Reviewerly/exHarmony huggingface dataset:
FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments

Massive data collection in modern systems has paved the way for data-driven machine learning, a promising technique for creating reliable and robust statistical models. By combining the data into centralized storage to develop a reliable learning model, there are concerns with privacy, ownership, and strict rules. It is self-evident that the samples in the typical machine learning centralized server paradigm have vastly different probability distributions of data supplied by each user. As a result, the typical model needs to be personalized for critical medical applications, and the deployment needs an efficient mechanism that can adapt to varying user inputs. Due to the heterogeneous and dynamic nature of critical medical IoT applications in such Edge/Fog scenarios, the privacy of patients become a crucial problem. Federated Learning, the model trained on diversity helps in addressing these concerns when used. This paper proposes the integration of Federated Learning for distributed Edge–Fog–Cloud architecture in the IoT smart healthcare sector. This paper presents FedSDM, the Federated Learning-based Smart Decision Making framework for the ECG data in microservice-based IoT medical applications. This proposal makes use of the advantages of Edge/Fog computing for real-time critical applications. It deploys the Federated Learning model at the Edge, Fog, and Cloud layers for performance comparison. The parameters considered for performance evaluation are energy consumption, network usage, cost, execution time, and latency. The proposed method shows that Edge-based deployment outperforms Fog and Cloud in terms of energy consumption, network usage, cost, execution time, and latency (i.e.) 0.3%, 2%, 15%, 11%, and 3% when compared with Fog and 1.6%, 31%, 41%, 24 % and 85% against Cloud respectively.
Mediation of Inventory Management in the Relationship Between Knowledge and Firm Performance

The role of inventory management is pivotal to improving the overall firm performance, but when it comes to the healthcare sector, then it becomes more crucial. This sector often countenances stocks-outs for life-saving drugs and equipment and competent staff that ascent the mortality rate. Thus, the current research aimed to analyze the effect of knowledge on firm performance through inventory management. For this research, a survey questionnaire with a deductive approach was utilized by collecting data from 200 respondents through multistage cluster sampling. IBM SPSS AMOS version 22.0 as a statistical tool was utilized to analyze the measurement model. Results of this research revealed a significant and positive mediation effect on the relationship between knowledge and firm performance. Finally, the results showed that the professionally well-equipped staff, accurate inventories, and stock availability improves the service quality and reduce the cost. Academicians, researchers, provincial healthcare ministries, state-funded organizations, and hospitals will all benefit from this research, which will also add to the current body of knowledge.
Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism

This paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-varying system parameters and multiple missing measurements (MMMs). The measurements from various sensor nodes to the fusion center may undergo the missing data, where this phenomenon is depicted by means of random variables governed by certain statistical principles. In addition, the DETM is adopted to regulate the communication process from each sensor node to fusion center, which can alleviate the network transmission situations with communication overload and energy consumption limitation. The purpose of the addressed issue is to construct a set of local resilient filters (LRFs) for stochastic nonlinear MSNSs with MMMs via the DETM, which can guarantee that the minimized upper bounds are derived and the desirable filter gain with easy-to-implementation form is given. Subsequently, via the obtained LRFs, a unified framework of the DRFF approach is formulated through using the ICI fusion criterion. In addition, the monotonicity analysis of the obtained upper bound in regard to the triggered parameter is examined by providing rigorous theoretical proof. Finally, the simulations with comparison experiment are provided to illustrate the validity of presented DRFF technique.
Deep learning for Arabic healthcare: MedicalBot

Since the COVID-19 pandemic, healthcare services, particularly remote and automated healthcare consultations, have gained increased attention. Medical bots, which provide medical advice and support, are becoming increasingly popular. They offer numerous benefits, including 24/7 access to medical counseling, reduced appointment wait times by providing quick answers to common questions or concerns, and cost savings associated with fewer visits or tests required for diagnosis and treatment plans. The success of medical bots depends on the quality of their learning, which in turn depends on the appropriate corpus within the domain of interest. Arabic is one of the most commonly used languages for sharing users' internet content. However, implementing medical bots in Arabic faces several challenges, including the language's morphological composition, the diversity of dialects, and the need for an appropriate and large enough corpus in the medical domain. To address this gap, this paper introduces the largest Arabic Healthcare Q &A dataset, called MAQA, consisting of over 430,000 questions distributed across 20 medical specializations. Furthermore, this paper adopts three deep learning models, namely LSTM, Bi-LSTM, and Transformers, for experimenting and benchmarking the proposed corpus MAQA. The experimental results demonstrate that the recent Transformer model outperforms the traditional deep learning models, achieving an average cosine similarity of 80.81% and a BLeU score of 58%.
ALSI-Transformer: Transformer-Based Code Comment Generation with Aligned Lexical and Syntactic Information

Code comments explain the operational process of a computer program and increase the long-term productivity of programming tasks such as debugging and maintenance. Therefore, developing methods that automatically generate natural language comments from programming code is required. With the development of deep learning, various excellent models in the natural language processing domain have been applied for comment generation tasks, and recent studies have improved performance by simultaneously using the lexical information of the code token and the syntactical information obtained from the syntax tree. In this paper, to improve the accuracy of automatic comment generation, we introduce a novel syntactic sequence, Code-Aligned Type sequence (CAT), to align the order and length of lexical and syntactic information, and we propose a new neural network model, Aligned Lexical and Syntactic information-Transformer (ALSI-Transformer), based on a transformer that encodes the aligned multi-modal information with convolution and embedding aggregation layers. Through in-depth experiments, we compared ALSI-Transformer with current baseline methods using standard machine translation metrics and demonstrate that the proposed method achieves state-of-the-art performance in code comment generation.
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF), that is specially designed for recommender scenario with implicit feedback. The proposed SelfCF framework simplifies the Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks. The main idea of SelfCF is to augment the output embeddings generated by backbone networks, because it is infeasible to augment raw input of user/item ids. We propose and study three output perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. The framework enables learning informative representations of users and items without negative samples, and is agnostic to the encapsulated backbones. We conduct comprehensive experiments on four datasets to show that our framework may achieve even better recommendation accuracy than the encapsulated supervised counterpart with a 2$\times$--4$\times$ faster training speed. We also show that SelfCF can boost up the accuracy by up to 17.79% on average, compared with a self-supervised framework BUIR.
Efficient data transmission on wireless communication through a privacy-enhanced blockchain process

In the medical era, wearables often manage and find the specific data points to check important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length, interruptions, and intensity, and physical activity like kind, duration, and levels. These digital biomarkers are created mainly through passive data collection from various sensors. The critical issues with this method are time and sensitivity. We reviewed the newest wireless communication trends employed in hospitals using wearable technology and privacy and Block chain to solve this problem. Based on sensors, this wireless technology controls the data gathered from numerous locations. In this study, the wearable sensor contains data from the various departments of the system. The gradient boosting method and the hybrid microwave transmission method have been proposed to find the location and convince people. The patient health decision has been submitted to hybrid microwave transmission using gradient boosting. This will help to trace the mobile phones using the calls from the threatening person, and the data is gathered from the database while tracing. From this concern, the data analysis process is based on decision-making. They adapted the data encountered by the detailed data in the statistical modeling of the system to produce exploratory data analysis for satisfying the data from the database. Complete data is classified with a 97% outcome by removing unwanted data and making it a 98% successful data classification.
RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today’s society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification.
A systematic review of multi perspectives on human cybersecurity behavior

Although the cornerstones of cybersecurity are technical and data-driven solutions, the human element cannot be disregarded. Understanding users’ behavior is essential to spot security incidents and data breaches. Although previous studies reviewed different angles on cybersecurity behavior, there is still a lack of an overview of the topic through the lenses of cybersecurity theories/models. This systematic review aims to provide multi perspectives on human cybersecurity behavior by evaluating and synthesizing cybersecurity theories/models, independent variables, target variables, moderators, methodologies, participants, units of analysis, technologies/services, countries, and domains. Of 2936 articles collected, 93 studies met the inclusion criteria and were thoroughly analyzed. The main results revealed that the protection motivation theory (PMT) and theory of planned behavior (TPB) were the most common theories in the analyzed literature. 76% of the analyzed studies did not involve a moderator to examine the relationships between predictors and target variables. Most of the studies were conducted at the individual level, mainly involving students and end-users. Social media and mobile devices were the most common tools examined for human cybersecurity behavior. Most of the studies were conducted in the education sector, with a limited number of studies examining the healthcare sector. Behavioral intention and cybersecurity behavior were the primary target/outcome variables in the analyzed studies. The review offers several theoretical contributions and practical implications and provides several research agendas that can be examined in future research.
Ordered Network Analysis

Collaborative Problem Solving (CPS) is a socio-cognitive process that is interactive, interdependent, and temporal. As individuals interact with each other, information is added to the common ground, or the current state of a group's shared understanding, which in turn influences individuals' subsequent responses to the common ground. Therefore, to model CPS processes, especially in a context where the order of events is hypothesized to be meaningful, it is important to account for the ordered aspect. In this study, we present Ordered Network Analysis (ONA), a method that can not only model the ordered aspect of CPS, but also supports visual and statistical comparison of ONA networks. To demonstrate the analytical affordances and interpretable visualizations of ONA, we analyzed the collaborative discourse data of air defense warfare teams. We found that ONA was able to capture the qualitative differences between the control and experimental condition that cannot be captured using unordered models, and also tested that such differences were statistically different.
Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms

Breast cancer exhibits one of the highest incidence and mortality rates among all cancers affecting women. The early detection of breast cancer reduces mortality and is crucial for prolonging life expectancy. Although mammography is the most often used screening technique in clinical practice, previous studies reviewing mammograms diagnosed by radiologists have commonly revealed false negatives and false positives. Ongoing advances in machine learning techniques have triggered new motivation for the development of computer-aided diagnosis (CAD) systems, which could be applied to assist radiologists in improving final diagnostic accuracy. In this study, an automated methodology for detecting breast cancer in mammography images is proposed based on an ensemble classifier and feature weighting algorithms. First, a novel region extraction approach is proposed to constrain the search area for suspicious breast lesions and an original pectoral removal method is proposed to avoid interference when identifying a region of interest (ROI). In addition, an effective segmentation strategy is developed to automatically identify ROIs whose textural and morphological features are then fused and weighted to generate new feature vectors using a feature weighting algorithm. Finally, an ensemble classifier model is designed using k-nearest neighbor (KNN), bagging, and eigenvalue classification (EigenClass) to determine whether a mammogram contains normal, benign, or malignant tumors based on a majority voting rule. A series of experiments was conducted using the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) datasets, the results of which demonstrated the proposed scheme outperformed comparable algorithms.
Recent Few-shot Object Detection Algorithms: A Survey with Performance Comparison

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans’ ability of learning to learn and intelligently transfers the learned generic object knowledge from the common heavy-tailed to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [ 58 , 59 , 74 , 78 ] that systematically study and compare them as the groups of fine-tuning/transfer learning and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.
3D human pose and shape estimation via de-occlusion multi-task learning

Three-dimensional human pose and shape estimation is to compute a full human 3D mesh given a single image. The contamination of features caused by occlusion usually degrades its performance significantly. Recent progress in this field typically addressed the occlusion problem implicitly. By contrast, in this paper, we address it explicitly using a simple yet effective de-occlusion multi-task learning network. Our key insight is that feature for mesh parameter regression should be noiseless. Thus, in the feature space, our method disentangles the occludee that represents the noiseless human feature from the occluder. Specifically, a spatial regularization and an attention mechanism are imposed in the backbone of our network to disentangle the features into different channels. Furthermore, two segmentation tasks are proposed to supervise the de-occlusion process. The final mesh model is regressed by the disentangled occlusion-aware features. Experiments on both occlusion and non-occlusion datasets are conducted, and the results prove that our method is superior to the state-of-the-art methods on two occlusion datasets, while achieving competitive performance on a non-occlusion dataset. We also demonstrate that the proposed de-occlusion strategy is the main factor to improve the robustness against occlusion. The code is available at https://github.com/qihangran/De-occlusion_MTL_HMR.
U-Net-Based Models towards Optimal MR Brain Image Segmentation

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture's performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.