Reviewerly/exHarmony huggingface dataset:
An Architectural Solution for Health Information Exchange

Health information technology (HIT) systems including electronic health records (EHRs) have a market saturation nearing 92% at individual institutions but are still unsuited for cross-institutional collaboration of stakeholders (e.g., medical providers such as physicians, hospitals, clinics, labs, etc.) in support of health information exchange (HIE) of different HIT systems in geographically separate locations. In the computer science field, software architectures such as service-oriented architecture, grid computing, publish/subscribe paradigm, and data warehousing are well-established approaches for interoperation. However, the application of these software architectures to support HIE has not been significantly explored. To address this issue, this paper proposes an architectural solution for HIE that leverages established software architectural styles in conjunction with the emergent HL7 standard Fast Healthcare Interoperability Resources (FHIR). FHIR models healthcare data with XML or JSON schemas using a set of 93 resources to track a patient's clinical findings, problems, allergies, adverse events, history, suggested physician orders, care planning, etc. For each resource, a FHIR CRUD RESTful Application Program Interface (API) is defined to share data in a common format for each of the HITs that can then be easily accessible by mobile applications. This paper details an architectural solution for HIE using software architectural styles in conjunction with FHIR to allow HIT systems of stakeholders to be integrated to facilitate collaboration among medical providers. To demonstrate the feasibility and utility of HHIEA, a realistic regional healthcare scenario is introduced that illustrates the interactions of stakeholders across an integrated collection of HIT systems.
Cryptanalysis of Deterministic and Probabilistic Multi-Copy PDP Schemes For Cloud Storage - Attacks and Countermeasures

We provide cryptanalysis of two versions of Provable Data Possession (PDP) constructions. These schemes are proposed for efficient verification of the availability of unmodified data stored in a remote cloud computing platform. We identify problems in the original constructions, as well as the source of errors in the proving methodology. Addressing this, we propose new improved schemes and subsequently, prove their security. To achieve this we reduce the problem of attacking our schemes into breaking the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\top \ell$</tex> -assumption, related to the coCDH problem. A benchmark analysis is conducted from proof-of-concept implementations in Python.
Enhancing Security in a Big Stream Cloud Architecture for the Internet of Things Through Blockchain

The Internet of Things (IoT) paradigm is foreseeing the development of our environment towards new enriched spaces in most areas of modern living, such as digital health, smart cities, and smart agriculture. Several IoT applications also have real-time and low-latency requirements and must rely on specific architectures. The authors refer to the paradigm that best fits the selected IoT scenario as “Big Stream” because it considers real-time constraints. Moreover, the blockchain concept has drawn attention as the next-generation technology through the authentication of peers that share encryption and the generation of hash values. In addition, the blockchain can be applied in conjunction with Cloud Computing and the IoT paradigms, since it avoids the involvement of third parties in a broker-free way. In this chapter, an analysis on mechanisms that can be adopted to secure Big Stream data in a graph-based platform, thus delivering them to consumers in an efficient and secure way, and with low latency, is shown, describing all refinements required employing federation-based and blockchain paradigms.
Research on Question Answering based on Knowledge Graph of Military Industry Domain

With the rapid development of natural language processing, large amount of information makes people’s requirements for the effectiveness and efficiency of information retrieval more urgent. For researchers in the military industry domain, how to effectively retrieve information is becoming an urgent problem. To this end, this research concentrates on the military industry domain, using relevant literature as the corpus, and constructs a knowledge graph that includes five types of entities and four types of relationships. On the basis of this knowledge graph, we achieve the function of intelligent question answering of scientific research literature in the military industry domain. The contribution of this research is to provide information service support for scientific research.
A Strategical Study on Cybersecurity Knowledge Dissemination for Teenager Based on Short Video Technology

In the era of digital information, digital life has become an indispensable part of teenagers. The age of teenagers that get access to the internet is getting lower. With insufficient self-protection awareness, they are prone to be influenced by unhealthy information. This paper proposes that short video technology enjoys an advantage over traditional dissemination channels such as pictures and words. Short video technology contains large sets of data. Through data analysis and data processing, we can further analyze the feasibility of applying short video technology to promote cybersecurity knowledge for teenagers and appeals the society, schools and families to educate teenagers on safe use of the internet.
Strategies for Evaluating Cloud System Providers During the Transformation of Businesses

This chapter takes a comprehensive look at the strategies and mechanisms developed for evaluating cloud services during business transformation. During business transformation, enterprises need to make decisions that would have long lasting impacts on the performance and profitability of the businesses. One of the important decisions business leaders are required to make is whether to own and manage their own IT infrastructure or outsource them from a public cloud provider. Due to the attractiveness of cloud computing, there are many cloud providers in the market creating a confusion in the minds of the customers who to select. Hence it is necessary to use proper strategies and mechanisms to evaluate the performance and the suitability of the service providers in meeting the customer requirements. This chapter takes an in depth look at some of the strategies, frameworks, mechanisms and tools proposed by researchers for evaluating cloud services in the literature with reference to their applicability, suitability, advantages and disadvantages.
Introduction

The present research in this chapter introduces a brief overview about the research topic, a clear understanding on the concepts of spam detection, the characteristics of spam detection, the challenges in development of image spam detection, the contribution of the research and outline that will be followed in the research. This outlines an introduction to the text and image-based email classification, followed by the motivations and problem statement, research objectives, research scope, novelty, and significance of the research. With advancements in the technologies to detect email spam emerged, spammers developed the concept of image spamming which tends to complicate the processes of detecting spam in image mails.
Mediated Embodiment in New Communication Technologies

Advanced communication technologies, such as virtual reality or certain types of robots, are able to induce the illusion of adopting a surrogate body as the own body. The sense of owning an artificial body, technologically mediated, in which one perceives to be located, is introduced as mediated embodiment. The concept has not been carefully conceptualized or clearly distinguished from other acceptations of embodiment, coming from a wide range of disciplines. A definition is provided which narrows the concept to accurately describe the experience of embodiment in new communication technologies. In addition, the text gathers the closest constructs related to embodiment in the communication literature, reviews the most important studies on mediated embodiment, provides potential approaches to its study, and outlines the direction that this field of study is taking.
Vibration Guided Automatic Vision for Enhanced Security

The existing security systems are secure but are not smart enough to handle arbitrary scenarios leading to many false triggers of the alert system. Furthermore, these systems require constant human intervention which isdifficult to achieve.They are also vulnerable as they contain many loopholesand the sensors used are easily manipulatable. The proposed system tries to solve this problem in an efficient and a smart way by the use of sensors, AI and IoT which makes the system robust and resistant againstattacks. The system implements advanced face detection via Single Shot Detection and face recognition via Inception Neural Network for recognition of object in a fast and accurate way. This helps the system act according to the situation, thus preventing any damage to theregion which implements this system. In this work the proposed system is implemented and tested as a Home Security System. The system can also be extended to work in other areas like banks, data hubs, museums etc.The overall accuracy of the system was recorded to be 97.95%.
ABC Optimized RBFNN for Classification of EEG Signal for Epileptic Seizure Identification

This chapter presents a further investigation of searching optimal parameters for RBFNN. A novel and modified version of artificial bee colony optimization algorithm has been proposed to get the optimal parameters for RBFNN such as center, spread, weights, and so on. Also, a comparative analysis has been performed to get the more efficient optimization algorithm between modified ABC and modified PSO in this specific domain only.
Measurement of Similarity and Design Interface for Soybean Disease Diagnosis

The similarity of the target case is determined by measuring how close each attribute of the target is similar to the stored case in the case base. Similarities are usually normalized to fall within a range of 0 to 1. The soybean is one of the most important bean in the world, providing vegetable protein for millions of people and ingredients for hundreds of chemical products. Several diseases, including root and stem rot, pod and stem blight, frogeye leaf spot, brown spot, downy mildew, leaf blight and purple seed stain, and stem rot (white mold). In a case-based reasoning system for the identification of diseases of soybean plants provide solutions recommended by experts in diseases of soybean in accordance with a similar case or a similar matches within the database storage plant disease cases. Similarity value where 0 is totally dissimilar and 1 is an exact match. if similarity value equal zero then system will keep a set of data will be save temporary and need validation as a new case from the expert. The system give a recommended solution from similarity formula with the threshold which given by the expert.
Research on the Influence of Big Data Information Processing Technology on English Writing Anxiety

For English learners, English writing is the most difficult to master, because in the process of writing, it is likely to be affected by mother tongue transfer and other factors. It is the difficulty and importance that lead to learners' fear of difficulties and anxiety in the process of writing practice, which affects their English learning level and expression. This article discusses the current English writing anxiety problems, analyzes the main causes of the problems, and puts forward relevant countermeasures and suggestions to solve the English writing anxiety problems with the help of the platform combined with big data technology.
Fast Distributed Stochastic Nesterov Gradient Descent Algorithm for Image Classification

Based on stochastic gradient descent, this paper presents a fast distributed stochastic Nesterov gradient descent algorithm denoted by SFDGND, which can effectively solve the problem of image classification with distributed neural nets. The proposed SFDGND algorithm allows data to be randomly and evenly assigned to agents. Each agent updates its parameters with a local subset of data, thus enabling parallel computation. Finally, we compare the effectiveness of the proposed stochastic algorithm for training distributed neural nets optimizing problems. The experimental results are implemented on MNIST and CIFAR-10 datasets, which clearly demonstrates that SFDNGD algorithm performs well in practical applications and compares favorably to the existed algorithms.
Blockchain Foundations

A blockchainBlockchain is a ledgerLedger of blocks of information (e.g., transactions, agreements, etc.) that are stored sequentially across a networkNetwork of computers. Rather than a simple algorithm, blockchainBlockchain is a technology construct and an enabling protocol that facilitates a decentralized brokering of data among participants, i.e., its revolutionary properties do not derive from what blockchainsBlockchain do (i.e., store data securely), but from the manner in which they are used and implemented (i.e., trustless and decentralized).
Prioritizing Urgent Messages on Instant Messaging Platforms

Instant Messaging (IM) has become integral to everyday life, with a plethora of information exchange between users. Whatsapp, an IM service has about 65 billion messages being exchanged between users, in a single day (2017). Whatsapp is just one of several such applications which share countless amount of messages where all messages received by a single user are not in equal priority. With an ever-increasing volume of messages that a person exchanges, there is a need for prioritizing important messages. The messages that are more importance or urgency is predicted with a classification algorithm. Moreover, the trained classification model is wrapped as an API service, allowing messages to be classified and predicted in real time by calling the REST API service. The paper provides a mechanism to predict important messages that require more attention or urgency and thereby, offering a way to maximize productivity of a person to check important messages.
Combined Side-Channel Attacks on a Lightweight Prince Cipher Implementation

Side-channel attacks have recently become one of primary threats that target modern computing devices. Several techniques have been proposed to improve the strength of side-channel attacks. Preprocessing is one of those techniques that improves the side-channel attacks by processing the measured leakage traces before performing the statistical analysis. The advantages of preprocessing techniques can potentially increase the performance and precision of side-channel attacks. Preprocessing of the measured leakage typically leads to faster side-channel attacks that require less number of measurements. A novel combined attack which utilizes the preprocessing techniques effectively is proposed in this work. Mutual information analysis (MIA) and correlation power analysis (CPA) are implemented for the preprocessing and attack steps interchangeably. For the first time, a combined attack is performed on a lightweight Prince cipher. The proposed preprocessing technique is demonstrated to reduce the required number of measurements to disclose a secret key by up to 25%.
A Matrix Factorization Technique with Word Embedding for Recommendation

In recent years, machine learning has achieved great success in computer vision and natural language processing.However, little attention has been paid to how to apply these explorations in the field of natural language processing to recommendation systems.In this paper, inspired by word embedding techniques, we take advantage of word embedding techniques and propose a word embedding based recommendation method.Specifically, word2vec is first used to train a corpus composed of item name and item attributes, the implicit item information is obtained, and then incorporate them into a matrix factorization model, which combines both latent and pre-learned features for recommendation.We have performed experiments on the MovieLens dataset, and the results show that our algorithm performs better than traditional matrix factorization algorithms and has a higher prediction rate.
Detecting Abnormal Activities using Computer Vision in Big Data Framework

The abnormal behaviour of any person can be detected using computer vision. This is an important area in the field of research which is driven by wide variety of domains like intelligent video surveillance. Various techniques can be used in the field of computer vision feature extraction and description scheme. In this paper we have shown the comparison of all the techniques and method used in computer vision for the detection of abnormal activities.
Forensic Investigation of Cross Platform Massively Multiplayer Online Games: Minecraft as a Case Study

Minecraft, a Massively Multiplayer Online Game (MMOG), has reportedly millions of players from different age groups worldwide. With Minecraft being so popular, particularly with younger audiences, it is no surprise that the interactive nature of Minecraft has facilitated the commission of criminal activities such as denial of service attacks against gamers, cyberbullying, swatting, sexual communication, and online child grooming. In this research, we simulate the scenario of a typical Minecraft setting, using a Linux Ubuntu 16.04.3 machine, acting as the MMOG server, and client devices running Minecraft. Then, we forensically examine both server and client devices to reveal the type and extent of evidential artefacts that can be extracted.
Variability modeling and implementation with EASy-producer

This tutorial introduces the EASy-Producer product line environment. EASy-Producer is an open-source toolset for engineering product lines, variability-rich software ecosystems, and dynamic software product lines. In particular, it features a variability modeling and a variability implementation DSL. It has been applied in several industrial case studies and research projects showing its practical applicability both from a stability and a capability point of view. While the tool-set also includes an interactive approach to product line definition and configuration through DSLs, the focus of this tutorial will be on the DSL-based techniques. We cover both the modeling of software product lines using the Integrated Variability Modeling Language (IVML) and its underlying conceptual approach, which differs from traditional feature modeling, but we will also provide an overview of the approach to variability instantiation with EASy-Producer (using the Variability Instantiation Language (VIL)).
Differential Deep Learning With Autodiff and Autoencoder

This paper builds on the differential machine learning concept and implementations devel- oped by Brian Huge and Antoine Savine. It discusses alternative technical implementations that utilize the Keras model framework in Tensorflow 2. The implementation includes a bespoke Keras layers for an explicit backpropation and the utilization of the Tensorflow GradientTape. It addition an autoencoder is added to the network to improve the calibration in absence of a differential PCA pre-processing.
An Optimized English Text Watermarking Method Based on Natural Language Processing Techniques

In this paper, the text analysis-based approach RTADZWA (Reliable Text Analysis and Digital Zero-Watermarking Approach) has been proposed for transferring and receiving authentic English text via the internet.Second level order of alphanumeric mechanism of hidden Markov model has been used in RTADZWA approach as a natural language processing to analyze the English text and extracts the features of the interrelationship between contexts of the text and utilizes the extracted features as watermark information and then validates it later with attacked English text to detect any tampering occurred on it.Text analysis and text zero-watermarking techniques have been integrated by RTADZWA approach to improving the performance, accuracy, capacity, and robustness issues of the previous literature proposed by the researchers.The RTADZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark.RTADZWA has been implemented using PHP with VS code IDE.The experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion, reorder, and deletion attacks, e.g., Comparison results with baseline approaches also show the advantages of the proposed approach.
Machine Recognition and Intelligent English Dialogue System Based on ELM Control Algorithm

In this study, a machine recognition and intelligent English dialogue system model based on the control algorithm of Extreme learning machines (ELM) is established. The system can preprocess and analyze the features of speech input signal, separate and recognize the signal through ELM classifier, and synthesize speech. The recognition experiments and performance tests of the system model show that the speech separation recognition of the system is feasible, and the recognition accuracy for English is as high as 88.9%, which is higher than that of the reference SVM classifier and DBN classifier.
Credit Card Fraud

This chapter provides a single person case study of Mr. Dan DeFilippi who was arrested for credit card fraud by the US Secret Service in December 2004. The chapter delves into the psychology of a cybercriminal and the inner workings of credit card fraud. A background context of credit card fraud is presented to frame the primary interview. A section on the identification of issues and controversies with respect to carding is then given. Finally, recommendations are made by the convicted cybercriminal turned key informant on how to decrease the rising incidence of cybercrime. A major finding is that credit card fraud is all too easy to enact and merchants need to conduct better staff training to catch fraudsters early. With increases in global online purchasing, international carding networks are proliferating, making it difficult for law enforcement agencies to be “policing” unauthorized transactions. Big data may well have a role to play in analyzing behaviors that expose cybercrime.
Petrochemistry of kimberlitic rocks and associated xenoliths of south-eastern Australia

Pourteen widely-separated areas of kimberlitic rocks have recently been discovered in south-eastern Australia in the States of South Australia, Tasmania, Victoria, and New South Wales (Pig.1).(Locality details, struct¬ ural relations and ages are given by Stracke et al., this volume).One of the occurrences exhibits an intimate spatial association with carbonatite.
Literature Review

As introduced in Chap. 1 , our main research tasks are to understand when customers purchase products, detect customer behaviour patterns, identify different types of customers and compare their responses to factors such as promotions and other marketing strategies. Therefore, we review related work from the following specific areas: 1) temporal modelling of customer behaviour; 2) customer segmentation; 3) factors impacting customer behaviour.