Reviewerly/exHarmony huggingface dataset:
TRTCD: trust route prediction based on trusted community detection

Social networks have become increasingly popular and are used for various activities. It is essential to evaluate the trustworthiness of the path between two unknown users in social networks. However, there are usually many social routes between them. In applications of trust, confidence relations among users need to be predicted. Trust route prediction predicts a new trust relationship between two users who are not currently connected. Thus, a challenging problem is finding which social trust route is optimal to yield the most trustworthy route. As a result, this process faces many challenges, such as the sparsity of user-specified trust relations, context awareness of trust, and changes in trust values over time. A new trust route prediction framework was proposed in this paper to enhance prediction accuracy. Considering community relations and node information for community detection, the proposed trust route prediction algorithm, TRTCD, is introduced. The effect of node and community information on link prediction accuracy was empirically investigated here using seven parameters. Experiments on eleven real-world datasets showed that the proposed method performed better than the fourteen existing methods. Based on the obtained experimental results, the proposed method performs better than other methods regarding accuracy and cost. The results show that the TRTCD has, on average, 22% better on eight directed datasets. The results of the NDCG measure show that the TRTCD can reach the average value of trust very close to 1, which is outstanding and performs better than other algorithms in terms of the ATCE criterion since more trusted and integrated communities are identified. In addition, the results show that the TRTCD can be successfully used in directed social networks but needs to work better in undirected social networks.
Variational Tobit Gaussian Process Regression

Abstract We propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires specialized techniques to perform inference since the resulting probabilistic models are typically analytically intractable. In this article we exploit the variational sparse Gaussian process inducing variable framework and local variational methods to compute an analytically tractable lower bound on the true log marginal likelihood of the probabilistic model which can be used to perform Bayesian model training and inference. We demonstrate the proposed framework on synthetically-produced, noise-corrupted observational data, as well as on a real-world data set, subject to artificial censoring. The resulting predictions are comparable to existing methods to account for data censoring, but provides a significant reduction in computational cost.
A cloud-oriented siamese network object tracking algorithm with attention network and adaptive loss function

Abstract Aiming at solving the problems of low success rate and weak robustness of object tracking algorithms based on siamese network in complex scenes with occlusion, deformation, and rotation, a siamese network object tracking algorithm with attention network and adaptive loss function (SiamANAL) is proposed. Firstly, the multi-layer feature fusion module for template branch (MFFMT) and the multi-layer feature fusion module for search branch (MFFMS) are designed. The modified convolutional neural networks (CNN) are used for feature extraction through the fusion module to solve the problem of features loss caused by too deep network. Secondly, an attention network is introduced into the SiamANAL algorithm to calculate the attention of template map features and search map features, which enhances the features of object region, reduces the interference of background region, and improves the accuracy of the algorithm. Finally, an adaptive loss function combined with pairwise Gaussian loss function and cross entropy loss function is designed to increase inter-class separation and intra-class compactness of classification branches and improve the accuracy rate of classification and the success rate of regression. The effectiveness of the proposed algorithm is verified by comparing it with other popular algorithms on two popular benchmarks, the visual object tracking 2018 (VOT2018) and the object tracking benchmark 100 (OTB100). Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The success rate and precision rate of the proposed algorithm SiamANAL on OTB100 are 0.709 and 0.883, respectively. With the help of cloud computing services and data storage, the processing performance of the proposed algorithm can be further improved.
Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

Digital filtering is essential for digital imaging, image recognition, and super-resolution technology. For example, the presence of noise in images captured by digital cameras causes deterioration of the image quality and image recognition rate. In order to improve the image recognition rate, noise reduction and edge preservation must be performed during preprocessing. Noise is generally reduced using low-pass filters, such as the Gaussian filter. Although they reduce noise, such filters also have the properties of blurring edge. A strong edge blur reduces the accuracy of the feature detection in image recognition. Therefore, in our previous study, a fast M-estimation Gaussian filter for images (FMGFI) was proposed as an image filter that simultaneously achieves denoising and edge preservation. In the FMGFI, the setting of the optimal basic width of the 2nd order B-spline basis functions is important for achieving simultaneous denoising and edge preservation. In this method, the optimal basic width of the FMGFI was determined not only by manually setting the basic width but also by human judgment of the filtered images. Consequently, the inability to automatically determine the optimal basic width hindered efficient denoising during image processing Therefore, in this research, we develop and propose a method that can automatically determine the optimal basic width of the FMGFI. The previously proposed method calculates using the same basic width for all the pixels over the entire image; in contrast, the proposed method calculates using the basic width automatically determined for each pixel. The experiments confirmed that the method proposed in this study achieves higher denoising and edge preservation performance than the ones used in previous research. The results also showed that it has the highest denoising performance against salt-and-pepper noise as compared to other filters: non-local mean filter, Gaussian filter, median filter, bilateral filter, adaptive bilateral filter, and FMGFI. The experimental results for the Gaussian noise sowed that the proposed method has the same denoising and edge preservation performance as the other filters in visual evaluation. From the above, the proposed method is expected to contribute to efficient denoising and improvement of image quality by using it as a preprocessing.
Neutron Activation Analysis for Geochemical Characterization of Rocks from Gold Mines in Egypt

The present work was carried out to determine the elemental composition of granitoid rock samples from three gold mines (Sukari, Hamash, and Um Hagalig) in the South-eastern part of Egypt and quantification of the extent of the elemental enrichment. A total of 37 samples from the mines were subjected to neutron activation analysis, and the mass fractions of the elements were measured in mg/kg. The results show a dominance of K (76.9%), Fe (11.0%), Ca (8.7%), and Na (3.1%) for Sukari mine; Na (31.2%), Ca (28.5%), K (23.1%), and Fe (9.9%) for the Hamash mine; and for the Um Hagalig mine Na (31.1%), K (22%), Ca (21.7%), and Fe (20.8%). In addition, significant mass fractions of uranium, thorium and rare earth elements were found in Hamash and Um Hagalig. The average mass fractions of U in the investigated areas are measured to be 59.7, 48.2, and 30.8 mg/kg for Hamash, Sukary, and Um Hagalig, respectively. Furthermore, the average mass fractions of Th were significant and measured to be 3905, 1673, and 7 mg/kg for Hamash, Um Hagalig, and Sukary, respectively. Multiple ratio indicators and discrimination diagrams were used to better understand the origin of the elements in the samples studied. The indicators suggest that the provenance of the elements is mainly from metavolcanic and volcanic rocks. The findings should make an important contribution to the study of ores and minerals and thus represent an important area for environmental studies.
Atlas-Based Shared-Boundary Deformable Multi-Surface Models through Multi-Material and Two-Manifold Dual Contouring

This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The dual contouring (DC) algorithm is a grid-based process used to generate surface meshes from volumetric data. The DC method enables the insertion of vertices anywhere inside the grid cube, as opposed to the marching cubes (MC) algorithm, which can insert vertices only on the grid edges. This multi-material DC method is then applied to initialize, by duality, a deformable multi-surface simplex model, which can be used for multi-surface atlas-based segmentation. We demonstrate our proposed method on synthetic and deep-brain atlas data, and a comparison of our method’s results with those of traditional DC demonstrates its effectiveness.
Full-BAPose: Bottom Up Framework for Full Body Pose Estimation

We present Full-BAPose, a novel bottom-up approach for full body pose estimation that achieves state-of-the-art results without relying on external people detectors. The Full-BAPose method addresses the broader task of full body pose estimation including hands, feet, and facial landmarks. Our deep learning architecture is end-to-end trainable based on an encoder-decoder configuration with HRNet backbone and multi-scale representations using a disentangled waterfall atrous spatial pooling module. The disentangled waterfall module leverages the efficiency of progressive filtering, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Additionally, it combines multi-scale features obtained from the waterfall flow with the person-detection capability of the disentangled adaptive regression and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes. Full-BAPose achieves state-of-the art performance on the challenging CrowdPose and COCO-WholeBody datasets, with AP of 72.2% and 68.4%, respectively, based on 133 keypoints. Our results demonstrate that Full-BAPose is efficient and robust when operating under a variety conditions, including multiple people, changes in scale, and occlusions.
HandFormer: A Dynamic Hand Gesture Recognition Method Based on Attention Mechanism

The application of dynamic gestures is extensive in the field of automated intelligent manufacturing. Due to the temporal and spatial complexity of dynamic gesture data, traditional machine learning algorithms struggle to extract accurate gesture features. Existing dynamic gesture recognition algorithms have complex network designs, high parameter counts, and inadequate gesture feature extraction. In order to solve the problems of low accuracy and high computational complexity in current dynamic gesture recognition, a network model based on the MetaFormer architecture and an attention mechanism was designed. The proposed network fuses a CNN (convolutional neural network) and Transformer model by embedding spatial attention convolution and temporal attention convolution into the Transformer model. Specifically, the token mixer in the MetaFormer block is replaced by the Spatial Attention Convolution Block and Temporal Attention Convolution Block to obtain the Spatial Attention Former Block and Temporal Attention Former Block. Firstly, each frame of the input image is quickly down-sampled by the PoolFormer block and then input to the Spatial Attention Former Block to learn spatial feature information. Then, the spatial feature maps learned from each frame are concatenated along the channel dimension and input to the Temporal Attention Former Block to learn the temporal feature information of the gesture action. Finally, the learned overall feature information is classified to obtain the category of dynamic gestures. The model achieves an average recognition accuracy of 96.72% and 92.16% on two publicly available datasets, Jester and NVGesture, respectively.
Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a Probabilistic Hough Transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive Probability Hough Transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.
Scaling Up Security and Efficiency in Financial Transactions and Blockchain Systems

Blockchain, the underlying technology powering the Bitcoin cryptocurrency, is a distributed ledger that creates a distributed consensus on a history of transactions. Cryptocurrency transaction verification takes substantially longer than it does for conventional digital payment systems. Despite blockchain’s appealing benefits, one of its main drawbacks is scalability. Designing a solution that delivers a quicker proof of work is one method for increasing scalability or the rate at which transactions are processed. In this paper, we suggest a solution based on parallel mining rather than solo mining to prevent more than two miners from contributing an equal amount of effort to solving a single block. Moreover, we propose the idea of automatically selecting the optimal manager over all miners by using the particle swarm optimization (PSO) algorithm. This process solves many problems of blockchain scalability and makes the system more scalable by decreasing the waiting time if the manager fails to respond. Additionally, the proposed model includes the process of a reward system and the distribution of work. In this work, we propose the particle swarm optimization proof of work (PSO-POW) model. Three scenarios have been tested including solo mining, parallel mining without using the PSO process, and parallel mining using the PSO process (PSO-POW model) to ensure the power and robustness of the proposed model. This model has been tested using a range of case situations by adjusting the difficulty level and the number of peers. It has been implemented in a test environment that has all the qualities required to perform proof of work for Bitcoin. A comparison between three different scenarios has been constructed against difficulty levels and the number of peers. Local experimental assessments were carried out, and the findings show that the suggested strategy is workable, solves the scalability problems, and enhances the overall performance of the blockchain network.
Accuracy verification of optical fingerprinting methods in sediment tracing study

Abstract Sediment fingerprints have been widely used in source identification studies. Optical features have become a powerful substitute for traditional fingerprints in recent years due to their fast measurement and low analysis costs. However, the accuracy of optical fingerprinting methods has received little attention. Here, artificial mixing and indoor scouring experiments were carried out to compare and assess the accuracy of optical fingerprinting results using three spectroscopic ranges [visible (VIS), near‐infrared (NIR), and mid‐infrared (MIR) spectroscopy] in multivariate models and using 19 colour parameters [such as red (R), green (G) and blue (B) in an RGB system; virtual component X (X), brightness (Y) and virtual component Z (Z) in a CIE XYZ system and so on] coupled with the conventional method. Furthermore, we examined how sediment sorting (particle sorting and organic matter enrichment) affects the accuracy of source apportionments. The results showed that VIS, NIR and MIR spectroscopic tracers presented high accuracy in scouring and artificial mixtures, with mean absolute error (MAE) values of 4.98% and 5.91%. In contrast, the colour parameters had weak performances in two experiments (MAE = 16.83% and 15.05%). Additionally, similar fingerprinting results of the scouring mixtures (MAE = 7.95%) and artificial mixtures (MAE = 8.20%) indicated that slight particle sorting and organic matter enrichment have little effect on the accuracy of optical fingerprinting results. Our study shows that sediment fingerprinting based on optical features, especially three spectroscopic ranges, has good applicability in sediment source identification.
A blockchain-based traceable and secure data-sharing scheme

The traditional data-sharing model relies on a centralized third-party platform, which presents challenges such as poor transaction transparency and unsecured data security. In this article, we propose a blockchain-based traceable and secure data-sharing scheme. Firstly, we designed an attribute encryption-based method to protect data and enable fine-grained shared access. Secondly, we developed a secure data storage scheme that combines on-chain and off-chain collaboration. The InterPlanetary File System (IPFS) is used to store encrypted data off-chain, and the hash value of encrypted data is stored on the blockchain. To improve data security, elliptic curve cryptography (ECC) encryption is performed before the hash value is stored. Finally, we designed a smart contract-based log tracking mechanism. The mechanism stores data sharing records on the blockchain and displays them in a visual form to meet the identity tracking needs of both data sharing parties. Experimental results show that our scheme can effectively secure data, track the identities of both parties sharing data in real-time, and ensure high data throughput.
LW-ViT: The Lightweight Vision Transformer Model Applied in Offline Handwritten Chinese Character Recognition

In recent years, the transformer model has been widely used in computer-vision tasks and has achieved impressive results. Unfortunately, these transformer-based models have the common drawback of having many parameters and a large memory footprint, causing them to be difficult to deploy on mobiles as lightweight convolutional neural networks. To address these issues, a Vision Transformer (ViT) model, named the lightweight Vision Transformer (LW-ViT) model, is proposed to reduce the complexity of the transformer-based model. The model is applied to offline handwritten Chinese character recognition. The design of the LW-ViT model is inspired by MobileViT. The lightweight ViT model reduces the number of parameters and FLOPs by reducing the number of transformer blocks and the MV2 layer based on the overall framework of the MobileViT model. The number of parameters and FLOPs for the LW-ViT model was 0.48 million and 0.22 G, respectively, and it ultimately achieved a high recognition accuracy of 95.8% on the dataset. Furthermore, compared to the MobileViT model, the number of parameters was reduced by 53.8%, and the FLOPs were reduced by 18.5%. The experimental results show that the LW-ViT model has a low number of parameters, proving the correctness and feasibility of the proposed model.
SS-BERT: A Semantic Information Selecting Approach for Open-Domain Question Answering

Open-Domain Question Answering (Open-Domain QA) aims to answer any factoid questions from users. Recent progress in Open-Domain QA adopts the “retriever-reader” structure, which has proven effective. Retriever methods are mainly categorized as sparse retrievers and dense retrievers. In recent work, the dense retriever showed a stronger semantic interpretation than the sparse retriever. When training a dual-encoder dense retriever for document retrieval and reranking, there are two challenges: negative selection and a lack of training data. In this study, we make three major contributions to this topic: negative selection by query generation, data augmentation from negatives, and a passage evaluation method. We prove that the model performs better by focusing on false negatives and data augmentation in the Open-Domain QA passage rerank task. Our model outperforms other single dual-encoder rerankers over BERT-base and BM25 by 0.7 in MRR@10, achieving the highest Recall@50 and the max Recall@1000, which is restricted by the BM25 retrieval results.
Cluster Validity Index for Uncertain Data Based on a Probabilistic Distance Measure in Feature Space

Cluster validity indices (CVIs) for evaluating the result of the optimal number of clusters are critical measures in clustering problems. Most CVIs are designed for typical data-type objects called certain data objects. Certain data objects only have a singular value and include no uncertainty, so they are assumed to be information-abundant in the real world. In this study, new CVIs for uncertain data, based on kernel probabilistic distance measures to calculate the distance between two distributions in feature space, are proposed for uncertain clusters with arbitrary shapes, sub-clusters, and noise in objects. By transforming original uncertain data into kernel spaces, the proposed CVI accurately measures the compactness and separability of a cluster for arbitrary cluster shapes and is robust to noise and outliers in a cluster. The proposed CVI was evaluated for diverse types of simulated and real-life uncertain objects, confirming that the proposed validity indexes in feature space outperform the pre-existing ones in the original space.