Reviewerly/exHarmony huggingface dataset:
On the Design and Engineering of a Zero Trust Security Artefact

Adequately informing the board of directors about operational security effectiveness is cumbersome. The concept of Zero Trust (ZT) approaches information and cybersecurity from the perspective of the asset, or sets of assets, to be protected, and from the value that it represents. Zero Trust has been around for quite some time. This paper continues on the authors previous research work on the examination of Zero Trust approaches, what is lacking in terms of operationalisation and which elements need to be addressed in future implementations and why and how this requires empirical validation. In the first part of the paper, we summarise the limitations in the state of the art approaches and how these are addressed in the Zero Trust Framework developed by ON2IT ‘Zero Trust Innovators’. Then we describe the design and engineering of a Zero Trust artefact (dashboard) that addresses the problems at hand, according to Design Science Research (DSR). The last part of this paper outlines the setup of an empirical validation trough practitioner-oriented research, in order to gain a better implementation of Zero Trust strategies. And how this validation was conducted in 2020 with 73 security practitioners. The final result is a proposed framework and associated technology which, via Zero Trust principles, addresses multiple layers of the organization to grasp and align cybersecurity risks and understand the readiness and fitness of the organization and its measures to counter cybersecurity risks.
A comprehensive investigation of knowledge discovered from historical operational data of a typical building energy system

Association rule mining has been widely utilized for extracting hidden operation patterns from huge volumes of building operational data. It usually generates a large number of association rules. However, most of them cannot reveal useful patterns. It is still unclear about how to develop effective methods to extract useful association rules. The major barrier is that characteristics of association rules mined from building operational data are still unknown. To reveal the characteristics, this study analyzes 101,787 association rules mined from the historical operational data of a typical chiller plant. The association rules are classified into useful ones and useless ones based on domain knowledge. Three insights are revealed successfully. Firstly, the useful association rules are related to component/sensor faults, control strategies, abnormal operation patterns, and normal operation patterns. And the useless association rules result from lacking physical meanings, insufficient information, and transient operation patterns. Secondly, the useful association rules only account for a very small proportion (4.64%) of all the association rules. Thirdly, three widely-used statistic indexes, i.e., support, confidence and lift, are ineffective in distinguishing between the useful and useless association rules actually in this field. Based on the three insights, five future research directions are proposed.
Mitigating Financial Fraud Using Data Science - “A Case Study on Credit Card Frauds”

In the early years of fast and active development of 80s and 90s, financial frauds happened to be pretty simple. They were not more than duplicity of forged cheques, draining peoples or investors' money through fake company formations, and minutest scrutinization of the loan documents etc. These were the only means for the fraudsters to play around. It is only after the advancement and adoption of desktop culture we have witnessed the whole new age of cybercrime and digital frauds. Investors, retailers, businesses and corporates none were spared and were hard hit. Since then, financial frauds have become intimidating for businesses and especially for the banks across the globe. With the advent and continuous advancement of technology it has further complicated the ways and means for the fraudsters ending up into catastrophic consequences. As data is growing many related connected challenge are also increasing. With the help of data science various aspect of data can be analyzed, various pattern of accessing the data can be understood, which can eventually help to understand the probability of risk associated with various pattern of storing / accessing / retrieving the data. This paper also presents the an analysis on open source dataset, taken from Kaggel, for the data analysis by using logistic regression, and the results of which are measure with confusion matrix, which provides more clear understand of the dataset.
Automated Sign Language Interpreter Using Data Gloves

Communication plays a very important role in the everyday life of human beings. Keeping this in mind, this project aims to aid the deaf and the dumb in communicating with others. Sign language is used for this form of communication and it is an incredible advancement that has grown over the years. This project helps in improving the communication with the deaf and the dumb using flex sensor technology. We convert the sign language so that it is understood by common people and will help them to communicate without any barriers. The main purpose of this project is to eliminate the barrier between the deaf and dumb and the others using an automated system to recognize the hand gestures and interpret them appropriately. The system uses a combination of sensors fixed onto a glove to capture various sign gestures. The sensor values for these signs are then recorded by the Arduino and processed using K nearest neighbor machine learning algorithm. There are many challenges that come with the usage of the sign language. Not everyone knows how to interpret the sign language. Hence, we use a sensor based glove to overcome this issue. Currently, the system is able to successfully classify 21 letters and basic phrases with an accuracy of 93% for the chosen value of K in the nearest neighbor algorithm.
Compressor Fault Diagnosis Knowledge: A Benchmark Dataset for Knowledge Extraction From Maintenance Log Sheets Based on Sequence Labeling

Compressor fault diagnosis requires expert knowledge. Using the sequence labeling technology, this expert knowledge can be automatically extracted from compressor maintenance log sheets. Previous studies indicate that sequence labeling methods often need a substantial amount of annotation data for knowledge extraction, Unfortunately, the annotation data are very scarce in the field of compressor fault diagnosis. In this paper, we introduce a benchmark dataset for extraction of knowledge suitable for air compressor fault diagnosis. First, we collected 11,418 pieces of information from air compressor maintenance log sheets. Fault description, service requests, causes and troubleshooting solutions were stored in a dataset for data preprocessing and masking. In addition, 6196 valid text pairs were developed after the “noises” in the raw dataset were cleaned. Second, five kinds of entities and sequences, such as equipment, faults, service requests, causes and troubleshooting solutions, were annotated by three subject experts. The annotation consistency was assessed with F1 scores. Furthermore, our proposed baseline model (or the BERT-BI-LSTM-CRF model) was compared against other five sequence labeling models (BI-LSTM-CRF, Lattice LSTM, BERT NER, ZEN, and ERNIE). The BERT-BI-LSTM-CRF model gives superior performance in extracting expert knowledge from the subject dataset. Although the baseline model is not the most cutting-edge model in the sequence labeling and named entity recognition fields, it indeed presents a great potential for compressor fault diagnosis. The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chentao1999/CFDK</uri> .
Robot path planning based on improved sparrow algorithm

Abstract Robot path planning is currently a research hotspot. To overcome the shortcomings of the traditional raster method, an improved sparrow search algorithm is proposed for path planning in the raster. On the basis of the new sparrow search algorithm, the K-medoids clustering method is introduced to classify the population dynamically and accelerate the information exchange among the population. Then, an adaptive weight factor is introduced to make the algorithm search more detailed and extensive, which enhances the optimization ability of the algorithm. The optimized routes of each algorithm on the raster graph show that the improved sparrow search algorithm can plan a simpler route with the least cost.
Blockchain: What Does It Mean to Industrial Electronics?: Technologies, Challenges, and Opportunities

Imagine you want to send money to a friend overseas. Wouldn’t it be good if you didn’t have to pay hefty fees to the intermediaries, and your friend received the funds very quickly? Now imagine ordering parts to make a product in your manufacturing plant. Wouldn’t it be great if you were able to verify where each part comes from and have access to a reliable certificate on its quality automatically? Also think about dealing with energy use or selling off your excess solar energy as a prosumer. Wouldn’t it be nice if you could purchase cheaper energy or sell it profitably at ease?
Fake-image detection with Robust Hashing

In this paper, we investigate whether robust hashing has a possibility to robustly detect fake-images even when multiple manipulation techniques such as JPEG compression are applied to images for the first time. In an experiment, the proposed fake detection with robust hashing is demonstrated to outperform state-of-the-art one under the use of various datasets including fake images generated with GANs.
Review Comments of Manipuri Online Video: Good, Bad or Ugly

Sentiment analysis is a novel approach towards analyzing the sentiment of a given text or document. In particular, the sentiment analysis of short texts such as single sentences and social media tweets and comments are one of the main challenges of natural language processing (NLP) given the limited contextual information they usually convey. In recent years, deep learning application has shown promising result in natural language processing. This paper presents the comparative analysis of various deep learning, traditional machine learning and lexicon based methodologies of sentiment analysis. We present the importance of pre-processing and feature engineering for sentiment analysis on a resource constrained data-set of Manipuri comments gathered from social media platform using these approaches.
Post COVID-19 Twitter user’s Emotions Classification using Deep Learning Techniques in India

Social Media platforms contain a huge data collection of shared and personal thinking with respect to a wide assortment of subjects, communicated and spread ceaselessly by their users. Among those platforms, Twitter is gaining immense popularity. This research work proposes a system to computationally measure the emotions of live tweets by their users and emotions regarding the government's decision on extending the lockdown. The system consists of dashboard with various functionalities. Main dashboard has country-wise data visualization of the emotions derived from the tweets, it has clickable map of India which shows state-wise data visualization as well. Live emotion prediction of tweets is achieved using Deep Learning tools. Tweet fetching is dynamic to get up-to-date data automatically. Resources tab is available for COVID-19 related statistics and news. 250 plus days since the initial stage COVID-19 case in the World, and 180+ days into the most punctual Lockdown Order of India, how is the people thinking in circumstances such as these? The Corona Virus imperils our physical wellbeing to be sure, however close by, social separating additionally represents a danger to our enthusiastic steadiness. Accordingly, it is vital to comprehend public emotions under COVID-19.
Fast Training of Adversarial Deep Fuzzy Classifier by Downsizing Fuzzy Rules With Gradient Guided Learning

While our recent deep fuzzy classifier DSA-FC, which stacks adversarial interpretable Takagi–Sugeno–Kang fuzzy subclassifiers, shares its promising classification, its training speed will become very slow and even intolerable for large-scale datasets, due to successive training on all training samples with their random gradient based updates along each layer of its stacked structure. In order to circumvent this bottleneck issue, a fast training algorithm FTA is developed in this study by downsizing fuzzy rules with the proposed gradient guided learning for each subclassifier at each layer of DSA-FC on large-scale datasets. The core of FTA is to assure fast training of each subclassifier at each layer of DSA-FC, which first generates first-order smooth gradient guided information by means of the proposed top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> fuzzy rules selected from all fuzzy rules in each subclassifier, and then quickly updates the current inputs in terms of such information, which will be taken as the inputs of the subclassifier at the next layer. Our theoretical analysis reveals that the proposed gradient guided learning indeed enhances the generalization capability of a deep fuzzy classifier with or without adversarial attacks on outputs. Experimental results on large datasets demonstrate that FTA indeed trains the deep fuzzy classifier DSA-FC quickly with enhanced generalization capability.
P2HBT: Partially Policy Hidden E‐Healthcare System with Black‐Box Traceability

Electronic health record (EHR), as the core of the e-healthcare system, is an electronic version of patient medical history, which records personal health-related information. EHR embodies the value of disease monitoring through large-scale sharing via the Cloud service provider (CSP). However, the health data-centric feature makes EHR more preferable to the adversaries compared with other outsourcing data. Moreover, there may even be malicious users who deliberately leak their access privileges for benefits. An e-healthcare system with a black-box traceable and robust data security mechanism is presented for the first time. Specifically, we propose an effective P2HBT, which can perform fine-grained access control on encrypted EHRs, prevent the leakage of privacy contained in access policies, and support tracing of traitors. Under the standard model, the scheme is proved fully secure. Performance analysis demonstrates that P2HBT can achieve the design goals and outperform existing schemes in terms of storage and computation overhead.
SDGchain: When Service Dependency Graph Meets Blockchain to Enhance Privacy

Nowadays, the number of services is increasing which allows users to perform their tasks easily. A huge amount of data is published regularly where the personal data takes the lion's share. In fact, personal data protection remains a big issue as far as how the service provider collected the data and for what purpose. Therefore, the privacy of the data owner has to be preserved by putting a strategy to control the data usage by the services. The concept of inter services privacy has been marginalized in the previous privacy preserving works. This paper proposes SDGchain, a secure and privacy-preserving decentralized model that uses secure service dependency graph (SDG) combined with permissioned blockchain Hyperledger fabric, which cooperates with an off-chain storage. In our design the SDG is used to control inter services interactions by building dependencies, measuring the level of trust, calculating the quality of service to ensure the privacy preservation of the service using blockchain to support authentication, access control and the logging operations for immutable and auditable history.
Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its modified search equation that depends on solutions of mean previous and global best is used. Furthermore, to solve the main issues of FCM, Automatic clustering algorithm was proposed based on the mean artificial bee colony called (AC-MeanABC). It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value. A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC. The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.
Social Media Content Categorization Using Supervised Based Machine Learning Methods and Natural Language Processing in Bangla Language

Social media has acquired the primary platform for people to connect. Millions of posts generate from social media consistently. The people of Bangladesh are habitually comfortable sharing their opinion on social media in the Bangla language. It is often arduous to place them in distinct categories relying on textual information. Classifying social media posts are challenging. It tends to be complicated to scrutinize when scripted in Bangla language. Our aspiration is to categorize these opinions from social platforms to enable searching, filtering, and organizing based on post sentiment. We employed the Sentiment Analysis to interpret the persuasion of the posts. We introduced a model that will classify the Bangla posts in several categories by using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, Logistic regression algorithms. We adopted the algorithm that provides the most reliable performance to classify the social media post with quite proficient in Bangla Language.
Optical Encryption Based on Computer Generated Holograms in Photopolymer

An optical encryption method based on computer generated holograms printing of photopolymer is presented. Fraunhofer diffraction is performed based on the Gerchberg-Saxton algorithm, and a hologram of the Advanced Encryption Standard encrypted Quick Response code is generated to record the ciphertext. The holograms of the key and the three-dimensional image are generated by the angular spectrum diffraction algorithm. The experimental results show that large-size encrypted Quick Response (QR) code and miniature keys can be printed in photopolymers, which has good application prospects in optical encryption. This method has the advantages of high-density storage, high speed, large fault tolerance, and anti-peeping.
Cryptocurrency bubble risk and the FOMC announcements during COVID-19 black swan event

Purpose After the COVID-19 outbreak, the Federal Reserve has undertaken several monetary policies to alleviate the pandemic consequences on the markets. This paper aims to evaluate the effects of the Federal Reserve monetary policy on the cryptocurrency dynamics during the COVID19 pandemic. Design/methodology/approach We examine the response and feedback effects via an event study methodology. For this purpose, abnormal returns (AR) and cumulative abnormal returns (CARs) around the first FOMC (Federal Open Market Committee) announcement related to the COVID-19 pandemic for the top five cryptocurrencies are explored. We, further investigate the effect of the eight FOMC statement announcements during the COVID19 pandemic on these cryptocurrencies (Bitcoin, Ethereum, Tether, Litecoin, and Ripple). In the above-mentioned crypto-currency markets, we investigate the presence of bubbles by using the PSY test. We then examine the concordance of the dates of these bubbles with the dates of the FOMC announcements. Findings The empirical results show that the first FOMC event has a negative significant effect after 4 days of the announcement date for all studied cryptocurrencies except Tether. The results also indicate that cumulative abnormal returns are significant during the event windows of (−3,8), (−3,9), and (−3,10). Besides, we find that Bitcoin, Ethereum and, Litecoin lived short bubbles lasting for a few days. However, Ripple and Tether markets present no bubbles and no explosive periods. Research limitations/implications This paper presents trained proof that FOMC announcements have a positive effect on volatility's predictive capacity. This work therefore promotes the study of the data quality of volatility in future research as well. Practical implications The justified effect of the FOMC announcements on cryptocurrency as a speculative asset has practical implications for investors in building their trading strategies in anticipation of the next FOMC announcement. Therefore, this study implies that the FOMC announcements contain very relevant information for investors in the cryptocurrency market. This research may not only encourage a better understanding of the evolution of the expectations of policymakers, but also facilitate a better understanding of how these expectations are developed. Originality/value The COVID-19 pandemic has disturbed the stability of financial markets, inciting the Fed to take some monetary regulations. To the best of our knowledge, this study is the first one that analyses the response of five major cryptocurrencies to FOMC announcements during COVID 19 pandemic and associates these dates with bubble occurrences.
Improving paragraph-level question generation with extended answer network and uncertainty-aware beam search

Question Generation (QG), which aims to generate a question given the relevant context, is essential to build conversational and question–answering systems. Existing neural question generation models suffer from the inadequate representation of the target answer and inappropriate techniques to reduce repetition. To address these issues, we propose an Extended Answer-aware Network (EAN) which is trained with Word-based Coverage Mechanism (WCM) and decoded with Uncertainty-aware Beam Search (UBS). The EAN represents the target answer by its surrounding sentence with an encoder and incorporates the extended answer to paragraph representation using gated paragraph-to-answer attention to tackle the problem of the inadequate representation of the target answer. To reduce undesirable repetition, the WCM penalizes repeatedly attending to the same words of different time-steps in the training stage. The UBS incorporates an uncertainty score into beam search to alleviate text degeneration and reduce repeated copying words of the paragraph. Experiments on two benchmark datasets demonstrate the effectiveness of our methods of paragraph-level question generation. Specifically, our model has achieved 4.2% and 27.2% improvement over BLEU-4 compared to the best paragraph-level QG baseline in SQuAD and NewsQA datasets respectively.