Reviewerly/exHarmony huggingface dataset:
Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China

Accurate daily solar radiation (Rs) data is crucial for the growth and development of crops, but its direct observation is relatively scarce in most regions around the world. Therefore, accurately estimating daily Rs is of great significance. Regarding the above practical requirement, this study developed a novel hybrid deep learning model (MEA-DBN-LSTM), which combined the Long Short-Term Memory model (LSTM), Deep Belief Network (DBN), and Mind Evolutionary Algorithm (MEA), to estimate the Rs across four different climate regions of China using five easily accessible meteorological input combination (sunshine-based, temperature-based and three complex-based). The results showed that MEA-DBN-LSTM was proved to be the most recommended Rs estimated model across four different climate regions of China, and the corresponding median R2, NSE, RMSE, MAE, and MAPE values ranged 0.805–0.999, 0.656–0.954, 1.069–4.289 MJ m−2 d–1, 0.889–3.532 MJ m−2 d–1, and 0.055–0.296 respectively. Correspondingly, compared to the empirical Rs model, the improvement values of MEA-DBN-LSTM were 12.33–52.13 % for R2, 14.80–69.38 % for NSE, 33.75–69.77 % for RMSE, 36.03–62.70 % for MAE, and 42.40–66.67 % for MAPE, respectively. Moreover, the MEA-DBN-LSTM exhibits improvements in R2, NSE, RMSE, MAE, and MAPE, ranging 1.40–13.50 %, 1.27–24.79 %, 1.81–61.18 %, 1.90–61.30 %, and 2.31–57.55 %, respectively, compared to other forms of LSTM-type models. Specifically, this study found the DBN modules significantly improved the complex-based single LSTM model, but without impact on sunshine-based and temperature-based single LSTM models. Regarding the MEA modules, it was effective in the hyperparameter optimization compared to DBN-LSTM. Furthermore, significant accuracy discrepancies exist among Rs models using different input combinations across four different climate regions, and the complex-based was generally superior to other input combinations. Overall, our findings can provide accurate daily Rs across four different climate regions of China using easily accessible meteorological data, which is of great significance for crop water consumption estimation, agricultural water resources management, crop growth conditions optimization, and sustainable agricultural development.
Recent advances in artificial intelligence for video production system

ABSTRACTThe emergence of AI has revolutionized video production, enabling innovative approaches in artistic creation. With self-media's rise and demand for user-generated content, AI drives personalized, efficient video production. However, research lacks comprehensive reviews of AI's impact on film and TV. This study fills the gap by analyzing AI's influence using a five-sub-field framework. Through literature integration, we assess AI video production's potential, proposing a new process paradigm. This research sheds light on AI's transformative role, guiding efficient, personalized film and TV production, and inspiring deep learning-based advancements. This paper acts as a roadmap for maximizing AI's potential in enhancing production quality and efficiency.KEYWORDS: Artificial intelligencedeep learningvideo productionfilm and television industries AcknowledgmentsWe would like to express our deepest gratitude and appreciation to everyone who contributed to the successful completion of this project.We extend our heartfelt thanks to the following individuals and organizations: The National Social Science Fund of China (18BXW060), Shenzhen Government (JCYJ20190806144609107).Furthermore, we extend our thanks to the professionals and experts in the field who provided us with valuable feedback, guidance, and support during the various stages of the project. Their expertise and constructive criticism significantly enhanced the quality and rigor of our work.Disclosure statementNo potential conflict of interest was reported by the author(s).
Assessing the Prevalence and Predictors of Incivility in Online News Comments Across Six Countries

ABSTRACTDrawing on discussions about the manifestation of incivility in online news comments sections, our research operationalizes the concept of incivility and suggests a methodological approach that relies on manual and automated text analysis and regression analysis to assess its prevalence and identify its predictors. Relying on a data analysis of over two million comments on immigration and unemployment retrieved from twelve newspapers websites from six countries (Brazil, Chile, Portugal, Spain, United Kingdom, and United States), our study confirms the prevalence of incivility in online news comments sections and shows that comments on the topic of immigration, with clear political orientation, particularly right-wing, and displaying populism and false information perception are more prone to include discursive features of incivility.KEYWORDS: Incivilitynews commentsopinionnewspaperspolitical orientationpopulism Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 Project reference: PTDC/CPO-CPO/28495/2017 funded by the Portuguese Foundation for Science and Technology.Additional informationFundingThis work was supported by Fundação para a Ciência e a Tecnologia [grant numbers: PTDC/CPO-CPO/28495/2017; 2020.04070.CEECIND/CP1615/CT0007].
Detection of adversarial attacks based on differences in image entropy

Abstract Although deep neural networks (DNNs) have achieved high performance across various applications, they are often deceived by adversarial examples generated by adding small perturbations. To combat adversarial attacks, many detection methods have been proposed, including feature squeezing and trapdoor. However, these methods rely on the output of DNNs or involve training a separate network to detect adversarial examples, which leads to high computational costs and low efficiency. In this study, we propose a simple and effective approach called the entropy-based detector (EBD) to protect DNNs from various adversarial attacks. EBD detects adversarial examples by comparing the difference in entropy between the input sample before and after bit depth reduction. We show that EBD can detect over 98% of the adversarial examples generated by attacks using fast-gradient sign method, basic iterative method, momentum iterative method, DeepFool and CW attacks when the false positive rate is 2.5% for CIFAR-10 and ImageNet datasets.
Variables are a Curse in Software Vulnerability Prediction

Deep learning-based approaches for software vulnerability prediction currently mainly rely on the original text of software code as the feature of nodes in the graph of code and thus could learn a representation that is only specific to the code text, rather than the representation that depicts the 'intrinsic' functionality of a program hidden in the text representation. One curse that causes this problem is an infinite number of possibilities to name a variable. In order to lift the curse, in this work we introduce a new type of edge called name dependence, a type of abstract syntax graph based on the name dependence, and an efficient node representation method named 3-property encoding scheme. These techniques will allow us to remove the concrete variable names from code, and facilitate deep learning models to learn the functionality of software hidden in diverse code expressions. The experimental results show that the deep learning models built on these techniques outperform the ones based on existing approaches not only in the prediction of vulnerabilities but also in the memory need. The factor of memory usage reductions of our techniques can be up to the order of 30,000 in comparison to existing approaches.
Dual-axis solar tracking system with different control strategies for improved energy efficiency

Photovoltaic (PV) systems are rapidly increasing worldwide but are often installed as fixed flat-plate systems with predefined angles. This paper focuses on constructing a closed-loop solar tracking system (STS) to accurately measure the sun's location in real time, enabling solar panels to collect maximum solar radiation. A sensor-based feedback controller compares sunlight intensity to a threshold, driving a motor to rotate the dual-axis tracking motor and turn the PV panel toward the sun. The system, consisting of an electrical and mechanical system, was designed using the SIMULINK platform and SOLIDWORKS platform for real-life solar tracker systems. Simulation results show that the tracker stand construction in the SP-13 program for three 335-watt PV panels has sufficient strength against normal and critical wind speeds. These findings will greatly benefit the development of a dual-axis real-time sun tracker PV system.
Role of Incentives in the Use of Blockchain-Based Platforms for Sharing Sensitive Health Data: Experimental Study

Blockchain is an emerging technology that enables secure and decentralized approaches to reduce technical risks and governance challenges associated with sharing data. Although blockchain-based solutions have been suggested for sharing health information, it is still unclear whether a suitable incentive mechanism (intrinsic or extrinsic) can be identified to encourage individuals to share their sensitive data for research purposes.This study aimed to investigate how important extrinsic incentives are and what type of incentive is the best option in blockchain-based platforms designed for sharing sensitive health information.In this study, we conducted 3 experiments with 493 individuals to investigate the role of extrinsic incentives (ie, cryptocurrency, money, and recognition) in data sharing with research organizations.The findings highlight that offering different incentives is insufficient to encourage individuals to use blockchain technology or to change their perceptions about the technology's premise for sharing sensitive health data. The results demonstrate that individuals still attribute serious risks to blockchain-based platforms. Privacy and security concerns, trust issues, lack of knowledge about the technology, lack of public acceptance, and lack of regulations are reported as top risks. In terms of attracting people to use blockchain-based platforms for data sharing in health care, we show that the effects of extrinsic motivations (cryptoincentives, money, and status) are significantly overshadowed by inhibitors to technology use.We suggest that before emphasizing the use of various types of extrinsic incentives, the users must be educated about the capabilities and benefits offered by this technology. Thus, an essential first step for shifting from an institution-based data exchange to a patient-centric data exchange (using blockchain) is addressing technology inhibitors to promote patient-driven data access control. This study shows that extrinsic incentives alone are inadequate to change users' perceptions, increase their trust, or encourage them to use technology for sharing health data.
Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach

This study examines the return dependence between Bitcoin and major international stock markets from January 1, 2018, to September 30, 2021. Utilizing a time-varying parameter VAR (TVP-VAR) model with stochastic volatility, we analyze the dynamics of the dependence structure while considering market uncertainty indices (UCRY Policy, UCRY Price and VIX) before and after the outbreak of COVID-19 pandemic. Our findings indicate that Bitcoin's role as a safe haven for stock markets has yet to be observed during the post-COVID-19 period. Furthermore, we observe varying impacts of the return correlation between different stock markets and Bitcoin, suggesting an asymmetric dependence. These results contribute to the ongoing discussion regarding Bitcoin's role as a safe haven asset and provide valuable insight for investors and policymakers.
A practical approach for discriminating tectonic settings of basaltic rocks using machine learning

Elucidating the tectonic setting of unknown rock samples has long attracted the interest of not only igneous petrologists but also a wide range of geoscientists. Recently, attempts have been made to use machine learning to discriminate the tectonic setting of igneous rocks. However, few studies have designed methods that are applicable to altered rocks. This study proposes a novel approach that utilizes the ratio of elements less susceptible to weathering, alteration, and metamorphism as feature values for analyzing altered basalts. The method was evaluated using six well-established machine learning algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). The results show that KNN achieved the highest classification score of 83.9% in the balanced accuracy of classifying the eight tectonic settings, closely followed by SVM with a score of 83.7%. In addition, oceanic and arc/continental basalts could also be discriminated against with an accuracy of more than ∼90% for KNN. This study suggested that the machine learning method can discriminate tectonic settings more accurately and reliably than previously used discrimination diagrams by designing appropriate feature values.
Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving

The spatial information provided by 3-D object detection is critical for autonomous driving. Although visual-based object detection has made significant progress in 2-D/3-D in recent years, nonvisual sensors such as 3-D light detection and ranging (LiDAR) still have inherent advantages in the accuracy (ACC) of object localization. However, the challenge still lies in the interpretability of the sparse point clouds it generates and the difficulty of being manually annotated. In this article, we propose an online transfer learning framework based on multimodal sensor systems for 3-D object detection of urban road participants including pedestrians, cyclists, and cars. The framework aims to automatically and efficiently transfer object detection capabilities from 2-D monocular camera to 3-D LiDAR through a multitarget tracker-based pipeline, enabling knowledge transfer between sensors of different modalities. Furthermore, online random forest (ORF), an inherently fast and multiclass learning method, is innovatively integrated into our system. Experiments on two very different datasets, including KITTI and Waymo, demonstrate that the proposed framework not only rapidly builds 3-D detection capabilities about road participants in a single dataset but also maintains this capability across datasets. This reveals that the proposed framework is particularly suitable for in situ deployment of unmanned vehicles and can also solve the problem of insufficient generalization ability of current offline-trained detectors.
Inverse Extended Kalman Filter—Part II: Highly Nonlinear and Uncertain Systems

Counter-adversarial system design problems have lately motivated the development of inverse Bayesian filters. For example, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverse Kalman filter</i> (I-KF) has been recently formulated to estimate the adversary’s Kalman-filter-tracked estimates and hence, predict the adversary’s future steps. The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverse extended Kalman filter</i> (I-EKF). The companion paper proposed the theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs). In this paper, we develop this theory for highly non-linear models, which employ second-order, Gaussian sum, and dithered forward EKFs. In particular, we derive theoretical stability guarantees for the inverse second-order EKF using the bounded non-linearity approach. To address the limitation of the standard I-EKFs that the system model and forward filter are perfectly known to the defender, we propose reproducing kernel Hilbert spacebased EKF to learn the unknown system dynamics based on its observations, which can be employed as an inverse filter to infer the adversary’s estimate. Numerical experiments demonstrate the state estimation performance of the proposed filters using recursive Cram´er-Rao lower bound as a benchmark.
Lightweight Automatic Modulation Classification via Progressive Differentiable Architecture Search

Automatic modulation classification (AMC) is a key step of signal demodulation that determines whether the receiver can correctly receive the transmitted signal without prior knowledge of the modulation type. Deep learning (DL) based AMC methods have been proven to achieve excellent performances. However, these DL-based methods rely heavily on expert experience to design neural network structures. These hand-designed networks have fixed architectures and lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) is a vital direction for automatic machine learning (AutoML), which can solve the shortcomings of hand-designed network architectures. In this paper, according to the specific modulation classification task, we propose a lightweight progressive differentiable architecture search-based AMC (PDARTS-AMC) method to search for a very lightweight network with great performance. In addition, the optimal architecture searched on dataset simulated by MATLAB is transferred to the RadioML2016.10B task, to verify the robustness and generalization of the proposed method. Experimental results show that the proposed PDARTS-AMC method both improves the classification accuracy and reduces the computational cost when compared with existing classical AMC methods.
Camouflaged Object Segmentation Based on Joint Salient Object for Contrastive Learning

In a broad sense, camouflaged objects generally refer to objects that have a high degree of similarity to the background. Therefore, camouflaged object segmentation (COS) is more challenging than traditional object segmentation. Current COS networks have high segmentation precision on datasets. However, the problems of object miss detection and false alarm still occur, mainly due to the different camouflage levels of the objects. In this paper, we propose a Joint Comparative Learning Network (JCNet) for camouflaged object segmentation based on joint salient object for contrastive learning to overcome the widespread challenges in COS. Specifically, the main innovation of JCNet is the Contrastive Network (CNet) design, which generates a unique feature representation of the camouflaged object different from others. In terms of details, we design an edge guidance module to enhance the edge extraction capability. Moreover, a global relationship capture module is proposed to improve the confidence level of the feature representation. Finally, we set positive and negative samples and loss functions in conjunction with sample types. We conducted comprehensive experiments using four COS datasets, and the results demonstrate its suitability for COS when compared with other state-of-the-art segmentation models. JCNet achieves optimal results on five evaluation metrics, including an average improvement of 1.93% and 2.7% on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F<sub>m</sub></i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ω</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>m</i></sub> , respectively. In summary, it has lower miss and false alarm rates, and better generalization in the COS task. In addition, the experiments demonstrate that JCNet also has strong segmentation capability in salient object segmentation, achieving a win-win situation for both tasks. The code will be available at https://github.com/jiangxinhao2020/JCNet.
Multimodal hate speech detection via multi-scale visual kernels and knowledge distillation architecture

People increasingly use social media platforms to express themselves by posting visuals and texts. As a result, hate content is on the rise, necessitating practical visual caption analysis. Thus, the relationship between image and caption modalities is crucial in visual caption analysis. Contrarily, most methods combine features from the image and caption modalities using deep learning architectures with millions of parameters already trained without integrating a specialized attention module, resulting in less desirable outcomes. This paper suggests a novel multi-modal architecture for identifying hateful memetic information in response to the above observation. The proposed architecture contains a novel "multi-scale kernel attentive visual" (MSKAV) module that uses an efficient multi-branch structure to extract discriminative visual features. Additionally, MSKAV utilizes an adaptive receptive field using multi-scale kernels. MSKAV also incorporates a multi-directional visual attention module to highlight spatial regions of importance. The proposed model also contains a novel "knowledge distillation-based attentional caption" (KDAC) module. It uses a transformer-based self-attentive block to extract discriminative features from meme captions. Thorough experimentation on multi-modal hate speech benchmarks MultiOff, Hateful Memes, and MMHS150K datasets achieved accuracy scores of 0.6250, 0.8750, and 0.8078, respectively. It also reaches impressive AUC scores of 0.6557, 0.8363, and 0.7665 on the three datasets, respectively, beating SOTA multi-modal hate speech identification models.
Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages

Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40% on SemEval-2018 and 53.45% on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models.