Reviewerly/exHarmony huggingface dataset:
A Tool for Extracting Text from Scanned Documents and Convert it into Editable Format

With the advent of Social media, now-a-days most of the data are stored in images. These data if processed correctly can provide large information. Hence there is a need to convert these data into information. A novel technique which can convert the data available in image into an editable format is proposed where the image can be acquired either by a camera, smart phone or directly from any source. The image is segmented into characters by using Connected Components (CC) and edge recombination using stroke width. This image is then converted to an editable format by using Optical Character Recognition (OCR) technology and Maximally Stable Extremal Region (MSER) for segmentation. The proposed system can also extract object from the images, which is done by using Artificial Neural Networks (ANN). The complete solution is developed using MATLAB and the output is stored in a variable. This can also be extracted to a word document if required where it can be edited. The performance of the system is measured by using two parameters namely precision rate and recall rate and has about 88% precision and 97% recall rate which is higher than most of the earlier proposed methods.
Development of the algorithm of keyword search in the Kazakh language text corpus

The issue of semantic text analysis occupies a special place in computational linguistics. Researchers in this field have an increased interest in developing an algorithm that will improve the quality of text corpus processing and probabilistic determination of text content. The results of the study on the application of methods, approaches, algorithms for semantic text analysis in computational linguistics in international and Kazakhstan science led to the development of an algorithm of keyword search in a Kazakh text. The first step of the algorithm was to compile a reference dictionary of keywords for the Kazakh language text corpus. The solution to this problem was to apply the Porter (stemmer) algorithm for the Kazakh language text corpus. The implementation of the stemmer allowed highlighting unique word stems and getting a reference dictionary, which was subsequently indexed. The next step is to collect learning data from the text corpus. To calculate the degree of semantic proximity between words, each word is assigned a vector of the corresponding word forms of the reference dictionary, which results in a pair of a keyword and a vector. And the last step of the algorithm is neural network learning. During learning, the error backpropagation method is used, which allows a semantic analysis of the text corpus and obtaining a probabilistic number of words close to the expected number of keywords. This process automates the processing of text material by creating digital learning models of keywords. The algorithm is used to develop a neurocomputer system that will automatically check the text works of online learners. The uniqueness of the keyword search algorithm is the use of neural network learning for texts in the Kazakh language. In Kazakhstan, scientists in the field of computational linguistics conducted a number of studies based on morphological analysis, lemmatization and other approaches and implemented linguistic tools (mainly translation dictionaries). The scope of neural network learning for parsing of the Kazakh language remains an open issue in the Kazakhstan science.The developed algorithm involves solving one of the problems of effective semantic analysis of the text in the Kazakh language
Formation of professional competencies of future specialists in the field of rocket technics and technology in the study of mathematical disciplines

The paper discusses ways to ensure a practice-oriented mathematical training of specialists in the field of rocket technics and technology. The experience of organizing term papers and interdisciplinary projects in mathematical disciplines is presented. The importance of using the project method in creating an innovative environment in a technical university, in the formation of professional competencies of future engineers is shown.
Copyright Issues with the 'Black Hole' Image and Their Legal Implications

The release of the world’s first-ever black hole image generated an immediate copyright dispute and revealed multiple copyright issues that remain unsettled. This paper argues that the black hole image should be left in the public domain without copyright protection because: first, the image’s copyrightability and copyright ownership are too uncertain to warrant a legal protection, making fair use and compulsory licensing largely irrelevant; second, the image is a work of world significance that was created through broad international collaboration with substantial public funding, which has a strong implication for public interest in access of the work; and third, Creative Commons 4.0 Attribution license cannot guarantee public access because it can be changed to a more restrictive license. This paper concludes that only by leaving the black hole image in the public domain can copyright’s objective of increasing public access of creative works and promoting scientific progress be achieved.
Agricultural Information Application Design using User Centered Requirements Engineering

Abstract In Indonesia there are 43,55 percent of low-education people where majority depend on life as farmer and the needs of farmers for information in agriculture are very diverse. So far, farmers have only used agricultural information for generations. Although technology has entered the agricultural sector, such as the use of smartphone devices. However, its use is still not productive because there are no mobile applications that can provide integrated agricultural information suitable to farmer needs. So that the User Centered Requirements Engineering (UCRE) method is needed as a process of classifying farmers information needs that are very diverse and packaged into an integrated agricultural information application design model. The results of the application design obtained usability size by 80.32% and correctness by 79% so that, it illustrates a good quality value of the application based on the need for usefulness and accuracy of information on the McCall method testing.
Unpaired Sketch-to-Line Translation via Synthesis of Sketches

Converting hand-drawn sketches into clean line drawings is a crucial step for diverse artistic works such as comics and product designs. Recent data-driven methods using deep learning have shown their great abilities to automatically simplify sketches on raster images. Since it is difficult to collect or generate paired sketch and line images, lack of training data is a main obstacle to use these models. In this paper, we propose a training scheme that requires only unpaired sketch and line images for learning sketch-to-line translation. To do this, we first generate realistic paired sketch and line images from unpaired sketch and line images using rule-based line augmentation and unsupervised texture conversion. Next, with our synthetic paired data, we train a model for sketch-to-line translation using supervised learning. Compared to unsupervised methods that use cycle consistency losses, our model shows better performance at removing noisy strokes. We also show that our model simplifies complicated sketches better than models trained on a limited number of handcrafted paired data.
Hyper-Parameter Selection on Convolutional Dictionary Learning Through Local ℓ<sub>0,∞</sub> Norm

Convolutional dictionary learning (CDL) is a widely used technique in many applications on the signal/image processing and computer vision fields. While many algorithms have been proposed in order to improve the computational run-time performance during the training process, a thorough analysis regarding the direct relationship between the reconstruction performance and the dictionary features (hyper-parameters), such as the filter size and filter bank's cardinality, has not yet been presented.As arbitrarily configured dictionaries do not necessarily guarantee the best possible results during the test process, a correct selection of the hyper-parameters would be very favorable in the training and testing stages. In this context, this works aims to provide an empirical support for the choice of hyper-parameters when learning convolutional dictionaries. We perform a careful analysis of the effect of varying the dictionary's hyper-parameters through a denoising task. Furthermore, we employ a recently proposed local ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0,∞</sub> norm as a sparsity measure in order to explore possible correlations between the sparsity induced by the learned filter bank and the reconstruction quality at test stage.
Cluster management framework for autonomic machine learning platform

Autonomic machine learning platforms must provide the necessary management tasks while monitoring the execution status of remotely running machine learning tasks and the performance of the model being trained. In this paper, we design a cluster management framework. The proposed cluster management framework monitors distributed computing resources so that it helps the autonomic machine learning platform to select the proper machine learning algorithm and to execute the proper machine learning model.
Session-based Search Behavior in Naturalistic Settings for Learning-related Tasks

In this research, we investigate the behavioral patterns exhibited in different search sessions as users attempt to complete search tasks of increasing cognitive complexity. The search tasks, which are exploratory in nature, have been designed using the Taxonomy of Educational Objectives, and are presented to the users hierarchically. We capture naturalistic search behavior of the users in real world (non-lab) setting using a Chrome browser plugin. The research analyzes the web log data of the users to assess if and how the web search behavior of the users changes over different search sessions. We also look at the different demographic factors like age and gender, educational factors like the academic background, read and write proficiency in English, and search skills to determine if these factors influence the web search behavior of the users. Our results indicate that search sessions have significant effects on the web search behavior of the users. Most of the web search behaviors differed significantly across search sessions. Of the secondary factors, gender showed significant effect on the query reformulations (measured using average edit distance between queries) and query length (measured using number of words per query) while year of study affected only the average query length. Search experience had significant effect on all the web search behaviors.
Neuromorphic Photonics for RF Signal Processing

Neuromorphic photonics use light to imitate the neural models and systems of nature for solving complex human problems that are challenging for conventional electronic approaches. Neural algorithms are natural designs that govern the survival of the organism, therefore, are highly effective for the designated tasks. In this paper, we review two small-scale neural algorithms - spike timing dependent plasticity process for learning and jamming avoidance response in Eigenmannia, discuss the marriage of those neural algorithm and photonics, as well as explore their real-life applications in human society.
Nursery Science: A Mobile Application for Learning Science for Pre-Schoolers

Mobile Learning or M-Learning has been gaining attention in research to determine its impact on the learning experiences of the students. The use of technology, particularly of mobile learning, is essential in education due to its capability to provide individual learning experiences. M-Learning can be defined as Learning using mobile devices like Smart Phones and Tablets. This platform can be integrated into Blended Learning where students learn inside and outside the four walls of the classroom. Given the advantages of M-Learning for students, this study sought to design and develop a mobile application that helps preschoolers enhance their learning experience in science in one Daycare Center in the Philippines. The application includes lessons from the Department of Social Welfare and Development (DSWD). Quizzes as a form of assessment can be taken after each lesson. Succeeding lessons are locked and cannot be accessed until the teacher unlocks these lessons. The mobile application was assessed using ISO 9126 to determine the acceptability among the teachers and parents of the pupils of the Daycare Center. The overall result yielded an average mean of 4.0 which is verbally interpreted as Very Satisfactory. A longitudinal-experimental study for the mobile application is recommended to further assess the impact of the application on the learning experience of the pupils.
A Minimum Rare-Itemset-Based Anomaly Detection Method and Its Application on Sensor Data Stream

In recent years, the scale of data stream is becoming much larger in real life. However, the anomaly data often exists in the collected data stream, while the existence of anomaly is a main reason for the decrease of the accuracy of data-based operations. The anomaly data have two main characteristics, that is, appear rarely and deviate much from most data elements, thus, the anomaly detection methods should accurately detect the anomaly data by considering these two attributes. Because the data stream is continuously generated and constantly flowing, thus, the previous static anomaly detection methods are not suitable for processing data streams. In addition, the large amount of data stream makes the time consumption and memory occupation of rare itemset mining phase very high. To effectively solve these problems, this paper first proposes an efficient MRI-Mine method for mining minimum rare itemsets, and then proposes an accurately anomaly detection method called MRI-AD based on anomaly index to identify the implicit anomaly data. The experiments indicate the proposed MRI-Mine method can mine the minimum rare itemsets in less time consumption and memory occupation, and the detection accuracy of MRI-AD method is also competitive.
A PatchMatch‐based Approach for Matte Propagation in Videos

Abstract Despite considerable advances in natural image matting over the last decades, video matting still remains a difficult problem. The main challenges faced by existing methods are the large amount of user input required, and temporal inconsistencies in mattes between pairs of adjacent frames. We present a temporally‐coherent matte‐propagation method for videos based on PatchMatch and edge‐aware filtering. Given an input video and trimaps for a few frames, including the first and last, our approach generates alpha mattes for all frames of the video sequence. We also present a user scribble‐based interface for video matting that takes advantage of the efficiency of our method to interactively refine the matte results. We demonstrate the effectiveness of our approach by using it to generate temporally‐coherent mattes for several natural video sequences. We perform quantitative comparisons against the state‐of‐the‐art sparse‐input video matting techniques and show that our method produces significantly better results according to three different metrics. We also perform qualitative comparisons against the state‐of‐the‐art dense‐input video matting techniques and show that our approach produces similar quality results while requiring only about 7% of the amount of user input required by such techniques. These results show that our method is both effective and user‐friendly, outperforming state‐of‐the‐art solutions.
RARE Shop: Real &amp; Augmented Retail Bookshop Experience using Projection Mapping

Augmented reality (AR) technology has been used to integrate online product information to the physical shopping experience by relying on hardware outputs such as head-mounted displays or mobile devices, which interferes with the customers natural in-store shopping experience. Different from traditional AR, spatial augmented reality (SAR) could potentially offer an unmediated augmented physical experience. In this paper, we present a proof-of-concept prototype that augments existing physical shops with online personalized product information without disrupting the customer's natural shopping behavior. We envision a real and augmented book shop experience, using SAR to provide immersive digital and online information overlaid onto physical products, breaking down the boundary between physical space and digital information at retail shops. Moreover, we design unmediated interaction that reacts to the relative spatial and temporal context of the users, based on user's natural interactions with the physical products, without the need for learning special interactions/gestures. We also discuss the design challenges and implications for this in-store shopping experience.
Retrieval by Classification: Discriminative Binary Embedding for Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) intends to use free-hand sketch drawings as query to retrieve correlated real-world images from database. Hashing based methods gradually become the mainstream approaches in SBIR with its low memory usage and high query speed. Existing hashing based methods are incapable of guiding hash codes to preserve inter-class relationship and improving object recognition ability of hash functions simultaneously, which limits the higher performance. Hence, we propose Discriminative Binary Embedding (DBE), a novel algorithm of considering inter-class relationship and object recognition ability in a joint manner by treating retrieval as classification. Specifically, we apply NLP methods to encode category labels as binary embedding and then build classifiers for images and sketches, so as to obtain hash codes of instances based on binary embedding of predicted labels. Experimental results on two benchmarks show that DBE outperforms several state-of-the-arts.
Design of Energy Monitoring System for Small Scale Wind Turbine Applications

Abstract In this paper, an energy monitoring system for small scale wind turbine is proposed. The monitoring system process includes measuring wind turbine power output (voltage and current) and storing systems. The measurement results will be stored in a micro SD at the real time. All processes for measuring and storing data in a data logger were carried out by a microcontroller through several sensors and storage media. The data logger were designed and implemented as small as possible to save material and enhance the performance of serial communication between components inside. Hence, most of components are installed by Surface Mounting Device (SMD) packaging resulted a compact data logger size as 7cm x 8cm. Furthermore, by using FAT32 system files, the data logger is able to operate a micro SD with a size of up to 32GB. Finally, this data logger shows a good capability for measuring voltage, current and storing the results into the SD card including the measurement period.
Face-aware Saliency Estimation Model for 360° Images

In this paper, a saliency estimation technique for omni-directional images is presented. Traditional approaches for estimating 360° image saliency rely on the exploitation of low and high-level image features, along with auxiliary data, such as head movement or eye-gazes. However, the image content plays an important role in saliency estimation. Based on this evidence, in the proposed method low-level features are combined with the detection of human faces. In this way it is possible to refine the saliency estimation based on the low-level features by assigning a larger weight to the regions containing faces. Experimental results on 360° image dataset show the effectiveness of the proposed approach.
Analysis and parameter estimation of time-varying signals: theory and methods

Abstract The need to capture signals, perform classification and make decisions are components of an Industry 4.0 manufacturing system. Since signals in practice are corrupted by noise, it is essential to optimize the signal processing and classification to ensure correct decision making. The time-varying nature of signals requires the use of time-frequency analysis over conventional methods such as spectrum estimation to ensure accurate estimation of signal parameters. The methods presented cover the main classes of time-frequency distributions – linear and quadratic- and compare their strengths and weaknesses for the appropriate choice of application.
Proposing Model for Recognizing User Position

In the progressive world of Internet, online banking or any other transaction is not secure because of methods such as brute force technique, dictionary attacks, and spoofing. It is because traditional methods of authentication require either password or pin which is not secure because it can be cracked easily using the methods mentioned above. Therefore, biometric authentication can be a viable option which can be used for identification of a genuine user. In this paper, we propose a model for identifying the user’s position that plays a vital role in identifying the genuinity of any user. This paper revolves around the effects on verification of genuine user if the data collected from user is done in both controlled and uncontrolled environment.
Big Data Encryption Storage System Design Under Cloud Environment

Contraposing to the security of cloud storage service network and data sharing characteristics, a ciphertext access control mechanism based on CP-ABE algorithm is proposed. The access control mechanism is studied from two aspects: access control and access control architecture. The corresponding data structure of security algorithm is provided, and its simulation and performance analysis are performed. The security mechanism ensures the security of data in cloud storage system under open environment and reduces the complexity of privilege management through attribute management, under the premise that service providers are not credible. The cloud storage system designed and implemented in this paper achieves the original design object and performs well in all aspects, which is of great importance to the safe and effective storage of user's information and data in the era of large data.
Case Study: Deep Convolutional Networks in Healthcare

Technological improvements lead big data producing, processing and storing systems. These systems must contain extraordinary capabilities to overcome complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis method. It becomes one of the most popular and up-to-date artificial neural network types with deep representation ability. Another powerful ability of deep learning is providing feature learning through convolutional neural networks. Deep learning has wide implementation areas in medical applications from diagnosis to treatment. Various deep learning methods are applied to the biomedical problems. In many applications, deep learning solutions are modified in accordance with the requirements of the problems. Through this chapter the most popular and up-to-date deep learning solutions to biomedical problems are discussed. Studies are analyzed according to problem characteristic, importance of solution, requirements and deep learning approaches to solve them. Since the deep learning systems have very effective image and pattern recognition ability, biomedical imaging becomes one of the most suitable application areas. During the first diagnosis and continuous tracking phase of the patients, deep learning systems offer very effective aids to the medicine. Although organ, disease or data type classifications are possible for biomedical application categorization, organ and disease combination are taken into consideration in the chapter.