Reviewerly/exHarmony huggingface dataset:
MapReduce Based Crow Search Adopted Partitional Clustering Algorithms For Handling Large Scale Data

Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic manner from terabytes to petabytes. To make proposed algorithms suitable to handle these voluminous data, the phenomena of parallel implementation of these clustering algorithms with Hadoop Mapreduce framework. The proposed algorithms are experimented with large scale data and the results are compared in terms of cluster evaluation measures and computation time with the number of nodes.
L-Interact: An Augmented Reality method for sharing information

There have been a lot of instances in the past which question the authenticity of a piece of information ashed over the social media and other news sharing organizations, spread all over the domains of the internet. In this global crisis of coronavirus pandemic, information is the most important thing which is desired by the people. Information regarding the virus and the ways to remain protected from it. The news has been displayed but due to the immense amount of time spent on the social media platform it becomes a chaos of information and our mind tends to divert to the fake sources. This might create havoc. Using this project, a module has been devised out that would eventually bring the genuine news to your doorsteps. Using Augmented Reality, the interface has been created that would bring entertainment in the visualization of the data factors. The app has been integrated with the major data channels over the internet which are the major sources of information during this time of crisis.
Statistical Method for Named Entity Recognition in Telugu, an Indian Language

One of the important tasks of Natural Language Processing (NLP) is Named Entity Recognition (NER). The primary operation of NER is to identify proper nouns i.e. to locate all the named entities in the text and tag them as certain named entity categories such as Entity, Time expression and Numeric expression. In the previous works, NER for Telugu language is addressed with Conditional Random Fields (CRF) and Maximum Entropy models however they failed to handle ambiguous named entity tags for the same named entity. This paper presents a hybrid statistical system for Named Entity Recognition in Telugu language in which named entities are identified by both dictionary-based approach and statistical Hidden Markov Model (HMM). The proposed method uses Lexicon-lookup dictionary and contexts based on semantic features for predicting named entity tags. Further HMM is used to resolve the named entity ambiguities in predicted named entity tags. The present work reports an average accuracy of 86.3% for finding the named entities.
The ecological project: Step into the new library mediaspace

Due to the concerns of Kstovo residents about the ecological situation, the need for reliable information increased. The goal was to design an efficient model of ecological education and promotion of ecological culture. Through the grant support of the number of accomplished projects, the solution was found. At A. S. Pushkin Central Library, Ecological Space and Comfort Zone has been established. It comprises computer ecological information database, multimedia equipment, professional scanner, and provides access to the Internet resources.
An Intelligent Fuzzy-induced Recommender System for Cloud-based Cultural Communities

The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services.
Workflow Demonstration part 1

Abstract This and the next chapter involve us working through the process of getting insights from data. We have chosen a research subject that should have some interest to us all: food. More specifically, what bats eat. In this chapter you will experience a clearly specified set of research questions, learn how the study was performed and why it was done that way, and learn how to prepare your computer, R, and RStudio for the project, and how to read the dataset into R. The chapter also covers how you can clean, tidy, and manipulate the data in R. These are the solid foundations from which robust and reliable insights can grow.
HomeKit

Ever since I was a kid I’ve been dreaming of home automation. Back in the day (I’m the Commodore 64 generation, which should tell you my age) it was only thinkable if you had a lot of money.
How Is the Use of Polite Language In Improving Students' Language Skills?

Politeness of speech is one of the studies of pragmatics and speech. Speakers in Indonesian now pay less attention to the maxims of courtesy in speech. This is due to the limited knowledge of speakers which includes several factors, namely the principle of courtesy in language, the principle of cooperation in language and language context. Speech politeness is one aspect of language that can improve the emotional intelligence of the speaker because in communication, speakers and speakers are not only required to convey the truth, but must remain committed to maintaining harmonious relationships.
Bayesian filtering in incoherent scatter plasma parameter fits

Earth and Space Science Open Archive This work has been accepted for publication in Journal of Geophysical Research - Space Physics. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v3]Bayesian filtering in incoherent scatter plasma parameter fitsAuthorsIlkka I.VirtaneniDHabtamu W.TesfawiDLassiRoinineniDSariLasaneniDAnita T.AikioiDSee all authors Ilkka I. VirtaneniDCorresponding Author• Submitting AuthorUniversity of OuluiDhttps://orcid.org/0000-0002-7111-8888view email addressThe email was not providedcopy email addressHabtamu W. TesfawiDUniversity of OuluiDhttps://orcid.org/0000-0001-8300-0732view email addressThe email was not providedcopy email addressLassi RoinineniDLappeenranta-Lahti University of TechnologyiDhttps://orcid.org/0000-0002-7014-6684view email addressThe email was not providedcopy email addressSari LasaneniDUniversity of OuluiDhttps://orcid.org/0000-0001-7837-385Xview email addressThe email was not providedcopy email addressAnita T. AikioiDUniversity of OuluiDhttps://orcid.org/0000-0001-8838-5125view email addressThe email was not providedcopy email address
How to Save Time and Money by Connecting the Writing Process to the Update and Translation Process

The cost of the second translation is obviously less than that of the original translation since only the updates and changes are translated. The customer's part in the process is to deliver the texts to be translated from in-house sources or contract writers to the translation vendor. The extreme requirements of time-to-market make translation a pressured activity, placing high demands on both writers and translators. The cost of translation, from the translator's viewpoint, has decreased over the last 15 years. The quality of a translation depends on the quality of the translator and on internal quality controls implemented by the translation vendor. The quality of a translation also depends on the translator's access to terminology, reference material, and previously translated versions. The translation vendor charges a fixed amount per translated word, and when the match is closer to 100%, the charge is lower.
Service Blueprinting: A Method for Assessing Library Technologies within an Interconnected Service Ecosystem

Service blueprinting is a method for designing, assessing, and improving services. This article provides a practical overview of the service blueprinting process for library technology services. We begin by outlining the recent conversation around library technologies, service design, and service blueprinting. We then detail an iterative case study for the creation process of a service blueprint, followed by a discussion of the service insights and improvements that resulted from this activity. We conclude by offering a set of recommendations for creating and analyzing service blueprints. Ultimately, the service blueprint is a useful tool for understanding the operation of a service, and for situating that service within a broader and interconnected library ecosystem.
Identification of Repeating Earthquakes: Misconception of Waveform Similarity and a Physics-based Solution

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Identification of Repeating Earthquakes: Misconception of Waveform Similarity and a Physics-based SolutionAuthorsDaweiGaoiDHonnKaoiDSee all authors Dawei GaoiDUniversity of VictoriaiDhttps://orcid.org/0000-0002-4294-2085view email addressThe email was not providedcopy email addressHonn KaoiDCorresponding Author• Submitting AuthorGeological Survey of CanadaiDhttps://orcid.org/0000-0001-9150-9416view email addressThe email was not providedcopy email address
Political Asceticism

Thoreau’s perspective on time and its utility was contrarian in the context of New England society. Poking fun at forms of economic thinking in which work could not be sacrificed for leisure, Thoreau wrote, about his life at Walden: “There were times when I could not afford to sacrifice the bloom of the present moment to any work, whether of the head or hands” (IV, 2). In this, he reversed a piece of economic wisdom common in the work ethic of the culture in which he lived – that work should always be the first priority, and that other ways of using time were only available if they could be “afforded.” As I discussed at the end of Chapter 2, the long, descriptive middle of the book puts forward another hypothesis.
TD‐P‐09: DISCOVERING SUBTYPES OF ALZHEIMER'S DISEASE USING ELECTRONIC HEALTH RECORDS

Electronic health records (EHR) offer higher diagnostic resolution and a breadth of clinically relevant features recorded over the lifecourse. Applying clustering methods in this large, representative, longitudinal data can potentially identify novel, clinically-actionable Alzheimer's Disease (AD) subtypes. The effect of the different clustering methods has not been systematically investigated. Here, we perform four clustering experiments using different methods, comparing the results internal and clinical validity. We identified AD patients using a previously-validated rule-based phenotyping algorithm based on diagnosis codes in longitudinal primary care EHR (CPRD) available in the CALIBER resource (Denaxas 2012). We identified relevant clinical features to include from literature and extracted them for each patient. Data were clustered using four methods k-means, kernel k-means, affinity propagation and latent class analysis (LCA). Clusters were manually labelled by examining clinical feature prevalence. Internal validity was assessed using the mean silhouette score (measuring cluster separation). Clinical validity was assessed using healthcare utilization and progression markers such as rate of decline in MMSE score. We identified 10,065 AD patients (66% female, median age: 81).We identified and extracted four symptomatic variables, 22 comorbidities (such as depression, cancer), and four lifestyle risk factors. The number of optimal clusters varied from 2 (kernel k-means) to 6 (LCA). The highest silhouette score of 0.19 was achieved for five clusters using the k-means algorithm. A common cluster was found in K-means, Affinity propagation and LCA. It had the earliest diagnosis age, and the highest prevalence of depression, anxiety, and current smokers and drinkers. It represented 13.7% of patients that are consistently grouped together. This group had significantly faster progression determined by examining MMSE scores. As there is no ground truth available when using clustering methods the fact that this cluster has appeared in 3/4 methodologies indicates that this is a distinct group of interest rather than an artefact of the clustering methods. Further research should be conducted into how this group of variables relates to faster progression and also possible interventions. This group has clinical significance due to the complications of treating AD patients with additional mental health diagnoses.
Dynamic String-Averaging Subgradient Projection Algorithm

In this chapter we consider a minimization of a convex function on a solution set of a convex feasibility problem in a general Hilbert space using the dynamic string-averaging (DSA) subgradient projection algorithm. Our goal is to obtain a good approximate solution of the problem in the presence of computational errors.
Human Computer Interaction Based on Hand Gesture

Many hand-controlled robots are developed for visually impaired people in order to make them live confidently. This project work proposes a Human Computer Interactions with the help of gestures recognition wireless to help physically handicapped persons to move robot in desired direction lives. The project work is framed into three stages. First, gesture capturing and recognition – gesture capturing uses a laptop or pc camera that takes input from our hands and gesture recognition based on the finger count algorithm. Secondly, Transmission of data wireless – ZigBEE Module is used for serial transmission of data, Finally, Movement of Robot - The robot will move based on the fingers opened or fingers closed and displays the direction in laptop or pc in which direction the robot is moving. This project work can be able to insist the physically enabled people in their daily life. The entire process will run on Arduino Uno, ZigBEE Module, L293D Motor Driver
A New Colour Image Encryption Approach using a Combination of Two 1D Chaotic Map

In this paper, we propose an improved chaotic map by coupling two existing methods. Numerical tests prove that the proposed technique presents closely complex behaviour and wider chaotic range than their seed maps. In this paper a new colour image encryption approach using the enhanced chaotic map has been suggested. The proposed scheme is based on the conventional confusion-diffusion structure that contains a new permutation process, it has designed to randomly scramble neighbouring pixels. The performance and the quality measurement of the proposed scheme are analysed by comparing it with some existing research.
Dynamic resource adaptation method for airborne network based on multi-objective optimisation

Aiming at the problem that the existing methods have low resource utilisation, low task submission success rate, high overhead and high consumption in the process of airborne network resource adaptation, a dynamic resource allocation method based on multi-objective optimisation for airborne network is proposed. Taking the RAGE delay of the airborne network system model as the objective function, the problem is transformed into the graph dyeing problem. By constructing the dynamic resource migration model on the airborne network server and the application-aware dynamic resource migration model, the average delay in the dynamic resource allocation process of the airborne network can be achieved. The speech completed the key technical analysis of the dynamic resource adaptation of the airborne network. The experimental results show that the method overcomes the shortcomings of the existing methods, improves resource utilisation, task submission success rate and deadline satisfaction rate, reduces system overhead and improves user experience.
Exploring Visually the Known and the Ill-Known About Kraków’s Center Urban Evolution

Analyzing and depicting in 3D the design and transformations of urban structures have been concerns ever since the nineteenth century, for both historians and urban planners. What exactly is the impact of the digital humanities on the way we use today’s means of investigation? How can we usefully support the analyst’s tasks, by visual means, in the context of historical research and on the large scale of a city? This chapter describes real experiments that were carried out on the city center of Kraków, former capital of Poland. Through these experiments, the authors consider the fundamental issues of imperfect knowledge in the historical sciences, the need to and constraints of handling ill-defined temporal data, and the necessity to at least complement “classic” virtual reconstruction efforts with visual solutions that better match the nature of the underlying data. The data are a heterogeneous collection of structures that differ in terms not only of morphology and use but also of documentation quality. The authors introduce an approach that is based on the idea that the gap between the historical sciences and information visualization (via InfoVis) should and can now be filled. They show the potential added value of InfoVis-inspired abstract graphics in supporting analysis tasks regarding the evolution of urban structures, with the hope that this methodological bridge can be a solution particularly in the context of urban analysis.
An activity theory model for dynamic evolution of attack graph based on improved least square genetic algorithm

Most of the risk assessments of the attack graph are static and have a fixed assessment scenario, which limit the real-time nature of the situation assessment. This paper presents an activity theory model to analyse the contradictions in the attack behaviour. In order to assess the maximum probability path of an attacker and dynamically remain in control for the overall situation, a definition of attacker's benefit (loss/gain) value calculated by contradictory vector is proposed. The attacker's budget is applied as an unbiased amount in the least square genetic algorithm, optimises the fitness function of the genetic algorithm. Experimental results reveal that the improved least square genetic algorithm with unbiased estimator effectuate higher gains owing to the high fit degree of fitness function. With the coming evidence, the maximum probability attack paths get a more accurate and dynamic risk assessment of the situation.
Formalized Semantics

Consciousness and thinking produce objects of a formalized nature. These objects are usually discrete; the denotative semantics of AI models are created by these objects. But consciousness and thinking themselves cannot be fully formalized.
Object Recognition in Image Sequence using Heuristic Convolution Neural Network

The human visual system can make a distinction of tiger from cat very easily without taking any efforts. But in case of a computer system, it is a very complicated job. Identifying and differentiating task has to deal with many challenges but the human brain makes it effortless. Self learning or heuristic techniques are most relevant in this area. The recognition task is to search for the particular object of same shape, color and texture and so on, of the trained objects and match with input. The geometrical distinction such as zoom in, zoom out, rotation etc result in poor performance. This paper uses convolution neural network models Alexnet and VGGNet on object recognition problems which are added with novel heuristic method. We have used CIFAR-10 dataset. The performance and computation speeds are found efficient.
Survey on High Utility Itemset Mining 

Utility-based data mining is a new research area involved in all types of utility factors in data mining processes. The main objective of Utility Mining is to categorize the itemsets with highest utilities, by making an allowance for other user preferences such as quantity, profit and cost.Mining high utility itemsets from a transactional database refers to the encounter of itemsets with high utility like profits. It experience the problem of producing a large number of candidate itemsets used for high utility itemsets. Such a large number of candidate itemsets destroys the mining concert in terms of execution time and space requirement
PE2 - A Service Oriented Meta Task Scheduling Framework in Cloud Environment

Cloud computing is an emerging computing environment which facilitates on demand services. As it contributes a large pool of computing resources, scheduling of tasks in an efficient manner is one of the main problems. Poor allocation of tasks affects the performance of the whole system. Hence it is very important to schedule the tasks for better utilization of resources by allocating them properly to particular resources in particular time. Efficient scheduling algorithms fulfill the user requirements and also satisfy the needs of the cloud service providers without affecting the performance of the environment. Execution Time based Sufferage Algorithm (ETSA), Cost and Completion Time based Sufferage Algorithm (CCTSA) and Modified Artificial Fish Swarm(MAFSA) Algorithm are efficient task scheduling approaches developed in cloud environment. These algorithms considered the parameters such as makespan, cost and resource utilization while scheduling the tasks and produced better performance. This paper presents a scheduling framework which converts the above said algorithms in to services and deployed in the cloud. Depends on the user’s requirements, the services will be delivered.
Donetsk Republican Library’s for the Youth addressing urgent ecological problems

The findings of the study of the attitude of young adults in Donetsk People’s Republic towards ecological problems are reviewed; main activities and methods of ecological education in Donetsk Republican Library for Young Adults are discussed.
Signal and Image Resampling, and Building Their Continuous Models

Many image processing applications must resample available digital images in positions other than the original ones. Such applications include fusing image data from different image modalities, building image mosaics from many partly overlapped images, reconstructing images from projections, producing image super-resolution from video sequences, stabilizing video images distorted by atmosphere turbulence, locating targets, and tracking with sub-pixel accuracy, to name a few.
Introduction

An introduction is provided to neurons; computation by biologically plausible networks of neurons; the representation of information in the brain; the functions of different brain areas; and the structure and connectivity of the cerebral neocortex.