Reviewerly/exHarmony huggingface dataset:
Towards the Journey to Accomplish the “Joy of Learning”

In this study we aimed to explore teachers’ decision to employ a number of ICT applications to enhance their digital literacy, thus, eliminating the “fear of technology”. At the same time we wished to investigate whether factors such as gender or type of school influenced their decision to use the tools. In the study which was conducted in two phases, both quantitative and qualitative research methods were employed. To provide answers for the research questions anonymous questionnaires were delivered to 98 teachers followed by short interviews to help us expand further on the research issues. The results have revealed that the notion of “computer literacy” need is gradually being cultivated in the teachers’ minds that have used the tools for both teaching practices and European projects. More interesting results are also discussed with implications for educational authorities and curricula designers.
Algorithms for Closest and Farthest String Problems via Rank Distance

A new distance between strings, termed rank distance, was introduced by Dinu (Fundamenta Informaticae, 2003). Since then, the properties of rank distance were studied in several papers. In this article, we continue the study of rank distance. More precisely we tackle three problems that concern the distance between strings.
State behavior analysis in validating the web service framework

Web service (WS) interactions correspond to states, and they can be modeled based on the type of interactions between the service consumer and the provider. Models like UML, OWL have been designed in the past but not all of them focus on state-based interaction among the requester and the provider. In this paper, we develop a framework that understands interactions between the requester and provider and learns behaviors using FSA. We first propose a model called the state machine representation model for analyzing the behavior of WSs and also supports for modeling the job fair service for identifying the appropriate services for the consumer. Next, we propose an implementation work which gives us the run time behavior of the Job Search Engine (JSE) in network and cloud environment. Finally, we conclude this paper by interpreting and analyzing the performance of the JSE with respect to the standard parameters such as access time for invoked state, discovered state, running state, halt state, disconnected state and show that the model framework designed and developed in this paper works for the JSE. In future the framework can be extended for other applications at the generic set of states analyzed in this paper.
Supervised Joint Nonlinear Transform Learning with Discriminative-Ambiguous Prior for Generic Privacy-Preserved Features

In this paper, we explicitly model a discriminative-ambiguous setup by two jointly learned parametric nonlinear transforms. The idea is to use one nonlinear transform for ambiguization and the other one for discrimination, and also to address a privacy-utility setup that is conditioned on ambiguization and discrimination priors, respectively, together with minimum information loss prior. The generic coupled representation is composed by linear combination using the two nonlinear transforms. The model parameters are learned by minimizing the empirical log likelihood of the model, where we propose an efficient solution using block coordinate descend alternating algorithm. The proposed model has low computational complexity and high recognition accuracy for the authorized parties while having low recognition accuracy for the unauthorized parties. We validate the potential of the proposed approach by numerical experiments.
Women Oppresion Of The Main Character In Alice Walker's “The Colour Purple”

The aim of this study is to analyze women oppression and its impact to the main character in The Color Purple 1982. The method used for this study is qualitative approach by describing and analyzing the character in a novel. The theory used is the theory of women oppression by Marilyn Frye. Women oppression issue found in the novel actually has a lot of impacts on women as the subordinate due to the social construction that considers women as inferior and men as superior. Therefore, because of that reason, women are always opressed by men that are always perceiving that they are able to control and dominate. The results of the study shows that women oppression happens verbally, physically, and mentally and the impacts of women oppression are trauma, confusion, unconfidence, and powerlessness. Keywords: Women, Oppression, Novel, The Color Purple, Alice Walker, Trauma, Confusion, Unconfidence, Powerlessness
Geometrical Computational Method to Locate Hypocenter by Signal Readings from a Three Receivers

The information about hypocenters is essential for engineering tasks to predict a danger created by earthquakes. A nonlinearity in the earthquake location problem, a one-sided distribution of seismic receivers lead to a search of solutions improving hypocenter location accuracy. The original earthquake location method has been developed by using a physical and geometrical representation of the time difference between first signals from seismic waves registered by three different receivers. Data processing is performed applying algorithms of a computational geometry. The solution error is formulated in terms of an analytical geometry. Thus the method differs from traditional approaches, which are based on a standard statistic error. Taking into account the promising testing results we guess that the geometrical method can be as an additional computational technique or as a tool to get the trial hypocenter, which is an input parameter of standard software for hypocenter determining.
Software inspections: comparing a formal method based with a classical reading methodology

In this paper, we present a rigorous comparison of two inspection techniques: our formal method-based approach, SOLIMVA 3.0, with a set of Object-Oriented Reading Techniques (OORTs). We evaluated efficiency (required time to analyse a scenario) and effectiveness (ability to find defects within UML diagrams). Results show that the classical OORT methodology is more efficient than SOLIMVA. However, we found a strong positive correlation between the required time by using OORT and SOLIMVA. For effectiveness, in general, both methodologies presented the same performance. However, SOLIMVA detected more inconsistency (incorrectness and extra information), ambiguity, and partial incompleteness defects while OORT identified more total incompleteness. Our overall conclusion is that a classical reading methodology (OORT) and a formal-based one (SOLIMVA) can be adopted in a complementary way within a software inspection process. While OORT is less costly to apply, SOLIMVA can be used to address some specific types of defects.
Neural networks and how machines learn meaning

column Share on Neural networks and how machines learn meaning Author: Oana Niculaescu View Profile Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 25Issue 3Spring 2019 pp 64–66https://doi.org/10.1145/3313144Published:10 April 2019Publication History 0citation153DownloadsMetricsTotal Citations0Total Downloads153Last 12 Months14Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Compact dictionary pair learning and refining based on principal components analysis

Although traditional dictionary learning (DL) methods have made great success in pattern recognition and machine learning, it is extremely time-consuming, especially in the training stage. The projective dictionary pair learning (DPL) learned the synthesis dictionary and the analysis dictionary jointly to achieve a fast and accurate classifier. However, the dictionary pair is initialized as random matrices without using any data samples information, it required many iterations to ensure convergence. In this paper, we propose a novel compact DPL and refining method based on the observation that the eigenvalue curve of sample data covariance matrix usually decrease very fast, which means we can compact the synthesis dictionary and analysis dictionary. For each class of the data samples, we utilize the principal components analysis (PCA) to retain global important information and compact the row space of a synthesis dictionary and the column space of an analysis dictionary in the first stage. We further refine the learned dictionary pair to achieve a more accurate classifier during compact dictionary pair refining, which combines the orthogonality of PCA with the redundancy of DL. We solve this refining problem in closed-form completely, naturally reducing the computation complexity significantly. Experimental results on the Extended YaleB database and AR database show that the proposed method achieves competitive accuracy and low computational complexity compared with other state-of-the-art methods.
The Use of Integrated Lessons in Professional Educational Organizations for the Formation of Skills to Solve Applied Problems

In the professional educational organizations implementing the educational program of secondary General education within the development of professional educational on the basis of the main General education, studying of mathematics has the features depending on a profile of secondary vocational education. Profiling goals of mathematical education are reflected in the choice of priorities in the organization of educational activities of students. The scientific and technological revolution in all areas of human activity imposes new requirements on knowledge, technical culture, General and applied nature of education. This poses new challenges for the professional educational to improve education. The applied orientation of the school course of mathematics is carried out in order to improve the quality of mathematical education of student’s, the application of their mathematical knowledge to solve problems of daily practice and further professional activities. The article is devoted to the peculiarities and importance of integrated classes ‘Mathematics and Chemistry’ for obtaining professional skills in secondary vocational education. Experimental work has yielded positive results. The significance of the research: the materials of the article can be useful for school teachers of mathematics.
PEDAGOGICS OF COOPERATION AS A WAY OF UNIVERSITY TEACHERS AND STUDENTS INTERACTION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 4396-4401ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1097Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Interactive Interior Design and Personal Data

Interactive Interior Design refers to the development of interactive elements within the built environment. These technologies offer new kinds of functionality and interaction capability embedded within decorative elements inside our buildings. In this chapter we introduce a variety of these technologies and explore some of their implications. In particular we focus on the challenges around the personal data that ‘Interactive Interiors’ will inevitably generate and use. We also present four short case studies of interactive technologies, contrasting their technopositive accounts (that are often common to research literature) with design fictions of dystopian alternative interpretations to support our deeper reflection on the potential pitfalls of increased interactivity in our habitable spaces. We conclude with a set of reflections on those case studies, pointing to the necessary challenges we face as we develop the field of Human-Building Interaction.
End-User Engagement, Protection and Education

This chapter will focus on the human factors and more specifically how end-users can be engaged, informed and associated with IoT deployments, to minimise the legal, financial and reputational risks. It will present the achievements of U4IoT and CREATE-IoT projects in the context of the five European large-scale pilots on IoT.
Secure and Dynamic Multi-Keyword Ranked Search Scheme Over Encrypted Cloud Data Using K-Means Clustering

A Secure and Dynamic Multi-keyword graded Search theme over Encrypted Cloud information attributable to the increasing fame of cloud computing, a lot of information homeowners are spurred to source their information to cloud servers for unimaginable accommodation and diminished expense in information management can also perform information dynamic operations on files. On the opposite hand, sensitive information needs to be encrypted before outsourcing for security conditions, that obsoletes information use like keyword-based document retrieval. A protected multi-keyword graded search theme over encrypted cloud information, that all the whereas underpins part update operations like deletion and insertion of documents. Especially, the vector area model and therefore the usually utilised TF_IDF model are consolidated as a neighbourhood of the index development and question generation. A unique tree-based index structure employing a "K-means Clustering" formula to provide practiced multi-keyword graded search. The secure KNN formula is employed to cipher the index and question vectors, so guarantee precise importance score calculation between encrypted index and question vectors. With a selected finish goal to oppose measurable attacks, phantom terms are accessorial to the index vector for glaring search results. Due to the employment of our exceptional tree-based index structure. Keyword: Reduplication, Authorized duplicate check, public auditing, shared data, Cloud computing.
Visual Servoing Controller Design Based on Barrier Lyapunov Function for a Picking System

This paper proposes a visual servoing controller design based on Barrier Lyapunov function for a picking system. Visual servoing uses feedback data provided by the camera to control the movement of a picking system in a closed loop system. Visual servoing requires an object in the field of view of the camera in order to control the picking system. To improve the visual servoing controller, the image-based visual servoing and the position-based visual servoing are presented. To apply this method an offline trajectory is developed to perform the image-based visual servoing and the position-based visual servoing tasks for the picking system. Two different control approaches i.e. the visual servoing controller with the limit orientation using the Barrier Lyapunov function and the visual servoing controller with a quadratic Lyapunov function are presented. The proof of asymptotic stability is presented and simulation results from two visual servoing controllers are presented to verify the effectiveness of the proposed controller.
LeucoDiff: Mobile Application for Hematology Students Improve their Skills in Calculating the Leukocyte Formula

This paper describes the study conducted to design, implement and evaluate the LeucoDiff mobile application, in terms of a tool to help hematology students and professionals learning or improving their competences in calculating the leukocyte formula. LeucoDiff also includes gaming features to motivate the students in the learning process. The application was evaluated with 25 hematology students using a database of 3494 images of blood samples, taken in an optical microscopy, properly evaluated by a set of experts in the field (clinical pathologists).
An XBOX-based key generation technique for vigenere algorithm

Vigenere algorithm is simple but considered unbreakable for nearly 300 years. However, the weakness of the algorithm is repeating its key nature. There were several studies conducted to address the drawback but as the key length increases compromises the speed performance in the key generation process. This paper modified the algorithm to increase its encryption and decryption speed through a simple but fast key generation techniques using XBOX and XOR bitwise operations. The key generation technique includes mapping (XBOX and XOR) to increase its security. The results show that the modified algorithm performs better and has increased its encryption and decryption speed.
Towards a Portable SLU System Applied to MSA and Low-resourced Algerian Dialects

As the most used approach to extend a Spoken language Understanding (SLU) from a language to another, Machine translation achieves high performance for English domains, which is not the case for other languages, especially low-resourced ones as Arabic and its dialects. To avoid Machine Translation approach which requires huge parallel corpora, we will investigate, in this paper, the problem of user’s intent interpretation from natural language queries to a system’s semantic representation format across the languages and dialects, namely: English, Modern Standard Arabic (MSA) and four vernacular Algerian dialects from different regions: Blida, Djelfa, Tenes and Tizi-Ouzou. We should note that the domain we have chosen to run our experiments is a special application of school management. For this, We use three classifiers: kNN, Gaussian Naive Bayes and Bernoulli Naive Bayes which led to an average accuracy of 90%.
A Robust Characterized Decimal Data Compression Algorithm by Using Replace Table (CDDC)

Data compression is a way of altering appendage by encoding or converting the bit formation of a data, in such a path that it exhaust, less space. Character encryption is fair of the same family to data compression which is a typical sample of a character by some genial of the encoding system. Encoding is the action which stands for individual characters using a commensurate encoding system made up with symbols and other types of data. Data compression spread over a huge area of applications incorporate data transmission, data storage, and database technology. In this paper, a data compression technique named CDDC is developed based on a character encoding system. This splits characters based on 3 groups with a number of branches and replaces the table. LZW and Huffman algorithm is used here to determine the comparison with the results of CDDC. The experimental results of the proposed method achieve average compression ratio 0.46 and average saving percentage of 54% which sometimes efficient than LZW and Huffman.
Digital Image Classification Techniques

Image classification is a technique to categorize an image in to given classes on the basis of hidden characteristics or features extracted using image processing. With rapidly growing technology, the size of images is growing. Different categories of images may contain different types of hidden information such as x-ray, CT scan, MRI, pathologies images, remote sensing images, satellite images, and natural scene image captured via digital cameras. In this chapter, the authors have surveyed various articles and books and summarized image classification techniques. There are supervised techniques like KNN and SVM, which classify an image into given classes and unsupervised techniques like K-means and ISODATA for classifying image into a group of clusters. For big images, deep learning networks can be employed that are fast and efficient and also compute hidden features automatically.
A New Bayesian Approach to Exploring Damaged Assets by Monitoring Mission Failures Caused by Undetected Attack

Modern military systems operated with a complex of computers and software may have mission failure which is caused by undetected attacks. In such situations, it is important to find out which assets are damaged. After identifying damaged assets, we need to immediately examine the damaged assets to defend against the attacks. However, it is not straightforward to explore the damaged assets because there are the complicated relationships among assets, tasks and missions. In this paper, we propose an effective methodology to infer the damaged assets given observed mission impacts in a Bayesian framework. We used Bayesian networks to model assets, tasks, missions and to set the relationships among them. Our approach visually infers and identifies the damaged assets with the probability. We show that proposed Bayesian framework is practical and useful with the use case experiment.
Design and Actualize the Bengali Consonant with Appropriate Appearances in the Segmented Display System

It is tough to represent the Bengali consonant letters holding up their actual shape over the segmented display system, as their structural patterns are not straight. Due to the curved structure, actualizing the consonant letter is not easy enough. In the recent past, bunches of research have done to bring the actual shape of the Bengali consonants in the segmented display system by the researchers. But they failed to maintain the actual shapes for the character. So, a display model to show all 39 Bengali consonant letters with their exact shapes and derived the simplified logical expressions for all segments have proposed by us, which can be utilized for IC manufacture for the display system. Besides this, contrasting our model with all published distributed works and has indicated unmistakable upgrades in the event of better shapes of the letters have done by us.
Single image super-resolution using fast sensing block

Single image super-resolution (SISR) is a classical task in computer vision. In recent years, convolutional neural network (CNN) has been widely used to solve this problem. CNN-based methods directly learn an end to end mapping between low-resolution (LR) and high-resolution (HR) images to achieve state-of-the-art performance. Recent studies show that larger receptive field in CNN is more beneficial for SISR. However, most CNN-based methods have to pass through a mass of serial convolutional layers to get a large size of receptive field. Consequently, computational efficiency is low. Moreover, it is difficult to fully use multi-scale information. In this paper, a fast sensing super-resolution network (FSSRN) built with parallel Fast Sensing Blocks (FSB) is proposed to extract multi-scale features from LR image more efficiently. Experimental results show that FSSRN achieves significant improvement of efficiency while achieves state-of-the-art performance.
UDLR Convolutional Network for Adaptive Image Denoiser

We propose a new convolutional network architecture called as UDLR Convolutional Network for improving the recently proposed Neural Adaptive Image DEnoiser (NAIDE). More specifically, we develop UDLR filters that meet the conditional independence constraint of NAIDE. By using the UDLR network, we could achieve a denoising result that significantly outperforms the state-of-the-art CNN-based methods on a standard benchmark dataset.