Reviewerly/exHarmony huggingface dataset:
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios, such as schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, including flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets, since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://pegasus.ac.cn .
How blockchain technology generates a trust-based competitive advantage in the wine industry: a resource based view perspective

Purpose This study aims to explore whether the blockchain technology (BCT), as a unique and distinctive resource, affects the development of unique capabilities and the integration and reconfiguration of internal physical and human resources necessary to gain a trusted competitive advantage. Design/methodology/approach A case study approach has been used to identify the internal resources and capabilities that affect the decision to implement BCT and to determine whether this new technology can become a strategic internal resource in the wine industry. Findings Results show the role of two strategic resources: human resources, such as IT specialists and software developers for BCT applications, and skills of companies’ management to grant sensitive and confidential data to IT specialists and the ability to read the analytics from the BCT application, becoming a distinctive enabling assets. Research limitations/implications The main limitation of this study is related to the analysis of a single case, which, on the one hand, does not allow generalisation of the empirical findings but, on the other hand, is themost appropriatemethod for the explorative nature of this research. Practical implications Our findings have useful practical implications for wineries and agri-food companies in overall because they can support managers and entrepreneurs in effectively implementing these innovative digital technologies in their valuecreation process. Originality/value The study of this underexplored topic gives interesting insights into the resources needed for the successful adoption of this emerging technology, which can support wineries in improving the value-creating process. In particular, compared to the previous studies, this research analyses the adoption process under the resource-based theory lens.
Blockchain-Based Design of a Government Incentive Mechanism for Manufacturing Supply Chain Data Governance

In the context of “double carbon”, constructing green supply chains is the only way to implement sustainable development strategies in the manufacturing industry. This paper, therefore, examines the manufacturing supply chain for low-carbon products. More recently, the lack of technical information flow due to data barriers up and down the supply chain has led to high energy consumption, the serious waste of raw materials, and the substandard production of green products. Therefore, the level of supply chain data governance must be improved to enhance the sustainability of the supply chain. By studying blockchain-based data governance and government policy incentives for manufacturing supply chains, this study constructed an evolutionary game model based on prospect theory for the tripartite relation of government, manufacturers, and retailers. The difference between the perceived and actual value was introduced into a three-way evolutionary game model based on prospect theory to optimize the practical implications of the model. The model was then simulated using system dynamics. Through the simulation, it could be concluded that the ability of the three-way evolutionary game to reach the optimal stability point is only related to the sensitivity of the retailer’s perceived value. Additionally, the outcome of the three-way evolutionary game can be unstable, with changes in perceived value sensitivity. Finally, relevant policy recommendations are made. The innovation of this study is establishing a data governance platform that uses data governance to build green supply chains. Additionally, the government was added to the subjects of the game to explore the role of government policy in data governance and sustainable development. In addition, the evolutionary game model was incorporated with prospect theory and traditional expected utility theory, and the rational deficits and preferences of decision makers were taken into account, which brings the results closer to the reality of the situation.
New MDS entanglement-assisted quantum codes from MDS Hermitian self-orthogonal codes

The intersection $$\textbf{C}\cap \textbf{C}^{\perp _H}$$ of a linear code $$\textbf{C} \subset \textbf{F}_{q^2}^n$$ and its Hermitian dual $$\textbf{C}^{\perp _H}$$ is called the Hermitian hull of this code. A linear code $$\textbf{C} \subset \textbf{F}_{q^2}^n$$ satisfying $$\textbf{C} \subset \textbf{C}^{\perp _H}$$ is called Hermitian self-orthogonal. Many Hermitian self-orthogonal codes were given for the construction of MDS quantum error correction codes (QECCs). In this paper we prove that for a nonnegative integer h satisfying $$0 \le h \le k$$ , a linear Hermitian self-orthogonal $$[n, k]_{q^2}$$ code is equivalent to a linear h-dimension Hermitian hull code. Therefore a lot of new MDS entanglement-assisted quantum error correction (EAQEC) codes can be constructed from previous known Hermitian self-orthogonal codes. Actually our method shows that previous constructed quantum MDS codes from Hermitian self-orthogonal codes can be transformed to MDS entanglement-assisted quantum codes with nonzero consumption parameter c directly. We prove that MDS EAQEC $$[[n, k, d; c]]_q$$ codes with nonzero c parameters and $$d\le \frac{n+2}{2}$$ exist for arbitrary length n satisfying $$n \le q^2+1$$ . Moreover any QECC constructed from k-dimensional Hermitian self-orthogonal codes can be transformed to k different EAQEC codes. We also prove that MDS entanglement-assisted quantum codes exist for all lengths $$n\le q^2+1$$ .
Augmented reality user interface design and experimental evaluation for human-robot collaborative assembly

Artificial intelligence (AI) has been applied to a wide spectrum of industrial sectors, but manual operations remain indispensable in most manufacturing systems due to high complexity or costs involving in automation. A more practical approach is enabling humans to collaborate with machines complementary to each other. One critical issue in human-robot collaboration (HRC) is to assure the operator's productivity, safety, and trust while interacting with the robot. This paper presents an experimental study on user interface design in augmented reality (AR) for human-robot collaborative assembly in a shared workspace. The experiment aims to verify and cross-compare the effectiveness of visual and haptic cues in various forms that convey the robot intent to human. Analysis of the work performance and gazing behavior of participants shows that both cues can reduce their visual attention on the moving robot during the collaboration. The interface that provides the proximity of robot using visual cues is considered most useful. It is not intuitive to recognize complex information by vibration on different parts of the human hand in the experiment. Finally, human trust to robot has a higher correlation on the usability of a user interface than the work performance assisted by the interface. These findings may work as design guidelines for AR assisted human-robot interaction in smart manufacturing.
The challenges of integrating explainable artificial intelligence into <scp>GeoAI</scp>

Abstract Although explainable artificial intelligence (XAI) promises considerable progress in glassboxing deep learning models, there are challenges in applying XAI to geospatial artificial intelligence (GeoAI), specifically geospatial deep neural networks (DNNs). We summarize these as three major challenges, related generally to XAI computation, to GeoAI and geographic data handling, and to geosocial issues. XAI computation includes the difficulty of selecting reference data/models and the shortcomings of attributing explanatory power to gradients, as well as the difficulty in accommodating geographic scale, geovisualization, and underlying geographic data structures. Geosocial challenges encompass the limitations of knowledge scope—semantics and ontologies—in the explanation of GeoAI as well as the lack of integrating non‐technical aspects in XAI, including processes that are not amenable to XAI. We illustrate these issues with a land use classification case study.
Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems

Owing to imaging equipment's environment and limitations, the images obtained in industrial cyber-physical systems (ICPSs) are degraded and available in various visual appearances. The process of highlighting the hidden contents in night-time contrast-distorted images is complex. Earlier approaches have solved this problem from a different perspective and achieved remarkable results that are generally unsatisfactory for images with diverse illumination distortions in ICPSs. Hence, an effective visibility enhancement model is proposed to eliminate inconsistent color casts while highlighting more hidden content for improved inspection, safety in large spaces, and monitoring of large systems. Our proposed model has four steps: 1) removal of the unnatural color cast via a white balance technique, 2) use of probability density and softplus functions to process the images with the actual color cast, 3) using an optimization algorithm to estimate the illumination and adjusting it using a nonlinear function, and 4) blending by multiscale fusion to obtain the most effective visual result. Evaluation of ten benchmark datasets using 14 quality metrics for 22 conventional and modern algorithms shows that our approach is robust, flexible, and applicable to numerous vision-based applications, such as ICPSs, autonomous vehicles, smart cameras, smart mobility, and transportation, especially in low-light environments.
DARGS: Image inpainting algorithm via deep attention residuals group and semantics

To solving the problems that the existing image inpainting methods lack authenticity, do not deal with the information of missing and non-missing regions flexibly, and do not deal with the image feature information on different stages effectively, we propose an image restoration method combining Semantic Priors and Deep Attention Residual Group. The image restoration method mainly consists of Semantic Priors Network, Deep Attention Residual Group, and Full-scale Skip Connection. The Semantic Priors Network learns the complete semantic prior information of the visual elements in the missing regions and completes the missing regions with the learned semantic information. The deep attention residual set allows the generator to focus more on not only the missing regions of the image but also to learn the features of each channel adaptive. The full-scale jump joins can combine the low-level feature maps containing image boundaries with the high-level feature maps containing image textures and details to repair the missing regions. In this paper, a full comparison experiment is conducted on the CelebA-HQ and the Paris StreetView datasets, and the experimental results show that proposed method outperforms the current representative state-of-the-art image restoration methods.
Temporal information processing induced by quantum noise

Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on their correction and mitigation techniques. But do these quantum noises always provide disadvantages? We show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data in a framework called quantum reservoir computing. We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile. We verified our perspective by implementing the framework in a number of IBM quantum processors and obtained similar characteristic memory profiles with model analyses. As a surprising result, information processing capacity increased with quantum devices' higher noise levels and error rates. Our study opens up a path for diverting useful information from quantum computer noises into a more sophisticated information processor.
An evaluation of conventional and deep learning‐based image‐matching methods on diverse datasets

Abstract Image matching plays an important role in photogrammetry, computer vision and remote sensing. Modern deep learning‐based methods have been proposed for image matching; however, whether they will surpass and take the place of the conventional handcrafted methods in the remote sensing field still remains unclear. A comprehensive evaluation on stereo remote sensing images is also lacking. This paper comprehensively evaluates the performance of conventional and deep learning‐based image‐matching methods by dividing the matching process into feature point extraction, description and similarity measure on various datasets, including images captured from close‐range indoor and outdoor scenarios, unmanned aerial vehicles (UAVs) and satellite platforms. Different combinations of the three steps are evaluated. The experimental results reveal that, first, the performance of the different combinations varies between individual datasets, and it is difficult to determine the best combination. Second, by using more comprehensive indicators on all of the datasets, that is, the average rank and absolute rank, the combination of scale‐invariant feature transform (SIFT), ContextDesc and the nearest neighbour distance ratio (NNDR), and also the original SIFT, achieve the best results, and are recommended for use in remote sensing. Third, the deep learning‐based Sub‐SuperPoint extractor obtains a good performance, and is second only to SIFT. The learning based ContextDesc descriptor is as effective as the SIFT descriptor, and the learning based SuperGlue matcher is not as stable as NNDR, but leads to a few top‐performing combinations. Finally, the handcrafted methods are generally faster than the deep learning‐based methods, but the efficiency of the latter is acceptable. We conclude that although a full deep learning‐based method/combination has not yet beaten the conventional methods, there is still much room for improvement with the deep learning‐based methods because large‐scale aerial and satellite training datasets remain to be constructed, and specific methods for remote sensing images remain to be developed. The performance of the different combinations of feature extractor, descriptor and similarity measure varies between individual datasets. The combination of SIFT, ContextDesc and NNDR, and also the original SIFT, achieve the best results when using more comprehensive indicators on all the datasets. For extractor, the learning based Sub‐SuperPoint is second only to SIFT; for descriptor, learning‐based ContextDesc is as effective as the SIFT descriptor; and for matcher, learning‐based SuperGlue is not as stable as NNDR.
Reservoir computing with noise

This paper investigates in detail the effects of measurement noise on the performance of reservoir computing. We focus on an application in which reservoir computers are used to learn the relationship between different state variables of a chaotic system. We recognize that noise can affect the training and testing phases differently. We find that the best performance of the reservoir is achieved when the strength of the noise that affects the input signal in the training phase equals the strength of the noise that affects the input signal in the testing phase. For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.
Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation

Session-based Recommendation aims at predicting the next interacted item based on short anonymous behavior sessions. However, existing solutions neglect to model two inherent properties of sequential representing distributions, i.e., hierarchy structures resulted from item popularity and collaborations existing in both intra- and inter-session. Tackling with these two factors at the same time is challenging. On the one hand, traditional Euclidean space utilized in previous studies fails to capture hierarchy structures due to a restricted representation ability. On the other hand, the intuitive apply of hyperbolic geometry could extract hierarchical patterns but more emphasis on degree distribution weakens intra- and inter-session collaborations. To address the challenges, we propose a Hierarchy-Aware Dual Clustering Graph Network (HADCG) model for session-based recommendation. Towards the first challenge, we design the hierarchy-aware graph modeling module which converts sessions into hyperbolic session graphs, adopting hyperbolic geometry in propagation and attention mechanism so as to integrate chronological and hierarchical information. As for the second challenge, we introduce the deep dual clustering module which develops a two-level clustering strategy, i.e., information regularizer for intra-session clustering and contrastive learner for inter-session clustering, to enhance hyperbolic representation learning from collaborative perspectives and further promote recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed HADCG.
A Comprehensive Survey of Transformers for Computer Vision

As a special type of transformer, vision transformers (ViTs) can be used for various computer vision (CV) applications. Convolutional neural networks (CNNs) have several potential problems that can be resolved with ViTs. For image coding tasks such as compression, super-resolution, segmentation, and denoising, different variants of ViTs are used. In our survey, we determined the many CV applications to which ViTs are applicable. CV applications reviewed included image classification, object detection, image segmentation, image compression, image super-resolution, image denoising, anomaly detection, and drone imagery. We reviewed the state of the-art and compiled a list of available models and discussed the pros and cons of each model.
A multivocal literature review of decentralized finance: Current knowledge and future research avenues

Abstract While decentralized finance (DeFi) has the potential to emulate and, indeed, outperform existing financial systems, it remains a complex phenomenon yet to be extensively researched. To make the most of this potential, its practitioners must gain a rigorous understanding of its intricacies, as must information systems (IS) researchers. Against this background, this study uses a multivocal literature review to capture the state of research in DeFi. Thereby, we (1) present a consolidating definition of DeFi as we (2) analyze, synthesize, and discuss the current state of knowledge in the field of DeFi. We do so while adapting the blockchain research framework proposed by (Risius and Spohrer, Business &amp; Information Systems Engineering 59:385–409, 2017). Furthermore, we (3) identify gaps in the literature and indicate future research directions in DeFi. Even though our findings highlight several shortcomings in DeFi that have prevented its widespread adoption, our literature review shows a large consensus on DeFi’s many promising features and potential to complement the traditional financial system. To that end, this paper is presented to encourage further research to mitigate the current risks of DeFi, the payoff of which will be an enriched financial ecosystem.
The Human-Centric Metaverse: A Survey

In the era of the Web of Things, the Metaverse is expected to be the landing site for the next generation of the Internet, resulting in the increased popularity of related technologies and applications in recent years and gradually becoming the focus of Internet research. The Metaverse, as a link between the real and virtual worlds, can provide users with immersive experiences. As the concept of the Metaverse grows in popularity, many scholars and developers begin to focus on the Metaverse’s ethics and core. This paper argues that the Metaverse should be centered on humans. That is, humans constitute the majority of the Metaverse. As a result, we begin this paper by introducing the Metaverse’s origins, characteristics, related technologies, and the concept of the human-centric Metaverse (HCM). Second, we discuss the manifestation of human-centric in the Metaverse. Finally, we discuss some current issues in the construction of HCM. In this paper, we provide a detailed review of the applications of human-centric technologies in the Metaverse, as well as the relevant HCM application scenarios. We hope that this paper can provide researchers and developers with some directions and ideas for human-centric Metaverse construction.
Memory-Based Sand Cat Swarm Optimization for Feature Selection in Medical Diagnosis

The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant features. As a result, it is critical to pick the most pertinent features for the classification task, which is referred to as Feature Selection (FS). Among the FS approaches, wrapper methods are designed to select the most appropriate subset of features. In this study, two intelligent wrapper FS approaches are implemented using a new meta-heuristic algorithm called Sand Cat Swarm Optimizer (SCSO). First, the binary version of SCSO, known as BSCSO, is constructed by utilizing the S-shaped transform function to effectively manage the binary nature in the FS domain. However, the BSCSO suffers from a poor search strategy because it has no internal memory to maintain the best location. Thus, it will converge very quickly to the local optimum. Therefore, the second proposed FS method is devoted to formulating an enhanced BSCSO called Binary Memory-based SCSO (BMSCSO). It has integrated a memory-based strategy into the position updating process of the SCSO to exploit and further preserve the best solutions. Twenty one benchmark disease datasets were used to implement and evaluate the two improved FS methods, BSCSO and BMSCSO. As per the results, BMSCSO acted better than BSCSO in terms of fitness values, accuracy, and number of selected features. Based on the obtained results, BMSCSO as a FS method can efficiently explore the feature domain for the optimal feature set.