Reviewerly/exHarmony huggingface dataset:
Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN

The semantic information presents in the scene images may be the useful information for the viewers who is searching for a specific location or any specific shop and address. This type of information can also be useful in licenseplate detection, controlling the vehicle on the road, robot navigation, and assisting visually impaired persons. An efficient method is presented in this paper to detect and extract Bangla texts from scene images based on a connected component approach along with rule-based filtering and vertical scanning scheme. Next, extracted characters are recognized by using Convolutional Neural Network (CNN). The method consists of the four basic consecutive steps such as detection and extraction of the Region of Interest (ROI), segmentation of the words, extraction of characters, and recognition of the extracted characters. After extracting the ROI from the input image, connected component(CC) analysis and bounding box technology are used for segmentation of Bangla words. To separate and extract Bangla characters from the segmented Bangla words, vertical scanning based method along with a dynamic threshold value has been applied. Finally, character recognition is carried out using CNN. The proposed algorithm is applied to 600 scene images of different writing styles and colors, and we have obtained 89.25% accuracy in text detection and 94.50% accuracy in the extraction of characters. We have achieved an accuracy of 99.30% and 95.76% in recognition of Bangla digits and characters respectively. By combining both the digits and characters, obtained recognition accuracy is 95.39%.
Study on assessment and factors supporting successful vocational high schools student of industrial class

The purpose of this study is to investigate the learning effectiveness of the industrial class of vocational high schools (VHS) and to identify the required factors to improve VHS students' skill. This research is an ex-post facto type of causal research to reveal occurred events and to see various factors related to the events. Data collection of learning effectiveness assessment and factors supporting successful VHS students' identification were done through focus group discussion (FGD), interviews, questionnaires, and pre-test-post-test. The samples were 30 students of the industrial class and 30 students of the regular class of class XI TP1. The result shows that the average grade of an industrial student is 81, while the average grade of a regular student is 70.
An Overview of a Crime Detection System Using the Art of Data Mining

To predict crime and analyze crime activity we need to proceed with a systematic approach with data mining. By using a data mining system one can predict locations that have a huge probability. And one also can inward eye by using this method. It will increase the computerized systems. Our main problem is that we are looking at the crime occurrences but not at the key factors of crime. If we use data mining we can form any unstructured data to structured data. By which we can find also some prediction before the crime. We can set a bridge between computer science and criminal justice board using the data mining process by which we can catch the factor very fast and also very easy. Culprits also are divided based on their data. Crime and illegal services are increasing rapidly now. We suggest a methodology by which we can detect culprits, predict crime by analyzing previous crime reports of the regions. For establishing this we need crime data from police station websites, govt. crime websites etc. Using the Naive Bayesian algorithm on these pre-processed data we can easily predict the stat of crimes. With this system we can establish a secure society which will free from crime and people can lead their life soundly and also happily. So that we need this data mining system and it will increase the usage of computer science and engineering in this era of science. We propose to the prediction of real-time. Though it will difficult to get accurate cause crimes are doing their crimes using different and complex methods. They are also clever. But the bridge of data between the police station and system of data mining will report about further and upcoming crimes. And for easy understanding of the police government we need to use graph data mining so that they will easily get points. This paper will describe kinds of criminal activities and criminal pathway methods after a crime which we will solve by using the data mining system. SO, we need to locate the maximum crime location area first. We can also detect the hotspot area and will get some data also from social sites. Data analysts of crime can help the law department to solve this problem. The Law department can also reduce crime hotspots. Using this method we need to extract previous criminal data. So, the bridge between computer science and the law will help the world to free from crime.
Software Defect Prediction Utilizing Deterministic and Probabilistic Approach for Optimizing Performance through Defect Association Learning

While developing software it is very important that the software should be of defect free.But, none of the software can be 100% defect free and various studies are in progress to build a model which minimizes the defect as much as possible by predicting it at an early stage of development.Based on the probability facts various researchers has used probabilistic model to predict defects in the program.To contribute in this research and enhancing the existing model of software defect prediction we are proposing a model based on the combination of probabilistic and deterministic model through defect association learning.The experimental evaluation in comparison with the existing methods shows the improvement in the accuracy of predicting the defect by using Deterministic and Probabilistic defect prediction.
Video classification with memory and computation-efficient convolutional neural network

Video understanding involves problems such as video classification, which consists in labeling videos based on their contents and frames. In many real world applications such as robotics, self-driv ...
Applied Ontologies for Managing Graphic Resources in Quantitative Spectroscopy

The report presents the tasks on graphical resources management thoroughly describing applied ontologies of GrafOnto research graphics collection used for solving problems of spectroscopy. Two groups of the tasks on graphical resources management are discussed in the paper. The first group is oriented on the quality analysis of graphical resources and the representation of these results as a graphical resources ontology. The second group of tasks is associated with the automatic class creation for the aforementioned ontology. The problems of ontology modularity and automatic classes’ generation are being discussed. Examples of solving reduction problem as well as applied ontologies metrics are presented.
Machine learning distributions of quantum ansatz with hierarchical structure

Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.
Models Are Functioning in Scenarios

Models are one of the universal instruments of humans. They are instruments that are used on purpose in scenarios. They are usable and useful within these scenarios. The scenario determines the function and thus the purpose of a model. This determination governs the acceptance in dependence of the utility that a model provides. We discuss in this paper the dependence of model kinds on four scenarios: communication scenario oriented on social aspects, comprehension scenarios targeting on representation aspects, search scenarios oriented on model usage and model enhancement, and analysis scenarios targeting on sense-making aspects.
Improving speaking ability using English "Shock Day" approach

The aim of this study was to measure the effectiveness of English "Shock Day" to improve university students' speaking ability. It employed a quantitative method using the pre-experimental design. The subject of the research was the fourth-semester students in one of the private universities in South Sulawesi, Indonesia, in the academic year of 2018/2019. The total population of this research was 76 students. The total number of the sample was 25 students were taken by cluster sampling technique. The instrument was a speaking test to obtain data on the students' speaking achievement through the implementation of English shock day. The data analysis found that the students' mean score in English speaking was significantly improved. The students' mean score in the pretest was 58.36, while their mean score in the posttest was 70.68. The t-table was also lower than the t-test, that is 2.093 &lt; 6.650. Based on the result, the researchers concluded that the implementation of English shock day improved students' ability to speak.
BLOCKCHAIN BASED E-VOTING APPROACH IN P2P NETWORK

E-voting is the process of conducting the voting process through online. The voters can cast their votes from different locations and these votes are collected and recorded electronically. Therefore, there is a need for a system to provide control and security to the whole procedure. Blockchain, a distributed ledger technology can be integrated to provide a decentralized system. Blockchain uses distributed ledger technology (DLT) to avoid forged voting option & non-repudiation and one time login of the user is also ensured. By integrating the above techniques, a secure user authentication for e-voting supported blockchain in p2p network is projected. This system would increase the safety by avoiding the forgery of votes. Similarly, the blockchain are often integrated to a spread of voting situations and a few alternative applications.
Discrete Circular Distributions with Applications to Shared Orthologs of Paired Circular Genomes

For structural comparisons of paired prokaryotic genomes, an important topic in synthetic and evolutionary biology, the locations of shared orthologous genes (henceforth orthologs) are observed as binned data. This and other data, e.g., wind directions recorded at monitoring sites and intensive care unit arrival times on the 24-hour clock, are counted in binned circular arcs, thus modeling them by discrete circular distributions (DCDs) is required. We propose a novel method to construct a DCD from a base continuous circular distribution (CCD). The probability mass function is defined to take the normalized values of the probability density function at some pre-fixed equidistant points on the circle. Five families of constructed DCDs which have normalizing constants in closed form are presented. Simulation studies show that DCDs outperform the corresponding CCDs in modeling grouped (discrete) circular data, and minimum chi-square estimation outperforms maximum likelihood estimation for parameters. We apply the constructed DCDs, invariant wrapped Poisson and wrapped discrete skew Laplace to compare the structures of paired bacterial genomes. Specifically, discrete four-parameter wrapped Cauchy (nonnegative trigonometric sums) distribution models multi-modal shared orthologs in <i>Clostridium</i> (<i>Sulfolobus</i>) better than the others considered, in terms of AIC and Freedman’s goodness-of-fit test. The result that different DCDs fit the shared orthologs is consistent with the fact they belong to two kingdoms. Nevertheless, these prokaryotes have a common favored site around 70° on the unit circle; this finding is important for building synthetic prokaryotic genomes in synthetic biology. These DCDs can also be applied to other binned circular data.
Esquema de certificación por conformidad de requisitos del estándar ISO/IEC 29110 para la calidad de las empresas software

Small software development companies require for their process management of their software processes proposals specific to their context, in order to they can improve in their daily work. This article presents a certification model for compliance requirements based on the ISO/IEC 29110-4-1 standard (analogously to that made for the ISO 9001 or ISO/IEC 27001 or ISO/IEC 20001 certifications) for supporting to small software development organizations interested in ensuring the software product quality through processes improvement and certification. A pilot project has been carried out to determine the suitability of the model proposed put in practice, which had as scope the defined processes by the company that were related to project management and software implementation. On these two processes, this company has satisfied the requirements established by the model for obtaining the certification in ISO/IEC 29110. From this application it can be evidenced that the proposed model is economical for the implementation of the standard and for its certification audit, given that: (i) it is limited only to two processes that all companies carry out when they develop software, (ii) the reference model establishes the “must be” (requirements) of the two proposed processes, which are those that must be satisfied, and (iii) the certification by conformity of process requirements is less complex, and therefore less expensive, than other certification schemes (such as, for example, process assessment schemes).
Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means

Indonesia has fertile soil, natural resources and abundant marine resources. However, Indonesia is also not immune to the risk of natural disasters which are a series of events that disturb and threaten life safety and cause material and non-material losses. Indonesia's strategic geological location causes Indonesia to be frequently hit by earthquakes, volcanic eruptions and other natural disasters. From the data collected, natural disasters that occurred in Indonesia consisted of several categories, namely earthquakes, volcanic eruptions, floods, landslides, tornados, and tsunamis. Many natural disasters in Indonesia have caused casualties, both fatalities and injuries, destroying the surrounding area and destroying infrastructure and causing property losses. The trend of increasing incidence of natural disasters needs to be further investigated to prevent the number of victims from increasing. This information can be obtained through a data mining approach given the large amount of data available. In relation to natural disaster data, clustering techniques in data mining are very useful for grouping natural disaster data based on the same characteristics so that the data can be adopted as a groundwork for predicting natural disaster events in the future. Thus, this research is supposed to group natural disaster data using clustering techniques using the k-means algorithm into several groups, in terms of natural disaster types, time of disaster, number of victims, and damage to various facilities as a result of natural disasters
A Reporting Assistant for Railway Security Staff

In this paper, we introduce RARSS, a reporting assistant for railway security staff. RARSS is a demonstration application with a multi-modal interface based on the Mobile Multimodal Interaction and Rendering (MMIR) framework. The system should support the security staff at railway premises (stations, trains, etc.) in Germany and inform about security relevant information about the travel of football fans or a group of people on their way to a major event. In the application we leverage multi keyword spotting (KWS) for detecting of the actual context and a grammar with specific voice commands to improve the semantic interpretation. The results of friendly user testing showed that the multimodal conversational interface was positively rated according the simplicity and the efficiency to make security reports by the security staff.
Mining Online Opinions and Reviews Using Bi-LSTM for Reputation Generation

Nowadays, people express their opinions and thoughts about an item (products, movies, services, and brands, etc.) directly online. They use platforms like IMDB, Amazon, eBay, and more. This large amount of people’s opinions is very valuable for an automatic computing of reputation score for companies. Consequently, developing a system able to generate reputation from textual opinions and their attached rating will be a helpful tool for companies to have an idea about the quality and the issues of their product, and for potential customers to assist them during their ecommerce decision-making. However, a very limited number of studies have investigated mining reviews expressed in natural language for reputation generation. Therefore, we propose an approach and a whole system for generating reputation using bidirectional long short-term memory Recurrent Neural Network (Bi-LSTM RNN) and Natural Language Processing (NLP) techniques. The experimental results conducted on the IMDB dataset show that our method provides the nearest reputation value to the ground truth (IMDb weighted average vote). This implies that the proposed approach can be applied in practice to generate reputation.
Ethical Use of Social Media in Student Teacher Communication with Special Reference to Whatsapp Groups in Pune Colleges

The study is done to find the ethics used while using Whatsapp as a mode of communication in colleges. The easy operations of this App have made it very popular amongst youth and other generations. It is also important that this mode of communication is operated systematically and follows ethical rules so that the right messages are circulated amongst the youth. The study aimed at finding the preference of use of Whatsapp in colleges and whether all the ethical rules are maintained. The survey done in various colleges of Pune showed the result of Whatsapp being preferred by number of respondents and that people find it time saving and good communication mode as it is fast and lot of information can be shared. From ethical point of view there are still gaps in understanding about not forwarding any fake messages. So the study suggests that more awareness about Whatsapp ethics should be spread in colleges where there are official groups.
Mineral Mapping at Lonar Crater Using Remote Sensing

Remote is the advanced technology used for various applications in mineral mapping, mineral identification, lithological discrimination and structural mapping etc. Lonar crater is a wellknown impact crater in basaltic crust of rocks.With Hyperion satellite imagery, it is possible to analyze the Lonar crater remotely and comment on the mineralogy associated with it.The various geological, geochemical, mineralogical characterization studies has been done on crater-cum-lake till today but the remote sensing is not used prominently indicating some fruitful results.So the present study focuses on the use of hyperspectral images for mineral mapping and identification near and at the Lonar crater.The preprocessing techniques like bad band removal, destriping, radiometric correction and atmospheric corrections were applied.Then noise removal and dimensionality reduction was performed using modified PCA known as minimum noise fraction.Then the classification techniques Spectral Feature Fitting and Spectral Correlation Mapper were applied on the significant endmembers obtained after Pixel Purity Index and n-D Visualizer.The spectral signatures obtained were compared with USGS standard spectral library.The Alunite mineral was successfully mapped and identified with 88%, 89% and 90% at the different wavelengths at the Lonar Crater.
Earthquake observations in the age before Lisbon: eyewitness observation and earthquake philosophy in the Royal Society, 1665–1755

Between 1665 and 1755 several members of the Royal Society collected eyewitness accounts of earthquakes, some of which were published in the Philosophical Transactions . While such observations have been recognized as crucial to early seismology in the nineteenth century, their impact on the seventeenth- and eighteenth-century natural philosophy of earthquakes has received limited attention. This article suggests that growing inclusion of observations considerably challenged existing theories and epistemologies and was indispensable to the emergence of seismology in the second half of the eighteenth century. Initially, observations of earthquakes served two uses: the provision of particulars and the verification of other observations. By the eighteenth century, a new approach to empiricism was beginning to influence the collection of observations, focusing on information that could be compared and synthesized to chart the extent and varying intensities of earthquakes. This practice promoted a conception of subterranean, travelling earthquakes that could be measured by their intensity, and which were hypothesized to have some form of centre or origin. While seismology could develop on the basis of sound theoretical assumptions, these assumptions were heavily indebted to the methodological practice of eyewitness observation.
We're here to help: Company image repair and user perception of data breaches

Data breaches involve information being accessed by unauthorized parties. Our research concerns user perception of data breaches, especially issues relating to accountability. A preliminary study indicated many people had weak understanding of the issues, and felt they themselves were somehow responsible. We speculated that this impression might stem from organizational communication strategies. We therefore compared texts from organizations with those from external sources, such as the news media. This suggested that organizations use well-known crisis communication methods to reduce their reputational damage, and that these strategies align with repositioning of the narrative elements involved in the story. We then conducted a quantitative study, asking participants to rate either organizational texts or news texts about breaches. The findings of this study were in line with our document analysis, and suggest that organizational communication affects the users' perception of victimization, attitudes in data protection, and accountability. Our study suggests some software design and legal implications to support users protecting themselves and developing better mental models of security breaches.