Reviewerly/exHarmony huggingface dataset:
Real-Time Input Text Recognition System for the Aid of Visually Impaired

It is estimated that 285 million people globally are visually impaired. A majority of these people live in developing countries and are among the elderly population. Reading is essential in daily life for everyone. Visually impaired persons can read only by use of special scripts specially designed for them such as Braille language. Further, only trained people can read and understand. Since every product does not provide the product information on product cover in Braille, the present work proposes an assistive text reading framework to help visually impaired persons to read texts from various products/objects in their daily lives. The first step in implementation captures the image of the required by extracting frames from real-time video input from the camera. This is followed by preprocessing steps which includes conversion to grey scale and filtering. The text regions are further extracted using MSER followed by canny edge detection. The text regions from the captured image are then extracted and recognized by using Optical Character Recognition software (OCR). The OCR engine Tesseract is used here. This extracts the text of various fonts and then sizes can be recognized individually and then combined to form a word. Further, producing audio output by using Text to Speech module. The result obtained is very much comparable with other existing methods with better time efficiency. The real-time input is taken and passed through the algorithm which applies filters and removes noise then later image is passed through MSER, OCR, Canny edge detection to get the final audio output.
Proposal of a Method to Evoke Cross-Cultural Communication by Using Digital Signage

A method of evoking cross-cultural communication between Japanese and foreign tourists with using digital signage has been proposed in this study aiming to improve foreigners’ sightseeing experience in Japan. The proposed method utilizes a digital signage with interactive design, which resembles some jigsaw puzzle and requires gestures and cooperative work to see complete information, to evoke communications between the signage users. The results of evaluation experiment showed that the proposed method can evoke cross-cultural communication significantly more than conventional slideshow-type information presenting method.
Multi-insight Monocular Vision System Using a Refractive Projection Model

The depth information of a scene, imaged from the inside of a patient’s body, is a difficult task using a monocular vision system. A multi-perception vision system idea has been proposed as a solution in this work. The vision system of the camera has been altered with the refractive projection model. The developed lens model recognises the scene with multiple perceptions. The motion parallax is observed under the different lenses for the single shot, captured through the monocular vision system. The presence of multiple lenses refracts the light in the scene at the different angles. Eventually, the appearance of the object dimension is augmented with more spatial cues that help in capturing 3D information in a single shot. The affine transformations between the lenses have been estimated to calibrate the multi-insight monocular vision system. The geometrical model of the refractive projection is proposed. The multi-insight lens plays a significant role in spatial user interaction.
Evaluation of Quality of Service Parameters for Mobile Cloud Service Using Principal Component Analysis

The cloud service is communicating between mobile devices through the Internet. They are prepended as platform independent and loosely conjoined. This paper’s objective is to obtain effective services with composition of services based on specific QoS parameters using principal component factor analysis method. The Factor analysis, analyzing the interrelationships among variables by characterizing highly correlated known as factors. In factor analysis, particularly statistical principal component analysis is used to consider the interrelationships among variables with a minimal loss of information. This analysis is made from the data collected from the cloud service providers. This result is based on components of cloud service availability, response time, throughput, reliability, successability, compliance, best practices, and web services resource framework. These are few parameters considered among the QoS parameters to find the best mobile cloud services. In this factor analysis based on principal component analysis considers these eight parameters which can be further improved to select the cloud services of QoS.
Iteration on single vector for extracting two extremal eigenpairs of symmetric matrices

It has been shown that the first two extremal eigenpairs of symmetric matrices are involved a lot in spectral clustering, dimensionality reduction, image segmentation, and graph theory. We also know that the eigengap directly affects the stability of principal eigenvector related algorithms, such as the Hyperlink-Induced Topic Search (HITS). To extract two extremal eigenpairs, conventional methods are generally iterated on two orthogonal vectors, i.e., 2-dimensional subspace. This paper introduces an approach that is iterated on single vector but can simultaneously extract the largest or smallest two eigenpairs of general symmetric matrices, which reduces the solution scale by half. The complete stability analysis is also presented here. Numerical experiments demonstrate the superior performance of the proposed algorithm.
Comparative Performance Analysis of Local Feature Descriptors for Biomedical Image Retrieval

Biomedical imaging field is growing enormously from last decade. The medical images have been used and stored continuously for diagnosis as well as research purposes. There exist several methods that tend to provide real-time retrieval of medical images from such storage repositories. Therefore, in this paper, we strive to present an exhaustive performance comparison of existing and recently published state-of-the-art local feature descriptors for retrieval of CT and MR images. All the compared methods have been tested on two standard test databases, namely, NEMA CT and NEMA MRI. Additional experiments have been conducted to analyze the noise robustness ability of all the compared approaches. Lastly, the methods are also compared in terms of their computational complexity and total CPU time taken to retrieve images corresponding to the given query image.
Limiting the Impact of Statistics as a Proverbial Source of Falsehood

This paper presents an early version of a decision-making “eco” system. We refer to it as an “eco” system because it is primarily based on mathematical logic and combines concepts and principles from the fields of statistics, decision theory, artificial intelligence and modeling of human behavior. The primary goal of the proposed approach is to address errors that occur resulting from the misuse of statistical methods. In practice, such errors often occur either owning to the use of inappropriate statistical methods or wrong interpretations of results. The proposed approach relies on the LPwNF (Logic Programming without Negation as Failure) framework of non-monotonic reasoning as provided by Gorgias. The proposed system enables automatic selection of the appropriate statistical method, based on the characteristics of the problem and the sample. The expected impact could be twofold: it can enhance the use of statistical systems like R and, combined with a Java-based interface to Gorgias, make non-monotonic reasoning easy to use in the proposed context.
High-Performance Computing (HPC) design to improve the quality of Introduction of Parallel Computing lectures

High-Performance Computing (HPC) has been created to improve the quality of Pengantar Komputasi Parallel lectures. HPC consists of two PCs with AMD Ryzen 7 processor connected by high-speed LAN. HPC is used to solve problems that students can't practice Parallel Computing because the computer used is not adequate. So it takes a machine that can used together that has many processors, so the problems encountered are resolved. The results obtained, participants can use more than four processors to solve tree problems directly.
Towards Computer-Aided Differential Diagnosis: A Holonic Multi-agent Based Medical Diagnosis System

The state of the art of the Medical Diagnosis Systems (MDSs) has demonstrated an exciting advancement in recent years. Clearly, the success of these systems is very much dependent on the quality of their input, however, so far no computer-aided decision support system has been introduced to address this issue. Such a system should be capable of performing the Differential Diagnosis (DDx) process, in which upon receiving the chief compliant, some potential diagnoses are considered, according to which enough evidence and supporting information will be gathered in order to shrink the probability of the other candidates. This paper shows that DDx domain is a holonic domain, and hence, this process can be implemented using a holonic multi-agent based MDS.
Cyber Risk in Critical Infrastructure

Existing approaches to evaluating cyber risk are summarized and explored for their applicability to critical infrastructure. The approaches cluster in three different spaces: network security, cyber-physical, and mission assurance. In all approaches, some form of modeling is utilized at varying levels of detail, while the ability to understand consequence varies, as do interpretations of risk. A hybrid approach can account for cyber risk in critical infrastructure and allow for allocation of limited resources across the entirety of the risk spectrum.
Detecting Anomalies in Dynamic Networks

This chapter deals with an important analysis task over dynamic networks, namely exploring the time varying characteristics of anomalies present in such networks. In this direction, a graph mining based framework is considered that takes a sequence of network snapshots as input for analysis. It defines various categories of temporal anomalies typically encountered in such an exploration and characterizes them appropriately to enable their detection. An experimental study of this framework over benchmark graph data sets is presented here to demonstrate the evolving behavior of the anomalies detected as per the categories defined.
A Survey of High Utility Pattern Mining Algorithms for Big Data

High utility pattern mining is an essential data mining task with a goal of extracting knowledge in the form of patterns. A pattern is called a high utility pattern if its utility, defined based on a domain objective, is no less than a minimum utility threshold. Several high utility pattern mining algorithms have been proposed in the last decade, yet most do not scale to the type of data we are nowadays dealing with, the so-called big data. This chapter aims to provide a comprehensive overview and a big-picture to readers of high utility pattern mining in big data. We first review the problem of high utility pattern mining and related technologies, such as Apache Spark, Apache Hadoop, and parallel and distributed processing. Then, we review recent advances in parallel and scalable high utility pattern mining, analyzing them through the big data point of view and indicate challenges to design parallel high utility pattern mining algorithms. In particular, we study two common types of high utility patterns, i.e., high utility itemsets (HUIs) and high utility sequential patterns (HUSPs). The chapter is concluded with a discussion of open problems and future directions.
Medical Data Storage and Compression

This chapter introduces medical imaging devices and its history. Further, the chapter discusses the history of the DICOM image and DICOM standard. The chapter also discusses image acquisition. Moreover, the chapter discusses the various software used for processing DICOM image. The chapter also discusses the limitations of DICOM and other medical image data formats. The basic structure of the DICOM is described in this chapter. Further, various research articles on medical image processing are discussed.
Cue abstraction and ideal prototype abstraction in estimation tasks

This study investigates abstracted processes and introduces a new prototype abstraction model adapted to estimation tasks. This prototype abstraction model assumes that the processing of whole exemplar patterns supports the detection of the underlying statistics necessary for the abstraction of two extreme prototypes on the continuous criterion dimension of the task. The prototypes are stored in memory as valid reference points for future similarity-based judgments. This prototype model was compared with the cue abstraction model, which assumes that people abstract cue weights in learning and add the cue information from exemplars to infer their criterion values varying on the continuous dimension. This study hypothesises that the training mode and the number of exemplars in training interact and affect subsequent model performance at test. The results from an experiment confirmed this hypothesis and showed that observational training supports an efficient prototype abstraction and feedback training supports an efficient cue abstraction.
Towards Automatic Comparison of Cloud Service Security Certifications

Cloud service providers who offer services to their users traditionally signal security of their offerings through certifications based on various certification schemes. Currently, a vast number of schemes and standards exists on one side (cloud service certifications), while another large set of security requirements stemming from internal needs or laws and regulations stand on the other side (users of cloud services). Determining whether a service with an arbitrary certificate in one country fulfills requirements imposed by the user in another country is a difficult task and therefore a project (EU-SEC) was started focusing on allowing cross-border usage of cloud services. In this paper, we propose automated comparison of cloud service security certification schemes and, subsequently, security of cloud services certified using these schemes. In the presented method, we map requirements in schemes, standards, laws, and regulations into a proposed cloud service security ontology. Due to the free-form text nature of these items, we also describe a supporting method for semi-automated conversion of free text into this ontology using natural language processing. The requirements described in ontology format are then easily compared against each other. We also describe an implementation of a prototype system supporting the conversion and comparison with preliminary results on describing and comparing two well-known schemes.
A Framework for Improving the Accuracy of Keystroke Dynamics-Based Biometric Authentication Using Soft Computing Techniques

The global access of information and resources from anywhere has increased the chance of intrusion and hacking of confidential data. Username with password is the commonly used authentication mechanism which is used for almost all online applications from net banking to online examinations. However, advanced safeguard mechanisms are sought against unauthorized access as the passwords can be hacked easily. To strengthen the password, it can be combined with biometric technology. Keystroke biometrics, a strong behavioral biometric, can be considered as a secure method compared to other methods even if the imposter knows the username and password as it is based on user habitual typing rhythm patterns. For any online application the accuracy level plays a vital role. But the accuracy of keystroke authentication when compared with other biometric authentication mechanisms is low. To improve the accuracy and minimize the training and testing time, this chapter proposes a wrapper-based classification using PSO-ELM-ANP algorithm which gives 99.92% accuracy.
Gender Differences in Mathematics and Science Achievement of Eighth Grade Students in Ukraine on TIMSS 2011

The purpose of the paper is in-depth analysis of gender differences in Ukrainian student achievement in mathematics and science on TIMSS 2011 results because the average scores similarity hides some significant differences between the achievements of boys and girls. Boys significantly outperform girls in some cognitive and content domains, while the benefit of girls never was significant. The procedure of Differential Item Functioning (DIF) on the basis of the Mantel-Haenszel method was used to test the fairness of assessment. The results show that the set of TIMSS 2011 items does not have any gender bias on Ukrainian students since there are insignificant number of items with large DIF, among which some favouring boys, and some favouring girls. Each of gender groups is equally represented among both stronger and weaker participants. Any significant relationship between the DIF size and the difficulty or type of the item is not detected. But the items in number, physics and earth science, and math problems on applying function in favour of boys. This is likely caused by the fact that in Ukrainian society still there is attitude to such fields of study as masculine.
Big Data Framework for Kenya’s County Governments

Digitalization is transforming governments across the globe.At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of massive digitalization.Digitalization is compelling governments at all levels to embrace voluminous data and institute appropriate multi-channel platforms to support digital transformation.While this is the case, most governments have been caught unprepared thwarting maximum benefits spurred by digitalization.Inherently, the social media and e-participation tools for generating huge amount of data have convoluted most governments' appetite in Big Data management.This situation is further compounded with the slow pace of adoption of these technological tools by citizens and the public sectors.For enhanced e-citizen satisfaction and engagement, as well as e-participation processes, public institutions need to promote engagement and collaboration.In view of advancing benefits to their citizens, public institutions need to institute appropriate measures to collect citizen's data.The information collected is vital for public institutions in actualizing what services the citizens want.Using literature reviews and cases, the authors examine Big Data benefits in counties and propose a Big Data model to improve efficiency of e-governance services and productivity in county governments.The authors demonstrate Big Data framework has the aptitude of molding citizen's opinion in county decision making process.Better use of e-technologies is shown in the proposed model which illustrates sharing resources among various data analytics sources.Our proposed framework based on Big Data analytics is a viable initiative to progress effectiveness and productivity, strengthen citizen engagement and participation and encourage decision-making in e-governance services delivery in the counties.
A Multi-layer Virtual Network Isolation Detection Method for Cloud Platform

In the trusted testing of cloud platforms, isolation testing of virtual networks is one of the important tasks. The traditional isolation detection method only extracts network configuration information from the database and reconstructs the network structure. However, these data do not necessarily reflect the current status of the virtual network and may affect the test results. To solve the above problems, this paper proposes a multi-layer virtual network isolation detection method based on trusted third party for distributed cloud platform environment. Firstly, The basic idea of the method is to extract the correct test input data from the database and each agent node, and then compare these data with the reference value, and then conduct a multi-layer evaluation of the isolation of the virtual network based on the comparison result. Then, a formal method is used to verify the effectiveness of the proposed scheme in detecting network isolation under both the policy updating and post-update scenarios. Finally, build a simulation experiment environment based on CloudSim4.0 to evaluate the performance of the multi-layer detection method, including the comparison with the traditional detection methods, and test the performance overhead of the test method on the test-end system. The experimental results show that the multi-layer detection method has less impact on the test-end system’s performance, and as the test scale increases, the gap with the traditional program’s overhead will gradually decrease.
Embedded Diversities: Design Explorations with the Tool of Type and Expressions

A script in India conveys a symbolic glimpse into a particular culture and the associated beliefs. There is an attractive diversity due to appearance of various such scripts. The diversity is not only in the existence of the scripts but also more critically in their visual representation. The aesthetic beauty and the traditional native identity are imbued as one harmonious unit in the multitude letterforms of any script. Indigenous ‘Scripts’ in India mirror the cultural essence of letterforms. Different forms of writing each script represent the origin of a script from the respective local context—where a particular language written in specific script defines the context (Nath et al. in Display Type Expressions: The cultural underpinnings of the sripts from two Royal Cities of India, 2004 [1]). This paper outlines the existence of three visual facets that are part of the representation of a script. The foremost facet is ‘Language,’ that provides a communicable/functional meaning to the form of the script. A language has a structure, syntax, and a cultural basis that are associated with the style/form of a particular script. The second facet consists of ‘Tools or Techniques of production,’ that provide the mechanics of letter structure, its construction, design, and style. The third facet consists of ‘Semantics (Expressions) and meaning based applications,’ that give a provision of a range of applications from unilingual to multi-lingual forms of visual display. The explorations of expressions of various local scripts and their type styles by design students showcase a range of functional meanings, cultural meanings and tool influenced meanings that nuance the compositions to a new level of aesthetics. The present paper brings forth the trifold visual approach from ‘Language’ to ‘Tools or Techniques of production’ to the final type forms that communicate semantics in the context of meaning-based communication aims. The methodology provides a scientific lens to craft the design process for better designs with type and their expressions. In conclusion, a conceptual approach in the form of an open framework has been discussed. This framework is envisioned to act as a scientific frame of reference to understand the design applications of scripts, languages, tools, and techniques, type expressions, etc.
An efficient user privacy preserving multi-server private information retrieval

The thirst of acquiring and sharing the knowledge has been increased exponentially in these days due to the availability of the Internet at the finger tips. As a consequence, the need of privacy at various levels and various contexts has been comprehensively studied by various cryptographers. One of the user privacy preserving concepts called Private Information Retrieval (PIR) also provides user privacy at various levels. Several years of efforts on PIR have succeeded to provide almost practical communication efficient privacy preserving solutions.