Reviewerly/exHarmony huggingface dataset:
Cross-site scripting detection with two-channel feature fusion embedded in self-attention mechanism

In the era of big data, stealing users’ private data has become one of the main targets of network hackers. In recent years, cross-site scripting (XSS) attacks to obtain users’ privacy data have been one of the main web attack methods of network hackers. Traditional antivirus software cannot identify such cross-site scripting attacks. To identify cross-site scripting attacks quickly and accurately, we proposed a cross-site scripting detection model (C-BLA) with two-channel multi-scale feature fusion embedded in a self-attention mechanism. The model first maps cross-site scripting payloads into spatial vectors by data preprocessing using Word2Vec. Then the two-channel network performs feature extraction on the data. Channel I: extract local features of cross-site scripting payloads at different scales by designing parallel one-dimensional convolutional layers with different convolutional kernel sizes; Channel II: extract semantic information of cross-site scripting payloads from two directions of positive and negative order using a bidirectional Long-Short Term Memory network, and then embed the self-attention mechanism to strengthen the semantic information features. Experiments show that the proposed model achieves a precision rate of 99.8% and a recall rate of 99.1% for cross-site scripting detection, which is a certain improvement in detection rate compared with a single deep learning model and traditional machine learning methods. The two-channel feature fusion of this model better solves the cross-site scripting detection problem.
A COMPUTATIONAL APPROACH TO SOLVE THE NONLINEAR BIOLOGICAL PREY–PREDATOR SYSTEM

This study is conducted to solve a nonlinear biological prey–predator system (NBPPS) using a novel design of the Levenberg–Marquardt backpropagation approach (LMBA). The LMBA-based supervised neural networks (SNNs) deal with three kinds of sample data, training, validation, and testing. The percentages for these data to solve three different cases of the NBPPS are selected: for training 75%, validation 10%, and testing 15%, respectively. The numerical performances of the Adams method are used for the reference dataset to solve the NBPPS. The obtained form of the numerical solutions of the NBPPS based on the SNNs along with LMBA is used to reduce the functions of mean square error (MSE). For the correctness, competence, and effectiveness of the proposed SNNs along with LMBA, the numerical procedures are proficient based on the proportional schemes and analyses in terms of MSE results, correlation, error histograms, and regression.
Fast virtual view synthesis for an 8K 3D light-field display based on cutoff-NeRF and 3D voxel rendering

Three-dimensional (3D) light-field displays can provide an immersive visual experience, which has attracted significant attention. However, the generating of high-quality 3D light-field content in the real world is still a challenge because it is difficult to capture dense high-resolution viewpoints of the real world with the camera array. Novel view synthesis based on CNN can generate dense high-resolution viewpoints from sparse inputs but suffer from high-computational resource consumption, low rendering speed, and limited camera baseline. Here, a two-stage virtual view synthesis method based on cutoff-NeRF and 3D voxel rendering is presented, which can fast synthesize dense novel views with smooth parallax and 3D images with a resolution of 7680 × 4320 for the 3D light-field display. In the first stage, an image-based cutoff-NeRF is proposed to implicitly represent the distribution of scene content and improve the quality of the virtual view. In the second stage, a 3D voxel-based image rendering and coding algorithm is presented, which quantify the scene content distribution learned by cutoff-NeRF to render high-resolution virtual views fast and output high-resolution 3D images. Among them, a coarse-to-fine 3D voxel rendering method is proposed to improve the accuracy of voxel representation effectively. Furthermore, a 3D voxel-based off-axis pixel encoding method is proposed to speed up 3D image generation. Finally, a sparse views dataset is built by ourselves to analyze the effectiveness of the proposed method. Experimental results demonstrate the method's effectiveness, which can fast synthesize novel views and 3D images with high resolution in real 3D scenes and physical simulation environments. PSNR of the virtual view is about 29.75 dB, SSIM is about 0.88, and the synthetic 8K 3D image time is about 14.41s. We believe that our fast high-resolution virtual viewpoint synthesis method can effectively improve the application of 3D light field display.
Stacked attention hourglass network based robust facial landmark detection

Deep learning based facial landmark detection (FLD) has made rapid progress. However, the accuracy and robustness of FLD algorithms are degraded heavily when the face is subject to diverse expressions, posture deflection, partial occlusion and other uncertain circumstances. To learn more discriminative representations and reduce the negative effect caused by outliers, a stacked attention hourglass network (SAHN) is proposed for FLD, where new attention mechanism is introduced. Basically, in the design of SAHN, a spatial attention residual (SAR) unit is constructed such that relevant areas of facial landmarks are specially emphasized and essential features of different scales can be well extracted, and a channel attention branch (CAB) is introduced to better guide the next-level hourglass network for feature extraction. Due to the introduction of SAR and CAB, only two hourglass networks are stacked as the proposed SAHN with fewer parameters, which is different from traditional SHNs stacked by four hourglass networks. Furthermore, a variable robustness (VR) loss function is introduced for the training of SAHN. The robustness of the proposed model for FLD is guaranteed with the help of the VR loss by adaptively adjusting a continuous parameter. Extensive experimental results on three public datasets including 300W, WFLW and COFW confirm that our method is superior to some previous ones.
Detecting racial stereotypes: An Italian social media corpus where psychology meets NLP

The generation of stereotypes allows us to simplify the cognitive complexity we have to deal with in everyday life. Stereotypes are extensively used to describe people who belong to a different ethnic group, particularly in racial hoaxes and hateful content against immigrants. This paper addresses the study of stereotypes from a novel perspective that involves psychology and computational linguistics both. On the one hand, it describes an Italian social media corpus built within a social psychology study, where stereotypes and related forms of discredit were made explicit through annotation. On the other hand, it provides some lexical analysis, to bring out the linguistic features of the messages collected in the corpus, and experiments for validating this annotation scheme and its automatic application to other corpora in the future. The main expected outcome is to shed some light on the usefulness of this scheme for training tools that automatically detect and label stereotypes in Italian. • The paper offers a psychological and computational perspective on racial stereotype. • It provides a novel fine-grained annotation scheme for racial stereotype. • It sheds light on how to describe and model stereotypes in hoaxes against immigrants. • A novel Italian corpus from FaceBook is annotated for validating this scheme. • The dataset is lexically analyzed and used for training classifiers.
MADLINK: Attentive multihop and entity descriptions for link prediction in knowledge graphs

Knowledge Graphs (KGs) comprise of interlinked information in the form of entities and relations between them in a particular domain and provide the backbone for many applications. However, the KGs are often incomplete as the links between the entities are missing. Link Prediction is the task of predicting these missing links in a KG based on the existing links. Recent years have witnessed many studies on link prediction using KG embeddings which is one of the mainstream tasks in KG completion. To do so, most of the existing methods learn the latent representation of the entities and relations whereas only a few of them consider contextual information as well as the textual descriptions of the entities. This paper introduces an attentive encoder-decoder based link prediction approach considering both structural information of the KG and the textual entity descriptions. Random walk based path selection method is used to encapsulate the contextual information of an entity in a KG. The model explores a bidirectional Gated Recurrent Unit (GRU) based encoder-decoder to learn the representation of the paths whereas SBERT is used to generate the representation of the entity descriptions. The proposed approach outperforms most of the state-of-the-art models and achieves comparable results with the rest when evaluated with FB15K, FB15K-237, WN18, WN18RR, and YAGO3-10 datasets.
Robots and AI in retailing and consumer services

The hospitality industry has experienced changes in communication and interaction due to the evolution of new technology. However, there has been limited research on the relationship between entrepreneurial passion, technology adoption propensity (TAP), perception of advantage, and non-fungible token (NFT) usage intention. Despite the challenges in data collection and limited NFT applications, this study gathered the perspectives of 292 hotels and investigated the effects of mediation and moderation on NFT adoption. Results show that entrepreneurial passion indirectly influences NFT adoption through the perception of advantage, TAP, and trust. Attitudes and images also have a moderating effect on the hotel NFT adoption process. The final section demonstrates an alternative model and second-order analysis to ensure the robustness of the proposed model. This study highlights the importance of considering multiple factors when examining NFT adoption in the hospitality industry.
Employing Mobile Learning in Music Education

This study sought to evaluate the effectiveness of introducing digital software in music education to improve academic performance and solfeggio class attendance. To assess academic performance, a test consisting of 25 theoretical questions and 25 practical tasks was developed. The Bologna model was used as an assessment system. The groups were compared by calculating the Student's t-test in terms of the "Test Score" and "Missed Classes" parameters. The collected data revealed that the mean score for the experimental group was 87.50 out of 100, while in the control group, it was much lower, namely 65.37. Likewise, the control group was characterized by satisfactory (D) performance in mastering solfeggio, with the mean score ranging between 64 and 73, whereas experimental group participants had an average of 82-89. The statistical data processing results showed that the six-month use of the ChordIQ app contributed to more effective mastering of solfeggio by amateur musicians. At the same time, the difference between the groups turned out to be significant in both cases, so the research hypothesis was confirmed for both of them (p < 0.05). Overall, the results showed that ChordIQ is a useful mobile app teaching the user music in an exciting and interactive way. The findings collected can be used to update traditional solfeggio curricula, conduct scientific studies in the field of interactive music education, as well as create new dedicated software.
MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification

Pathological diagnosis is considered as the benchmark for the detection of breast cancer. With the increasing number of patients, computer-aided histopathological image classification can assist pathologists in improving breast cancer diagnosis accuracy and working efficiency. However, a single model is insufficient for effective diagnosis, and this also does not accord with the principle of centralized decision-making. Starting from the real pathological diagnosis scenario, we propose a novel model fusion framework based on online mutual knowledge transfer (MF-OMKT) for breast cancer histopathological image classification. The OMKT part based on deep mutual learning (DML) imitates the mutual communication and learning between multiple experienced pathologists, which can break the isolation of single models and provides sufficient complementarity among heterogeneous networks for MF. The MF part based on adaptive feature fusion uses the complementarity to train a powerful fusion classifier. MF imitates the centralized decision-making process of these pathologists. We used the MF-OMKT model to classify breast cancer histopathological images (BreakHis dataset) into benign and malignant as well as eight subtypes. The accuracy of our model reaches the range of [99.27 %, 99.84 %] for binary classification. And that for multi-class classification reaches the range of [96.14 %, 97.53 %]. Additionally, MF-OMKT is applied to the classification of skin cancer images (ISIC 2018 dataset) and achieves an accuracy of 94.90 %. MF-OMKT is an effective and versatile framework for medical image classification.
A systematic literature review on sensitive data protection in blockchain applications

Summary Since inception, blockchain has earned significant attention due to its exclusive characteristics and advantages. It has changed the way the transactions are conducted by eradicating the role of third parties and promises to ensure trust among the participants. This technology is emerging as a potential solution to several issues but not without certain security vulnerabilities. In particular, protection of sensitive data is a more critical issue in the absence of a third party. This paper is aimed to report and share the state of the art of sensitive data protection in blockchain applications. The covered aspects include identification of sensitive data, existing techniques to protect sensitive data and to know how real time data compromised by security risks, attacks, threats and vulnerabilities concerning blockchain applications. This paper analysis the tools and techniques used in the past for protecting sensitive data and categorized them. On the basis of research and intuitive findings, methods and techniques are elaborated which can contribute in future in the designing a framework for protection of sensitive data in blockchain applications.
Padding-only Defenses Add Delay in Tor

Website fingerprinting is an attack that uses size and timing characteristics of encrypted downloads to identify targeted websites. Since this can defeat the privacy goals of anonymity networks such as Tor, many algorithms to defend against this attack in Tor have been proposed in the literature. These algorithms typically consist of some combination of the injection of dummy "padding'' packets with the delay of actual packets to disrupt timing patterns. For usability reasons, Tor is intended to provide low latency; as such, many authors focus on padding-only defenses in the belief that they are "zero-delay.'' We demonstrate through Shadow simulations that by increasing queue lengths, padding-only defenses add delay when deployed network-wide, so they should not be considered "zero-delay.'' We further argue that future defenses should also be evaluated using network-wide deployment simulations.
Dimensions of Perceived Value That Influence the Intention to Adopt the Metaverse

This chapter examines antecedents and dimensions of perceived value that influence the intention to adopt metaverse experiences provided by popular spectator sports organisations. It further offers recommendations for the conceptualisation and implementation of immersive virtual experiences. Recommendations are based upon an extensive literature review and empirical research undertaken specifically for this publication. The chapter first defines what the metaverse is and explores examples of how it can offer new opportunities for spectator sports brands to attract fans to experience the metaverse. A conceptual model is then offered upon a discourse on the relevant dimensions of perceived value and their antecedents. The analysis and discussion of the collected quantitative data (n=504) offers the foundation for recommendations on how spectator sports organisations can design and promote immersive metaverse experiences and foster continuous fan engagement.
Mounting Cases of Cyber-Attacks and Digital Payment

Digital transformation in financial transactions has changed the method of payment. We have witnessed a many-fold and rapid increase in the digital payment. As more individuals opt for digital payments, the potential of being exposed to cyber-attacks such as online fraud, theft of identity, and spyware or virus attacks is rising. Transaction on digital mode has led to an increase in internet-based crimes known by the term ‘cybercrime'. Cybercrime is an illegal act practiced by hackers on web applications, web browsers, and websites. Secured payment is critical for any company that deals with electronic payments and transactions. One of the most vital issues confronting players in the digital payment ecosystem is cyber security. The growth of such cyber-attacks can be attributed to various reasons, including a lack of knowledge and a poor digital payment infrastructure. To safeguard against threats of cybercrime, there are various cyber security techniques. This chapter deals in understanding the causes, threats, and solutions to cyber-attacks in digital payment methods.
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow

Multiple human tracking is a fundamental problem for scene understanding. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning have focused on accuracy and require substantial running time. This study aims to improve running speed by performing human detection at a certain frame interval because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that complements the detection results with optical flow, based on the fact that someone's appearance does not change much between adjacent frames. To maintain the tracking accuracy, we introduce robust interest point selection within human regions and a tracking termination metric calculated by the distribution of the interest points. On the MOT20 dataset in the MOTChallenge, the proposed SDOF-Tracker achieved the best performance in terms of the total running speed while maintaining the MOTA metric. Our code is available at https://github.com/hitottiez/sdof-tracker.
Pyramid attention object detection network with multi-scale feature fusion

With the development of deep learning, object detection has made substantial progress. However, when the object to be detected in the image is small or partially occluded, the detection network often fails to detect it successfully. We propose a multi-scale feature fusion pyramid attention module, which effectively combines the global average pooling results of multiple scales with the upper features in the residual blocks of the feature extraction network to obtain more spatial context information in the original feature map. We added the multi-scale feature fusion pyramid attention module proposed in this paper based on YoloV3 and conducted experiments on the PASCALL VOC and MS COCO datasets. The experimental results show that the attention module can effectively help the network detect small objects and accurately detect partially occlusion objects.
Breast lesion identification and categorization using mammography screening based on combined convolutional recursive neural network framework with parameters optimized using multi‐objective seagull optimization algorithm

Summary In recent years, a number of learning methods have been adopted for classifying the mammogram images, which helps the early detection and diagnosis of breast cancer. The breast lesion identification and categorization using mammography screening based on combined convolutional neural network and recursive neural network (CRNN) framework with parameters optimized using multi‐objective seagull optimization algorithm (BLIC‐CRNN‐MOSOA) is proposed in this article. Initially, the unnecessary noise components are taken away from the mammogram images and the quality of the images are enhanced based on altered phase preserving dynamic range compression filtering approach. Then, the deep CRNN model with weight parameters optimized using multi‐objective seagull optimization algorithm is adopted for classifying the mammogram images into three categories: (i) normal, (ii) benign, and (iii) malignant masses. The proposed BLIC‐CRNN‐MOSOA approach is executed in MATLAB platform, and its performance is compared with other deep learning classification approaches. Then the simulation performance of the proposed BLIC‐CRNN‐MOSOA method attains higher accuracy 99.67%, 98.38%, and 97.45%, higher sensitivity 98.33%, 89.34%, and 88.96%, higher specificity 93.15%, 91.25%, and 92.88% compared with existing methods, like BLIC‐FrCN, BLIC‐ICS‐ELM, and BLIC‐DCNN‐BO. By this, the proposed method achieves higher classification accuracy with less misclassified error. Finally, the simulation results show that the proposed method is more efficient than the other classification methods.
Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals

This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal's useful information content over time. In order to maximize the correct recovery of the useful part of the signal, this paper proposes a denoising method that uses a criterion based on amplitude segmentation and local Rényi entropy estimation which are limited over short time blocks of the signal spectrogram. Local estimation of the signal features reduces the denoising problem to the stationary noise case. Results, presented for synthetic and real data, show consistently better performance gained by the proposed adaptive method compared to denoising driven by global criteria.
Constrained future brightening of solar radiation and its implication for China's solar power

As Earth's primary energy source, surface downward solar radiation (Rs) determines the solar power potential and usage for climate change mitigation. Future projections of Rs based on climate models have large uncertainties that interfere with the efficient deployment of solar energy to achieve China's carbon-neutrality goal. Here we assess 24 models in the latest Coupled Model Intercomparison Project Phase 6 with historical observations in China and find systematic biases in simulating historical Rs values likely due to model biases in cloud cover and clear-sky radiation, resulting in largely uncertain projections for future changes in Rs. Based on emergent constraints, we obtain credible Rs with narrowed uncertainties by ∼56% in the mid-twenty-first century and show that the mean Rs change during 2050-2069 relative to 1995-2014 is 30% more brightening than the raw projections. Particularly in North China and Southeast China with higher power demand, the constrained projections present more significant brightening, highlighting the importance of considering the spatial changes in future Rs when locating new solar energy infrastructures.