Reviewerly/exHarmony huggingface dataset:
HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin-eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.
Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.
Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset’s behaviour several days in advance.
A novel detection method for hot spots of photovoltaic (PV) panels using improved anchors and prediction heads of YOLOv5 network

Accurate classification and detection of hot spots of photovoltaic (PV) panels can help guide operation and maintenance decisions, improve the power generation efficiency of the PV system, and ensure power stations’ safe and stable operation. Considering that, in this paper, the hot spots of PV panels collected on site are taken as the research object, and their formation mechanism is studied. Based on this, the morphological characteristics possessed by the hot spots of PV panels are classified into circular, linear, and array ones. A novel method for detecting hot spots of PV panels based on improved anchors and prediction heads of the YOLOv5 (AP-YOLOv5) network is proposed. Besides, to improve the detection precision of the YOLOv5 network at different scales in hot spots of PV panels, the K-means clustering algorithm is employed to cluster the length–width ratio of the data annotation frame, and a group of the anchors with smaller values is added so as to realize the detection of small targets by optimizing the cluster number. Apart from that, the corresponding prediction heads are constructed for the new anchor parameters to improve the detection precision concerning hot spots of PV panels. Furthermore, the model is verified by training experimental data and comparing test set results. The results showed that compared with other one-stage object detection models, the mean average precision (mAP) of the proposed network can achieve 87.8%, while the average recall rate is 89.0%, and the F1 score reaches 88.9%. In addition, the precision of this model is better than that of other models while maintaining a high frame rate; the frames per second (FPS) is as high as 98.6, thus laying a foundation for developing rapid detection tool of hot spots of PV panels, improving the safety of power station operation, and providing a method for intelligent operation and maintenance of PV power stations.
Attestation Mechanisms for Trusted Execution Environments Demystified

Attestation is a fundamental building block to establish trust over software systems. When used in conjunction with trusted execution environments, it guarantees the genuineness of the code executed against powerful attackers and threats, paving the way for adoption in several sensitive application domains. This paper reviews remote attestation principles and explains how the modern and industrially well-established trusted execution environments Intel SGX, Arm TrustZone and AMD SEV, as well as emerging RISC-V solutions, leverage these mechanisms.
FLAME

Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human activity recognition (HAR), FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by being not independent and identically distributed (non-IID) across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy- and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.3-25.8% higher F1 score, 1.02-2.86x greater energy efficiency, and up to 2.06x speedup in convergence to target accuracy through fair distribution of the FL workload.
CT image super-resolution reconstruction based on global hybrid attention

Computer tomography (CT) has played an essential role in the field of medical diagnosis, but the blurry edges and unclear textures in traditional CT images usually interfere the subsequent judgement from radiologists or clinicians. Deep learning based image super-resolution methods have been applied for CT image restoration recently. However, different levels of information of CT image details are mixed and difficult to be mapped from deep features by traditional convolution operations. Moreover, features representing regions of interest (ROIs) in CT images are treated equally as those for background, resulting in low concentration of meaningful features and high redundancy of computation. To tackle these issues, a CT image super-resolution network is proposed based on hybrid attention mechanism and global feature fusion, which consists of the following three parts: 1) stacked Swin Transformer blocks are used as the backbone to extract initial features from the degraded CT image; 2) a multi-branch hierarchical self-attention module (MHSM) is proposed to adaptively map multi-level features representing different levels of image information from the initial features and establish the relationship between these features through a self-attention mechanism, where three branches apply different strategies of integrating convolution, down-sampling and up-sampling operations according to three different scale factors; 3) a multidimensional local topological feature enhancement module (MLTEM) is proposed and plugged into the end of the backbone to refine features in the channel and spatial dimension simultaneously, so that the features representing ROIs could be enhanced while meaningless ones eliminated. Experimental results demonstrate that our method outperform the state-of-the-art super-resolution methods on restoring CT images with respect to peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indices.
Kalman Filtering and Bipartite Matching Based Super-Chained Tracker Model for Online Multi Object Tracking in Video Sequences

Object tracking has gained importance in various applications especially in traffic monitoring, surveillance and security, people tracking, etc. Previous methods of multiobject tracking (MOT) carry out detections and perform object tracking. Although not optimal, these frameworks perform the detection and association of objects with feature extraction separately. In this article, we have proposed a Super Chained Tracker (SCT) model, which is convenient and online and provides better results when compared with existing MOT methods. The proposed model comprises subtasks, object detection, feature manipulation, and using representation learning into one end-to-end solution. It takes adjacent frames as input, converting each frame into bounding boxes’ pairs and chaining them up with Intersection over Union (IoU), Kalman filtering, and bipartite matching. Attention is made by object attention, which is in paired box regression branch, caused by the module of object detection, and a module of ID verification creates identity attention. The detections from these branches are linked together by IoU matching, Kalman filtering, and bipartite matching. This makes our SCT speedy, simple, and effective enough to achieve a Multiobject Tracking Accuracy (MOTA) of 68.4% and Identity F1 (IDF1) of 64.3% on the MOT16 dataset. We have studied existing tracking techniques and analyzed their performance in this work. We have achieved more qualitative and quantitative tracking results than other existing techniques with relatively improved margins.
A real-time rural domestic garbage detection algorithm with an improved YOLOv5s network model

An increasing number of researchers are using deep learning technology to classify and process garbage in rural areas, and have achieved certain results. However, the existing garbage detection models still have problems such as high complexity, missed detection of small targets, low detection accuracy and poor real-time performance. To address these issues, we train a model and apply it to garbage classification and detection in rural areas. In general, we propose an attention combination mechanism based on the YOLOv5 algorithm to build a better backbone network structure, add a new small object detection layer in the head network to enhance the model's ability to detect small objects, adopt the CIoU loss function to optimize the output prediction bounding box, and choose the Adam optimization algorithm to train the model. Our proposed YOLOv5s-CSS model detects a single garbage image in 0.021 s with a detection accuracy of 96.4%. Compared with the YOLOv5 algorithm and the classic detection algorithm, the improved algorithm has better detection speed and detection accuracy. At the same time, the complexity of the network model is reduced to a certain extent, which can meet the requirements of real-time detection of rural domestic garbage.
Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40% in some cases. In summary, our work provides new insight into studying adversarial transferability and provides a strong counterpart for future research on adversarial defense.
CNN-LSTM Facial Expression Recognition Method Fused with Two-Layer Attention Mechanism

When exploring facial expression recognition methods, it is found that existing algorithms make insufficient use of information about the key parts that express emotion. For this problem, on the basis of a convolutional neural network and long short-term memory (CNN-LSTM), we propose a facial expression recognition method that incorporates an attention mechanism (CNN-ALSTM). Compared with the general CNN-LSTM algorithm, it can mine the information of important regions more effectively. Furthermore, a CNN-LSTM facial expression recognition method incorporating a two-layer attention mechanism (ACNN-ALSTM) is proposed. We conducted comparative experiments on Fer2013 and processed CK + datasets with CNN-ALSTM, ACNN-ALSTM, patch based ACNN (pACNN), Facial expression recognition with attention net (FERAtt), and other networks. The results show that the proposed ACNN-ALSTM hybrid neural network model is superior to related work in expression recognition.
Short-Term Solar Irradiance Forecasting Using CNN-1D, LSTM, and CNN-LSTM Deep Neural Networks: A Case Study With the Folsom (USA) Dataset

Abstract In this paper, solar irradiance short-term forecasts were performed considering time horizons ranging from 5 min to 30 min, under a 5 min time-step. Global horizontal irradiance (GHI) and direct normal irradiance (DNI) were computed using deep neural networks with 1-dimensional convolutional neural network (CNN-1D), long short-term memory (LSTM), and CNN-LSTM layers on the benchmarking dataset FOLSOM, which is formed by predictors obtained by recursive functions on the clear sky index time series and statistical attributes extracted from images collected by a camera pointed to the zenith, characterizing endogenous and exogenous variables, respectively. To analyze the endogenous predictors influence on the accuracy of the networks, the performance was evaluated for the cases with and without them. This analysis is motivated, to our best knowledge, by the lack of works that cite the FOLSOM dataset using deep learning models, and it is necessary to verify the impact of the endogenous and exogenous predictors in the forecasts results for this specific approach. The accuracy of the networks was evaluated by the metrics mean absolute error (MAE), mean bias error (MBE), root-mean-squared error (RMSE), relative root mean squared error (rRMSE), determination coefficient (R2), and forecast skill (s). The network architectures using isolated CNN-1D and LSTM layers generally performed better. The best accuracy was obtained by the CNN-1D network for a horizon of 10 min ahead reaching an RMSE of 36.24 W/m2, improving 11.15% on this error metric compared to the persistence model.
Image denoising in the deep learning era

Over the last decade, the number of digital images captured per day has increased exponentially, due to the accessibility of imaging devices. The visual quality of photographs captured by low cost or miniaturized imaging devices is often degraded by noise during image acquisition and data transmission. With the re-emergence of deep neural networks, the performance of image denoising techniques has been substantially improved in recent years. The objective of this paper is to provide a comprehensive survey of recent advances in image denoising techniques based on deep neural networks. We begin with a thorough description of the fundamental preliminaries of the image denoising problem, followed by an overview of the benchmark datasets and commonly used metrics for objective assessment of denoising algorithms. We study the existing deep denoisers in the supervised and unsupervised training paradigms and review the technical specifics of some representative methods within each category. We conclude the survey by remarking on trends and challenges in the development of state-of-the-art algorithms and future research.
Adoption of Blockchain Technology Facilitates a Competitive Edge for Logistic Service Providers

Blockchain is attracting tremendous attention in the logistics industry on account of its cutting-edge appeal, potential for integration within the sector, and capacity to disrupt established practices. Among other things, blockchain technology promises to open up new horizons in traceability, transparency, accuracy, and safety throughout the supply chain. However, as an emerging technology, blockchain is still relatively nascent, and familiarity with the technology is limited, as are its implementations. In addition, there is a dearth of studies concerning blockchain technology specifically, as it pertains to transport and logistics, as opposed to finance and cryptocurrency. It remains unknown what factors enable blockchain adoption by logistics service providers and how its adoption affects firm performance and capabilities. This research examines extant literature and conducts research on an eminent global transportation company to elucidate the potential influence of blockchain adoption on firm performance. The results of this work support the model that advancing theory-driven and empirical blockchain studies will increase firm capabilities and foster a competitive edge in the emerging digitalized era.
DC-SSL: Addressing Mismatched Class Distribution in Semi-supervised Learning

Consistency-based Semi-supervised learning (SSL) has achieved promising performance recently. However, the success largely depends on the assumption that the labeled and unlabeled data share an identical class distribution, which is hard to meet in real practice. The distribution mismatch between the labeled and unlabeled sets can cause severe bias in the pseudo-labels of SSL, resulting in significant performance degradation. To bridge this gap, we put forward a new SSL learning framework, named Distribution Consistency SSL (DC-SSL), which rectifies the pseudolabels from a distribution perspective. The basic idea is to directly estimate a reference class distribution (RCD), which is regarded as a surrogate of the ground truth class distribution about the unlabeled data, and then improve the pseudo-labels by encouraging the predicted class distribution (PCD) of the unlabeled data to approach RCD gradually. To this end, this paper revisits the Exponentially Moving Average (EMA) model and utilizes it to estimate RCD in an iteratively improved manner, which is achieved with a momentum-update scheme throughout the training procedure. On top of this, two strategies are proposed for RCD to rectify the pseudo-label prediction, respectively. They correspond to an efficient training-free scheme and a training-based alternative that generates more accurate and reliable predictions. DC-SSL is evaluated on multiple SSL benchmarks and demonstrates remarkable performance improvement over competitive methods under matched- and mismatched-distribution scenarios.
Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization

Image harmonization aims to modify the color of the composited region according to the specific background. Previous works model this task as a pixel-wise image translation using UNet family structures. However, the model size and computational cost limit the ability of their models on edge devices and higher-resolution images. In this paper, we propose spatial-separated curve rendering network (S $$^2$$ CRNet), a novel framework to prove that the simple global editing can effectively address this task as well as the challenge of high-resolution image harmonization for the first time. In S $$^2$$ CRNet, we design a curve rendering module (CRM) using spatial-specific knowledge to generate the parameters of the piece-wise curve mapping in the foreground region and we can directly render the original high-resolution images using the learned color curve. Besides, we also make two extensions of the proposed framework via cascaded refinement and semantic guidance. Experiments show that the proposed method reduces more than 90% parameters compared with previous methods but still achieves the state-of-the-art performance on 3 benchmark datasets. Moreover, our method can work smoothly on higher resolution images with much lower GPU computational resources. The source codes are available at: http://github.com/stefanLeong/S2CRNet .