Reviewerly/exHarmony huggingface dataset:
Designing an optimized confidential-data management system using preeminent access-control and block-chain

Purpose The blockchain system is required for coordinating and managing the information across the organizations. The internal assurance of the information system is under threat and requires a defined and arbitrary system for protective information sharing. The primary analysis of defining and providing the access to the data or information is proposed in this article. The major challenge faced by organizations is providing and maintaining security to the cumulated data infrastructure, protecting the confidential data flow within the infrastructure and to ascertain ethical operations within the organization. Design/methodology/approach In this paper, a top-down approach is utilized to solve the issues faced during confidential data storage, track and alert internal malignant access. The web content management system (WCMS) market is growing and the challenging desideratum of solving security issues are incrementing. This research proposes a top-down security implementation methodology which alters the access control of the organization running the WCMS, it is a highly secure access-control with activity tracker that records who did what activity and who is responsible for malignant access tracker. Findings The upgraded access control system is implemented utilizing the most influential blockchain technology where the hash engendered by the Inter Planetary File System (IPFS) cloud is stored on the block to implement a confidential data tracker; a supplemental level of security is integrated to the admin by verifying its account ID by storing it on to the astute contracts and applying two-step authentication. A caliber of security is integrated during storage by integrating RHS encryption. Research limitations/implications This system is proposed for the confidential student data storage (for e.g. educational documents, marks cards etc.) and tracking maleficent access. Originality/value The upgraded access control system is implemented using the most influential blockchain technology where the hash generated by the IPFS cloud is stored on the block to implement a confidential data tracker, an additional level of security is added to the admin by verifying its account ID by storing it on to the smart contracts and applying two-step authentication.
DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes

Clinical named entity recognition (NER) is an essential building block for many downstream natural language processing (NLP) applications such as information extraction and de-identification. Recently, deep learning (DL) methods that utilize word embeddings have become popular in clinical NLP tasks. However, there has been little work on evaluating and combining the word embeddings trained from different domains. The goal of this study is to improve the performance of NER in clinical discharge summaries by developing a DL model that combines different embeddings and investigate the combination of standard and contextual embeddings from the general and clinical domains. We developed: 1) A human-annotated high-quality internal corpus with discharge summaries and 2) A NER model with an input embedding layer that combines different embeddings: standard word embeddings, context-based word embeddings, a character-level word embedding using a convolutional neural network (CNN), and an external knowledge sources along with word features as one-hot vectors. Embedding was followed by bidirectional long short-term memory (Bi-LSTM) and conditional random field (CRF) layers. The proposed model reaches or overcomes state-of-the-art performance on two publicly available data sets and an F1 score of 94.31% on an internal corpus. After incorporating mixed-domain clinically pre-trained contextual embeddings, the F1 score further improved to 95.36% on the internal corpus. This study demonstrated an efficient way of combining different embeddings that will improve the recognition performance aiding the downstream de-identification of clinical notes.
Random Forest Regression to Predict Catalyst Deactivation in Industrial Catalytic Process

Catalyst deactivation has become a great concern in an industry with heterogenous catalystbased production. An accurate model to predict catalyst performance is needed to optimize the maintenance schedule, avoid an unplanned shutdown, and ensure reliable operation. This research work applies a machine learning model to predict catalyst deactivation based on actual data from relevant multitube-reactor sensors. The product conversion is a crucial indicator of the catalyst performance degradation over time. Random forest regression (RFR) algorithm is chosen to construct the model. Hyperparameter tuning is applied and shows improvement over the default model. The result showed that the RFR model could predict the conversion as a time series function. The feature importance analysis shows the most influencing factor and facilitates the model interpretation.
Chapter 4: Traveling Waves

PreviousNext No AccessBasic Wave AnalysisChapter 4: Traveling Waveshttps://doi.org/10.1190/1.9781560803737.ch4 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Waves can be classified as either standing waves or traveling waves. A standing wave, also known as a stationary wave, is a wave that remains in a constant position. A traveling wave is not confined to a given space in the medium, but propagates through the medium. The waves on a string of a musical instrument are examples of standing waves. Such waves are the result of interference of traveling waves propagating in opposite directions. Permalink: https://doi.org/10.1190/1.9781560803737.ch4FiguresReferencesRelatedDetails Basic Wave AnalysisISBN (print):978-1-56080-372-0ISBN (online):978-1-56080-373-7Copyright: 2020 Pages: 406 publication data© 2020 Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transcribed in any form or by any means, electronic or mechanical, including photocopying and recording, without prior written permission of the publisher.Publisher:Society of Exploration Geophysicists HistoryPublished in print: 24 Aug 2020 CITATION INFORMATION (2020), "Chapter 4: Traveling Waves," Geophysical Monograph Series : 121-137. https://doi.org/10.1190/1.9781560803737.ch4 Plain-Language Summary PDF DownloadLoading ...
An improvement of the AES protocol to optimize energy consumption in IoT

This paper focuses on the security of low-resource connected objects. In order to ensure the security of communications with these connected objects, most cryptographic protocols use lightweight encryption algorithms adapted to the context of low-resource equipment. This article first presents the most widely used lightweight encryption algorithms, then analyzes lightweight AES block ciphers such as LAES, and finally proposes a new lightweight encryption algorithm derived from AES called R-LAES. The R-LAES algorithm optimizes energy consumption while improving security level compared to the LAES algorithm.
Smart Contract Based Next-Generation Public Key Infrastructure (PKI) Using Permissionless Blockchain

The traditional Public Key Infrastructure (PKI) solutions are centralized and non-transparent schemes which struggle to combat the current era of distributed applications. One of the major challenging issues of the trusted third party is the issuance of rouge certificates. So the centralized certification authorities can’t be trusted as they may be compromised by disrupting the TLS method for various hosts. In this paper, we have proposed a distributed and transparent PKI system that uses smart contracts while employing a web-of-trust model and runs on the Ethereum blockchain. This work is inspired by the SCPKI system proposed by Mustafa Al-Bassam, which is a Smart Contract-based PKI and Identity System. Our scheme can detect authority certificates after they are printed. We have presented the working prototype of proposed scheme on permissionless ethereum blockchain, with detailed implementation of smart contracts.
Multi-scale local-global architecture for person re-identification

Abstract With the emergence of deep learning method, which has been driven a great success for the field of person re-identification (re-ID). However, the existing works mainly focus on first-order attention (i.e., spatial and channels attention) statistics to model the valuable information for person re-ID. On the other hand, most existing methods operate data points respectively, which ignores discriminative patterns to some extent. In this paper, we present an automated framework named multi-scale local-global for person re-ID. The framework consists of two components. The first component is that a high-order attention module is adopted to learn high-order attention patterns to model the subtle differences among pedestrians and to generate the informative attention features. On the other hand, a novel architecture named spectral feature transformation is designed to make for the optimization of group wise similarities. Furthermore, we fuse the components together to form an ensemble model for person re-ID. Extensive experiments were conducted on the three benchmark datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, showing the superiority of the proposed method.
An Enhanced Hybrid Watermarking Method and Imaging System for Securing Medical Images

Medical image processing typically deals with the exploration of several medical image datasets for attaining an effective solution in diagnosing the affected patients. Medical image of the patients are typically stored in digital form as Electronic patient’s record (EPR), which must be dealt with utmost security and confidentiality, as the patient’s data are linked with external open platforms for future diagnosis. Medical image watermarking and encryption schemes assist in meeting the above requirements in effectively securing the patient’s image data. The ultimate objective of this research inclines towards securing medical images so as to achieve maximum effectiveness over health related areas. In this paper, an enhanced hybrid medical image watermarking and equivalent encryption strategy is typically investigated for attaining an effective solution towards medical image processing. The proposed methodology works with the integration of image watermarking algorithm together with an encryption algorithm. Image watermarking is achieved by a system based on Redundant discrete wavelet transform and Singular value decomposition. Moreover, by utilizing the property of chaotic signals for improving the integrity, a hybrid medical image watermarking technique is proposed by upgrading the Arnold cat map (ACM) with Logistic map. For image encryption, Symmetric block encryption algorithm based on Feistel structure is proposed. The efficiency of the proposed strategy is estimated in terms of Peak signal to noise ratio (PSNR), Mean square error (MSE) and Correlation coefficient (CC).
Yonder: A Python Package for Data Denoising and Reconstruction

We present a standalone implementation of a data-deconvolution method based on singular value decomposition. The tool is written in python and packaged in the open-source yonder package. yonder receives as input two matrices, one for the data and another for the errors, and outputs a denoised version of the original dataset. In this Research Note, we briefly describe the methodology and show a demonstration of the yonder on a simulated dataset.
Yet Another Wearable LED Matrix Sign System for Campus Guiding

Yet another wearable LED Matrix sign system, teleport dresser, is presented. We have shown a wearable LED Matrix sign system, sign system 2015, which shows tweets of Twitter, in SIGUCCS 2015. The sign system 2015 could not change the color of the sign. The sign system could not display pictures and animations either. The teleport dresser can show color pictures and color animation. The contents can be controlled by a script on a wiki page on the internet. Superimposing of letters on a picture or an animation is also possible.
A Cybersecurity Culture Framework for Grassroots Levels in Zimbabwe

Cybersecurity is a combination of technologies, processes and operations that are designed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access1.The research was purposed to develop a cybersecurity culture framework which ensures that grassroot users of cyberspace are secured from cyber threats. Literature review showed that in Zimbabwe, no research had attempted to come up with a cybersecurity culture framework for grassroot users of cyberspace.The research was guided by the interpretivist paradigm and employed a qualitative methodology. A descriptive research design was used to answer the research questions and unstructured interviews were done to ascertain the cybersecurity needs and challenges of grassroot users of cyberspace. A cybersecurity culture framework was then crafted based on the research findings. The researchers recommended that Zimbabwe should have a cybersecurity vision and strategy that cascades to the grassroot users of cyberspace. Furthermore, the education curricula should be revised so that it incorporates cybersecurity courses at primary and secondary school level .This will then ensure that ICT adoption is matched with cyber hygiene and responsible use of cyberspace.
A Random Irregular Blocking Image Encryption Algorithm Based on Improved Digital Chaotic Maps at Bit Level

To improve the security level of image encryption based on chaos theory, a novel bit-level transform method which could enhance the dynamical performance of chaotic maps is proposed and a new image encryption algorithm based on a random irregular blocking method is also designed in this paper. Firstly, in the bit-level transform method, three chaotic maps are combined to generate a novel chaotic sequence. From a series of performance analyses results, the generated sequence not only successfully passes NIST tests, but also performs better than the original sequence, with a larger chaotic parametrial range, high complexity, etc. Then, an image encryption algorithm based on the newly generated chaotic sequence is proposed, in which a random irregular blocking method is adopted. Random irregular blocking could solve the problem of insecurity in the fixed blocking method. With a color Lena image as an encryption example, we analyze the security of the encryption algorithm by conducting a sufficiently large number of experiments using the encrypted image. For the encrypted Lena image, the average values of information entropy, NPCR, and UACI are 7.9931, 0.9961, and 0.3352, respectively. The experimental results and security analyses results provide evidence that the proposed encryption algorithm has good performance and can resist various typical attacks.
Stock Market Prediction Using Support Vector Regressor with Radial Basis Function Kernel

Currently, one of the most popular types of investment by the Indonesian people is stocks. This is evidenced by data from the Indonesia Stock Exchange, until the end of 2020, the number of capital market investors increased 56% from 2019 to 3.88 million investors. The purpose of this study is to increase public interest, especially the millennial generation, to invest in stocks, to increase profits from investing in stocks, and to reduce suicides due to losses from investing in stocks. In addition, the method used in this study is the Support Vector Regressor. From this research, the results of the Support Vector Regressor Train results for Root Mean Square Error are 179.17, R2 are 1.0, and Mean Absolute Error are 1.48 while the Support Vector Regressor Test results for Root Mean Square Error are 228.79, R2 are 0.99, and Mean Absolute Error are 13.80. Therefore, the Support Vector Regressor model is suitable for predicting stock prices.
Micro hand gesture recognition system using hybrid dilated convolution

Gesture recognition is the latest human-computer interaction (HCI) technology, which allows users to naturally control electronic devices through the movement of fingers and palms without operating redundant devices. Radar gesture recognition technology offers significant advantages in terms of privacy and security, device reliability and design flexibility. In this paper, a model GestureNet suitable for radar gesture recognition is designed by using the smooth pseudo Wigner Ville processing of millimeter wave radar gesture echo and the knowledge of hybrid zero convolution neural network in deep learning. The results show that the recognition accuracy of the validation set of GestureNet reached 97.35% and the recognition accuracy of the test set reached 91.75%, indicating that the model has good generalisation ability, thus providing a strong guarantee for radar gesture recognition.
Manifold semi-supervised learning for aluminum electrolysis temperature identification based on regularized hierarchical extreme learning machine

The aim of this article is to develop a soft approach for a real-time cell temperature prediction in the aluminum electrolysis reduction. Under the limited labeled data constraint, Laplacian semi-supervised learning methods, which can fully utilize the underlying structure of the data distribution and further extract information contained in all available data, has recently received extensive attention in the field of soft sensor modeling. Since the Laplacian underlying manifold is a constant, it remains a challenging task to improve the extrapolating ability for the case that only a few labeled samples are available. This study presents a soft modeling method based on a semi-supervised deep learning structure, which was developed from the hierarchical autoencoders with extreme learning machine. Furthermore, a Laplacian–Hessian semi-supervised extreme learning machine is built to learn all the geometric distribution information. The Laplacian–Hessian semi-supervised extreme learning machine method is applied to estimate the cell temperature in an aluminum reduction process. The experimental results demonstrate the performance and robustness of the proposed algorithm are superior to those of the existing state-of-the-art methods.
Anonymous Traceability Protocol Based on Group Signature for Blockchain

In blockchain technology, data are stored on decentralized nodes and public to each node in the blockchain network. Hence, the focus of privacy protection in the blockchain guarantees the anonymity of transactions such that attackers cannot attain the users’ personal information through data analysis. Among the existing privacy protection technologies, the scheme based on group signature has good anonymity, but the existing scheme exists a large number of operations that are difficult to apply to wireless terminals. In this paper, using the powerful offloading capability of edge computing, we propose a blockchain node traceable identity privacy technology scheme based on threshold group signature, and the scheme greatly reduces the computing burden of nodes while achieving node privacy protection.
Enhancements to the Deep Learning Signal Detection Model in Non-Orthogonal Multiple Access Receivers and Noisy Channels

This paper presents an enhanced deep learning-based Non-Orthogonal Multiple Access (NOMA) receiver that can mainly be used in low signal-to-noise channels. We show how a better dataset generation strategy for training Deep Learning (DL) could result in better generalization capabilities. Then, we apply hyperparameter tuning using exhaustive search to optimize the DL network. A Long-Short-Term-Memory (LSTM) DL architecture is used. The results show superior Symbol Error Rate vs Signal-to-Noise Ratio performance compared to the state-of-the-art methods such as Maximum Likelihood, Minimum Mean Square Error, and Successive Interface Cancellation, even though the network is only half as complex as previously proposed DL networks in the literature.
An Empirical Evaluation of Network Representation Learning Methods

Network representation learning methods map network nodes to vectors in an embedding space that can preserve specific properties and enable traditional downstream prediction tasks. The quality of the representations learned is then generally showcased through results on these downstream tasks. Commonly used benchmark tasks such as link prediction or network reconstruction, however, present complex evaluation pipelines and an abundance of design choices. This, together with a lack of standardized evaluation setups, can obscure the real progress in the field. In this article, we aim at investigating the impact on the performance of a variety of such design choices and perform an extensive and consistent evaluation that can shed light on the state-of-the-art on network representation learning. Our evaluation reveals that only limited progress has been made in recent years, with embedding-based approaches struggling to outperform basic heuristics in many scenarios.
Blockchain Interoperability Method for the Cross-organizational Transactions: A Meta-Meta-Model

Nowadays, blockchain systems are attracting attention in both academic and industry fields. Their main advantages are to their provided capabilities in security, and immutability. However, these systems are still immature growth, as there is a lack of templates for blockchain development. Interoperability can be considered one of the challenges while developing new blockchain systems, due to the heterogeneity of the blockchain systems infrastructure, consensus protocol, privacy level, etc. Although many organizations aim to utilize blockchain systems, they suffer from the limitations of running cross-organizational transactions over different blockchain systems. The current cross-organizational solutions depend on how to make a connection between the different organizations’ applications to reach choreography. In this paper, we will address the current interoperability challenges in blockchain systems and their impact on the running of cross-organizational transactions. Moreover, we will illustrate the usage of Model-Driven Engineering (MDE) to model the different aspects of blockchain systems. Finally, we will present an MDE-based solution to reach organizational interoperability as a basis for modeling hybrid applications that can process cross-organizational transactions.
Executive summary

E-commerce presents an important opportunity to address some of the major challenges faced by the Jordanian economy and to become a driver of sustainable economic growth. Jordan has experienced an economic slowdown in recent years, with GDP growth dropping from an average of 5.1 per cent in 2000-2015 to 2.0 per cent in 2016-2019. In 2020, the global COVID-19 pandemic had negative consequences for the Jordanian economy, albeit to a lesser degree than in neighboring economies, with GDP falling by 1.6 per cent.1 The economy is gradually returning to a growth path, with an expected 1.9 per cent increase in GDP in 2021.