Reviewerly/exHarmony huggingface dataset:
An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.
UAV’s Status Is Worth Considering: A Fusion Representations Matching Method for Geo-Localization

Visual geo-localization plays a crucial role in positioning and navigation for unmanned aerial vehicles, whose goal is to match the same geographic target from different views. This is a challenging task due to the drastic variations in different viewpoints and appearances. Previous methods have been focused on mining features inside the images. However, they underestimated the influence of external elements and the interaction of various representations. Inspired by multimodal and bilinear pooling, we proposed a pioneering feature fusion network (MBF) to address these inherent differences between drone and satellite views. We observe that UAV's status, such as flight height, leads to changes in the size of image field of view. In addition, local parts of the target scene act a role of importance in extracting discriminative features. Therefore, we present two approaches to exploit those priors. The first module is to add status information to network by transforming them into word embeddings. Note that they concatenate with image embeddings in Transformer block to learn status-aware features. Then, global and local part feature maps from the same viewpoint are correlated and reinforced by hierarchical bilinear pooling (HBP) to improve the robustness of feature representation. By the above approaches, we achieve more discriminative deep representations facilitating the geo-localization more effectively. Our experiments on existing benchmark datasets show significant performance boosting, reaching the new state-of-the-art result. Remarkably, the recall@1 accuracy achieves 89.05% in drone localization task and 93.15% in drone navigation task in University-1652, and shows strong robustness at different flight heights in the SUES-200 dataset.
Clustering models for hospitals in Jakarta using fuzzy c-means and k-means

After facing the COVID-19 pandemic, national and local governments in Indonesia realized a gap in the distribution of health care and human health practitioners. This research proposes two unsupervised learning methods, K-Means and Fuzzy C-Means (FCM), for clustering a list of hospital data in Jakarta, Indonesia, which contains information about the number of its human health resources. The datasets used in this study were obtained from the website the Ministry of the Health Republic of Indonesia provided through the content scraping method. The result shows that implementing K-Means and FCM clustering results in the same number of clusters. Nevertheless, both results have different areas and proportions that can be observed by three distance metrics, such as Hamming, Euclidean, and Manhattan distance. By using the clustering result using the K-Means algorithm, the hospital list was separated into three clusters with a proportion of 84.82%, 14.66%, and 0.52% for clusters 0, 1, and 2, respectively. Meanwhile, using the FCM algorithm, the hospital list was separated into three clusters with a proportion of 17.80%, 73.82%, and 8.38% for clusters 0, 1, and 2, respectively. To the best of our knowledge, this is the first discussion of clustering healthcare facilities in Indonesia, especially hospitals, based on their health professionals.
BERT base model for toxic comment analysis on Indonesian social media

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897.
Integrated physical approach to assessing urban-scale building photovoltaic potential at high spatiotemporal resolution

Assessing the urban-scale building photovoltaic (PV) potential is important for designing urban environments, retrofitting existing structures, or integrating PVs with grids. However, few studies have considered high-temporal-resolution simulations, the facade PV potential, and a comprehensive PV model simultaneously; thus, the overall accuracy of the estimation of PV potential may be limited. Therefore, this study developed an integrated framework to assess the urban-scale PV potential of rooftops and facades at high spatiotemporal resolution. The proposed approach integrates an anisotropic sky diffuse model, a vector-based shading calculation method, and a temperature-related PV performance model. The annual PV potential and spatial/temporal characteristics were analyzed in a case study of over 170,000 buildings in Beijing. The results showed that the estimated rooftop PV power generation was 7.55 TWh/y, whereas the facade PV power generation was 18.07 TWh/y, which was 239% of the rooftop PV yield. The integrated model estimated PV yield with higher accuracy than the simplified models by depicting more details. The proposed approach can be applied to the large-scale assessment of future energy systems with increasing penetration of PVs, and the results can support effective policies for the integration of PVs into the built environment in dense cities.
Feature selection via maximizing inter-class independence and minimizing intra-class redundancy for hierarchical classification

Hierarchical feature selection has proven to be significant in reducing classification difficulty. Many existing hierarchical feature selection methods use the hierarchy in the class space as structural information to improve performance. However, these methods focus on the inter-class structural relations and ignore the intra-class feature correlations, which is challenging as the numbers of classes and features in current datasets are both growing rapidly. In this paper, we propose a hierarchical feature selection method that maximizes inter-class independence and minimizes intra-class redundancy using structure and feature relations. First, we investigate the class hierarchy dependency in the tree structure as the structural relation regularization, which maximizes the independence between unrelated classes in the class structure. Second, we transform the feature correlations into a mathematical representation as feature relation regularization, which minimizes the redundancy in each class on the premise of sparsity. Finally, we unify the two regularization terms into a hierarchical feature selection method to trade off the structural and feature relations. Our method exhibits excellent efficiency and effectiveness compared with other feature selection methods.
How does smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) awareness affect hotel employees’ career perceptions? A disruptive innovation theory perspective

ABSTRACTABSTRACTThe extensive implementation of smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) in hospitality services has accelerated the need to understand their potential influence on hotel employees' career perceptions. This study conducted two scenario-based experiments based on disruptive innovation theory and the Stimulus-Organism-Response (SOR) model to examine how STAARA awareness shapes hotel employees' job insecurity and mobility. In addition, this study investigated career progression as a counterstrategy to attenuate the substitution effect of STAARA. Study 1 demonstrated that hotel employees' negative (vs. positive) awareness of STAARA leads to higher job insecurity and mobility. Furthermore, according to Study 2, for hotel employees with low-level career progression, negative (vs. positive) awareness of STAARA induces higher job insecurity and mobility. However, among employees with high-level career progression, there were no significant differences, which means that high-level career progression attenuates the impact of STAARA. This study also considers theoretical and practical influences.随着智能技术、人工智能、自动化、机器人和算法 (简称STAARA) 在酒店服务中的广泛应用, "科技意识将如何影响酒店员工的职业认知"成为学界和业界亟需了解的重要问题。为此,本研究根据破坏性创新理论和刺激-机体-反应 (SOR) 模型, 设计了2个情境实验, 以此来探讨STAARA意识对酒店员工的工作不安全感和职业迁移的影响。此外, 本研究还检验了职业进展对STAARA取代员工效应的调节作用。实验1的结果表明:与积极的STAARA意识相比, 消极的STAARA意识会引发酒店员工更高水平的工作不安全感和职业迁移。实验2的结果表明:对于职业进展水平较低的酒店员工而言, 消极的STAARA意识会引发更高水平的工作不安全感和职业迁移。然而, 对于职业进展水平较高的酒店员工而言, 上述差异并不显著,这意味着高水平的职业进展削弱了STAARA的影响。本研究在理论和实践层面上均作出了有价值的贡献。KEYWORDS: STAARA awarenessdisruptive innovationjob mobilityjob insecuritycareer progressioncareer perceptions Disclosure statementNo potential conflict of interest was reported by the author(s).
A Secure Data Dissemination Scheme for IoT-Based e-Health Systems using AI and Blockchain

In Internet of Things (IoT)-based e-Health Systems (IoTEHS), medical devices form a large network that continuously sense and share the healthcare data with the nearby edge devices or cloud servers. The health data is subsequently made available to various IoTEHS stakeholders (such as doctors, nurses and patients) to track and monitor patients under observation. However, the entire IoTEHS stakeholders communicate with each other over a wireless unsecured public communication channel. This is a major security and privacy loophole wherein the attacker can exploit the vulnerability of the system and can launch various attacks on the ongoing communication. Motivated by the aforementioned challenges, a secure data dissemination scheme using AI and blockchain is proposed. In this scheme, the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices. The collected data is first filtered using AI-based intrusion detection system located at the edge of the network. Second, a secure health monitoring network is designed using blockchain. Specifically, the filtered or normal transactions are transmitted to centralized cloud servers where the smart contact-enabled consensus mechanism is used to validate the transactions. Once the transaction gets validated, it is stored on distributed InterPlanetary File System (IPFS) of cloud and returned transaction hash is stored on the blockchain ledger located at edge devices making data exchange faster. The detailed experimental investigation demonstrates that the proposed schemes are efficient (in terms of computing and processing time) as well as its resistance to a variety of security attacks.
MirageNet - Towards a GAN-based Framework for Synthetic Network Traffic Generation

With the emergence of machine learning technology that supports the development of synthetic models, many new use cases and challenges are emerging in the fields of computer vision and security. The main model behind this technology is Generative Adversarial Networks (GANs), with their ability to model unknown distributions accurately and perform well in generating synthetic data such as images and videos. However, the application of this technology by the networking community has been lacking. Given this motivation, we introduce MirageNet; our vision for a GAN-based synthetic network traffic generation framework, which can automatically create synthetic network models of protocols, applications, and devices. With the potential to build many applications for privacy, security, and network optimization. In this paper, we present MiragePkt; the first component of MirageNet. It is a GAN-based model to synthetically generate network packets. We describe the different challenges, limitations, and solutions for generating synthetic network packets. Finally, we validate and evaluate the performance of our framework with the synthesizing DNS packets.
Creating deep neural networks for text classification tasks using grammar genetic programming

Text classification is one of the Natural Language Processing (NLP) tasks. Its objective is to label textual elements, such as phrases, queries, paragraphs, and documents. In NLP, several approaches have achieved promising results regarding this task. Deep Learning-based approaches have been widely used in this context, with deep neural networks (DNNs) adding the ability to generate a representation for the data and a learning model. The increasing scale and complexity of DNN architectures was expected, creating new challenges to design and configure the models. In this paper, we present a study on the application of a grammar-based evolutionary approach to the design of DNNs, using models based on Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Graph Neural Networks (GNNs). We propose different grammars, which were defined to capture the features of each type of network, also proposing some combinations, verifying their impact on the produced designs and performance of the generated models. We create a grammar that is able to generate different networks specialized on text classification, by modification of Grammatical Evolution (GE), and it is composed of three main components: the grammar, mapping, and search engine. Our results offer promising future research directions as they show that the projected architectures have a performance comparable to that of their counterparts but can still be further improved. We were able to improve the results of a manually structured neural network in 8,18% in the best case.
Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

Objective During the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model. Methodology A hybrid model consists of an upgraded AlexNet with Inception v-4 for salient object detection and ocean environment monitoring. For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). Moreover, we used preprocessing techniques before the classification task for underwater image enhancement, segmentation, noise and fog removal, restoration, and color constancy. Conclusion The Real-Time Underwater Image Enhancement (RUIE) dataset and the Marine Underwater Environment Database (MUED) dataset are used in this research project’s data fusion and experimental activities, respectively. Root mean square error (RMSE), computing usage, and accuracy are used to construct the model’s simulation results. The suggested model’s relevance form optimization and conspicuous item prediction issues in the seas is illustrated by the greatest accuracy of 95.7% and low RMSE value of 49 when compared to other baseline models.
An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Acute lymphoblastic leukemia (ALL) is the most serious type of leukemia that develops because it causes an abnormal increase in the production of immature white blood cells in the bone marrow. ALL spreads rapidly in children's bodies and leads to their death. The main objective of this paper is to introduce an enhanced methodology based on the k-means clustering algorithm for classifying all subtypes of ALL. Image preprocessing is the first step of the proposed methodology. For obtaining the images' descriptive features, feature extraction is the second step that has been used. To select the most vital features that can characterize the histology of the blood cells, Enhanced Grey Wolf Optimization (EGWO) algorithm has been used in the third step. Hence, EGWO will start to select the search agents based on certain criteria as the best cluster center by using the k-means clustering algorithm. Several supervised classifiers as Random Forest (RF), K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) have been compared. The proposed methodology achieves a high degree of accuracy of 99.22%, precision of 99%, and sensitivity of 99%. A comparative study has been established in order to verify the effectiveness of the proposed methodology.
Image Embedding and Classification using Pre-Trained Deep Learning Architectures

The use of deep learning in image analysis has been a game-changer. Common methods have relied on problem-specific algorithms to characterize pictures using properties like cell shape, count, intensity, and texture that were painstakingly constructed by human experts. With deep convolutional neural networks, feature learning occurs implicitly, and training the network often zeroes down on specific tasks. To get a vector representation of a picture by embedding, the image is fed into a preexisting deep neural network. An image embedding is a representation of the picture in fewer dimensions. It is a dense vector representation of the picture that may be used for things like categorization and plenty of other things. In this research, we profile pictures by using vectors of features and pre-trained deep learning architectures (Inception V3, VGG16, VGG19, Painter, SqueezeNet, and DeepLoc). The process of picture categorization makes use of these vectors. For experimentation purposes, we used the dataset containing images of yoga positions and classified them using Multilayer Perceptron (MLP) Classifier.
Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet

Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible–near-infrared–shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements.
Blockchain for medical collaboration: A federated learning-based approach for multi-class respiratory disease classification

The scarcity and diversity of medical data have made it challenging to build an accurate global classification model in the healthcare sector. The prime reason is privacy concerns and legal obstacles which limit data-sharing scope among institutions in healthcare. On the other hand, data from a single source is hardly sufficient to develop a universal diagnosis model. While federated learning is a potential solution to privacy and data diversity concerns (allows distributed model training), an apt aggregation process for multi-class and heterogenous medical data is still at the outset. This study aims to propose a federated learning mechanism that can effectively learn from multi-class and heterogenous respiratory medical data. The proposed system trains and aggregates the local model by leveraging blockchain technology, ensuring privacy. While aggregating the local models, we introduced the weight manipulation technique that, unlike any other studies, uses the local model test accuracy as the principal parameter. The resulting metric scores show that learning from diverse and heterogenous data, the performance of the proposed federated model is analogous to a single-source model (learning from single source data). Using the novel aggregation technique, the highest testing accuracy of 88.10% has been achieved for five classes, compared to the less complex single source model, which achieved 88.60% testing accuracy. A similar trend has been observed for models with three and four classes. For developing better synergy among organizations, this study introduces an incentive mechanism for the contributing institution while the blockchain stores the records to make the system transparent and trustworthy. The proposed mechanism has been implemented using a web system, which demonstrates how the weight manipulation technique can effectively learn from heterogeneous and multi-sourced data while preserving privacy.