Reviewerly/exHarmony huggingface dataset:
Dynamic phase measurement in shearography by clustering method and Fourier filtering

&lt;p&gt;Quantitative phase extraction is a key step in optical measurement. While phase shifting technique is widely employed for static or semi-static phase measurement, it requires several images with known phase shifts at each deformed stage, thus is not suitable for dynamic phase measurement. Fourier transform offer a solution to extract phase information from a single fringe pattern. However, a high frequency spatial carrier which is sometimes not easy to generate is required to solve the phase ambiguity problem. In this paper, we aim to propose an ideal solution for dynamic phase measurement. Four images with known phase shift are captured at the reference stage to analyze the initial phase information. After the object starts continuous deformation, only one image is captured at each deformed stage. A clustering phase extraction method is then applied for deformation phase extraction utilizing the phase clustering effect within a small region. This method works well for speckle image with low and medium fringe density. When the fringe density is high, especially in the case of shearographic fringe, information insufficiency inherent with merely one deformed speckle image often results in poor quality wrapped phase map with plenty of phase residues, which make phase unwrapping a difficult task. In the light of this limitation, a Fourier transform based phase filtering method is proposed for fringe frequency analysis and adaptive filtering, and effectively removes most of the phase residues to reconstruct a high quality wrapped phase map. Several real experiments based on shearography are presented. Comparison between the proposed solution and standard phase evaluation methods is also given. The results demonstrate the effectiveness of the proposed integrated dynamic phase extraction method.&lt;/p&gt;
The Analysis of Computerized Accounting Practicum: Study Case of Faculty of Economic, Universitas Catur Insan Cendekia

The purpose of this study is to analyze and compare whether the computerized accounting laboratory is in accordance with computerized accounting practices in the work environment to support teaching and learning activities. The subjects of this study are Kantor Jasa Akuntan Langgeng, accounting lecturers, and accounting students. This is a qualitative study with a case study approach. The data collection techniques used for this study were interviews and observation. Based on the research that has been done, similarities between the two computerized accounting practices were found. The similarities found are from the workflow in general, such as data collection and database creation. Meanwhile, during the pandemic, learning can be done using online methods and also using blended learning methods.
A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

&lt;p&gt;In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.&lt;/p&gt;
Employee Daily Report Application Using Flutter Framework (Case Study: PT. Planet Selancar Mandiri)

Planet Surf Retail company is a company that moves under PT. Planet Selancar Mandiri management has the first Planet Surf store located at Galleria Shopping Mall, Jogjakarta, since 1997 and still operates. Planet Surf has 54 stores spread across 31 cities throughout Indonesia and continues to grow to meet customer fashion needs. Each store has one leader and eight to ten employees. Nowadays, Planet Surf still uses third-party applications to help their jobs daily. The employees report their activity, employment, and progress using third-party applications, and the leader sees it. But the problem is third-party applications can not organize their report by name, date, and category. It makes there a condition that the leader misses seeing employees' reports, employees find it hard to report their activities, and misunderstandings between employees and the leader. In this research, the Author tries to develop an Android application to see if it can help employees and leaders finish their jobs. Features from the Application are daily reports, job reports, and progress reports, and the leader can create reports for all employees.
Pengembangan Media Pembelajaran Interactive Android Game Petualangan Syariahku pada Mata Pelajaran Layanan Lembaga Keuangan Syariah

As technology develops rapidly, adjustments the learning process are needed to achieve learning objectives. Curriculum 2013 with a scientific approach and technology ICT as an interactive learning tool is one of the methods to increase student interest and learning outcomes. Interactive Android game has a positive impact on motivation, enjoyment of learning, and confidence in the new information obtained. Equipped with various HOTS-based questions, appropriate designs, effective and efficient Android-based. This research aims to create interactive Android games for Islamic financial institution services class XI semester 1. The development process includes the analysis, design, development, implementation, and evaluation according to the ADDIE model by Dick and Carry. The media that has been developed is analyzed and tested on 20 students of Islamic banking class XI State Vocational High School 2 Mojokerto City. Average percentage results by experts and learners were obtained in the categories of "very decent" as well as "excellent". So it can be concluded that interactive Android game learning media for Islamic financial institution service subjects for class XI semester 1 is very suitable for use in the learning process and obtains positive responses from students. Suggestions for the next researcher is to develop interactive Android game learning media for class X, XI semester 2 and XII.
Teachers’ Perception on The Aim of Reading Material in an English Textbook

Teaching material takes an important part to the success of teaching and learning. Moreover, Teachers’ perception on the reading materials is essential because it gives valuable input about teaching materials used in the teaching learning process. The main source of teaching materials comes from textbook. This research analyzes reading materials in students’ English textbook for grade XI semester I. The purpose of this research is to describe how are the reading materials in the aspects of aims viewed from the teachers’ perception. Source of the data in this research is divided into two. First is the analysis checklist of reading materials in the students’ English textbook for grade XI semester I filled by three analysts. Two of the analysts are the English teachers at different schools while the other one is the researcher herself. Second is the interview gotten from the teacher who is also one of the analysts in order get more data about the teacher’s perception on the reading material especially about the strength and weakness of the aims of the material. The analyses found that the aims of the reading material fulfill the good criteria of reading aim.
Intuitionistic fuzzy neural network with filtering functions. An index matrix interpretation

Biological neurons and their connection in neural networks have motivated the creation of the architecture of artificial neural networks. In the previously considered cases, the description of the neural networks and their connections are described with standard matrices where the values for the weighting coefficients and biases are placed. By recalculating them, the artificial neural network is trained. The paper presents an approach for describing multilayer neural networks with Intuitionistic Fuzzy Index Matrix (IFIM). The neural network input was described in IFIM form, then the weight coefficients of the connections between the nodes of the input vector, and then activation functions of the neurons. The use of IFIM extends the understanding and description as well as the structure and use of multilayer neural networks.
InterCriteria Analysis with weight coefficients of objects or criteria

The idea of the InterCriteria Analysis (ICA) was generated by the authors in 2014. For the next (already 9) years, ICA has been an object of intensive research and applications. In the present paper, an extension of the ICA is introduced. In it, the criteria or the objects have their own weight coefficients, or priorities. In the particular case, when these coefficients are equal to 1, the standard ICA appears.
InterCriteria Analysis applied to the health problems declared by individuals in Bulgaria

In the current investigation the health problems in Bulgaria through the years 2008, 2014 and 2019 are observed. The datasets for self-reported chronic morbidity datasets by Eurostat data browser are used. InterCriteria Analysis (ICA) is applied to estimate the relationships between the health problems in different countries. The outcomes will present tendencies of chronic diseases appearance in the selected countries. The prevalence of health problems by countries and years is observed.
Evaluating the performance of catalyst and feedstocks in the fluid catalytic cracking process: Application of InterCriteria Analysis with weight coefficients of the objects

In the current investigation an evaluation of the performance of catalyst and feedstocks in the fluid catalytic cracking process is proposed. An application of the newly modified method of InterCriteria Analysis—with weight coefficients of the objects—is performed. The obtained results are discussed in the light of a comparison with the standard ICrA.
Evaluating the performance of catalyst and feedstocks in the fluid catalytic cracking process: Application of InterCriteria Analysis with weight coefficients of the criteria

In this paper, we will apply InterCriteria Analysis to evaluate the performance of two catalysts and two feedstocks in the fluid catalytic cracking process. For the purposes of this analysis, each object is given a weight coefficient which affects the final evaluation between every pair of criteria. After presenting our results, we discuss their implications.
Design of a Mobile Manipulator for Curtain Wall Installation

Abstract The installation of the high-altitude curtain wall is mainly completed manually with simple mechanical assistance. The construction process is characterized by high labor intensity, high risk, low efficiency and low quality. To improve the automation and intelligence of curtain wall installation, improve construction efficiency and reduce labor intensity, a mobile manipulator for curtain wall installation is developed. Firstly, the curtain wall installation process is analyzed. Secondly, kinematics modeling is established, and the forward and inverse kinematics are analyzed and solved. Finally, the experiment is carried out to complete the tasks of grasping, rolling over and transporting stone, glass, ALC plate and other dense and semi-dense materials.
Improving Performance and Energy Efficiency of the Classification of Data Streams on Edge Computing

In this work, we propose a loop transformation to improve performance and energy efficiency of ensembles. We compare the performance of our technique with three other strategies for improving energy efficiency and throughput in data stream classification using six state-of-the-art ensemble algorithms and four benchmark datasets. Our results show that software strategies can significantly reduce energy consumption. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, mini-batching with loop fusion improved energy efficiency by 136% on average and 456% in the best case.
APLIKASI E-MARKET SECARA ONLINE DENGAN MENGGUNAKAN CODE IGNITER DAN BARCODE

Nowadays, of course, friends are no longer strangers to Barcodes. Almost on every product packaging sold in shops and supermarkets, be it bread, masks, mineral water, to snacks, a box is printed consisting of thick and thin vertical lines. This is what is called a barcode, and is usually printed neatly in one corner of the product packaging. The goal is to design a payment application between sellers and buyers in supermarkets that is able to handle work in data management. The second is running a payment application as a solution for handling databases with accurate results and not easy to make mistakes in handling payments.Keywords : Barcode , code igniter
Implementation Of Gamification In Learning Media To Improve Student Activity And Learning Outcomes

The demands of the learning process during the current pandemic force teachers and students to carry out digital transformation in the learning process. Especially in the new normal where the learning process is done online. Thus, the learning media made by the teacher must also be adapted to online media so that the learning process can be maximized and increase the activeness of students in learning while online. This study aims to create an online learning media by applying the concept of gamification. The method used in this research is R&amp;D (Research and Development) using analysis, design, and development development models. Stages of analysis are carried out to determine the needs of students when learning online. Then in the design stage, the selection of media formats and media design is carried out, and finally at the development stage, the advanced process of making games with programming languages is carried out as a learning medium. The use of gamification learning media is very practical, which can be accessed by students through their devices at home via the internet network. In the learning process, this game can be used to assist teachers in achieving learning objectives so that they can increase learning activity and the value of students in one basic competency.
Sentiment Analysis of Citayam Fashion Week Phenomenon Using Support Vector Machine

&#x0D; &#x0D; &#x0D; &#x0D; Citayam Fashion Week is a phenomenon that displays a model doing a fashion show using distinctive and unique clothing when crossing a zebra cross as a catwalk. This phenomenon has received extraordinary attention and discussion from various circles and led to numerous pros and cons among the public and observers of society in Indonesia. Therefore, it is great importance to conduct the study on sentiment analysis of this phenomenon to determine society's sentiment tendency to provide government references and help decision-makers improve their policies. Sentiment analysis was performed using the Support Vector Machine based on the polynomial kernel. The results shows that the accuracy, recall, precision and F1-Score value of 95.61%, 95.66%, 96% and 95.55%, respectively. This study proved that the Support Vector Machine classifier with the polynomial kernel provides higher algorithm performance on text classification. Therefore, the government can use the result of this study to evaluate the existence of the citayam fashion week which may be followed by other phenomena.&#x0D; &#x0D; &#x0D; &#x0D;
Multichannel Approach for Sentiment Analysis Using Stack of Neural Network with Lexicon Based Padding and Attention Mechanism

Abstract Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance.
“Explain Thyself Bully”: Sentiment Aided Cyberbullying Detection with Explanation

Cyberbullying has become a big issue with the popularity of different social media networks and online communication apps. While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying. Recent laws like “right to explanations” of General Data Protection Regulation, have spurred research in developing interpretable models rather than focusing on performance. Motivated by this we develop the first interpretable multi-task model called mExCB for automatic cyberbullying detection from code-mixed languages which can simultaneously solve several tasks, cyberbullying detection, explanation/rationale identification, target group detection and sentiment analysis. We have introduced BullyExplain, the first benchmark dataset for explainable cyberbullying detection in code-mixed language. Each post in BullyExplain dataset is annotated with four labels, i.e., bully label, sentiment label, target and rationales (explainability), i.e., which phrases are being responsible for annotating the post as a bully. The proposed multitask framework (mExCB) based on CNN and GRU with word and sub-sentence (SS) level attention is able to outperform several baselines and state of the art models when applied on BullyExplain dataset. Disclaimer: The article contains offensive text and profanity. This is owing to the nature of the work, and do not reflect any opinion or stand of the authors.(The code and dataset are available at https://github.com/MaityKrishanu/BullyExplain-ICDAR .)
MemeGraphs: Linking Memes to Knowledge Graphs

Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies on the understanding of visual elements, language, and background knowledge. Thus, it is important to meaningfully represent these sources and the interaction between them in order to classify a meme as a whole. In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture. We compare our approach with ImgBERT, a multimodal model that uses only learned (instead of structured) representations of the meme, and observe consistent improvements. We further provide a dataset with human graph annotations that we compare to automatically generated graphs and entity linking. Analysis shows that automatic methods link more entities than human annotators and that automatically generated graphs are better suited for hatefulness classification in memes.
Improving Information Extraction from Semi-structured Documents Using Attention Based Semi-variational Graph Auto-Encoder

In this paper, we propose a semi-supervised system for information extraction from administrative documents, that learns from both labeled and unlabeled data. The document is modeled as a words graph where each node contains the textual, layout and visual features of the word and it is connected to its spatially close neighbors. Semi-supervised variational graph auto-encoders (VGAE) have proven efficient on graph-based tasks, but they usually separate the classifier from the encoder and decoder and don’t take full advantage of the VGAE model for the benefit of the classification. To optimize the classification as much as possible, we propose a semi-VGAE with an attention-based classifier that shares its layers with the VGAE encoder. This is further enhanced by proposing a VGAE loss managed by the classification loss. Experiments show that our model helps improve nodes prediction accuracy. We tested the architecture on two artificially generated datasets: Gen-Invoices and Gen-Payslips and one real dataset: receipts issued from the SROIE ICDAR 2019 competition. The latter data set yielded an important F1 score of 97.94%, placing our system among the best systems on this dataset.
TACTFUL: A Framework for Targeted Active Learning for Document Analysis

Document Layout Parsing is an important step in an OCR pipeline, and several research attempts toward supervised, and semi-supervised deep learning methods are proposed for accurately identifying the complex structure of a document. These deep models require a large amount of data to get promising results. Creating such data requires considerable effort and annotation costs. To minimize both cost and effort, Active learning (AL) approaches are proposed. We propose a framework TACTFUL for Targeted Active Learning for Document Layout Analysis. Our contributions include (i) a framework that makes effective use of the AL paradigm and Submodular Mutual Information (SMI) functions to tackle object-level class imbalance, given a very small set of labeled data. (ii) an approach that decouples object detection from feature selection for subset selection that improves the targeted selection by a considerable margin against the current state-of-the-art and is computationally effective. (iii) A new dataset for legacy Sanskrit books on which we demonstrate the effectiveness of our approach, in addition to reporting improvements over state-of-the-art approaches on other benchmark datasets.