and Multi-Layer Perceptron model (MLP) by working on their hyperparameters for the best-achieved classification result. The proposed solution attains the accuracy of 82.34% using best hyperparameter selection.
Can AI Tell Emerging Technologies: Evaluating the Importance of Quantitative Features of Technology

Many researchers and organizations have been archiving and analyzing vast documents and data for technology evaluations and emerging technology mining. Korea Institute of Science and Technology Information (KISTI) as one of them, has been collecting various technological data from technical literatures, such as patents and papers, and developing techniques to analyze and retrieve various quantitative features from it. Lately, the demand of utilizing our resources, data and technologies, for developing an intelligent technology information system which output is objective, consistent, and explainable, has been increase. By applying the latest advanced artificial intelligent techniques, deep learning, to our data and system, it is possible to improve our capability of evaluating technology and mining future emerging technology. For this reason, it is necessary to investigate and evaluate the effectiveness of each quantitative features of technology which are retrieved from technical literature analysis. In this paper, we present the results of our study of testing the effectiveness of various quantitative features of technology, which are being referred by human experts in technology evaluation and future emerging technology mining process, in both empirical and statistical ways. In the empirical approach, an artificial intelligent model is built to simulate the human expert group for emerging technology mining and the change of the performance is observed while the training features are changed. In the statistical approach, the relations between the basic distribution variables of data and the decision making is analyzed.
Supporting data-driven exploration in NSW

SummaryNSW legislative changes in 2016 mean that from 1 June 2021, confidential mineral exploration company reports will become open file 5 years after submission. This will result in a large amount of previously confidential data being released on 1 June 2021.In conjunction with the data release, substantial work is being undertaken to improve the quality, accessibility and usability of the datasets. To provide the most functional regional scale geochemical datasets possible, the usability of the datasets must be assessed by following a standard workflow of data validation > exploratory data analysis (EDA) > normalisation > interpolation.Due to the large size of the datasets, programmatical solutions that expedite the validation and EDA processes are necessary. This presentation will give examples of methods used to condition the NSW geochemical data. An innovative data normalisation process will also be demonstrated, addressing the complexity of dealing with spatially overlapping and varied sample methods. An interpolation process that is suitable for use on large volume, large-scale datasets will also be demonstrated.
FORMATION OF STUDENTS' MOTIVATION TO STUDY BUSINESS ENGLISH

Purpose of the study: The article describes the view of authors to the formation of students' motivation to study Business English.&#x0D; Methodology: In the research, the authors investigate a number of tasks that teachers need to solve in the course of formation of students' motivation. The article shows the analysis of category of motivation in Russian and foreign pedagogics, studies the concept of educational motives and conditions of emergence of motivation in pedagogical process.&#x0D; Results: The authors theoretically describe the problem of the formation of students' motivation to study Business English, the model of formation of motivation, pedagogical conditions that make effective impact on increase in students' motivation when they study Business English. The article describes experimental work on efficiency check of formation model of students' motivation to study Business English.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: In this research, the model of the Formation of Students' Motivation to Study Business English is presented in a comprehensive and complete manner.
PENINGKATAN KETERAMPILAN MENULIS DENGAN PENDEKATAN KOMUNIKATIF PRAGMATIK PADA MUATAN PEMBELAJARAN BAHASA INDONESIA DI KELAS IV SDN 04 TAPI SELO

Lack of writing skills to express ideas in class IV SDN 04 Tapi Selo is a problem that must be resolved, because it will have a negative impact on other learning outcomes in school. The problem in writing skills is certainly directly related to skills, listening, reading and speaking. To solve this problem, the authors made improvements by carrying out the Pragmatic Communicative approach to Indonesian language learning, which is a language approach with the context of the meaning of expressions that must be understood by others and in accordance with the situation and conditions of language. The study was conducted in three cycles, from August 24, 2018 to September 22, 2018. The results of the study have shown that the pragmatic communicative approach to learning Indonesian has improved the skills of students, to write their ideas in writing reaching 50%.
Criteria for evaluating the technical universities of the world using Forbes rating

The analysis of quantitative and quality indicators of ratings of technical and electrotechnical universities of the world by the international, national and private criteria is considered and made Forbes rating . The similar analysis will give the chance to be guided at choice of profession taking into account many factors.
Crime Scene Sketches Classification Based on CNN

Crime scene sketch plays a significant role in criminal investigation. In China, the crime scene sketches of all criminal cases should be uploaded to the National Criminal Scene Investigation Information System (NCSIIS). However, there are wrong images and low quality sketches frequently being uploaded to NCSIIS, which would make crime scene sketches unable to undertake their tasks. Yet, checking the sketches uploaded to NCSIIS still reamins as a manual work by the police officers. In this paper, we focus on a new problem of crime scene sketches classification. Firstly, a crime scene sketches database was constructed, sampled from NCSIIS. Secondly, an automatic crime scene sketches classification method is proposed based on CNN. A new architecture, namely Crime Scene Sketch Net (CSS-Net) is designed for high accuracy. Experiments are conducted on the database constructed. The experimental results show that the method proposed by this paper is of good performance.
The Probability of Dual Hesitant Fuzzy Event and Its Applications to Bayesian Network Inferences

The concepts of probabilities and conditional probabilities of dual hesitant fuzzy events have been introduced. The basic properties, the countable additive formula, the law of total probability, the Bayes formula and the continuity of probability of classical probability theory are generalized to that of the probabilities of dual hesitant fuzzy events in frequently used \( t \)-norm and \( s \)-norm. The correlation coefficient between two dual hesitant fuzzy sets has been defined in random environment. An example of the dual hesitant fuzzy Bayesian network inference is presented finally.
Geology Controls Grade Variance at Grasberg Cu-Au Porphyry Deposit, Papua-Indonesia

Abstract The Grasberg mining district is located in the core of the highlands of West New Guinea island, in the central ranges of the Papua Province of Indonesia. The collision between Australia Plate and Pacific Plate on Late Miocene generate NW-SE compressional structures orientation that produced reactivation of NE striking dilation to control the multiple series intrusion of host to Cu-Au porphyry deposit (Sapiie &amp; Cloos, 2004). The oldest of Dalam Intrusion characterized by andesitic rock texture, phyllic to potassic alteration, low-moderate Cu-Au grade (0.25&lt;EqCu&lt;1). It is intruded by Main Grasberg Intrusion (MGI) with potassic alteration dominantly, quartz monzodioritic, high Cu-Au grade (EqCu&gt;1). The latest Kali Intrusion characterized by propylitic to potassic alteration, quartz monzodioritic, dominant barren with EqCu is below the cut-of-grade (EqCu &lt;0.25). The forecast block model is built in quarterly basis based on technical sampling assay method combination between diamond drill core and blasthole cutting samples. The actual grade model develop in daily basis following incremental blasthole cutting samples and be used for the ore reconciliation in monthly basis. The Cu-Au grade variance can be a loss and gain comparing to the forecast block model up to +/-30%. Focused on the negative variance or grade loss within pit radial sector 300°-330° at Dalam Intrusion and Southeast Kali host to phyllic-potassic alteration, and positive variance or grade gain observed dominantly along contact intrusion host to propylitic-potassic alteration. The veinlet relationships observed early vein bearing Cu-sulfide in NE direction intensively cut by SE and NS veins that filled by barren quartz-anhydrite±gypsum and quartz-sericite-pyrite as the latest hydrothermal fluid and affected on grade variance results. The spatial data distribution also influenced on grade variance model related to the alteration and mineralization style within the Grasberg Cu-Au porphyry deposit.
DROPS Survey and Reporting Based on Asset Inspection Management System a Reliable Inspection Tool to Manage DROPS in Offshore &amp; Onshore Drilling Rigs, Empowering the Work Site Supervisor to Enhance Overall Efficiency of the System to Performance

Abstract Dropped Objects pose the number-one risk of serious injuries, fatalities, and equipment damage in several industries across the globe and is more prominent in Off shore drilling condition, where more personnel perform their duties within a confined space. Dropped Object Prevention Scheme [DROPS] is intended to be applied to operations in which Dropped Objects could cause harm to people, equipment, and/or the environment. Though Companies develop documented DROPS at relevant levels/departments within the organization as per objectives and targets developed as part of their company's existing Health, Safety, and Environment and/or operational goal-setting processes. Third Party DROPS Survey and Reporting is a catalyst to integrate &amp; guide the scheme to meet its objectives. This paper emphasizes the importance of a robust DROPS, reinforced by repeatable and reliable DROPS Survey &amp; Reporting, in compliance with present industry standard and at the end extend its SMARTER possibilities of expansion with use of RFID Tags and Artificial Intelligence. Our cloud-based Asset Inspection Management System "SOSINSPECTIONEERING" through which (1)Each individually identified DROPS Item shall have a Unique Asset ID,(2)Generate individual DROPS Report for each DROPS Asset, (3)Generate a DROPS Master Asset Register capturing the main features of DROPS Reports and linking it to the latest DROPS Report, (4)Option for Client to download a excel picture book with all 3 levels of Securing and a detailed item specific checklist, (5)The Check list downloaded to ‘Tab’ or any equivalent device provides the work site supervisor to use it in his/her regular verification inspection and report compliance/non-compliance report for further analysis. We are also developing the system to next level of application such as, (a)Tagging each DROPS Asset with RFID Tags, (b) RFID Tag to load with latest DROPS Report for that Asset ID, (c)Each DROPS Reports of the Asset to linked to its other documentation like (i)Manufacture Certificate; (ii)OEM/Other DATA Sheets; (iii)Latest Lifting Certificate &amp;NDT Inspection, (d)RFID scanner with the Work Site Supervisor to Download the DROPS Report or ‘Drone’ to scan the RFID Tag to Down load the latest DROPS Report to the Remote System for verification, (e)Work Site Supervisor of Third Party DROPS Surveyor with Camera can take real time Photos to edit the DROPS Report and modify the ‘Photo Book’ in real time, (f)similarly Camera fitted in a ‘Drone’ can go one step beyond the checklist to use artificial intelligence to recognize patterns and takes a probabilistic approach to verify the compliance by using more historical data.
Web Log Analysis Based on Hadoop Technology

The world is now in the information age. It is important to explore the valuable information from massive and diverse data. The analysis of the website log has great significance in practical application. Hadoop framework can provide reliable, scalable, distributed processing of large data sets. Hadoop series framework is utilized to conduct offline data processing and analysis for the website access log. The number of different browser, which visits to the master site access log, is counted. Different browser kernel, operating system, terminal type and other content access times are calculated and analyzed.
Decompressing Lempel-Ziv Compressed Text

We consider the problem of decompressing the Lempel-Ziv 77 representation of a string S of length n using a working space as close as possible to the size z of the input. The folklore solution for the problem runs in O(n) time but requires random access to the whole decompressed text. Another folklore solution is to convert LZ77 into a grammar of size O(z log(n/z)) and then stream S in linear time. In this paper, we show that O(n) time and O(z) working space can be achieved for constant-size alphabets. On general alphabets of size σ, we describe (i) a trade-off achieving O(n log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sup> σ) time and O(z log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-δ</sup> σ) space for any 0≤ δ≤ 1, and (ii) a solution achieving O(n) time and O(z log log (n/z)) space. The latter solution, in particular, dominates both folklore algorithms for the problem. Our solutions can, more generally, extract any specified subsequence of S with little overheads on top of the linear running time and working space. As an immediate corollary, we show that our techniques yield improved results for pattern matching problems on LZ77-compressed text.
A Guide for Mobile-Assisted Language Learning in Informal Settings

The evolution of mobile devices, web technologies, and learning paradigms has been shaping MALL practice. Today, informal learning has become more important than ever since the number of resources increased and the interaction styles became multidimensional. Computer-assisted language learning was converted into different forms, and mobile-assisted language learning is one of them. In its early history, the pedagogical dimension of MALL was criticized for being very behavioristic. However, the collaborative and creative nature of Web 2.0 tools contributed to methodological changes in the MALL tradition. Although there are many mobile applications designed to improve language proficiency, cloud-based tools and other mobile applications can feed the language skills informally if the designers benefit from big data.
A Novel Approach to Overcome the Limitations of Power Iteration Algorithm Designed for Clustering

The rise of Big Data has generated a new challenge to develop and refine algorithms to process and extract useful information. Power Iteration Clustering is the most efficient and scalable algorithms that can deal with larger datasets. It uses the power method for finding the eigen values and vectors. Despite its advantages the main disadvantage is that the convergence of this method relies on the magnitude of the leading eigen values that do not guarantee convergence. Moreover, it gives only one pseudo eigen value and it seems to be difficult when it is multideimensional. Hence, the algorithm has been refined using various acceleration techniques to make it more efficient to handle larger datasets. Various experiments was conducted to check its validity and it has been proved that the power method modified is efficient and suitable for processing larger datasets more accurately.
Review on Preliminary Study on Student’s Motivation in Learning History

Learning history is compulsory to every student in Malaysia. However, most of the students are not interested in this subject. This study aims to gather the preliminary data to investigate student’s motivation in learning history and their perspective on integrating technology into the classroom. This study used convenience sampling technique (n = 83 students) and collected data through an instrument distributed among the students. The results indicate that the current materials for learning history are not sufficient and satisfactory. Therefore, this study proposed a new technology namely Virtual Reality technology in learning history and positive feedbacks received in incorporating this technology in learning history subject into the classroom.
Practical Insights for XR Devised Performances

Devised performances are produced through improvisations and theater games in order to develop a story. Devising has a tradition of using supplementary media. However, its collaborative storytelling process presents design challenges when using Reality Media (XR). We present a series of challenges and design insights derived from the production of a devised performance with XR, The Safety Show. This novel performance utilized 20 mobile devices networked to a stage manager running a shared XR experience with live actors. The work sketches a rich space for research and creative expression.
Large-Scale Ontology Alignment- An Extraction Based Method to Support Information System Interoperability

Ontology alignment is an important way of establishing interoperability between Semantic Web applications that use different but related ontologies. Ontology alignment is the process of identifying semantically equivalent entities from multiple ontologies. This is not always obvious because technical constraints such as data volume and execution time are determining factors in the choice of an alignment algorithm. Nowadays, partitioning and modularization are two main strategies for breaking down large ontologies into blocks or ontology modules respectively to align ontologies. This article proposes ONTEM as an effective alignment method for large-scale ontology based on the ontology entities extraction. This article conducts a comprehensive evaluation using the datasets of the OAEI 2018 campaign. The obtained results are promising, and they revealed that ONTEM is one of the most effective systems.