Reviewerly/exHarmony huggingface dataset:
A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.
Artificial Intelligence in academic library strategy in the United Kingdom and the Mainland of China

There is growing recognition of the value of applying Artificial Intelligence (AI) in libraries. This study explores how academic libraries have responded to this opportunity at the level of strategy, what is the status of the application of AI, if any, and what are the different emphases of development comparing the UK and China. The data for the study was strategy documentation from high-ranking universities and their libraries. The sample consisted of the top 25 universities from the United Kingdom and top 25 from the Mainland of China according to the QS world university rankings. Explicit mention of Artificial Intelligence and related technologies is rarely found in strategic plans of universities in the UK but most Chinese universities mention them in their vision statements which focus on the development of new majors and research of the technology. Though several libraries have already implemented applications based on AI or claim to be “smart” or “intelligent” most academic library strategic plans or agendas do not emphasize AI. This is one of the first studies to explore the current status of AI applied in academic libraries as a sector and to compare experiences internationally.
Multi-Scale Video Super-Resolution Transformer With Polynomial Approximation

Video super-resolution techniques aim to obtain high-resolution equivalents of existing low-resolution videos through a series of operations. In recent research, transformers have been increasingly popular because of their remarkable abilities in parallel computing and efficient extraction of space-time sequence features from videos. Moreover, combining self-attention and multi-scale methods has yielded excellent results. However, the combination of the two methods has limitations, current up-sampling methods struggle to match the global modeling capacity of self-attention mechanisms. Therefore, this paper proposes three strategies to combine the two methods. Based on the approximation strategy, we first construct a new bilinear up-sampling method for multi-scale acquisition. Convolution and cross-attention techniques are then used to correct and align features at different scales to prevent large deviations in feature extraction at a specific scale, which can affect subsequent feature extraction. Finally, to effectively solve the common computational complexity, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ C^{0} $ </tex-math></inline-formula> continuity, and neuron death problems of existing activation functions, a new method to construct the activation function is proposed. The cubic spline function is used to construct a new activation function approximating tanh. The new activation function is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ C^{2} $ </tex-math></inline-formula> continuous, which is piecewise defined by cubic polynomial curves. In this study, better results were achieved on three public video super-resolution test sets: REDS4, Vid4, and Vimeo-90K-T. Experiments demonstrated that the proposed method could provide a new solution for video super-resolution tasks.
Database Reconstruction Is Not So Easy and Is Different from Reidentification

Abstract In recent years, it has been claimed that releasing accurate statistical information on a database is likely to allow its complete reconstruction. Differential privacy has been suggested as the appropriate methodology to prevent these attacks. These claims have recently been taken very seriously by the U.S. Census Bureau and led them to adopt differential privacy for releasing U.S. Census data. This in turn has caused consternation among users of the Census data due to the lack of accuracy of the protected outputs. It has also brought legal action against the U.S. Department of Commerce. In this article, we trace the origins of the claim that releasing information on a database automatically makes it vulnerable to being exposed by reconstruction attacks and we show that this claim is, in fact, incorrect. We also show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques. We further show that the geographic level at which exact counts are released is even more relevant to protection than the actual SDC method employed. Finally, we caution against confusing reconstruction and reidentification: using the quality of reconstruction as a metric of reidentification results in exaggerated reidentification risk figures.
Block attention network: A lightweight deep network for real-time semantic segmentation of road scenes in resource-constrained devices

Deep-learning-based semantic segmentation networks typically incorporate object classification networks in their backbone. This leads to a loss of context because classification networks have a smaller field of view. The architecture has been extended to recover context with additional downsampling feature maps, a parallel context branch, or pyramid pooling modules after the backbone. However, these extensions increase multiply–accumulate operations and memory requirements, thus, making them unsuitable for resource-constrained devices. To overcome this limitation, a novel convolutional building block with attention-based context guidance is proposed. The block is repeated to build an efficient encoder–decoder network. Our network runs in real-time, has a lightweight design with only 0.72 Million parameters, and achieves 70.1%, and 66.3% mean intersection-over-union scores on the highly competitive Cityscapes and CamVid datasets, respectively. An efficient decoder is also designed to replace other semantic segmentation network decoders with minimal performance loss. The performance measures on mobile platforms show that our network suits resource-constrained devices. Further, experimental results show that the proposed method can optimally balance the model size-inference speed and segmentation accuracy.
Structural displacement estimation by a hybrid computer vision approach

Structural displacement is an important indicator of structural safety in structural health monitoring (SHM). As one of the vision-based methods, optical flow can provide displacement measurement of pixels in images. However, estimating structural displacement by predicting the optical flow between the current frame and the initial frame may be subject to limited accuracy due to environmental variations. Alternatively, calculating structural velocity by predicting the optical flow of adjacent frames and integrating to obtain displacement may introduce displacement drift attributable to cumulative errors. This study proposes a hybrid structural displacement estimation method to eliminate the effects of environmental variations and cumulative errors. In comparison to the existing methods, the novelty of the proposed method is to effectively integrate deep learning-based dense optical flow and correlation template matching (CM), for achieving both high accuracy and improved robustness. Deep learning-based dense optical flow was used for optical flow prediction between adjacent frames through correlation calculations and iterative updating to obtain the structural velocity. Pyramid-accelerated CM was employed to locate the regions of interest (ROI) of the structure in each frame, and the structural displacement was then estimated by counting temporal changes in these locations. By fusing estimated structural velocity and displacement using the Kalman filter, optimized structural displacement results were obtained, and temporal cumulative errors using dense optical flow could be eliminated. The proposed method was validated in an indoor shaking table test of a three-story reinforced concrete structure, and an outdoor shaking table test of a cold-formed steel wall system. The results indicated that the proposed method reduced the root mean square error of estimated displacement by over 89% compared with dense optical flow and by over 36% compared with CM. Furthermore, the proposed method was able to process 1080p high-definition images at a rate of 5.43 frames per second, indicating its high efficiency for applications.
Graph Attention Guidance Network with Knowledge Distillation for Semantic Segmentation of Remote Sensing Images

Deep learning has become a popular method for studying the semantic segmentation of high-resolution remote sensing images (HRRSIs). Existing methods have adopted convolutional neural networks to achieve better segmentation accuracy of HRRSIs, and the success of these models often depends on the model complexity and parameter quantity. However, the deployment of these models on equipment with limited resources is a significant challenge. To solve this problem, a lightweight student network framework—a graph attention guidance network (GAGNet) with knowledge distillation, called GAGNet-S*—is proposed in this study, which distills knowledge from pretrained large teacher network (GAGNet-T) and builds reliable weak labels to optimize untrained student network (GAGNet-S). Inspired by the graph convolution network, this study designs a graph convolution module called the attention-graph decoder, which combines attention mechanisms with graph convolution to optimize image features and improve segmentation accuracy in the semantic segmentation task of HRRSIs. In addition, a dense cross-decoder was designed for multiscale dense fusion, which utilizes rich semantic information in the high-level features to guide and refine the low-level features from the bottom up. Extensive experiments showed that GAGNet-S* (GAGNet-S with knowledge distillation) achieved excellent segmentation performance on two widely used datasets: Potsdam and Vaihingen. The code and models are available at https://github.com/F8AoMn/GAGNet-KD.
Counseling Psychological Understanding and Considerations of the Metaverse: A Theoretical Review

The COVID-19 pandemic has triggered the interest in and demand for online platforms that can replace traditional face-to-face activities. Accordingly, metaverses have been increasingly used across society, especially in the Mental health field. This broad use of metaverses is now recognized as a major trend that will drive various industries across healthcare and society. In response to this societal change, this study provides a theoretical framework for understanding and applying metaverses as therapeutic spaces in mental health fields through a conceptualization and characterization of metaverses for a range of technologies and services. The value of metaverses as a human-centered "field of experience" was identified and proposed based on the essential aspect of the user, the human being, rather than from the technology used. According to existing psychotherapeutic theories, four therapeutic concepts for the metaverse were proposed: metaverse as an "average expectable environment" in the developmental and therapeutic categories of the self, a transitional stage for adaptation to reality, a creative space for memory and communication for the self, and an expanded or "surplus" reality. This work is expected to be a useful basis for expanding new psychotherapeutic strategies and methods as therapeutic spaces for maintaining mental health through the metaverse.
Exploring the Opportunities of AR for Enriching Storytelling with Family Photos between Grandparents and Grandchildren

Storytelling with family photos, as an important mode of reminiscence-based activities, can be instrumental in promoting intergenerational communication between grandparents and grandchildren by strengthening generation bonds and shared family values. Motivated by challenges that existing technology approaches encountered for improving intergenerational storytelling (e.g., the need to hold the tablet, the potential view detachment from the physical world in Virtual Reality (VR)), we sought to find new ways of using Augmented Reality (AR) to support intergenerational storytelling, which offers new capabilities (e.g., 3D models, new interactivity) to enhance the expression for the storyteller. We conducted a two-part exploratory study, where pairs of grandparents and grandchildren 1) participated in an in-person storytelling activity with a semi-structured interview 2) and then a participatory design session with AR technology probes that we designed to inspire their exploration. Our findings revealed insights into the possible ways of intergenerational storytelling, the feasibility and usages of AR in facilitating it, and the key design implications for leveraging AR in intergenerational storytelling.
Random forest clustering for discrete sequences

As one of the most popular machine learning methods, random forests have been successfully applied to different data analysis tasks such as classification, regression and cluster analysis. Recently, the random forest clustering method has received much attention due to its simplicity, accuracy and robustness. However, we cannot directly employ the random forest clustering algorithm to solve the discrete sequence clustering problem because of the lack of explicit features and “negative” sequences. In this paper, we propose a new random forest clustering algorithm for discrete sequences. The proposed method firstly injects a set of decoy sequences and then constructs the random forest in a supervised and adaptive manner by generating features on the fly. Experimental results on real data sets show that the proposed method can achieve better performance than those state-of-the-art discrete sequence clustering algorithms.
Brightness segmentation-based plateau histogram equalization algorithm for displaying high dynamic range infrared images

Third-generation thermal cameras produce images with high dynamic range (HDR), low contrast, and blurry edges, which makes them difficult to visualize on traditional display devices. Thus, tone mapping methods are required to adapt the recorded signal to the display in order to maintain, and possibly improve, object's visibility and contrast. Since the traditional global tone mapping methods cannot take account of the trade-off between clearly displaying the details of dark regions and bright regions of the infrared image, this paper proposes a global tone mapping algorithm based on brightness segmentation for the enhancement and display of HDR infrared images. Firstly, the HDR infrared image is divided into different brightness regions in accordance with the characteristics of the human visual system's ability to perceive brightness. Secondly, the determination of the boundary value of each brightness region is optimized by combining the gray statistics of the HDR infrared image, to achieve the adaptive brightness region segmentation of the infrared image from various scenes. Thirdly, the plateau histogram equalization method is designed to enhance different brightness regions separately, according to their brightness levels. Finally, the enhanced brightness regions are recombined to produce a more visually pleasing output image. The effectiveness of the proposed method is analyzed of HDR infrared images taken from different scenes. A comparison of the proposed method with well-established global tone mapping techniques is provided, along with subjective and objective evaluation methods that demonstrate the potential benefits of this method in enhancing brightness, compressing dynamic range, and improving global contrast.
Global and local similarity learning in multi-kernel space for nonnegative matrix factorization

Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global and local similarity information from data. In this paper, we propose a novel local similarity learning approach in the convex NMF framework, which encourages inter-class separability that is desired for clustering. Thus, the new model is capable of enhancing intra-class similarity and inter-class separability with simultaneous global and local learning. Moreover, the model learns the factor matrices in an augmented kernel space, which is a convex combination of pre-defined kernels with auto-learned weights. Thus, the learnings of cluster structure, representation factor matrix, and the optimal kernel mutually enhance each other in a seamlessly integrated model, which leads to informative representation. Multiplicative updating rules are developed with theoretical convergence guarantee. Extensive experimental results have confirmed the effectiveness of the proposed model.
Window-based transformer generative adversarial network for autonomous underwater image enhancement

The estimation of high-quality underwater images is an important step towards the development of computer vision systems in marine environments. This fundamental step contains numerous computer vision and robotics applications including marine exploration, robotics manipulation, navigation, object detection, tracking, and sea life monitoring. However, this pre-processing step becomes more challenging in the presence of back-scattering of underwater particles and attenuation issues, which lead to the formation of hazy underwater images. Vision transformers have recently demonstrated outstanding performance in many computer vision applications. Window-based Transformers (WT) show promising enhancement performance by computing self-attention within non-overlapping local windows. WT has been identified as an essential component in improving representation capabilities; however, it has received less attention in improving the performance of Underwater Image Enhancement (UIE). Therefore, we propose a novel end-to-end Underwater window-based Transformer Generative Adversarial Network (UwTGAN). Our proposed algorithm consists of two main components, including a transformer generator that generates a restored underwater image and a transformer discriminator that classifies the generated underwater image. Both components are equipped with Window-based Self-Attention Blocks (WSABs), which maximize efficiency by limiting self-attention computation to non-overlapping local windows and provide relatively low computational costs. WSAB-based transformer generators and discriminators are trained end-to-end. We formulated an efficient loss function to ensure that the variables are closely integrated. Extensive experimental evaluations are performed on four independent underwater image datasets. Our results demonstrate that the proposed UwTGAN algorithm has outperformed several state-of-the-art UIE methods in terms of both quantitative and qualitative metrics by a significant margin.
DASS: Differentiable Architecture Search for Sparse Neural Networks

The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available computational power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current methods do not support sparse architectures in their search space and use a search objective that is made for dense networks and does not focus on sparsity. This paper proposes a new method to search for sparsity-friendly neural architectures. It is done by adding two new sparse operations to the search space and modifying the search objective. We propose two novel parametric SparseConv and SparseLinear operations in order to expand the search space to include sparse operations. In particular, these operations make a flexible search space due to using sparse parametric versions of linear and convolution operations. The proposed search objective lets us train the architecture based on the sparsity of the search space operations. Quantitative analyses demonstrate that architectures found through DASS outperform those used in the state-of-the-art sparse networks on the CIFAR-10 and ImageNet datasets. In terms of performance and hardware effectiveness, DASS increases the accuracy of the sparse version of MobileNet-v2 from 73.44% to 81.35% (+7.91% improvement) with a 3.87× faster inference time.