Reviewerly/exHarmony huggingface dataset:
Digital Image Correlation Analysis of Strain Fields in Fibre-Reinforced Polymer–Matrix Composite under ±45° Off-Axis Tensile Testing

This study presents an experimental investigation of an in-plane shear of a glass lamina composite using a ±45° off-axis tension test. Typically, the shear stress curve, shear modulus, and in-plane shear strength for composite lamina-type materials are identified. Previous research indicated that a loading rate affects the strength of this composite. This study extends the existing literature by utilising a non-contact optical digital image correlation (DIC) method to measure strain distribution during the test. Two cross-head displacement rates were examined. The obtained strain maps reveal an uneven distribution resembling fabric texture. As the deformation progresses, the differences in the strain pattern increase. Subsequently, a quantitative analysis of the differences between regions with extreme (minimum and maximum) strain values and regions with average values was conducted. Based on these measurements, shear stress-strain curves, indicating variations in their courses, were constructed. These differences may reach several percent and may influence the analysis of numerical simulations. The DIC results were validated using strain gauge measurements, a commonly utilised method in this test. It was demonstrated that the location of the strain gauge installation impacts the results. During the tests, the occurrence of multiple microcracks in the resin was observed, which can contribute to the nonlinearity observed in the shear stress-shear strain curve.
Smart Fuzzy Petri Net-Based Temperature Control Framework for Reducing Building Energy Consumption

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems.
Adaptive Multi-Scale Difference Graph Convolution Network for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have obtained remarkable performance in skeleton-based action recognition. However, previous approaches fail to capture the implicit correlations between joints and handle actions across varying time intervals. To address these problems, we propose an adaptive multi-scale difference graph convolution Network (AMD-GCN), which comprises an adaptive spatial graph convolution module (ASGC) and a multi-scale temporal difference convolution module (MTDC). The first module is capable of acquiring data-dependent and channel-wise graphs that are adaptable to both samples and channels. The second module employs the multi-scale approach to model temporal information across a range of time scales. Additionally, the MTDC incorporates an attention-enhanced module and difference convolution to accentuate significant channels and enhance temporal features, respectively. Finally, we propose a multi-stream framework for integrating diverse skeletal modalities to achieve superior performance. Our AMD-GCN approach was extensively tested and proven to outperform the current state-of-the-art methods on three widely recognized benchmarks: the NTU-RGB+D, NTU-RGB+D 120, and Kinetics Skeleton datasets.
High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

Pose recognition in character animations is an important avenue of research in computer graphics. However, the current use of traditional artificial intelligence algorithms to recognize animation gestures faces hurdles such as low accuracy and speed. Therefore, to overcome the above problems, this paper proposes a real-time 3D pose recognition system, which includes both facial and body poses, based on deep convolutional neural networks and further designs a single-purpose 3D pose estimation system. First, we transformed the human pose extracted from the input image to an abstract pose data structure. Subsequently, we generated the required character animation at runtime based on the transformed dataset. This challenges the conventional concept of monocular 3D pose estimation, which is extremely difficult to achieve. It can also achieve real-time running speed at a resolution of 384 fps. The proposed method was used to identify multiple-character animation using multiple datasets (Microsoft COCO 2014, CMU Panoptic, Human3.6M, and JTA). The results indicated that the improved algorithm improved the recognition accuracy and performance by approximately 3.5% and 8–10 times, respectively, which is significantly superior to other classic algorithms. Furthermore, we tested the proposed system on multiple pose-recognition datasets. The 3D attitude estimation system speed can reach 24 fps with an error of 100 mm, which is considerably less than that of the 2D attitude estimation system with a speed of 60 fps. The pose recognition based on deep learning proposed in this study yielded surprisingly superior performance, proving that the use of deep-learning technology for image recognition has great potential.
Task-Incremental Learning for Drone Pilot Identification Scheme

With the maturity of Unmanned Aerial Vehicle (UAV) technology and the development of Industrial Internet of Things, drones have become an indispensable part of intelligent transportation systems. Due to the absence of an effective identification scheme, most commercial drones suffer from impersonation attacks during their flight procedure. Some pioneering works have already attempted to validate the pilot’s legal status at the beginning and during the flight time. However, the off-the-shelf pilot identification scheme can not adapt to the dynamic pilot membership management due to a lack of extensibility. To address this challenge, we propose an incremental learning-based drone pilot identification scheme to protect drones from impersonation attacks. By utilizing the pilot temporal operational behavioral traits, the proposed identification scheme could validate pilot legal status and dynamically adapt newly registered pilots into a well-constructed identification scheme for dynamic pilot membership management. After systemic experiments, the proposed scheme was capable of achieving the best average identification accuracy with 95.71% on P450 and 94.23% on S500. With the number of registered pilots being increased, the proposed scheme still maintains high identification performance for the newly added and the previously registered pilots. Owing to the minimal system overhead, this identification scheme demonstrates high potential to protect drones from impersonation attacks.
MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario

To tackle the challenges posed by dense small objects and fuzzy boundaries on unstructured roads in the mining scenario, we proposed an end-to-end small object detection and drivable area segmentation framework for open-pit mining. We employed a convolutional network backbone as a feature extractor for both two tasks, as multi-task learning yielded promising results in autonomous driving perception. To address small object detection, we introduced a lightweight attention module that allowed our network to focus more on the spatial and channel dimensions of small objects without impeding inference time. We also used a convolutional block attention module in the drivable area segmentation subnetwork, which assigned more weight to road boundaries to improve feature mapping capabilities. Furthermore, to improve our network perception accuracy of both tasks, we used weighted summation when designing the loss function. We validated the effectiveness of our approach by testing it on pre-collected mining data which were called Minescape. Our detection results on the Minescape dataset showed 87.8% mAP index, which was 9.3% higher than state-of-the-art algorithms. Our segmentation results surpassed the comparison algorithm by 1 percent in MIoU index. Our experimental results demonstrated that our approach achieves competitive performance.
Federated Learning Incentive Mechanism Design via Shapley Value and Pareto Optimality

Federated learning (FL) is a distributed machine learning framework that can effectively help multiple players to use data to train federated models while complying with their privacy, data security, and government regulations. Due to federated model training, an accurate model should be trained, and all federated players should actively participate. Therefore, it is crucial to design an incentive mechanism; however, there is a conflict between fairness and Pareto efficiency in the incentive mechanism. In this paper, we propose an incentive mechanism via the combination of the Shapley value and Pareto efficiency optimization, in which a third party is introduced to supervise the federated payoff allocation. If the payoff can reach Pareto optimality, the federated payoff is allocated by the Shapley value method; otherwise, the relevant federated players are punished. Numerical and simulation experiments show that the mechanism can achieve fair payoff allocation and Pareto optimality payoff allocation. The Nash equilibrium of this mechanism is formed when Pareto optimality payoff allocation is achieved.
A Multi-Scale Dehazing Network with Dark Channel Priors

Image dehazing based on convolutional neural networks has achieved significant success; however, there are still some problems, such as incomplete dehazing, color deviation, and loss of detailed information. To address these issues, in this study, we propose a multi-scale dehazing network with dark channel priors (MSDN-DCP). First, we introduce a feature extraction module (FEM), which effectively enhances the ability of feature extraction and correlation through a two-branch residual structure. Second, a feature fusion module (FFM) is devised to combine multi-scale features adaptively at different stages. Finally, we propose a dark channel refinement module (DCRM) that implements the dark channel prior theory to guide the network in learning the features of the hazy region, ultimately refining the feature map that the network extracted. We conduct experiments using the Haze4K dataset, and the achieved results include a peak signal-to-noise ratio of 29.57 dB and a structural similarity of 98.1%. The experimental results show that the MSDN-DCP can achieve superior dehazing compared to other algorithms in terms of objective metrics and visual perception.
Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering

Surface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee products, the number of models has to be reduced and products with similar characteristics have to be grouped and overseen. In this paper, we show that it is possible to handle a large number of new products using latent vectors obtained from the autoencoder model. By hierarchically clustering latent vectors, the model finds product groups with similar characteristics and oversees them by group. Furthermore, we validate our multi-product operation strategy for anomaly detection with a newly collected SMD dataset. Experimental results show that the anomaly detection method using hierarchical clustering of latent vectors is a practical management method for SMD anomaly detection.
Cobaltite-gersdorffite solid solution as a collector of platinum-group elements in the Ban Phuc Ni-Cu-(PGE) sulfide deposit, northern Vietnam

The Ban Phuc Ni-Cu-(PGE) sulfide deposit in northern Vietnam contains no or few platinum-group minerals (PGM) in massive sulfide ores, and most PGM grains occur in altered disseminated sulfide ores, including sperrylite (PtAs2), irarsite-orarsite-hollingworthite [IrAsS, OsAsS, (Rh,Ir)AsS], and froodite-sobolevskite-sudburyite [PdBi2, PdBi, Pd(SbBi) and PdSb]. Discrete PGM grains are enclosed within serpentines and base metal sulfides (BMS) of the disseminated ores. In contrast, small amounts (0.004 vol%) of cobaltite-gersdorffite solid solution (CGss) in massive sulfide ores commonly contain high PGE concentrations relative to the BMS. Textural relationships reveal that the CGss grains crystallize earlier than the host BMS in massive sulfide ores. The CGss grains are found to contain all six PGE and act as the major hosts for the PGE in the massive sulfide ores. The fast Fourier transform (FFT) patterns of high resolution TEM images show that the irarsite (IrAsS) enclosed within the CGss shares the same zone axis of (2¯ 1 2¯), which results from the exsolution of irarsite from the CGss. On the other hand, the CGss grains without visible PGM show an even distribution of PGE in the crystal and respective FFT patterns of CGss crystals. This is an example of a growing number of cases that massive sulfide ores have been found to contain PGE in As-bearing minerals, either PGM including sperrylite and members of the irarsite-orarsite-hollingworthite solid solution series or CGss. These results argue that PGE in the sulfide melt may enter the CGss at high temperature before the crystallization of monosulfide solution, rather than soluble in BMS, even if the sulfide melt was not As-saturated.
A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

In recent years, there has been an unprecedented upsurge in applying deep learning approaches, specifically convolutional neural networks (CNNs), to solve image denoising problems, owing to their superior performance. However, CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. To meet this objective, we introduce a new selective convolutional (SeConv) block. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets. The results illustrate that the proposed SeConvNet model effectively restores images corrupted by SAP noise and surpasses all its counterparts at both quantitative criteria and visual effects, especially at high and very high noise densities.
HyperTP: A unified approach for live hypervisor replacement in datacenters

Maintenance of virtualized datacenters is often needed for the purposes of introducing new features, fixing bugs or mitigating security problems. However, current maintenance methods are either highly disruptive to the operation of VMs, utilize large amounts of computing resources or require high development efforts. We build HyperTP, a generic framework which combines in a unified way two approaches: in-place server micro-reboot-based hypervisor transplant (noted InPlaceTP) and live VM migration-based hypervisor transplant (noted MigrationTP). HyperTP hinges on a VM state hierarchy for organizing different types of hypervisor memory states in terms of their relation to VM execution, and an Unified Intermediate State Representation that abstracts VM-relevant memory states between multiple different hypervisors. We describe our implementations of both approaches, including technical details of our UISR design and the transplant process. Our evaluation results show that HyperTP delivers satisfactory performance: (1) MigrationTP changes a VM's underlying hypervisor while taking the same time and impacting virtual machines (VMs) with the same performance degradation as normal live migration; and (2) InPlaceTP imposes minimal VM downtime, even under increasing number of VMs and memory sizes. Finally, we discuss how the combination of InPlaceTP and MigrationTP can be used to address the challenges of upgrading a hypervisor cluster, and to mitigate known unpatched hypervisor vulnerabilities.
Semi-supervised progressive dehazing network using unlabeled contrastive guidance

Image dehazing aims to restore the missing high-quality content from its original hazy observation. Most of the existing learning-based methods achieve promising achievements by designing various networks. However, these approaches cannot generalize well on real-world scenes, since they fail to exploit natural haze priors. Towards this end, we propose a novel Semi-supervised Progressive Dehazing Network (Semi-PDNet), which leverages both synthetic and real-world images in training process. The overall network follows a progressive architecture, and can be divided into three core stages: image encode stage (IES), feature enhance stage (FES) and hierarchical reconstruction stage (HRS). Specifically, IES is responsible for encoding shallow features from the corrupted hazy image. Then, our FES tries to distill finer local and global features via the well-designed dual stream attentive block (DSAB). The HRS is to estimate semantic and contextual information based on a hierarchical structure, and accurately reconstructs the final clear image. This stage-by-stage paradigm can make full use of informative features from shallow to deep, thus facilitating network for better haze removal. Furthermore, we utilize an unlabeled contrastive guidance (UCG) to bridge the domain gap between synthetic and real-world images. Extensive experimental comparisons show that our Semi-PDNet can obtain comparable results with other state-of-the-art dehazing algorithms.
An efficient semi-supervised balanced cut with hard pairwise constraints and partial labels

With a small scale of prior information, including pairwise constraints and partial labels, semi-supervised clustering algorithms can improve clustering performance by imposing certain restrictions on the clustering results, and have accordingly attracted a lot of attention in recent years. Moreover, a large number of graph-based semi-supervised clustering algorithms have been proposed, since graphs are naturally suited to pairwise constraints. However, most graph-based approaches involve a two-step process and are not scalable to large-scale datasets due to their high time complexity. Additionally, they also encounter a serious problem of constraint violation. To deal with this issue, we propose a new non-parametric model to directly obtain the indicator matrix by minimizing the cut of the graph with a regularization term that corresponds to the balance of the clusters. To simultaneously support the two forms of prior information, our approach restricts the indicator matrix during optimization, while also ensuring that the clusters obtained by our approach do not violate any constraints. The computational complexity of the proposed approach is nearly linear to the data size n. Experiments show that the algorithm proposed in this paper achieves significant improvements in both clustering performance and efficiency.
TABASCO: A transformer based contextualization toolkit

Ambiguity means that a single reader can interpret the natural language (NL) software requirement in more than one way or that multiple readers come to different interpretations. Ambiguous NL software requirements may result in the production of poor quality software artifacts in later stages of software development life cycle. In the literature, several approaches have been proposed to identify multiple meanings of commonly used terms in different application domains, i.e., cross-domain ambiguities. Unfortunately, none of these approaches are able to identify different contexts in which a term has been used within the requirements document of a single application domain or in a multidisciplinary project document. We call this type of ambiguity as intra-domain ambiguity. We have designed and developed TABASCO, a tool for detecting and identifying such ambiguities present in the software requirements and other project-related documents. TABASCO focuses on detecting nouns which have been used in different contexts by computing contextual embeddings represented as real-valued vector for every instance of a candidate noun present in the document using the Bidirectional Encoder Representations from Transformers (BERT) as a language model. In the next step, K-means clustering algorithm with cosine similarity metric is applied which allows creating multiple clusters for a candidate noun such that each cluster contains all those instances which have been used in a similar context. TABASCO provides an intuitive graphical user interface which can be used to generate a summary report and a detailed report for each target noun providing the details of different contexts in which this noun has been used in the project. The detailed report also presents the most similar words for every occurrence of the target noun and some example sentences from the corpus to show the context-specific meaning. Our demonstrative experiments using two case studies show that TABASCO can be very useful for identifying intra-domain ambiguities in software requirements and other project-related documents written using NL text.
$$B^2$$-FedGAN: Balanced Bi-directional Federated GAN

In Federated Learning (FL), a shared model is learned across dispersive clients each of which often has small and heterogeneous data. As such, datasets in FL setting may suffer from the non-IID (Independent and identically distributed) problem. In this paper, we propose a BAGAN as machine learning model which has the ability to create data for minority classes, and a Bi-FedAvg model as a new approach to mitigate non-IID problems in FL settings. The performance comparison between FedAvg and Bi-FedAvg in both IID and Non-IID environments will be shown in terms of accuracy, converge stability and category cross-entropy loss. On the other hand, the training and testing performance among FedAvg, FedAvg with a conditional GAN model, and FedAvg with BAGAN-GP model, on IID and Non-IID environments with three imbalanced datasets will be compared and discussed. The results indicate that Bi-FedAvg fails to outperform Fed-Avg, for Bi-FedAvg suffers from model quality loss or even divergence when running on non-IID data partitions. In addition to that, our experiments demonstrate that higher quality images for complex image datasets can be generated by BAGAN and combining federated learning and Balancing GAN model together is conducive to obtaining a high-level privacy-preserving capability and achieving more competitive model performance. The project will give an inspired further exploration of the implementation of a combination between Federated learning and BAGAN on image classification in real-world scenarios.