Reviewerly/exHarmony huggingface dataset:
Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.
Efficient Resource Allocation in Blockchain-Assisted Health Care Systems

Smart health care will be a major application in future smart cities. Timely and precise delivery of patients’ data to their medical consultant, to allow the necessary actions, is one of the basic needs in health care systems. Blockchain technology, with the provisioning of recording and tracking of data blocks, guarantees secure and error-free data delivery. The vital sign data from patients’ sensors are placed in different data blocks. To become a part of the blockchain, the block must contain a valid key, based on a hash function. Mining nodes with high processing capabilities generate the required key using a 32-bit number, known as a nonce, which is changed for every new block. Finding a nonce that meets the hash function requirements is a time-intensive process in blockchain technology and is performed by several fog mining nodes. However, an efficient resource allocation that results in the fair placement of data in these fog mining nodes, while maintaining the priority and sensitivity of patients’ data, is a challenge. This work proposes two algorithms for the resource allocation of mining nodes. The first algorithm uses a load balancing technique to distribute the load of nonce computing tasks. The second algorithm utilizes the knapsack algorithm to allocate the caching space of the mining nodes. The simulation results highlighted that the proposed resource allocation techniques outperformed the existing techniques, in terms of quick mining of the most sensitive patient data blocks.
Smooth Autonomous Patrolling for a Differential-Drive Mobile Robot in Dynamic Environments

Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment with smooth, efficient, and safe motion. In this paper, we propose an online smooth-motion-planning method that generates a smooth, collision-free patrolling trajectory based on clothoid curves. Moreover, the proposed method combines global and local planning methods, which are suitable for changing large environments and enabling efficient path replanning with an arbitrary robot orientation. We propose a method for planning a smoothed path based on the golden ratio wherein a robot's orientation is aligned with a new path that avoids unknown obstacles. The simulation results show that the proposed algorithm reduces the patrolling execution time, path length, and deviation of the tracked trajectory from the patrolling route compared to the original patrolling method without smoothing. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity, and its performance was validated through the results of an experiment employing a differential-drive mobile robot.
Current status and future perspectives for Augmented Reality navigation in neurosurgery and orthopedic surgery.

Augmented reality (AR) for surgical navigation is a relatively new but rapidly evolving and promising field. AR can add navigation to the surgical workflow in a more intuitive way, improving ergonomics and precision. Neurosurgery has a long tradition in computer-assisted surgical navigation and was the first discipline to use this technology to navigate interventions. Orthopedic surgery is following this path with a wide range of new use cases currently in development. In this chapter, we will describe the evolution of AR as a surgical navigation tool, focusing on application developed for neurosurgery and orthopedic surgery. Based on our own experience, we will also discuss future perspectives and the hurdles to be overcome before the widespread breakthrough of this technology.
A Multi-Task Classification Method for Application Traffic Classification Using Task Relationships

As IT technology advances, the number and types of applications, such as SNS, content, and shopping, have increased across various fields, leading to the emergence of complex and diverse application traffic. As a result, the demand for effective network operation, management, and analysis has increased. In particular, service or application traffic classification research is an important area of study in network management. Web services are composed of a combination of multiple applications, and one or more application traffic can be mixed within service traffic. However, most existing research only classifies application traffic by service unit, resulting in high misclassification rates and making detailed management impossible. To address this issue, this paper proposes three multitask learning methods for application traffic classification using the relationships among tasks composed of browsers, protocols, services, and application units. The proposed methods aim to improve classification performance under the assumption that there are relationships between tasks. Experimental results demonstrate that by utilizing relationships between various tasks, the proposed method can classify applications with 4.4%p higher accuracy. Furthermore, the proposed methods can provide network administrators with information about multiple perspectives with high confidence, and the generalized multitask methods are freely portable to other backbone networks.
Wavelet and Earth Mover’s Distance Coupling Denoising Techniques

The widely used wavelet-thresholding techniques (DWT-H and DWT-S) have a near-optimal behavior that cannot be enhanced by any local denoising filter, but they cannot utilize the similarity of small-size image patches to enhance the denoising performance. Two of the latest improvements (WNLM and NLMW) introduced the Euclidean distance to measure the similarity of image patches, and then used the non-local meaning of similar patches for further denoising. Since the Euclidean distance is not a good similarity measurement, these two improvements are limited. In this study, we introduced the earth mover’s distance (EMD) as the similarity measure of small-scale patches within the wavelet sub-bands of noisy images. Moreover, at higher noise levels, we further incorporated joint bilateral filtering, which can filter both the spatial domain and the intensity domain of images. Denoising simulation experiments on BSDS500 demonstrated that our algorithm outperformed the DWT-H, DWT-S, WNLM, and NLMW algorithms by 4.197 dB, 3.326 dB, 2.097 dB, and 1.162 dB in terms of the average PSNR, and by 0.230, 0.213, 0.132, and 0.085 in terms of the average SSIM.
Fibonacci Group Consensus Algorithm Based on Node Evaluation Mechanisms

In response to challenges posed by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, where all nodes are involved in the consensus process, issues such as high communication overhead in the three-phase procedure, random selection of master nodes, and the absence of reward and penalty mechanisms are addressed. This leads to the proposal of a Speculative Practical Byzantine Fault Tolerance (SP-PBFT) consensus algorithm based on a node evaluation mechanism for speculative grouping. Firstly, the consensus protocol was optimized, and a timeout mechanism was proposed to divide the consensus process into an optimistic mode and a pessimistic mode, which reduced the communication overhead under the premise of resisting Byzantine node attacks. Secondly, a node evaluation mechanism was proposed to calculate the node reputation value according to the node’s historical behavior and give the corresponding reward or punishment, which reduced the possibility of malicious nodes participating in the consensus process. Finally, the Fibonacci grouping mechanism was used to reduce the number of nodes participating in the consensus process, fundamentally improving the consensus efficiency, avoiding the problem of centralization of the consensus process caused by the cumulative reputation value of nodes, and improving the enthusiasm of consensus nodes. Simulation experiments using Docker containers to simulate multiple nodes show that the SP-PBFT consensus algorithm proposed in this paper has better performance than the PBFT consensus algorithm and other improved algorithms in terms of consensus delay, throughput, fault tolerance and communication complexity.
Feature fusion over hyperbolic graph convolution networks for video summarisation

Abstract A novel video summarisation method called the Hyperbolic Graph Convolutional Network (HVSN) is proposed, which addresses the challenges of summarising edited videos and capturing the semantic consistency of video shots at different time points. Unlike existing methods that use linear video sequences as input, HVSN leverages Hyperbolic Graph Convolutional Networks (HGCNs) and an adaptive graph convolutional adjacency matrix network to learn and aggregate features from video shots. Moreover, a feature fusion mechanism based on the attention mechanism is employed to facilitate cross‐module feature fusion. To evaluate the performance of the proposed method, experiments are conducted on two benchmark datasets, TVSum and SumMe. The results demonstrate that HVSN achieves state‐of‐the‐art performance, with F1‐scores of 62.04% and 50.26% on TVSum and SumMe, respectively. The use of HGCNs enables the model to better capture the complex spatial structures of video shots, and thus contributes to the improved performance of video summarisation.
Seismology in the cloud: guidance for the individual researcher

The commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a reproducible earthquake detection and association workflow that runs on Microsoft Azure. The Python-based workflow runs on continuous time-series data using both template matching and machine learning. We provide tutorials for constructing cloud resources (both storage and computing) through a desktop portal and deploying the code both locally and remotely on the cloud resources. We report on scaling of compute times and costs to show that CPU-only processing is generally inexpensive, and is faster and simpler than using GPUs. When the workflow is applied to one year of continuous data from a mid-ocean ridge, the resulting earthquake catalogs suggest that template matching and machine learning are complementary methods whose relative performance is dependent on site-specific tectonic characteristics. Overall, we find that the commercial cloud presents a steep learning curve but is cost-effective. This report is intended as an informative starting point for any researcher considering migrating their own processing to the commercial cloud.
INCHAIN: a cyber insurance architecture with smart contracts and self-sovereign identity on top of blockchain

Abstract Despite the rapid growth of the cyber insurance market in recent years, insurance companies in this area face several challenges, such as a lack of data, a shortage of automated tasks, increased fraudulent claims from legal policyholders, attackers masquerading as legal policyholders, and insurance companies becoming targets of cybersecurity attacks due to the abundance of data they store. On top of that, there is a lack of Know Your Customer procedures. To address these challenges, in this article, we present , an innovative architecture that utilizes Blockchain technology to provide data transparency and traceability. The backbone of the architecture is complemented by Smart Contracts, which automate cyber insurance processes, and Self-Sovereign Identity for robust identification. The effectiveness of ’s architecture is compared with the literature against the challenges the cyber insurance industry faces. In a nutshell, our approach presents a significant advancement in the field of cyber insurance, as it effectively combats the issue of fraudulent claims and ensures proper customer identification and authentication. Overall, this research demonstrates a novel and effective solution to the complex problem of managing cyber insurance, providing a solid foundation for future developments in the field.
Dynamic finegrained structured pruning sensitive to filter texture distribution

Pruning of neural networks is undoubtedly a popular approach to cope with the current compression of large-scale, high-cost network models. However, most of the existing methods require a high level of human-regulated pruning criteria, which requires a lot of human effort to figure out a reasonable pruning strength. One of the main reasons is that there are different levels of sensitivity distribution in the network. Our main goal is to discover compression methods that adapt to this distribution to avoid deep architectural damage to the network due to unnecessary pruning. In this paper, we propose a filter texture distribution that affects the training of the network. We also analyze the sensitivity of each of the diverse states of this distribution. To do so, we first use a multidimensional penalty method that can analyze the potential sensitivity based on this texture distribution to obtain a pruning-friendly sparse environment. Then, we set up a lightweight dynamic threshold container in order to prune the sparse network. By providing each filter with a suitable threshold for that filter at a low cost, a massive reduction in the number of parameters is achieved without affecting the contribution of certain pruning-sensitive layers to the network as a whole. In the final experiments, our two methods adapted to texture distribution were applied to ResNet Deep Neural Network (DNN) and VGG-16, which were deployed on the classical CIFAR-10/100 and ImageNet datasets with excellent results in order to facilitate comparison with good cutting-edge pruning methods. Code is available at https://github.com/wangyuzhe27/CDP-and-DTC.
Improving Medical Imaging with Medical Variation Diffusion Model: An Analysis and Evaluation

The Medical VDM is an approach for generating medical images that employs variational diffusion models (VDMs) to smooth images while preserving essential features, including edges. The primary goal of the Medical VDM is to enhance the accuracy and reliability of medical image generation. In this paper, we present a comprehensive description of the Medical VDM approach and its mathematical foundation, as well as experimental findings that showcase its efficacy in generating high-quality medical images that accurately reflect the underlying anatomy and physiology. Our results reveal that the Medical VDM surpasses current VDM methods in terms of generating faithful medical images, with a reconstruction loss of 0.869, a diffusion loss of 0.0008, and a latent loss of 5.740068×10-5. Furthermore, we delve into the potential applications of the Medical VDM in clinical settings, such as its utility in medical education and training and its potential to aid clinicians in diagnosis and treatment planning. Additionally, we address the ethical concerns surrounding the use of generated medical images and propose a set of guidelines for their ethical use. By amalgamating the power of VDMs with clinical expertise, our approach constitutes a significant advancement in the field of medical imaging, poised to enhance medical education, research, and clinical practice, ultimately leading to improved patient outcomes.
A lattice-based efficient heterogeneous signcryption scheme for secure network communications

Ensuring the security of transmitted content is an important task in network communication. Signcryption technology combines signature and encryption operations in a single step to achieve message authentication and confidentiality. In practical applications, users are usually in different cryptographic systems, the ordinary signcryption technology cannot realize communication between two different cryptographic systems. And the ciphertext structure of the existing signcryption schemes is relatively complex, signcryption is not efficient. Therefore, in order to solve the problem of efficient communication between different cryptosystems and ensure quantum security, this paper proposes a lattice-based efficient heterogeneous signcryption scheme for secure network communications. This signcryption scheme accomplishes signcryption through matrix operations and hash functions, which avoids complex signcryption structures, efficiently realizes bidirectional message communication between identity-based cryptosystems and certificateless cryptosystems.
A survey of high utility sequential patterns mining methods

In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given.
Text localization and recognition of Chinese characters in natural scenes based on improved faster R-CNN

Text detection and recognition are widely used in daily life. Although it is a very rich market, it has very difficult challenges in practical application. The complex and changeable natural scenes lead to the complex background of the text in the image, which also reflects the research value of text information recognition and extraction in natural scenes. To solve the many problems faced by the recognition of Chinese characters, such as complex shapes and diverse structures, this paper uses VGG16 to extract features and introduces a two-layer bidirectional LSTM network. It improves Faster R-CNN by using a RPN to extract candidate boxes and adjust the position of candidate regions. In this paper, the improved model Faster BLSTM-CNN is tested, and the effectiveness experiment of feature extraction, the difference comparison before and after the improvement of the algorithm, and the comparison experiment with the traditional recognition algorithm are carried out respectively. And it finally carried out an experimental comparison of the combination of text recognition and positioning, and obtained the results. The algorithm Faster BLSTM-CNN in this paper is better in the localization and recognition of Chinese characters in the dataset. In the natural scene, the recognition rate of Faster BLSTM-CNN in this paper is 81.54%, the positioning accuracy is 88.14%, and the detection speed is 86 ms, which has improved performance. Therefore, the improvement of Faster R-CNN is effective. It can effectively locate and recognize Chinese characters in natural scenes.
The Role of Internal Auditing in Improving the Accounting Information System in Jordanian Banks by Using Organizational Commitment as a Mediator

In light of the function of Internal Auditing and its significance in assessing and ensuring the validity of data, information, reports, and high lists generated by the Accounting Information System and improving its credibility and dependability, the purpose of this study was to investigate the relationship between Internal Auditing (IA) and Accounting Information System (AIS) in Jordanian banks, with a focus on the mediator role of Organizational Commitment (OC). A cross-sectional survey method was used to collect data from a sample of employees who work in banks, including those who work in the internal audit department. The collected data were analyzed using SPSS 26.0 and PROCESS V4.1. The study sample includes 193 employees who work in banks, including those who work in the internal audit department. Descriptive statistical methods, such as frequencies, percentages, means, and standard deviations, were employed to depict both the characteristics of the sample and the participants’ responses to the study items. The results indicate that IA has a positive relationship with AIS. Moreover, the results indicate that OC partially mediates the relationship between IA and AIS in Jordanian banks.
Scaffolding Strategies to Support English Language Learning in Reading Comprehension: A Case Study

Reading is one of the most challenging language skills for elementary school students. To obtain a literacygrasp of reading English texts, appropriate learning tactics and approaches are required. This study aimsto explore the scaffolding strategy and the students’ perception on the strategy they used to understandreading text. The study used the descriptive method by collecting data from interviews and questionnaires.Twenty-nine students were given questionnaires to know the impact and usefulness of the Scaffolding.Observation and interviews were used to see the situation in the learning process. Data collected wasanalyzed using qualitative data analysis techniques. Based on the result, there are three types of strategies,namely the pre-reading stage, the reading stage, and the post-reading stage. The types of Scaffoldinggiven by the teacher are Modeling, Bridging, Contextualizing, Schema Building, and Metacognition. Itindicates that scaffolding strategy helps the student pronounce the words and understand the meaning.The study implies that the scaffolding technique is expected to be a reference material and choice forevery teacher in learning to read and understand English texts. Scaffolding is a structured learning supporttechnique used at an early stage to encourage students to learn independently.