Reviewerly/exHarmony huggingface dataset:
Predictability and forecast skill of solar irradiance over the contiguous United States

Current solar forecast verification processes place much attention on performance comparison of a group of competing methods. However, forecast verification ought to further answer how the best method within the group performs relative to the best-possible performance which one can attain under that forecasting situation, which makes the quantification of predictability and forecast skill immediately relevant. Unfortunately, the literature on the quantification of relative performance of solar irradiance has hitherto been lacking, and very few studies have focused on the spatial distributions of predictability and forecast skill of solar irradiance. The predictability and forecast skill of an atmospheric process depend on two concepts: (1) the growth of initial error in unresolved scale of motion, and (2) the forecast performance of the standard of reference. Based upon this formalism, predictability and forecast skill of solar irradiance in the United States are quantified and mapped. Through this study, a couple of common misconceptions in regard to irradiance predictability are refuted, and the original formulation of skill score revived.
Spatial complementary and self-repair learning for occluded person re-identification

Occluded person re-identification (ReID) is more challenging than holistic person ReID, as the appearance clues are insufficient and misaligned in cluttered scenarios. By digging pose/foreground priors, numerous previous methods focus on visible body regions to achieve alignment but they are complicated. To alleviate these issues, we present spatial complementary and self-repair learning (SCSRL), a simple yet effective approach that constructs holistic representations without extra priors. Specifically, SCSRL includes two learning branches, a spatial complementary learning (SCL) one and a self-repair learning (SRL) one. The SCL branch extracts aggregation features that reflect integral information of the human appearance. The SRL branch not only mines visible features for each input but also repairs missing/invisible appearance clues via a proposed repair loss. Moreover, we construct a large-scale occluded dataset namely Occluded-Market which contains more percentage of occlusion samples than existing datasets. The experiments show that our approach achieves state-of-the-art performance on Occluded-Market and other occluded datasets.
Level-set evolution for medical image segmentation with alternating direction method of multipliers

Segmentation via level-set methods plays a significant role in natural and medical image analysis. Recently, the distance regularized level-set evolution (DRLSE) model, which is based on variational level-set formulation has been proven very useful. However, the DRLSE model suffers from difficulties related to its computational cost due to the sign distance function (SDF) and Courant-Friedrichs-Lewy (CFL) condition applied by the gradient descent method. This paper analyzes and tests the well-known fast computing algorithm known as alternating direction method of multipliers (ADMM) for level-set based image segmentation. That is, we compute the DRLSE model by using the ADMM algorithm. The comparison between the classical gradient descent method and the proposed ADMM for two-phase and multi-phase level-set formulation is provided. The numerical experiments on standard public data sets, particularly in the case of medical image segmentation, demonstrate the effectiveness and efficiency of the proposed method. The average segmentation coefficients for Dice and Jaccard against ground truth are 0.97 and 0.92, respectively, and the average running time is 1.70 seconds. 0.0932, 0.993, 0.981, and 0.964 are average estimated values for mean absolute distance (MAD), accuracy, sensitivity, and specificity, respectively. The source code can be found at link https://github.com/WaliSamad/DRLSE-ADMM-Medical-Image-Segmentation.
Parallel binary arithmetic optimization algorithm and its application for feature selection

Arithmetic Optimization Algorithm (AOA) has simple structure, few parameters and is easy to implement. It utilizes the distribution behavior of the main arithmetic operators in mathematics. The Multiplication and Division operators are used for exploration, while the Subtraction and Addition are used for exploitation. In this manuscript, the AOA algorithm was converted into binary form with the Multiplication Mathematical Optimizer Operator (MOO) redesigned for better exploration. Then, four families of transfer functions are used in the binary AOA (BAOA). Moreover, in order to further improve the performance, the parallel mechanism is introduced to the BAOA and proposed the Parallel Binary AOA (PBAOA) algorithm. The proposed algorithms are applied for feature selection problem on 10 low-dimensional and 10 high-dimensional datasets from UCI and scikit-feature repository. The results show that the proposed BAOA and PBAOA algorithms are superior to the classical and state-of-the-art algorithms. The Z-shaped transfer functions are more suitable for the proposed algorithms on low-dimensional datasets, while the V-shaped transfer functions are more suitable for high-dimensional datasets. Moreover, no matter what transfer function is used, the parallel mechanism is effective for BAOA.
Efficient Multiclass Classification Using Feature Selection in High-Dimensional Datasets

Feature selection has become essential in classification problems with numerous features. This process involves removing redundant, noisy, and negatively impacting features from the dataset to enhance the classifier’s performance. Some features are less useful than others or do not correlate with the system’s evaluation, and their removal does not affect the system’s performance. In most cases, removing features with a monotonically decreasing impact on the system’s performance increases accuracy. Therefore, this research aims to propose a dimensionality reduction method using a feature selection technique to enhance accuracy. This paper proposes a novel feature-selection approach that combines filter and wrapper techniques to select optimal features using Mutual Information with the Sequential Forward Method and 10-fold cross-validation. Results show that the proposed algorithm can reduce features by more than 75% in datasets with large features and achieve a maximum accuracy of 97%. The algorithm outperforms or performs similarly to existing ones. The proposed algorithm could be a better option for classification problems with minimized features.
Virtual reality and collaborative learning: a systematic literature review

Background: While research on Virtual Reality’s potential for education continues to advance, research on its support for Collaborative Learning is small in scope. With remote collaboration and distance learning becoming increasingly relevant for education (especially since the COVID-19 pandemic), an understanding of Virtual Reality’s potential for Collaborative Learning is of importance. To establish how this immersive technology can support and enhance collaboration between learners, this systematic literature review analyses scientific research on Virtual Reality for Collaborative Learning with the intention to identify 1) skills and competences trained, 2) domains and disciplines addressed, 3) systems used and 4) empirical knowledge established. Method: Two scientific databases—Scopus and Web of Science—were used for this review. Following the PRISMA method, a total of 139 articles were analyzed. Reliability of this selection process was assessed using five additional coders. A taxonomy was used to classify these articles. Another coder was used to assess the reliability of the primary coder before this taxonomy was applied to the selected articles Results: Based on the literature reviewed, skills and competences developed are divided into five categories. Educational fields and domains seem interested in Virtual Reality for Collaborative Learning because of a need for innovation, communities and remote socialization and collaboration between learners. Systems primarily use monitor-based Virtual Reality and mouse-and-keyboard controls. A general optimism is visible regarding the use of Virtual Reality to support and enhance Collaborative Learning Conclusion: Five distinct affordances of Virtual Reality for Collaborative Learning are identified: it 1) is an efficient tool to engage and motivate learners, 2) supports distance learning and remote collaboration, 3) provides multi- and interdisciplinary spaces for both learning and collaborating, 4) helps develop social skills and 5) suits Collaborative Learning-related paradigms and approaches. Overall, the reviewed literature suggests Virtual Reality to be an effective tool for the support and enhancement of Collaborative Learning, though further research is necessary to establish pedagogies.
TruCert: Blockchain-based trustworthy product certification within autonomous automotive supply chains

Supply chain networks are complex structures which introduce significant challenges regarding transparency in production and traceability of components, making product certification non-trivial. Visibility within quality assurance processes is critical to this and is particularly important for autonomous & driver-less vehicles which rely on correct operation of individual parts (in the absence of human intervention) to achieve safety of such vehicles. Failure to ascertain quality assurance of parts can result in rogue behaviour among driver-less vehicles which can result in a risk to human lives. In this paper, a blockchain-based approach - TruCert, is proposed which achieves trustworthy product certification through enhanced visibility within tier 1 and beyond for complex automotive supply chains. Leveraging blockchain technology, TruCert’s potential to improve product quality assurance is demonstrated whilst also strengthening supply chain resilience to combat risks and uncertainties. Utilising the use-case of autonomous connected vehicles manufacturing, design and development of TruCert solution is presented including detailed system design, data model, and smart contracts & oracle implementations. TruCert enables trustworthy part certification across supply chain beyond tier 1 whilst achieving interoperability with heterogeneous systems across suppliers and other stakeholders. Outcomes of evaluation with respect to cost, performance, and security are also presented which highlight the effectiveness of the approach whilst identifying directions for future work.
NanBDOS: Adaptive and parameter-free borderline oversampling via natural neighbor search for class-imbalance learning

Learning class-imbalance data has become a challenging task in machine learning. Oversampling is an effective way to achieve rebalancing between classes by generating new minority instances. However, most existing oversampling methods exhibit significant ill-posedness due to the involvement of k-nearest neighbors. Recently, several novel techniques have been developed using natural neighbors instead of k-nearest neighbors. But these budding techniques fail to take into account the natural distribution of data, resulting in degraded adaptability of natural neighbors to data characteristics. In this paper, we propose an adaptive and completely parameter-free synthetic oversampling method called NanBDOS (short for borderline oversampling via natural neighbor search). NanBDOS first determines the intrinsic neighbor relationship between instances through a natural neighbor search procedure. Then, those informative borderline instances are identified and used as base instances, i.e., sampling seeds. NanBDOS assigns dynamic sampling weights to the base instances so that the data complexity can be well represented. Finally, new synthetic instances are generated by interpolating between the base instances and their natural neighbors. NanBDOS is experimentally compared with seven baseline methods on twenty-four real-world datasets. The results confirm the effectiveness of the proposed method. Moreover, the statistical analysis also indicates its higher-level Friedman ranking.
Critical success factors of Blockchain technology adoption for sustainable and resilient operations in the banking industry during an uncertain business environment

Blockchain technology (BCT) adoption in banks can bring resilience and make banking operations sustainable in terms of security in times of increased risks and uncertainty. Along this line, this study aims to identify and model the critical success factors (CSFs) of blockchain technology adoption for sustainable and resilient banking operations. Seventeen CSFs were identified from the literature and semi-structured interviews. After that, 15 CSFs were confirmed through the fuzzy Delphi method, and cause-effect relationships were developed through the Decision-Making Trial and Evaluation Laboratory method. Findings of the study highlighted that ease of local and international legislation and regulation and user data privacy are the most significant CSFs of blockchain technology, and these demand a high level of attention from management and decision-makers to realise sustainability and resilience in the banking sector. The study will guide practitioners and policymakers in understanding the importance of CSFs and thereby help devise a strategy for successful blockchain technology adoption in the banking sector.
SeCoM: An Outsourced Cloud-Based Secure Communication Model for Advanced Privacy Preserving Data Computing and Protection

A malignant entity may access outsourced healthcare data in Internet of Things (IoT) and cloud environments during data sharing, analysis, and communication among the involved entities to procure sensitive information that can be misused or leaked to other illegal entities. To tackle this pivotal and challenging issue, this article presents a novel efficient, privacy-preserving, secure communication model (SeCoM) for healthcare data protection in the cloud and IoT systems by minimizing the threat of data leakage, identifying, and terminating malicious entities against data leakage, and addressing security threats. The model provides enhanced security to the system by synchronously performing secure data storage, analysis, sharing, and communication. The experimental results and comparison of the proposed model with existing approaches demonstrate that it significantly improves privacy, security, and detection efficiencies as well as data utilization of up to 9.49%, 77.17%, 83%, and 43.25% along with a reduction of leakage and unsafe communication links by 97.62% and 98%, respectively.
Application of Metaverse in the Hospitality Industry

The chapter's analysis of the possible advantages and disadvantages of the metaverse is centered on studies in the tourist and food and wine industries. Open issues include compatibility and technological problems, cost and financial issues, user experience and accessibility issues, privacy and security issues, and cultural and ethical issues. The metaverse offers chances to enable interaction, engagement, and trip planning, successfully changing consumer behavior. It is anticipated that engaging with and visiting destinations virtually would inspire actual travel, not replace it. As the technology becomes more widely used, both the industry and the public are beginning to recognize the potential that this technology offers for their respective hospitality and tourist interests. VR/AR application areas for hospitality and tourism are currently under development. In order for the tourism industry to fully capitalize on the capabilities and opportunities presented by the metaverse as well as identify challenges for the future, the report recommends research directions.
Corner-Point and Foreground-Area IoU Loss: Better Localization of Small Objects in Bounding Box Regression

Bounding box regression is a crucial step in object detection, directly affecting the localization performance of the detected objects. Especially in small object detection, an excellent bounding box regression loss can significantly alleviate the problem of missing small objects. However, there are two major problems with the broad Intersection over Union (IoU) losses, also known as Broad IoU losses (BIoU losses) in bounding box regression: (i) BIoU losses cannot provide more effective fitting information for predicted boxes as they approach the target box, resulting in slow convergence and inaccurate regression results; (ii) most localization loss functions do not fully utilize the spatial information of the target, namely the target's foreground area, during the fitting process. Therefore, this paper proposes the Corner-point and Foreground-area IoU loss (CFIoU loss) function by delving into the potential for bounding box regression losses to overcome these issues. First, we use the normalized corner point distance between the two boxes instead of the normalized center-point distance used in the BIoU losses, which effectively suppresses the problem of BIoU losses degrading to IoU loss when the two boxes are close. Second, we add adaptive target information to the loss function to provide richer target information to optimize the bounding box regression process, especially for small object detection. Finally, we conducted simulation experiments on bounding box regression to validate our hypothesis. At the same time, we conducted quantitative comparisons of the current mainstream BIoU losses and our proposed CFIoU loss on the small object public datasets VisDrone2019 and SODA-D using the latest anchor-based YOLOv5 and anchor-free YOLOv8 object detection algorithms. The experimental results demonstrate that YOLOv5s (+3.12% Recall, +2.73% mAP@0.5, and +1.91% mAP@0.5:0.95) and YOLOv8s (+1.72% Recall and +0.60% mAP@0.5), both incorporating the CFIoU loss, achieved the highest performance improvement on the VisDrone2019 test set. Similarly, YOLOv5s (+6% Recall, +13.08% mAP@0.5, and +14.29% mAP@0.5:0.95) and YOLOv8s (+3.36% Recall, +3.66% mAP@0.5, and +4.05% mAP@0.5:0.95), both incorporating the CFIoU loss, also achieved the highest performance improvement on the SODA-D test set. These results indicate the effectiveness and superiority of the CFIoU loss in small object detection. Additionally, we conducted comparative experiments by fusing the CFIoU loss and the BIoU loss with the SSD algorithm, which is not proficient in small object detection. The experimental results demonstrate that the SSD algorithm incorporating the CFIoU loss achieved the highest improvement in the AP (+5.59%) and AP75 (+5.37%) metrics, indicating that the CFIoU loss can also improve the performance of algorithms that are not proficient in small object detection.
Construction Safety Risks of Metro Tunnels Constructed by the Mining Method in Wuhan City, China: A Structural Equation Model-Fuzzy Cognitive Map Hybrid Method

A hybrid method based on the structural equation model (SEM) and a fuzzy cognitive map (FCM) was developed to study the influences of the construction safety risks (CSR) of metro tunnels constructed by the mining method on the project risk (PR). An SEM was used to identify and verify the correlations between the CSR and PR. An FCM was developed adopting the verified standardized causality and path coefficient in the SEM. On this basis, predictive reasoning and diagnostic reasoning were performed in the framework of an FCM to simulate the CSR and PR. The feasibility of the developed method was validated based on the construction project of a metro tunnel in Wuhan City, China. The results show that (1) the cause concept node C2 (tunnel excavation) shows the strongest positive correlation with the target concept node CT (PR). According to their risks, the construction stages are ranked in descending order as C2 (tunnel excavation), C1 (advanced support), C6 (auxiliary measures), C4 (structure waterproofing), C3 (primary support), and C5 (secondary lining). (2) The target concept node CT is most sensitive to changes in the cause concept nodes C2 and C1. (3) Close attention should be paid to key risks, such as X9 (over-excavation and under-excavation) and X8 (unreasonable determination of excavation footage and sequence) in stage C2. Originality and significance: (1) A hybrid method that can simulate the dynamic interaction between the CSR and PR is proposed. (2) A new perspective for the dynamic evaluation of a PR is provided. (3) The method can be used as a decision-making tool for controlling the PR.
Decentralized federated learning through proxy model sharing

Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.
Asymptotics of quantum channels

We discuss several aspects concerning the asymptotic dynamics of dicrete-time semigroups associated with a quantum channel. By using an explicit expression of the asymptotic map, which describes the action of the quantum channel on its attractor manifold, we investigate the role of permutations in the asymptotic dynamics. We show that, in general, they make the asymptotic evolution non-unitary, and they are related to the divisibility of the quantum channel. Also, we derive several results about the asymptotics of faithful and non-faithful channels, and we establish a constructive unfolding theorem for the asymptotic dynamics.
Assessment of mine post-closure strategies by integrating evidential reasoning and fuzzy cognitive maps: Toward sustainable mining policy

This study proposes a novel framework based on evidential reasoning and fuzzy cognitive map to evaluate post-closure strategies for open-pit mine reclamation, with a focus on sustainable mining. The framework enables a reasonable assessment of multiple strategies and criteria within a complex dynamic decision analysis system under uncertainty. This study introduces two novel aspects: linking the weight outputs of fuzzy cognitive maps to the evidential reasoning process and considering a broad range of criteria and reclamation strategies toward sustainable mining. This study considers various strategies, focusing on tailings, waste rocks, and demolition materials as filling materials to reclaim pits and reduce mine waste footprints. In evidential reasoning, causal weights were determined by a fuzzy cognitive map, which revealed that social criteria with a causal weight >0.96 were found the highest impact on cause-and-effect. The framework was applied to a large-scale mine with high discontinuities and acidity potentials, a local ecosystem with a high piezometric level, and low rainfall. A post-closure strategy using tailings as the main filling material was found most suitable, as it eliminates material transport and supply costs and reduces the adverse environmental effects and social complaints.