Reviewerly/exHarmony huggingface dataset:
Automated identification of borrowings in multilingual wordlists

<ns4:p>Although lexical borrowing is an important aspect of language evolution, there have been few attempts to automate the identification of borrowings in lexical datasets. Moreover, none of the solutions which have been proposed so far identify borrowings across multiple languages. This study proposes a new method for the task and tests it on a newly compiled large comparative dataset of 48 South-East Asian languages. The method yields very promising results, while it is conceptually straightforward and easy to apply. This makes the approach a perfect candidate for computer-assisted exploratory studies on lexical borrowing in contact areas.</ns4:p>
A View from the CISO: Insights from the Data Classification Process

ABSTRACT Data security is a critical concern for organizations. In a rush to protect data, some IT managers overlook the important first step of data classification and instead focus on implementing the strictest controls on all data to reduce risk. To investigate organizational processes surrounding data classification, we conduct interviews with 27 CISOs in 23 organizations. We develop a model that identifies the common themes of data classification and their interrelationships. The most common driver for data classification is compliance with data privacy regulations and security standards. Collaboration and employee education are essential to the process. Increases in employee awareness of data security risk and improvements in data hygiene are outcomes. Challenges to data classification include the increase in IT landscape complexity, maintenance of an accurate data inventory, immaturity of automated tools, limited resources, and user compliance. Our model provides insights for practitioners and identifies areas of interest for researchers.
Building Semantic Information Mediator Runtime Application using Native API From Heterogeneous Data Sources

Semantic Information Mediator Runtime Application Using Native API(S.I.M.R.A.N.) is Artificial Intelligence based android mobile Application in which the agent is assigned to perform a particular operation or task. It is like an intermediate agent for the user who want to search the string and check given string matched or not. In this paper author provide a proposed model which is able to perform searching based on artificial Intelligent including semantic web features also
Data Wrangling using Python

The term Data Engineering did not get much popularity as the terminologies like Data Science or Data Analytics, mainly because the importance of this technique or concept is normally observed or experienced only during working with data or handling data or playing with data as a Data Scientist or Data Analyst. Though neither of these two, but as an academician and the urge to learn, while working with Python, this topic ‘Data engineering’ and one of its major sub topic or concept ‘Data Wrangling’ has drawn attention and this paper is a small step to explain the experience of handling data which uses Wrangling concept, using Python. So Data Wrangling, earlier referred to as Data Munging (when done by hand or manually), is the method of transforming and mapping data from one available data format into another format with the idea of making it more appropriate and important for a variety of relatedm purposes such as analytics. Data wrangling is the modern name used for data pre-processing rather Munging. The Python Library used for the research work shown here is called Pandas. Though the major Research Area is ‘Application of Data Analytics on Academic Data using Python’, this paper focuses on a small preliminary topic of the mentioned research work named Data wrangling using Python (Pandas Library).
Scene Recognition using Significant Feature Detection Technique

Scene classification is basic problem in robotics and computer vision application. In Scene classification focused on complete view or event that contains both low and high level features. The main purpose of scene classification is to diminish the semantic gap in between social life &amp; computer system. The main issue in scene classification is recognizing tall buildings, mountain, open country and inside city. We applied combination algorithms of feature extraction on trained datasets. Our proposed algorithm is hybrid combination of SIFT+ HOG named as HFCNN. As compare with the existing CNN architecture, HFCNN perform betters with high accuracy rate. Accuracy rate for proposed architecture is more than 96% as calculated with better time consumption and cost effective.
Network optimisation solutions of university computer laboratory based on virtualisation technology

Information technology is widely used in our work and life. Colleges and Universities have set up a large number of information related courses to meet the needs of information training, and built a large number of computer laboratories to meet the teaching needs of these courses. In the process of laboratory construction and management, there are various problems, such as high failure rate, low utilization and complex management requirements. This paper carries out a detailed analysis of these problems, in order to solve these problems, we provide some suggestions for the construction of computer laboratory based on the virtualisation technology, so as to effectively improve the utilisation rate of the laboratory equipment and reduce the construction cost and management difficulty. Considering the difficulties encountered in the construction of laboratory virtualisation, this paper analyses various factors that will affect the network performance, and puts forward the network optimisation solution of university computer laboratory based on virtualisation.
Optimizing the Impact of Security Attributes in Requirement Elicitation Techniques using FAHP

Software security is a key issue in the domain of software engineering which attracts attention from both the industry and academia. Besides, due to the massive investment in software development, security is in much demand. The selection of appropriate software development model is an increasingly challenging task. Security attributes play a vital role while designing security during software development. Each attribute has its importance during requirement elicitation procedure. This is based upon the user’s demand, organization resources, and sensitivity of the information. Hence, developers should understand the significance of each attribute while collecting the user requirements for developing software. In this paper, authors have proposed an approach for prioritization of these attributes using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method. A literature survey reveals that critical security attributes such as Integrity, confidentiality, Authentication, Effectiveness, Availability, Access Control and Authorization. This will help developers to improve software security for longer.
Financial Performance Analysis in the Influence of Environmental Performance and Environmental Disclosure with Moderating by Organization Culture

This study aims to analyze financial performance that is influenced by environmental performance and environmental disclosure by moderating by organization culture.The analysis used is descriptive and verification analysis.The sample used is companies listed on the Indonesia Stock Exchange from 2014 to 2017 in the manufacturing sector.The company is a PROPER with gold, green and blue ratings and has a positive ROA value.Samples that met these criteria during the period 2014 -2017 were 163 samples.The results showed that financial performance was positively influenced by environmental performance by 5.2 and positively environmental disclosure by 3.463.Another thing, organizational culture is not able to increase the effect of environmental performance and environmental disclosure on financial performance and even weaken environmental performance by 70,701.Based on this research, environmental management and disclosure is a positive signal for investors in considering investment decision making so that it can improve the company's financial performance, while the organization culture is still implemented to maintain the company's performance in other fields.
Keyword Extraction from Tweets using Graph-Based Methods

Social media refers to a set of different web sites like Twitter is a microblogging service that generates a huge amount of textual content daily. These methods based on text mining, natural language processing, and information retrieval are usually applied. The text mining approaches, documents are represented using the well-known vector space model, which results in sparse matrices to be dealt with computationally. A technique to extract keywords from collections of Twitter messages based on the representation of texts employing a graph structure, from which it is assigned relevance values to the vertices, based on graph centrality measures. The proposed approach, called TKG, relies on three phases: text pre-processing; graph building and keyword extraction. The first experiment applies TKG to a text from the Time magazine and compares its performance with TFID [1] and KEA[6], having human classifications as benchmarks. Finally, these algorithms are designed to the sets of tweets of increasing size were used and the computational time necessary to run the algorithms was recorded and compared. The results obtained in these experiments showed that building the graph using an all neighbors edging scheme invariably provided superior performance, and assigning weights to the edges based on the weight as the inverse co-occurrence frequency was superior cases. One possible future work is to apply centrality measures TKG showed to be faster for all its variations when compared with TFIDF and KEA, except for the weighting scheme based on the inverse co-occurrence frequency. TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets.
Exception Included, Ordered Rule Induction from the Set of Exemplars (ExIORISE)

An expert system is one which uses collection of data comprising the knowledge to offer guidance or make inferences. Its work in most cases can be seen as classification which is basically the task of assigning objects to different categories or classes, determined by the properties of those objects. Numerous research works have been and being done to develop efficient knowledge acquisition techniques for expert systems. Some state-of-the-art algorithms are great performers but need extensive learning whereas older rule / decision- tree based algorithms perform pretty well with small data sets. Moreover, co-existence of learners of different levels of expertise and accuracy is believed to be encouraged to achieve a cumulative intelligence just like the human beings have. RISE is one such algorithm that infuses both instance-based learning and rule induction. It proved to be quite efficient handling binary and multi-class classification problems for small data sets in terms of accuracy and cost as well. In this work, features like exclusion of inefficient rules, inclusion of exceptions in the rule set and ordering of the rules using weights beforehand are integrated with the classical RISE algorithm to develop a more efficient classifier system named as ExIORISE. Empirical study shows that ExIORISE outperforms RISE, C4.5 and CN2 significantly.
A dependability and cost optimisation method for private cloud infrastructures

Creating a cost-effective and highly available private cloud infrastructure from scratch is challenging. In order to improve availability, system administrators commonly employ redundant hardware, which incurs increased acquisition cost. There are plenty of decisions involved in this process. It is necessary to specify how many servers will be employed, and each of them will have a role in the infrastructure either as a worker node or as a management node. One question that arises is: among all possible ways to arrange the servers, and considering a set of requirements (e.g., the minimum number of available workers, minimum availability), which configurations can sustain a minimal availability level (steady state and capacity oriented)? This work proposes an optimisation model for private clouds planning supported by analytical modelling for computing the cost of solutions. By combining the optimisation model with the bisection method, it is possible to minimise the acquisition cost while maintaining the high availability of the cloud. The proposed method can also be applied to other parallel or distributed systems with the same properties as the private clouds used in this study.
Pediatric Hematology

Improving diagnostics has been one of the most important challenges facing health systems in recent years, due to innovation and scientific research, image classification techniques, medical information management systems brought about by health technology, and developments never seen before that enable major breakthroughs in the areas of prevention, early disease detection, and disease control. Blood is investigated through blood count, which is subordinate to manual and/or automated procedures. This reliance on medical fields for new innovations directs this chapter to the development of a deep learning framework for classifying white cell subtypes in digital images that fits the criteria for reliability and efficiency of blood cell detection, making the methodology more accessible to diverse populations. Because laboratory medical examinations are often costly and inaccessible to populations of underdeveloped and developing countries, it is of great importance to create tools that facilitate the obtaining of medical reports at low cost and with high reliability. For this, Python using a Jupyter notebook was developed and experiments were conducted using a dataset containing 12,500 digital images of human blood smear fields comprising nonpathological leukocytes. As a result, the accuracy of the approach was 86.17%, demonstrating the high reliability of the methodology. Therefore the algorithm is considered a reliable, accurate, and inexpensive method and can be employed as the third most practicable procedure for blood counts for often underprivileged people of developing and underdeveloped countries.
Multiobjective Cost Function Based Digital Vide Watermarking Technique

Video watermarking is a key process for copyright protection of digital data.In the video sequence, there is a huge quantity of data and these data are more susceptible to attacks.Hence, this paper intends to develop a novel video watermarking technique with the aid of multi-objective cost function.Initially, the keyframes are extracted from the input video sequence and these extracted video frames are subjected to wavelet transform for achieving the wavelet coefficients.Then, a multi-objective cost function is proposed with the help of multiple criteria like edge, brightness, the intensity of pixel, coverage, energy and frequency coefficient.The bit plane technique is utilized here to develop multiple binary images for the secret message by means of partitioning the secret messages.Further, with the aid of the multi-objective cost function, the message bit is embedded in the frame (wavelet coefficient).The embedded image is transmitted from the sender to the receiver via a communication channel and on the receiver side, the retrieval process takes place.The original image is retrieved in the receiver side with the help of a multi-objective cost function.Finally, a comparative analysis is accomplished between the proposed wavelet+Cost (wavelet transform based on a multi-objective cost function) and the existing models like wavelet model, Linear Significant Bit model (LSB) and LSB + cost model in terms of peak-to-signal noise ratio (PSNR) and correlation coefficients.
Multiple key Generation for Securing Data Sharing and Backup

Currently a data sharing and data backup is been maintained by the intermediator of cloud admin provider also to reduce the cost of intermediator management and to get an authentication. It is introducing to provide a security and efficient access to large amount of outsourced data. In this project we are proposing a key distribution to solve the problem (encryption and decryption). As the user is provided two types of key by the admin to secure the data outsourcing and data backup which is been encrypted and decrypted so that threading and attacks can be reduced using cryptography based key mechanism. We are introducing a key mechanism using RSA and AES for generating keys files to secure the data. By this cryptographic key mechanism, the cloud users are free from the data management, consumption of time, cost.
Exploration of Fuzzy System With Applications

Fuzzy system was altered from a ‘buzz word' to an important technological area, with various publications in international conferences and transactions. Several Japanese products applying fuzzy logic concepts, such as household appliances and electronic equipment, power engineering, robotics, and optimization have been manufactured. This system is capable to process and learn mathematical data as well as linguistic data. Fuzzy system user linguistic explanations for the variables and linguistic procedures for the I/P-O/P behavior. In this chapter, present the application of fuzzy system with data mining, neural networks, fuzzy automata, and genetic algorithms. It also presents the foundation of fuzzy data Mining, with the fuzzification inference procedure and defuzzification procedure, fuzzy systems and neural networks with feed forward neural network, FNN with it features generalization of Fuzzy Automata, and sixth fuzzy systems and genetic algorithms. The chapter explores a popular fuzzy system model to show complex systems and an application of fuzzy system.
Evaluation of feature selection techniques on network traffic for comparing model accuracy

The accuracy and performance of any machine learning model are highly dependent on the number of qualitative features taken into consideration while training the model. The selection of qualitative features depends on the considerate choice of feature selection technique. In this study, feature selection is performed using different techniques such as information gain, Gini decrease, Chi2 and FCBF on the same dataset, and subsequently, the accuracy has been measured. The results showed that the FCBF method has dramatically reduced the number of features and moderated the accuracy compared with other feature selection methods.
Deep Learning-designed Diffractive Neural Networks

We report deep learning-based design of diffractive neural networks. Following its fabrication, a diffractive neural network can all-optically perform user-defined tasks through diffraction. We demonstrate the applications of this framework for classification and imaging tasks.
Digital Module 17: Data Visualizations: Effective Evidence‐Based Practices https://ncme.elevate.commpartners.com

Abstract In this digital ITEMS module, Nikole Gregg and Dr. Brian Leventhal discuss strategies to ensure data visualizations achieve graphical excellence. Data visualizations are commonly used by measurement professionals to communicate results to examinees, the public, educators, and other stakeholders. To do so effectively, it is important that these visualizations communicate data efficiently and accurately. These visualizations can achieve graphical excellence when they simultaneously display data effectively, efficiently, and accurately. Unfortunately, measurement and statistical software default graphics typically fail to uphold these standards and are therefore not suitable for publication or presentation to the public. To illustrate best practices, the instructors provide an introduction to the graphical template language in SAS and show how elementary components can be used to make efficient, effective, and accurate graphics for a variety of audiences. The module contains audio‐narrated slides, embedded illustrative videos, quiz questions with diagnostic feedback, a glossary, sample SAS code, and other learning resources.
A Survey on Proxy Re-Signature Schemes for Translating One Type of Signature to Another

Abstract Proxy Re-Signature (PRS) complements well-established digital signature service. Blaze-Bleumer-Strauss discussed PRS in 1998 for translating a signature on a message from Alice into a signature from Bob on the same message at semi-trusted proxy which does not learn any signing-key and cannot produce new valid signature on new message for Alice or Bob. PRS has been largely ignored since then but it has spurred considerable research interest recently for sharing web-certificates, forming weak-group signatures, and authenticating network path. This article provides a survey summarizing and organizing PRS-related research by developing eight-dimensional taxonomy reflecting the directional feature, re-transformation capability, re-signature key location, delegatee involvement, proxy re-signing rights, duration-based revocation rights, security model environment, and cryptographic approach. Even though multi-dimensional categorization is proposed here, we categorize the substantial published research work based on the eighth dimension. We give a clear perspective on this research from last two-decades since the first PRS-protocol was proposed.
Research on centralised matching method of teaching knowledge categories based on intelligent language recognition

In order to improve the accuracy and efficiency of teaching knowledge classification matching, a method of teaching knowledge classification matching based on intelligent language recognition is proposed. The deep learning network was constructed, the language files were pre-processed, and the extracted language feature vectors were used to train the network and optimise the deep learning network. The intelligent language recognition network model is established and the k-means clustering algorithm is used to acquire the model. Classification of teaching knowledge is processed centrally and the classification system of teaching knowledge is obtained. Matching problem of teaching knowledge classification system is modelled, and the corresponding undirected graph is constructed, which is converted into the matching problem with the greatest weight, and the optimal matching scheme under the category of teaching knowledge concentration is obtained. Experimental results show that the matching results based on intelligent language recognition are more accurate and more efficient.
Verification of Quantum Computation:An Overview of Existing Approaches

Quantum computers promise to effciently solve not only problems believed to be intractable for classical computers, but also problems for which verifying the solution is also considered intractable. This raises the question of how one can check whether quantum computers are indeed producing correct results. This task, known as quantum verification, has been highlighted as a significant challenge on the road to scalable quantum computing technology. We review the most significant approaches to quantum verification and compare them in terms of structure, complexity and required resources. We also comment on the use of cryptographic techniques which, for many of the presented protocols, has proven extremely useful in performing verification. Finally, we discuss issues related to fault tolerance, experimental implementations and the outlook for future protocols.