Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Massive Data Dissemination Handling in IIoT Environment

Rapid development in Information and Communications Technologies (ICT), Internet of Things (IoT), and Big Data (BD) analytics has revolutionized the manufacturing industry, which introduces the Industrial Internet of Things (IIoT) in Industry 4.0. IIoT includes machinery, manufacturing processes, and automation mechanisms. The existing IIoT system uses a centralized architecture, where the trusted third party (TTP) performs transactions, which raises security and privacy concerns and may have a single point of failure. The emerging technology Blockchain is a prominent solution to address the aforementioned issues. Motivated by these facts, in this article we highlight the issues of data dissemination in the IIoT environment and present a blockchain-based decentralized model for IIoT (DMIIoT). The proposed model uses a secure Peer-to-Peer (P2P) network, where each node interacts with other nodes. Then we highlight the potential of the DMIIoT to improve various services in IIoT, such as better production visibility and Quality of Service (QoS). Finally, we present a case study on a Smart Grid (SG) system to evaluate the efficacy of the proposed model with data load balance, energy management costs, and transmission delay parameters.
Predicting pulsar stars using a random tree boosting voting classifier (RTB-VC)

The recent exponential growth in the data volume and number of identified pulsar stars is due to pulsar candidate search experiments and surveys. In this study, we investigated the existing methods and techniques used for pulsar prediction, such as applying filters based on pulsar observations, which can adversely affect the success of accurate pulsar prediction. Some of the existing methods are not capable of dealing with large volumes of data and others fail to accurately select the best candidates from pulsar observations. Thus, we developed a new approach based on the traditional supervised machine learning algorithm, which yields faster and more accurate results. In this study, we present our hybrid machine learning classifier called the random trees boosting voting classifier (RTB-VC) for predicting pulsar stars. RTB-VC combines tree-based classifiers and it employs the High Time Resolution Universe 2 (HTRU2) data set comprising a set of eight features related to pulsars and non-pulsars. The HTRU2 data set is imbalanced and we solve this problem by using the synthetic minority oversampling technique to generate artificial data and obtain a balanced data set. A feature set is used to separate pulsar and non-pulsar candidates because the different distributions of variables in the data set are helpful for training models. In the proposed approach, the prediction stage of RTB-VC is based on a combination of soft voting, hard voting, and weighted voting to obtain highly accurate and relevant criteria for finally predicting pulsars or non-pulsars. The ensemble-based structure of RTB-VC yields accurate results based on pulsar observations with a high F1 score for pulsars (98.3%). We evaluated the learning algorithm in terms of its accuracy, precision, recall, and F1 score.
Removal of multisource noise in airborne electromagnetic data based on deep learning

Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.
Hypercompositional Algebra, Computer Science and Geometry

The various branches of Mathematics are not separated between themselves. On the contrary, they interact and extend into each other’s sometimes seemingly different and unrelated areas and help them advance. In this sense, the Hypercompositional Algebra’s path has crossed, among others, with the paths of the theory of Formal Languages, Automata and Geometry. This paper presents the course of development from the hypergroup, as it was initially defined in 1934 by F. Marty to the hypergroups which are endowed with more axioms and allow the proof of Theorems and Propositions that generalize Kleen’s Theorem, determine the order and the grade of the states of an automaton, minimize it and describe its operation. The same hypergroups lie underneath Geometry and they produce results which give as Corollaries well known named Theorems in Geometry, like Helly’s Theorem, Kakutani’s Lemma, Stone’s Theorem, Radon’s Theorem, Caratheodory’s Theorem and Steinitz’s Theorem. This paper also highlights the close relationship between the hyperfields and the hypermodules to geometries, like projective geometries and spherical geometries.
Lexicon-based Sentiment Analysis Using the Particle Swarm Optimization

This work belongs to the field of sentiment analysis; in particular, to opinion and emotion classification using a lexicon-based approach. It solves several problems related to increasing the effectiveness of opinion classification. The first problem is related to lexicon labelling. Human labelling in the field of emotions is often too subjective and ambiguous, and so the possibility of replacement by automatic labelling is examined. This paper offers experimental results using a nature-inspired algorithm—particle swarm optimization—for labelling. This optimization method repeatedly labels all words in a lexicon and evaluates the effectiveness of opinion classification using the lexicon until the optimal labels for words in the lexicon are found. The second problem is that the opinion classification of texts which do not contain words from the lexicon cannot be successfully done using the lexicon-based approach. Therefore, an auxiliary approach, based on a machine learning method, is integrated into the method. This hybrid approach is able to classify more than 99% of texts and achieves better results than the original lexicon-based approach. The final hybrid model can be used for emotion analysis in human–robot interactions.
Analysis of Connected Word Recognition systems using Levenberg Marquardt Algorithm for cockpit control in unmanned aircrafts

Due to advances in computation, the computer system needs sufficient input data, and it allows it a better computer tool for efficient operation of the human-computer, such as the fast-moving Automatic Speech Recognition System. This paper aims in particular to provide an insight into the contact distance between humans and computers in unmanned aircraft vehicles. While there are several algorithms, a critical analysis of algorithms suitable for large-scale applications is still important. The aircraft without a human pilot on board is an unmanned aerial vehicle. Continuous Word Recognition systems for voice enhancement (commanding) based cockpit control are commonly used in unmanned aircraft. The goal is to evaluate the efficiency of the Levenberg Marquardt algorithm by using these recognition systems. To do this, optimal preparation can be selected using neural networks to increase the machine recognition effectiveness. MATLAB verify simulated findings and tests show that a high accuracy of recognition of over 87 percent is obtained.
Trust, Identity, Privacy, and Security Considerations for Designing a Peer Data Sharing Platform Between People Living With HIV

Resulting from treatment advances, the Human Immunodeficiency Virus (HIV) is now a long-term condition, and digital solutions are being developed to support people living with HIV in self-management. Sharing their health data with their peers may support self-management, but the trust, identity, privacy and security (TIPS) considerations of people living with HIV remain underexplored. Working with a peer researcher who is expert in the lived experience of HIV, we interviewed 26 people living with HIV in the United Kingdom (UK) to investigate how to design a peer data sharing platform. We also conducted rating activities with participants to capture their attitudes towards sharing personal data. Our mixed methods study showed that participants were highly sophisticated in their understanding of trust and in their requirements for robust privacy and security. They indicated willingness to share digital identity attributes, including gender, age, medical history, health and well-being data, but not details that could reveal their personal identity. Participants called for TIPS measures to foster and to sustain responsible data sharing within their community. These findings can inform the development of trustworthy and secure digital platforms that enable people living with HIV to share data with their peers and provide insights for researchers who wish to facilitate data sharing in other communities with stigmatised health conditions.
Classification of industrial Augmented Reality use cases

Due to rapid technical advancements and the increasing diffusion of information and communications technologies, Augmented Reality (AR) has left its fledgling stages and is now on the verge of a breakthrough. Especially in industrial applications, AR holds great potentials for performance improvements: By enriching real world environments with context-specific virtual information, manual operations can be carried out more efficiently, costly mistakes can be avoided and the mental effort for transferring information from an abstract representation (e.g. monitor) to the physical world can be significantly reduced. However, a lack of transparency regarding the equally numerous and manifold applications and benefits makes it difficult for companies to weigh the impact of AR for their business. Existing classification approaches in scientific literature mainly focus on technical aspects of AR, neglecting information essential for the strategic planning of AR. The current paper aims at filling this gap by providing a classification of industrial AR use cases. The basis is a classification scheme consisting of eight classification criteria, each with several predefined characteristics, specifically tailored to the industrial use of AR. In a comprehensive literature research, 75 industrial application examples for AR were determined and described using the classification scheme. By performing a cluster analysis and multidimensional scaling, the AR application examples were grouped according to their similarity, resulting in 16 clusters, each representing a generic use case for AR. Further information was researched for each use case (e.g. benefits, opportunities, risks) and summarized in a profile. Thus, the paper provides two core results: (1) A classification scheme for describing industrial AR applications, extending the state of the art with an use-case-related classification approach. (2) A catalogue with 16 AR use case profiles, providing transparency on industrial applications and potential benefits of AR and inspiring companies on how to utilize AR for their own business.
Landsat-7 and ASTER remote sensing satellite imagery for identification of iron skarn mineralization in metamorphic regions

The use of satellite remote sensing imagery for mineral exploration is a fast and low-cost approach for indicating high potential zones. Exploration of iron skarn mineralization in metamorphic regions is challenging during the fieldwork campaign. In this study, Landsat-7 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery were used for mapping alteration zones and geological structures associated with iron skarn mineralization in the Galali region, NW, Iran. Band ratio and Directed Principal Component Analysis (DPCA) and Automatic Lineament Extraction methods were applied to Landsat-7 data. Relative Absorption Band Depth (RBD) and Constrained Energy Minimization (CEM) algorithms were implemented to ASTER data. Fieldwork and laboratory analysis were conducted to verify the remote sensing results. Results indicate that propylitic alteration zone and intersection of N–S and NW–SE and curvilinear features are typically associated with iron skarn mineralization and some high potential zones are identified for future exploration projects.
Domain Specific Language for Smart Contract Development

The notion to digitally articulate, execute, and enforce agreements with smart contracts has become a feasible reality today. Smart contracts have the potential to vastly improve the efficiency and security of traditional contracts through their self-executing autonomy. To realize smart contracts several blockchain-based ecosystems exist. Today a prominent representative is Ethereum. Its programming language Solidity is used to capture and express contractual clauses in the form of code. However, due to the conceptual discrepancy between contractual clauses and corresponding code, it is hard for domain stakeholders to easily understand contracts, and for developers to write code efficiently without errors. Our research addresses these issues by the design and study of a domain-specific smart contract language based on higher level of abstraction that can be automatically transformed to an implementation. In particular, we propose a clause grammar close to natural language, helpful coding abstractions, and the automatic integration of commonly occurring design patterns during code generation. Through these measures, our approach can reduce the design complexity leading to an increased comprehensibility and reduced error susceptibility. Several implementations of exemplary smart contract scenarios, mostly taken from the Solidity documentation, are used to demonstrate the applicability of our approach.
A combined multi-objective optimization model for degradation trend prediction of pumped storage unit

Pumped storage unit (PSU) operated under the combination of different condition, which may cause equipment wear, fatigue and fault issues. It is difficult to achieve accurate and stable degradation trend prediction (DTP) considering only either the accuracy or stability. In this paper, a combined multi-objective optimization model is proposed for DTP of PSU. Firstly, the health model of PSU is constructed with back propagation neural network (BPNN) and condition parameters of active power, working head and guide vane opening. Subsequently, the performance degradation index (PDI) is developed to characterize degradation level of PSU. Then, considering high accuracy and strong stability in predicted model, kernel extreme learning machine (KELM) is optimized by multi-objective particle swarm optimization (MOPSO) to achieve DTP. To validate the effectiveness of the proposed model, the monitoring data collected from PSU in China is taken as case studies. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability.
Structural representation learning for network alignment with self-supervised anchor links

Network alignment, the problem of identifying similar nodes across networks, is an emerging research topic due to its ubiquitous applications in many data domains such as social-network reconciliation and protein-network analysis. While traditional alignment methods struggle to scale to large graphs, the state-of-the-art representation-based methods often rely on pre-defined anchor links, which are unavailable or expensive to compute in many applications. In this paper, we propose NAWAL, a novel, end-to-end unsupervised embedding-based network alignment framework emphasizing on structural information. The model first embeds network nodes into a low-dimension space where the structural neighborhoodship on original network is captured by the distance on the space. As the space for the input networks are learnt independently, we further leverage a generative adversarial deep neural network to reconcile the spaces without relying on hand-crafted features or domain-specific supervision. The empirical results on three real-world datasets show that NAWAL significantly outperforms state-of-the-art baselines, by over 13% of accuracy against unsupervised methods and on par or better than supervised methods. Our technique also demonstrate the robustness against adversarial conditions, such as structural noises and graph size imbalance.
Universal Face Photo-Sketch Style Transfer via Multiview Domain Translation

Face photo-sketch style transfer aims to convert a representation of a face from the photo (or sketch) domain to the sketch (respectively, photo) domain while preserving the character of the subject. It has wide-ranging applications in law enforcement, forensic investigation and digital entertainment. However, conventional face photo-sketch synthesis methods usually require training images from both the source domain and the target domain, and are limited in that they cannot be applied to universal conditions where collecting training images in the source domain that match the style of the test image is unpractical. This problem entails two major challenges: 1) designing an effective and robust domain translation model for the universal situation in which images of the source domain needed for training are unavailable, and 2) preserving the facial character while performing a transfer to the style of an entire image collection in the target domain. To this end, we present a novel universal face photo-sketch style transfer method that does not need any image from the source domain for training. The regression relationship between an input test image and the entire training image collection in the target domain is inferred via a deep domain translation framework, in which a domain-wise adaption term and a local consistency adaption term are developed. To improve the robustness of the style transfer process, we propose a multiview domain translation method that flexibly leverages a convolutional neural network representation with hand-crafted features in an optimal way. Qualitative and quantitative comparisons are provided for universal unconstrained conditions of unavailable training images from the source domain, demonstrating the effectiveness and superiority of our method for universal face photo-sketch style transfer.
Verified Development and Deployment of Multiple Interacting Smart Contracts with VeriSolid

Smart contracts enable the creation of decentralized applications which often handle assets of large value. These decentralized applications are frequently built on multiple interacting contracts. While the underlying platform ensures the correctness of smart contract execution, today developers continue struggling to create functionally correct contracts, as evidenced by a number of security incidents in the recent past. Even though these incidents often exploit contract interaction, prior work on smart contract verification, vulnerability discovery, and secure development typically considers only individual contracts. This paper proposes an approach for the correct-by-design development and deployment of multiple interacting smart contracts by introducing a graphical notation (called deployment diagrams) for specifying possible interactions between contract types. Based on this notation, it later presents a framework for the automated verification, generation, and deployment of interacting contracts that conform to a deployment diagram. As an added benefit, the proposed framework provides a clear separation of concerns between the internal contract behavior and contract interaction, which allows one to compositionally model and analyze systems of interacting smart contracts efficiently.
Do you have confidence in how your rolling stock has been maintained? A blockchain-led knowledge-sharing platform for building trust between stakeholders

This study explores the potential of blockchain technology in enhancing trust among the stakeholders responsible for the maintenance of rolling stock. Although the technology has been widely successful in the financial sector, it is still novel in the maintenance field. Given the customized nature of maintenance processes, it is unclear if a suitable consensus protocol can be identified that can enhance trust among stakeholders. This problem is investigated through the lens of the Design Science Research Methodology. First, the theoretical background of blockchain technology and its role in enhancing trust are explained, followed by the analysis of a current case at a Railway company to test the proof of concept. A business network archive is developed for the maintenance management of the sliding step of the train door system. The archive encompasses the business logic and transactional data required to enhance trust among stakeholders in the quality of performed maintenance. The developed archive is deployed on Hyperledger Fabric and the effectiveness of the solution is evaluated through a survey. The results show that the developed business network, deployed on a customized Hyperledger Fabric consensus protocol, enhanced trust among the stakeholders involved.
On the assessment of software defect prediction models via ROC curves

Abstract Software defect prediction models are classifiers often built by setting a threshold t on a defect proneness model, i.e., a scoring function. For instance, they classify a software module non-faulty if its defect proneness is below t and positive otherwise. Different values of t may lead to different defect prediction models, possibly with very different performance levels. Receiver Operating Characteristic (ROC) curves provide an overall assessment of a defect proneness model, by taking into account all possible values of t and thus all defect prediction models that can be built based on it. However, using a defect proneness model with a value of t is sensible only if the resulting defect prediction model has a performance that is at least as good as some minimal performance level that depends on practitioners’ and researchers’ goals and needs. We introduce a new approach and a new performance metric (the Ratio of Relevant Areas) for assessing a defect proneness model by taking into account only the parts of a ROC curve corresponding to values of t for which defect proneness models have higher performance than some reference value. We provide the practical motivations and theoretical underpinnings for our approach, by: 1) showing how it addresses the shortcomings of existing performance metrics like the Area Under the Curve and Gini’s coefficient; 2) deriving reference values based on random defect prediction policies, in addition to deterministic ones; 3) showing how the approach works with several performance metrics (e.g., Precision and Recall ) and their combinations; 4) studying misclassification costs and providing a general upper bound for the cost related to the use of any defect proneness model; 5) showing the relationships between misclassification costs and performance metrics. We also carried out a comprehensive empirical study on real-life data from the SEACRAFT repository, to show the differences between our metric and the existing ones and how more reliable and less misleading our metric can be.