Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Double-Layer Byzantine Fault Tolerance for Scalability Enhancement for Building Information Modeling Information Exchange

A Practical Byzantine Fault Tolerance (PBFT) is a consensus algorithm deployed in a consortium blockchain that connects a group of related participants. This type of blockchain suits the implementation of the Building Information Modeling (BIM) information exchange with few participants. However, when much more participants are involved in the BIM information exchange, the PBFT algorithm, which inherently requires intensive communications among participating nodes, has limitations in terms of scalability and performance. The proposed solution for a multi-layer BFT hypothesizes that multi-layer BFT reduces communication complexity. However, having more layers will introduce more latency. Therefore, in this paper, Double-Layer Byzantine Fault Tolerance (DLBFT) is proposed to improve the blockchain scalability and performance of BIM information exchange. This study shows a double-layer network structure of nodes that can be built with each node on the first layer, which connects and forms a group with several nodes on the second layer. This network runs the Byzantine Fault Tolerance algorithm to reach a consensus. Instead of having one node send messages to all the nodes in the peer-to-peer network, one node only sends messages to a limited number of nodes on Layer 1 and up to three nodes in each corresponding group in Layer 2 in a hierarchical network. The DLBFT algorithm has been shown to reduce the required number of messages exchanged among nodes by 84% and the time to reach a consensus by 70%, thus improving blockchain scalability. Further research is required if more than one party is involved in multi-BIM projects.
The Use of Blockchain Technology and Its Reflection in the Financial Performance of Investment Projects Developed by the Ministry of Sports

Blockchain has received a lot of attention in financial technology, as it combines many computer technologies, including data storage, point-to-point transmission, and consensus mechanisms, as it is considered a decentralized technology for managing transactions and data that has been developed. The study aimed to demonstrate the impact of the use of blockchain technology on the financial performance of investment projects developed by the Ministry of Sports. We used the descriptive approach (survey study method) as an appropriate method to achieve the objectives of the study due to the suitability of its procedures. The participants in the study included some leaders of the Ministry of Sports and experts in the field of sports investment, as well as some leaders working in the directorates of youth and sports, and some members of the board of directors of the Olympic Committee, sports federations, sports clubs, and youth centers. The study sample was selected in a deliberate way based on the categories of the study population. There were 300 participants in the study, and the researchers used two questionnaires as tools to collect the data. The results revealed a lack of the use of blockchain technology at the Ministry of Sports and a low level of financial performance at the Ministry of Sports. With a correlation between the level of financial performance and the use of blockchain technology, the level of the financial performance of the investment projects developed by the Ministry of Sports could be predicted based on the use of blockchain technology. Finally, the study also provides insight into political implications, limitations, and future directions.
Non-Intrusive Head Movement Control for Powered Wheelchairs: A Vision-Based Approach

Joystick is the most common wheelchair controlling interface, however, it might not be applicable for cases of severely disabled people, for example quadriplegic users.The few solutions currently available on the market, such as the four switches on the headrest, the sip-n-puff, or the tongue drive system, can be cumbersome to use, while they offer limited control to the user.For such cases, a vision-based headcontrolled wheelchair is a unique alternative solution, which despite its benefits, has not been equally well studied.In this work, we propose and evaluate a novel method for operating an Electric Powered Wheelchair (EPW) via head movements in a non-invasive way.This is based on computer vision techniques and Deep Learning to estimate accurately the orientation of the user's head.It allows calibration for individual users and driving in a continuous navigation space as opposed to the discrete commands imposed by alternatives such as head switches.Our approach enables the design of an efficient and cost-effective solution utilising a simple RGB camera that captures the user's face orientation.Our system is implemented and tested real-time on an EPW using its existing commercial controller, while it can work with any commercial controller the manufacturer allows interfacing with (i.e., a direct plugin).Performance is evaluated through trials conducted with healthy participants.The results (96% successful track completion) show that our head driving system can be reliably used as an alternative solution to the conventional joystick interface, with only a small trade-off in travelling time and distance (reduction by 9.4% and 21% respectively).The participants' experience in terms of mental and physical load, subjectively assessed following the trials, suggests relatively low mental and physical demand imposed by our system.Users also expressed high confidence in the system's performance indicating trust to the safety aspects of our implementation.Analysis of our findings and experimental observations provide a new knowledge base for potential system improvements and future designs.
An Extensive Study and Review on Dark Web Threats and Detection Techniques

The Dark Web is a difficult and anonymous network used by cybercriminals, terrorists, and state-sponsored agents to carry out their illicit goals. Dark web cybercrime is very similar to offline crime. However, the vastness of the Dark Web, its unpredictable ecology, and the anonymity it provides are all obstacles that must be overcome in order to track down criminals. To reach the dark web, which is not indexed by search engines, you must use the anonymous Tor browser. The anonymity and covert nature of the network make it ideal for criminal activity and the launch of carefully orchestrated, malicious assaults. Online criminal activity is rampant and getting more intense, according to specialists in cyber security. This chapter has provided a thorough analysis of the various attacks and attack strategies utilized on the dark web. In addition, the authors examine the strengths and weaknesses of the various methods currently in use for threat detection, and how they apply to anonymity networks such as Tor, I2P, and Freenet.
Fano-qubits for quantum devices with enhanced isolation and bandwidth

Quantum devices often suffer from reflections and noise during readout, a problem traditionally addressed by magneto-optical isolators and circulators. However, these solutions are hindered by limited bandwidth, low tunability, high losses, and incompatibility with planar technologies like circuit QED. To overcome these challenges, we introduce an approach to quantum non-reciprocity, leveraging the inherent nonlinearity of qubits and spatial symmetry disruption. Our method transforms a circuit with Lorentz-type qubits into one with Fano-type qubits, which exhibit an asymmetric spectral response. This transformation leads to a significant enhancement in isolation (up to 40 dB) and a doubling of spectral bandwidth (up to 200 MHz). We base our analysis on realistic circuit parameters and substantiate it with existing experimental results and comprehensive quantum simulations. Our research paves the way for creating compact, high-performance, planar-compatible non-reciprocal quantum devices. These devices could revolutionize quantum computing, communication, and sensing by offering improved noise protection and broader bandwidth.
From Earlier Exploration to Advanced Applications: Bibliometric and Systematic Review of Augmented Reality in the Tourism Industry (2002–2022)

Augmented reality has emerged as a transformative technology, with the potential to revolutionize the tourism industry. Nonetheless, there is a scarcity of studies tracing the progression of AR and its application in tourism, from early exploration to recent advancements. This study aims to provide a comprehensive overview of the evolution, contexts, and design elements of AR in tourism over the period (2002–2022), offering insights for further progress in this domain. Employing a dual-method approach, a bibliometric analysis was conducted on 861 articles collected from the Scopus and Web of Science databases, to investigate the evolution of AR research over time and across countries, and to identify the main contexts of the utilization of AR in tourism. In the second part of our study, a systematic content analysis was conducted, focusing on a subset of 57 selected studies that specifically employed AR systems in various tourism situations. Through this analysis, the most commonly utilized AR design components, such as tracking systems, AR devices, tourism settings, and virtual content were summarized. Furthermore, we explored how these components were integrated to enhance the overall tourism experience. The findings reveal a growing trend in research production, led by Europe and Asia. Key contexts of AR applications in tourism encompass cultural heritage, mobile AR, and smart tourism, with emerging topics such as artificial intelligence (AI), big data, and COVID-19. Frequently used AR design components comprise mobile devices, marker-less tracking systems, outdoor environments, and visual overlays. Future research could involve optimizing AR experiences for users with disabilities, supporting multicultural experiences, integrating AI with big data, fostering sustainability, and remote virtual tourism. This study contributes to the ongoing discourse on the role of AR in shaping the future of tourism in the post COVID-19 era, by providing valuable insights for researchers, practitioners, and policymakers in the tourism industry.
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework

Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown impressive performance for the video analytics task. However, these newly introduced methods either exclusively focus on model performance or the effectiveness of these models in terms of computational efficiency, resulting in a biased trade-off between robustness and computational efficiency in their proposed methods to deal with challenging HAR problem. To enhance both the accuracy and computational efficiency, this paper presents a computationally efficient yet generic spatial-temporal cascaded framework that exploits the deep discriminative spatial and temporal features for HAR. For efficient representation of human actions, we propose an efficient dual attentional convolutional neural network (DA-CNN) architecture that leverages a unified channel-spatial attention mechanism to extract human-centric salient features in video frames. The dual channel-spatial attention layers together with the convolutional layers learn to be more selective in the spatial receptive fields having objects within the feature maps. The extracted discriminative salient features are then forwarded to a stacked bi-directional gated recurrent unit (Bi-GRU) for long-term temporal modeling and recognition of human actions using both forward and backward pass gradient learning. Extensive experiments are conducted on three publicly available human action datasets, where the obtained results verify the effectiveness of our proposed framework (DA-CNN+Bi-GRU) over the state-of-the-art methods in terms of model accuracy and inference runtime across each dataset. Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.
A superior image inpainting scheme using Transformer-based self-supervised attention GAN model

Image inpainting is the task of reconstructing the missing regions in diverse imaging as well as graphic applications which includes object elimination, image restoration, as well as manipulation. Although various deep learning techniques have attained a notable progress in restoration of images, because of local nature of different operations, they cannot effectively capture both global as well as semantic information, leading to issues such as structural ambiguity and semantic incompleteness, particularly for larger missing areas. To overcome these limitations, this article proposes a novel Transformer-based self-supervised attention-generating adversarial image painting method that leverages the Transformer's self-attention strategy to capture global semantic data. The proposed technique establishes a self-supervised attention module in the Transformer to overcome the limitations of convolutional operations. Additionally, a hierarchical Swin Transformer with a shift window is used in the discriminator to remove the image's contextual features, while semantic feature learning is performed using the transformer structure. The generator also employs a depthwise over-parameterized convolutional layer (DO-Conv) to extract features and enhance model performance. The experimental evaluations are carried out and result analysis demonstrated that the proposed technique outperformed various other existing approaches. Overall, the proposed method effectively addresses the limitations of existing approaches and demonstrates the superiority of the Transformer-based generative adversarial network structure over the full convolution approach.
Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering

Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. Firstly, the algorithm combines two detection methods, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to extract descriptive features by setting a low contrast threshold. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm removes mismatched pairs and reduces false positives. To improve the accuracy of forgery localization, the algorithm uses the original image and the image transformed by the affine matrix to compare similarities in the same position in order to locate the forged region. The proposed method was tested on two datasets (Ardizzone and CoMoFoD). The experimental results show that the method effectively improved the accuracy of forgery detection in smooth regions, reduced computational complexity, and exhibited strong robustness against post-processing operations such as rotation, scaling, and noise addition.
Four-Dimensional Trajectory Planning Algorithm for Fixed-Wing Aircraft Formation Based on Improved Hunter—Prey Optimization

The aircraft four-dimensional trajectory planning is an important technology for multiple aircraft to achieve cooperation. However, the current four-dimensional trajectory planning technology is mainly used for civil aviation and helicopters and is difficult to meet the requirements of fixed-wing aircraft. This paper proposed a four-dimensional trajectory planning algorithm for a fixed-wing aircraft formation, considering the speed range, turning radius and maximum overload. The improved tau-J strategy (ITJS) is used to generate the four-dimensional trajectory of the aircraft. This strategy is a bio-inspired trajectory planning algorithm that can generate a four-dimensional trajectory with continuous acceleration. Furthermore, the improved hunter–prey optimization (IHPO) algorithm is used to optimize the trajectory to make the generated trajectory meet the constraints and speed up the algorithm convergence. This algorithm improves the updated strategy and initialization strategy based on the hunter–prey optimization (HPO) algorithm, which prevents the algorithm from falling into local optima. The results of the benchmark test function show that the optimization result of the algorithm is improved by more than 10% compared with the original HPO algorithm. The simulation results show that the proposed algorithm jumps out of local optima and generates a trajectory that meets the constraints.
Complete coverage path planning for wheeled agricultural robots

Abstract In the agricultural industry, an evolutionary effort has been made over the last two decades to achieve precise autonomous systems to perform typical in‐field tasks, including harvesting, mowing, and spraying. One of the main objectives of an autonomous system in agriculture is to improve the efficiency while reducing the environmental impact and cost. Due to the nature of these operations, complete coverage path planning (CCPP) approaches play an essential role to find an optimal path which covers the entire field while taking into account land topography, operation requirements, and robot characteristics. The aim of this paper is to propose a CCPP approach defining the optimal movements of mobile robots over an agricultural field. First, a method based on tree exploration is proposed to find all potential solutions satisfying some predefined constraints. Second, a similarity check and selection of optimal solutions method is proposed to eliminate similar solutions and find the best solutions. The optimization goals are to maximize the coverage area and to minimize overlaps, nonworking path length, and overall travel time. To explore a wide range of possible solutions, our approach is able to consider multiple entrances for the robot. For fields with a complex shape, different dividing lines to split them into simple polygons are also considered. Our approach also computes the headland zones and covers them automatically which leads to a high coverage rate of the field.
CFRWD-GAN for SAR-to-Optical Image Translation

Synthetic aperture radar (SAR) images have been extensively used in earthquake monitoring, resource survey, agricultural forecasting, etc. However, it is a challenge to interpret SAR images with severe speckle noise and geometric deformation due to the nature of radar imaging. The translation of SAR-to-optical images provides new support for the interpretation of SAR images. Most of the existing translation networks, which are based on generative adversarial networks (GANs), are vulnerable to part information loss during the feature reasoning stage, making the outline of the translated images blurred and semantic information missing. Aiming to solve these problems, cross-fusion reasoning and wavelet decomposition GAN (CFRWD-GAN) is proposed to preserve structural details and enhance high-frequency band information. Specifically, the cross-fusion reasoning (CFR) structure is proposed to preserve high-resolution, detailed features and low-resolution semantic features in the whole process of feature reasoning. Moreover, the discrete wavelet decomposition (WD) method is adopted to handle the speckle noise in SAR images and achieve the translation of high-frequency components. Finally, the WD branch is integrated with the CFR branch through an adaptive parameter learning method to translate SAR images to optical ones. Extensive experiments conducted on two publicly available datasets, QXS-SAROPT and SEN1-2, demonstrate a better translation performance of the proposed CFRWD-GAN compared to five other state-of-the-art models.
Using association rules analysis to determine favorable mineralization sites in the Jiaojia gold belt, Jiaodong Peninsula, East China

Mesozoic orogenic gold deposits in the Jiaojia goldfield, Eastern China show the features of regular mineralization spatial distribution and have long been captivated the attention of geologists. This study integrates 3D modeling, model space analysis, and association rule analysis to extract the relationship between structural deformation and gold mineralization. The Apriori algorithm with spatial constraints is used to quantify the favorable mineralization sites in the Jiaojia gold field using different combinations of ore-controlling indicators. The first favorable mineralization site has a 44% probability of mineralization and is primarily located in segments of the fault footwall within 25 m of the fault buffer where the transition from steep to gentle occurs (∼1–24). Its uniform distribution indicates that the ore-bearing space near the main fault plane is relatively wide, and hydrothermal diffusion is relatively uniform. Site No. 2 has a 45% probability of mineralization and is located in convex segments (6–35 m) between 25 and 55 m of the fault footwall. The equidistant zonal distribution in this area indicates that secondary faults and fractures have facilitated the migration of hydrothermal fluids, resulting in the formation of extensive ore-bearing spaces. Site No. 3 sited in convex segments (∼0–6 m) distal to the fault footwall (∼55–515 m) of the fault buffer where the dip is gentle (∼30°–35°). The binary structure of differing dips formed by the intersection of steeply dipping orebodies and the slowly dipping orebodies in the central and southern parts of the Nanlü-Xinmu deposit exhibit the complex channel away from the main fault. In this study, the correlation between ore-controlling factors and mineralization in Jiaojia fold field is quantitatively expressed as favorable mineralization sites in the form of association rules, it would contribute to a deeper understanding of the underlying mechanisms governing the formation of structure-controlled orogenic gold deposits in the Jiaojia goldfield.
ADMM and spectral proximity operators in hyperspectral broadband phase retrieval for quantitative phase imaging

The hyperspectral broadband phase retrieval is developed for a scenario where both object and modulation phase masks are spectrally varying. The proposed iterative algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and the novel Spectral Proximity Operators derived for Gaussian and Poissonian multiple intensity observations. These proximity operators solve two problems. First, the complex-domain spectral components of the object are extracted from the total intensity observations calculated as the sums of the spectral intensities of diffractive patterns. Second, noisy observations are filtered, compromising noisy intensity observations and their predicted counterparts. The simulation and physical tests confirm that the broadband hyperspectral phase retrieval in the proposed formulation can be successfully resolved.