Reviewerly/exHarmony huggingface dataset:
A space-reduction based three-phase approach for large-scale optimization

With the rapid development of science and technology, large-scale global optimization (LSGO) has received more and more attention. However, large-scale problem is very challenging due to its high dimensionality, nonlinearity, and countless local optimal solutions. In this paper, a space-reduction based three-phase approach (SRTP) is proposed to solve this problem. In the first phase, a space-reduction based line search method is designed to roughly locate good solutions as well as to explore important information such as promising search regions and characteristics of each variable (dimension). In the second phase, a multi-grouping strategy is proposed for fully non-separable large-scale problems. This new decomposition method provides dozens of grouping results for non-separable large-scale problems that not only reduces the dimensionality but also maintains the correlation of variables to the most extent. Then in the third phase, a space-reduction based group search method is designed to optimize the sub-groups. By decomposing the large-scale problem as well as reducing the huge search space to focus on promising areas, this group search method can gain better efficiency. Experiments are conducted on 35 widely used benchmark functions and the proposed SRTP is compared with state-of-the-art LSGO algorithms. Experimental results show that SRTP is effective and efficient.
A dynamic support ratio of selected feature-based information for feature selection

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. Existing methods concentrate on the classification information from candidate features while seldom considering the changing information supported by selected features. In this paper, we construct a dynamic support ratio (DSR), which employs the new information of selected features to support classification. DSR explicitly describes the dynamic interactions between selected features and candidate features. Simultaneously, the feature relevance and feature redundancy are treated adaptively. Thus, distinctive features can be noticed sensitively. Afterward, a novel feature selection method based on a dynamic support ratio (DSRFS) is proposed. The proposed method is established on 18 benchmark data sets with four different classifiers. Classification accuracy, standard deviation, recall and statistical validations are employed to measure the classification performance. Extensive experiments demonstrate that DSRFS not only reduces the dimension of the feature space effectively, but also obtains the best average classification accuracy.
Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features

Skin cancer is the most common type of cancer in United States with 9,500 new cases diagnosed daily. It is one of the deadliest forms, however early detection and treatments can lead to recovery. More and more modern medical systems employs deep learning (DL) vision models as an assistive secondary diagnostic tool. This progress is derived from the superior performance by convolutional neural networks (CNNs) across a wide number of medical applications. However, recent discovery has revealed that adding small but faint noises to images can cause these models to make classification errors. These adversarial attacks can undermine defense measures and hamper the operations of deep learning models in real-world settings. The objective of this paper is to explore the effects of image degradation on popular off-the-shelf Deep Learning (DL) vision models. First, the investigation of the effects of adversarial attacks on image classification accuracy, sensitivity, and specificity are evaluated. Then we introduce pepper noise as an adversarial attack, which is an extension of the one-pixel attack on deep learning models. Second, we propose a novel texture descriptor Ordered statistics Local Binary Patterns (OS-LBP) for recognizing potential skin cancer areas. Third, we will demonstrate how our OS-LBP is successful in mitigating some of the effects of image degradations caused by adversarial attacks.
An efficient GPU-based method to compute high-order Zernike moments

The utilization of Zernike moments has been extensive in various fields, including image processing and pattern recognition, owing to their desirable characteristics. However, the application of Zernike moments is hindered by two significant obstacles: computational efficiency and accuracy. These issues become particularly noticeable when computing high-order moments. This study presents a novel GPU-based method for efficiently computing high-order Zernike moments by leveraging the computational power of the Single Instruction Multiple Data (SIMD) architecture. The experimental results demonstrate that the proposed method can compute Zernike moments up to order 500 within 0.5 seconds for an image of size 512×512. To achieve greater accuracy in Zernike moments computation, a k×k sub-region scheme was incorporated into our approach. The results show that the PSNR value of the Lena image reconstructed from 500-order Zernike moments computed using the 9×9 scheme can reach 39.20 dB.
Dual frame-level and region-level alignment for unsupervised video domain adaptation

Most existing unsupervised video domain adaptation methods focus on extracting frame-level features and using temporal attention to create a domain-invariant feature space. However, this ignores the importance of different image regions and temporal dynamics. In this study, we propose a method that considers both frame-level and image-region level alignment to learn a dual spatial and temporal domain-invariant feature space. This is achieved by: 1) extracting frame-level image features and aligning them into a domain-invariant latent feature space; 2) extracting region-level temporal features and aligning them into a domain-invariant latent feature space. Optimal frame-level correlation is achieved through video-frame level alignment, while optimal image-region level correlation is demonstrated through image-region level alignment between the source and target videos. We use an LSTM network to parameterize this dual feature alignment, which is more efficient than attention mechanisms and results in significantly improved performance and reduced GPU memory cost by over 40% compared to current state-of-the-art methods.
Application of multivariate change detection in automated lithofacies classification from well-log data in a nonstationary subsurface

There have been research attempts to automate the process of lithology identification from the well-logs using machine learning algorithms. However, a single algorithm cannot be applied on a universal scale due to the nonstationary nature of geological data sets. The notion of changes in the well-log signal must be considered to resolve the issue. This paper presents a multistage method using multivariate change detection and statistical machine learning to (a) identify the significant changes in the well-logs, (b) map multivariate change in log signal with the lithofacies classes, (c) enhance the data set by removing the highly populated class, (d) using statistical machine learning tools, i.e., Support Vector Machine (SVM) to classify target classes. We apply the algorithm to the data collected from a mostly shale-contained subsurface environment in the Krishna-Godavari (KG) basin, India. The primary objective of the research is to locate thin layers of hydrocarbon-contained sand in the subsurface while dealing with challenges such as intra-well and inter-well heterogeneity and a severe class imbalance. The algorithm satisfactorily performs in lithofacies classification with significantly better performance than the standard machine learning algorithm.
Self-relation attention networks for weakly supervised few-shot activity recognition

Traditional sensor-based human activity recognition (HAR), which requires a large amount of high-quality data of a new class for training, can hardly classify new-class activities given only a few labeled samples. Using these models to customize and recognize personalized (new class) activity for a better user experience is a great challenge. It is time-consuming work for users to label abundant new-class time series data. Additionally, these activity sequences are weakly-labeled with existing annotation technology. The purpose of this work is to learn a meta-classifier that generalizes well in weakly-labeled new-class activities even given few noisy samples. On this basis, we combine the power of few-shot learning and weakly supervised classification to achieve the above goal. We propose a novel self-relation attention Prototypical Networks for personalized HAR to extract more discriminative target features from the spatiotemporal features of noisy sensor data. Attention-based meta-learning generates each subsequence contribution to the object activity features automatically on the subsequence-level relation mining in one weakly labeled sample. Therefore, the highlighted object activity features are less affected by the noisy subsequences. Furthermore, we design an augmentation operation for the highlighted features to avoid the biased target activity prototype in each class. Finally, we evaluate the proposed models in extensive experiments, including accuracy evaluations, ablation studies and the influence of thresholds. The results show that our proposal achieves a better performance in weakly-supervised HAR compared with other models.
Regularization-Adapted Anderson Acceleration for multi-agent reinforcement learning

Originating from model-free reinforcement learning (RL), many modern multi-agent reinforcement learning (MARL) algorithms are usually armed with the paradigm of Centralized Training with Decentralized Execution (CTDE) to mitigate the non-stationary problem and make the training process stable. However, these methods still suffer from sample inefficiency and slow training convergence as in the single-agent reinforcement learning setting. Many common methods aiming to tackle these problems utilize the experience buffer and parallel training mechanism to speed up the training process, which would cost more computing resources and may still underuse the sampled experiences. In this paper, we propose Regularization-Adapted Anderson Acceleration (RA3) for model-free, off-policy MARL algorithms. Under the CTDE paradigm, this specific RA3 approach treats the joint action-value function update as a fixed-point iteration task and speeds up the training process with the same amount of sampled experiences as in the baseline algorithms. Furthermore, our RA3 employs an adaptive regularization strategy related to Bellman residuals to stabilize the update process and enhance the training performance. Experimental results demonstrate that the improved learning speed and superior performance of our proposed method are significantly improved on the predator–prey game and the challenging StarCraft II micromanagement benchmark tasks.
Intra-hour PV power forecasting based on sky imagery

This paper introduces an upgraded version of the PV2-state model [Paulescu et al. Renew Energy 195 (2022) 322] for intra-hour photovoltaic (PV) power forecasting. The model incorporates real-time adjustments to the estimated clear-sky PV power or the transmittance of clouds, considering the Sun coverage by clouds. A physics-based approach for processing cloud field information from an all-sky imager is proposed to intra-hour forecast SSN, a binary quantifier stating whether the Sun is shining (SSN = 1) or not (SSN = 0). The model performance with the new procedure is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality 1-min data collected from a micro-PV plant, on a sample of days with scattered clouds. PV2-state demonstrates notable performance particularly under challenging conditions, where models typically struggle to perform well. In terms of skill score, the overall model performance ranges between 10 and 20%, and under very high variability conditions even exceeding four times the purely statistical forecast. For the operationally relevant 15-min horizon, PV2-state exhibits a noteworthy precision, with two-thirds of forecasts falling within a 10% tolerance interval. Even under severe conditions roughly one third falls in this 10% interval.
Fraud analytics: A decade of research

The literature on fraud analytics and fraud detection has seen a substantial increase in output in the past decade. This has led to a wide range of research topics and overall little organization of the many aspects of fraud analytical research. The focus of academics ranges from identifying fraudulent credit card payments to spotting illegitimate insurance claims. In addition, there is a wide range of methods and research objectives. This paper aims to provide an overview of fraud analytics in research and aims to organize the discipline and its many subfields. We analyze a sample of almost 300 records on fraud analytics published between 2011 and 2020. In a systematic way, we identify the most prominent domains of application, challenges faced, performance metrics, and methods used. In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research. One of the key challenges in fraud analytics is access to public datasets. To further aid the community, we provide eight requirements for suitable data sets in research motivated by our research. We structure our sample of the literature in an online database. The database is available online for fellow researchers to investigate and potentially build upon.
Quantum-like implicit sentiment analysis with sememes knowledge

Sentiment analysis, which can discriminate the sentiment tendency of subjective texts, is one of the important research works in the field of natural language processing. Sentiment analysis can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether the text contains explicit sentiment words. Most of the current research work focuses on explicit sentiment analysis, while implicit sentiment analysis has become one of the most challenging research tasks because the sentiment characteristics are too implicit. In this paper, “Fused with Sememe Knowledge Quantum-like Chinese Implicit Sentiment Analysis (FSKQ)” is proposed, which introduces the density matrix in quantum theory and takes sememe, the smallest common sense semantic unit in natural language, as an external knowledge base to build a sememe-based density matrix. The matrix can be regarded as a complete knowledge system with strong generalization, which models the global information of the most fine-grained semantic knowledge. Its incorporation into the text vector results in a high quality of text representation, which effectively improves the performance of the model in Chinese implicit sentiment analysis. Ablation experiments and comparison experiments are conducted in the SMP ECISA2019 dataset, and the results show that the F1 score of the model is improved by 2.6% compared with the best model, which proves the effectiveness and superiority of the idea. In addition, in order to verify the performance of the proposed method in terms of text representation quality, it is also applied to existing models in the aspect-level sentiment analysis and event detection, and it is compared with the original model without using the idea and the baseline model on Twitter, Lap14, Rest14/15/16 and ACE2005 datasets. The results show that compared with the original model and the baseline model in this field, the model combined with the idea improves the accuracy and F1 score, which further proves the effectiveness, superiority and generalization of the FSKQ model.
The effect of hands synchronicity on users perceived arms Fatigue in Virtual reality environment

The use of virtual reality (VR) system has become more common with the arrival of a new generation of headsets. This increase in accessibility has in turn led to VR being used for applications which require longer sessions, such as video games, artistic expression, rehabilitation, and so on. Taking muscular fatigue in account for the VR applications is therefore becoming essential to ensure a comfort of usage and also to avoid injuries. In addition, usual interactions in these diverse applications are very varied and may require the use of one or two hands, synchronized or not. However, little research has focused on fatigue produced by one and two handed interactions especially during mid-air interaction in VR environment. In this paper, we examine the effect of hands synchronicity and movement direction on the user perceived arm fatigue in VR environment during a simple (one line of targets) and a composite (two lines of targets) pointing tasks performed using both controlled and free hands synchronicity. Our findings indicate that to optimize the relationship between fatigue and efficiency, it seems necessary to select the synchronicity of hands depending on the performed task. Furthermore, it is desirable to permit the users to use the hands synchronicity they prefer. In addition, distance covered by the hand and changes in user posture could be used in real-time as indicators of fatigue to trigger changes in the interface, or notifications. Finally, our findings reveal that the directions of movement along the vertical axis and some diagonals are more tiring than those of the horizontal plane, which suggests that it would be better to favor the use of horizontal directions.
Anomaly detection for multivariate times series through the multi-scale convolutional recurrent variational autoencoder

To realize the anomaly detection for industrial multi-sensor data, we develop a novel multi-scale convolutional recurrent variational autoencoder (MSCRVAE) model. It is a hybrid of convolutional autoencoder and convolutional long short-term memory with variational autoencoder (ConvLSTM-VAE). The ConvLSTM-VAE part helps the MSCRVAE model not only capture the spatial and temporal dependence of data but also learn robust high-level representations by estimating the distributions of latent variables. Moreover, a typical loss function that combines the characteristics of both autoencoder and variational autoencoder is designed for the MSCRVAE model. The experimental results on three public data illustrate the superiority of the MSCRVAE model in anomaly detection with the best average F1-score up to 0.90. The interquartile range of the boxplots on the public data also proves the robustness of the MSCRVAE model. We also illustrate the role of the two components in the loss function by exploring their changes during training. What is more, on the private data, the MSCRVAE model also performs well and the residual matrices provide reasonable interpretations to the anomaly detection results.
NeoDescriber: An image-to-text model for automatic style description of neoclassical architecture

Architectural style description is important for the renewal and reuse of historical monuments. However, the identification of Neoclassical buildings is more difficult than that of other European architectural styles owing to the revival of classicism and its numerous variants. This paper proposes the image-to-text model NeoDescriber for the automatic identification and description of Neoclassical buildings based on the combination of dedicated visual classification and detection modules organized by expert knowledge in architecture. NeoDescriber takes building façade images as input and follows a coarse-to-fine description logic to generate structured annotations covering the whole building and key building elements. We built several building image datasets composed of more than 300 historical monuments to train and validate different visual modules. The results showed the acceptance performance of both classification and detection modules, with average precisions ranging from 73.6% to 95.2%. The generated text descriptions were then compared with results from BLIP-2 by qualitative and quantitative analyses. With human descriptions as a reference, the results showed that NeoDescriber’s generated descriptions were more precise and detailed, having a higher BERTScore for Neoclassical monuments. Thus, NeoDescriber could successfully recognize and describe Neoclassical buildings.
A multi-channel framework based Local Binary Pattern with two novel local feature descriptors for texture classification

Local binary pattern (LBP) has been widely used in various application fields, including object detection, texture analysis, and remote sensing. To improve discrimination performance, many LBP-based methods have been proposed for extracting different local feature information for texture classification. However, current LBP-based algorithms typically describe local features at a single sampling scale, but some significant and discriminative texture feature information is contained between different scales. Therefore, the lack of capability to extract cross-scale features can lead to losing critical texture features. Moreover, low-frequency texture information should be accorded great importance in texture classification. Additionally, if the diversity and validity of local feature extraction are lacking, the effectiveness of classification capability suffers. To address these issues, (1) we propose a completed cross-scale Local binary pattern (ccsLBP) operator to extract cross-scale texture features. (2) A mean-filtered Local binary pattern (LBPmf) operator is presented to highlight the important low-frequency texture information of texture images. (3) We build a high-performing multi-channel framework based Local binary pattern (MC-LBP) for texture classification, which combines complementary features extracted by LBP, ccsLBP, and LBPmf hybridly to form a final feature vector of the texture image. The effectiveness of the proposed MC-LBP framework is verified on 6 representative texture databases, and the experimental results demonstrate its state-of-art texture classification performance.
Single image fast dehazing based on haze density classification prior

Most of existing the dehazing methods have high computational complexity and poor dehazing quality. Therefore, a fast haze removal method based on the haze density classification prior is proposed. Haze density is reflected by the difference between maximum channel and minimum channel. Based on this, haze density prior (HDP) is proposed. The HDP can effectively distinction between mist image and dense haze image, and quickly estimate the atmospheric light veil. In addition, an optimized method for estimating atmospheric light using the mid-channel of haze image is proposed, which overcomes limitations of global atmospheric light. Our method can fast and efficiently recover clear image because there is no estimation of transmission map. Experiments show that the proposed method outperforms existing methods in haze removal, especially for dense haze images. Comparison of objective evaluation and running time shows that the proposed method is effective and real-time.