Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Noise Removal from Hand Written Manuscript using Enhanced Gibbs Sampling Algorithm

Hand written documents are one of the most important sources of writing media in ancient Kerala. Due to improper storage of manuscripts, about 80% of them got degraded. Noises are one of the most common degrading factor which affects the visibility of the images and makes them unclear. The main noises found in manuscripts are pepper salt noise, speckle noise and holes on billings etc. There are many denoising methods available. In this paper we have made a literature analysis, both basic and advanced algorithms. Finally compared the output of each methods, Gibbs Sampling technique gives more accurate output in handwritten manuscripts. But holes in documents were not removed. For this we have given an enhancement using Circle Hough Transform(CHT) Which detects the circles and can identify exact hole by comparing the boundary pixels of circle and background of the script. In future we can recognize the characters using Support Vector Machine(SVM) and H2istogram of orientation Gradients(HOG).
State Early Warning Technology for UHV AC Substation Equipment

Abstract With the development of UHV power transmission and transformation projects in China, as a key technology to prevent power grid accidents caused by faults of power transmission and transformation equipment, the state early warning technology of UHV AC power transformation equipment urgently needs to carry out and perfect corresponding research. This paper introduces the key technology of state warning for UHV AC substation equipment, and analyzes the principle and method of state alarm technology. And put forward the views on the current problems and future development of UHV AC equipment status warning technology.
Night Vision Colorization from Color Mapping to Color Transferring

Multispectral imaging greatly supports executing nighttime object assessment tasks. In such a context, analyzing colorized multisensory images definitely improves observer situational awareness, reaction time, and perceptual analysis (human vision). Night vision (NV) colorization is such a technique that renders multispectral images (in grayscale) with daylight colors. This paper provides a brief review of NV colorization techniques that includes well-known color mapping methods that use statistical matching, and the recently-introduced color transferring methods that use a convolutional neural network (CNN) to map colors. Objective evaluation index (OEI) is used as a quantitative metric to compare colorization results. The experimental results show that the color transferring methods using CNN is very promising in colorizing NV imagery.
Detection of Object Changes in Low Contrast Image Using Template Matching Method

Abstract This research uses a contrast stretching method to improve the detected object image. This process is needed because of the lack of lighting in the room so it is difficult to identify properly. This research develops an image object change detection system to detect the movement of objects contained in a room through a Closed Circuit Television (CCTV) camera which is conducted simulations using a webcam camera using a template matching method with normalized cross-correlation techniques. The results of detecting changes in image objects are then made to compare between the reference image and image before there is an object change with the image after the object changes. System testing is carried out at a certain time, namely 07.00-17.00 WIB. The level of accuracy to identify the detection of changes in image objects using the template matching method with normalized cross-correlation technique is 85% for the category of no change in min (0.01025) and max (0.02088) and changes in min (0.01640) and max (-0.00513).
Architecture of Software for Automated Generation of Technical Specifications

Describing requirements and building entity-relation diagrams are processes that cannot be avoided when creating an information system. With the advent of modern technologies, such as Natural Language Processing and Automatic Speech Recognition, opportunities for automation of the mentioned activities have appeared.The paper describes the process of creating the web-based application that allows user to generate formalized requirements for the system and its entity-relation diagram based on the informal audio description of the system. The paper describes possibilities and limitations of the offered approach. As the main result, the paper discusses architecture of the solution and possibilities of using freeware components and modules. Existing approaches and software to solve discussed tasks also described.
Hybrid Feature Extraction based on HOGHT to Detect Tumor in Mammogram Images

The Mammogram image segmentation and feature extraction techniques still need improvement in tumor detection and play a vital process of diagnosing. This paper optimizes the novel approaches of hybrid feature extraction technique for mammogram tumor detection with implicit knowledge. The main objective of this paper is to apply the double threshold segmentation (DTS) with a new hybrid feature extraction called Histogram oriented gradients with Hough transform (HOGHT) and PCA Algorithm. This Extraction can be classified in to benign, normal and malignant. This approach consists of a better result and reduces the Computation time. Quality measure can be done by using this predictive accuracy and no one can be applied for this combination of work. This technique ensures that decision is taken ahead of tumor detection rather than the existing system. The experiments have been made from the dataset taken from MIAS. The obtained results by using this method are highly encouraged.
A multi-objective competitive co-evolutionary approach for classification problems

This paper proposes a multi-objective competitive co-evolutionary algorithm (MOCPCEA) based on the PreyPredator model to solve classification problems. In the MOCPCEA, a data population acts as preys. To be specific, each prey represents a selected subset of the training dataset. Another population is ANN classifiers which play as Predators. The task of the Predators is to try to classify the data sets as correctly as possible, whereas the Preys try to find the data sets that are difficult to be classified. Through this interaction process, MOCPCEA generates a set of classifiers that are able to classify difficult data sets. The final classification result is given by the ensemble voting mechanism among these sets of classifiers. The performance of the proposed algorithm is performed on seven benchmark problems. Through comparison with other algorithms, the proposed algorithm indicates that it could create an ensemble of ANN networks that give high and stable classification results.
Innovative management in the system of modern secondary education

In this article the scientific-theoretical analysis of innovative management in the system of secondary education is carried out; the basic conditions of their implementation are determined. The model, which will be implemented in the management process, is characterized by the use of pedagogical conditions, the production and implementation of innovation changes, taking into account regional specifics, external and internal factors. The technology of implementation of the implementation and models of implementation of innovation management in the secondary education system is disclosed; the step-by-step introduction of the components of the model technology based on the pedagogical conditions, application of modern principles and innovations for the perspective development of the secondary education system of the region is reflected. The level of pedagogical conditions in the implementation of innovation management is determined; The significance of innovation management for the management of secondary education system is determined and the factors that stimulate the managerial activity of the head of the institution of general secondary education are identified and ensure the progressive development of the institution of education and the system of secondary education in general.
Simplicity is The Golden Rule: Lesson Learned from The Development of Smart Mobile Apps for Rural Community

Many government agencies have been implementing digital-based public facilities and amenities reporting and management systems. However, these systems need further improvement as there exist some contradictions between the requirements of the rural users and the complex idea of the developer. Exploring the views of rural citizens on these systems, this paper shares some lesson learned from the mobile app development point of view and real user's expectations. An Android smart mobile app prototype was developed based on the current requirement of necessary facilities and amenities reporting system. It was then tested by 40 participants from various level of community members in Langkawi Island, based on the specific real reporting scenarios. The overall analysis revealed that rural users prefer simplicity as opposed to the complexity of mobile apps.
Data Access Management System in Azure Blob Storage and AWS S3 Multi-Cloud Storage Environments

Multi-cloud storage offers better Quality of Service (QoS) such as availability, durability, and users' perceived latency. The exploitation of price differences across cloud-based storage services is a motivate example of storing data in different Geo-graphically data stores, where data migration is also a choice to achieve more cost optimization. However, this requires migrating data in tolerable time from the perspective of users. This chapter first proposes a comprehensive review on different classes of data stores inspiring data migration within and across data stores. Then, it presents the design of a system prototype spanned across storage services of Amazon Web Services (AWS) and Microsoft Azure employing their RESTful APIs to store, retrieve, delete, and migrate data. Finally, the experimental results show that the data migration can be conducted in a few seconds for data with a magnitude of Megabytes.
Binary Form through the Music of Underrepresented Composers

This chapter presents material for an introduction to binary form, appropriate for form and analysis classes or chromatic harmony classes with an introduction to small forms. Skills developed include how to identify large-scale two-part forms both aurally and in score study and to determine how binary form manifests in various musical contexts, such as in continuous and sectional, balanced, simple and rounded designs. Pieces for analysis include Valerie Capers’ (b. 1935) “Billie’s Song” from Portraits in Jazz (1976), and Elisabeth Jacquet de la Guerre’s (1665–1729) Allemande from the Suite in D Minor from Pieces de Clavecin (1687).
CMP-based saliency model for stereoscopic omnidirectional images

Although many saliency models for images had been proposed, the saliency model for stereoscopic omnidirectional image (SOI) is still a very important but not yet deeply studied topic. In addition, one of main disadvantages of the existing saliency models is difficult to simulate the characteristics of a wide field of view (FOV) of SOIs. To solve the problems, this paper proposes a novel cube map projection (CMP) based saliency model for SOIs. Considering that the equi-rectangular projection (ERP) representation of SOI will result in obvious stretch distortion, and what the human eyes view are the viewport images by using head mounted display (HMD), we construct the saliency model of the SOI in its less shape distorted CMP plane. First, the SOI with ERP format is converted into the CMP format. In order to reduce the boundary effect between the faces in CMP plane, its four horizontal faces are moved to obtain the horizontal global face and the horizontal local faces for which the corresponding saliency maps are subsequently computed by combining color distance and depth distance in the graph model. Then, to establish the correlations of these faces, the horizontal face saliency map is calculated by weighting the horizontal global face saliency map to the horizontal local face saliency map. Meanwhile, the vertical face saliency map is defined and obtained. Finally, the horizontal face saliency map and vertical face saliency map are spliced and re-projected to ERP plane to obtain the final saliency map of the ERP-mapped SOI. The experiments on the public ODI database are performed to compare the proposed methods with the state-of-the-art methods, and the results show that the proposed method achieves better performance of estimating SOI saliency map in terms of six well-known quantitative metrics.
Fast Positioning Method for Concentric Circle Marks Based on Color and Shape Features

The most classic method for finding a circle mark in an image and positioning the center of the circle is the Hough circle detection. However, this method has many problems, such as large amount of computation, low detection accuracy and large memory usage, which cannot meet the requirement of real-time performance. This paper proposes a positioning method based on color and shape features, first by color segmentation area of interest, then use median filter to filter the noise in image, using edge detection and contour to find the outline of the image, finally from the view of the mathematical description of circular shape characteristics, the contour filter, so as to quickly find the center of concentric circles. Experiments show that compared with the improved Hough gradient method, the proposed method has lower complexity, faster calculation speed and can find the marker area more effectively, especially suitable for real-time requirements such as drone vision guidance and accurate landing.
Analysis and Prioritization of App Reviews

Smartphone apps market is a billion dollar industry and is growing rapidly. Thus, app developers are constantly on the lookout for efficient and reliable data analytics and prioritization tools that analyze crucial feedback present in app reviews and at the same instance prioritize the reviews for remedial actions. Such analysis and prioritization tools significantly assist app developers to identify and address requirements raised in the app reviews by the app's customers (end-users). This, in general, aids the app maintenance and evolution cycles. In this study, we propose and develop analytics and prioritization methods and represent the methods in the form of a software tool to support app developers of the MyTracks app towards the app's maintenance and evolution cycles. Our findings through the means of system usability scores reveal that the app developers had significantly agreed with the usability, reliability, and efficiency of our tool.
Multifrontal Non-negative Matrix Factorization

Non-negative matrix factorization (Nmf) is an important tool in high-performance large scale data analytics with applications ranging from community detection, recommender system, feature detection and linear and non-linear unmixing. While traditional Nmf works well when the data set is relatively dense, however, it may not extract sufficient structure when the data is extremely sparse. Specifically, traditional Nmf fails to exploit the structured sparsity of the large and sparse data sets resulting in dense factors. We propose a new algorithm for performing Nmf on sparse data that we call multifrontal Nmf (Mf-Nmf) since it borrows several ideas from the multifrontal method for unconstrained factorization (e.g. LU and QR). We also present an efficient shared memory parallel implementation of Mf-Nmf and discuss its performance and scalability. We conduct several experiments on synthetic and realworld datasets and demonstrate the usefulness of the algorithm by comparing it against standard baselines. We obtain a speedup of 1.2x to 19.5x on 24 cores with an average speed up of 10.3x across all the real world datasets.
An Effective Web Service Anti-Pattern Prediction Model Using SMOTE

An anti-pattern is a common response to a recurring problem that is usually ineffective and risks being highly counterproductive. In this work, we empirically investigate the association between the occurrence of four different types of anti-patterns and source code metrics. SMOTE is being used for data sampling as the dataset considered is imbalanced. Principle component analysis and Rough set analysis are applied for feature extraction and selection. The features selected from this two techniques along with the significant features(SIGF) are considered as input for building the predictive models for the detection of antipatterns. The effectiveness of these techniques are evaluated using Logistic Regression(LOGR), Decision Tree(DT) and Least Square Support Vector Machine(LSSVM) with three different kernels:Linear(LSVVML), Polynomial(LSSVMP) and Radbas(LSSVMR). Experimental results reveal that the model developed using SMOTE is yielding better results when compared to the models developed with the original dataset. Furthermore, we also observe that the predictive model developed using LSSVM with linear and polynomial is more effective than the models developed using other classifier techniques.
Particle Flow Filters: Biases and Bias Avoidance

Particle flow filters are appealing due to their potential resistance to particle collapse. However, common implementations exhibit undesirable biases or particle divergence. This paper shows that the explicit and incompressible flows, unlike the Gromov flow, are inherently biased. Another issue is errors in the numerical integration of the flow. The benefits of implicit stochastic-integration methods are demonstrated and a new adaptive step-size selection heuristic is presented.
A Path Planning Algorithm based on Leading Rapidly-exploring Random Trees

Nowadays, unmanned vehicles for navigation applications, such as factory unmanned vehicles and delivery dining systems, are becoming more and more extensive and gradually playing an indispensable role in our lives. Basically, navigation can be divided into three fields, the mapping, the localization and the path planning, in which the path planning is to use a pre-built map to plan a feasible path given the starting and the ending points. Hence, the path planning is the core part of the navigation operation, which is very important at the robot application level, such as the automatic driving and the driverless driving. For aircraft and space exploration, the path planning algorithms can be roughly divided into two types, the graph-based searching and the sampling-based searching. Among the two, the path planning based on random sampling provides with fast operation speed, high success rate on high-dimensional and complex problems, and disuse of extra considerations. The constraint of non-holonomic constraints, and the fast search for random trees are an algorithm based on random sampling. This paper mainly focuses on improving the path divergence in fixed iteration and on leading the direction from root to goal in fixed iteration condition. From the comparison of the experimental results, our approach is approximately 1.5 times better than the RRT on average path length.
Keystroke Dynamics in Mobile Platform

Recently popularity in current digital devices with touch sense system such as smartphones, iPad, iPod, Nintendo DS, Automated Teller Machine (ATM), Windows 10 devices and etc. Smartphone is the one of main communication device in current global because it is portable with-it size and ease to carry. Other than that, it has numerous features combine into one device. User can do their transaction through online banking with their phone for online shopping, fund transfer, pay bills and view current balance using smartphone. A smartphone is become most important device in daily life so the smartphone security is an important issue. To secure the sensitive data for stored and accessed from, smartphone device has made user authentication become an importance major issue. Most of the smartphone devices using traditional way such as setting pin and password. Pin is using 4 random numerical number 1 to 9 while Password using 4 to 32 numerical number 1 to 9. A different security lock in smartphone is pattern lock while user can draw different pattern into the 9 dots to lock the phone. While for current security using biometric fingerprint security for unlock phone. It was dominant to analyze users' daily behaviors and evolve mobile phones into truly intelligent personal devices to secure users. While making the device truly intelligent should implement machine learning with biometric together and let the device learn and predict future. However, keystroke dynamics is the one of the behavior biometric that can implement with machine learning to create continuously authentication to track the owner of the devices.
Pedagogical coaching in the modern educational process

In the article the conditions of acquiring of coaching function of pedagogical instrument are considered. The aspects of this problem, which are already being studied by Ukrainian educators - both practitioners and theorists, are outlined. The factors of the entry of coaching technology’s entering in Ukrainian educational process in XXI century are found out in the article. Conditions and nature of cooperation of the student with the coach-teacher are highlighted. Their duties and opportunities in such work are revealed. The principles of pedagogical coaching, the peculiarities of methods of work with the students, the role of coaching-technology in the formal education in Ukraine are described here.
A Tool for Requirements Analysis of Safety-Critical Cyber-Physical Systems

One of the key challenges in the design of a Safety-Critical Cyber-Physical Systems is Requirements Analysis. Current Requirements Analysis approaches range from informal, human-centered ones that are hard to automate, to formal approaches that often lack freedom of expression. Furthermore, most approaches are general-purpose and do not focus on a particular domain, which makes identifying the specific requirements of a given domain less trivial. To overcome these challenges, this paper presents aDSL, a Domain-Specific Language and toolset for Requirement Analysis of Safety-Critical Cyber-Physical Systems. The approach comprises a mixture of informal and formal elements to enable both automation and freedom of expression; a number of stakeholders introduce and negotiate about their requirements. The aDSL language is used to precisely, concisely and unambiguously describe all such requirements. We have validated aDSL, using simulation techniques and actors that represent the stakeholders, on a case in the agro-machines domain. The proposed approach allows the discovery of requirements in a semi-automatic way.
Interpolation of Real and Complex Discrete Signals in the Spatial Domain

The problem solution of two-dimensional signal interpolation on the reference plane in the spatial domain is considered. The difference between the interpolation and approximation problems of two-dimensional discrete signals is shown. The mathematical apparatus of matrix and algebraic descriptions of two-dimensional discrete signals in the spatial and spatial-frequency domain is given. The methods of interpolation of real and complex discrete two-dimensional signals in the spatial domain are developed. The validity, accuracy, and efficiency of the proposed interpolation algorithms are illustrated. The results of experimental study using two-dimensional discrete signal model are presented.