Reviewerly/exHarmony huggingface dataset:
MoS<sub>2</sub> as Nonlinear Optical Material for Optical Neural Networks

Despite the significant advancements in photonic computation in recent years, the inadequacy of optical nonlinearities limits the scalability of optical deep networks (ONNs). Molybdenum disulfide (MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), with excellent nonlinear properties, is emerging as a promising candidate for nonlinear processing. Here, we investigate the saturable absorption of MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> by continuous wave lasers and illustrate the capability of MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> as an activation unit for nonlinear mapping in ONNs. Moreover, a simulation-based fully connected neural network is fabricated for mimicking the operation of ONNs and demonstrating image classification. The results show that the recognition accurateness ranged from 89% to 94%, depending on the morphology of MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . This article provides a guideline for the selection of nonlinear units and opens up the possibility of implementing all-optical neural networks.
Semantic Learning for Analysis of Overlapping LPI Radar Signals

The increasingly complex radio environment may cause the received low probability of intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping LPI radar signals requires identifying the modulation type and estimating the parameters of each component. Prior research performs overlapping signal analysis as a multistage task, where each stage is designed to perform a part of the task. The multistage system will increase the calculation burden and cannot be optimized as a whole. Instead, this article proposes a novel framework for analyzing overlapping signals in a single stage. Specifically, we develop a joint semantic learning deep convolutional neural network (JSLCNN) that jointly learns three tasks, i.e., feature restoration, modulation classification, and parameter regression. Since the whole cognitive pipeline is a single network, it can be optimized end-to-end directly on cognitive performance. To verify the validity of the proposed JSLCNN, numerous comparative experiments are carried out in terms of modulation recognition and parameter estimation of overlapping signals. Experimental results demonstrate that the JSLCNN has desirable extensibility for identifying unseen signal combinations and robustness against unknown jamming. Furthermore, we show that the JSLCNN outperforms other existing approaches in generic real-time parameter estimation for LPI radar signals.
STGL-GCN: Spatial–Temporal Mixing of Global and Local Self-Attention Graph Convolutional Networks for Human Action Recognition

Human action recognition methods based on skeleton data have been widely studied owing to their strong robustness to illumination and complex backgrounds.Existing methods have achieved good recognition results; however, they have certain challenges, such as the fixed topological structure of the graph, the omission of nonphysical joint correlation, and the inability to extract local spatial-temporal features.Herein, we propose spatial-temporal mixing of global and local self-attention graph convolutional networks (STGL-GCN) using skeleton data.The global self-attention matrix captures the potential dependencies of nonphysical correlations between joints, and the local self-attention matrix determines the connection strength of the physical edges of joints.The matrices are updated together with the convolution parameters in each network layer as the model is trained for optimal graph structure to achieve accurate action expressions Experiments on the NTU-RGBD dataset demonstrate that our model accurately recognizes actions.
TUSR-Net: Triple Unfolding Single Image Dehazing With Self-Regularization and Dual Feature to Pixel Attention

Single image dehazing is a challenging and illposed problem due to severe information degeneration of images captured in hazy conditions. Remarkable progresses have been achieved by deep-learning based image dehazing methods, where residual learning is commonly used to separate the hazy image into clear and haze components. However, the nature of low similarity between haze and clear components is commonly neglected, while the lack of constraint of contrastive peculiarity between the two components always restricts the performance of these approaches. To deal with these problems, we propose an end-to-end self-regularized network (TUSR-Net) which exploits the contrastive peculiarity of different components of the hazy image, i.e, self-regularization (SR). In specific, the hazy image is separated into clear and hazy components and constraint between different image components, i.e., self-regularization, is leveraged to pull the recovered clear image closer to groundtruth, which largely promotes the performance of image dehazing. Meanwhile, an effective triple unfolding framework combined with dual feature to pixel attention is proposed to intensify and fuse the intermediate information in feature, channel and pixel levels, respectively, thus features with better representational ability can be obtained. Our TUSR-Net achieves better trade-off between performance and parameter size with weight-sharing strategy and is much more flexible. Experiments on various benchmarking datasets demonstrate the superiority of our TUSR-Net over state-of-the-art single image dehazing methods.
Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization

In order to solve the problem of data sparsity and credibility in collaborative filtering, a recommendation system based on sentiment analysis and matrix factorization (SAMF) is proposed in this paper, which uses topic model and deep learning technology to fully mine the implicit information in reviews to improve the rating matrix and assist recommendation. Firstly, user topic distribution and item topic distribution are generated from reviews(consisting user reviews and item reviews) through LDA(Latent Dirichlet Allocation). The user feature matrix and item feature matrix are created based on topic probability. Secondly, user feature matrix and item feature matrix are integrated to create user-item preference matrix. Thirdly, the user-item preference matrix and the original rating matrix are integrated to create the user-item rating matrix. Fourthly, BERT(Bidirectional Encoder Representation from Transformers) is used to quantify the sentiment information contained in the reviews and integrate the sentiment information with the user-item rating matrix, to modify and update the user-item rating matrix. Finally, the updated user-item rating matrix is used to achieve rating prediction and Top-N recommendation. Experiments on Amazon datasets demonstrates that the proposed SAMF has better recommendation performance than other classical algorithms.
Phishing and Fraudulent Email Detection through Transfer Learning using pretrained transformer models

Phishing is a type of social engineering attack used by malicious users and cyber criminals for stealing sensitive information, installation of unwanted and malicious software, ransomware, and other advanced persistent threats on a victim’s computer or mobile device. With the widespread adoption of the Internet, phishing attacks are on the rise and the recent work-from-home paradigm has increased the risk of phishing attacks targeted at large and small organizations alike. The consequences of phishing scams are often severe, ranging from financial loss, identity theft, data loss, and data theft. Though there are many ways of launching phishing attacks, phishing emails are one of the most commonly used techniques employed by cybercriminals. This is primarily because email addresses are easily obtainable and sending bulk emails is quite cheap, enabling attackers to send out a large number of emails hoping a few users will fall prey to the scam. Phishing emails are used for stealing sensitive information and credentials, delivering unwanted and malicious software, and delivering ransomware. This paper proposes a deep learning approach for detecting phishing and fraudulent emails. The proposed approach uses state-of-the-art pretrained transformer models and achieves very high accuracy, recall, and f1 score of 0.99.
Deep Learning Based Optimal Traffic Classification Model for Modern Wireless Networks

The Network traffic monitoring, identification, and classification is an active research area to identify the content, service, and the application which has generated this traffic. The network is classified into many different traffics, and encryption of that traffic is necessary for sensitive data privacy and information security. The encryption is done using TLS encryption, although formerly it was known as Secure Sockets Layer (SSL) in HTTPS traffic. So Identifying any information from encrypted traffic is a challenging task. The previous machine learning-based works on classification involve a high level of feature extraction from the traffic dataset to develop a model for traffic identification. Here the proposed deep learning model uses the SNI attribute. The model has Convolutional Neural Network layers and between these layers, we insert a small Multi-Layer Perceptron(MLP) and then connected it to the next Convolution layer. Unlike using fully connected layers, this model ends with Global Average Pooling which helps in reducing the overfitting and the complexity of the model. Thus resulting in the model with an accuracy of 99% for this dataset. On increasing the number of CNN layers, the model starts to overfit which in turn deteriorates the performance of the model.
Ethereum Compatible Faster Atomic Payment Splitting Network

The major challenge is the scalability issue, which the second layer of blockchain must solve. But the transfer of micro-payment creates another problem: node idleness due to the insufficient channel balance. So that we aim to implement an atomic payment splitting network using the ethereum blockchain, which can transfer the micro-payments quickly in different paths from sender to receiver. It is the first Ethereum-based atomic payment splitting model. This model works faster than other Bitcoin-based models, and smart contracts make it easier with the help of random seeds. Again the payment splitting decreases the node idleness. The significant challenges in Bitcoin lightning-based networks are intermediary insecurity and flood, and loot attack is not happening in our system that of the random seed and smart contracts. Our resultant model guarantees that the honest intermediaries have not lost funds, and the payment split network is not a failure after the path confirmation. Using negative acknowledgment also ensures the payment’s failure in the model’s initial phase. The final payment confirmation happens only by receiving all the unique seeds within the ∆ time. The proposed model has been analyzed with different existing models, and the success ratio is better.
Complete Separable Reversible Data Hiding for Encrypted Digital Images Using Code Division Multiplexing with Versatile Bit Depth Management

A reversible data hiding in the encrypted domain (RDH-ED) aims to hide data within encrypted images, protecting its content, while allowing additional information to be distributed. This paper presents a complete separable RDH-ED scheme, whose main contribution is allowing the receiver to extract data and restore the image, either from the cryptogram with hidden data or from the directly decrypted version. With versatile bit-depth management, the most significant bits of each pixel are encrypted with AES-CTR cipher algorithm, while the additional data will be inserted inside the least significant bit planes of the encrypted pixels, by means of the code division multiplexing technique. Considering the marked/encrypted images, and encryption/data-hiding keys, a receiver could: (a) directly decrypt the encrypted image and obtain its approximate version, (b) extract the error-free hidden data, and (c) recover the data and original image. Considering an image approximation version and the data hiding key, a receiver could: (d) extract the hidden data from the plaintext domain, and (e) restore the image to its original state, while accessing the hidden data without any loss. Experimental results show the performance of the developed algorithm, evaluating the capacity and imperceptibility of the proposed scheme with respect to current state of the art.
Broad Embedded Logistic Regression Classifier for Prediction of Air Pressure Systems Failure

In recent years, the latest maintenance modelling techniques that adopt the data-based method, such as machine learning (ML), have brought about a broad range of useful applications. One of the major challenges in the automotive industry is the early detection of component failure for quick response, proper action, and minimizing maintenance costs. A vital component of an automobile system is an air pressure system (APS). Failure of APS without adequate and quick responses may lead to high maintenance costs, loss of lives, and component damages. This paper addresses classification problem where we detect whether a fault does or does not belong to APS. If a failure occurs in APS, it is classified as positive class; otherwise, it is classified as negative class. Hence, in this paper, we propose broad embedded logistic regression (BELR). The proposed BELR is applied to predict APS failure. It combines a broad learning system (BLS) and logistic regression (LogR) classifier as a fusion model. The proposed approach capitalizes on the strength of BLS and LogR for a better APS failure prediction. Additionally, we employ the BLS’s feature-mapped nodes for extracting features from the input data. Additionally, we use the enhancement nodes of the BLS to enhance the features from feature-mapped nodes. Hence, we have features that can assist LogR for better classification performances, even when the data is skewed to the positive class or negative class. Furthermore, to prevent the curse of dimensionality, a common problem with high-dimensional data sets, we utilize principal component analysis (PCA) to reduce the data dimension. We validate the proposed BELR using the APS data set and compare the results with the other robust machine learning classifiers. The commonly used evaluation metrics, namely Recall, Precision, an F1-score, to evaluate the model performance. From the results, we validate that performance of the proposed BELR.
Smile Back at Me, But Only Once: Social Norms of Appropriate Nonverbal Intensity and Reciprocity Apply to Emoji Use

Abstract In computer-mediated communication, small graphical icons ( emojis ) can be used to compensate for the lack of nonverbal cues such as facial expressions or hand gestures. Accordingly, literature suggests that the use of emojis may also be subject to social norms—similar to nonverbal behavior in face-to-face interactions. However, actual empirical investigations into this assumption remain lacking. To remedy this research gap, I explored whether traditional norms of appropriate emotional intensity and reciprocity also apply to emoji usage. A first online experiment ( N = 188) revealed that excessive emoji use in a first-contact scenario leads to diminished interpersonal outcomes, corresponding to the drawbacks of overly intense nonverbal displays in natural interactions. Proceeding to a different communicative stage, Experiment 2 ( N = 242) explored nonverbal reciprocity with acquainted interaction partners. Inviting participants to reply to fictitious text messages (at varying levels of interpersonal intimacy), it was observed that stimulus messages containing more emojis also evoke stronger emoji use in return—indicating that principles of nonverbal attunement are in full effect during text-based online interactions.
Lightweight and Energy-Aware Monocular Depth Estimation Models for IoT Embedded Devices: Challenges and Performances in Terrestrial and Underwater Scenarios

The knowledge of environmental depth is essential in multiple robotics and computer vision tasks for both terrestrial and underwater scenarios. Moreover, the hardware on which this technology runs, generally IoT and embedded devices, are limited in terms of power consumption, and therefore, models with a low-energy footprint are required to be designed. Recent works aim at enabling depth perception using single RGB images on deep architectures, such as convolutional neural networks and vision transformers, which are generally unsuitable for real-time inferences on low-power embedded hardware. Moreover, such architectures are trained to estimate depth maps mainly on terrestrial scenarios due to the scarcity of underwater depth data. Purposely, we present two lightweight architectures based on optimized MobileNetV3 encoders and a specifically designed decoder to achieve fast inferences and accurate estimations over embedded devices, a feasibility study to predict depth maps over underwater scenarios, and an energy assessment to understand which is the effective energy consumption during the inference. Precisely, we propose the MobileNetV3S75 configuration to infer on the 32-bit ARM CPU and the MobileNetV3LMin for the 8-bit Edge TPU hardware. In underwater settings, the proposed design achieves comparable estimations with fast inference performances compared to state-of-the-art methods. Moreover, we statistically proved that the architecture of the models has an impact on the energy footprint in terms of Watts required by the device during the inference. Then, the proposed architectures would be considered to be a promising approach for real-time monocular depth estimation by offering the best trade-off between inference performances, estimation error and energy consumption, with the aim of improving the environment perception for underwater drones, lightweight robots and Internet of things.
A serial attention module‐based deep convolutional neural network for mixed Gaussian‐impulse removal

Abstract The removal of mixed noise is a challenging task because the attenuation of the noise distribution cannot be described precisely. The coupling of additive white Gaussian noise and impulse noise (IN) is a typical case. At present, most methods use a two‐phase strategy, that is, IN detection coupled with additive white Gaussian noise removal, often leading to poor denoising results with an increase in the ratio of IN. In this paper, an effective convolutional neural network (CNN) model is proposed, namely a serial attention module‐based CNN (SACNN), for mixed noise removal. In contrast to the existing two‐phase methods, SACNN unifies the denoising process into a single CNN framework. In SACNN, residual learning and batch normalization are used to train the model, which speeds up the convergence and improves the mixed noise removal performance. Meanwhile, the serial attention module is applied to better preserve the texture details. The experimental results reveal that SACNN achieves superior quality metrics and visual appearance when compared to several leading approaches.
Designing NoSQL databases based on multiple requirement views

In recent years, new data characteristics led to the development of new database management systems named NoSQL. As opposed to the mature Relational databases, design methods for the new databases receive little attention and mainly consider the data-related requirements. In this paper, we present methods for designing two types of NoSQL databases – Document and Graph databases – that consider not only the data-related but also functional-related requirements. We empirically evaluate the methods and apply the design methods to two leading database systems. We found that the databases designed with the consideration of functional requirements perform better, with respect to time of execution and database I/O operations, compared to when designed without considering them.
Hybrid token transformer for deep face recognition

Although Convolutional Neural Networks have achieved remarkable successes in face recognition, they still suffer a critical limitation on capturing long range relations among facial regions. The recent vision transformers can naturally alleviate this problem, by learning global token dependencies. However, They are insufficient to discover high-level facial semantics since tokens in these transformers are based on small and fixed regions. To tackle such difficulty, we propose a novel Hybrid tOken Transformer (HOTformer) module to identify key facial semantics for effective recognition with cooperation of atomic and holistic tokens. Specifically, atomic tokens are generated from small fixed-size regions that can learn fine-grained core representation. Alternatively, holistic tokens are constructed from big adaptively-learned regions that can capture coarse-grained contextual representation. Furthermore, our HOTformer is a plug-and-play module. By hierarchically inserting it into convolutional networks, we can build a concise HOTformer-Net that achieves a preferable computation like CNN while boosting accuracy like transformer.
Appearance and geometry transformer for facial expression recognition in the wild

Facial expression recognition (FER) in the wild is considered to be one of the most challenging tasks in computer vision because it requires identifying the categories of expressions presented by the human face under adverse conditions, such as dynamic poses, extreme illuminations, and partial occlusion. In this paper, we propose a model called the appearance and geometry transformer (AGT), which is a self-attention-based deep neural network, to improve the accuracy of recognition of FER in the wild. The AGT is designed to simultaneously perform feature extraction and fusion on heterogeneous data, including images and graphs, by using two transformer pathways. A pre-trained DeiT model is introduced to the image transformer pathway to explore the most discriminative facial regions. A multi-scale graph transformer network consisting of a self-attention module based on several multi-scale graphs is presented in the graph transformer pathway to adaptively extract features from the facial graph. A distillation mechanism is employed in the AGT to improve training in case of a limited number of training samples. We evaluated the proposed method on two benchmark databases: FERplus and RAF-DB. The results of comprehensive experiments on these two databases show that the proposed model can achieve comparable results to state-of-the-art methods of FER in the wild.