Reviewerly/exHarmony huggingface dataset:
Interaction between Language Testing Research and Classroom Testing Practice

The existing gap between research and practice in language testing has posed a huge challenge to language teachers.In particular, this study intended to examine language testing research and classroom testing activities for their degree of interaction from Iranian EFL teachers' points of view.The analysis drew on the questionnaire developed by Nassaji ( 2012), which consists of quantitative and qualitative sections.The data were collected from 200 language institute teachers in Bushehr, Iran.The findings indicated that most of the teachers confirmed their familiarity with language testing research, while only a few believed that they were able to conduct or publish testing research.Although the participants acknowledged that the information gained from reading language testing could be valuable for their testing performances, they found their experience more relevant than knowledge gained from language testing research.This study also illustrated that there is a growing consensus among teachers in that there should be collaboration between researchers and teachers in testing practices.
Mathematical Model of Data Processing System for Information Support of Innovative Cluster Works

Information systems of innovation management are an important component of cluster organization infrastructure. The creation of such systems involves working with a wide range of commercial information resources. These resources are often used to form thematic databases designed to meet the information needs of cluster organizations. It is highly relevant to find the topology of an information processing system which would minimize overall operational costs for information support of innovative cluster activities. The paper presents the mathematical statement of the problem concerning forming optimal topology of such system. It is shown that finding optimal topology of a distributed information processing system can be reduced to solving the problem of integer linear programming, which would allow minimizing the system operation costs.
No-reference quality assessment for contrast-altered images using an end-to-end deep framework

No-reference image quality assessment (NR-IQA) aims to predict image quality consistently with subjective scores with no prior knowledge of reference images. However, contrast distortion, which is an uncommon distortion, has been largely overlooked. To address this issue, we explore the NR-IQA metric by predicting the quality of contrast-altered images, using deep-learning techniques. We adopt a two-stage training strategy due to a gap between the deep learning's sample requirements and the insufficiency of samples in the IQA domain. A deep convolutional neural network (CNN) is first designed and is pretrained to the classification task with the help of an additional synthetic contrast-distorted dataset. Then, the pretrained CNN is fine-tuned on the target IQA dataset using an end-to-end training approach. An effective pooling method is employed to map the image representation into a subjective quality score during the fine-tuning stage. Experimental results on five public IQA databases containing contrast-altered images show that the proposed method achieves competitive results and has good generalization ability compared to other NR-IQA methods.
Proposed Framework for Spam Recognition in Big Data for Social Media Networks in Smart Environment

Social Media Networks (SMNs) are becoming more and more popular across the globe in the past few years. Netizens share all their personal information regarding day to day activities, views, and opinions across various SMNs. Simultaneously, it can be observed that throughout the most popular SMNs face frequent social spam problems in various formats. Big Data theory is gaining much more attention and it is expected that SMNs will have more interactions with each other shortly. This would enable a spam link, content or profile attack to easily move from one social network like Twitter to other social networks like Facebook. Consequently, effective discovery of spam has turn out to be a noteworthy and prevalent issue. Proposed research highlights spam discovery across several SMNs by leveraging the data of sensing analogous spam inside an OSN (Open Source Network) and using it across various SMNs. Authors have selected Twitter and Facebook as the research marks, In this research paper, the authors proposed and presented a spam detection framework to find out spam on more than one social network those are most common features in terms of contents, behavior, posts and user involvement etc. Spam detection techniques can significantly facilitate in various social network to measure the vulnerability. Based on this fact the researchers have proposed and presented a spam detection framework to find out spam on more than one social network.
A Quantum Algorithm for Minimising the Effective Graph Resistance upon Edge Addition

In this work, we consider the following problem: given a graph, the addition of which single edge minimises the effective graph resistance of the resulting (or, augmented) graph. A graph’s effective graph resistance is inversely proportional to its robustness, which means the graph augmentation problem is relevant to, in particular, applications involving the robustness and augmentation of complex networks. On a classical computer, the best known algorithm for a graph with N vertices has time complexity $$\mathcal {O}(N^5)$$ . We show that it is possible to do better: Dürr and Høyer’s quantum algorithm solves the problem in time $$\mathcal {O}(N^4)$$ . We conclude with a simulation of the algorithm and solve ten small instances of the graph augmentation problem on the Quantum Inspire quantum computing platform.
Mobile Complex Factors: An Approach for the Prediction of Mobile Size Parameters

Background: Mobile application and Effort estimation have direct relationship where on the basis of size, mobile application development efforts can be determined. Inaccuracy or inappropriateness in this approach can cause underestimation or overestimation. The main phase of Mobile application development is to standardize the approach to predict the size of an application. Objectives: The primary objective of this study is to quantify the functionality provided by the software to the end users it is necessary to know the size of an application. This paper focuses on the background of Mobile application size measures, Mobile complexity factors and the future work of the size measure. Methods: This is a survey based study where the primary endpoint was to see the resemblance of selected parameters with modern day mobile application development, a list of questions commonly known as questionnaire was prepared and was sent to more than 140 people including practitioners, researchers and industry people. Results: Out of 40 Parameters 9 parameters were selected to be includes as Mobile complex factors in order to calculate the functional size of a mobile application. Hence new concept for mobile size measures is introduced. Conclusion: Mobile complexity factors were proposed to form a standard to be used as an input in proposed size metrics for estimation of Mobile application development. To validate the effectiveness of this research work, there is something that is to be achieved in future: Conclusion: a) Propose a New Sizing metrics to calculate size of a Mobile application. Conclusion: b) Proposing a model for estimation of Cost in Mobile application development so that the there will be more accuracy in the resultant value and the process of estimation will be more streamlined.
Attribute-Based Encryption with Efficient Keyword Search and User Revocation

Ciphertext policy attribute-based encryption (CP-ABE) is a promising cryptographic technology and a key component that enable secure data sharing in a cloud environment through fine-grained access control. Since it was introduced, many interesting schemes have been proposed. However, in addition to managing data sharing through access control, a comprehensive scheme should also cater for user revocation and ciphertext queries. This is because in a cloud environment new users may join while existing users may leave the system. At the same time, given the potentially large amount of data stored in a cloud storage, user should be able to retrieve the required files efficiently in a privacy-preserving manner. To address the above issue, in this paper, we propose a practical searchable CP-ABE scheme supporting user revocation. In contrast to existing schemes that provide only single keyword query, our efficient search function provides conjunctive search, which allows user to locate a ciphertext related to a set of keywords. The computation overhead of our user revocation is at least on par with existing schemes. Besides, the security analysis indicates that the proposed scheme is secure under the decisional Bilinear Diffie-Hellman assumption. We also provide extensive experimental results to confirm the efficiency and feasibility of our proposed construction.
New format of regulative support of BA and MA programs in library and information sciences

The current regulation of higher education in library and information sciences reflects the prospective trends and orientation toward practical demands of the industry. The author examines the main concepts of the higher professional education reform under the way, i. e. multilevel structure and consistency, competence approach, connection with practical activities, integrated control and evaluation of obtained knowledge. Regulation novations of the updated federal national education standards of higher education in specializations 51.03.06 “Library and information activities”, BA, and 51.04.06 “Library and information activities”, MA, are discussed. The author also examines the methodological approaches toward modeling of the professional competences to be developed within BA and MA programs; the complex of mandatory and recommended professional competences to be developed within BA and MA programs. Based on the comprehensive analysis, the conclusion is made that the higher library and information education, like the Russian education system on the whole, has been going through the transformations; while library professional training corresponds with the general requirements of theBolognaprocess.
To Have and Vehold: Marrying Museum Objects and Virtual Collections via AR

The Veholder.org project is aiming for enhanced, innovative museum exhibitions, enabled by collaborative use of Augmented Reality. Complementing physical with relevant 3D virtual objects widens impact of interrelated collections. Objects brought together, from display or storage, provide blended environments for visitors and researchers. Veholder (Virtual Beholder, or Virtual Environment for Holdings and Online Digital Educational Repositories) is developing collaborations between suitable institutions. Early tests at the University of Cambridge are promising, while challenges such as calibration and scaling stress the need for shared solutions to blend collections. Following initial discussions with developers of IIIF, the International Image Interoperability Framework, about standards for digital library sharing of historical manuscripts and other disparate 2D images, we hope that working together can accelerate the process for a standardised approach to sharing 3D images. This could extend the concept of universal digital library viewers to incorporate and integrate 3D and AR images as well.
Breast Cancer Detection Using Polynomial Fitting Applied on Contrast Enhanced Spectral Mammography

This paper presents a polynomial fitting technique on pixels inside the Region Of Interest (ROI) to obtain a new feature set that can be used to differentiate between normal and cancerous cases. In this paper few features have been used if it compared to other previous works that have been used many feature sets, and then used different techniques to reduce the dimensionality, which might complicate the system also require a great computational cost. These few features are used without any need to reduce the dimensionality then it is applied on two different classifiers, the K-nearest neighbor (KNN) and the artificial neural network (ANN). The proposed techniques have been applied on 50 real cases from `BAHEYA Foundation for Early Detection & Treatment of Breast Cancer' with the help of physicians and radiologists in the hospital. The used images are Contrast Enhanced Spectral Mammograms (CESM) that has clearer and more contrast images than the conventional mammograms. Applying the proposed feature set on these CESM mammograms using the two different classifiers-the K-nearest neighbor (KNN) and the artificial neural network (ANN) - give accuracies of 96% and 92% respectively.
Limiting behavior of the ratio of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e19" altimg="si7.gif"><mml:mi>k</mml:mi></mml:math>th records

Let R0(k),R1(k),… be kth record values derived from samples consisting of independent identically distributed discrete random variables. In the present paper, using the theory of regular variation, we discuss the asymptotic behavior of Rn+m(k)∕Rn(k) as n→∞.
Use of Big Data in Aviation

The expansion of data and its efficient handling is becoming a more popular tendency in recent times bringing new difficulties to learn new avenues. Data analytics can be done more proficiently with the availability of distributed architecture of not only SQL (NoSQL) databases. Technological advancements around us are changing very rapidly, and major shift is being carried out, a switch from relational to non-relational world. When moving from relational to non-relational models, database administrators face common problems due to the fact that NoSQL is a no-schema database. The purpose of conducting this research is to propose a mechanism by which the schema of a relational database management system and its data can be transformed into big data by following some standardize guidelines. This model can be quite useful for relational database administrators by enabling them to give attention to logical modeling rather than procedural writing for each and every SQL to NoSQL transition.
Optimal selection of ensemble classifiers using particle swarm optimization and diversity measures

Multiple Classifier Systems (MCSs) or ensemble methods have recently attracted genuine consideration due to their capacity to enhance prediction performance appreciably. Both experimental and theoretical investigations have demonstrated that MCSs can be useful in improving overall classification in the area of pattern recognition. Usually, such systems make an aggregate decision by combining the responses of several classifiers that form a committee. One of the most challenging issues in the classifier ensemble is selecting a suitable subset of base classifiers. Researchers have shown that MCSs produce an extensive amount of classifiers and, consequently, those classifiers have redundancy between each other. Since smaller ensembles are favored on account of storage and efficiency reasons, ensemble pruning is a critical step in the development of classifier ensembles. Two most common choices for selection criteria are combined performance and diversity measures. Nevertheless, there isn’t an agreement on this inquiry that which of them is better. In this paper, we utilized binary particle swarm optimization algorithm for pruning redundant base classifiers and acquiring an ideal ensemble from a given pool of classifiers. The proposed accuracy-diversity based pruning algorithm takes into account the accuracy of combined classifiers as well as the pairwise diversity amongst these classifiers. Comparing the performance of the proposed method using ten databases taken from the UCI Machine Learning Repository demonstrated that using diversity measures and combined performance simultaneously is appropriate for selecting a subset of classifiers.
Artistic Research Creation for Publicly Engaged Scholarship

In this paper I discuss the adoption of artistic research creation methodologies, the creation and exhibition of artistic works closely aligned with scholarly research, as a way to increase public engagement with academic research. I begin by discussing the need for scholars to consider the ‘public first’ when developing research communication plans, and draw upon the emergence of ‘mobile first’ interface design as a metaphor. With mobile first development, also known as progressive enhancement, ‘You start by establishing a basic level of user experience that all browsers will be able to provide when rendering your web site,’ but you also build in more advanced functionality that will automatically be available to devices, such as desktop computers (W3C 2015). I argue that we need to prioritize public first research outputs if we are truly serious about engaging the public in our research. I then move into a discussion of various research creation methodologies and explain how they are similar to, and differ from, critical making, another emergent humanities research practice that is based upon the making of physical objects. Finally I provide examples of successful research creation activities, including some related to my current SSHRC-funded project, The Post-Digital Book Arts.
Internet of Things – striking the balance between competition and security

With a growing number of companies now recognising the vast benefits of the Internet of Things (IoT), it is critical they put in place the right measures to protect their cyber security against the rising risk of scammers. With a growing number of companies now recognising the vast benefits of the Internet of Things (IoT), it is critical they put in place the right measures to protect themselves against scammers. As the pressure to increase the level of connectivity intensifies, so too does the opportunity for systems to become compromised. It is vital that in their efforts to monetise services and keep pace with competitors, businesses are not overwhelmed by the applications on offer and become unwittingly vulnerable. The challenge is to strike a balance between enhancing their proposition and continuing to protect their security, explains Robert Rutherford of QuoStar.
Malicious Websites Identification Based on Active-Passive Method

Nowadays, massive numbers of malicious websites are endeavored to change their hosts/IP addresses to avoid tracking. This paper fills a gap in the study of tracking this kind of websites and offers approaches to detection and identification by combining both active and passive methods. The active method, as bootstrap, is based on crawling traffic from Internet, we can extract title, keywords and picture as features and store them as feature sets. What we do in passive filtering is to match online traffic using the feature sets. Other than finding out those malicious websites, we can extract extra features such as cookie and users information, which is unavailable by using active method, from online traffic and add them to the feature sets created by proceeding active method. According to the experiment, we can have 95.43% true positive rate and 3.90% false positive rate under real data flow in this way.
Evaluation of Scheduling Techniques in Cloud Data Centers: Simulation and Experimentation

Cloud Computing technology is becoming a challenging technology requirement for many enterprises and individuals nowadays. Due to its numerous advantages offered to its clients such as elasticity, scalability and cost-effectiveness the cloud computing industry has grown much since it emerged and is foreseen to continue this growth through the coming years. This growth requires cloud providers to expand the size and number of their data centers to accommodate the needs of their users. As the number of data centers increases, efficient scheduling algorithms are required in order to manage the scheduling of arrivals to a data center where it can be served optimally. This paper discusses two proposed scheduling algorithms for two distributed cloud data centers, one for cases of real-time requests with bounded delays and another for data centers having diverse geographical locations. An OMNeT++ simulation model is introduced as well as a test-bed implementation in order to test the proposed algorithms and their effectiveness under general assumptions and more realistic conditions.
Segmentation Algorithm for Surface Reconstruction According to Data Provided by Laser-Based Scan Point

The paper presents the results of the elaboration of algorithms of image segmentation and segmentation of the surface based on the calculation of the local geometric properties of the surface (the method of the segmentation of the point cloud obtained at the stage of rough scanning of the surface). The problem of reconstructing a surface from a spontaneous point cloud has been solved to create a CAD model based on laser based scan data of the object. The development of the method of automatic reconstruction of accurate and piecewise smooth surfaces from spontaneous 3D-points were carried out for designing an automatic system of path planning for an industrial robot manipulator. The proposed procedure of automatic segmentation is based on the local analysis of the Gaussian K and mean H curvatures, obtained by applying a non-parametric analytical model.
Legal Entity Identifier Blockchained by a Hyperledger Indy Implementation of GraphChain

The main idea behind GraphChain is to use blockchain mechanisms on top of abstract RDF graphs. This paper presents an implementation of GraphChain in the Hyperledger Indy framework. The whole setting is shown to be applied to the RDF graphs containing information about Legal Entity Identifiers (LEIs). The blockchain based data management system presented in the paper preserves all the benefits of using RDF data model for the representation of LEI system reference data, including powerful querying mechanisms, explicit semantics and data model extensibility with the security and non-repudiation of LEIs as the digital identifiers for legal entities.
A highly efficient system for Mammographic Image Classification Using NSVC Algorithm

Nowadays, classification is the most efficient method for breast cancer detection using mammography images. During the last two decades, researchers achieved very good results using different kinds of classification methods. In this context, we will focus in this work on the quality of mammography image classification by proposing a new approach that us compares with the state-of-art methods. Our method consists of two phases; a semi-supervised step based on SKDA and a second step based on SVM. As will be shown in the experiments section, results on Mammography data set show that the proposed algorithm can get very good results. The application of our algorithm on five well known real data sets allow us to validate our method and show its interest in the context of image based medical diagnosis. The best precision obtained by NSVC exceeded 99 %.
Development and Evaluation of a Novel Robotic System for Search and Rescue

Search and Rescue robotics is a relatively new field of research, which is growing rapidly as new technologies emerge. However, the robots that are usually applied to the field are generally small and have limited functionality, and almost all of them rely on direct control from a local operator. In this paper, a novel wheeled Search and Rescue robot is proposed which considers new methods of controlling the robot, including using a wireless “tether” in place of a conventional physical one. A prototype is then built which acts as a proof of concept of the robot design and wireless control. The prototype robot is then evaluated to prove its mobility, wireless control and multi-hop networking. The experimental results demonstrate the effectiveness of the proposed design incorporating the rocker-bogie suspension system and the multi-hop method of “wireless tethering”.