Reviewerly/exHarmony huggingface dataset:
Stability of the optimal distribution for the searching effort to find the Markovian targets by using fuzzy maximum discounted effort reward search: case of the cooperative search techniques

The aim of this paper is to study the stability of the optimal distribution for the searching effort to detect two related Markovian targets by using multiple cooperative searchers. This effort, at each fixed number of time intervals, is a random variable with a normal distribution. The optimal solution (which makes the discounted effort reward with fuzzy parameter maximum) is obtained from solving a fuzzy stochastic optimisation problem. Rather than presenting an algorithm that shows the stability of the optimal distribution of an effort, we study more interesting special cases of located spider landmines and unrestricted effort. Two illustrative examples of Markovian and randomly located spider landmines are discussed.
Underwater Image Enhancement and Object Detection Using Edge Preserving and Multiscale Contextual Neural Network

The submerged perception circumstances cause incredible difficulties to the issue of article location from the low-goals submerged pictures. In this paper, we acquaint an effective strategy with improve the pictures caught submerged and corrupted in light of the medium dispersing and retention. It expands on the mixing of 2 pictures that are legitimately gotten from a shading redressed and white-adjusted adaptation of the first corrupted picture. In the wake of improving the submerged picture, plans to identify object that present in the submerged by utilizing novel edge saving and multiscale logical neural network. We concentrated for the most part on discovery of an item in the submerged that they are utilized to isolate them an article from the foundation by utilizing a mix of programmed difference extending pursued by picture number-crunching task, worldwide edge, and least channel. Our system could be a solitary picture approach that doesn't need particular equipment or information about the submerged conditions or scene structure. our upgraded pictures are described by better exposedness of the dull area, improved worldwide complexity and edge sharpness and our striking article recognition accomplishes both clear identification limit and multi-scale logical vigor at the same time in this manner accomplishes an enhanced presentation.
Hybrid BATGSA: A Meta Heuristic Model For Classification of Breast Cancer Data

Nature-inspired algorithms have a vast range of applications. One such application is in the field of medical data mining where, major focus is on building models for the classification and prediction of various diseases. Breast cancer has grabbed the interest of numerous researchers because, it is the major killer disease, killing millions of women across the globe. In this paper, we propose a hybrid diagnostic model which is a fusion of Bat algorithm (Bat), Gravitational Search Algorithm (GSA), and Feed-Forward Neural Network (FNN). Here, the potential of the FNN and the advantages of nature inspired algorithms have been exploited to build a hybrid model used for classification of breast cancer data. The proposed model consists of two modules. First, is the training module where the data is properly trained using a feed-forward neural network and the second, is an error minimising module, which is built using Bat and GSA metaheuristic algorithm. The hybrid model minimises the error thus, producing better classification results. The accuracy obtained for Wisconsin Diagnostic Breast Cancer Diagnostic (WBCD) data set is found to be 94.28% and 92.10% for training and testing respectively.
An ensemble random forest algorithm for privacy preserving distributed medical data mining

As the voluminous amount of data is generated because of inexorably widespread proliferation of electronic data maintained using the Electronic Health Records (EHRs). Medical health facilities have great potential to discern the patterns from this data and utilize them in diagnosing a specific disease or predicting outbreak of an epidemic etc. This discern of patterns might reveal sensitive information about individuals and this information is vulnerable to misuse. This is, however, a challenging task to share such sensitive data as it compromises the privacy of patients. In this paper, a random forest-based distributed data mining approach is proposed. Performance of the proposed model is evaluated using accuracy, f-measure and appa statistics analysis. Experimental results reveal that the proposed model is efficient and scalable enough in both performance and accuracy within the imbalanced data and also in maintaining the privacy by sharing only useful healthcare knowledge in the form of local models without revealing and sharing of sensitive data.
Content Based Image Retrieval for Community Retrieval from Given Nationality using an Efficient Combination Algorithm

There is tremendous requirement of such technique which can fulfill the entire requirement for retrieval of an image from available dataset which comes under computer vision. In this paper we discussed about the one of the application of CBIR using an efficient combination of two techniques. The application is retrieval of people images from database that comes under minority. In this paper we used an efficient combination of color image histogram technique and edge orientation histogram technique by dividing original image into small subblocks. The feature vector is formed by combination of two features obtained by above methodologies. The final features obtained by query image will be compared with the feature vector of database images using a new Canberra Distance classifier. Proposed method is designed for multiple self-prepared and some collected from internet databases. Our method includes the efficient integration of features such as color, texture, shape and orientation. The proposed method is compared with state of art techniques to prove the stable and highest accuracy of proposed work.
Bimanual gesture recognition based on convolution neural network

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image.
An Environment-Visualization System with Image-Based Retrieval and Distance Calculation Method

Many environmental issues have occured in this world and these issues are common to all human beings.It is considered that environmental issues caused by humans exist in the "border" between nature and human society.In other words, there is the possibility that finding the "border" leads to determine the cause of environmental issues and discover the solution.This paper presents an environment-visualization system with image-based retrieval and distance calculation method as the first step of research for finding the "border".We focused on the plastic garbage issue which is related to SDGs14 and this study was made to find the "border", source of the plastic garbage which is scattered on the coast area.In addition, we aim to realize the system which enables people to share the knowledge about the plastic issue in order to acquire knowledge of the environment issues and to promote concrete action to realize sustainable nature and society. In this system, there are 3 features: (1) Composition-Based Image Retrieval Function, (2) Spatio-Temporal-Based Mapping Function, and (3) Coast-area Location-Checking for Selected Images Function.(1) is the image retrieval function for detecting highly-related images to a query-image with dividing one image to three images for separating with nature, human society, and "border".We used Euclid calculation to calculate the similarity and show the results in the ranking format.( 2) is the mapping function with using the spatio-temporal information which is accompanied with images.( 3) is the locationchecking function to judge whether the photographing spot is near the ocean or not with image processing metric and select the images only which are near the ocean.We present several experimental results to clarify the feasibility and effectiveness of our method.
Image recognition for vehicle traffic analysis at intersections

We have been conducting research on traffic simulation for traffic jam analysis.Before building a traffic simulation system, it is necessary to perform actual road observations.However, in the past, a great deal of labor has been required to acquire data on vehicles running on the road.In this study, we propose a method to easily obtain car driving data by using image recognition.In recent years, cameras are becoming smaller and higher performance.Therefore, an image can be taken from a free position.In general vehicle traffic, traffic congestion often occurs due to the flow of vehicles at intersections.Therefore, in this research, we decided to take a bird's-eye view from the top of the building in contact with the intersection.We have taken many images from buildings in the city and made them sample images so that the vehicle can be recognized from the overhead position.And we built a system to track the position of the vehicle by using image recognition technology by machine learning.This system makes it easy to analyze the movement of vehicles at intersections.This vehicle tracking system in the intersection constructed makes it possible to measure the transit time of the intersection.Furthermore, by using this system, the traveling time of a group of vehicles can be automatically measured.Our experimental system makes it easier to analyze the driving characteristics of vehicles at intersections.Then, we compared the YOLO (Redmon, J., et al. 2016) adopted for the image recognition method this time with the OpenCV cascade classifier used so far, and analyzed the characteristics of each method.As a result of this research, we will be able to analyze vehicle traffic in detail, so that we can construct an effective traffic simulation, and will be able to perform more accurate traffic congestion analysis.
Identification of Accurate Classification Technique for Crime Investigation Using Ensemble Approach

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
File Format s for Big Data Storage Systems

Big data is one of the most influential technologies of the modern era. However, in order to support maturity of big data systems, development and sustenance of heterogeneous environments is requires. This, in turn, requires integration of technologies as well as concepts. Computing and storage are the two core components of any big data system. With that said, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings the facet of big data file formats into picture. This paper classifies available big data file formats into five categories namely text-based, row-based, column-based, in-memory and data storage services. It also compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Lastly, it provides a discussion on tradeoffs that must be considered while choosing a file format for a big data system, providing a framework for creation for file format selection criteria.
INFORMATIONAL ASPECT OF MODERN TECHNICAL EDUCATIONAL PROCESS IN THE HIGHER SCHOOL SYSTEM

The article is devoted to the analysis of the influence of informatization of society on the mechanisms of functioning and implementation of the higher education system.The authors highlight the basic values of the information society, such as knowledge; qualification; independence of thinking; ability to work with information and make a decision.The global epidemiological challenge (COVID-19) has affected absolutely all spheres of life in modern society, including the higher education system.The purpose of this study: is to analyze the impact of the "cavid crisis" on the domestic system of higher education in general and on modern technical education in particular.
Study on the sound quality of classical guitar with the new material nut and saddle

We paid attention to the change in sound quality due to the material change of the nut and saddle, which are the elastic support parts of the classical guitar. The materials used are ivory and carbon. There was a comment from the user that there was a difference in sound quality. As a result of comparing the two, the carbon material had a smaller sound elongation and tended to suppress the time-series change of the envelope. It was also found that the singularity of sound elongation may be related to the behavior as an antiresonance point around the saddle bridge in a specific harmonics.
UML-based process model for mobile cloud forensic application framework - a preliminary study

The mobile cloud forensics has received a great deal of attention within the digital forensics community. Collecting potential evidence from the mobile cloud by applying forensically sound methods faces severe setback due to technological advancements. The latest forensic investigation tools and methods are inevitable to cope up with the increasing criminal activities on the mobile cloud platform. Thus, a forensically sound process is essential to develop critical methods for mobile cloud forensic investigators. This preliminary study presents a mobile cloud forensic process framework with the help of UML diagrams that primarily consists of identification, collection and preservation, examination and analysis, and evidence correlation and presentation phases. The proposed mobile cloud forensic process introduces the evidence correlation phase between the mobile and cloud. The proposed methods improve the evidence traceability in the cloud by determining the evidence related to the malicious activity of the suspected device in an efficient manner.
Dynamic Access Control Scheme for Personal Health Record in Cloud Computing

Presently, usage of Cloud computing is increasing, due to internet availability most of Personal Health Record (PHR) owners outsourcing their records to the cloud, but it is untrusted, so a security mechanism needed in this paper proposing Dynamic Time-based encryption (DTBE), it derived from classic ABE. In the past, many researchers suggested different access controls for secure PHR. Still, most of the access control mechanisms introduce burden to the PHR owner while performing dynamic operations insertion, PHR user revocation, and when it updates, PHR users attribute list. Most of the ABE schemes have several limitations as it cannot efficiently handle adding or revoking users or identity attributes. It needs to keep multiple encrypted copies of the same key that incurs high computational costs. So, there is a need for a suitable access control mechanism that should support effective policies.
The Effect of Edge Stopping in Perona Malik Anisotropic Diffusion Model as Image Denoising on Digital Radiographic Image

Image denoising is process of reducing image noise in order to improve the visual quality of the image.Many conventional filtering techniques are able to remove noise with the blurring effect that reduces the image quality.The introduction of nonlinear partial differential equations approach in image processing has grown significantly and gives varieties in filtering techniques.Perona Malik Anisotropic Diffusion (PMAD) is one of the nonlinear models that able to preserve the edge features well.But the sharp edge and fine details sometimes are not preserved well due to the used of inappropriate edge stopping function.Hence, the following objectives are been set up to propose a new edge stopping function as part of PMAD model and analyse the performance of the proposed function with the existing edge stopping function.In this paper, the PMAD model is solved using finite difference method.Then, different edge stopping functions are applied as part of the model.For experimental purposes, a set of digital radiographic image of welding defect is used.The performances of each function are evaluated using Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR).The results show that different edge stopping function give different effect on the image quality after the denoising process.As the conclusion, utilizing edge stopping function preserves the sharp edge in the denoised image and hence the image quality is also improved.
A STUDY OF THE BENEFITS AND DRAWBACKS OF NOSQL DATABASES

NoSQL database management solutions are a departure from standard relational database management systems (RDBMS). Non-tabular NoSQL databases store data in a distinct way when compared to relational database tables. The data model of NoSQL databases is used to classify them. The most popular types are document, key-value, wide-column, and graph. They feature customizable schemas and can easily handle large amounts of data as well as significant user loads. They have adaptable schemas and can handle big amounts of data and high user loads with ease. Furthermore, their data models' flexibility and ease of use, in comparison to the relational model, can faster the development, especially in the field of cloud computing. This paper discusses the benefits of NoSQL databases, such as the ability to handle large volumes of data at high speeds with a scale-out architecture, the ability to store unstructured, semi-structured, or structured data, and the ability to easily update schemas and fields, making them developer friendly. Furthermore, this paper discusses the downsides, such as the lack of reliability features found in Relational Databases.
Concluding Thoughts

Chapter 6 offers concluding thoughts on various methods of blurring the line between score and sound design and their common themes such as integration, inclusion, the erasure of boundaries, and the toppling of hierarchies. The chapter notes the inner dialectics of this trend, epitomized in the forces of change understood as progression and those explained by the cyclical reappearance of certain tendencies. The former are manifested in the use of a contemporary musical language that draws on the whole world of sound and the use of digital technology; the latter include the musical approach to soundtrack, aesthetic trends such as the aesthetics of reticence, and a fascination with the materiality, texture, and hapticity of sound, which all have precedents of some kind in the past. This chapter asserts that the main methods of blurring the line between score and sound design are inspired by music or informed by musical logic.
A novel single scan distributed pattern mining algorithm for frequent pattern identification

In data mining, the extraction of frequent patterns from large databases is still a challenging and difficult task due to the various drawbacks such as, high response time, communication cost to alleviates such issues, a new algorithm namely single scan distributed pattern mining algorithm (SSDPMA) is proposed in this paper for frequent mining. The frequent patterns are extracted in a single scan of the database. Then, it is split into multiple files, which will be shared to multiple virtual machines (VMs) to store and compute the weight for the distinct records. Then, the support, confidence and threshold values are estimated. If the limit is greater than the given data, the frequent data are mined by using the proposed SSDPMA algorithm. The experimental results evaluate the performance of the proposed system in terms of response time, message size, execution time, run time and memory usage.
Session details: Theme: Distributed systems: CC - Cloud computing track

Cloud Computing, the computing paradigm of the decade, maintains its accelerated pervasion into newer domains, achieving faster solutions and improved data security. Cloud computing has already proved its potential in meeting the demands of challenging applications, and is well on the way to forming the backbone of the Internet of Everything. The wide acceptability shows the increasing confidence of user and provider communities alike, on the underlying cloud computing technology. The leading companies, startups, and the middle order companies in the IT sector, almost invariably choose clouds as the platform for offering their services. The academia has also sustained its keen interest in this hot area, with an abundance of funded research projects at the leading universities across the globe.
Image Encryption Using Image Division and Suffling Technique

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.