Reviewerly/exHarmony huggingface dataset:
What do Asian and non-Asian scriptures have in common? An applied statistical machine learning inquiry

This paper presents a substantially detailed statistical machine learning approach to the analysis of several aspects of sacred texts from both the Asian and Biblical scriptural canons.The corpus herein considered consists of 4 Asian sacred scriptures, namely the Tao Te Ching, the teachings of the Buddha, the Yogasutras of Patanjali, and the Upanishads, and 4 non-Asian sacred texts essentially four books from the Bible, namely the Book of Proverbs, the Book of Wisdom, the Book of Ecclesiastes and the Book of Ecclesiasticus.Standard text mining tools are used, like the creation of Document Term Matrices (DTM) to pre-process raw English translations into word frequencies, and both unsupervised and supervised learning methods are used to answer some foundational questions featuring similarities and dissimilarities within each canon and interesting differences between all the canons considered.Despite the vast disparities between the translators of the original texts, our findings reveal sharp differences between Asian and non Asian scriptures regardless of whether clustering techniques or pattern recognition methods are used.We provide several compelling visualizations to help highlight our striking findings, chief of which are the persistent groupings of the scriptures based on geography.
Explaining IT programming concepts using NSM explications: The case of ‘variable’ and ‘constant’

The paper seeks to explore a practical application of Natural Semantic Metalanguage in defining two core concepts in computer programming, i.e. the concept of a variable and the concept of a constant. The investigation of both programming concepts is carried out with reference to Apple’s Swift programming language, which is now the dominant language in creating applications designed for Apple’s devices. The explications of a variable and a constant developed in this paper are tentative definitions of the most fundamental functionalities behind the two programming concepts. They are meant to ease the learning experience of programming enthusiasts who are at the early stages of their learning path.
Deep Learning-based Vehicle Image Matching for Flooding Damage Estimation

Images representing flooding damages can provide valuable information, such as the damage location and severity.Automated and quantifiable analyses of those images allow asset managers to accurately understand the vulnerability of the infrastructure.To this end, this paper proposes a methodology to match a vehicle in a flooding image to a 3D vehicle image.The proposed method is a part of a framework for flooding depth estimation.As the initial step of the framework, the proposed method uses Mask R-CNN and VGG network to extract the vehicle object and its features, respectively.The features of the vehicle images are compared with those of 3D vehicle image, to find a good match.A total of 87 vehicle objects were used to validate the proposed method, and promising levels of matching accuracy were obtained.Once the framework is completed, the proposed method is expected to automatically analyze flooding images for its damage assessment.
Thermal-difference states of light: true states of heralded photons

We introduce a three-parameter family of single-mode optical states whose density operator is a weighted difference of two thermal states, the thermal-difference states. We identify the parameter values for which these states have a negative non-singular P -function, implying they are nonclassical. We show that the states of the “heralded photons” generated via spontaneous parametric downconversion belong to this family, with the three parameters corresponding to the nonlinear gain and the losses in the signal and the idler channels. The thermal-difference states yield new benchmark states for the analysis of nonclassicality and quantum macroscopicity criteria.
Digital Methods of Marketing Resources Distribution of a Scientific Organization

In relation to the needs of the implementation of the marketing function of a scientific organization (SO), marketing models and characteristics of perspective information and communication tools are being analyzed. These tools are systematized from a view of ensuring visualization of scientific results, efficiently targeted and at the same time more dissemination of information about new scientific results. Comparative characteristics of perspective channels of sources interaction and potential consumers of scientific products are given. It has been proven that the most effective marketing tools for SO should be a marketing strategy formation for their activities, website development, the participation of scientists in scientific and applied events, the preparation of popular science information materials for network platforms, etc. The necessity of developing a strategy for the marketing direction of the SO is substantiated. The marketing strategy of a scientific organization should include mechanisms for identifying priority and perspective marketing channels used for scientific communication, as well as for direct interaction and cooperation of developers with each other, as well as with potential consumers. In practice, the SO marketing strategy can be implemented by mean of social networks using, being created and developed an organization’s website, or joined multilateral platforms, etc.
Efficient Removal of Noise from an Image using HSV Filtering

Noise removal in images is an important and growing field of research in the present day. Many image processing techniques exist with new techniques being proposed quite frequently, among which median filtering based techniques are popular for their relative simplicity compared to other non-linear techniques, as well as the accuracy they generally provide in denoising an image, especially compared to linear methods. Typical categories of noise may exist solely or in combination in a noisy image, such as impulse noise, Gaussian noise and speckle noise. The current paper presents a technique which uses hue, saturation and intensity values based filtering for denoising an image. The comparative results obtained indicate the superiority of the HSV technique combined with median filtering over the standard median filtering technique.
An Improved Quality and Security of LSB Based Steganography Technique Using XOR Bitwise Operation in 24 Bit Color Image

Information privacy has become the overborne issue today with the increasing use and efficiency of electronic data processing. The main purpose on privacy of communication is to provide secure connection with target without being captured by the third persons or by bringing them in such a way that they cannot understand. In the most general sense, steganography is the study and practice of concealing information to provide security of communication. The classic LSB (least significant bit) technique and methods which are derived from classic lsb, are used in steganography techniques on spatial domains. For the case of security, the classic LSB technique is very weak because it is very simple and predictable. Also, it causes distortion of image. In this paper, we propose an improved LSB Steganography technique to increase security and decrease the distortion of image. The proposed method is based on hiding two bits of secret data in one color (rgb) pixel with only one least significant bit change in one of the layers. LSB value of red layer is used in XOR operation with both LSB values of green and blue layers respectively. Only with one LSB bit change, 2 bits of secret data can be hidden, because nature of XOR bitwise operation. XOR operation provide the more secure and unpredictable communication than classic LBS. This method performed on to different images. Furthermore, it observed that the improved method reveals good result as the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) than classic LSB method.
Agency in the Smart Home of the Future

Agency in the Smart Home of the Future is a short documentary film about a unique design fiction social experiment. Comprised mostly of trace evidence video footage recorded by field researchers on their smart phones, the film takes audiences directly into the reflexive lived experience of four Australian households, each of whom have had their real homes transformed into fully functional smart homes. Smart speakers, smart TVs, smart fridges, robo vacs, sensor lighting, etcetera! is installed into their homes as a gestalt to generate a paradigmatic shift in their everyday living and interactions. In the process, we discover how human agency and structure reproduce in this potential living environment of the future.
A conversation with Richard Skarbez

In this series of interviews with innovation leaders, Ubiquity Associate Editor and software engineer, Dr. Bushra Anjum sits down with Dr. Richard Skarbez, a lecturer in the Department of Computer Science and Information Technology at La Trobe University, to discuss augmented, mixed, or virtual realities and how users interact with such technologies.
Towards Light Weight Cryptography Schemes for Resource Constraint Devices in IoT

The Internet of Things (IoT) is becoming the most relevant next Internet-related revolution in the world of Technology. It permits millions of devices to be connected and communicate with each other. Beside ensuring reliable connectivity their security is also a great challenge. Abounding IoT devices have a minimum of storage and processing capacity and they usually need to be able to operate on limited power consumption. Security paths that depend maximum on encryption are not good for these resource constrained devices, because they are not suited for performing complicated encryption and decryption tasks quickly to be able to transmit data securely in real-time. This paper contains an overview of some of the cryptographic-based schemes related to communication and computational costs for resource constrained devices and considers some approaches towards the development of highly secure and lightweight security mechanisms for IoT devices.
Quality of experience prediction model for video streaming in SDN networks

To evaluate the network performance, network operators rely on quality of service. This measure has shown limits and great deal of effort has been put into putting in place a new metric that more accurately reflects the quality of service offered. This measure is known as Quality of Experience (QoE). QoE reflects the user's satisfaction for a service. Today, evaluating the QoE has become paramount for service providers and content providers. This necessity pushed us to innovate and design new methods to estimate the QoE. This paper comprises two parts: the first part defines our subjective method which evaluates the video quality over SDN networks. In the second part we try to cover the impairments of subjective methods by a novel method that predicts the QoE (MOS) based on machine learning, so we employ ML-classifiers, then we calculate the performance metrics to measure the performance of each algorithm to deduce the best algorithm.
Junkbox, a waste management educational game for preschool kids

Waste management is becoming an increasingly important issue in contemporary society. The growth in consumption, the increase in urbanization and economic development have as main consequence an increase in the production of waste. Moreover, the population's bad recycling habits are becoming more and more entrenched. In this context, sustainable development has gained considerable importance and the need to teach new generations the value of protecting the environment is key and what through our research we are experimenting is the way of playing. In fact, basic but very significant behaviours can be activated through a game and they may become stable habits and then proper lifestyles. This paper describes an educational game that might support educators and trainers to raise environmental consciousness in kids. Starting from several co-design sessions conducted with teachers, children and parents, we elaborated an idea to target the stakeholders’ needs: making the separate collection an enjoyable experience by collectively awarding children after the correct disposal of waste. This idea foresaw a touchpoint finalized to become an integral part of every child's routine during school hours. “Junkbox” thus becomes a vehicle for learning how to recycle and turning waste separation into a game. This ritual helps children to achieve one of the most important goals of recent years: actively contribute to reduce the environmental costs of our bad habits and lifestyles.
Voter privacy in an era of big data

The Cambridge Analytica/Facebook data scandal has brought to light not only the data-driven nature of much modern political campaigning but also the lack of an international consensus on the appropriate regulation and enforcement regarding the use of personal information in the political arena. In Australia, the Privacy Act 1988 (Cth) (Privacy Act) does not apply to registered political parties and also contains an exemption for the political acts and practices of certain organisations. These exemptions were justified on the basis of the implied constitutional freedom of political communication in the Australian Constitution. This chapter argues that the political exemptions in the Australian Privacy Act have become anachronistic in an era of big data analytics and political micro-targeting of electors. In the pursuit of this argument, the chapter provides an overview of the political exemptions in the Privacy Act and discusses the data protection issues raised by the current trend towards micro-targeting of Australian electors. It then explores the case law on the implied freedom of political communication, including developments that have taken place since the introduction of the political exemption, and submits that there are no constitutional impediments to rolling back the political exemptions in the Privacy Act. The removal of the political exemptions would enhance the privacy of voters and improve transparency in political campaigning by requiring political parties and other political actors to adopt fair data-handling practices and subjecting their practices to regulatory scrutiny.
Identifikasi Pola Suara Pada Bahasa Jawa Meggunakan Mel Frequency Cepstral Coefficients (MFCC)

Voice Recognition is a process of developing systems used between computer and human. The purpose of this study is to find out the sound pattern of a person based on the spoken Javanese language. This study used the Mel Frequency Cepstral Coefficients (MFCC) method to solve the problem of feature extraction from human voices. Tests were carried out on 4 users consisting of 2 women and 2 men, each saying 1 word "KUTHO", the word pronounced 5 times. The results of the testing are to get a sound pattern from the characteristics of 1 person with another person so that research using the MFCC method can produce different sound patterns
Content based video retrieval using histogram of gradients and frame fusion

In the present article, we present an algorithm of content based video retrieval using Frame fusion and Histogram of Oriented Gradients (HOG). Representative frames of database videos are pre-processed using frame fusion to get a high resolution representative frames and HOG descriptor of this high resolution representative frames represents corresponding database video. On other side, query frame also undergo frame fusion and the HOG descriptor of high resolution query frame is used to represent query frame. To retrieve videos similar to query frame, matching is done using Euclidean distance between HOG features of query frame and database representative frames. The proposed method is tested on news category videos. The proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Performance is assessed with the parameters precision, recall, accuracy and Jaccard index. The experimental results have shown that the performance of the proposed method is performing better than other state-of-art methods.
OctopusKing: A TCT-Aware Task Scheduling on Spark Platform

The majority of large-scale data intensive applications executed by data centers are based on Map-Reduce or its open-source implementation, Spark. However, the native Spark's data locality has significant side effects. Spark prioritizes high-priority tasks. But tasks at the next level of data locality are never started because Spark's delay scheduling mechanism. Additionally, the current scheduling approach on Spark only considers allocating reasonably computing resources for each task, but ignores the balance between computing resources and network resources. Network-intensive jobs will generate a large number of cross-rack tasks on heterogeneous clusters. More extensively, geographically distributed big data analytics clusters will produce greater network latency due to network transfer, which will decrease the performance of the whole cluster. Therefore, we propose OctopusKing a scheduling optimization approach based on the task completion time awareness. OctopusKing employs a deep learning algorithm to predict task completion time(TCT), which comprehensively considers four influence factors including the data size of each task, data nodes heterogeneity, the computation complexity of each task and network transfers. Based on the prediction of task completion time, our approach balances non-local task scheduling and network transfer time, which decreases the overall computation time of a job and improves the utilization of cluster resources effectively. Finally, we have implemented OctopusKing on Spark cluster with a 10 nodes. We show the convincing evidence that OctopusKing reduces average job completion time by 21% in comparison to Spark's default scheduler with little overhead.
General algorithm for searching user data in social media of the Internet

The research work presented within this paper solves the problem of automated search for heterogeneous data in social media of the Internet (SMI). Building a system for obtaining and subsequent analysis of heterogeneous data in SMI – a complex multi-stage process in which specialists of various profiles and qualifications participate. Therefore, one of the main problems in the design of such systems is the coverage of all aspects of the functioning of the software-analytical complex, providing a common language for specialists, which allows us to uniquely, and clearly, understandably formulate the basic concepts of the projects. One of the main and basic tasks in analyzing the pages of a SMI user is to build algorithms for analyzing the user data environment (UDE). The quality of software will depend on the implemented algorithms. The construction of such algorithms, on the one hand, provides an understanding of the process of forming functional individual modules of the system and their interaction, on the other hand, laying a qualitative foundation in the future system. Algorithms for data analysis in the SMI will be designed based on the basic principles of behavior of the user registered in it.
End-to-End Joint Entity Extraction and Negation Detection for Clinical Text.

Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.
Improved Algorithm of Blocking the Selected Edges in the Digraph

Iris Publishers, open access journals of biostatistics, open access journal of biostatistics and biometrics applications, Peer reviewed journal of biostatistics and biometrics, open access journal of applied biostatistics, open access journal of biostatistics & biometrics, international journal of biostatistics & computational biology, international journal of clinical biostatistics and biometrics, journal of biostatistics impact factor, journals on biostatistics.
The Choice Between EBooks and Printed Books: A Study Among Hospitality and Tourism Educators and Learners

Emergence of electronic books (e-book) changed the position of printed books in the learning space. This study examines the perceptions and preferences of hospitality and tourism educators and learners. Similarities and dissimilarities in the preference, perception and its link with gender, and scope of the degree course what the respondents teach and learn are analyzed. Data was collected from both students and teachers of hospitality, tourism and culinary degree program at a private university located in Karnataka, India. A structure questionnaire was used to collect the data from the respondents. The result of the study shows that printed books are preferred mostly for their easy usage and reading. Both student and teachers of hospitality and tourism feel pleasure in reading printed books. However both hospitality and tourism educators have more intention to use the e-books in the future. The findings obtained will help the educational service providers, publishers and librarians in their decisions related to investment and management of library resource very effectively which facilitate teaching and learning process.
Criteria Prioritization in Adaptive Security Activities Selection, ASAS Model using Analytic Network Process, ANP

Organizations have proposed the best practices of security activities in software development life cycle as guidance for development team. However, the implementation of the security activities are influenced by criteria which are interdependent to each other and dynamic. Those criteria are experience, skill and knowledge; teams workload; development team size; development timeline; and cost/budget. Therefore, the selection of security activities must be based on the priority of the criteria and adaptive. This paper addresses the adaptive security activities selection model as a multi-criteria decision making (MCDM) issue. Analytic Network Process (ANP) is proposed for weight calculation and criteria prioritization. Hypothetical examples were presented based on hard constraints faced by in-house web-based application development team in two different scenarios. The Decision Maker made the judgement by using the online ASAS model. The results show that experience, skill and knowledge get the highest priority in the first scenario while budget/ cost get the highest priority in the second scenario. These prioritization will be used as a major requirement in security activities selection.