Reviewerly/exHarmony huggingface dataset:
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, (1) we collected 562 breast ultrasound images and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.
A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities

Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.
Tripartite Information Mining and Integration for Image Matting

With the development of deep convolutional neural networks, image matting has ushered in a new phase. Regarding the nature of image matting, most researches have focused on solutions for transition regions. However, we argue that many existing approaches are excessively focused on transition-dominant local fields and ignored the inherent coordination between global information and transition optimisation. In this paper, we propose the Tripartite Information Mining and Integration Network (TIMI-Net) to harmonize the coordination between global and local attributes formally. Specifically, we resort to a novel 3-branch encoder to accomplish comprehensive mining of the input information, which can supplement the neglected coordination between global and local fields. In order to achieve effective and complete interaction between such multi-branches information, we develop the Tripartite Information Integration (T I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) Module to transform and integrate the interconnections between the different branches. In addition, we built a large-scale human matting dataset (Human-2K) to advance human image matting, which consists of 2100 high-precision human images (2000 images for training and 100 images for test). Finally, we con-duct extensive experiments to prove the performance of our proposed TIMI-Net, which demonstrates that our method performs favourably against the SOTA approaches on the alphamatting.com (Rank First), Composition-1K (MSE- 0.006, Grad-11.5), Distinctions-646 and our Human-2K. Also, we have developed an online evaluation website to perform natural image matting.
InsuDet: A Fault Detection Method for Insulators of Overhead Transmission Lines Using Convolutional Neural Networks

One of the key tasks of the overhead line power equipment inspection based on aerial images acquired by unmanned aerial vehicles is to determine whether the insulators are faulty. However, the fault area on the insulator string occupies a relatively small portion of the entire image, which will make detection difficult. This article presents an intelligent fault detection method for overhead line insulators based on aerial images and improved you only look once (YOLOv3) deep learning technology. In our model, a densely connected feature pyramid network (FPN) is proposed. First, this network can improve the utilization rate of the strong semantic information of deep features and the localization information of shallow features, thereby improving the small insulator fault (missing-cap) detection performance of the YOLOv3 model. Second, this network reduces the number of parameters of the YOLOv3 model, resulting in a low risk of network over-fitting for small datasets. The experimental results on the CPLID dataset show that our model has higher detection accuracy in localization of overhead line insulators and detection of insulator missing-cap faults compared with the existing works.
Adaptive differential evolution with ensembling operators for continuous optimization problems

Differential evolution is one of the most popular evolutionary algorithms for continuous optimization. In this paper, we introduce a new algorithm named the adaptive differential evolution with ensembling populations. In the proposed algorithm, two sets of mutation and crossover operators are utilized to generate offspring to better balance the exploitation and exploration abilities of the algorithm. Besides, an adaptive parameter control strategy is integrated to dynamically adjust the parameter setting of the algorithm so as to further improve the search efficacy. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance on benchmark functions as well as on the real-world wireless sensor localization application, in terms of global search ability and search efficiency.
Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation

Segmentation is a fundamental task in biomedical image analysis. Unlike the existing region-based dense pixel classification methods or boundary-based polygon regression methods, we build a novel graph neural network (GNN) based deep learning framework with multiple graph reasoning modules to explicitly leverage both region and boundary features in an end-to-end manner. The mechanism extracts discriminative region and boundary features, referred to as initialized region and boundary node embeddings, using a proposed Attention Enhancement Module (AEM). The weighted links between cross-domain nodes (region and boundary feature domains) in each graph are defined in a data-dependent way, which retains both global and local cross-node relationships. The iterative message aggregation and node update mechanism can enhance the interaction between each graph reasoning module's global semantic information and local spatial characteristics. Our model, in particular, is capable of concurrently addressing region and boundary feature reasoning and aggregation at several different feature levels due to the proposed multi-level feature node embeddings in different parallel graph reasoning modules. Experiments on two types of challenging datasets demonstrate that our method outperforms state-of-the-art approaches for segmentation of polyps in colonoscopy images and of the optic disc and optic cup in colour fundus images. The trained models will be made available at: https://github.com/smallmax00/Graph_Region_Boudnary.
Blockchain-based Data Trading in Edge-cloud Computing Environment

With the continuous growth in the amount of data generated in the edge-cloud environment, security risks in traditional centralized data management platforms have been concerned. Blockchain technology can be applied to guarantee safety and information transparency in data caching and trading processes. Therefore, a blockchain-based secure cost-aware data caching scheme is proposed to optimize the placement and prevent the tampering of cache data. In this scheme, under the constraints of transmission cost, edge cache size, a quantum particle swarm optimization (QPSO) algorithm is used to solve the data cache placement problem with the greatest content caching gain. A blockchain-based secure decentralized data trading model is proposed to solve the trust problem among the buyers, sellers, and agent nodes and increase incentives for users to trade data. A double auction mechanism is used to maximize social welfare. The experimental results reveal that the proposed data caching and trading scheme can reduce the data transmission cost, improve the cache hit ratio, and maximize social welfare.
A reinforcement learning brain storm optimization algorithm (BSO) with learning mechanism

Brain storm optimization algorithm (BSO), which is inspired by brain storm process of human, has been adopted as an efficient optimizer for various complex problems. A reinforcement learning brain storm optimization algorithm (RLBSO) to improve the performance of BSO is proposed in this paper. Four mutation strategies are designed to enhance the search capability of the algorithm in different stages. Elites are adopted as the guidance to ensure the quality of the population. The global best is utilized to guide the search direction of individuals. The cluster centers are employed to improve the exploitation ability of the algorithm. The historical individuals are utilized to the increase the diversity of the population. The Q-learning mechanism is introduced to guide the selection of strategies according to the historical information fed back by the corresponding strategies. A self-learning mechanism, which is based on the evolutionary state of the population and the experience of previous successful individuals, is utilized to determine the update method of individual. The RLBSO algorithm is tested on the CEC 2017 benchmark test suite and a practical engineering problem. The results show that RLBSO has better performance than the other state of the arts algorithms.
Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic

The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.
A probabilistic generalization of isolation forest

The problem of finding anomalies and outliers in datasets is one of the most important challenges of modern data analysis. Among the commonly dedicated tools to solve this task one can find Isolation Forest (IF) that is an efficient, conceptually simple, and fast method. In this study, we propose the Probabilistic Generalization of Isolation Forest (PGIF) that is an intuitively appealing and efficient enhancement of the original approach. The proposed generalization is based on nonlinear dependence of segment-cumulated probability from the length of segment. Introduction of the generalization allows to achieve more effective splits that are rather performed between the clusters, i.e. regions where datapoints constitute dense formations and not through them. In a comprehensive series of experiments, we show that the proposed method allows us to detect anomalies hidden between clusters more effectively. Moreover, it is demonstrated that our approach favorably affects the quality of anomaly detection in both artificial and real datasets. In terms of time complexity our method is close to the original one since the generalization is related only to the building of the trees while the scoring procedure (which takes the main time) is kept unchanged.
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers

This paper presents channel-aware adversarial attacks against deep learning-based wireless signal classifiers. There is a transmitter that transmits signals with different modulation types. A deep neural network is used at each receiver to classify its over-the-air received signals to modulation types. In the meantime, an adversary transmits an adversarial perturbation (subject to a power budget) to fool receivers into making errors in classifying signals that are received as superpositions of transmitted signals and adversarial perturbations. First, these evasion attacks are shown to fail when channels are not considered in designing adversarial perturbations. Then, realistic attacks are presented by considering channel effects from the adversary to each receiver. After showing that a channel-aware attack is selective (i.e., it affects only the receiver whose channel is considered in the perturbation design), a broadcast adversarial attack is presented by crafting a common adversarial perturbation to simultaneously fool classifiers at different receivers. The major vulnerability of modulation classifiers to over-the-air adversarial attacks is shown by accounting for different levels of information available about the channel, the transmitter input, and the classifier model. Finally, a certified defense based on randomized smoothing that augments training data with noise is introduced to make the modulation classifier robust to adversarial perturbations.
Development of a soil heavy metal estimation method based on a spectral index: Combining fractional-order derivative pretreatment and the absorption mechanism

Visible and near-infrared (Vis-NIR) reflectance is an effective way to estimate soil heavy metal content. In this study, in order to magnify the spectral information of the soil heavy metals and solve the collinearity and redundancy of hyperspectral datasets, we aimed to explore the potential of the fractional-order derivative (FOD) spectral pretreatment method and the band combination algorithm in soil heavy metal estimation. A total of 120 soil samples were collected in Xuzhou city, Jiangsu province, China, and their heavy metal contents and spectra were measured. The FOD (intervals of 0.25, range of 0-2) and a new three-band spectral index which take into account the electronic transition of metal ions in the visible region and organic matter and clay minerals in the near-infrared region were utilized for the spectral pretreatment and the selection of characteristic bands, respectively. FOD with an order of 0.75 exhibited the best model performance for estimating Cr and Zn, yielding RP2 values of 0.74 and 0.81, respectively. As regards Pb, the highest estimation accuracy was achieved with the 0.5-order reflectance, yielding RP2 values of 0.56. The three-band spectral indices with the best performance were then combined for a better estimation. To improve the estimation accuracy and generalization, partial least squares (PLS), support vector machine (SVM), random forest (RF), ridge regression (RR), XGBoost and extreme learning machine (ELM) were used to estimate the heavy metals by incorporating multiple spectral indices, and it was found that ELM outperformed other counterparts (the highest RP2 = 0.77 for Cr, the highest RP2 = 0.86 for Zn, the highest RP2 = 0.63 for Pb). The main spectral absorption mechanisms and modes of heavy metals were also analyzed. This estimation method combining FOD and a three-band index will provide a reference to estimate soil heavy metals using Vis-NIR spectra over a large scale.
PACO-VMP: Parallel Ant Colony Optimization for Virtual Machine Placement

The Virtual Machine Placement (VMP) problem is a challenging optimization task that involves the assignment of virtual machines to physical machines in a cloud computing environment. The placement of virtual machines can significantly affect the use of resources in a cluster, with a subsequent impact on operational costs and the environment. In this paper, we present an improved algorithm for VMP, based on Parallel Ant Colony Optimization (PACO), which makes effective use of parallelization techniques and modern processor technologies. We achieve solution qualities that are comparable with or superior to those obtained by other nature-inspired methods, with our parallel implementation obtaining a speed-up of up to 2002x over recent serial algorithms in the literature. This allows us to rapidly find high-quality solutions that are close to the theoretical minimum number of Virtual Machines.
Blockchain-Based Wine Supply Chain for the Industry Advancement

The wine sector is one of the most ‘amazing’ and significant agri-food sectors worldwide since ancient times, considering revenue or employment as well as health aspects. This article aims to describe the impact of the implementation of blockchain technology (BCT) in the wine supply chain. After the literature review, the study is based on Agent Based Models (ABMs) and carried out by the GAMA program. Then, the model and simulation of BCT wine supply chain is designed. Finally, the paper compares traditional and BCT-based supply chains, and the advantages of the last one are evident. Blockchain is a useful tool to ensure a traceability system and to protect the production from any type of fraud and contamination.
An <scp>IoT</scp> and <scp>Blockchain</scp>‐based approach for the smart water management system in agriculture

Abstract Agriculture in rural areas facing critical issues such as irrigation with the increase in water crises followed by some other issues line seed quality, poor fertilizers and many others. The recent advances suggest that IoT and Blockchain Technology along with artificial intelligence will be most dominant technologies in near future. In this article, the integration of Internet of Things (IoT) with Blockchain technology is implemented for monitoring agricultural fields efficiently. An efficient seed quality monitoring and smart water management system is design using IoT and Blockchain Technology for managing and coordinating the use of good quality seeds and water resources among communities. The Blockchain network is implemented for securing the information and supporting trust among the members of community. The Blockchain network is also implemented for sporting trust among commercial resource constrained systems, which are communicating with the Blockchain network consisting of a hardware platform. The design of a prototype and its performance evaluation based on implementation is also presented.
Improving data and model quality in crowdsourcing using co-training-based noise correction

Crowdsourcing makes it much faster and cheaper to obtain labels for a large amount of data used in supervised learning. In the crowdsourcing scenario, an integrated label is inferred from a multiple noisy label set for each instance using ground truth inference algorithms, which is called label integration. However, a certain level of label noise remains in the integrated dataset, which degrades the performance of the models trained on it. To the best of our knowledge, existing label noise correction algorithms only use the original attribute space and do not use the information contained in the multiple noisy label sets for building models. To solve these problems, we propose a novel integrated label noise correction algorithm called co-training-based noise correction (CTNC). In CTNC, the weight is first calculated from the information provided by the multiple noisy label set for each instance. Subsequently, a label noise filter is used to identify noisy instances; a clean set and a noisy set are thus obtained. Another attribute view of each instance in both the clean and noisy sets is then generated by the classifiers trained on the original attribute view of the clean set. Finally, a co-training framework is used to train two classifiers to relabel the integrated instances. The performance on 34 simulated datasets and 2 real-world datasets demonstrates that our proposed CTNC outperforms all state-of-the-art label noise correction algorithms used for comparison.
Immersive Analytics with Abstract 3D Visualizations: A Survey

Abstract After a long period of scepticism, more and more publications describe basic research but also practical approaches to how abstract data can be presented in immersive environments for effective and efficient data understanding. Central aspects of this important research question in immersive analytics research are concerned with the use of 3D for visualization, the embedding in the immersive space, the combination with spatial data, suitable interaction paradigms and the evaluation of use cases. We provide a characterization that facilitates the comparison and categorization of published works and present a survey of publications that gives an overview of the state of the art, current trends, and gaps and challenges in current research.