Reviewerly/exHarmony huggingface dataset:
ASCA-squeeze net: Aquila sine cosine algorithm enabled hybrid deep learning networks for digital image forgery detection

The basic element in resolving numerous challenges, particularly social concerns like those in court cases and Facebook, is image forgery detection. The primary objective of this study is to build and develop an effective system for detecting digital image forgery utilising the recently proposed technique called the Aquila Sine Cosine Algorithm (ASCA). The forgery from the digital image is detected in this study using a hybrid deep learning technique that incorporates Deep Convolutional Neural Network (DCNN) and Squeeze Net. Additionally, the training time and computational complexity of the detection process are decreased by updating the weight of both the DCNN and the Squeeze Net using the developed ASCA technique. Additionally, the developed ASCA is produced by combining the update functions of the Aquila Optimizer (AO) with the Sine Cosine Algorithm (SCA). As a result, the hybrid deep learning classifier provides the classified output as either the authentic image or the forged image using a copy-move forgery detection dataset. The experimentation of the developed model has provided higher performance, as shown by testing accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 0.980, 0.976, and 0.956, respectively. Furthermore, by varying the iteration, testing accuracy, TNR, and TPR obtained by the devised technique are 0.944, 0.947, and 0.936, and by varying the population size obtained testing accuracy values of 1, TNR values of 1.003, and TPR values of 0.991, respectively, by algorithmic analysis.
Discriminative subspace learning via optimization on Riemannian manifold

Discriminative subspace learning is an important problem in machine learning, which aims to find the maximum separable decision subspace. Traditional Euclidean-based methods usually use Fisher discriminant criterion for finding an optimal linear mapping from a high-dimensional data space to a lower-dimensional subspace, which hardly guarantee a quadratic rate of global convergence and suffers from the singularity problem. Here, we propose the manifold optimization-based discriminant analysis (MODA) which is constructed by using the latent subspace alignment and the geometry of objective function with orthogonality constraint. MODA is solved by using Riemannian version of trust-region algorithm. Experimental results on various image datasets and electroencephalogram (EEG) datasets show that MODA achieves the best separability and is significantly superior to the competing algorithms. Especially for the time series of EEG signals, the accuracy of MODA is 20–30% higher than existing algorithms. The code for MODA is available at https://github.com/ncclabsustech/MODA-algorithm.
How to Cope With Emergency Remote Teaching for University Academics: The Case of a High-Profile Language University in China

Despite the growing body of literature on the structural problems of emergency remote teaching during the COVID-19 pandemic, far too little empirical research has been conducted on university academics’ challenges in online pedagogy and what is needed to facilitate their teaching during a time of educational crisis. Thus, this study selects a high-profile Chinese language university as a case study to explore how university academics in China have dealt with the challenges of emergency remote teaching during the pandemic. This paper conducts open-ended interviews with 22 academic faculty members and adopts TPACK concepts to interpret the findings. The results demonstrate how participants find avenues to deal with emergency remote teaching, effective measures universities need to adopt, and ways to facilitate TPACK with academic faculty.
Tracking down financial statement fraud by analyzing the supplier-customer relationship network

The supplier-customer relationships in the supply chain reflect the transaction activities between companies, which can also imply the relationships across the financial data disclosed in companies’ financial statements, thus helping to discover financial statement fraud. However, few studies can systematically analyze these complex relationships in the entire supply chain network and apply them to financial statement fraud detection studies. This paper introduces the supplier-customer relationships between companies to improve the accuracy of financial statement fraud detection. Based on the suppliers and customers data of Chinese listed companies, a supplier-customer knowledge graph is constructed to aggregate companies’ complex supply-customer relationships in the whole supply chain network, and the graph neural network (GNN) methods are innovatively applied to analyze companies’ financial data and their relationships in the graph to detect financial statement fraud. The empirical results indicate that the accuracy of GNN methods considering the supplier-customer relationships is significantly improved than the common machine learning methods. The AUC of the Heterogeneous Graph Transformer (HGT) method achieves 85.10%, which improves 5.19% over the result of the best-performing machine learning method. Furthermore, the results of fraud detection for different years using different periods of historical supplier-customer relationships are all improved, which shows the robustness of this study. This paper demonstrates the effectiveness of introducing supplier-customer relationships in financial statement fraud detection, providing a new perspective for regulators, investors, and researchers in future anti-fraud practices.
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters. There are many studies corroborating these trends, but does this translate into an exponential increase in energy consumption? In order to answer this question we focus on inference costs rather than training costs, as the former account for most of the computing effort, solely because of the multiplicative factors. Also, apart from algorithmic innovations, we account for more specific and powerful hardware (leading to higher FLOPS) that is usually accompanied with important energy efficiency optimisations. We also move the focus from the first implementation of a breakthrough paper towards the consolidated version of the techniques one or two year later. Under this distinctive and comprehensive perspective, we analyse relevant models in the areas of computer vision and natural language processing: for a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated. The only caveat is, yet again, the multiplicative factor, as future AI increases penetration and becomes more pervasive.
Inclusivity induced adaptive graph learning for multi-view clustering

Graph-based multi-view clustering, with its ability to mine potential associations between data samples, has attracted extensive attention. However, existing methods directly learn affinity graphs from multiple feature views without removing noisy information mixed with the original data, resulting in limited performance. In addition, only shared information is included in the graph fusion process, which excludes inconsistent information, resulting in insufficient exploration of complementary information among multi-view data. In this study, we propose an inclusivity-induced adaptive graph learning (IiAGL) method to address these issues. First, inclusivity is defined as the capability of a view to accommodate other views. Specifically, we extended subspace learning to graph construction, enabling view-specific graphs to capture the underlying data distribution. Furthermore, an inclusivity-induced regularizer is introduced to guide graph rebuilding and graph fusion, and the entire process is performed using a self-weighted strategy. In this manner, we can obtain a clean consensus containing shared representations and complementary information from different views. An alternating direction method with augmented Lagrangian multiplier (ADM-ALM) was designed to solve the resulting optimization problem. Extensive experiments on diverse datasets demonstrated that the clustering performance of the proposed method outperformed that of other state-of-the-art methods. The source code for this study is publicly available at https://github.com/ryan-xinzou/IiAGL-MC.
EOSA-GAN: Feature enriched latent space optimized adversarial networks for synthesization of histopathology images using Ebola optimization search algorithm

Generative adversarial networks (GAN) represent two deep learning (DL) models positioned in an adversarial manner to generate and evaluate images. This area of research promises to address several issues associated with medical image analysis using deep learning architectures and has been applied to medical image synthesis. The histopathology image samples are the gold standard for detecting and staging cancer since they contain rich latent information. However, this medical image modality is highly problematic: imbalanced class distribution in datasets, the rareness of publicly accessible pathologic findings, the burdensome task of image annotation, the increasing need to anonymize pathology samples, segmentation of regions of interest, and the demand for high-quality with super-resolution histopathology images. In this paper, we present a highly optimized, locally attenuated and two-level optimization strategy to improve the performance of GAN. First, a novel feature space-to-latent space mapping mechanism is designed to enrich the latent space of input to the generator. We applied a DL model to extract discriminant features and used dimensionality reduction to match the number of features to latent space. Secondly, a new metaheuristic algorithm, the Ebola optimization search algorithm (EOSA), optimizes the EOSA-GAN architecture and is experimentally applied to benchmark datasets. Results obtained showed that the quality of generated samples achieved an impressive outcome when evaluated using the Feature Similarity Indexing Method (FSIM), Peak Signal to Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM) and others. The finding from this study demonstrates the impact of optimization algorithms in stabilizing and speeding up GANs to convergence.
Large-Kernel Attention for 3D Medical Image Segmentation

Abstract Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.
Adversarial pan-sharpening attacks for object detection in remote sensing

Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to fuse texture-rich PAN images and multi-spectral MS images to obtain texture-rich MS images. With the development of deep learning, CNN based Pan-sharpening methods have received more and more attention in recent years. Since Pan-sharpening technique can integrate the complementary information of Pan and MS images, researchers usually apply object detectors on these pan-sharpened images to achieve reliable detection results. However, recent studies have shown that Deep Learning-based object detection methods are vulnerable to adversarial examples, i.e., adding imperceptible noise to clean images can fool well-trained deep neural networks. It is interesting to combine the pan-sharpening technique with adversarial examples to attack object detectors in remote sensing. In this paper, we propose a framework to generate adversarial pan-sharpened images. Specifically, we propose a two-stream network to generate the pan-sharpened images, and then utilize the shape loss and label loss to perform the attack task. To guarantee the quality of pan-sharpened images, a perceptual loss is utilized to balance spectral preservation and attacking performance. Experimental results demonstrate that the proposed method can generate effective adversarial pan-sharpened images that maintain a high success rate for white-box attacks and achieve transferability for black-box attacks.
Re-ranking and TOPSIS-based ensemble feature selection with multi-stage aggregation for text categorization

Aiming at reducing data dimensionality, feature selection (FS) could improve the accuracy and reduce computational cost of machine learning model, especially those with high-dimensional text datasets. To improve the robustness, ensemble feature selection (EFS) has been developed with considerable attention recently where different aggregation methods are applied. This paper proposed a four-stage EFS method called re-ranking and TOPSIS-based ensemble feature selection (RTEFS). In the first stage of RTEFS, features are extracted from the text corpus. The second one is to construct a union subset yielded by six filter-based FS methods out of preprocessing feature vectors. Then a re-ranking stage is applied to evaluate those features from such subset. The TOPSIS method is used to aggregate the ranking lists ranked by two FS groups in the ensemble feature ranking stage. In the final stage, the two fused rankings are ensembled via a multi-objective genetic algorithm NSGA-III. To demonstrate the superiority of the proposed method, experiments are performed using 20-Newsgroups and Reuters-21,578 datasets with Support Vector Machine and K-Nearest Neighbors classifiers. Results show that RTEFS produces higher accuracy and F-measure scores over the base counterparts.
Comparing filter and wrapper approaches for feature selection in handwritten character recognition

It is generally agreed that the selection of an appropriate set of features is a fundamental process in the development of any pattern recognition system. Its purpose is to identify the truly distinctive subset of features to reduce the size of the search space, without decreasing the classification performance. This problem is particularly relevant in the field of handwriting recognition, due to the enormous variability of character shape, which has led to the development of a large variety of feature sets that are becoming increasingly larger in terms of the number of attributes. While promising, the results achieved so far have several limitations, which include, among others, the computational complexity of selecting and evaluating feature subsets and the difficulty in evaluating the interactions among features. In a previous study, we tried to overcome some of the above limitations by adopting a feature-ranking-based technique: a large study was carried out considering different filter-based techniques for feature subset evaluation. The aim of this work is to extend the previous study by presenting a broad comparison between filter and wrapper techniques for feature selection in the field of handwritten character recognition. In the experiments, we analysed one of the most effective and widely used set of features in handwriting recognition, applied to standard real-word databases of handwritten characters. The experimental results confirmed that filter and wrapper approaches achieve similar performances, with the former selecting fewer features at a lower computational cost.
EA-GAN: restoration of text in ancient Chinese books based on an example attention generative adversarial network

Abstract Ancient Chinese books are of great significance to historical research and cultural inheritance. Unfortunately, many of these books have been damaged and corroded in the process of long-term transmission. The restoration by digital preservation of ancient books is a new method of conservation. Traditional character restoration methods ensure the visual consistency of character images through character features and the pixels around the damaged area. However, reconstructing characters often causes errors, especially when there is large damage in critical locations. Inspired by human’s imitation writing behavior, a two-branch structure character restoration network EA-GAN (Example Attention Generative Adversarial Network) is proposed, which is based on a generative adversarial network and fuses reference examples. By referring to the features of the example character, the damaged character can be restored accurately even when the damaged area is large. EA-GAN first uses two branches to extract the features of the damaged and example characters. Then, the damaged character is restored according to neighborhood information and features of the example character in different scales during the up-sampling stage. To solve problems when the example and damaged character features are not aligned and the convolution receptive field is too small, an Example Attention block is proposed to assist in restoration. Qualitative and quantitative analysis experiments are carried out on a self-built dataset MSACCSD and real scene pictures. Compared with current inpainting networks, EA-GAN can get the correct text structure through the guidance of the additional example in the Example Attention block. The peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) value increased by 9.82% and 1.82% respectively. The learned perceptual image patch similarity (LPIPS) value calculated by Visual Geometry Group (VGG) network and AlexNet decreased by 35.04% and 16.36% respectively. Our method obtained better results than the current inpainting methods. It also has a good restoration effect in the face of untrained characters, which is helpful for the digital preservation of ancient Chinese books.
Metaverse virtual social center for elderly communication in time of social distancing

The lack of social activities in the elderly for physical reasons can make them feel lonely and prone to depression. With the spread of COVID-19, it is difficult for the elderly to conduct the few social activities stably, causing the elderly to be more lonely. The metaverse is a virtual world that mirrors reality. It allows the elderly to get rid of the constraints of reality and perform social activities stably and continuously, providing new ideas for alleviating the loneliness of the elderly. Through the analysis of the needs of the elderly, a virtual social center framework for the elderly was proposed in this study. Besides, a prototype system was designed according to the framework. The elderly can socialize in virtual reality with metaverse-related technologies and human-computer interaction tools. Additionally, a test was jointly conducted with the chief physician of the geriatric rehabilitation department of a tertiary hospital. The results demonstrated that the mental state of the elderly who had used the virtual social center was significantly better than that of the elderly who had not used it. Thus, virtual social centers alleviated loneliness and depression in older adults. Virtual social centers can help the elderly relieve loneliness and depression when the global epidemic is normalizing and the population is aging. Hence, they have promotion value
Secured and Privacy-Preserving Multi-Authority Access Control System for Cloud-Based Healthcare Data Sharing

With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.
Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder

A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictions on them thereafter.