Reviewerly/exHarmony huggingface dataset:
Ranking products through online reviews considering the mass assignment of features based on BERT and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si22.svg" display="inline" id="d1e1760"><mml:mi>q</mml:mi></mml:math>-rung orthopair fuzzy set theory

A product ranking method is an effective tool that can analyze a significant number of online product reviews to recommend suitable products to consumers. However, existing product ranking methods have two main limitations: (1) the high manual annotation costs and (2) the inability to express consumers’ purchasing decisions because the information is limited to a single feature of each product. To overcome the limitations, this paper proposes a novel product ranking method considering the mass assignment of features based on bidirectional encoder representations using transformers (BERT) and q-rung orthopair fuzzy set theory. First, BERT is adopted to identify sentiment orientations of online product reviews and product features from online product reviews. Subsequently, the product features are clustered into groups and the relative frequencies of product features are obtained. Second, the relative frequencies of product features are transformed into q-rung orthopair fuzzy numbers based on mass assignment theory. Third, the q-rung orthopair fuzzy numbers are aggregated by the q-rung orthopair fuzzy generalized weighted Heronian mean operator to rank the products. Finally, we implement the method using a case study of six different phones to verify its feasibility. Using the case study, we also perform comparisons and sensitivity analyses, which demonstrate the superiority of our method.
Deep learning for visible-infrared cross-modality person re-identification: A comprehensive review

Visible-infrared cross-modality person re-identification (VI-ReID) is currently a prevalent but challenging research topic in computer vision, since it can remedy the poor performance of existing single-modality ReID models under insufficient illumination, thus enabling the 24/7 surveillance systems. Although extensive research efforts have been dedicated to VI-ReID, a systematic and comprehensive literature review is still missing. Considering that, in this paper, a comprehensive review of VI-ReID approaches is provided. First, we clarify the importance, definition and challenges of VI-ReID. Secondly and most importantly, we elaborately analyze the motivations and the methodologies of existing VI-ReID methods. Accordingly, we will provide a comprehensive taxonomy, including 4 categories with 8 sub-items, for those state-of-the-art (SOTA) VI-ReID models. After that, we elaborate on some widely used datasets and evaluation metrics. Next, comprehensive comparisons of SOTA methods are made on the benchmark datasets. Based on the results, we point out the limitations of current methods. At last, we outline the challenges in this field and future research trends.
Silicon photonic architecture for training deep neural networks with direct feedback alignment

There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture to harness the potential for massively parallel, efficient, and fast data operations. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation, and can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. The photonic architecture exploits parallelized matrix-vector multiplications using arrays of microring resonators for processing multi-channel analog signals along single waveguide buses to calculate the gradient vector for each neural network layer in situ. We also experimentally demonstrate training deep neural networks with the MNIST dataset using on-chip MAC operation results. Our novel approach for efficient, ultra-fast neural network training showcases photonics as a promising platform for executing AI applications.
A survey of fuzzy clustering validity evaluation methods

As an unsupervised learning method, clustering does not need to know prior knowledge of the datasets in advance. How determining the optimal number of clusters becomes an important method to judge the quality of clustering results. For fuzzy clustering algorithms, the introduction to fuzzy partition makes it more consistent with the structure of real datasets than hard clustering algorithms. Therefore, it is necessary to carry out the research on the validity evaluation methods of fuzzy clustering. At present, the research on fuzzy clustering validity mainly focuses on the fuzzy clustering validity index (FCVI) and the combined fuzzy clustering validity evaluation method (CFCVE). From these two aspects, this paper reviews fuzzy clustering validity functions and combined fuzzy clustering validity evaluation methods. Then FCVI and CFCVE are discussed in details from different points on fuzzy clustering validity functions, and the research status and construction strategies of different fuzzy clustering validity evaluation methods are analyzed. The accuracy and stability of each fuzzy clustering validity evaluation method are analyzed through comparative experiments. Finally, the paper summarizes the shortcomings and advantages of the current research on fuzzy clustering validity and looks forward to the research direction and improved methods of the evaluation methods.
Low-light image enhancement with knowledge distillation

Low-light image enhancement studies how to improve the quality of images captured under poor lighting conditions, which is of real-world importance. Currently, convolutional neural network (CNN)-based methods with state-of-the-art performance have become the mainstream of research. However, most CNN-based methods improve the performance of the algorithm by increasing the width and depth of the neural network, which requires large computing device resources. In this paper, we propose a knowledge distillation method for low light image enhancement. The proposed method uses a teacher-student framework in which the teacher network tries to transfer the rich knowledge to the student network. The student network learns the knowledge of image enhancement under the supervision of ground truth images and under the guidance of the teacher network simultaneously. Knowledge transfer between the teacher-student network is accomplished by distillation loss based on attention maps. We designed a gradient-guided low-light image enhancement network that can be divided into an enhancement branch and a gradient branch, where the enhancement branch is learned under the guidance of the gradient branch to better preserve structural information. The teacher and student networks use a similar structure, but they have different model sizes. The teacher network has more parameters and more powerful learning capabilities than the student network. With the help of knowledge distillation, our approach can improve the performance of the student network without increasing the computational burden during the testing phase. The qualitative and quantitative experimental results demonstrate the superiority of our method compared to the state-of-the-art methods.
ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.
Security issues in IoT applications using certificateless aggregate signcryption schemes: An overview

Security is one of the major concerns in IoT networks which consists of a huge number of objects that have given an alarm with the risen number of threats in our day-to-day life. Mobile IoT users are dynamically categorized into multiple social groups based on their velocity, directions, and accelerations. In each group, all IoT nodes broadcast their collected content bundles in a short range of communication and share a dynamically updated group key negotiated by all of them. To make the whole IoT infrastructure secure, efficient, robust cryptographic techniques are developed and required to handle data authentication, data privacy, confidentiality, integrity, etc. Furthermore, with the large-scale connectivity and group key management policy, cryptographic processes like Signcryption techniques used for authentication and confidentiality increase the overhead complexity of the network resources. To reduce this overhead, numerous research has been conducted on Certificateless Aggregate Signcyption Schemes (CLASC) to reduce the time complexity, computational and communication overheads. Keeping the effectiveness of CLASC in mind, an attempt has been made to provide a detailed review of the security-related challenges and issues in IoT applications using CLASC hoping that it will provide a good insight to the current researchers, practitioners, and relevant stakeholders to carry out further research.
MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images

Recent progress in salient object detection (SOD) has been fueled substantially by the development of convolutional neural networks. However, several SOD methods do not fully exploit information from different modalities, consequently performing only marginally better than methods using a single modality. Therefore, we propose a multitype fusion and enhancement network (MFENet), following three steps “Encoder- Pre-decoder- Decoder” for RGB-thermal (RGB-T) SOD by completely exploiting the advantages of the RGB and thermal modalities through feature integration and enhancement. To better fuse two modalities' features, we have designed the cross-modality fusion module (CMFM) in the encoder part. As shallow features describe details and deep features provide semantic information, a multiscale interactive refinement module is designed in the pre-decoder part to complement multilevel features. Additionally, to further sharpen salient objects, we have proposed a high-level, low-level module that takes inputs from adjacent layers for gradual translation into a saliency map in the decoder part. This module provides semantic information for shallower features and the boundaries of salient objects can be gradually sharpened with subtle details. Extensive experiments show the effectiveness and robustness of the proposed MFENet and its substantial improvement over state-of-the-art RGB-T SOD methods. The codes and results will be available at: https://github.com/wujunyi1412/MFENet_DSP.
Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.
HELPHED: Hybrid Ensemble Learning PHishing Email Detection

Phishing email attack is a dominant cyber-criminal strategy for decades. Despite its longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit critical situations to lure victims. Plenty of detectors have been proposed over the years, which mainly focus on the contents or the textual information of emails; however, to cope with the evolution of phishing emails more sophisticated approaches should be introduced that will exploit all the emails’ traits to enhance the detection capability of Machine Learning/Deep Learning classifiers. To tackle the limitations of existing works, this paper proposes a phishing email detection methodology, named HELPHED that focuses on the detection of phishing emails by combining Ensemble Learning methods with hybrid features. The hybrid features provide an accurate representation of emails by fusing their content and textual traits. We propose two methods of HELPHED, the first one employs the Stacking Ensemble Learning method, while the second method utilizes the Soft Voting Ensemble Learning. Both methods deploy two different Machine Learning algorithms to handle the hybrid features separately, yet in parallel, minimizing the features’ complexity and improving the model’s performance. A thorough evaluation analysis is carried out considering innovative guidelines that aim to prevent partial and misleading results. Experimental tests verified that the combination of hybrid features with Ensemble Learning, overall, accomplishes better detection performance than when employing only content-based or text-based features. Numerical results on a rich imbalanced dataset (i.e., 32,051 benign and 3,460 phishing email samples) that considers the evolution of phishing emails show that Soft Voting Ensemble Learning outperforms other prominent Machine Learning/Deep Learning algorithms and existing works yielding F1-score equal to 0.9942.
Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting and reconstructing turbulence more challenging. This study proposes a deep-learning approach to reconstruct three-dimensional (3D) high-resolution turbulent flows from spatially limited data using a 3D enhanced super-resolution generative adversarial networks (3D-ESRGAN). In addition, a novel transfer-learning method based on tricubic interpolation is employed. Turbulent channel flow data at friction Reynolds numbers Reτ = 180 and Reτ = 500 were generated by direct numerical simulation (DNS) and used to estimate the performance of the deep-learning model as well as that of tricubic interpolation-based transfer learning. The results, including instantaneous velocity fields and turbulence statistics, show that the reconstructed high-resolution data agree well with the reference DNS data. The findings also indicate that the proposed 3D-ESRGAN can reconstruct 3D high-resolution turbulent flows even with limited training data.
A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants

Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM.
Low-cost mobile augmented reality service for building information modeling

Informed decision-making is crucial for construction site operators. Cyber-physical systems, including various technologies such as augmented reality and building automation tools, are gathering popularity within the infrastructure management sector. However, they are expensive and inaccessible to adopt for most small organizations. This paper describes a prototype of low-cost mobile augmented reality service for BIM and demonstrated its usability for pipe maintenance by supporting inspection, workflow management, data reduction, and augmented reality. User views about the decision support, integration and ease of use of the prototype were also collected from the organisations in the UK and South Korea. The results showed that integrating the augmented reality service with the building automation tool connected to the BIM server enhances decision-making for on-site operations by generating a closed loop. This paper also highlights the need for developing low-cost digital solutions to foster the digitalization of construction organisations with limited budgets.
Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT

The data produced by millions of connected devices and smart sensors in the Industrial Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped. These time-stamped data are the core of IIoT automation and have the potential to affect industrial processes intensely. It poses significant challenges to effectively detect anomalies from time-series data and deliver actionable insights in real time to drive improvements to industrial processes. In most practical applications, where data are used to make automated decisions, real-time anomaly detection is critical. With this focus, in this article, we advise a hybrid end-to-end deep anomaly detection (DAD) framework to accurately detect anomalies and extremely rare events on sensitive, Internet of Things (IoT) streaming data in real time or near real time. The proposed framework is based on a convolutional neural network (CNN) and a two-stage long short-term memory (LSTM)-based Autoencoder (AE). We exploit a two-stage LSTM AE in parallel to detect anomalies and extremely rare events hidden in massive sensor data by identifying short- and long-term variations in actual sensor values from the predicted values. We design and train a hybrid model using the Keras/TensorFlow framework as the backend. The experimental results on one simulation and two real datasets demonstrate that the proposed framework achieved better performance and outperforms other state-of-the-art competitive models. Moreover, to prove that the proposed model can be designed for the network edge, we train, optimize, and quantize the model to run-on resource-constrained (i.e., edge) devices. Further evaluation indicates that the training and inference time for each sample is short enough to carry out anomaly detection on edge.
Transformer-Based Language Models for Software Vulnerability Detection

The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these models in one domain to other related domains, and the closeness of natural languages to high-level programming languages, such as C/C++, this work studies how to leverage (large) transformer-based language models in detecting software vulnerabilities and how good are these models for vulnerability detection tasks. In this regard, firstly, we present a systematic (cohesive) framework that details source code translation, model preparation, and inference. Then, we perform an empirical analysis of software vulnerability datasets of C/C++ source codes having multiple vulnerabilities corresponding to the library function call, pointer usage, array usage, and arithmetic expression. Our empirical results demonstrate the good performance of the language models in vulnerability detection. Moreover, these language models have better performance metrics, such as F1-score, than the contemporary models, namely bidirectional long short term memory and bidirectional gated recurrent unit. Experimenting with the language models is always challenging due to the requirement of computing resources, platforms, libraries, and dependencies. Thus, this paper also analyses the popular platforms to efficiently fine-tune these models and present recommendations while choosing the platforms for our framework.