Reviewerly/exHarmony huggingface dataset:
Most severe risk factors in software development projects in Kuwait

&lt;p&gt;&lt;span&gt;Software development projects are famous of high rate failure, this made an encouragement for researcher to investigate reasons of failure. However, literature covering under developed countries is uncommon. This research investigates the most severe risk factors in software development project in the country of Kuwait. A face-to-face questionnaire with 109 IT practitioners in Kuwait from government and private organizations was conducted. The findings reveal and identify risk factors that have a high impact on the budget, quality, and schedule. The aim of this study is to validate risk factor taxonomy through quantitative methods by experts in the field of IT. Another purpose of this research is to provide a comprehensive and updated review of risk factors from Kuwait to be presented for IT practitioners and researchers.&lt;/span&gt;&lt;/p&gt;
Using Timestamp Data to Assess the Impact of Voice Recognition on the Efficiency of Grossing Biopsies

Context.— Studies on the adoption of voice recognition in health care have mostly focused on turnaround time and error rate, with less attention paid to the impact on the efficiency of the providers. Objective.— To study the impact of voice recognition on the efficiency of grossing biopsy specimens. Design.— Timestamps corresponding to barcode scanning for biopsy specimen bottles and cassettes were retrieved from the pathology information system database. The time elapsed between scanning a specimen bottle and the corresponding first cassette was the length of time spent on the gross processing of that specimen and is designated as the specimen time. For the first specimen of a case, the specimen time additionally included the time spent on dictating the clinical information. Therefore, the specimen times were divided into the following 2 categories: first-specimen time and subsequent-specimen time. The impact of voice recognition on specimen times was studied using both univariate and multivariate analyses. Results.— Specimen complexity, prosector variability, length of clinical information text, and the number of biopsies the prosector grossed that day were the major determinants of specimen times. Adopting voice recognition had a negligible impact on specimen times. Conclusions.— Adopting voice recognition in the gross room removes the need to hire transcriptionists without negatively impacting the efficiency of the prosectors, resulting in an overall cost saving. Using computer scripting to automatically enter clinical information (received through the electronic order interface) into report templates may potentially increase the grossing efficiency in the future.
THE APPLICATION MODEL LEARNING GAMES ABC TO IMPROVE RESULT OF LEARNING

The main problem in this study is the low student learning outcomes of class V on natural science subjects due the lack of creative students to solving problems in science learning. The idea of student focus on the material in recognized. This study aims to improve student learning outcomes of natural science subjects through the application of ABC Games learning models in fifth grade students of SDN Biro. This type of research is a classroom action research consisting of 2 cycles with stages of planning, implementation, observation and reflection. The subjects of this study were the fifth grade students of SDN Biro with a total of 22 students consisting of 7 male students and 15 female students. Data collection techniques used were teacher observation skills sheets, student activity observation sheets, and student learning outcomes tests in the form of multiple choice questions totaling 10 numbers. The results obtained in cycle 1 are the percentage of classical learning completeness 75%, the percentage of teacher skills 75.83%, and the percentage of student activities 73.33%. In cycle 2 the classical learning completeness obtained 85.71%, the percentage of teacher skills 85.83, and the percentage of student activity 85%. So it can be concluded that the application of ABC Games learning model can improve the learning outcomes of science subjects in Class V Students of SDN Biro seen from the increase in learning outcomes from cycle 1 to cycle 2.
Comparative Analysis of Segmentation and Recognition Techniques for Offline Handwritten Words

A Pre-processing is the initial and vital phase in optical character recognition is the Pre-processing. Segmentation deals with the extraction of individual component from a document image. Number of techniques like projection profile, connected components, gaps between characters/components is reported in the literature for component extraction followed by feature extraction and recognition of the individual component. These techniques gives good results if components are isolated but fails if components are touched, shadowed or skewed. A novel technique is required to address such issues to enhance the recognition rate. The problem of segmentation for Roman script cursive handwriting is addressed by various authors but not enough addressed for Indian script especially Devanagari script.This paper is a review which is confined to offline handwritten script domain.It attempt to review various techniques for character segmentation considering touching characters for offline handwritten words in Devanagari script and scripts sharing similar characteristics (like Bangla, Gurumukhi), database used and their accuracy reported in the literature.
Knowledge discovery from gene expression dataset using bagging lasso decision tree

&lt;p&gt;Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.&lt;/p&gt;
Local Self-Attention based Connectionist Temporal Classification for Speech Recognition

Connectionist temporal classification (CTC) has been successfully applied to end-to-end speech recognition tasks, but its main body recurrent neural network makes parallelization very difficult.Since the attention mechanism has shown very good performance on a series of tasks such as machine translation, handwriting synthesis, and image caption generation for loop sequence generators conditioned on input data.This paper applies the attention mechanism to CTC, and proposes a connectionist temporal classification based on the local self-attention mechanism, in which the cyclic neural network module in the traditional CTC model is replaced by the self-attention module.It shows that it is attractive and competitive in end-to-end speech recognition.The proposed mechanism is based on local self-attention, which uses a sliding mechanism to obtain acoustic features locally.This mechanism effectively models long-term scenarios by stacking multiple sliders to obtain a larger receiving field to achieve online decoding.Moreover, the CTC training joint cross-entropy criterion makes the model converge better.We have completed experiments on the AISHELL-1 dataset.The experiments show that the basic model has a lower character error rate than the existing state-of-the-art models, and the model after cross entropy has been further improved.
Towards Hybrid Model for Human-Computer Interaction in Latvian

Human-computer interaction, especially in form of dialogue systems and chatbots, has become extremely popular during the last decade. The dominant approach in the recent development of practical virtual assistants is the application of deep learning techniques. However, in case of less resourced language (or domain), the application of deep learning could be very complicated due to the lack of necessary training data. In this paper, we discuss possibility to apply hybrid approach to dialogue modelling by combining data-driven approach with the knowledge-based approach. Our hypothesis is that by combining different agents (general domain chatbot, frequently asked questions module and goal oriented virtual assistant) into single virtual assistant we can facilitate adequacy and fluency of the conversation. We investigate suitability of different widely used techniques in less resourced settings. We demonstrate feasibility of our approach for morphologically rich less resourced language Latvian through initial virtual assistant prototype for the student service of the University of Latvia.
The Italian Serie A TV Rights Selling Model – Historical Study

In mid 2000s the tv rights income reaches a 873 million euro for the 2005-06 season, with the Serie A tv rights annual income to gradually increase from a 844 million euro for the 2009-10 season to an amazing approximate 1,4 billion euro for the 2018-21 season. All these years Lega Calcio experimented a lot in the designing and application of a tv rights selling mechanism and its accompanied tv rights income distribution model. The Greek Superleague followed similar paths with Lega Calcio, when for many years in early 2000s clubs could exploit their tv rights by themselves. Even later, in late 200s and after the establishment of SL and with the collective selling mechanism in use, this mechanism didn’t include all participating clubs. The study aims to show how could Greek Superleague higher management staff learn from the Serie A tv rights deal history and follow the league’s recent tv rights optimal collective selling practices.
Predicting Single Observer’s Votes from Objective Measures using Neural Networks

The last decades witnessed an increasing number of works aiming at proposing objective measures for media quality assessment, i.e. determining an estimation of the mean opinion score (MOS) of human observers. In this contribution, we investigate a possibility of modeling and predicting single observer’s opinion scores rather than the MOS. More precisely, we attempt to approximate the choice of one single observer by designing a neural network (NN) that is expected to mimic that observer behavior in terms of visual quality perception. Once such NNs (one for each observer) are trained they can be looked at as “virtual observers” as they take as an input information about a sequence and they output the score that the related observer would have given after watching that sequence. This new approach allows to automatically get different opinions regarding the perceived visual quality of a sequence whose quality is under investigation and thus estimate not only the MOS but also a number of other statistical indexes such as, for instance, the standard deviation of the opinions. Large numerical experiments are performed to provide further insight into a suitability of the approach.
KLASIFIKASI TEKS LAPORAN MASYARAKAT PADA SITUS LAPOR! MENGGUNAKAN RECURRENT NEURAL NETWORK

The existence of public dissatisfaction with public services causes the public to be provided with facilities to make complaints.One of the sites that can be used to make complaints is the Public Service Complaint Management System (SP4N LAPOR!).With this site, complaints made by the public can be handled quickly, transparently and accountably in accordance with the authority of each organizer.However, the large number of complaints that had to be processed caused the process of data verification and sorting of reports by respective departments to take quite a long time, so the report classification process was needed to speed up the handling and follow-up of a report.The purpose of this research is to classify each complaint report from the public in preparation for the verification process of each public report document, which is expected to have an impact on the accelerated process of handling and follow-up of each related institution or agency.In this study, Long Short-Term Memory Recurrent Neural Network was used to perform the classification process for each public report document.The learning model is evaluated using k-fold cross-validation of 10 parts of data.The evaluation results show that the average f-measure percentage is 85.69% for the balanced dataset and 79.44% for the unbalanced dataset, while the highest evaluation value of all evaluations results in an f-measure of 88.82%.The high accuracy of the modeling indicates that the proposed method can be used to classify public report documents.
Harnessing the Power of OrCam

The OrCam reader is an AI enabled device that helps sight challenged readers to access print materials. This article is a first person account of a public library's experience in employing the OrCam technology.
Diagnose Disease Expert System Respiratory Tract Infection Method Using Certainty Factor

Respiratory tract infections are infectious diseases that interfere with the process of human breathing. When the breathing process takes place, there are often various kinds of diseases, most of which can only be treated by a lung specialist. The arrival of a pulmonary specialist for consultation can take hours and is expensive. Then we need an expert system that can quickly find out the type of disease in human breathing and how to handle it and the solutions that will be provided. Expert system is a system that uses human knowledge to find out the system that is entered into a computer and then is used to solve problems that usually require expertise or human expertise. One application of an expert system to diagnose respiratory tract infections is to use the certainty factor method. The certainty factor method is a method used to solve problems from uncertain answers, and also produce uncertain answers. This uncertainty is influenced by two factors, namely uncertain rules and uncertain user answers. The research aims to build an expert system application for handling respiratory tract infection problems with Visual Studio 2010 as a tool for designing applications and using Microsoft Access 2007 Database as a database. This expert system is able to calculate similarity in weight calculation based on symptoms of respiratory tract infection using certainty factor methods and provide reports using crystal reports
MODEL CNN LENET DALAM PENGENALAN JENIS GOLONGAN KENDARAAN PADA JALAN TOL

Research in the field of transportation, especially vehicle classification with various methods, is a widely developed field of study. Vehicles can be categorized by shape, dimension, logo, and type. The vehicle dataset is also not difficult to find because it is general in nature. Based on the research that has been done, the introduction of group types based on the number of axles with CNN, the dataset is not yet available to the public. In this paper, we discuss the introduction of the types of groups using the Convolutional Neural Network method. The architecture used is the LeNet model. The trial scenario is carried out in 4 stages, namely 25 epochs, 50 epochs, 75 epochs and 100 epochs. Based on the test results, the accuracy obtained continues to increase at 50 epochs and 100 epochs iterations. Starting from an accuracy of 82%, 94% to the highest accuracy of 95%. Likewise in the prediction the data has increased from 80%, 85% to the highest accuracy that can be 86%. From 50 epochs to 75 epochs, the accuracy of both training and testing has decreased.
Proposing "analogy experiment" as a sub-category of "analog experiment" in earth science

&amp;lt;p&amp;gt;In the earth and planetary sciences, the term &amp;quot;analog experiment&amp;quot; indicates laboratory experiments that use analog materials to investigate natural processes. Scaled experiments constitute a representative sub-category of analog experiments. They are designed to have the same dominant dimensionless parameter in the same range as the targeted natural processes. Other primary uses of analog experiments are education and outreach. Reproducing similar phenomena in front of the audience is useful in explaining the essence of the complex dynamics of natural processes. However, it is often the case that we do not fully understand the physics of the experimental systems or the targeted natural phenomena. In such cases, especially when the process is complex, it is difficult to guarantee the scaling similarity. When we take such laboratory phenomena as a research subject of earth science, we encounter critical comments about the scaling issue.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Nevertheless, I think it scientifically important to consider questions like follows. What is the mechanism of the experimental phenomena? Why the behaviors of the experiment look similar to the natural phenomena? To what extent the laboratory and the natural systems are similar. To indicate experimental studies to elucidate these questions, I would like to define &amp;quot;analogy experiment&amp;quot; as a new sub-category of analog experiments.&amp;amp;#160; Some recent experiments are presented as examples.&amp;lt;/p&amp;gt;
The Influence of Cinematherapy Against Academic Anxiety in Students

Students with high academic anxiety can cause a decrease in learning motivation. This situation will damage the learning achievement achieved by students. The purpose of this study was to look at the effect of cinematherapy on reducing student academic anxiety. This study uses a one-group pretest-posttest design experimental design. A total of 7 students in SMP Metro 1 were the subjects in this study. Subject selection uses a purposive sampling technique based on high academic anxiety criteria from the results of the academic anxiety scale score. The data analysis technique used is the Wilcoxon test. Statistical test results show that there is an effect of cinematherapy on the decrease in students' academic anxiety. The findings of this study prove that cinema is effective in reducing student academic anxiety at SMPN 1 in the city of Metro, Lampung.
Developing a Taxonomy for Software Engineering Education Through an Empirical Approach

Background: Software engineering has been formalized to a greater extent since 2000; this can be seen in the development of different bodies of knowledge. Objective: This work aims to contribute to organizing software engineering education knowledge, a sub-area in which formalization is still necessary.Method: We propose a process for the construction of controlled vocabularies. We instantiated this process twice; first, using automatic clustering techniques to analyze over 1,000 articles; and then, we focused on concepts related to teaching techniques and methods.Findings: We present a taxonomy with 60 terms organized in three facets at its highest level. The `teaching approaches and methods' category covers 26 terms with their definitions and most relevant references.Implications: The taxonomy can be used by teachers and researchers to understand the breadth of the field, to place their research initiatives in a broader context and to conduct more rigorous searches in the literature. We believe it is necessary to continue working on the taxonomy's expansion and also to carry out validation activities, if possible, including experts' validation.
Implementation of a Crypto-Steganographic System Based on the Aes-Cbc Algorithm

Safeguarding information has been one of the oldest needs of mankind, as well as protecting privacy from malicious attacks, thus steganography arises as a response to that need. Common steganography techniques, such as LSB, remove the natural entropy of the least significant bits in an image, which can raise suspicions about the existence of hidden information. The objective of this paper is to embed an AES-CBC encrypted secret message in an image using the LSB method to eliminate any kind of patterns that may be generated when applying steganographic techniques, in addition to providing double security to the information using a standard block cipher. A high-level software tool was developed that works with image processing and the use of cryptographic libraries that have the standard of block cipher such as AES-CBC. After this implementation, a series of tests were carried out with different sizes of images and information to be ciphered, which measured the time it takes to perform the entire process of mixing and encryption of information, thus it was possible to give a double security scheme to the information in terms of using LSB without generating patterns easily detectable by any tool and making a basic and secure encryption scheme of information, allsimultaneously.
Can Terrestrial Restoration Methodologies be Transferred to Planetary Hyperspectral Imagery? A Quantitative Intercomparison and Discussion

Hyperspectral imaging is a significant remote sensing technology for deep space exploration to understand the planetary geological evolution. However, hyperspectral images (HSIs) usually suffer from various noises because of the complicated environment and equipment limitations, which leads to inconvenience to subsequent applications. In this work, a comprehensive and systematic investigation on planetary noise categories is summarized initially, and an intercomparison among state-of-the-art terrestrial joint spatial-spectral restoration models is performed to test their capability on planetary datasets. The Compact Reconnaissance Imaging Spectrometer for Mars and Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité are adopted as examples. An improved nonreference quantitative evaluation method based on the high-resolution imaging science experiment imagery is proposed. The processed high-resolution classification result of Russell Dune can be used as the reference of unmixing after denoising. Then, spectral and spatial fidelity can be assessed indirectly. Experimental results emphasize that denoising approaches with modeling for non-independently and identically (non i.i.d.) noise characteristics are more suitable for planetary HSIs because of the diversity and complexity of their noises. This kind of method is flexible under practical circumstances and maintains the intrinsic information of HSIs better.