Reviewerly/exHarmony huggingface dataset:
Associating Field Components in Heterogeneous Handwritten form Images using Graph Autoencoder

We propose a graph-based deep network for predicting the associations pertaining to field labels and field values in heterogeneous handwritten form images. We consider forms in which the field label comprises printed text and field value can be the handwritten text. Inspired by the relationship predicting capability of the graphical models, we use a Graph Autoencoder to perform the intended field label to field value association in a given form image. To the best of our knowledge, it is the first attempt to perform label-value association in a handwritten form image using a machine learning approach. We have prepared our handwritten form image dataset comprising 300 images from 30 different templates having 10 images per template. Our framework is experimented on different network parameter and has shown promising results.
Physical meets digital

The high utility and usability of paper sticky notes support workflows and social dynamics of collaborative design activities and methods like affinity diagramming. In this chapter, we show how these natural collaboration activities can be blended with computational power by applying our framework Blended Interaction for a case study on affinity diagramming. Based on four domains of design, we embed our design solutions in a specific physical environment, preserve workflows, and emphasize individual and social interaction. Our proposed design solution to augment sticky notes with digital power blends the benefits of physical materials with the digital power of interactive surfaces, tangibles, and digital pens in an outstanding way. We hope that our design solutions inspire other researchers and practitioners to find innovative solutions that carefully blend real-world practices with the power of digital computing.
A Simple and Convex Formulation for Multi-label Feature Selection

In recent years, multi-label study has received extensive attention and research in many fields. The feature dimensions of a multi-label data set are high but contain a large amount of noise as well as irrelevant and redundant features. This not only leads to huge storage and time overhead, but also brings serious dimensional disaster problems, making multi-label learning tasks very difficult. Therefore, how to effectively select multi-label features is an important research content in multi-label learning. However, most of the current methods are converted from the methods of single-label feature selection, and feature selection is easy to fall into the local optimal heuristic search strategy. Time complexity has always been the biggest problem of such methods. Based on those considerations, our paper proposes a fast and effective multi-label feature selection method, which uses the optimization strategy to replace the previous search strategy for multi-label feature selection, and transforms the search problem into convex optimization problem. Therefore, the time performance of the traditional method is improved by two to three orders of magnitude. Finally, the experimental results of five evaluation indicators on the four data sets show that our method is superior to many popular methods in feature selection field.
Implementasi Forward Chaining Dan Breadth First Pada Sistem Pakar Diagnosa Gangguan Kehamilan

Expert system is a computer-based system that uses knowledge, facts and reasoning techniques in solving problems that can usually only be solved by an expert. Inference method is a thinking mechanism and reasoning patterns used by the system to reach conclusions, while forward chaining is an inference method that traces starting from a set of facts by looking for rules that match the guesswork / hypothesis that leads to conclusions. Expert systems can be applied in various fields, including in medicine, for example how to diagnose a disease. Pregnancy is a happy moment for a mother, but there are still many problems with pregnancy problems in pregnant women because many do not know about pregnancy disorders that usually occur. Puskesmas Bontang Utara I has a program to reduce mortality in pregnant women and infants, by providing guidance and socialization on how to maintain a correct pregnancy. To facilitate public understanding and provide information related to pregnancy disorders, the researchers made the application system expert pregnancy disorders built using the PHP programming language, Dreamweaver 8 as an editor and MySql as the database. Software development methods use the waterfall model and system testing using blackbox. The expert system application in this study provides information on diagnosing pregnancy disorders in pregnant women such as consulting with experts. The diagnostic process starts with the way the user selects or inputs the type of complaint that is felt in accordance with the symptoms provided by the system. By using the forward chaining method and breadth first search search technique, the complaints that have been entered will be matched with the symptoms and types of disturbances stored in the database. So that the results of the diagnosis will be produced as the results of the diagnosis of experts. With this information helps pregnant women recognize pregnancy disorders during pregnancy.
Fully Adaptive Savitzky-Golay Type Smoothers

The problem of adaptive signal smoothing is considered and solved using the weighted basis function approach. In the special case of polynomial basis and uniform weighting the proposed method reduces down to the celebrated Savitzky-Golay smoother. Data adaptiveness is achieved via parallel estimation. It is shown that for the polynomial and harmonic bases and cosinusoidal weighting sequences, the competing signal estimates can be computed in both time-recursive and order-recursive way.
Document Typist Identification by Classification Metrics Applying Keystroke Dynamics Under Unidealised Conditions

Keystroke Dynamics is the study of typing patterns and rhythm for personal identification and traits. Keystrokes may be analysed as fixed text such as passwords or as continuous typed text such as documents. This paper reviews different classification metrics for continuous text, such as the A and R metrics, Canberra, Manhattan and Euclidean and introduces a variant of the Minkowski distance. To test the metrics, we adopted a substantial dataset containing 239 thousand records acquired under real, harsh, and unidealised conditions. We propose a new parameter for the Minkowski metric, and we reinforce another for the A metric, as initially stated by its authors.
Automated Functional Size Measurement: A Multiple Case Study in the Industry

Automating functional size measurement (FSM) for software applications that use specific development frameworks is a challenge for the industry. Although FSM automation brings benefits such as savings in time and costs, and better measure reliability, it is difficult to implement. In this paper, we present a multi-case study that evaluates the accuracy of an automated procedure for software size estimation in the context of a software development company. This procedure is implemented by a tool called FastWorks FPA, which obtains the IFPUG FPA function point estimation of software applications modeled in the company’s FastWorks framework. We describe the measurement process used by the tool, and discuss the results of the case studies. The accuracy (magnitude of relative error) of the measurements computed by the tool ranged between 3.9% and 12.9%, based on the total unadjusted function points. These results provide evidence for the feasibility of automating the counting process, as the tool’s estimated functional size reasonably approximates the result of specialists’ manual counting.
Grey Fault Detection Method Based on Context Knowledge Graph in Container Cloud Storage

In the field of container cloud storage cluster resource scheduling, the activities, such as how to schedule resources according to load changes, and migrate according to resource conditions, are mainly considered. These activities bring about frequent changes in the context and also changes in the application’s operating environment. They pose great difficulties in locating fault, especially the location of grey faults, which affect the operation of the application in the containers. Therefore, in order to ensure the normal operation of the application, grey fault detection method is proposed, which establishes a relationship knowledge graph for the relationship between the context change and the grey fault by studying the change of the application attention feature, which are brought by the context change. The method introduces temporal and spatial snapshot group architecture to solve a large number of situational temporal queries caused by too large structure of knowledge graph. The method is validated in the container cluster project and the Google open source dataset, which can effectively detect grey fault scenarios and the accuracy rate has been improved by more than 90%.
Future Teachers’ Training in Identification and Development of IT Giftedness at Schools of the Republic of Tyva

The article studies the issue of future bachelors’ of education professional training improvement (education profile — “computer science”) in order to identify and develop children’s IT‑giftedness in cultural and educational environment of the Republic of Tyva. The research aims at developing the supplement to the system of methodical training, the use of which will ensure the future bachelors’ willingness to identify and develop the Tuvan students’ IT‑giftedness in the course of teaching Computer Science. Dynamics of changes in the future bachelors’ training is determined during the pedagogical experiment, based on a set of empirical methods (observation, questionnaire survey, testing, prolonged pedagogical experiment, the method of expert evaluation, self-evaluation) as well as statistical ones (measurement and mathematical processing of experimental data). The authors propose to introduce additional components (the complex of methodically oriented courses) to the curriculum, the courses based on ethno-psychological, ethno-cultural, ethno-social features of Tuvan cultural and educational environment. Effectiveness of the approach, proposed for the bachelors’ of computer science improved training for working with gifted children in Tuvan schools, is experimentally proved
MedNLU: Natural Language Understander for Medical Texts

Natural Language Understanding is one of the essential tasks for building clinical text-based applications. Understanding of these clinical texts can be achieved through Vector Space Models and Sequential Modelling tasks. This paper is focused on sequential modelling i.e. Named Entity Recognition and Part of Speech Tagging by attaining a state of the art performance of 93.8% as F1 score for i2b2 clinical corpus and achieves 97.29% as F1 score for GENIA corpus. This paper also states the performance of feature fusion by integrating word embedding, feature embedding and character embedding for sequential modelling tasks. We also propose a framework based on a sequential modelling architecture, named MedNLU, which has the capability of performing Part of Speech Tagging, Chunking, and Entity Recognition on clinical texts. The sequence modeler in MedNLU is an integrated framework of Convolutional Neural Network, Conditional Random Fields and Bi-directional Long-Short Term Memory network.
A breast tissue characterization framework using PCA and weighted score fusion of neural network classifiers

This work proposes a 4-class breast tissue pattern classification using principal component analysis (PCA) and weighted score fusion of neural network classifiers. The fixed size ROIs are extorted from the middle of the breast around the glandular ducts for 480 mammographic images acquired from Digital Database for Screening Mammography. The GLCMmean feature descriptors are figured out for every input sample, i.e., ROI for {d ϵ [1, 2, …,15]} where d is known as interpixel distance. The proposed breast density classification system consisting of 2-stages, (i) stage one: It consists of single 4-class PCA-NN1,2,3,4, which yields the probability score vector that indicates the probability score to test the belongings of an ROI to a specific class; (ii) stage two: this stage consists of six binary PCA-NN classifiers for classification between individual BIRADS classes. After passing all testing instances through stage two, the obtained accuracy is 92.1% with ICA values of 96.6%, 91.6%, 80.0%, and 100% for B1, B2, B3, and B4 classes respectively. It can be observed that after passing all testing instances through stage two, the accuracy boost is from 79.6% to 92.1%.
Fast Korean Syllable Recognition with Letter-Based Convolutional Neural Networks

Convolutional neural networks are widely used for character recognition. However, a large number of characters (that is, a number of classes) in CJK (Chinese, Japanese, Korean) languages can lead to problems in a straightforward approach. Also the fact that the distribution of the characters in these languages is far from a uniform distribution can cause additional problems. For example, about 10% of Korean syllables cover 99.9% of Korean texts, so image search of rare syllables for a training set can be challenging. In this paper we introduce a letter-based Korean syllable recognition approach which is able to resolve the problems described above. The core idea of the approach is to recognize the letters that make up a syllable separately instead of recognizing the whole syllable. Additional advantages of the proposed approach are high computational speed and small size of the model. The paper also briefly describes model deployment.
Understanding Instant Messaging in the Workplace

In the workplace, effective communication plays a vital role. Many work tasks require communication which includes planning, organizing, motivating and controlling. Communication establishes relationship between superiors and their subordinates, and it determines the quality of relationship between colleagues. Currently, computer mediated communication such as Instant Messaging (IM) is used in complimentary to or as a replacement of e-mails to complete tasks. This paper discusses how IM has evolved and being adapted at work. Reviews conducted from previous studies show the impact and how people practice IM in workplaces to create a better communication and relationship in areas such as education, health care, business and others. Research shows that communication through IM applications can support or improve the quality of communication, work task and the relationship between colleagues. Based on the current trend of IM usage is the workplace, the paper proposes a qualitative method to study WhatsApp mobile application usage in the workplace. This approach will be used to observe how IM is adapted in WhatsApp group conversation in the workplace. The goal of this proposed study is to observe the use of WhatsApp at work using the existing framework, develop an IM usage model at the workplace and test and validate the IM model to make it more workplace-friendly that encourages to full implementation as a medium of communication in the workplace.
Sequential prediction for imbalanced data stream via weighted OS-ELM and dynamic GAN

This paper focuses on the problem of sequential prediction for imbalanced data streams. A novel hybrid algorithm called Weighted OS-ELM and Dynamic Generative Adversarial Nets (GAN-WOSELM) is presented for handling this issue. In the data reconstruction stage, GAN is utilised for generating lifelik e minority class samples to equilibrate the data distribution. Then a principal component score threshold helps judge unusual data. In the model update stage, the new constructive ELM is applied to forecast time-varying data chunk. After concerning fitting accuracy and data change, the analytical relationship between the new weight and the shifting imbalance ratio is determined. Therefore, the GAN-WOSELM can update weight quantificationally, and it avoids interactive parameter optimization. According to the suitable weight for the arriving chunk, the proposed method is able to perceive the changeable data distribution and do the adaptation by itself, thus building a reliable model with a low fitting deviation. Numerical experiments are conducted on four different kinds of UCI datasets. The results demonstrate that the proposed algorithm not only has better generalisation performance but also provides higher numerical stability.
Bags of tricks for learning depth and camera motion from monocular videos

Based on the seminal work proposed by Zhou et al., much of the recent progress in learning monocular visual odometry, i. e. depth and camera motion from monocular videos, can be attributed to the tricks in the training procedure, such as data augmentation and learning objectives. Herein, we categorize a collection of such tricks through the theoretical examination and empirical evaluation of their effects on the final accuracy of the visual odometry. By combining the aforementioned tricks, we were able to significantly improve a baseline model adapted from SfMLearner without additional inference costs. Furthermore, we analyzed the principles of these tricks and the reason for their success. Practical guidelines for future research are also presented.
Functional Model of the Head of the Third-Generation University Department

This article is dedicated to the development of a functional model of the head of a third-generation university department and introduction thereof into the activity of a modern university. The relevance of this model is determined by the fact that it allows universities efficiently respond, integrate and, the most important, manage the processes of accelerated technological development that drastically change all global economic and social landscapes today.The purpose of this study is to develop a functional model of the head of university structural unit; in the University of a third generation it’s not a chair, but a department. Departments are established in accordance with the principles of project and process approaches. Departments are subdivisions that unite larger academic communities than ones existing within the chairs and provide lecturers with higher academic freedom.We showed that universities seeking to be ranked “third-generation” need a new model of a leader who is able to be a director general for a dynamic and business institution, which meets the challenges of the 21st century, and an experienced research scholar who can develop the intellectual authority of the university.We revealed the factors affecting the qualification of the head of the university department for management activities in terms of a third-generation university: the strategic goals of the institute; quality of work performed; business qualities; leadership skills.We developed the process of building a leader’s qualification: it is a complex of organization and management bodies that provide an assessment of the management process, the results thereof and identification of factors affecting their quality.As a result of the study, we discovered the factors affecting the activity of department heads and proved their low efficiency using the method of expert assessments and diagnostic tools, such as Ishikawa and Pareto charts, and also developed requirements for the leader of new generation.The content of the study is of practical value, the conclusions and recommendations can be used to improve the current system of training the heads for management activities.
THE ANALYSIS OF ACADEMIC INFORMATION SYSTEM IN DIRGANTARA MARSEKAL SURYADHARMA UNIVERSITY USING ISO/IEC 25010

Dirgantara Marsekal Suryadarma University (Unsurya) needs to improve the information system for the students. So that it needs to do a software quality test in SIAKAD and to ensure the feature and the function run well. It has a goal to know the quality of a software by testing from functional suitability, performance efficiency, compatibility, security and maintainability standardized by ISO/IEC 25010. It is done to improve the quality of academic information system to be better. This research analyzed the software quality insurance of academic information system in Unsurya by testing the aspects from main menu such as transcript, study plan, lecturer’s evaluation of learning activity, the print out of exam card, the consultation of academic supervisor, consultation from Head of Study Program and student’s financial. Based on the suitability functional and efficiency performance testing using black box in Unsurya SIAKAD system, it runs well although some functions don’t work well. The result for compatibility in SIAKAD Unsurya, it works in line with the browser in the desktop or mobile. Based on the security test using audit system, it results the value 90 (B) with 1 medium risk and 9 low risk. Based on the maintainability test, it results SIAKAD which is able to give a system needed by the students.
Multivariate Geochemical Tectonic Discrimination: Practical Approaches, Limitations and Opportunities

SummaryModern machine learning approaches to multivariate geochemical tectonic discrimination overcome limitations of classical graphical methods, and leverage large public geochemical databases to achieve high classification accuracy. Here we document practical approaches to developing classification models using free and open source tools, and investigate methods for visualising classification uncertainties and high-dimensional spatial relationships. In applying these classification methods into deep time, issues of biases and secular change become apparent. We discuss some opportunities to build upon these models and provide richer insight for future classification models.
Hybrid Requirement Elicitation Techniques with Lean Six Sigma Methodology for an Enhanced Framework

Requirement Elicitation (RE) is the most important phase in software development life cycle. It is a complex process because it involves the communications between two group of people to get into understanding of what to be built and what need to be delivered from software perspective. The purpose of this paper is to introduce a hybrid framework which combine the RE techniques from analytical and synthetic category with Lean Six Sigma (LSS) method, aimed at improving RE process to produce a quality software requirement. A framework consists of three phases; design, implementation and analysis are proposed where an industry case study is demonstrated for validation. The new framework observed a significance acceptance by analysts where the new hybrid approach is easy to adopt though at absence of users at the early stage of requirement study. The result also shown that the entire RE process cycle time improved tremendously. The limitation of this paper because it only tested in one company and at a specific project. The main contribution of the paper is to provide new knowledge in combining the RE techniques with LSS at absence of end user.
Chinese-Vietnamese News Documents Summarization Based on Feature-related Attention Mechanism

The association analysis of cross-linguistic information is still a challenging problem in the task of multi-language summarization. To address this issue, we propose an LSTM framework based on feature-related attention mechanism to extract the summarization of Chinese-Vietnamese bilingual news. Firstly, the word embedding with multi-features is used as input to the model such as word frequency, sentence position and relevance. Then, the degree of elements co-occurrence in bilingual documents is analyzed, and the attention mechanism based on bilingual features is proposed to calculate the importance scores of sentences. Finally, the sentence with high score is selected and the redundant information is deleted according to the similarity analysis to generate summary. The results of comparison experiments show that the method has achieved good results.