Reviewerly/exHarmony huggingface dataset:
RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization

Stain variations often decrease the generalization ability of deep learning based approaches in digital histopathology analysis. Two separate proposals, namely stain normalization (SN) and stain augmentation (SA), have been spotlighted to reduce the generalization error, where the former alleviates the stain shift across different medical centers using template image and the latter enriches the accessible stain styles by the simulation of more stain variations. However, their applications are bounded by the selection of template images and the construction of unrealistic styles. To address the problems, we unify SN and SA with a novel RandStainNA scheme, which constrains variable stain styles in a practicable range to train a stain agnostic deep learning model. The RandStainNA is applicable to stain normalization in a collection of color spaces i.e. HED, HSV, LAB. Additionally, we propose a random color space selection scheme to gain extra performance improvement. We evaluate our method by two diagnostic tasks i.e. tissue subtype classification and nuclei segmentation, with various network backbones. The performance superiority over both SA and SN yields that the proposed RandStainNA can consistently improve the generalization ability, that our models can cope with more incoming clinical datasets with unpredicted stain styles. The codes is available at https://github.com/yiqings/RandStainNA.
Identification of RVE length scale in fiber composites via combined optical and SEM digital image correlation

The representative volume element (RVE) in fiber composites refers to the smallest volume of material that contains all constituents of the composite, i.e., fiber and matrix, and is a domain in which the homogenization of microscale response will lead to the macroscale behavior. Therefore, the RVE size in fiber composites is the length scale at which micro to macroscale transition occurs. In this work, a multiscale experimental approach is developed to experimentally characterize the length scale of RVE in a carbon fiber composite with a random fiber distribution. The approach is based on combining optical and scanning electron microscope (SEM) digital image correlation (DIC) measurements. A statistical procedure based on spatial averaging of local strain data is applied to the strain fields obtained from optical and SEM DIC measurements. The convergence between local strains averaged over a carefully selected domain and global strains is used as the criterion for identifying the RVE size in the examined composite. The RVE sizes determined using this approach are stress dependent and increase with the evolution of internal damage in the composite.
Sentiment Lexical Strength Enhanced Self-supervised Attention Learning for sentiment analysis

In Natural Language Processing (NLP), attention mechanism is often used to quantify the importance of the context word in sentiment prediction. However, it tends to focus on high-frequency words, while ignoring low-frequency words that have an active effect in some positions. In this paper, we propose a Sentiment Lexical Strength Enhanced Self-supervised Attention Learning (SLS-ESAL) approach. Specifically, we iteratively mine attention supervision information from all input sentences. Then we use weights quantified by sentiment lexical strength to enhance attention learning in final training, which enables our model to continue to focus on the active context words in different positions and eliminate the effects of the misleading context ones. Experiments on three datasets show that our approach can improve sentiment analysis performance and verify attention weights can be used as an explanation for text classification.
Composition of construction services with hierarchical planning on digital platform

The digital platform business model offers opportunities for the construction industry to address the problem of extensive development by elaborately orchestrating a range of construction services to meet complex construction requirements. The presence of isomeric and coupling relationships between two construction services leads to alternative execution paths in the service network. However, the problem of generating a service network with alternative branches has been scarcely investigated. This study proposes a hierarchical task network (HTN) approach to address the service composition problem. Specifically, the approach performs a depth-first traversal search to address the isomeric and coupling relationships. A branch merging mechanism is integrated to eliminate repeated planning caused by equivalent planning nodes. The experimental studies demonstrate that the approach can generate valid and correct branches in the construction service network and that the devised branch merging mechanism can significantly improve the efficiency of the approach. • An HTN planning-based approach is proposed to orchestrate a range of construction services. • Depth-first traversal search is used to handle isomeric and coupling relations between services. • A branch merging mechanism is designed to avoid repeated planning. • Two comparisons are carried out to demonstrate the approach's feasibility and effectiveness.
Performance evaluation of machine learning for breast cancer diagnosis: A case study

Breast cancer (BC) is one of the most common and aggressive malignancies in women worldwide. It is proven that machine learning (ML) could rapidly and cost-effectively diagnose BC. This study aimed to develop and test predictive models for BC based on women's lifestyle factors using several basic and ensemble machine learning (ML) classifiers. Data of 1503 suspected BC cases were retrospectively extracted from a hospital-based electronic database. First, important risk factors were identified using wrapper-J48, wrapper-SVM, wrapper-NB, logistic regression (LR), and correlation-based feature selection (CFS) methods. Then the performance of five basic ML algorithms, including Naïve Bayes (NB), Bayesian network (BNeT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), C4.5, eXtreme Gradient Boosting (XGBoost), decision tree and two ensemble algorithms, including Confidence weighted voting and Voting were compared to predict BC before and after performing feature section (FS). We utilized SPSS 20 and Weka software version 3.8.4 to analyze the data. Implementation of ML models was also performed in R 3.5.0. The RF algorithm presented the best performance before and after performing FS with AUC of 0.799 and 0.798, respectively. Also, the best model's combination using the Confidence weighted voting method improved the classifier performance and achieved the best result with an 80% AUC. The results showed that ensemble ML algorithms represented higher ability than basic methods. The developed models can accurately classify individuals who are at high risk for BC, and can be employed as a screening tool for the early BC detection.
A novel grid-based many-objective swarm intelligence approach for sentiment analysis in social media

Sentiment analysis is a field of study that analyses people's opinions, evaluations, feelings, ratings, sentiments, and attitudes towards entities such as products, organizations, individuals, services, topics, titles, events, and qualifications. Studies on sentiment analysis problems in social media have generally adopted intelligent classification methods. However, there are conflicting and contradictory objectives to simultaneously optimize, and active research continues into developing a more effective analysis model in terms of many metrics in order to achieve effective usage. This study considers sentiment analysis as a many-objective optimization problem for the first time. For this purpose, it first proposes a Grid-based Adaptive Many-Objective Grey Wolf Optimizer (GAM-GWO) based on the Grey Wolf Optimizer algorithm. Then, it adapts this proposed method for the sentiment analysis problem in order to obtain more successful results in terms of different metrics. The study tests the performance of the proposed approach with three different data sets. Experimental results show that GAM-GWO can achieve non-dominated and competitive results in all data set classes.
Differentiable Automatic Data Augmentation by Proximal Update for Medical Image Segmentation

Dear editor, This letter presents an automatic data augmentation algorithm for medical image segmentation. To increase the scale and diversity of medical images, we propose a differentiable automatic data augmentation algorithm based on proximal update by finding an optimal augmentation policy. Specifically, on the one hand, a dedicated search space is designed for the medical image segmentation task. On the other hand, we introduce a proximal differentiable gradient descent strategy to update the data augmentation policy, which would increase the searching efficiency. Results of the experiments indicate that the proposed algorithm significantly outperforms state-of-the-art methods, and search speed is 10 times faster than state-of-the-art methods.
Accurate dynamic 3-D shape measurement based on the fringe pattern super-reconstruction technique

Fringe projection profilometry (FPP) has been widely used for three-dimensional (3-D) shape measurement. However, measuring the dynamic objects is always problematic due to the movement of objects during the inter-frame time gap. Many dynamic solutions have been developed, but they are fragile for practical applications. In this paper, we propose a new method for dynamic 3-D measurement with flexible implementation and high measurement accuracy, which utilizes the inverse relationship between camera sampling speed and sampling resolution and image super-reconstruction technique. The camera selects a low sampling resolution to capture fringe patterns, thus improving the sampling speed and alleviating the influence of object’s dynamic motion. However, insufficient resolution will affect the accuracy and details of the reconstructed 3-D shape. Inspired by the successes of computational imaging techniques, the resolution and details lost by the front-end camera can be restored by the back-end image processing. Therefore, we specifically designed a details restoration and super-reconstruction network (DRSRNet) to transform the captured low-resolution fringes into the desired high-quality and high-resolution fringes. The desired 3-D shapes are calculated from these transformed fringes combined with the correspondence system calibration parameters. The provided experiments verify that the proposed method can improve the 3-D profiling speed by 5 times while ensuring the profiling accuracy, and the motion-induced errors can be obviously eliminated compared with FPP using the camera with a high sampling resolution. Furthermore, the proposed method can achieve different 3-D profiling speed increases to flexibly suit different measurement requirements.
The impact of digital trust on firm value and governance: an empirical investigation of US firms

Purpose This study aims to investigate the effects of firm- and governance-specific characteristics on digital trust (DT) and firm value. Firm-specific factors include return on assets (ROA), market-to-book ratio (M/B ratio), size and leverage, whilst governance-related factors comprise board size, percentage of female board members, board independence and institutional ownership. All listed US firms over the period of 2011–2016 were analysed in this study. Design/methodology/approach This study provides a novel method to empirically measure DT by combining multiple variables to create a combined DT score. The variables include security and privacy scores, security rankings and data breaches, amongst others. Subsequently, a linear regression was performed to evaluate the effect of firm- and governance-specific characteristics on DT, as well as the effect of DT on firm value. Findings By using signalling theory, this study finds significant evidence that a firm’s profitability (ROA) decreases whilst its size increases DT. This could be due to the fact that firms with lower DT monetise data more actively, decrease DT and increase short-term profitability. Significant evidence also shows that increasing DT leads to an increase in firm value. Originality/value Although numerous studies have been conducted on developing customers’ trust by incorporating corporate social responsibility to improve firm value, the literature remains still on its digital analogue. Therefore, this study extends the knowledge of corporate digital responsibility (CDR) by providing a novel method for calculating DT across industries as an antecedent of CDR. Specifically, it sheds light on how firms can enhance DT by utilising firm- and governance-level factors. This enhanced DT can subsequently increase firm value. The study provides important managerial implications by providing empirical evidence that cybersecurity investments increase firm value. This value increase is related to the rise in shareholder value amongst investors and the increase in the organisation’s consumer perceptions as the latter’s interests are better managed.
Smart Contract System Architecture for Pharma Supply chain

In this paper, we offer a blockchain-based approach for efficient product tracing in the pharmaceutical supply chain that uses smart contracts and decentralized storage. The smart contract ensures data provenance, eliminates the need for mediators, and provides all participants with a safe, immutable transaction history. We provide the smart contract system architecture as well as the detailed algorithms that regulate our proposed solution’s operating principles. We test and validate it to see how effective it is at improving traceability in pharmaceutical supply chains.
Optimal 3D object reconstruction and classification by separable moments via the Firefly algorithm

The 3D Hahn-Hahn-Charlier moments (HHCMs) are a novel collection of 3D separable Discrete Orthogonal Moments (DOM) that we introduce in this paper for the purpose of using them in classification and reconstruction applications. It should be noted that HHCM moments are characterized by parameters $\varphi_{1}, \gamma_{1}, \varphi_{2}, \gamma_{2}, a_{1}$ However, it was very important to optimize these parameters in order to obtain good results in classification and reconstruction. In this context, this paper adopts an approach to optimize the parameters $\varphi_{1}, \gamma_{1}, \varphi_{2}, \gamma_{2}, a_{1}$ of HHCMs based on the Firefly (FA) algorithm. The simulation results suggest that the proposed HHCM moments, based on the FA algorithm, provide a high level of quality in the reconstruction and classification of objects. Moreover, the comparison with other algorithms clearly demonstrates the superiority of the studied method.
Fine-grained-based multi-feature fusion for occluded person re-identification

Many previous occluded person re-identification(re-ID) methods try to use additional clues (pose estimation or semantic parsing models) to focus on non-occluded regions. However, these methods extremely rely on the performance of additional clues and often capture pedestrian features by designing complex modules. In this work, we propose a simple Fine-Grained Multi-Feature Fusion Network (FGMFN) to extract discriminative features, which is a dual-branch structure consisting of global feature branch and partial feature branch. Firstly, we utilize a chunking strategy to extract multi-granularity features to make the pedestrian information contained in it more comprehensive. Secondly, a spatial transformer network is introduced to localize the pedestrian's upper body, and then introduce a relation-aware attention module to explore the fine-grained information. Finally, we fuse the features obtained from the two branches to obtain a more robust pedestrian representation. Extensive experiments verify the effectiveness of our method under the occlusion scenario.
Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.
Developing Tourism Users’ Profiles with Data-Driven Explicit Information

In recommender systems (RSs), explicit information is often preferred over implicit because it is much more accurate than implicit or predicted information; for example, the user can enter information about his interests directly into the system, and the system will generate accurate recommendations for him. Receiving explicit information, however, may be difficult for a system. Explicit demographic information might be uncomfortable for some users, and extremely common questions, such as race, gender, income, and age, can lead to bias and unfair recommendations. As a result, in this study, we propose a method, in which information collected from a new user does not contain demographic information, and enquired explicit information is data driven. Users’ interest in tourism activities is used to identify seven categories of tourism. The mapping between extracted categories and activities is established with a multilabel classification (MLC) algorithm. The user’s interest in 18 tourism activities is predicted by rating only seven tourism categories. Common MLC algorithms with different classifiers were used to evaluate the proposed method. The best result relates to binary relevance with the Naïve Bayes classifier, which also outperforms the entitled algorithms in collaborative filtering (CF) systems as baseline models. The proposed method can capture users’ interests and develop their profiles without receiving demographic information. Also, compared to CF, in addition to a slight advantage in metrics, it only requires seven ratings to predict user interest in 18 activities. In contrast, CF algorithms require at least 15 user ng records to predict user interest in unknown activities (3-4 activities) to achieve a performance close to the proposed method.
HyVulDect: A hybrid semantic vulnerability mining system based on graph neural network

In recent years, software programs tend to be large and complex, software has become the infrastructure of modern society, but software security issues can not be ignored. software vulnerabilities have become one of the main threats to computer security. There are countless cases of exploiting source code vulnerabilities to launch attacks. At the same time, the development of open source software has made source code vulnerability detection more and more critical. Traditional vulnerability mining methods have been unable to meet the security analysis needs of complex software because of the high false-positive rate and false-negative rate. To resolve the existing problems, we propose a graph neural network vulnerability mining system named HyVulDect based on hybrid semantics, which constructs a composite semantic code property graph for code representation based on the causes of vulnerabilities. A gated graph neural network is used to extract deep semantic information. Since most of the vulnerabilities are data flow associated, we use taint analysis to extract the taint propagation chain, use the BiLSTM model to extract the token-level features of the context, and finally use the classifier to classify the fusion features. We introduce a dual-attention mechanism that allows the model to focus on vulnerability-related code, making it more suitable for vulnerability mining tasks. The experimental results show that HyVulDect outperforms existing state-of-the-art methods and can achieve an accuracy rate of 92% on the benchmark dataset. Compared with the rule-based static mining tools Flawfinder, RATS, and Cppcheck, it has better performance and can effectively detect the actual CVE source code vulnerabilities.
Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.