Reviewerly/exHarmony huggingface dataset:
Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making.This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems.PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting.The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts.Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research.A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures.
Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.
Development of Education Field Student Digital Competences—Student and Stakeholders’ Perspective

Alongside reading, writing, and numeracy digital literacy has become an increasingly crucial component of functional competences in the modern era. Digital competences are essential for educators who wish to stay updated with the changing needs of their students and the education sector. These competences can help them engage their students better, improve their teaching effectiveness, and advance their careers. Considering the ongoing changes in both the field of education and the field of technologies, it is important to explore the current needs for improving digital competences of educators including students in the field of education, to provide them with the necessary support. This study aims to develop recommendations for improving digital competence of educators by combining students’ self-assessment through surveys with stakeholder opinions concerning digital competence development of educators ascertained in a focus group discussion. Results of the research reveal that education students do not have a statistically different self-assessment of their digital competences than students from other study fields. And although students evaluate their digital competences as high, stakeholders point out that there is a lack of digital competences to carry out technology-enhanced learning and a negative attitude towards digitalization of education among all educators’ age groups. Improving the availability of digital resources and technological support as well as incorporating organisational level strategies that require application and improvement of digital competence could remedy the issue.
A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to Random Forests, XGBoost, and RETAIN, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.
Cross-Channel: Scalable Off-Chain Channels Supporting Fair and Atomic Cross-Chain Operations

Cross-chain technology facilitates the interoperability among isolated blockchains on which users can freely communicate and transfer values. Existing cross-chain protocols suffer from the scalability problem when processing on-chain transactions. Off-chain channel, as a promising blockchain scaling technique, can enable micro-payment transactions without involving on-chain transaction settlement. However, existing channel schemes can only be applied to operations within a single blockchain, failing to support cross-chain services. Therefore in this paper, we propose <inline-formula><tex-math notation="LaTeX">$\mathsf {Cross}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathsf {Channel}$</tex-math></inline-formula> , the first off-chain channel to support cross-chain services. We introduce a novel hierarchical channel structure with a hierarchical interaction protocol, a new hierarchical settlement protocol, and a smart general fair exchange protocol, to ensure scalability, fairness, and atomicity of cross-chain interactions. Besides, <inline-formula><tex-math notation="LaTeX">$\mathsf {Cross}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathsf {Channel}$</tex-math></inline-formula> provides strong security and practicality by avoiding high latency in asynchronous networks.Through a 50-instance deployment of <inline-formula><tex-math notation="LaTeX">$\mathsf {Cross}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathsf {Channel}$</tex-math></inline-formula> on AliCloud, we demonstrate that <inline-formula><tex-math notation="LaTeX">$\mathsf {Cross}$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$\mathsf {Channel}$</tex-math></inline-formula> is well-suited for processing cross-chain transactions in high-frequency and large-scale, and brings a significantly enhanced throughput with a small amount of gas and delay overhead.
Analyzing Traffic Characteristics of Instant Messaging Applications on Android Smartphones

Instant Messaging Applications (IMAs), such as WhatsApp and Messenger, have become one of the main communication tools for smartphone users. However, there is limited research analyzing the nature of encrypted network traffic produced by IMAs. In this paper, we employ a data driven approach using machine learning classification models to analyze and identify encrypted traffic from six different IMAs. Our results show that it is possible to distinguish the behaviour of different IMAs with high F1 scores.
Heterogeneous Hyper-Graph Neural Networks for Context-Aware Human Activity Recognition

Context-aware Human Activity Recognition (CHAR) is challenging due to the need to recognize the user's current activity from signals that vary significantly with contextual factors such as phone placements and the varied styles with which different users perform the same activity. In this paper, we argue that context-aware activity visit patterns in realistic in-the-wild data can equivocally be considered as a general graph representation learning task. We posit that exploiting underlying graphical patterns in CHAR data can improve CHAR task performance and representation learning. Building on the intuition that certain activities are frequently performed with the phone placed in certain positions, we focus on the context-aware human activity problem of recognizing the <Activity, Phone Placement> tuple. We demonstrate that CHAR data has an underlying graph structure that can be viewed as a heterogenous hypergraph that has multiple types of nodes and hyperedges (an edge connecting more than two nodes). Subsequently, learning <Activity, Phone Placement> representations becomes a graph node representation learning problem. After task transformation, we further propose a novel Heterogeneous HyperGraph Neural Network architecture for Context-aware Human Activity Recognition (HHGNN-CHAR), with three types of heterogeneous nodes (user, phone placement, and activity). Connections between all types of nodes are represented by hyperedges. Rigorous evaluation demonstrated that on an unscripted, in-the-wild CHAR dataset, our proposed framework significantly outperforms state-of-the-art (SOTA) baselines including CHAR models that do not exploit graphs, and GNN variants that do not incorporate heterogeneous nodes or hyperedges with overall improvements 14.04% on Matthews Correlation Coefficient (MCC) and 7.01% on Macro F1 scores.
Deploy Machine Learning Model for Effective Bank Telemarketing Campaign

Marketing is a strategy that brings goods and services to all kinds of people. Most of the time marketing is not done to target the audience but to everyone to increase the chance of people opting for that particular product or service. Organizations use various marketing techniques like social media, telemarketing, advertisements, etc. The banking industry frequently chooses to speak with the user directly. The majority of the time, the customer hangs up as soon as they realize it was a telemarketing call because they are irritated. This is a result of a lack of essential client information. A deep learning model that analyses numerous parameters and forecasts whether a person is in a position to choose a loan or deposit money in the bank was developed to address this issue. For this purpose, a database of various parameters that the individual's demands depend on is compiled from Kaggle. On this dataset, several preprocessing approaches are then used. The procedures involve the elimination of missing values, category characteristics, outliers, undesirable features, and data balancing. To produce three machine learning (ML) models, three different techniques were applied. For this, the linear regression algorithm, the K-nearest neighbor algorithm, and the random forest (RF) algorithm are employed. To find the best algorithm for calling-list filtering, this is done. The results of testing these ML models using the preprocessed dataset are next analyzed. According to the results of the analysis, the RF algorithm is the best among the three algorithms. The RF algorithm's accuracy is somewhat more than 95%, which is higher than many other algorithms currently in use. In the future, the algorithm can be used as the backend process for a website or an application.
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence

.Policy optimization, which learns the policy of interest by maximizing the value function via large-scale optimization techniques, lies at the heart of modern reinforcement learning (RL). In addition to value maximization, other practical considerations arise commonly as well, including the need of encouraging exploration, and that of ensuring certain structural properties of the learned policy due to safety, resource, and operational constraints. These considerations can often be accounted for by resorting to regularized RL, which augments the target value function with a structure-promoting regularization term. Focusing on an infinite-horizon discounted tabular Markov decision process, this paper proposes a generalized policy mirror descent (GPMD) algorithm for solving regularized RL. As a generalization of policy mirror descent [G. Lan, Math. Program., 198 (2023), pp. 1059–1106], the proposed algorithm accommodates a general class of convex regularizers as well as a broad family of Bregman divergence in cognizance of the regularizer in use. We demonstrate that our algorithm converges linearly to the global solution over an entire range of learning rates, in a dimension-free fashion, even when the regularizer lacks strong convexity and smoothness. In addition, this linear convergence feature is provably stable in the face of inexact policy evaluation and imperfect policy updates. Numerical experiments are provided to corroborate the applicability and appealing performance of GPMD.Keywordspolicy mirror descentBregman divergenceregularizationpolicy optimizationMSC codes68Q2568Q3290C26
A Lightweight Multi-View Learning Approach for Phishing Attack Detection Using Transformer with Mixture of Experts

Phishing poses a significant threat to the financial and privacy security of internet users and often serves as the starting point for cyberattacks. Many machine-learning-based methods for detecting phishing websites rely on URL analysis, offering simplicity and efficiency. However, these approaches are not always effective due to the following reasons: (1) highly concealed phishing websites may employ tactics such as masquerading URL addresses to deceive machine learning models, and (2) phishing attackers frequently change their phishing website URLs to evade detection. In this study, we propose a robust, multi-view Transformer model with an expert-mixture mechanism for accurate phishing website detection utilizing website URLs, attributes, content, and behavioral information. Specifically, we first adapted a pretrained language model for URL representation learning by applying adversarial post-training learning in order to extract semantic information from URLs. Next, we captured the attribute, content, and behavioral features of the websites and encoded them as vectors, which, alongside the URL embeddings, constitute the website’s multi-view information. Subsequently, we introduced a mixture-of-experts mechanism into the Transformer network to learn knowledge from different views and adaptively fuse information from various views. The proposed method outperforms state-of-the-art approaches in evaluations of real phishing websites, demonstrating greater performance with less label dependency. Furthermore, we show the superior robustness and enhanced adaptability of the proposed method to unseen samples and data drift in more challenging experimental settings.
Vision-Based Hand Detection and Tracking Using Fusion of Kernelized Correlation Filter and Single-Shot Detection

Hand detection and tracking are key components in many computer vision applications, including hand pose estimation and gesture recognition for human–computer interaction systems, virtual reality, and augmented reality. Despite their importance, reliable hand detection in cluttered scenes remains a challenge. This study explores the use of deep learning techniques for fast and robust hand detection and tracking. A novel algorithm is proposed by combining the Kernelized Correlation Filter (KCF) tracker with the Single-Shot Detection (SSD) method. This integration enables the detection and tracking of hands in challenging environments, such as cluttered backgrounds and occlusions. The SSD algorithm helps reinitialize the KCF tracker when it fails or encounters drift issues due to sudden changes in hand gestures or fast movements. Testing in challenging scenes showed that the proposed tracker achieved a tracking rate of over 90% and a speed of 17 frames per second (FPS). Comparison with the KCF tracker on 17 video sequences revealed an average improvement of 13.31% in tracking detection rate (TRDR) and 27.04% in object detection error (OTE). Additional comparison with MediaPipe hand tracker on 10 hand gesture videos taken from the Intelligent Biometric Group Hand Tracking (IBGHT) dataset showed that the proposed method outperformed the MediaPipe hand tracker in terms of overall TRDR and tracking speed. The results demonstrate the promising potential of the proposed method for long-sequence tracking stability, reducing drift issues, and improving tracking performance during occlusions.
Efficient Roundabout Supervision: Real-Time Vehicle Detection and Tracking on Nvidia Jetson Nano

In recent years, a significant number of people in Morocco have been commuting daily to Casablanca, the country’s economic capital. This heavy traffic flow has led to congestion and accidents during certain times of the day as the city’s roads cannot handle the high volume of vehicles passing through. To address this issue, it is essential to expand the infrastructure based on accurate traffic-flow data. In collaboration with the municipality of Bouskoura, a neighboring city of Casablanca, we proposed installing a smart camera on the primary route connecting the two cities. This camera would enable us to gather accurate statistics on the number and types of vehicles crossing the road, which can be used to adapt and redesign the existing infrastructure. We implemented our system using the YOLOv7-tiny object detection model to detect and classify the various types of vehicles (such as trucks, cars, motorcycles, and buses) crossing the main road. Additionally, we used the Deep SORT tracking method to track each vehicle appearing on the camera and to provide the total number of each class for each lane, as well as the number of vehicles passing from one lane to another. Furthermore, we deployed our solution on an embedded system, specifically the Nvidia Jetson Nano. This allowed us to create a compact and efficient system that is capable of a real-time processing of camera images, making it suitable for deployment in various scenarios where limited resources are required. Deploying our solution on the Nvidia Jetson Nano showed promising results, and we believe that this approach could be applied in similar traffic-surveillance projects to provide accurate and reliable data for better decision-making.
Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.
Metaverse

The term “metaverse” is derived from the concept of a “universe” or “multiverse” which is a virtual world, digital space, or immersive parallel reality. The metaverse is a concept that refers to a virtual world that is fully immersive and interactive, where the users can interact with each other in a seamless and realistic way. The metaverse is being driven by advancements in technology, such as the Internet, artificial intelligence, and virtual and augmented reality, and with this potential to revolutionize the Internet, the metaverse is gaining a lot of attention and investment from tech giants around the world. Currently, major players like Roblox and Facebook have already announced plans to enter the space. The architecture of the metaverse is based on a decentralized network, where the users will have full control over their data and experiences. The purpose of this chapter is to provide readers with a comprehensive view of the metaverse and an understanding of how Internet giants are positioning themselves within the metaverse, as well as the fundamental architecture of the metaverse.