Reviewerly/exHarmony huggingface dataset:
A Hybridized Optimal Algorithm for Multimodal Optimal Design of Inverse Problems

Particle swarm optimization (PSO) is an intelligent searching technique for solving complicated and multimodal design optimization’s problems. The classical PSO algorithm is more flexible and efficient because of its ability to solve a diverse range of complex and real-world issues. Moreover, the primary deficiency of this method that it trapped and stuck to local minima during the optimization of multimodal, complex and inverse objective function. We introduce a crossover and mutation vectors in the conventional PSO to solve this deficiency. The differential evolution strategies inspired the novel vectors. The central idea of the proposal is that, the novel global best particle is updated through a mutation vector and crossover vector. The introduction of the global best particle maintains the swarm diversity at the final steps of the evolution process. Also, we designed a novel strategy for the control parameter, which will maintain a decent alignment of the candidates between the global and local searches. The performance evaluation table and trajectory curves illustrate that our proposed approach is the best compared to other methods.
Exploring Colocated Synchronous Use of Tablets Based on Split Screen Feature

This study analyzes task performance, perceived satisfaction, and behavior of students completing a similar set of mathematical tasks on paper, tablet in 1:1 (one-per-one), and tablet in 1:m (one-per-many) settings with implemented split screen feature. The primary goal of the study was to explore whether the proposed split screen feature could support the learning activities of students sharing a mobile device. A customized tablet mobile application was designed and developed to support up to four colocated users, generating independent instances of a math activity in a visually independent subinterface. This feature was tested among 80 students attending the first four grades of elementary education and the results showed a statistically significant difference in task performance between the paper and tablet conditions, but no significant differences between the 1:1 and the 1:m conditions. Moreover, no significant differences were found between the tested 1:m settings (1:2, 1:3, and 1:4). The students’ perceived satisfaction was significantly higher while working with tablets compared to the paper condition, but the results showed no significant difference between the 1:1 and the 1:m conditions. Furthermore, the observed higher frequency of verbal interaction between group peers in the 1:3 and 1:4 distributions compared to the 1:2 setting could indicate that working in pairs provides the most similar learning environment to the 1:1 distribution. The overall results suggest that the proposed split screen approach could effectively support the organization of both individual educational activities and collaborative learning on a shared mobile device implementing the split screen feature.
3D Reconstruction Cost Function Algorithm Based on Stereo Matching in the Background of Digital Museums

Stereo matching plays an important role in 3D reconstruction in the context of digital museums. At present, it has problems such as occlusion, weak texture, and discontinuous disparity, which restrict the development of binocular vision. In response to this type of problem, Census transformed algorithms based on mean discrimination and Sobel edge detection were introduced to calculate the cost function. At the same time, the algorithm also incorporated the absolute value method of grayscale difference, making it more adaptable to situations such as discontinuous disparity and weak textures. The results show that the Census transform algorithm, which introduces edge gradients, has the lowest error matching rate on different images, with a minimum value of 25.1%. The classical Census transformation method and the AD Census classical transformation algorithm are 28.3% and 27.4%, respectively.Compared with the other two algorithms, the Census transformation of edge gradient improves the matching performance of the algorithm in the discontinuous area of edge disparity and improves the anti-interference ability of the algorithm. At the same time, the algorithm has the lowest error matching rate in the disparity discontinuous regions of Teddy, Cones, Venus, and Tsukuba images, and the lowest value is only 32.1%. Compared to the classic Census transformation method and the AD Census classical transformation algorithm, the minimum error matching rate has decreased by 13.2% and 4.5%, respectively. In addition, the algorithm has the lowest average effective runtime on all four types of images, with an effective average of 4.6 seconds on Venus images with rich texture features, which is 0.6 seconds lower than the classic Census transform algorithm.The improved Census transform algorithm not only has high matching accuracy but also low time complexity, providing a reliable method reference for modern 3D reconstruction fields.
An Extended Survey Concerning the Significance of Artificial Intelligence and Machine Learning Techniques for Bug Triage and Management

Bug reports are generated in large numbers during the software development processes in the software industry. The manual processing of these issues is usually time consuming and prone to errors, consequently delaying the entire software development process. Thus, a properly designed bug triage and management process implies that essential operations, such as duplicate detection, bug assignments to proper developers, and determination of the importance level, are sustained by efficient algorithmic models and implementation approaches. Designing and implementing a proper bug triage and management process becomes an essential scientific research topic, as it may significantly optimize the software development and business process in the information technology industry. Consequently, this paper thoroughly surveys the most significant related scientific contributions analytically and constructively, distinguishing it from similar survey papers. The paper proposes optimal algorithmic and software solutions for particular real-world use cases that are analyzed. It concludes by presenting the most important open research questions and challenges. Additionally, the paper provides a valuable scientific literature survey for any researcher or practitioner in software bug triage and management systems based on artificial intelligence and machine learning techniques.
A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

With the development of industrial informatization, video processing technology is receiving more and more attention. Extracting background is a prerequisite for many video processing techniques, so video background modeling technology is becoming highly sought-after. Currently, there are a variety of approaches to estimating background; however, many of these methods have the fault of not being able to accurately distinguish between foreground and background, especially when objects move slowly or remain still for a period of time. In this paper, a novel background modeling scheme is proposed for surveillance video, based on keyframe and particle shape properties. The model consists of three parts: the first part is to reduce running time and eliminate the ghost phenomenon caused by adjacent redundant frames by extracting keyframe and dividing the extracted frames into several groups; the second part includes three steps, computing binarized difference, characterizing the binarized difference and screening the difference where a quadruple, composed of particle shape properties, is designed to quantitatively describe binarized differences; the third part involves generating the temporary background and updating the temporary background according to the similarity of data obtained at the newly proposed locations. Experiment results on SBMnet and SBI datasets and comparisons with some emerging algorithms show that the performance of the proposed model is superior or comparable to the other state-of-the-art methods particularly when dealing with stationary objects. Furthermore, the proposed method ranks as the second for intermittent category video, compared to the other 31 state-of-art methods. Moreover, the speed of the proposed method, 5.38 Frame Per Second ( FPS ) for SBMnet dataset and 10.94 FPS for SBI dataset, is faster than most public methods.
An Adaptive and Robust Method for Oriented Oversampling With Spatial Information for Imbalanced Noisy Datasets

Imbalanced datasets have a large negative impact on the classifiers, biasing the classification results towards the majority class. Since imbalanced data distribution is an inevitable and significant challenge in the real world, many variants of SMOTE have been proposed. However, current oversampling methods still need improvement because they rely on hyperparameter optimization, overgeneralize due to emphasizing specific synthetic regions, randomly synthesize samples or suffer from noise performance degradation. To overcome the above problems, we propose an adaptive and robust method (OOSI) for oriented oversampling with spatial information to deal with imbalanced noisy datasets. OOSI is a rare adaptive and effective oversampling method that can fill the gaps of existing methods through dataset-specific spatial partitioning and information quantization, three-stage noise suppression, and spatially-informed generation path improvement. Firstly, a specific and adaptive clustering space is adaptively derived through the data space division of the characteristics of datasets. Then, all minority clusters are assigned a reasonable number of synthetic samples to simultaneously address intra- and inter-class imbalances by integrating the cluster samples’ intra-cluster sparsity and the multi-class density information. After differentiating and identifying the noise, oriented weights are assigned based on the multi-class information level to guide the enhancement of the generation path of the synthetic samples and prevent the generation of extra noisy and overlapping samples. Extensive experiments demonstrate that the proposed algorithm outperforms 11 prominent oversampling algorithms on 11 real-world datasets with varying noise levels.
YOLO-UAV: Object Detection Method of Unmanned Aerial Vehicle Imagery Based on Efficient Multi-Scale Feature Fusion

As Unmanned Aerial Vehicle (UAV) remote sensing technology progresses, the utilization of deep learning in UAV imagery object detection has become more prevalent. However, detecting small targets in complex backgrounds and distinguishing dense targets remains a major challenge. To address these issues and improve object detection efficiency, this study proposes an UAV imagery object detection method called YOLO-UAV by optimizing YOLOv5. YOLO-UAV first reconstructs the backbone and feature fusion networks by simplifying the network structure and reducing computational burden. The employment of a Dense_CSPDarknet53 backbone network, fashioned via the incorporation of dense connections, facilitates the extraction of latent image information through the recurrent utilization of features. In the Neck structure, an efficient feature fusion block with structural re-parameterization and ELAN strategies is integrated to effectively reduce interference from complex background noise while extracting more accurate and rich features. In addition, by proposing GS-Decoupled Head, this approach diminishes the parameter count of the decoupled head without compromising accuracy. It also separates classification tasks from regression tasks to lessen the influence of task disparities on prediction bias. To tackle the discrepancy between positive and negative samples in bounding box regression tasks, this study introduces a new loss function, Focal-ECIoU, capable of expediting network convergence and improve model positioning ability. Experimental findings from the public VisDrone2019 dataset indicate that YOLO-UAV outperforms other advanced object detection methods in comprehensive performance. Compared with the baseline model YOLOv5s, YOLO-UAV increased mAP0.5 from 35.1% to 46.7%, while mAP0.5:0.95 increased from 19.1% to 27.4%. For small-scale targets, AP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>small</i></sub> increased from 10.2% to 17.3%. The experiment proves that YOLO-UAV performs well in improving object detection accuracy and has strong generalization ability, satisfying the practical requirements of UAV imagery object detection tasks.
Coarse-to-Fine Context Aggregation Network for Vehicle Make and Model Recognition

Vehicle Make and Model Recognition (VMMR) is one of the fundamental elements in Intelligent Transportation System (ITS) that becomes the enabler for plenty of downstream tasks. Most of the past studies advance the recognition performance by focusing on the top-level feature maps but this practice hinders the ability of the network to learn features. Although the top-level feature maps are rich in global context information, they do not incorporate the fine-scale details that are embedded within the low-level feature maps. In this work, we bridge the gap by proposing a Coarse-to-Fine Context Aggregation (CFCA) module which effectively integrates information from feature maps of various scales. In particular, the cross-scale features are generated by first refining the scale-specific components independently and then fusing them in a nonlinear manner through convolution. The resultant multi-scale feature maps are highly discriminative, as they contain both local subtle details and global abstract information. This is proven when the proposed framework achieves astounding classification performance on five publicly available datasets i.e. web-nature Comprehensive Cars (CompCars), Stanford Cars, Car-FG3K, surveillance-nature CompCars and Mohsin-VMMR. Moreover, the neurons exhibit high feature responses on the discriminative vehicle parts, corresponding to the superior feature extraction ability of the CFCA module. The CFCA module is also highly generalizable to other networks as it elevates the performance of VGG16, Inceptionv3, ResNet50 and DenseNet169.
A Blockchain-Assisted Certificateless Public Cloud Data Integrity Auditing Scheme

The utilization of cloud storage is increasingly prevalent as the field of cloud computing continues to expand. Several cloud data auditing schemes have been proposed within the academic community to guarantee the availability and integrity of cloud data. Nevertheless, several schemes rely on public key infrastructure and identity-based encryption, introducing intricate challenges associated with certificate management and key escrow. Consequently, we present a certificateless encryption-based blockchain-assisted public cloud data integrity auditing scheme for data integrity. Furthermore, our proposed scheme incorporates blockchain technology to oversee the activities of semi-trusted third-party auditors and resolve the concerns mentioned above. To enhance the efficiency of dynamic data updating and ensure data privacy security, we introduce a new data structure that combines a novel counting bloom filter and a Multi-Merkel hash tree approach. The assumption of the discrete logarithm issue determines the system’s security. In contrast, the security model of the scheme is comprehensively delineated. In the part dedicated to performance analysis, we assess the scheme’s functionality and computational cost within the framework of existing literature. The experimental results provide proof of the scheme’s comprehensive functionality and effectiveness.
SCMA: Exploring Dual-Module Attention With Multi-Scale Kernels for Effective Feature Extraction

Feature space enrichment is an integral part of the development of attention mechanisms in Convolutional Neural Networks (CNNs). The ability to efficiently extract channel and spatial information across a variety of scales is crucial. Furthermore, balancing model parameter efficiency while ensuring higher accuracy is a key objective. To create a compelling and robust attention mechanism, channel and spatial attention must be carefully incorporated into CNN architecture. This research work addresses these challenges and presents an attention mechanism called Spatial and Channel aware Multi-scale kernel Attention (SCMA) for CNNs. Our approach leverages the combination of two separate attention modules, one for channel-wise attention and another for spatial attention, in sequential order to refine intermediate feature representations in a CNN. The SCMA module is designed to be compact and universal, capable of being seamlessly integrated into any baseline CNN architecture with minimal parameter overhead, and can be trained in an end-to-end manner. Our empirical findings regarding the utilization of SCMA in conjunction with various CNN architectures for image classification tasks on multiple benchmark datasets including Imagenette, Imagewoof, CIFAR-10, CIFAR-100, and CINIC, affirm the intuition that multi-scale kernels are pivotal for effectively capturing dependencies across both spatial and channel dimensions. In many instances, SCMA exhibits higher performance in terms of accuracy than its state-of-the-art counterparts while keeping the parameter overhead to a minimum.
Design of an Intelligent Laboratory Facial Recognition System Based on Expression Keypoint Extraction

The number of clever facial recognition systems has been growing as artificial intelligence and robotics have advanced. However, due to the limited collection of biometric features by intelligent facial recognition systems compared to authentication methods such as iris and fingerprint, there are errors in the recognition process, low recognition accuracy, and low operational efficiency. To improve laboratory security and the efficiency of intelligent facial recognition systems, a study was conducted to extract facial feature information through facial expression key points, and a spatiotemporal graph convolutional network fused with attention mechanism was used to organize and match feature data. Finally, an improved facial recognition system was designed. A simulation experiment was conducted, and the results showed that the intelligent laboratory facial recognition system constructed by the research had a facial recognition accuracy of 89% in different datasets. Compared with the Line Hausdorff distance facial recognition method, the facial feature data was more concentrated, proving that the facial recognition system designed by the research has high recognition accuracy, wide application range, and strong application value, It helps to improve the optimization of facial recognition systems in the laboratory and improve the operational efficiency of the laboratory.
Virtual Human: A Comprehensive Survey on Academic and Applications

As a creative method for virtual human individuals based on multiple fusion technologies such as artificial intelligence, computer graphics, and speech synthesis, virtual human technology has developed rapidly since its birth, and continuous discussions and studies have been conducted in both academia and industry. Starting from the film and television industries, the cross-disciplinary application of virtual human has been continuously recognized and applied in fields such as media, games, and finance. Although virtual human has achieved sufficient development and innovation, it faces many challenges such as emotion recognition, privacy, and security, as well as the uncanny valley effect. This article starts with the development history of virtual human and analyzes the current academic research status and application scenarios in combination with the characteristics, technical architecture, and application of virtual human technology. At the same time, this article sorts out seven mainstream application scenarios of virtual human and analyzes their main advantages and possible future challenges. This article provides a valuable reference for subsequent related research by exploring development trends, application fields, and future research trends in virtual human.
Robust Spatial-Temporal Motion Coherent Priors for Multi-View Video Coding Artifact Reduction

Multi-view video (MVV) data processed by three-dimensional (3D) video systems often suffer from compression artifacts, which can degrade the rendering quality of 3D spaces. In this paper, we focus on the task of artifact reduction in multi-view video compression using spatial and temporal motion priors. Previous MVV quality enhancement networks using a warping-and-fusion approach employed reference-to-target motion priors to exploit inter-view and temporal correlation among MVV frames. However, these motion priors were sensitive to quantization noise, and the warping accuracy was degraded, when the target frame used low-quality features in the corresponding search. To overcome these limitations, we propose a novel approach that utilizes bilateral spatial and temporal motion priors, leveraging the geometry relations of a structured MVV camera system, to exploit motion coherency. Our method involves a multi-view prior generation module that produces both unidirectional and bilateral warping vectors to exploit rich features in adjacent reference MVV frames and generate robust warping features. These features are further refined to account for unreliable alignments cross MVV frames caused by occlusions. The performance of the proposed method is evaluated in comparison with state-of-the-art MVV quality enhancement networks. Synthetic MVV dataset facilitates to train our network that produces various motion priors. Experimental results demonstrate that the proposed method significantly improves the quality of the reconstructed MVV frames in recent video coding standards such as the multi-view extension of High Efficiency Video Coding [1] and the MPEG immersive video standard [2].
Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum – Minimum (Max – Min), Minimum Completion Time (MCT), Minimum – Minimum (Min – Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms FCFS, Max – Min, MCT, Min – Min, and RR were 51.98 %, 41.71 %, 51.98 %, 59.43 %, and 52.17 %, respectively, across all four phases. Lastly, the Reinforcement Learning (RL) model is suggested to add an intelligence mechanism to LB and optimize the cloud resource utilization using these RS algorithms to provide the best Quality of Service (QoS).