Reviewerly/exHarmony huggingface dataset:
Multi-Dimensional Data Fusion for Mineral Prospectivity Mapping (MPM) Using Fuzzy-AHP Decision-Making Method, Kodegan-Basiran Region, East Iran

Analyzing and fusing information layers of exploratory parameters is a crucial stride for increasing the accuracy of pinpointing mineral potential zones in the reconnaissance stage of mineral exploration. Remote sensing, geophysical, geochemical, and geology data were analyzed and fused for identify metallic mineralization in the Kodegan-Basiran region (East Iran). Landsat 7 Enhanced Thematic Mapper Plus (ETM+), aeromagnetic data, geological data, and geochemical stream sediment samples were utilized. The study area contains some copper indices and mines. Thus, the main focus of this study was identifying the zones with high potential for metallic copper mineralization. A two-stage methodology was implemented in this study: First, extraction of the exploratory parameters related to metallic mineralization and second is data fusion by the hybrid fuzzy-analytic hierarchy process (Fuzzy-AHP) method. Hydrothermal alterations and iron oxides in the area were mapped by applying the optimum index factor (OIF), band ratio (BR), and least squared fit (LS-Fit) to ETM+ data. Intrusive masses were positioned as one of the effective parameters in identifying metallic mineralization zones using the gradient tensor method to assess aeromagnetic data. In order to determine the threshold concentration and the location of mineralization anomalies, the K-means clustering algorithm, vertical geochemical zonality (Vz) index, as well as concentration-area (C-A) multi fractal and singularity analysis were implemented on the geochemical data. In conclusion, the potential zones of metallic mineralization in the Kodegan-Basiran region were displayed in a mineral prospectivity map (MPM) derived from the Fuzzy-AHP decision-making method. Finally, to validate the prospectivity map of metallic mineralization, a control area was selected and surveyed by collecting mineralogical, petrological, and stream sediment samples. Field works confirmed the mineralization of Cu and Fe sulfides, oxides, and hydroxides. The high potential areas identified in the MPM can be considered as targets for future Cu exploration in the Kodegan-Basiran area.
Technology-enabled financing of sustainable infrastructure: A case for blockchains and decentralized oracle networks

The capital required to maintain infrastructure in good repair falls short globally. This is commonly referred to as the “infrastructure finance gap”. To address climate change, transitioning from conventional to sustainable infrastructure further imposes financing challenges. This study employs the Model approach to justify and predict how blockchain technologies can leverage infrastructure data to close the finance gap by introducing new financing mechanisms. A semisystematic literature review was carried out for infrastructure finance, sustainable infrastructure and smart cities finance mechanisms, as well as blockchains and oracles. Conventional infrastructure finance via debt and equity lacks benchmarks that reflect the risk-return characteristics of the asset class. Performance-based financing addresses this issue by integrating performance data in valuations. However, a lack of trust in data veracity remains. Blockchains provide trust and transparency in data and transactions. They utilize oracles to access off-chain information for on-chain decision-making. With smart contracts and decentralized oracle networks, a general approach is presented where data from internet-of-things inform on-chain transactions, delivering performance benchmarks that accurately reflect the risk-return characteristics in an infrastructure investment. Capital can then be more readily deployed, thus closing the finance gap. An end-to-end example of financing sustainable stormwater infrastructure, Open Storm, is also presented.
Cultivating security culture for information security success: A mixed-methods study based on anthropological perspective

The continuous information security failures in organizations have led focus toward organizational culture. It is argued that the development of culture of information security would subsequently lead to a secure organization. However, limited studies have been conducted to understand information security culture. This study aims to understand information security culture and its impact on success with information security efforts in an organization. The research model is based on the theory of primary message systems, which is an established theory from the anthropology discipline. We followed a mixed-methods research design involving two phases of the study. In the first phase, 25 semi-structured interviews with experienced cybersecurity practitioners were conducted to develop the research model. The second phase empirically validated the research model using survey data from 473 participants who completed a web-based survey in Southeast USA from multiple companies. For data analysis, we employed Partial Least Squares - Structural Equation Modeling using SmartPLS. Our findings indicate that group cohesiveness, professional code, information security awareness, and informal work practices have significant influence on information security culture. Further, the security culture has positive impact on information security success perception. The contribution of this research lies in establishing the role of security culture and information security awareness in contributing toward information security success.
Enhancing supply chain flows through blockchain: a comprehensive literature review

Applications of blockchain in supply chain management (SCM) have received extensive attention among academics and industrial practitioners. Most current blockchain-related review papers focus on the values, methodologies, barriers, trends, and challenges of blockchain applications in the supply chain (SC) context. Despite some papers discussing blockchain’s role in SCs from a specific perspective, the existing review papers mainly concentrate on blockchain’s influence on one of the three critical SC flows. Hence, this study comprehensively reviews 251 academic papers to capture the precise impacts of blockchain on the material, information, and money flows in SCM. Following the above analyses, a conceptual framework is put forward to accentuate blockchain’s influence on SCM. By unveiling a comprehensive research landscape, this study offers valuable viewpoints and vital information for scholars and practitioners to better identify research frontiers and themes of blockchain applications in SCM.
Deep learning detection network for peripheral blood leukocytes based on improved detection transformer

Leukocytes serve as an important barrier to healthy immunity in the body and play an important role in fighting diseases. Manual morphological examination of leukocytes is the gold standard for the diagnosis of certain diseases but is undoubtedly labour-intensive and requires a high level of expertise. Therefore, conducting research on computer-aided diagnostics is important. With the development of deep learning techniques in computer vision, an increasing number of deep learning-based methods are now being applied in the field of medical imaging. Recently, the detection transformer (DETR) model, which is based on the transformer architecture, has exhibited outstanding performances in object detection tasks and has attracted considerable attention. Our study aims to propose a pure transformer-based end-to-end object detection network based on DETR and apply it to a practical medical scenario of leukocyte detection. First, we introduce the pyramid vision transformer and deformable attention module into the DETR model to improve the model performance and convergence speed. Second, we train the improved model on the challenging Common Objects in Context dataset to obtain the pretrained weights. Third, we perform transfer learning on the modified Raabin leukocyte dataset to obtain the optimal model. The improved DETR shows a mean average precision detection performance of up to 0.961 and is therefore superior to the original DETR and convolutional neural network. The study findings are expected to be useful for the development of a transformer structural model for leukocyte detection.
Entanglement and coherence in the Bernstein-Vazirani algorithm

Quantum algorithms allow to outperform their classical counterparts in various tasks, most prominent example being Shor's algorithm for efficient prime factorization on a quantum computer. It is clear that one of the reasons for the speedup is the superposition principle of quantum mechanics, which allows a quantum processor to be in a superposition of different states at the same time. While such superposition can lead to entanglement across different qubits of the processors, there also exists quantum algorithms which outperform classical ones using superpositions of individual qubits without entangling them. As an example, the Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle. While the classical version of the algorithm requires multiple calls of the oracle to learn the bit string, a single query of the oracle is enough in the quantum case. In this Letter, we analyze in detail the quantum resources in the Bernstein-Vazirani algorithm. For this, we introduce and study its probabilistic version, where the goal is to guess the bit string after a single call of the oracle. We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state. We further demonstrate that a large amount of entanglement in the initial state prevents the algorithm from achieving optimal performance. We also apply our methods to quantum computation with mixed states, proving that pseudopure states achieve optimal performance for a given purity in the Bernstein-Vazirani algorithm. We further investigate quantum resources in the one clean qubit model, showing that the model can exhibit speedup over any known classical algorithm even with arbitrary little amount of multipartite entanglement, general quantum correlations, and coherence.
Graduating nursing students' user experiences of the immersive virtual reality simulation in learning – A qualitative descriptive study

To describe nursing students' user experiences' (UX) regarding highly immersive virtual reality (VR) simulation with head mounted display used for learning.Qualitative descriptive study.Graduating nursing students (n = 41). Individual interviews were held right after VR simulation. The usability was assessed using System Usability Scale (SUS).User experiences emphasized three themes about the highly immersive VR simulation: nursing care in the immersive VR simulation, technology in the immersive VR simulation, and learning nursing in the immersive VR simulation. Usability was evaluated as fair (SUS score 62.3 out of 100). Scores revealed that the VR simulation was easy to use. The support of a technical person was highlighted.This study supports strongly the use of highly immersive VR simulation for nursing education. VR simulations should replicate the most authentic nursing care. Technical assistance is crucial when adopting new technologies in education. The results highlighted the importance of UX in an education context.
Adaptive niching particle swarm optimization with local search for multimodal optimization

Multimodal optimization problems (MMOPs), in which multiple optimal solutions need to be found for decision-makers, are common in real-world applications. Finding as many peaks as feasible and enhancing the accuracy of solutions on already identified peaks are the objectives of solving MMOPs. An adaptive particle swarm optimization (PSO) based on speciation, species regulation, and local search, termed SR-PSO-MAES, is proposed to achieve these objectives. First, an adaptive speciation strategy is introduced to divide the population, which forms species according to the similarity of particles and does not need to set the radius of species in advance. Second, a species regulation strategy is proposed to avoid a few species occupying most of the population during the iterative process. Third, a matrix adaptation evolution strategy (MAES) with a restart scheme as a local search strategy is used to evaluate the species for further improving the accuracy of the solutions. By comparing with 15 state-of-the-art multimodal optimization algorithms, the experimental results through a benchmark test problem and a real wet spinning process validate the superiority of the proposed SR-PSO-MAES algorithm.
Recurrent wavelet structure-preserving residual network for single image deraining

The combination of deep learning and image prior has been widely used in single image deraining since 2017. Recent studies have demonstrated an excellent deraining effect on the high-frequency part of rain images, but less attention was paid to the low-frequency part of rain images. The rain streaks remain in the low-frequency part of rain images, thus limiting the deraining effect. Since the rain streaks in rain images are often mixed with object edges and background scenes, it is challenging to separate rain from them by directly learning the deraining function in the image domain. To solve these problems, we propose a novel Recurrent Wavelet Structure-preserving Residual Network (RWSRNet), which mainly preserves and introduces the low-frequency sub-images of each level into the low-frequency rain removal sub-networks that are greatly different from the state-of-the-art approaches introducing wavelet transform. In addition, we also share the low-frequency structure information to the high-frequency sub-networks through block connection, which further enriches the detailed information, facilitates convergence, and strengthens the ability of our network to remove rain streaks in high frequency. Finally, we fuse the derained low-frequency sub-images of each level through the proposed image weighted blending module and finally reconstruct the low- and high-frequency sub-images into clean images through inverse wavelet transform recursively. The experimental results indicate that the proposed method achieves an excellent deraining effect on both low- and high-frequency parts of rain images and has better performance in low-frequency preservation and high-frequency enhancement in comparison with the state-of-the-art approaches on synthetic and real image datasets.
Supervised term-category feature weighting for improved text classification

Text classification is a central task in Natural Language Processing (NLP) that aims at categorizing text documents into predefined classes or categories. It requires appropriate features to describe the contents and meaning of text documents, and map them with their target categories. Existing text feature representations rely on a weighted representation of the document terms. Hence, choosing a suitable method for term weighting is of major importance and can help increase the effectiveness of the classification task. In this study, we provide a novel text classification framework for Category-based Feature Engineering titled CFE. It consists of a supervised weighting scheme defined based on a variant of the TF-ICF (Term Frequency-Inverse Category Frequency) model, embedded into three new lean classification approaches: (i) IterativeAdditive (flat), (ii) GradientDescentANN (1-layered), and (iii) FeedForwardANN (2-layered). The IterativeAdditive approach augments each document representation with a set of synthetic features inferred from TF-ICF category representations. It builds a term-category TF-ICF matrix using an iterative and additive algorithm that produces category vector representations and updates until reaching convergence. GradientDescentANN replaces the iterative additive process mentioned previously by computing the term-category matrix using a gradient descent ANN model. Training the ANN using the gradient descent algorithm allows updating the term-category matrix until reaching convergence. FeedForwardANN uses a feed-forward ANN model to transform document representations into the category vector space. The transformed document vectors are then compared with the target category vectors, and are associated with the most similar categories. We have implemented CFE including its three classification approaches, and we have conducted a large battery of tests to evaluate their performance. Experimental results on five benchmark datasets show that our lean approaches mostly improve text classification accuracy while requiring significantly less computation time compared with their deep model alternatives.
Quantum-inspired algorithm for direct multi-class classification

Over the last few decades, quantum machine learning has emerged as a groundbreaking discipline. Harnessing the peculiarities of quantum computation for machine learning tasks offers promising advantages. Quantum-inspired machine learning has revealed how relevant benefits for machine learning problems can be obtained using the quantum information theory even without employing quantum computers. In the recent past, experiments have demonstrated how to design an algorithm for binary classification inspired by the method of quantum state discrimination, which exhibits high performance with respect to several standard classifiers. However, a generalization of this quantum-inspired binary classifier to a multi-class scenario remains nontrivial. Typically, a simple solution in machine learning decomposes multi-class classification into a combinatorial number of binary classifications, with a concomitant increase in computational resources. In this study, we introduce a quantum-inspired classifier that avoids this problem. Inspired by quantum state discrimination, our classifier performs multi-class classification directly without using binary classifiers. We first compared the performance of the quantum-inspired multi-class classifier with eleven standard classifiers. The comparison revealed an excellent performance of the quantum-inspired classifier. Comparing these results with those obtained using the decomposition in binary classifiers shows that our method improves the accuracy and reduces the time complexity. Therefore, the quantum-inspired machine learning algorithm proposed in this work is an effective and efficient framework for multi-class classification. Finally, although these advantages can be attained without employing any quantum component in the hardware, we discuss how it is possible to implement the model in quantum hardware.
ThirdEye: Attention Maps for Safe Autonomous Driving Systems

Automated online recognition of unexpected conditions is an indispensable component of autonomous vehicles to ensure safety even in unknown and uncertain situations. In this paper we propose a runtime monitoring technique rooted in the attention maps computed by explainable artificial intelligence techniques. Our approach, implemented in a tool called ThirdEye, turns attention maps into confidence scores that are used to discriminate safe from unsafe driving behaviours. The intuition is that uncommon attention maps are associated with unexpected runtime conditions.
Revocable Blockchain-Aided Attribute-Based Encryption With Escrow-Free in Cloud Storage

The massive amount of data generated by the Internet of Things (IoT) and the need to store that data presents a huge challenge for storage. However, meeting this challenge has also driven the development of storage technologies, especially those related to cloud storage. Although attribute-based encryption (ABE) schemes are commonly used to achieve data confidentiality and fine-grained access control in cloud storage, there is still an inherent problem with ABE schemes, namely the key escrow problem. In this paper, we propose a revocable blockchain-aided ABE with escrow-free (BC-ABE-EF) system that resolves the key escrow problem by replacing the traditional key authority with a consortium blockchain. The keys are generated between the blockchain and the data user through a secure key issuing protocol, and the blockchain cannot obtain the user's full key alone. Furthermore, utilize the decryption cloud server to schedule pre-decryption operations in cloud and introduce a group manager to update the group keys of unrecovered users and generate re-encryption keys. The security analysis shows that our scheme is secure under the Decisional Computation Diffie Hellman (DCDH) assumption. The effectiveness of the scheme is demonstrated by simulating the BC-ABE-EF scheme and comparing it based on performance analysis.
A Systematic Literature Review of Empirical Research on Stablecoins

This study reviews the current state of empirical literature on stablecoins. Based on a sample of 22 peer-reviewed articles, we analyze statistical approaches, data sources, variables, and metrics, as well as stablecoin types investigated and future research avenues. The analysis reveals three major clusters: (1) studies on the stability or volatility of different stablecoins, their designs, and safe-haven-properties, (2) the interrelations of stablecoins with other crypto assets and markets, specifically Bitcoin, and (3) the relationship of stablecoins with (non-crypto) macroeconomic factors. Based on our analysis, we note future research should explore diverse methodological approaches, data sources, different stablecoins, or more granular datasets and identify five topics we consider most significant and promising: (1) the use of stablecoins in emerging markets, (2) the effect of stablecoins on the stability of currencies, (3) analyses of stablecoin users, (4) adoption and use cases of stablecoins outside of crypto markets, and (5) algorithmic stablecoins.
Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition

Human Action Recognition (HAR) is a branch of computer vision that deals with the identification of human actions at various levels including low level, action level, and interaction level. Previously, a number of HAR algorithms have been proposed based on handcrafted methods for action recognition. However, the handcrafted techniques are inefficient in case of recognizing interaction level actions as they involve complex scenarios. Meanwhile, the traditional deep learning-based approaches take the entire image as an input and later extract volumes of features, which greatly increase the complexity of the systems; hence, resulting in significantly higher computational time and utilization of resources. Therefore, this research focuses on the development of an efficient multi-view interaction level action recognition system using 2D skeleton data with higher accuracy while reducing the computation complexity based on deep learning architecture. The proposed system extracts 2D skeleton data from the dataset using the OpenPose technique. Later, the extracted 2D skeleton features are given as an input directly to the Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) architecture for action recognition. To reduce the complexity, instead of passing the whole image, only extracted features are given to the CNN-LSTM architecture, thus eliminating the need for feature extraction. The proposed method was compared with other existing methods, and the outcomes confirm the potential of the proposed technique. The proposed OpenPose-CNNLSTM achieved an accuracy of 94.4% for MCAD (Multi-camera action dataset) and 91.67% for IXMAS (INRIA Xmas Motion Acquisition Sequences). Our proposed method also significantly decreases the computational complexity by reducing the number of inputs features to 50.