Reviewerly/exHarmony huggingface dataset:
Breast cancer histopathology image classification based on dual-stream high-order network

The early diagnosis of breast cancer using pathological images is of the vital importance. Recently, breast cancer histopathology image classification methods based on convolution neural network (CNN) are constantly innovating with the development of computer-aided diagnosis technology. To obtain pathological tissue features with more discriminant presentation capability for classification, this work proposes a novel dual-stream high-order breast cancer pathological image classification network named DsHoNet. To be precise, a shallow network composed of six convolution layers is built as the backbone of the dual-stream network firstly, in which one stream utilizes batch normalization (BN) layer to retain the original feature information with clearer feature distribution, while another stream introduces the Ghost module to extract richer supplementary features by utilizing a series of linear transformations. Then, outputs of the two streams are further enhanced via a covariance pooling layer to achieve more powerful deep high-order statistic features for classification. Extensive evaluation experiments carried out on the public BreakHis dataset demonstrate that the optimal recognition rates of DsHoNet are 99.01% and 99.25% respectively at the image-level and patient-level, performing favorably against its counterparts.
All‐Dielectric Metasurface Empowered Optical‐Electronic Hybrid Neural Networks

Abstract Optical computing has a series of advantages over its electronic counterpart, e.g., low energy consumption, high speed, and intrinsic parallelism. Diffraction deep neural networks (D 2 NNs) are a prominent example capable of processing images directly without addressing the spatial locations of each element. Despite the great successes, the D 2 NNs typically utilize the multilayer framework and face the severe challenge of misalignment in the optical region. Herein, a single metasurface‐based optical‐electronic hybrid neural network (OENN) is proposed and experimentally demonstrated. The OENN is composed of a titanium dioxide (TiO 2 ) metasurface and a fully‐connected electronic layer. The combination of nonlocal neural layer and nonlinear transformation has significantly expanded the neural network capacity. Consequently, the classification accuracy on handwritten digits recognition can still be as high as 98.05% without employing the architecture of cascaded metasurfaces. The OENN shall shed light on the practical applications of optical computing in the visible spectrum.
Ontology of Mathematical Modeling Based on Interval Data

An ontological approach as a tool for managing the processes of constructing mathematical models based on interval data and further use of these models for solving applied problems is proposed in this article. Mathematical models built using interval data analysis are quite effective in many applications, as they have “guaranteed” predictive properties, which are determined by the accuracy of experimental data. However, the application of mathematical modeling methods is complicated by the lack of software tools for the implementation of procedures for constructing this type of mathematical models, creating an ontological model that operates by the categories of the subject area of mathematical modeling, regardless of the modeling object proposed in this article. This approach has made it possible to generate tools for mathematical modeling of various objects based on the interval data analysis for any software development environment selected by the user. The technology of creating the software on the basis of the developed ontological superstructure for mathematical modeling using the interval data for different objects, as well as various forms of user interface implementation, is presented in this article. A number of schemes, which illustrate the technology of using the ontological approach of mathematical modeling based on interval data, are presented, and the features of its interpretation when solving environmental monitoring problems are described.
CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&amp;E-stained Histopathological Images

Visual representation extraction is a fundamental problem in the field of computational histopathology. Considering the powerful representation capacity of deep learning and the scarcity of annotations, self-supervised learning has emerged as a promising approach to extract effective visual representations from unlabeled histopathological images. Although a few self-supervised learning methods have been specifically proposed for histopathological images, most of them suffer from certain defects that may hurt the versatility or representation capacity. In this work, we propose CS-CO, a hybrid self-supervised visual representation learning method tailored for H&E-stained histopathological images, which integrates advantages of both generative and discriminative approaches. The proposed method consists of two self-supervised learning stages: cross-stain prediction (CS) and contrastive learning (CO). In addition, a novel data augmentation approach named stain vector perturbation is specifically proposed to facilitate contrastive learning. Our CS-CO makes good use of domain-specific knowledge and requires no side information, which means good rationality and versatility. We evaluate and analyze the proposed CS-CO on three H&E-stained histopathological image datasets with downstream tasks of patch-level tissue classification and slide-level cancer prognosis and subtyping. Experimental results demonstrate the effectiveness and robustness of the proposed CS-CO on common computational histopathology tasks. Furthermore, we also conduct ablation studies and prove that cross-staining prediction and contrastive learning in our CS-CO can complement and enhance each other. Our code is made available at https://github.com/easonyang1996/CS-CO.
Centroids-guided deep multi-view K-means clustering

With the progress of deep learning used in unsupervised learning, deep approach based multi-view clustering methods have been increasingly proposed in recent years. However, in most of these methods, deep representation learning is not organically integrated into the multi-view clustering process. They either conduct deep representation learning and clustering in a separate manner, or use the pseudo cluster labels to supervise deep representation learning. In this paper, we propose a centroids-guided deep multi-view k-means clustering method, which organically incorporates deep representation learning into the multi-view k-means objective by using the cluster centroids in multi-view k-means to guide the deep learning of each view. In turn, more k-means-friendly representations are produced to further optimize the multi-view k-means objective. The cluster centroids of each view obtained under a common clustering partition not only represent the semantic information of the clusters but also imply consistency among different views. By reducing the loss between each representation and its assigned cluster centroid with respect to the network parameters of each view, the representations of different views will be more k-means-friendly toward a common partition. Experiments on several datasets demonstrate the effectiveness of our method.
New framework for code-mapping-based reversible data hiding in JPEG images

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion.
Adaptive CNN filter pruning using global importance metric

The depth and width of CNNs have increased over the years so as to learn a better representation of the input–output mapping of a dataset. However, a significant amount of redundancy exists among different convolutional kernels. Several methods on pruning suggest that trimming redundant parameters can produce compact structures with minor degradation in classification performance. Existing pruning methods reduce the number of filters at a uniform rate (i.e. pruning same percentage of filters from each layer) in every convolutional layer, which is suboptimal. In this paper, we conduct experiments to observe the sensitivity of each and every filter towards the final performance of the neural network. The essence of comparing filter importance on a global scale and subsequently pruning the neural network adaptively, is highlighted for the first time in this paper. Based on our observations, we propose a novel method named 'Global Filter Importance based Adaptive Pruning (GFI-AP)' that assigns importance scores to all filters based on how the network learns the input–output mapping of a dataset, which can then be compared across all the other convolutional filters. Our results show that non-uniform pruning achieves better compression as compared to uniform pruning. We demonstrate that GFI-AP significantly decreases the number of FLOPs (floating point operations) of VGG and ResNet networks in ImageNet and CIFAR datasets, without substantial drop in classification accuracy. GFI-AP reduces more number of FLOPs compared to existing pruning methods, for example, the ResNet50 variant of GFI-AP provides an additional 11% reduction in FLOPs over Taylor-FO-BN-72% while achieving higher accuracy.
Deep Learning-based Real-time Object Detection for Empty-Dish Recycling Robot

The world is facing a shrinking workforce by the sagging birth rate and an aging population. Robot techniques are one of the best solutions for taking place of humans and overcoming this emergency issue. This paper introduces a deep learning-based empty-dish recycling robot for realizing the automatic empty-dish recycling after breakfast, dinner, or lunch in a restaurant, canteen, or cafeteria. A deep learning model–You Only Look Once (YOLO)–is equipped for dish detection such as cups, bowls, chopsticks, towels et al., and catch points are calculated for controlling the robot arm to recycle the target dishes. Finally, the YOLOv4 model is quantized by TensorRT and deployed on Jetson Nano. The real-time dish detection YOLO is focused on this paper, the experimental results show that after the YOLO model quantization, the detection time of a single image is increased from 3.93s to 0.44s, with more than 96.00% high accuracy on Precision, Recall, and F1 values. The functions of empty-dish recycling are realized, which will lead to further development toward practical use.
Proxy Re-Encryption Scheme for Decentralized Storage Networks

Storage is a promising application for permission-less blockchains. Before blockchain, cloud storage was hosted by a trusted service provider. The centralized system controls the permission of the data access. In web3, users own their data. Data must be encrypted in a permission-less decentralized storage network, and the permission control should be pure cryptographic. Proxy re-encryption (PRE) is ideal for cryptographic access control, which allows a proxy to transfer Alice’s ciphertext to Bob with Alice’s authorization. The encrypted data are stored in several copies for redundancy in a permission-less decentralized storage network. The redundancy suffers from the outsourcing attack. The malicious resource provider may fetch the content from others and respond to the verifiers. This harms data integrity security. Thus, proof-of-replication (PoRep) must be applied to convince the user that the storage provider is using dedicated storage. PoRep is an expensive operation that encodes the original content into a replication. Existing PRE schemes cannot satisfy PoRep, as the cryptographic permission granting generates an extra ciphertext. A new ciphertext would result in several expensive replication operations. We searched most of the PRE schemes for the combination of the cryptographic methods to avoid transforming the ciphertext. Therefore, we propose a new PRE scheme. The proposed scheme does not require the proxy to transfer the ciphertext into a new one. It reduces the computation and operation time when allowing a new user to access a file. Furthermore, the PRE scheme is CCA (chosen-ciphertext attack) security and only needs one key pair.
Multifeature Transformation and Fusion-Based Ship Detection With Small Targets and Complex Backgrounds

With the development of deep learning, synthetic aperture radar (SAR) image ship detection based on the convolutional neural network has made significant progress. However, there are two problems. 1) The false alarm detection rate is high due to complex background and coherent speckle noise interference. 2) For smaller ship targets, missed detection is prone to occur. In this letter, a novel ship detection model (MFTF-Net) based on multi-feature transformation and fusion is proposed to address the issues. First, to avoid the randomness of initial point selection and the influence of outlier points, the anchor frame clustering approach based on the K-medians++ algorithm is presented to cluster the object candidate frames. Second, the low-level feature information is passed to the high level by constructing a local enhancement network; then, an improved Transformer structure is introduced to replace the last convolutional block of the backbone network to obtain rich contextual information. Finally, a four-scale residual feature fusion network is designed, which fully fuses the object&#x2019;s detailed and semantic information. In addition, improved convolutional block attention module (CBAM) and squeeze and excitation (SE) attention mechanisms are applied in the lower two layers and upper two layers of the network output to reduce the interference of confusing information, respectively. The experimental results demonstrate that the proposed method is superior to the state-of-the-art thirteen baseline models on SAR ship detection dataset (SSDD), high-resolution SAR images dataset (HRSID), and SAR-ship-dataset public datasets in terms of the mAP, recall, accuracy, and F1 metrics.
Hierarchical Federated Learning With Quantization: Convergence Analysis and System Design

Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server’s access to many clients’ data and the edge servers’ closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.
An Intelligent Gender Classification System in the Era of Pandemic Chaos with Veiled Faces

In the world of chaos, the pandemic has driven individuals around the globe to wear face masks for preventing the virus’s transmission, however, this has made it difficult to determine the gender of the person wearing a mask. Gender information is part of soft biometrics, which provides extra information about a person’s identification, thus, identifying a gender based on a veiled face is among the urgent challenges that must be advocated for in the next decade. Therefore, this study exploited various pre-trained deep learning networks (DenseNet121, DenseNet169, ResNet50, ResNet101, Xception, InceptionV3, MobileNetV2, EfficientNetB0, and VGG16) to analyze the effect of the mask while identifying the gender using facial images of human beings. The study comprises two strategies. First, the experimental part involves the training of models using facial images with and without masks, while the second strategy considers images with masks only, to train the pre-trained models. Experimental results reveal that DenseNet121 and Xception networks performed well for both strategies. Besides this, the Inception network outperformed all others by attaining 98.75% accuracy for the first strategy, whereas EfficientNetB0 performed well for the second strategy by securing 97.27%. Moreover, results suggest that facemasks evidently impact the performance of state-of-the-art pre-trained networks for gender classification.
Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&#x0026;A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the great success achieved by pre-trained models on many software engineering tasks, this study fine-tunes six pre-trained models for the aspect-based API review classification task and compares them with the current SOTA solution on an API review benchmark collected by Uddin et al. The investigated models include four models (BERT, RoBERTa, ALBERT and XLNet) that are pre-trained on natural languages, BERTOverflow that is pre-trained on text corpus extracted from posts on Stack Overflow, and CosSensBERT that is designed for handling imbalanced data. The results show that all the six fine-tuned models outperform the traditional machine learning-based tool. More specifically, the improvement on the F1-score ranges from 21.0&#x0025; to 30.2&#x0025;. We also find that BERTOverflow, a model pre-trained on the corpus from Stack Overflow, does not show better performance than BERT. The result also suggests that CosSensBERT also does not exhibit better performance than BERT in terms of F1, but it is still worthy of being considered as it achieves better performance on MCC and AUC.
Efficient Hand Gesture Recognition for Human-Robot Interaction

In this paper, we present an efficient and reliable deep-learning approach that allows users to communicate with robots via hand gesture recognition. Contrary to other works which use external devices such as gloves [1] or joysticks [2] to tele-operate robots, the proposed approach uses only visual information to recognize user's instructions that are encoded in a set of pre-defined hand gestures. Particularly, the method consists of two modules which work sequentially to extract 2D landmarks of hands –ie. joints positions– and to predict the hand gesture based on a temporal representation of them. The approach has been validated in a recent state-of-the-art dataset where it outperformed other methods that use multiple pre-processing steps such as optical flow and semantic segmentation. Our method achieves an accuracy of 87.5% and runs at 10 frames per second. Finally, we conducted real-life experiments with our IVO robot to validate the framework during the interaction process.
Emergency triage of brain computed tomography via anomaly detection with a deep generative model

Triage is essential for the early diagnosis and reporting of neurologic emergencies. Herein, we report the development of an anomaly detection algorithm (ADA) with a deep generative model trained on brain computed tomography (CT) images of healthy individuals that reprioritizes radiology worklists and provides lesion attention maps for brain CT images with critical findings. In the internal and external validation datasets, the ADA achieved area under the curve values (95% confidence interval) of 0.85 (0.81-0.89) and 0.87 (0.85-0.89), respectively, for detecting emergency cases. In a clinical simulation test of an emergency cohort, the median wait time was significantly shorter post-ADA triage than pre-ADA triage by 294 s (422.5 s [interquartile range, IQR 299] to 70.5 s [IQR 168]), and the median radiology report turnaround time was significantly faster post-ADA triage than pre-ADA triage by 297.5 s (445.0 s [IQR 298] to 88.5 s [IQR 179]) (all p < 0.001).
Parallel computing strategies for Sanitized Teaching Learning Based Optimization

Teaching-Learning-Based Optimization is a metaheuristic technique that imitates the knowledge transfer in a classroom. The competitive performance with other state-of-the-art metaheuristic algorithms, innate simplicity, and the requirement of only two user-defined parameters are the primary reason behind the popularity of this algorithm. However, due to the inherent nature of the Teaching Learning Based Optimization, it is not amenable to parallel computing for the multiple evaluations of the fitness function, thereby limiting its application to computationally intensive problems. This article considers the Teaching Learning Based Optimization algorithm without the duplicate removal step as Sanitized Teaching Learning Based Optimization. In this work, three competitive parallelized strategies of Sanitized Teaching Learning Based Optimization are suggested that can harness the benefits of parallel computing. The efficacies of the proposed strategies are demonstrated on 14 benchmark problems and the CEC’14 real parameter benchmark test suite. Additionally, the impact on the computational time with respect to the computational complexity of the objective function and the number of resources has been studied. It is observed that the proposed strategies can lead up to a 90% reduction in computational time over Sanitized Teaching Learning Based Optimization algorithm.