Reviewerly/exHarmony huggingface dataset:
Rethinking general underwater object detection: Datasets, challenges, and solutions

In this paper, we conduct a comprehensive study of Underwater Object Detection (UOD). UOD has evolved into an attractive research field in the computer vision community in recent years. However, existing UOD datasets collected from specific underwater scenes are limited in the number of images, categories, resolution, and environmental challenges. These limitations can lead to the settings and effectiveness of models trained on existing datasets being impaired in general underwater situations. These limitations also constrain the comprehensive exploration of UOD. To alleviate these issues, we first present a new real-world UOD dataset called RUOD that places UOD in the context of general scene understanding. The dataset contains 14,000 high-resolution images, 74,903 labeled objects, and 10 common aquatic categories. The dataset also has various marine objects and rich environmental challenges including haze-like effects, color casts, and light interference. Second, we conduct extensive and systematic experiments on RUOD to evaluate the development of general underwater scene detection from the perspective of algorithms, complex marine objects, and environmental challenges. The findings from these explorations highlight the challenges of UOD and suggest promising solutions and new directions for UOD. Finally, UOD in practice typically uses underwater image enhancement during preprocessing to improve image quality. We thus characterize object detection performance on enhanced images and find an effective auxiliary framework of image enhancement for UOD. Our dataset is available at https://github.com/dlut-dimt/RUOD.
BUViTNet: Breast Ultrasound Detection via Vision Transformers

Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew’s correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance.
Spatio-temporal segments attention for skeleton-based action recognition

Capturing the dependencies between joints is critical in skeleton-based action recognition. However, the existing methods cannot effectively capture the correlation of different joints between frames, which is very useful since different body parts (such as the arms and legs in “long jump”) between adjacent frames move together. Focus on this issue, a novel spatio-temporal segments attention method is proposed. The skeleton sequence is divided into several segments, and several consecutive frames contained in each segment are encoded. And then an intra-segment self-attention module is proposed to capture the relationship of different joints in consecutive frames. In addition, an inter-segment action attention module is introduced to capture the relationship between segments to enhance the ability to distinguish similar actions. Compared with the state-of-the-art methods, our method achieves better performance on two large-scale datasets.
MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software

Machine Learning (ML) software can lead to unfair and unethical decisions, making software fairness bugs an increasingly significant concern for software engineers. However, addressing fairness bugs often comes at the cost of introducing more ML performance (e.g., accuracy) bugs. In this paper, we propose MAAT, a novel ensemble approach to improving fairness-performance trade-off for ML software. Conventional ensemble methods combine different models with identical learning objectives. MAAT, instead, combines models optimized for different objectives: fairness and ML performance. We conduct an extensive evaluation of MAAT with 5 state-of-the-art methods, 9 software decision tasks, and 15 fairness-performance measurements. The results show that MAAT significantly outperforms the state-of-the-art. In particular, MAAT beats the trade-off baseline constructed by a recent benchmarking tool in 92.2% of the overall cases evaluated, 12.2 percentage points more than the best technique currently available. Moreover, the superiority of MAAT over the state-of-the-art holds on all the tasks and measurements that we study. We have made publicly available the code and data of this work to allow for future replication and extension.
QQLMPA: A quasi-opposition learning and Q-learning based marine predators algorithm

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Lévy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer.
A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information

Ensuring the credibility of online consumer reviews (OCRs) is a growing societal concern. However, the problem of fake reviewers on online platforms significantly influences e-commerce authenticity and consumer trust. Existing studies for fake reviewer detection mainly focus on deriving novel behavioral and linguistic features. These features require extensive human labor and expertise, placing a heavy burden on platforms. Therefore, we propose a novel end-to-end framework to detect fake reviewers based on behavior and textual information. It has two key components: (1) a behavior-sensitive feature extractor that learns the underlying patterns of reviewing behavior; (2) a context-aware attention mechanism that extracts valuable features from online reviews. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks on two real-world datasets from http://Yelp.com. Experimental results demonstrate that our method achieves state-of-the-art results on fake reviewer detection. Our method can be considered a tentative step toward lowering human labor costs in realizing automated fake reviewer detection on e-commerce platforms.
Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems

The equilibrium optimizer (EO) is a recently proposed physics-based metaheuristic algorithm inspired by the dynamic mass balance on a control volume. However, EO may encounter local convergence and low accuracy when solving high-dimensional optimization problems. In this work, we propose the opposition-based learning EO algorithm with the Levy flight and the evolutionary population dynamics for high-dimensional global optimization problems, called EOOBLE. Firstly, the opposition-based learning strategy is embedded into the initialization and updating process of EO. Then, the Levy flight strategy is used to improve the exploration ability of EO in the updating mechanism. Moreover, the strategy of evolutionary population dynamics is used in the proposed algorithm to avoid falling into the local optimum. The performance of the proposed algorithm is tested by 25 benchmark functions with dimensions from 100 to 5000, and is compared with 8 state-of-the-art metaheuristic algorithms. The statistical results indicate that the proposed algorithm has better convergence capacity than the compared algorithms. Besides, the proposed algorithm is also compared with different variants of EO, which outperforms the original EO and variants of EO with one or two operators. Therefore, the EOOBLE is a competitive algorithm in solving high-dimensional global optimization problems. Finally, a high-dimensional engineering design problem demonstrates the effectiveness of the EOOBLE.
Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

Nowadays, the world has achieved tremendous economic development at the expense of the long-term habitability of the planet. With the rapid economic development, the global greenhouse effect caused by excessive carbon dioxide (CO2) emissions is also accumulating, which generates the negative impact of global warming on nature and human beings. Meanwhile, economy and CO2 emissions prediction methods based on traditional neural networks lead to gradient disappearance or gradient explosion, making the economy and CO2 emissions prediction inaccurate. Therefore, this paper proposes a novel economy and CO2 emissions prediction model based on a residual neural network (RESNET) to optimize and analyze energy structures of different countries or regions in the world. The skip links are used in the inner residual block of the RESNET to alleviate vanishing gradients due to increasing depth in deep neural networks. Consequently, the proposed RESNET can optimize this problem and protect the integrity of information by directly bypassing the input information to the output, which can increase the precision of the prediction model. The needs for natural gas, hydroelectricity, oil, coal, nuclear energy, and renewable energy in 24 different countries or regions from 2009 to 2020 are used as inputs, the CO2 emissions and the gross domestic product (GDP) per capita are respectively used as the undesired output and the desired output of the RESNET to build an economy and CO2 emissions prediction model. The experimental results show that the RESNET has higher correctness and functionality than the traditional convolutional neural network (CNN), the radial basis function (RBF), the extreme learning machine (ELM) and the back propagation (BP). Furthermore, the proposed model provides guidance and development plans for countries or regions with low energy efficiency, which can improve energy efficiency, economic development and reasonably control CO2 emissions.
Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions

Although solar energy harnessing capacity varies considerably based on the employed solar energy technology and the meteorological conditions, accurate direct normal irradiation (DNI) prediction remains crucial for better planning and management of concentrating solar power systems. This work develops hybrid Long Short-Term Memory (LSTM) models for assessing hourly DNI using meteorological datasets that include relative humidity, air temperature, and global solar irradiation. The study proposes a unique hybrid model, combining a balance-dynamic sine–cosine (BDSCA) algorithm with an LSTM predictor. Combining optimizers and predictors, such hybrid models are rarely developed to estimate DNI, especially in smaller prediction intervals. Therefore, various commonly adopted algorithms in relevant studies have been considered references for evaluating the new hybrid algorithm. The results show that the relative errors of the proposed models do not exceed 2.07%, with a minimum correlation coefficient of 0.99. In addition, the dimensionality of inputs was reduced from four variables to the two most cost-effective variables in DNI prediction. Therefore, these suggested models are reliable for estimating DNI in the arid desert areas of Algeria and other locations with similar climatic features.
Imbalanced data classification: Using transfer learning and active sampling

Recently, deep learning models have made great breakthroughs in the field of computer vision, relying on large-scale class-balanced datasets. However, most of them do not consider the class-imbalanced data. In reality, the class-imbalanced distribution can lead to the degradation of model performance, reducing the generalization of these models. In addition, in the era of big data, many applications need to use real-time visual data. These data come from different mobile devices, which continuously generate a huge number of visual data. However, there are few studies using real-time data from information systems, real-time data is easy to capture but difficult to use. In order to solve the above problems, we propose a new model (Transfer Learning Classifier, TLC) based on transfer learning to deal with class-imbalanced data. The model includes active sampling module, real-time data augmentation module and DenseNet module. Among them, (1) the newly proposed active sampling module can dynamically adjust the number of samples with skewed distribution; (2) the data augmentation module can expand the real-time data to avoid over-fitting and insufficient data; (3) the DenseNet module is a standard DenseNet network pre-trained on the ImageNet dataset and transferred to TLC for relearning, and then we adjust the memory usage of the standard DenseNet to make it more efficient. In addition, we have applied a new end-to-end real-time data storage and analysis system. A large number of experiments have been carried out on four different long mantissa data sets. Experimental results show that the proposed TLC model can effectively deal with the static data as well as the real-time data, and the classification effect of imbalanced data is better than that of existing models.
Prediction of Alzheimer's progression based on multimodal Deep-Learning-based fusion and visual Explainability of time-series data

Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment and has no known treatment. The premise for delivering timely therapy is the early diagnosis of AD before clinical symptoms appear. Mild cognitive impairment is an intermediate stage in which cognitively normal patients can be distinguished from those with AD. In this study, we propose a hybrid multimodal deep-learning framework consisting of a 3D convolutional neural network (3D CNN) followed by a bidirectional recurrent neural network (BRNN). The proposed 3D CNN captures intra-slice features from each 3D magnetic resonance imaging (MRI) volume, whereas the BRNN module identifies the inter-sequence patterns that lead to AD. This study is conducted based on longitudinal 3D MRI volumes collected over a six-months time span. We further investigate the effect of fusing MRI with cross-sectional biomarkers, such as patients' demographic and cognitive scores from their baseline visit. In addition, we present a novel explainability approach that helps domain experts and practitioners to understand the end output of the proposed multimodal. Extensive experiments reveal that the accuracy, precision, recall, and area under the receiver operating characteristic curve of the proposed framework are 96%, 99%, 92%, and 96%, respectively. These results are based on the fusion of MRI and demographic features and indicate that the proposed framework becomes more stable when exposed to a more complete set of longitudinal data. Moreover, the explainability module provides extra support for the progression claim by more accurately identifying the brain regions that domain experts commonly report during diagnoses.
Research on significant factors affecting adoption of blockchain technology for enterprise distributed applications based on integrated MCDM FCEM-MULTIMOORA-FG method

Blockchain technology-powered applications infuse trust in the system without intermediary entities. Researchers’ interest in blockchain technology has increased dramatically in recent years as a result of this distinguishing feature. However, the adoption of this novel paradigm in enterprise distributed applications is not very encouraging. To adopt a new technology in an industry, managers and technocrats have to do multiple-criteria decision-making (MCDM) in a fuzzy environment. The aim of this study is to propose a system model for the identification and monitoring of significant factors responsible for the slow adoption rate of blockchain technology. In this research, the MULTIMOORA method for group decision-making, a popular MCDM approach, has been further extended by integrating the information entropy weight–fuzzy comprehensive evaluation model (IEW-FCEM) for an uncertain environment. The proposed method of FCEM-MULTIMOORA-FG combines the benefits of four best-in-class ranking methods, namely, the fuzzy ratio system, the fuzzy reference point method, the fuzzy full multiplicative form, and the IEW-FCEM. The idea behind integrating the IEW-FCEM with MULTIMOORA-FG is that information entropy will derive the objective weights of the criteria, thus removing the dependency upon subject matter expert experience only. An empirical study demonstrates the efficacy and practicability of the FCEM-MULTIMOORA-FG method. The simulation results demonstrate that the proposed method is computationally straightforward with better position accuracy than previous algorithms and that its basic idea is logical and understandable, making it easier to apply in a computer system. Additionally, a comparative analysis with five more MCDM methods was executed to validate the results.
Unsupervised feature selection through combining graph learning and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-norm constraint

Graph-based unsupervised feature selection algorithms have been shown to be promising for handling unlabeled and high-dimensional data. Whereas, the vast majority of those algorithms usually involve two independent processes, i.e., similarity matrix construction and feature selection. This incurs a poor similarity matrix that is obtained from original data, which retains constant for the following feature selection process and heavily affects the corresponding performance. Aiming to integrate these two processes into a unified framework, this paper proposes a novel unsupervised feature selection algorithm, named Graph Learning Unsupervised Feature Selection (GLUFS) to ensure the two processes proceed simultaneously. In particular, a new similarity matrix is derived from the original one, while the new matrix can adaptively maintain the manifold structure of data. Due to the fact that good individual features do not necessarily guarantee efficient combinations, the GLUFS algorithm adopts the ℓ2,0-norm sparsity constraint to achieve group feature selection. Eventually, we perform experiments on six public datasets with sufficient analysis, while the obtained results illustrate the effectiveness and superiority of our GLUFS over the considered algorithms.
A novel compound fault-tolerant method based on online sequential extreme learning machine with cycle reservoir for turbofan engine direct thrust control

Sensors are the primary information source of the aeroengine control system, their measurement accuracy is closely related to whether the engine can operate safely and efficiently. Aiming at the direct thrust control system of aeroengines, this paper proposes an intelligent prediction algorithm combining feedforward and recurrent networks and a fault-tolerant control strategy combining analytical redundancy and controller switching. First, the cycle reservoir with regular jumps is introduced into the online sequential extreme learning machine. The CR-OSELM is developed, which maps the inputs from the low-dimensional space to the high-dimensional space. Then the CR-OSELM is adopted to build the sensor measurement prediction module, and identify and reconstruct the sensor bias and drift fault. Furthermore, in allusion to the rotor speed sensor and temperature/pressure sensor faults, analytic redundancy and controller switching strategies are designed respectively, realizing the fault-tolerant control of thrust feedback. The main contribution of this paper is to come up with the CR-OSELM algorithm with the ability to save historical input information, which overcomes the limitations of traditional feedforward neural networks in identifying time series. And the compound fault-tolerant control scheme is put forward based on the fault characteristics and their impact on the direct thrust control system, which can deal with different types of sensor faults. Simulations are performed on some benchmark datasets and a turbofan engine model to investigate the time series prediction accuracy, sensor fault diagnosis, and thrust fault-tolerant control effect. The results show that the system can detect various faults rapidly, realize the analytic redundancy with high accuracy, reduce the influence of sensor faults and restore the engine performance.