Reviewerly/exHarmony huggingface dataset:
Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

Abstract White blood cells (WBC), which form the basis of the immune system, protect the body from foreign invaders and infectious diseases. While the number and structural features of WBCs can provide important information about the health of people, the ratio of the subtypes of these cells and observable deformations are a good indicator in the diagnostic process. The recognition of cells of the type of lymphocytes, neutrophils, eosinophils, basophils and monocytes is critical. In this article, Deep Learning based Hybrid CNN (Convololutional Neural Network) model is proposed for classification of eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The model presented is based on pretrained Alexnet and Googlenet architectures. The feature vector in the last pooling layer of both CNN architectures has been merged, and the resulting feature vector is classified by the Support Vector Machine. To determine the superiority of the proposed method, the classification was also performed and compared using pretrained Alexnet and Googlenet. Hybrid Alexnet-Googlenet-SVM model provides higher accuracy than pretrained Alexnet and Googlenet. The proposed method has been tested with WBC images from Kaggle and LISC database. Accuracy and F1-score were 99.73%, 0.99 and 98.23%, 0.98 for both data sets, respectively.
Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation

Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.
A deep learning based hybrid method for hourly solar radiation forecasting

• Proposed a deep learning clustering method for solar irradiance feature learning. • Hourly solar forecasting with Feature Attention based Deep Forecasting (FADF). • RMSE reduced by 11.88%–12.65% as compared to smart persistence. Solar radiation forecasting is a key technology to improve the control and scheduling performance of photovoltaic power plants. In this paper, a deep learning based hybrid method for 1-hour ahead Global Horizontal Irradiance (GHI) forecasting is proposed. Specifically, a deep learning based clustering method, deep time-series clustering, is adopted to group the GHI time series data into multiple clusters to better identify its irregular patterns and thus providing a better clustering performance. Then, the Feature Attention Deep Forecasting (FADF) deep neural network is built for each cluster to generate the GHI forecasts. The developed FADF dynamically allocates different importance to different features and utilizes the weighted features to forecast the next hour GHI. The solar forecasting performance of the proposed method is evaluated with the National Solar Radiation Database. Simulation results show that the proposed method yields the most accurate solar forecasting among the smart persistence and state-of-the-art models. The proposed method reduces the root mean square error as compared to the smart persistence by 11.88% and 12.65% for the Itupiranga and Ocala dataset, respectively.
Enhanced Beetle Antennae Search with Zeroing Neural Network for online solution of constrained optimization

This paper proposes a continuous-time enhanced variant of Beetle Antennae Search (BAS), a metaheuristic algorithm that mimics the food searching nature of beetles. Beetles register the smell of the food on their two antennae, and based on the intensity of smell, they move left or right. Likewise, discrete-time BAS computes the value of objective function three times and moves toward the optimal solution. However, the computation of objective function three times in each iteration known as a “virtual particle,” makes it computationally expensive, inefficient, and time-consuming, especially while dealing with complex systems, e.g., redundant manipulators. Our proposed, Enhanced Beetle Antennae Search with Zeroing Neural Network (BASZNN) algorithm overcomes this problem by introducing a delay factor in objective function and input. This delay allows BASZNN to compute objective function value once, making it computationally robust and efficient. BASZNN includes the flexible random searching nature of BAS and the parallel processing nature of ZNN, making it computationally fast and less time-consuming, especially for complex problems. As a testbed, we employed BASZNN on two types of problems: Unconstrained (unimodal, multimodal) and Constrained (real-world) and compared the results with state-of-the-art metaheuristic algorithms.
A compound of feature selection techniques to improve solar radiation forecasting

The prediction of Global Horizontal Irradiance (GHI) allows to estimate in advance the future energy production of photovoltaic systems, thus ensuring their full integration into the electricity grids. This paper investigates the effectiveness of using exogenous inputs in performing short-term GHI forecasting. To this aim, we identified a subset of relevant input variables for predicting GHI by applying different feature selection techniques. The results revealed that the most significant input variables for predicting GHI are ultraviolet index, cloud cover, air temperature, relative humidity, dew point, wind bearing, sunshine duration and hour-of-the-day. The predictive performance of the selected features was evaluated by feeding them into five different machine learning models based on Feedforward, Echo State, 1D-Convolutional, Long Short-Term Memory neural networks and Random Forest, respectively. Our Long Short-Term Memory solution presents the best prediction performance among the five models, predicting GHI up to 4 h ahead with a Mean Absolute Deviation (MAD) of 24.51%. Then, to demonstrate the effectiveness of using exogenous inputs for short-term GHI forecasting, we compare the multivariate models against their univariate counterparts. The results show that exogenous inputs significantly improve the forecasting performance for prediction horizons greater than 15 min, reducing errors by more than 22% in 4 h ahead predictions, while for very short prediction horizons (i.e. 15 min) the improvements are negligible.
A comprehensive study of bloated dependencies in the Maven ecosystem

Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application's code and its external dependencies, and automate several software development tasks. However, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that the build tool packages with the application's compiled code but that are actually not necessary to build and run the application. This phenomenon artificially grows the size of the built binary and increases maintenance effort. We propose a tool, called DepClean, to analyze the presence of bloated dependencies in Maven artifacts. We analyze 9,639 Java artifacts hosted on Maven Central, which include a total of 723,444 dependency relationships. Our key result is that 75.1% of the analyzed dependency relationships are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts up to 1/4 of its current count. We also perform a qualitative study with 30 notable open-source projects. Our results indicate that developers pay attention to their dependencies and are willing to remove bloated dependencies: 18/21 answered pull requests were accepted and merged by developers, removing 131 dependencies in total.
Interpreting and Unifying Graph Neural Networks with An Optimization Framework

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although most of GNNs basically follow a message passing manner, litter effort has been made to discover and analyze their essential relations. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed propagation mechanisms are the optimal solution optimizing a feature fitting function over a wide class of graph kernels with a graph regularization term. Our proposed unified optimization framework, summarizing the commonalities between several of the most representative GNNs, not only provides a macroscopic view on surveying the relations between different GNNs, but also further opens up new opportunities for flexibly designing new GNNs. With the proposed framework, we discover that existing works usually utilize naïve graph convolutional kernels for feature fitting function, and we further develop two novel objective functions considering adjustable graph kernels showing low-pass or high-pass filtering capabilities respectively. Moreover, we provide the convergence proofs and expressive power comparisons for the proposed models. Extensive experiments on benchmark datasets clearly show that the proposed GNNs not only outperform the state-of-the-art methods but also have good ability to alleviate over-smoothing, and further verify the feasibility for designing GNNs with our unified optimization framework.
Photovoltaic output power performance assessment and forecasting: Impact of meteorological variables

Meteorological variables have an important effect on the performance of a grid-connected photovoltaic station, in this paper, the impact of meteorological variables on the 6 mWp grid-connected photovoltaic station in the desert of Adrar has been explored through performance assessment and output power forecasting. The impact of the meteorological variables on performance parameters has been investigated by performing interdependence and correlation studies. A clear interdependence between some variables has been observed, but the complete separation between each variable correlation effect has proven to be a difficult task. A combination of random forest method and pre-processing techniques namely feature selection and Principal component analysis has been developed in order to predict power production using meteorological variables as inputs. The forecasting models have been evaluated in terms of computation time, accuracy, and several statistical indicators.
Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study

Background To combat the global COVID-19 pandemic, contact tracing apps have been discussed as digital health solutions to track infection chains and provide appropriate information. However, observational studies point to low acceptance in most countries, and few studies have yet examined theory-based predictors of app use in the general population to guide health communication efforts. Objective This study utilizes established health behavior change and technology acceptance models to predict adoption intentions and frequency of current app use. Methods We conducted a cross-sectional online survey between May and July 2020 in a German convenience sample (N=349; mean age 35.62 years; n=226, 65.3% female). To inspect the incremental validity of model constructs as well as additional variables (privacy concerns, personalization), hierarchical regression models were applied, controlling for covariates. Results The theory of planned behavior and the unified theory of acceptance and use of technology predicted adoption intentions (R2=56%-63%) and frequency of current app use (R2=33%-37%). A combined model only marginally increased the predictive value by about 5%, but lower privacy concerns and higher threat appraisals (ie, anticipatory anxiety) significantly predicted app use when included as additional variables. Moreover, the impact of perceived usefulness was positive for adoption intentions but negative for frequency of current app use. Conclusions This study identified several theory-based predictors of contact tracing app use. However, few constructs, such as social norms, have a consistent positive effect across models and outcomes. Further research is required to replicate these observations, and to examine the interconnectedness of these constructs and their impact throughout the pandemic. Nevertheless, the findings suggest that promulgating affirmative social norms and positive emotional effects of app use, as well as addressing health concerns, might be promising strategies to foster adoption intentions and app use in the general population.
Mitigating backdoor attacks in LSTM-based text classification systems by Backdoor Keyword Identification

It has been proved that deep neural networks are facing a new threat called backdoor attacks, where the adversary can inject backdoors into the neural network model through poisoning the training dataset. When the input containing some special pattern called the backdoor trigger, the model with backdoor will carry out malicious task such as misclassification specified by adversaries. In text classification systems, backdoors inserted in the models can cause spam or malicious speech to escape detection. Previous work mainly focused on the defense of backdoor attacks in computer vision, little attention has been paid to defense method for RNN backdoor attacks regarding text classification. In this paper, through analyzing the changes in inner LSTM neurons, we proposed a defense method called Backdoor Keyword Identification (BKI) to mitigate backdoor attacks which the adversary performs against LSTM-based text classification by data poisoning. This method can identify and exclude poisoning samples crafted to insert backdoor into the model from training data without a verified and trusted dataset. We evaluate our method on four different text classification datset: IMDB, DBpedia ontology, 20 newsgroups and Reuters-21578 dataset. It all achieves good performance regardless of the trigger sentences.
Decentralized secure storage of medical records using Blockchain and <scp>IPFS</scp>: A comparative analysis with future directions

Abstract Most of the hospitals store their patient's data locally and some even do not have any backup storage. This poses a real threat of data loss or data corruption. Although many hospitals are migrating to cloud storage, the clouds have their own threat vectors. Recently, various health care providers were hit by ransomware and Distributed Denial of Service attacks during the COVID‐19 outbreak. Due to these attacks, many emergency services were halted, affecting hundreds of thousands without any healthcare. Another problem with these traditional database practices is that they often misplace or mix the patient's data, which, needless to say, have severe complications. Many researchers are working on IPFS and Blockchain technology to improve the storage of medical records. This article presents a detailed study of the IPFS and Blockchain based healthcare secure storage solutions. It analyzes the existing solutions and their architecture, which will further facilitate the future research and development of emerging IPFS and Blockchain technologies.
Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors

This paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detection task. Even with slightly lower resolution, the performance is just as good as a camera in visible range. The detector performance as a function of the sensor-to-target distance is also investigated. In addition, using sensor fusion, the system is made more robust than the individual sensors, helping to reduce false detections. To counteract the lack of public datasets, a novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented 1.1. https://github.com/DroneDetectionThesis/Drone-detection-dataset. The database is complemented with an audio dataset of the classes drones, helicopters and background noise.
DSANet: Dilated spatial attention for real-time semantic segmentation in urban street scenes

Efficient and accurate semantic segmentation is particularly important in scene understanding for autonomous driving. Although Deep Convolutional Neural Networks(DCNNs) approaches have made a significant improvement for semantic segmentation. However, state-of-the-art models such as Deeplab and PSPNet have complex architectures and high computation complexity. Thus, it is inefficient for realtime applications. On the other hand, many works compromise the performance to obtain real-time inference speed which is critical for developing a light network model with high segmentation accuracy. In this paper, we present a computationally efficient network named DSANet, which follows a two-branch strategy to tackle the problem of real-time semantic segmentation in urban scenes. We first design a Semantic Encoding Branch, which employs channel split and shuffle to reduce the computation and maintain higher segmentation accuracy. Also, we propose a dual attention module consisting of dilated spatial attention and channel attention to make full use of the multi-level feature maps simultaneously, which helps predict the pixel-wise labels in each stage. Meanwhile, Spatial Encoding Network is used to enhance semantic information and preserve the spatial details. To better combine context information and spatial information, we introduce a Simple Feature Fusion Module. We evaluated our model with state-of-the-art semantic image semantic segmentation methods using two challenging datasets. The proposed method achieves an accuracy of 69.9% mean IoU and 71.3% mean IoU at speed of 75.3 fps and 34.08 fps on CamVid and Cityscapes test datasets respectively.
NovidChain: Blockchain‐based privacy‐preserving platform for COVID‐19 test/vaccine certificates

The COVID-19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID-19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re-establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self-sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID-19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self-sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof-of-concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer-indistinguishability, and revocation.
Improved YOLOv5 Network Model and Application in Safety Helmet Detection

Due to the complex background of the building site and the diverse sorts of construction personnel, relying on traditional manual inspections and video surveillance methods to detect the wearing of personnel helmets has poor timeliness and missed inspections. This article provides a method based on deep learning to resolve the above issues. First, improvement based on YOLOv5, added a functionality detection scale to allow it to get smaller targets; second, by introducing the DloU-NMS instead of NMS, DIoU also considers the overlap area and the center distance of the two boxes, making it more accurate in suppressing the predicted bounding box. The experimental results show that the proposed algorithm significantly improves the accuracy compared to the YOLOv5 network model, and detection speed is 98 frames per second, which can meet the needs of real-time detection.