Reviewerly/exHarmony huggingface dataset:
Assessing Cybersecurity Resilience of Distributed Ledger Technology in Energy Sector Using the MITRE ATT&amp;CK<sup>®</sup> ICS Framework

Digitization in the power industry enables wide connectivity among multiple new entrants such as DERs, prosumers, and P2P counterparts within or outside the Distributed Ledger Technology (DLT). The use of DLT to improve resilience in the power grid has growing support, but new technology provides new opportunities for adversaries to cause harm. This work completed by the Cybersecurity- focused task force of IEEE SA P2418.5 evaluates the potential risks by applying the MITRE ATT&CK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> ICS matrix to the DLT Engineering and Cybersecurity Stack designed for power systems applications.
HFabD+M: A Web-based Platform for Automated Hyperledger Fabric Deployment and Management

Hyperledger Fabric is an open-source private permissioned blockchain that supports the use of smart contracts (chaincode). It is aimed mainly at private networks of companies. To serve the different needs of each company and to be flexible in customer requirements, it consists of various adaptive components. Although this structure efficiently addresses a wide range of needs, deploying such a network for research purposes or rapid development is complex. In this paper, we present a web-based system architecture for the automated deployment of a Hyperledger Fabric network, and in addition, we describe the tools needed to manage and update such a network. Finally, as a proof-of-concept, we implement the proposed architecture to demonstrate the feasibility of our approach.
Web3 for sensitive data, enterprise, government, private, and permissioned use

Web3 represents an evolution of the centralized and siloed topology of Web2 by focusing on more connected, decentralized, and open technology to democratize data and services between databases, tools, devices, people, teams and organizations to provide secure single sources of truth on decentralized trust layers. However, sensitive data, such as intellectual property, classified information, personal data, or medical records, cannot be distributed to those without explicit access. A standard solution is to encrypt the data before uploading and then distribute the decryption key to select persons. This solution does not sufficiently protect against data leakage. In addition, legal, contractual, and GDPR requirements are not met in current Web3 systems. This study suggests critical requirements for any Web3 system that targets sensitive data, private and permissioned needs, and enterprise and governmental applications. A preliminary framework is then introduced for building compliant Web3 systems to the suggested requirements. The study proposes changes to the Inter Planetary File System (IPFS) protocol, with the main change being that each request checks against a ledger if the requestor has access to the data. Two novel Ethereum smart contracts, Enterprise Architecture and Smart Legal Contract, are presented to meet the suggested requirements.
IPv4 and IPv6 for Blockchain Networks: a Comparative Analysis

Blockchain technology enables new possibilities for the peer-to-peer exchange of both monetary value and information. A key challenge when designing high-performance blockchain networks is to ensure their scalability and capacity to support many concurrent users and applications. Blockchain networks are typically implemented on top of the TCP/IP stack, offering a choice between the IPv4 and IPv6 protocols. While most blockchain networks have not achieved significant scale, recent improvements in blockchain implementations offer increasing levels of transaction throughput. The improving capacity of such blockchains warrants an examination of the relative applicability of IPv4 and IPv6 in supporting scalable blockchain infrastructure.In this paper, we provide a comprehensive comparative analysis of IPv4 and IPv6 with respect to the requirements and properties of high-performance blockchains. We find that IPv6 is more suitable for such networks regarding privacy, security, and scalability. Moreover we outline how IPv6 offers additional functionalities over IPv4 by supporting diverse peer-to-peer payment mechanisms and advanced identity-management using cryptographically generated addresses.
Integrating Blockchain for Healthcare Group Decision Making

Group Decision-making (GDM) is very much required and practiced in healthcare, also through the healthcare decision support system (HDSS). However, it may lack transparency and trust in balancing the risk and expected decision. Also, the absence of trust can heavily undermine the decision quality. We here propose a blockchain-based solution using smart contract to facilitate the decision-making process in healthcare based on the decentralized technology. In general, in a GDM and particularly in healthcare, the opinions expressed by the expert may have some degree of risk associated with it, and we address this issue through 2-Tuple fuzzy preference representation. We propose a consensus-based GDM model that uses a 2-Tuple fuzzy preference relation for smart contract. Remix IDE is used to develop and test the smart contract code in the Ethereum blockchain environment. We demonstrate the applicability of the proposed blockchain-based solution and its security and cost analysis.
Towards a Distributed Autonomous Organisation for Financing, Governing and Disbursing Revenues of a Battery Energy Storage System

This paper presents a framework for a blockchain-based decentralised autonomous organisation that mediates the financing, governance, and revenue dispersal for a battery energy storage system fully controlled by remote token holders. In the proposed framework, the participants can buy a fractional share of an energy storage asset, embodied as a blockchain token, with corresponding rights and privileges enforced by smart contracts. The token holders continually vote to govern the battery’s operation (charge/float/discharge); these votes are autonomously aggregated by the smart contract, which is also empowered to directly dispatch the battery’s mode of operation by issuing physical control signals. Furthermore, the smart contract itself maintains a financial payment channel with the electricity market, transacting stable coins back and forth as it buys and sells energy from the spot market, per the token holders’ consensus wishes. This paper presents a case study simulation of this radical conception of asset ownership and control, whereby token holders vote based on the real-time price of electricity, their particular electricity price forecast, and the current state of charge of the battery.
A Trustless Federated Framework for Decentralized and Confidential Deep Learning

Nowadays, deep learning models can be trained on large amounts of web data on power hungry servers and be deployment-ready for specific real-world applications. With a state-of-the-art model architecture and a large publicly available dataset for pre-training, convolutional neural network models can be further fine-tuned via transfer learning for a related task. Nonetheless, the training process required of privacy-sensitive applications needs to protect data confidentiality and simultaneously boost performance using limited training data. For such data-deprived and privacy-centric learning, we introduce a trustless federated learning framework that seamlessly integrates deep learning models from different edge nodes using a blockchain-based architecture. Our framework performs federated learning without the need of a central server by leveraging a smart contract blockchain platform with a distributed file system for model storage. Users can locally train on their data while routinely benefiting from an enhanced model obtained by merging the models from all users in a decentralized fashion. Most importantly, this framework is free of the potential single point of failure in centralized federated learning. Besides, our framework has in-built incentive mechanisms to prevent model corruption and temper bad actors. We tested our framework on various computer vision datasets. The experimental results show that the merged model accuracy is on-par compared to a centralized federated training setup. To the best of our knowledge, this work represents the first systematic attempt at building a blockchain-based federated deep learning framework for computer vision. The code is publicly made available at: https://github.com/s-elo/DNN-Blockchain.
User Experience of a Conversational User Interface in a Museum

In this paper, we summarize the initial results of a field test with the ChiM (Chatbot in the Museum) system, a conversational user interface for the museum. The system contains a Natural Language Understanding (NLU) component that translates the user input into intentions and produces a multimodal (mainly spoken and textual) output. Museum visitors can use the system to freely ask questions about the exhibits in the exhibition. We conducted a field test with 140 participants in the Städel Museum, Frankfurt, and recorded over 4600 interactions between the participants and the system. After the test, participants gave their perceived feedback on the user experience (UX) and completed a custom system-specific questionnaire. We exploratively analyzed the feedback. The results show an overall medium UX for the system. We assume that the NLU component must be improved. Participants who rarely or never use audio guides rate the pragmatic quality (PQ) of the system significantly better than people who often or always use audio guides. People who rated the speech quality of the system as good also rated the attractiveness of the system significantly better than people who rated the speech quality as bad. In our future work, we will deepen the UX analysis and further put focus on recorded interaction data.
Review of Dematerialization Models and Systems in Public Procurement

Dematerialization through digital technologies offers an opportunity to modernize public administration. The manual management, the demand for more transparency of citizens with the use of open data, fighting corruption, the search for better efficiency of the process and the difficulties of archiving can be taken care of through the process of dematerialization of public order. The objective of this paper is to review the literature on recent research on models or systems in public procurement. Recently, with the emergence of Blockchain technology, exciting practical solutions are being explored. Also, with the use of smart contracts, better automation is possible. In this paper we present a comprehensive review of electronic procurement models and systems using web and centralized database technologies as well as Blockchain technology using smart contracts and cryptographic mechanisms.
Sentiment analysis of students’ Facebook comments toward university announcements

Students’ opinions are among the critical indicators to evaluate the university teaching process. However, due to the absence of an official online system in most universities that provides a mechanism for obtaining students’ opinions on several university announcements, most students use various social networks to express their feelings and provide their opinions toward these announcements. We present, through this paper, sentiment analysis of Facebook comments written in the Moroccan Arabic dialect. These comments reflect the opinions of students about university announcements during the COVID-19 pandemic, especially those related to teaching mode and ex-am planning. Then, the comments collected were cleaned, preprocessed, and manually classified into four categories, namely positive, neutral, negative, and bipolar. Further, data dimensionality reduction is applied using TF-IDF and Chi-square test. Finally, we evaluated the performance of three standard classifiers, i.e., Naïve Bayesian (NB), Support Vector Machines (SVM), and Random Forests (RF) using k-fold cross-validation. The results showed that the SVM-based classifier performs as well as the RF-based classifier regarding the classification’s accuracy and F1-score, while the NB-based classifier lags behind them.
A Survey on Lightweight Cryptography Approach For IoT Devices Security

Lightweight cryptography (LWC) has a paramount role in the context of securing IoT diverse applications, these last originate massive amount of data due to the enormous number of connected devices. The main concern of cryptographic based solutions is to hide the plain value of the exchanged data by providing confidentiality and preserving the integrity of transmissions. IoT devices employed in these environments, make applying traditional cryptography solutions complex to be utilized. An in-depth survey on lightweight cryptography has been carried out in this paper, for this reason, an evaluation of recommended LWC algorithms proposed by a variety of well-known organisations and committees has been realized.
Towards Highly-Efficient k-Nearest Neighbor Algorithm for Big Data Classification

the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a test point in a feature space. A large number of works have been developed in the literature to accelerate the speed of data classification using kNN. In parallel with these works, we present a novel K-nearest neighbor variation with neighboring calculation property, called NCP-kNN. NCP-kNN comes to solve the search complexity of kNN as well as the issue of high-dimensional classification. In fact, these two problems cause an exponentially increasing level of complexity, particularly with big datasets and multiple k values. In NCP-kNN, every test point’s distance is checked with only a limited number of training points instead of the entire dataset. Experimental results on six small datasets, show that the performance of NCP-kNN is equivalent to that of standard kNN on small and big datasets, with NCP-kNN being highly efficient. Furthermore, surprisingly, results on big datasets demonstrate that NCP-kNN is not just faster than standard kNN but also significantly superior. The findings, on the whole, show that NCP-kNN is a promising technique as a highly-efficient kNN variation for big data classification.
Determining Natural Disaster Mitigation Level using Unsupervised k-means Clustering

This work intends to categorize the number of districts/cities based on their mitigation efforts using unsupervised clustering approaches to generate decision support systems. Using data mining techniques and k-means clustering algorithms, it is possible to address problems involving data on the number of districts/cities based on natural disaster mitigation measures. Ms. Excel is used to estimate the value of the centroid for three clusters: the high expectation level cluster (C1), the medium anticipation level cluster (C2), and the low anticipation level cluster (C3) (C3). Our trials highlighted the outcomes of categorizing districts/cities based on their natural disaster preparedness efforts with two high-level districts/cities, namely Mentawai Islands Regency and South Lampung Regency, 47 regencies/cities at medium level 32 other districts/cities including low-level clusters. The results can be adopted as a recommendation for the district/city government to improve facilities and infrastructure in the district/city conforming to the efforts of natural disaster mitigation based on the clusters.
Schutz von Gesundheitspersonal und Patienten über 3 Jahre COVID-19

Healthcare professionals, particularly those in test centers, laboratories, or specialized COVID-19 wards, are in danger of becoming infected. Patients with special underlying health conditions are at an increased risk of getting very sick, being hospitalized, or dying from COVID-19. Age is a leading risk factor in this context. Currently, FFP2 (Filtering Facepiece 2, European standard), N95 (US standard), and KN95 (Chinese standard) face masks remain the simplest measure of protection. Coronavirus warning apps installed on smartphones have been recommended for anonymous contact tracing and quickly disrupting chains of infection. Preventive testing two to three times per week for healthcare personnel, on the day of hospital admission for patients, and upon facility entry for visitors has been routinely performed or has been requested from external test centers in most medical institutions. However, vaccination is regarded the most effective protective measure against COVID-19. The general recommendation of the World Health Organization is that countries continue to work toward vaccinating at least 70% of their populations, prioritizing the vaccination of 100% of healthcare workers and 100% of the most vulnerable groups, including people who are over 60 years of age and those who are immunocompromised or have underlying health conditions. The most vulnerable individuals among patients and healthcare workers should be identified and then their vaccination status should be checked and, if necessary, optimized by booster administration. In Germany, seasonal and institutional recommendations for individual protection by face masks, for hygiene measures, and for preventive testing must follow the updated coronavirus protection regulations (Coronavirus-Schutzverordnungen).
Application of data-driven multi-index overlay and BWM-MOORA MCDM methods in mineral prospectivity mapping of porphyry Cu mineralization

The present study applies proposed hybrid mathematical frameworks, namely Data-driven Multi-Index Overlay Method (DMIO) and Best Worst Method-multi objective optimization by ratio analysis (BWM-MOORA) techniques, for generating robust predictive models enhancing mineral prospectivity analysis is demonstrated. These are used for (i) weighting multiple groups of evidence maps acquired from geological databases, such as remote sensing, geological, geochemical, and geophysical, and then (ii) integrating these weighted layers into robust predictive models to assess porphyry copper prospectivity. This case study of the Chahargonbad area is a district of the volcano-plutonic Urumieh–Dokhtar magmatic arc belt. Porphyry copper deposit (exploration and development) models were used for distilling geoscientific datasets that are available into a group of evidence-based layers. The BWM-MOORA method combines two separate processes in which BWM is applied to weight each criterion and sub-criteria, whereas the MOORA approach helps rank these attributes. Each layer is first converted to another space of [0,1] by the sigmoidal logistic function to perform the critical process of integrating each of the evidence layers. Then, a concentration-area fractal model was used to classify evidences into some geospatial populations. In addition, a prospectivity / mineral potential mapping (MPM) analysis is performed using the DMIO, a well-proven, and accurate data-based methodology, to verify the satisfactoriness and validation of the applied methodology. Prediction-area (P-A) and normalized density (Nd) analysis (plots) were performed as validation methods to estimate the reliability and evaluate the relative efficiency of the MPM models. Therefore, these detailed outcomes confirm the validity of the BWM-MOORA model (Nd = 3.17), which is slightly higher than the valid and proper model DMIO (Nd = 3) in determining the specific potential mineralization regions.
Gaussian kernelized feature selection and improved multilayer perceptive deep learning classifier for software fault prediction

Software fault prediction is the significant process of identifying the errors or defects or faults in a software product. But, accurate and timely detection is the major challenging issue in different existing approaches to predicting software defects. A novel Gaussian linear feature embedding-based statistical test piecewise multilayer perceptive deep learning classifier (GLFE-STPMPDLC) is introduced to improve software fault prediction accuracy and minimize time consumption. First, the input data are collected from the dataset. Next, the software metrics selection is carried out to select the significant metrics using Gaussian kernelized locally linear embedding with lesser software fault prediction. Then classification is carried out by Kaiser Meyer piecewise multilayer perceptive deep learning classifier for software fault prediction. The novelty of Kaiser–Meyer–Olkin (KMO) correlation test analyzes testing and training instances. The innovation of the Heaviside step activation function is applied for analyzing the KMO correlation test results and providing the final software fault prediction results. Finally, accurate fault prediction outcomes are achieved at the output layer with lesser error. Simulation of proposed GLFE-STPMPDLC technique achieves better 5%, 3%, 3% and 3% enhancement of fault prediction accuracy, precision, recall, and f-measure and 13% faster prediction time compared to conventional methods.
Attentive ExFeat based deep generative adversarial network for noise robust face super-resolution

A new noise robust face super-resolution model using an attentive ExFeat-based generative adversarial network is proposed in this paper. The proposed model introduces the Exigent Feature Attention Unit (ExFAU) which consists of an Exigent Feature (ExFeat) block with a spatial attention unit to enhance the visual quality of the generated face images. The ExFAU block assists the model in reducing the noise and extracting the micro and high-level facial features. Further, the ExFeat block is followed by a spatial attention unit to focus on specific facial features. This allows us to give more attention to key face attributes related features and less to the remaining features. The proposed model repeats the ExFAU block to focus on different facial components and enhance them to improve the overall quality of the resultant face images. Experimental outcomes exhibit that the proposed model gains state-of-the-art performance on the standard datasets, namely CelebAHQ, Helen, and LFW face.