Reviewerly/exHarmony huggingface dataset:
Computer Aided Breast Cancer Detection and Classification using Optimal Deep Learning

Medical imaging is receiving considerable interest in the field of healthcare, involving breast cancer (BC). BC is one of the cancer-related death amongst women around the world. At present, analysis of histopathology image is the clinical gold standard in cancer diagnoses. But the histopathological analysis of BC is labor-intensive, non-trivial, and might results in a higher degree of disagreement amongst pathologists. Thus, an automated diagnosis method could help pathologists to enhance the efficiency of diagnosis process. Extracting features for BC classification is challenging, owing to the nature of large intraclass and small inter-class variances in BC histopathological image (H1). This study develops an automated breast cancer classification using deep learning with aquila optimizer (ABCC-DLAO) technique on histopathological images. The proposed ABCC-DLAO technique intends to identify and classify the BC on Hli. To accomplish this, the presented ABCC-DLAO technique employs contrast enhancement approach as a preprocessing step. Next, the ABCC-DLAO technique exploits EfficientNet model as feature extractor and the hyperparameter tuning process take place by AO algorithm. For BC classification, Elman neural network (ENN) model is utilized. The experimental assessment of the ABCC-DLAO technique take place on Breast Cancer Histopathological Database (BreakHis) dataset. The simulation outcomes reported that the ABCC-DLAO technique is proficient in automated classification of HIs for BC diagnosis.
LAB: a leader–advocate–believer-based optimization algorithm

This manuscript introduces a new socio-inspired metaheuristic technique referred to as leader–advocate–believer-based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behavior exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (leader, advocate, believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. The algorithm is validated using the CEC 2005 and CEC 2017 benchmark functions. The algorithm was applied to solve engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes and turning of titanium alloy in a minimum quantity lubrication environment. LAB algorithm was validated using the Friedman rank test. The results were compared with other algorithms such as FA, CI, GA, SA, PSO, Multi-CI, CMAES, ABC, SADE, CLPSO, BSA, IA, WOA, SHO, AVOA, LSHADE-Cn-EpsiN, FDB-SFS and LSHADE. For real-world problems, LAB outperformed SA, $$f_\textrm{best}$$ and $$f_\textrm{better}$$ by achieving 76%, 85% and 75% minimization of $$R_a$$ , respectively, for micro-milling with 0.7 mm tool diameter. For real-world problems, LAB achieved 81%, 72%, 85% minimization of $$R_a$$ when compared to SA, $$f_\textrm{best}$$ and $$f_\textrm{better}$$ for 1 mm tool diameter. LAB also achieved 24% and 34% minimization of $$B_\textrm{h}$$ and $$B_\textrm{t}$$ as compared to SA for micro-drilling with a tool diameter 0.5 mm. For tool diameters 0.8 mm and 0.9 mm, 16% and 3% minimization of $$B_\textrm{t}$$ , respectively, were achieved as compared to SA. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the algorithm are also discussed.
Moderating effect of ICT on the relationship between governance quality and income inequality in sub-Saharan Africa

The ICT-income inequality relationship and the governance quality-income inequality nexus have been investigated in recent years. However, the moderating effect of ICT on the governance quality-income inequality linkage has been largely ignored. To fill this gap in the literature, this study examines the moderating effect of ICT on the relationship between governance quality and income inequality for a panel of 42 sub-Saharan African economies over the period 1996–2020. To achieve this goal, the generalized method of moments (GMM) estimation technique has been adopted. The results reveal that while ICT contributes to the improvement of income distribution, governance quality contributes to the exacerbation of income inequality. Interestingly, the results unveil that the promotion of E-governance could contribute to improve social welfare and reduce income inequality. Policy implications are given based on the findings of this study. For instance, institutional reforms must be conducted by considering ICT as an important factor to improve income distribution.
Corpus-based insights into multimodality and genre in primary school science diagrams

This article presents a data-driven analysis of multimodal genre in a corpus of primary school science diagrams that contains multiple layers of cross-referenced annotations for multimodal discourse structure. The aim is to identify diagram genres in the corpus and describe their multimodal characteristics. To do so, information about expressive resources used in the diagrams and the discourse relations between them is extracted from the corpus, and computer vision is used to approximate the visual appearance of the diagrams. The article also presents a new method for quantifying information about the use of layout space. The resulting description of multimodal discourse structure is processed using UMAP, an unsupervised machine-learning algorithm, in order to identify diagrams that exhibit similar structural characteristics. The analysis allows the identification and characterization of four diagram genres in the corpus, which adopt different rhetorical strategies in combining expressive resources into discourse structures. The analysis also reveals that layout plays a major role in shaping the genre space, which can be further refined using information about the discourse structure. Overall, the results suggest that computational methods can be used to characterize multimodal genre from a bottom-up perspective using low-level information about expressive resources and layout.
Breast cancer: A hybrid method for feature selection and classification in digital mammography

Abstract In this article, a hybrid approach based on the Whale optimization algorithm (WOA) and the Dragonfly algorithm (DA) is proposed for breast cancer diagnosis. The hybrid WOADA method selects features based on the fitness value. These features are used to predict the breast masses as benign or malignant using artificial neural networks (ANN) and adaptive neuro‐fuzzy inference systems (ANFIS) as classifiers. The proposed solution is evaluated by using 651 mammograms. The results demonstrate that the WOADA technique outperforms the basic WOA and DA approaches. The accuracy of the suggested WOADA algorithm is 97.84%, with a Kappa value of 0.9477 and an AUC value of 0.972 ± 0.007 for the ANN classifier. Likewise, the ANFIS classifier achieved 98.00% accuracy with a Kappa value of 0.96 and an AUC value of 0.998 ± 0.001. In addition, the viability of the hybrid WOADA technique was evaluated on four benchmark datasets and then compared with four state‐of‐the‐art algorithms and published approaches.
SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction

Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph are extensively adopted. However, existing LBP-based methods that do not utilize node attributes often assume a global or predefined homophily strength of edges in the social graph, while different user’s discrimination and preferences may vary, resulting in local homogeneity differences. Another issue is that the existing message-passing paradigm uses the same edge potential when propagating belief to both sides of a directed edge, which does not agree with the trust interaction in one-way social relationships. To bridge these gaps, we present SybilHP, a Sybil-detection method optimized for directed social networks with adaptive homophily prediction. Specifically, we incorporate an iteratively updated edge homophily estimation into the belief propagation to better adapt to the personal preferences of real-world social network users. Moreover, we endow message passing on edges with directionality by a direction-sensitive potential function design. As a result, SybilHP can better capture the local homophily and direction pattern in real-world social networks. Experiments show that SybilHP works with high detection accuracy on synthesized and real-world social graphs. Compared with various state-of-the-art graph-based methods on a large-scale Twitter dataset, SybilHP substantially outperforms existing methods.
Multilevel Data and Decision Fusion Using Heterogeneous Sensory Data for Autonomous Vehicles

Autonomous vehicles (AVs) are predicted to change transportation; however, it is still difficult to maintain robust situation awareness in a variety of driving situations. To enhance AV perception, methods to integrate sensor data from the camera, radar, and LiDAR sensors have been proposed. However, due to rigidity in their fusion implementations, current techniques are not sufficiently robust in challenging driving scenarios (such as inclement weather, poor light, and sensor obstruction). These techniques can be divided into two main groups: (i) early fusion, which is ineffective when sensor data are distorted or noisy, and (ii) late fusion, which is unable to take advantage of characteristics from numerous sensors and hence yields sub-optimal estimates. In this paper, we suggest a flexible selective sensor fusion framework that learns to recognize the present driving environment and fuses the optimum sensor combinations to enhance robustness without sacrificing efficiency to overcome the above-mentioned limitations. The proposed framework dynamically simulates early fusion, late fusion, and mixtures of both, allowing for a quick decision on the best fusion approach. The framework includes versatile modules for pre-processing heterogeneous data such as numeric, alphanumeric, image, and audio data, selecting appropriate features, and efficiently fusing the selected features. Further, versatile object detection and classification models are proposed to detect and categorize objects accurately. Advanced ensembling, gating, and filtering techniques are introduced to select the optimal object detection and classification model. Further, innovative methodologies are proposed to create an accurate context and decision rules. Widely used datasets like KITTI, nuScenes, and RADIATE are used in experimental analysis to evaluate the proposed models. The proposed model performed well in both data-level and decision-level fusion activities and also outperformed other fusion models in terms of accuracy and efficiency.
A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients' prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.
Learning from crowds with robust logistic regression

Crowdsourcing systems provide an easy way to obtain labels for data. Each instance in data will usually be labeled by multiple crowd labelers who are not experts. Thus, it is very important to design considerate ground truth inference algorithms to infer integrated labels from multiple crowd labels. While almost all ground truth inference algorithms show good performance when the number of crowd labels is large, few algorithms can perform well with few crowd labels. This paper considers how to deal with noise in multiple crowd labels as a key to good ground truth inference. This paper solves ground truth inference using robust classifiers. This paper proposes two versions of ground truth inference algorithm based on robust logistic regression to solve the following two problems: (1) how to embed noise level into the loss function of logistic regression and (2) how to estimate the parameters that model noise level in the crowdsourcing scenario. We call our algorithms robust logistic regression inference (RLRI). By employing the idea of robust classifiers, RLRI can still perform well in the case of a small number of labels. We also theoretically compare the advantages and disadvantages of the two versions of RLRI. Finally, the performance of our algorithms is verified on benchmark and real-world datasets.
Digital Citizenship and Digital Literacy

The rapid development of technology and communication had given birth to a new world order that impacted all aspects of citizens' lives. This new world order was often known as a world driven by digital tools and innovations. In digital mobility, citizens' lives become faster so that the interaction among individuals has no boundaries. This resulted in the development of digital citizenship. The emergence of social media certainly disrupted citizens' behavior. Therefore, digital literacy was necessary for this era of disrupted society, so becoming a smart and good citizen would be someone's necessity, not a luxury.
A Fine-Grained Unsupervised Domain Adaptation Framework for Semantic Segmentation of Remote Sensing Images

Unsupervised domain adaptation (UDA) aims at adapting a model from the source domain to the target domain by tackling the issue of domain shift. Cross-domain segmentation of remote sensing images (RSIs) remains a big challenge due to the unique properties of RSIs. On the one hand, the divergence of data distribution in different local regions leads to negative transfer by directly applying the global alignment method in RSIs. On the other hand, the underlying category-level structure in the target domain is often ignored, which confuses the decision of semantic boundaries on the dispersed category features caused by large intraclass variance and small interclass variance in RSIs. In this study, we propose a novel fine-grained adaptation framework combining two stages of global-local alignment and category-level alignment to solve the above-mentioned problems. In the first stage of global-local adaptation, an attention map is derived from an intermediate discriminator and focuses on hard-to-align regions to mitigate negative transfer due to global adversarial learning. In the second stage of category-level adaptation, the category feature compact module is utilized to address the issue of dispersed features in the target domain attained by the cross-domain network, which will facilitate the fine-grained alignment of categories. Experiments under various scenarios, including geographic location variation and spectral band composition variation, demonstrate that the local adaptation and category-level adaptation of RSIs are complementary in the cross-domain segmentation, and the integrated framework helps achieve outstanding performance for UDA semantic segmentation of RSIs.
An improved nighttime people and vehicle detection algorithm based on YOLO v7

To solve the problem that the detection of people and vehicles in the night scenario is inaccurate and the detection speed is slow, which can not meet the needs of the scene, this paper proposes an improved nighttime people and vehicle detection algorithm based on YOLOv7, which aims to give consideration to the detection speed and detection accuracy. The improved algorithm is based on YOLOv7-tiny. In the backbone part, the algorithm adds a Ghostnet V2 module to reduce parameters. The activation function in the convolution layer is also changed from leaklyReLU to FReLU function. And Wise-10 U boundary box loss is introduced to accelerate the convergence speed and increase the detection accuracy; In the head part, the parameter-free attention module SimAM, the C3 module and Omni-Dimensional Dynamic Convolution (ODConv) module are introduced into the model. Finally, according to the dataset experiment, improved algorithm reduces the number of floating-point operations by 59%, the mAP value by 0.64 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and the inference speed reaches 47 fps, which shows that the algorithm can have a good performance.
The State-Oriented Soviet Model of Library Support for Communication between Science and Society

It is devoted to the history of the formation of the activities of Soviet libraries on communication between society and science. It is proposed to single out the state-oriented service model, which was formed in Soviet times as a tool of social management through political and scientific and technical education. The goals of the enlightenment were to involve the population in the construction of a new socialist society, industrial in nature. Technologically, this was realized during the library reading of specially selected scientific and popular science literature. This reading is of a controlled nature. The formation of the model began in 1920. In its development, it went through three stages: formation (1920–1940), heyday (1950–1970s) and extinction, which occurred in the late 1980s – early 1990s. Its theoretical and methodological basis was the works by V. I. Lenin and N. K. Krupskaya. It is concluded that the Soviet model of the organization of library communication between science and society was 1) vertical, systematic and directive in nature, providing, on the one hand, the formation of a natural-scientific materialistic worldview, coupled with the propaganda of Soviet science and its achievements, on the other hand, countering “pseudoscience” and religious worldview; 2) effective in the fight against illiteracy and initial political and natural-scientific (scientific and technical) education. The achievement of a higher level of development by Soviet readers in the second half of 1970–1980 showed that this model gradually exhausted its potential. This, in turn, required the development of new forms of library management and library reading management technologies aimed at a well-educated and active reader. The beginning of the reform took place in the 1990s.
LPI Radar Signals Modulation Recognition Based on ACDCA-ResNeXt

For low probability of intercept (LPI) radar waveform identication accuracy (ACC) problem at low Signal-to-Noise Ratios (SNRs), an approach based on time-frequency analysis (TFA) and Asymmetric Dilated Convolution Coordinate Attention Residual networks (ACDCA-ResNeXt) is proposed to recognize twelve kinds of LPI radar signals automatically.First, we apply Choi-Williams distribution (CWD), which shows superior performance at low SNRs, to transforming radar signals into time-frequency images (TFI).Then, in order to obtain the high-quality TFIs, a series of image processing techniques, including 2D Wiener ltering, image cutting, and image resize, are used to remove the background noise and redundant frequency bands of the TFI and obtain a xed-size gray scale image containing main morphological features of the TFI.Finally, the TFIs are input into ACDCA-ResNeXt network that can extract and learn deep features to recognize radar waveforms.Furthermore, a fusion loss function, which is composed of a soft-label smoothed cross entropy loss function and a center loss function, improves the generalization capability performance of network and achieves a better clustering eect.Experimental results demonstrate that, for twelve kinds of LPI radar waveforms, the overall recognition ACC of the proposed approach achieves 97.94% when SNR is -8 dB.
Classical vs. Quantum Machine Learning for Breast Cancer Detection

Breast cancer is one of the top causes of mortality in women throughout the world, and early identification is critical for successful treatment. The accuracy of breast cancer diagnosis has been improved thanks to machine learning. This research compares the effectiveness of conventional and quantum machine learning systems for detecting breast cancer in great detail. Using a publically accessible data set, the project will examine several quantum machine learning models and compare them to classical machine learning algorithms. The results of this study could provide insights into the potential benefits of quantum machine learning for breast cancer detection and ultimately contribute to improving the accuracy of breast cancer diagnosis.
Blockchain-based Self-Sovereign Identity Solution for Vehicular Networks

Identity and access management frameworks address data governance and system access rights for users, organizations, and vendors. Emerging identity management models such as Self-Sovereign Identity (SSI) that is based on Distributed Ledger Technology (DLT) technology, have emerged to address the challenges associated with centralized authority. The main goal of SSI is to help users self-manage their data shared with services. In this paper, we explore a possible application of the SSI concept to vehicular networks. We propose a new methodology, that is alternative to the conventional blockchain-based SSI, which ensures confidentiality, authentication, and integrity of vehicle users identity and their data. At the end of the paper, we also compare SSI-based and Non-fungible token (NFT)-based blockchain solutions, the challenges and future directions of SSI solutions in the context of vehicular networks.