Reviewerly/exHarmony huggingface dataset:
Learning Enhancement of Online Handwritten Telugu Character Modeling for Various Features Sets

Feature extraction plays vital role in online hand written character recognition. Local Features captured through co-ordinate system approach plays significant role in determining the online telugu character recognition. In this paper, we have instigated the performance of various features using Artificial Neural Networks( ANNs). ANN model is tested with various combination such as (x,y) co-ordinates , pen-up and pen-down ) , ( y x  , ( ) y x 2 2 , . Finally it is observed that ( ) y x 2 2 , features have given better accuracy. 95.18 % performance is obtained for 300 epochs for 52 Telugu characters. The database used for the study is HP-online Telugu database.
ABILITY IN USING CHINESE ADVERB “TONGCHANG” AND “CHANGCHANG” OF STBA-PIA SECOND YEAR STUDENT 2017/2018 ACADEMIC YEAR

The research is aimed at discovering the ability of STBA-PIA Second Year Students 2017/2018 Academic Year in using the Chinese Adverb “Tongchang通常” and “Changchang 常常”. This research uses a descriptive quantitive design method with a total population of 67 students. As the total population is under 100, the whole students are taken as the research sample. Students’ ability in using both Chinese Adverbs “Tongchang通常” and “Changchang 常常” is measured by using the test in which is the research instrument. The validity and reliability of the research instrument are examined by using content validity and internal reliability. Internal reliability is examined by using Kuder Richardson 20 formula. The reliability of the instrument shows the coefficient of 0.618 in which may be interpreted as quite a strong value. After passing the reliability score, the instrument is given a test to students. The result of the test is the ability of STBA-PIA second-year students in using the Chinese Adverb “Tongchang通常” and “Changchang常常” can be categorized as quite competent as 61.18% of students reach the average score of 74.72 for “Tongchang通常” and 63.18% of students reach the average score of 72.04 for “Changchang 常常”.
Concluding Remarks

This chapter summarizes the major contributions in this book and discusses their possible positions and requirements in some future scenarios. Section 8.1 follows the book structure to revisit the key contributions of this book in both theories and applications. The developed algorithms, such as the VA-ARTs for hyperparameter adaptation and the GHF-ART for multimedia representation and fusion, and the four applications, such as clustering and retrieving socially enriched multimedia data, are concentrated using one paragraph and three paragraphs, respectively. In Sect. 8.2, the roles of the proposed ART-embodied algorithms in social media clustering tasks are highlighted, and their possible evolutions using the state-of-the-art representation learning techniques to fit the increasingly rich social media data and demands are discussed.
Introduction

Unconditional education (UE) is driven by the belief that public schools are responsible for all students. This may seem a foregone conclusion: national trends in education never seem weary of exclaiming that no child shall be left behind, that all children in this nation will have the opportunity to attain an excellent education or, more recently, that every student shall succeed. The concept of unconditional education is not new. However, public schools have yet to fully deliver on its promise, failing to build truly inclusive systems of care capable of serving students with the most exceptional needs. The UE model, as presented in this book, serves as an organizing framework for education leaders who are looking to reshape their schools in order to better meet the broad range of needs of their students, families, and staff. This book may appear to focus only on students with the most exceptional needs. That focus, however, is not limited. UE is founded on the belief that improving the educational experience of our most vulnerable children, in the end, benefits all children. Its aim is to create learning environments where students receive the support they need when they need it, not only after they have reached a certain, designated threshold of failure. UE seeks to build truly engaging and exhaustively positive environments where all students, their families, and the school’s staff feel safe and deeply connected to the community, laying the foundation necessary for academic success. This is a book about transformational change in schools. Such change, by its very definition, requires a fundamental shift in the way schools do business. To be even more clear: this is a book that seeks to disrupt the well-established ways in which schools operate, from the daily experiences of students, families, and staff to the organizing structures that influence them, such as schedules, discipline practices, working conditions, service delivery, and physical space. This is a book about how to create actual changes within actual schools.
Recursion

In this chapter, we consider an immensely powerful technique for solving problems: recursion. Recursion involves recognizing that a problem really consists of the same kind of problem, just on a smaller scale. For example, to sort n elements, we can first find the smallest, then sort all the others, and put them after the smallest. This is, in its essence, what selection sort does; we just didn’t explain it in these terms. When we do describe the algorithm like this, the recursive part is that we sort all but the smallest object as part of sorting all the items. To sort n elements, we need to sort n – 1. This is what recursion is.
Priority based Agile Estimation for Size and Time (PAEST)

Agile estimation process is an emerging research area because of agile practice of accepting requirements at any stage. Some agile practitioners are using agile estimation of size and time based on expert opinion and planning poker game. These methods are non-algorithmic method whereas algorithmic method of estimation always useful for novice estimator as it is based on empirical study. In this paper, we have proposed algorithmic method, Priority based Agile Estimation for Size and Time (PAEST) for projects using agile practices. PAEST incorporates the identification of factors estimating size of projects based on its priority and impact on the project. Size and Time estimation are computed as function of priority factor of project attributes and uncertainty in requirements of the projects. Paper also elaborates the case studies of various domain of software projects along with priority and intensity level of factors affecting the project estimation. We have incorporated three domains: web application, MIS project and critical software and seven project attributes out of 21 attributes used by generalized estimation method in our case studies. Number of project attributes can be scalable to any number depending on project domain. Algorithm prioritizes the project attributes and generates the estimates of the project more realistic. Introduction of prioritization helps novice to get estimates more precise. In our study we observed that uncertainty in project have more impact than project attributes.
Real-Time Security Using Computer Vision

Security systems involve using methods to analyze the process going on inside a system and prevent the unauthorized access to the system. Any system can be accessed physically. This possesses a potential threat to the system. The real-time security system will keep watch on the operator and validate the operator and if the operator is not the original one, then the system will deny access and changes. Security system uses verification and authentication, which is contained in the protocols used by the system, has no means to authenticate that the user is the valid one. Computer vision has the capacity to view its surrounding and make decisions like human beings. Vision can be used as a tool to design a very secure system or network. Using computer vision technology will make computers recognize its owner and verify the user. This is a biometric system, which in spite of working for just one, works all the way around. This type of security system will be helpful in the long run, which not only focus on login and verification of user but it will keep an eye on the user. As this will keep working all the time if somehow anyone hacked it, the next moment it will be difficult for the hack to work because the system will again try to recognize its user. Biometric security system are of great use nowadays because they do not need any key to match; they need the user as a key for the access. The system keeps on updating its database and that means it keeps on increasing its efficiency with more and more training.
Novel Procedure for Generating Continuous Flash Suppression: Seurat Meets Mondrian

Continuous flash suppression (CSF), used in hundreds of studies to manipulate visual awareness, entails presentation of a stationary image (the ‘target’) to one eye and an animated sequence of arrays of geometric figures (the ‘mask’) to the other eye. The prototypical CFS sequence comprises different-sized rectangles of various colors, dubbed ‘Mondrians’ after the Dutch painter Piet Mondrian whose abstract compositional style used simple forms and colors devoid of figurative quality. When presented as a rapid, changing sequence to one eye, Mondrians - or other, similarly constructed textured arrays - can abolish awareness of a target viewed by the other eye for many seconds at a time, producing target suppression durations much longer than those associated with conventional binocular rivalry. We have devised a novel visual animation technique that replaces meaningless Mondrian figures with recognizable visual objects and scenes as inducers of CFS, allowing explicit manipulation of the visual semantic content of those masks. By converting each image of these CFS sequences into successively presented objects or scenes each comprised of many small, circular patches of color, we can create ‘pointillist’ (cf. Seurat) CFS sequences closely matched in terms of their spatio-temporal power spectra. Moreover, randomly rearranging the positions of the pointillist patches yields scrambled versions of these images, thus destroying their recognizability. Using poin-tillist CFS sequences, we have discovered that CSF sequences comprising a stream of different objects produces more robust interocular suppression than do sequences comprising a stream of different scenes, even though the two categories of CSF are matched in low-level feature strength. Color is not critical for creating this superiority of objects over scenes, as demonstrated by CFS sequences made from achromatic object- and scene-images. Poin-tillist CFS provides a useful means for examining the impact of high-level image meaning in the modulation of visual awareness.
Methods

The three statistical analysis methods used are presented: multivariate linear regression, random forests, and artificial neural networks. Their respective advantages and disadvantages are discussed as well as how they can complement each other. Using three independent methods increases the robustness of results considerably. A further subchapter describes the operationalization of success factors through the development of an indicator system. Particular attention is paid to the validation of this system through external experts, its practical use, and operationalization of ecological success. Different ways of operationalizing ecological success are compared for forests. The surprising conclusion is that experts’ judgment is equivalent to expensive quantitative measurements.
Resource Provision Scheduling in Cloud using Game Theory

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.
Self-Learning Based Emotion Recognition using Data Analytics

An outward appearance feeling acknowledgment based human-robot collaboration analytic framework used for which a layered framework structure is planned. This analytic framework empowers the robots not exclusively to perceive human feelings, yet additionally to create outward appearance for adjusting to human feelings. A facial feeling acknowledgment strategy dependent on multiclass extraordinary learning machine classifier is introduced, which is connected to ongoing outward appearance acknowledgment for robots. Here, a half and half component descriptor based technique is proposed perceive human feelings from facial articulations. Blend of spatial sack of highlights with spatial scale-invariant component change, and with spatial speeded up hearty change are used to enhance the capacity to perceive outward appearances. For arrangement of feelings, K-closest neighbor and bolster vector machines with direct, polynomial, and spiral premise work bits are connected. Descriptor produces a settled length include vector for all example pictures independent of their measure. Spatial SIFT and SURF highlights are free of scaling, turn, interpretation, projective changes, and mostly to brightening changes. An altered type of pack of highlights is utilized by including highlight's spatial data for facial feeling acknowledgment. The proposed strategy varies from ordinary techniques that are utilized for basic item categorization without utilizing spatial data. Tests have been performed on expanded muk–ken (MK+) and Japanese female outward appearance informational indexes. SVM brought about an acknowledgment precision of 98.5% on MK+ and 98.3% on informational index. Pictures are resized through specific pre-handling, in this way holding just the data of intrigue and decreasing calculation time.
Software Product Quality Management Methodology &amp; the Quantitative Assessment of Analyzability Indicators

The research paper developed a new software metric methodology for evaluating the analyzability indicator for software products. The proposed research methodology provided an objective and quantitative assessment in accordance with the requirements, limitations, purpose and specific features of software products. Forty-one (41) java programs were analyzed to extract and evaluate the software metrics described in ‘Halstead metrics. The mathematical classification model was developed to replace the expert output in the evaluating process as related to the software metric indicators. The output of the algorithm was applied to identify the metrics with the greatest analyzability influence. The result indicated that 13 measured metrics with 98% of “analyzability” are relevant to seven (7) software code metrics with the remaining six (6) metrics making up only ~ 5% of “analyzability”. The analyzed ROC-curves were similarly computed to test the performance of the proposed methodology compared to the expert’s metric evaluation. The ROC-curves indicator for the proposed methodology showed resultant scores of ROC = 7.4 as compared to 7.3 from the experts’ evaluation. However, both methods were correlated effectively after analytical computations with a resultant performance which showed that the proposed method outperforms the expert’s evaluation.
Application of convolution neural network in web query session mining for personalised web search

In this paper, a deep learning convolution neural network (CNN) is applied in web query session mining for effective personalised web search. The CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic clusters means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.
First Foray into Text Analysis with R

In this chapter readers learn how to load, tokenize, and search a text. Several methods for exploring word frequencies and lexical makeup are introduced. The practice exercise at the end of the chapter introduces the plot function.
Introduction

This introduction presents an overview of the key concepts discussed in the subsequent chapters of the book. This book aims to privilege the context of the digital age by offering a comprehensive text that highlights the impacts this has, and will continue to have, on the aspects of learning and teaching mathematics in formal education systems and settings. In the intervening years, the mathematics education research field has expanded to consider the teacher in all of his/her roles within the context of both pre-service and in-service teacher education, with increasing emphasis on how to prepare and support teachers to conduct their professional lives in this fast-moving digital age. The book adopts a historical approach to the analysis of major national or regional professional development initiatives in Colombia, Italy, Mexico and Portugal to aim at making explicit the “pragmatic theories” that have underpinned their design, implementation and (in some cases) evaluation.
Data Governance

In most cases, the direction of a data-driven organization will be different from the organization’s previous direction.
DEEP LEARNING BASED LOAD BALANCING USING MULTIDIMENSIONAL QUEUING LOAD OPTIMIZATION ALGORITHM FOR CLOUD ENVIRONMENT.

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.
Moving object detection and description

This chapter is concerned with how we can find and describe moving objects. This implies that we do not have a single image, but a sequence of images (or video frames). The objects we seek to find and describe are those which move from place to place in one image to the next. We shall first describe methods that extract the moving objects, separating them from their background. We shall then consider ways to describe the trajectories made by these objects, using the motion of the shape and its trajectory for recognition purposes. This is tracking and we shall give an insight into what is a very large research field. We shall then move on to techniques for extracting and describing moving objects, including articulated ones.
Towards a Taxonomy of Inline Code Comment Smells

Welcome to the 21st IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2021)! We hope that you enjoy the conference and that it will inspire you in your future research. Should you require a certificate of participation please contact Alexander Serebrenik, the general chair of SCAM, at a.serebrenik@tue.nl. For questions related to the Morressier virtual platform please contact support@morressier.com.
Eingabe-Dateien erzeugen

No AccessFinite Elemente Analyse für IngenieureJul 2019Eingabe-Dateien erzeugenFrank Rieg, Reinhard Hackenschmidt, Bettina Alber-LaukantFrank RiegSearch for more papers by this author, Reinhard HackenschmidtSearch for more papers by this author, Bettina Alber-LaukantSearch for more papers by this authorhttps://doi.org/10.3139/9783446460621.011SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2019Pages: 393-417Print ISBN: 978-3-446-45639-6eISBN: 978-3-446-46062-1 Copyright & Permissions© 2019 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
KKR/XPV Water Partners platform rebrands as Axius Water

Axius Water is the new corporate name for the water quality solutions platform established by KKR’s Global Impact Fund and XPV Water Partners in 2019 with the acquisitions of Nexom and Environmental Operating Solutions Inc (EOSi) (see Filtration Industry Analyst, December 2019).
Data Storage Energy Efficiency in the Zettabyte Era

Data storage has become an increasingly significant issue with the widespread availability of cheap storage technology and the development of global data centres for cloud storage. This paper reports a study of energy consumption for data storage in data centres. Unlike previous estimates, which have been extrapolations of broad energy usage within the data centre, we take a bottom-up approach. Firstly, we describe the technologies that can be used for data storage. Then we build up a model of energy consumption in data centres based on tiers of data storage. Based on published forecasts of data storage growth and assumptions about improving energy efficiency in technology and data centres, we then estimate the energy consumption for data storage in data centres in 2020. We show that energy consumption will rise substantially if a “business as usual” approach is adopted. We further show that the growth in energy consumption can be mitigated somewhat by adopting a more aggressive policy of data archiving on long-term, low-energy, “cold” storage; or more ideally purging data of little or no future value. This, however, would require a change in the popular assumption that data will be available readily online and forever.