Reviewerly/exHarmony huggingface dataset:
Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model's inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04 ± 0.0043, and 6.6016 × 10−06 ± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.
An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction models

Machine learning (ML) approaches were employed to tackle the software fault prediction (SFP) issue due to their consistent and rigorous performance. Multilayer perceptron (MLP) neural networks are one of the most effective ML models for prediction problems. Unfortunately, MLP suffers from chronic shortcomings related to the gradient-descent learning mechanism that can easily get stuck in local minima, leading to inappropriate control parameters. For SFP tackling, MLP boosted with an improved version of Salp Swarm Optimizer (SSA), a metaheuristic swarm intelligence technique, is presented in this research. The MLP learning approach is updated with SSA to remedy these flaws. The key benefit of such an algorithm is its ability to avoid local minima through convergence behavior. Two improvements were developed in the SSA optimization loop to link SSA functionality with MLP. The first improvement is elitism (SSA-elitism), while the second is MSSA, which stands for search improvement. The performance of proposed SSA versions is evaluated using 18 benchmark SFP datasets using ROC, sensitivity, specificity, and accuracy performance metrics. Several evaluations and validations were performed by contrasting the results of the developed versions with those of the conventional MLP, SSA, and 10 state-of-the-art approaches. The evaluations and validation findings prove that the proposed versions have superior capabilities to efficiently optimize MLP parameters, which raises the quality of their predictability.
Integral Neural Networks

We introduce a new family of deep neural networks, where instead of the conventional representation of network layers as N-dimensional weight tensors, we use a continuous layer representation along the filter and channel dimensions. We call such networks Integral Neural Networks (INNs). In particular, the weights of INNs are represented as continuous functions defined on N-dimensional hypercubes, and the discrete transformations of inputs to the layers are replaced by continuous integration operations, accordingly. During the inference stage, our continuous layers can be converted into the traditional tensor representation via numerical integral quadratures. Such kind of representation allows the discretization of a network to an arbitrary size with various discretization intervals for the integral kernels. This approach can be applied to prune the model directly on an edge device while suffering only a small performance loss at high rates of structural pruning without any fine-tuning. To evaluate the practical benefits of our proposed approach, we have conducted experiments using various neural network architectures on multiple tasks. Our reported results show that the proposed INNs achieve the same performance with their conventional discrete counterparts, while being able to preserve approximately the same performance (2% accuracy loss for ResNet18 on Imagenet) at a high rate (up to 30%) of structural pruning without fine-tuning, compared to 65% accuracy loss of the conventional pruning methods under the same conditions. Code is available at gitee.
Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration

Training Generative Adversarial Networks (GANs) on high-fidelity images usually requires a vast number of training images. Recent research on GAN tickets reveals that dense GANs models contain sparse sub-networks or “lottery tickets” that, when trained separately, yield better results under limited data. However, finding GANs tickets requires an expensive process of train-prune-retrain. In this paper, we propose Re-GAN, a data-efficient GANs training that dynamically reconfigures GANs architecture during training to explore different sub-network structures in training time. Our method repeatedly prunes unimportant connections to regularize GANs network and regrows them to reduce the risk of prematurely pruning important connections. Re-GAN stabilizes the GANs models with less data and offers an alternative to the existing GANs tickets and progressive growing methods. We demonstrate that Re-GAN is a generic training methodology which achieves stability on datasets of varying sizes, domains, and resolutions (CIFAR-10, Tiny-ImageNet, and multiple few-shot generation datasets) as well as different GANs architectures (SNGAN, ProGAN, StyleGAN2 and AutoGAN). Re-GAN also improves performance when combined with the recent augmentation approaches. Moreover, Re-GAN requires fewer floating-point operations (FLOPs) and less training time by removing the unimportant connections during GANs training while maintaining comparable or even generating higher-quality samples. When compared to state-of-the-art StyleGAN2, our method outperforms without requiring any additional fine-tuning step. Code can be found at this link: https://github.com/IntellicentAI-Lab/Re-GAN
ScaleDet: A Scalable Multi-Dataset Object Detector

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone.
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation

Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks. Experimental results on three benchmark datasets validated our effectiveness in performance and efficiency in annotation. For example, with only 10 annotated slices, our method reaches a Dice up to 86.93% on KiTS19 dataset. Our code and models are available at https://github.com/HengCai-NJU/DeSCO.
A Systematic Literature Review of Virtual Technology in Hospitality and Tourism (2013–2022)

Since 2019, the number of journal articles on virtual reality (VR), augmented reality (AR) and mixed reality (MR) has increased dramatically, but existing virtual technology literature reviews have mainly been based on earlier articles. A comprehensive literature review is thus necessary to gain an accurate understanding of the progress and trends in research in this field. Based on the Scopus database, this study systematically reviewed the research on virtual technology in the field of hospitality and tourism in the past 10 years; a total of 274 journal papers were reviewed. The results of bibliometric analysis using VOSviewer identified the research progress and influential literature. Four research clusters were identified based on the bibliometric map: VR research, AR research, VR experiences and virtual technology applications. Thirteen research themes under different clusters were discussed based on content analysis, and research agendas in seven areas were proposed to guide future research. This study contributes to the research on virtual technology in hospitality and tourism by identifying existing research themes and proposing further research directions. It also provides implications for hospitality and tourism marketers to develop marketing strategies using virtual technology.
<scp>MozzieNet</scp>: A deep learning approach to efficiently detect malaria parasites in blood smear images

Abstract Our study presents MozzieNet, a customized CNN model aimed at improving the identification of malaria parasites in blood smear microscopic images. By optimizing hyperparameters and incorporating techniques like data augmentation, batch normalization, and dropout, our model enhances robustness and generalization, addressing overfitting issues. Using the open‐source NIH malaria dataset with 27,558 images, we achieve a classification accuracy of 96.73%, recall rate of 97.90%, precision of 95.67%, area under the curve (AUC) of 99.35%, and F1 score of 96.77%. We performed feature maps and Grad‐CAM analysis on our proposed MozzieNet model to visualize and examine the targeted regions that are crucial for accurate predictions. Statistical analysis shows that the proposed architecture achieves promising performance and is superior to pre‐trained models and existing methods for malaria detection. MozzieNet is designed for cloud and low‐end smartphones, enabling malaria diagnosis in remote areas, thereby assisting physicians in informed malaria diagnosis and decision‐making.
A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection

Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic methods, different approaches can be integrated and methods can be hybridized to achieve faster convergence with high accuracy by balancing the exploitation and exploration stages. Chaotic maps are effectively used to improve the performance of metaheuristic methods by escaping the local optimum and increasing the convergence rate. In this study, chaotic maps are included in the TSO search process to improve performance and accelerate global convergence. In order to prevent the slow convergence rate and the classical TSO algorithm from getting stuck in local solutions, 10 different chaotic maps that generate chaotic values instead of random values in TSO processes are proposed for the first time. Thus, ergodicity and non-repeatability are improved, and convergence speed and accuracy are increased. The performance of Chaotic Transient Search Algorithm (CTSO) in global optimization was investigated using the IEEE Congress on Evolutionary Computation (CEC)'17 benchmarking functions. Its performance in real-world engineering problems was investigated for speed reducer, tension compression spring, welded beam design, pressure vessel, and three-bar truss design problems. In addition, the performance of CTSO as a feature selection method was evaluated on 10 different University of California, Irvine (UCI) standard datasets. The results of the simulation showed that Gaussian and Sinusoidal maps in most of the comparison functions, Sinusoidal map in most of the real-world engineering problems, and finally the generally proposed CTSOs in feature selection outperform standard TSO and other competitive metaheuristic methods. Real application results demonstrate that the suggested approach is more effective than standard TSO.
Evaluating the Performance of the LI3P in Latent Profile Analysis Models

AbstractNumerous methods exist to determine the optimal number of classes when using latent profile analysis (LPA), but none are consistently correct. Recently, the likelihood incremental percentage per parameter (LI3P) was proposed as a model effect-size measure. To evaluate the LI3P more thoroughly, we simulated 50,000 datasets, manipulating factors including sample size, class distance, number of indicators, and number of classes. Results indicate the LI3P performs similarly to established techniques, reflecting class separation and the number of classes. The novel simulation method also yields new findings for the standard approaches. We then demonstrate the LI3P by applying it to a study searching for subgroups of formerly-religious individuals.Keywords: Finite mixture modelinglatent profile analysislikelihood incremental percentagemodel comparison Disclosure statementNo potential conflict of interest was reported by the author(s).
Blockchain Technology Applied to Supply Chain Management: A Systems’ Analysis

Blockchain technology can be used to record real data at various nodes in the entire supply chain and can achieve timely data sharing and full traceability of the whole lifecycle of products in the supply chain. This study conducts a systematic literature review based on the WOS database, focusing on the research topic of the application of blockchain technology in supply chain management. We propose clear search and screening criteria based on 4 research questions and finally obtain 591 target studies. We conduct a detailed study on the distribution of the target literature in this research area, country/region, institution, and journal. Different countries/regions cooperate with each other and, thus, are clustered into five significant countries regions’ cooperation networks. There is a strong coupling relationship between research institutions, forming four major institutional cooperation networks. According to the analysis of those papers with a high number of citations, most of such papers were published in 2019, while the cross-citation phenomenon between papers occurred more frequently in 2021 and 2022. We use VOSviewer for visual coupling analysis of all keywords, which are automatically clustered into three research hotspots. Using CiteSpace to perform timeline-based keyword cooccurrence analysis, we find that scholars have gone through a “discovery-acceptance-question-improvement” process for the application of blockchain technology in supply chain management. According to the intensity and duration of burst detection words in different years, we draw three key points for future research. (i) Blockchain technology should be used to restructure and optimize the high-end food and medical cold chain supply chains. (ii) Blockchain technology should be used to enhance the cooperative relationship between supply chain members and the overall competitiveness of the supply chain. (iii) Innovate blockchain encryption technology that should be used to reduce the risk of information and privacy leakage in the supply chain.
Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm

The study of unmanned systems has garnered significant academic attention worldwide. The advancements in tracking technologies, particularly the unmanned aerial vehicle (UAV) developed by the US Air Force, have motivated scientists to extensively explore this field due to the abundance of available aerial resources. However, the UAV’s effectiveness in motion is influenced by its three fundamental motions: pitch, roll, and yaw. To address this, scientists have been developing optimization algorithms and models to enhance the UAV’s performance. Nonetheless, in practical scenarios, each motion requires specific measurements, and their relative importance varies. This presents several challenges, including the existence of multiple criteria for selecting optimization values, determining the criteria’s significance, and evaluating trade-offs between performance across different optimization cases. Consequently, the selection and evaluation of UAV motion control algorithms becomes complex. This study proposes a novel selection process that employs the cuckoo optimization algorithm (COA) and real measured resizable margins for goal detection instead of traditional fixed-size evaluation criteria. The framework consists of enhancing PID motion gains using the COA algorithm, focusing on altitude and attitude functional PID controllers. The results of the selection process demonstrate the significance of various criteria for different motions. Rigorous evaluation and analysis were conducted to validate the proposed research framework.
Text with Knowledge Graph Augmented Transformer for Video Captioning

Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented transformer (TextKG)for video captioning. Notably, TextKG is a two-stream transformer, formed by the external stream and internal stream. The external stream is designed to absorb additional knowledge, which models the interactions between the additional knowledge, e.g., pre-built knowledge graph, and the built-in information of videos, e.g., the salient object regions, speech transcripts, and video captions, to mitigate the long-tail words challenge. Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e.g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results. In addition, the cross attention mechanism is also used in between the two streams for sharing information. In this way, the two streams can help each other for more accurate results. Extensive experiments conducted on four challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions, MSR-VTT, and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Specifically, the proposed TextKG method out-performs the best published results by improving 18.7% absolute CIDEr scores on the YouCookII dataset.
Masked Wavelet Representation for Compact Neural Radiance Fields

Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. However, using a multi-layer perceptron (MLP) to represent a 3D scene or object requires enormous computational resources and time. There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees. Despite the promising performance, the explicit data structure necessitates a substantial amount of memory. In this work, we present a method to reduce the size without compromising the advantages of having additional data structures. In detail, we propose using the wavelet transform on grid-based neural fields. Grid-based neural fields are for fast convergence, and the wavelet transform, whose efficiency has been demonstrated in high-performance standard codecs, is to improve the parameter efficiency of grids. Furthermore, in order to achieve a higher sparsity of grid coefficients while maintaining reconstruction quality, we present a novel trainable masking approach. Experimental results demonstrate that non-spatial grid coefficients, such as wavelet coefficients, are capable of attaining a higher level of sparsity than spatial grid coefficients, resulting in a more compact representation. With our proposed mask and compression pipeline, we achieved state-of-the-art performance within a memory budget of 2 MB. Our code is available at https://github.com/daniel03c1/masked_wavelet_nerf.