Reviewerly/exHarmony huggingface dataset:
A Stacking Ensemble Prediction Model for the Occurrences of Major Adverse Cardiovascular Events in Patients With Acute Coronary Syndrome on Imbalanced Data

The major adverse cardiovascular events (MACE) often occur with high morbidity and mortality globally.It is very important to predict the MACE occurrences accurately in patients with acute coronary syndrome (ACS).Therefore, this paper proposes a stacking ensemble model for the prediction of MACE occurrences in patients with ACS at early stage.Our research contents are summarized as follows.First, we use the Korea Acute Myocardial Infarction Registry National Institutes of Health (KAMIR-NIH) dataset, and our experimental data are extracted from the raw data and preprocessed.Second, we apply three data sampling approaches, such as borderline synthetic minority oversampling technique (Borderline-SMOTE1), cluster centroids undersampling, and synthetic minority oversampling techniques (SMOTE) plus Tomek Links (SMOTETomek) hybrid technique, to solve the class imbalance problem.Third, to develop a stacking ensemble prediction model for the occurrences of MACE, we apply seven widely used machine learning algorithms, such as logistic regression (LR), support vector machine (SVM), K-Nearest Neighbors (KNN), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base learners.Fourth, the performance of the proposed stacking ensemble model is compared with the seven base learners using the three data sampling techniques.In the result, the proposed stacking ensemble model with the SMOTETomek shows the best performance with accuracy of 0.9862, precision 0.9976, recall 0.975, F1-score 0.9862, g-mean 0.9863, and AUC 0.9863 and provided a better solution for imbalanced dataset.Consequently, our finding is that the proposed stacking ensemble model with the SMOTETomek outperforms the base learners and improves the accuracy of diagnosis and prediction of the MACE occurrences in patients with ACS at early stage.
ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition

Arabic text classification is a process to simultaneously categorize the different contextual Arabic contents into a proper category. In this paper, a novel deep learning Arabic text computer-aided recognition (ArCAR) is proposed to represent and recognize Arabic text at the character level. The input Arabic text is quantized in the form of 1D vectors for each Arabic character to represent a 2D array for the ArCAR system. The ArCAR system is validated over 5-fold cross-validation tests for two applications: Arabic text document classification and Arabic sentiment analysis. For document classification, the ArCAR system achieves the best performance using the Alarabiya-balance dataset in terms of overall accuracy, recall, precision, and F1-score by 97.76%, 94.08%, 94.16%, and 94.09%, respectively. Meanwhile, the ArCAR performs well for Arabic sentiment analysis, achieving the best performance using the hotel Arabic reviews dataset (HARD) balance dataset in terms of overall accuracy and F1-score by 93.58% and 93.23%, respectively. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, understanding, and classification.
Identifying the Characteristics of Virtual Reality Gamification for Complex Educational Topics

Multidisciplinary topics in education pose a major challenge for traditional learning and teaching methods. Such topics can deter students from selecting particular courses or hinder their study progress. This study focused on the subject of medicinal chemistry, which is a discipline combining medicine and chemistry. This combination of applied and basic science creates a complex field of education that is challenging to both teach and learn. Chemical and pharmacological principles are typically presented in 2D molecular structures and, recently, 3D molecular models have been utilized to improve the visualization of chemical compounds and their chemical interactions. Contemporary studies have presented Virtual Reality (VR) as an alternative method for improving the learning and teaching of multidisciplinary specialties such as this. However, current educational efforts employing VR offer limited interactivity and a traditional teaching method previously presented in 2D. This reduces students’ interest and concentration in the taught subjects. This paper presents the development rationale of a novel VR educational application based on the evaluation of the user requirements by 405 pharmacy undergraduate students. The results informed the development and preliminary evaluation of a proposed VR serious game application, which was deployed in a real-life class environment and evaluated in contrast to traditional teaching methods by 15 students. The derived results confirmed the advantages of VR technology as a learning and teaching tool, in addition to the end-users’ willingness to adopt VR systems as a learning aid.
Impact of graph structures for QAOA on MaxCut

The quantum approximate optimization algorithm (QAOA) is a promising method of solving combinatorial optimization problems using quantum computing. QAOA on the MaxCut problem has been studied extensively on graphs with specific structure; however, little is known about the general performance of the algorithm on arbitrary graphs. In this paper, we investigate how different graph characteristics correlate with QAOA performance at depths at most three on the MaxCut problem for all connected non-isomorphic graphs with at most eight vertices. Some good predictors of QAOA success relate to graph symmetries, odd cycles, and density. For example, on eight vertex graphs, the average probability for selecting an optimal solution for graphs that contain no odd cycles after three iterations of QAOA is $$60.6\%$$ compared to $$48.2\%$$ for those that do. The data generated from these studies are shared in a publicly accessible database to serve as a benchmark for QAOA calculations and experiments. Knowing the relationship between structure and performance can be used to identify classes of combinatorial problems that are likely to exhibit a quantum advantage.
Lightweight Object Detection Ensemble Framework for Autonomous Vehicles in Challenging Weather Conditions

The computer vision systems driving autonomous vehicles are judged by their ability to detect objects and obstacles in the vicinity of the vehicle in diverse environments. Enhancing this ability of a self-driving car to distinguish between the elements of its environment under adverse conditions is an important challenge in computer vision. For example, poor weather conditions like fog and rain lead to image corruption which can cause a drastic drop in object detection (OD) performance. The primary navigation of autonomous vehicles depends on the effectiveness of the image processing techniques applied to the data collected from various visual sensors. Therefore, it is essential to develop the capability to detect objects like vehicles and pedestrians under challenging conditions such as like unpleasant weather. Ensembling multiple baseline deep learning models under different voting strategies for object detection and utilizing data augmentation to boost the models' performance is proposed to solve this problem. The data augmentation technique is particularly useful and works with limited training data for OD applications. Furthermore, using the baseline models significantly speeds up the OD process as compared to the custom models due to transfer learning. Therefore, the ensembling approach can be highly effective in resource-constrained devices deployed for autonomous vehicles in uncertain weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and were able to identify objects from the images captured in the adverse foggy and rainy weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and reached 32.75% mean average precision (mAP) and 52.56% average precision (AP) in detecting cars in the adverse fog and rain weather conditions present in the dataset. The effectiveness of multiple voting strategies for bounding box predictions on the dataset is also demonstrated. These strategies help increase the explainability of object detection in autonomous systems and improve the performance of the ensemble techniques over the baseline models.
IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.
A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study

Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer.We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI.In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm.We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.
‘Just What do You Think You’re Doing, Dave?’ A Checklist for Responsible Data Use in NLP

A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear.This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them.We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community.Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.
Survey of Interoperability in Electronic Health Records Management and Proposed Blockchain Based Framework: MyBlockEHR

Interoperability in Electronic Health Records (EHR) is significant for the seamless sharing of information amongst different healthcare stakeholders.Interoperability in EHR aims to devise agreements in its interpretation, access, and storage with security, privacy, and trust.A study and survey of state-of-the-art literature, prototypes, and projects in standardization of the EHR structure, privacy-preservation, and EHR sharing are very essential.The presented work conducts a systematic literature review to address four research questions. 1) What are the different standards for common interpretation, representation, and modeling of EHR to achieve semantic interoperability?2) What are the different privacy-preservation techniques and security standards for EHR data storage?3) How mature is blockchain technology for building interoperable, privacy-preserving solutions for EHR storage and sharing?4) What is the state-of-the-art for cross-chain interoperability for EHR sharing?An exhaustive study of these questions establishes the potential of a blockchain-based EHR management framework in privacy preservation, access control and efficient storage.The study also unveils challenges in the adoption of blockchain in EHR management with the state-of-theart maturity of cross-chain interoperable solutions for sharing EHR amongst stakeholders on different blockchain platforms.The research gaps culminate in proposing a blockchain-based EHR framework with privacy preservation and access control design.The proposed framework employs partitioning of EHR to onchain and off-chain storages for performance guarantees with the retrieval of valid off-chain data.The framework is deployed on the Ethereum test network with Solidity smart contracts.It is observed that different test cases on the partitioning of the EHR data, yielded better read-write throughput and effective gas price than fully on-chain storage.
Feedforward neural networks initialization based on discriminant learning

In this paper, a novel data-driven method for weight initialization of Multilayer Perceptrons and Convolutional Neural Networks based on discriminant learning is proposed. The approach relaxes some of the limitations of competing data-driven methods, including unimodality assumptions, limitations on the architectures related to limited maximal dimensionalities of the corresponding projection spaces, as well as limitations related to high computational requirements due to the need of eigendecomposition on high-dimensional data. We also consider assumptions of the method on the data and propose a way to account for them in a form of a new normalization layer. The experiments on three large-scale image datasets show improved accuracy of the trained models compared to competing random-based and data-driven weight initialization methods, as well as better convergence properties in certain cases.
Self-Similarity Features for Multimodal Remote Sensing Image Matching

Multimodal remote sensing image matching is a challenging task because of the existence of significant radiometric differences. To address the problem, we develop a novel multimodal remote sensing image matching method based on selfsimilarity features. The offset mean filtering method is proposed first to calculate the self-similarity features fast based on the symmetry of the self-similarity. The self-similarity features are presented through a multi-channel self-similarity map (SSM) and a corresponding multi-channel symmetric SSM. On this basis, we develop the image matching method, including a feature detector named improved maximal self-dissimilarities (IMSD) and a feature descriptor named oriented self-similarity (OSS). The IMSD detector is designed by introducing the two multi-channel SSMs into the maximal self-dissimilarities (MSD) detector for feature point detection. The OSS descriptor is proposed based on the orientations of the self-similarities extracted from the multichannel SSMs. We conduct experiments with a variety of optical, synthetic aperture radar, and light detection and ranging data. Our results demonstrate the advantages of our proposed IMSD detector and OSS descriptor in comparison with state-of-the-art detectors and descriptors, respectively. The image registration results further confirm the effectiveness of the proposed method.
Machine Learning-Based Detection of Spam Emails

Social communication has evolved, with e-mail still being one of the most common communication means, used for both formal and informal ways. With many languages being digitized for the electronic world, the use of English is still abundant. However, various native languages of different regions are emerging gradually. The Urdu language, coming from South Asia, mostly Pakistan, is also getting its pace as a medium for communications used in social media platforms, websites, and emails. With the increased usage of emails, Urdu’s number and variety of spam content also increase. Spam emails are inappropriate and unwanted messages usually sent to breach security. These spam emails include phishing URLs, advertisements, commercial segments, and a large number of indiscriminate recipients. Thus, such content is always a hazard for the user, and many studies have taken place to detect such spam content. However, there is a dire need to detect spam emails, which have content written in Urdu language. The proposed study utilizes the existing machine learning algorithms including Naive Bayes, CNN, SVM, and LSTM to detect and categorize e-mail content. According to our findings, the LSTM model outperforms other models with a highest score of 98.4% accuracy.
Deep Learning Based Emotion Recognition and Visualization of Figural Representation

This exploration aims to study the emotion recognition of speech and graphic visualization of expressions of learners under the intelligent learning environment of the Internet. After comparing the performance of several neural network algorithms related to deep learning, an improved convolution neural network-Bi-directional Long Short-Term Memory (CNN-BiLSTM) algorithm is proposed, and a simulation experiment is conducted to verify the performance of this algorithm. The experimental results indicate that the Accuracy of CNN-BiLSTM algorithm reported here reaches 98.75%, which is at least 3.15% higher than that of other algorithms. Besides, the Recall is at least 7.13% higher than that of other algorithms, and the recognition rate is not less than 90%. Evidently, the improved CNN-BiLSTM algorithm can achieve good recognition results, and provide significant experimental reference for research on learners' emotion recognition and graphic visualization of expressions in an intelligent learning environment.
Quadratic Speed-Up for Simulating Gaussian Boson Sampling

We introduce an algorithm for the classical simulation of Gaussian boson sampling that is quadratically faster than previously known methods. The complexity of the algorithm is exponential in the number of photon pairs detected, not the number of photons, and is directly proportional to the time required to calculate a probability amplitude for a pure Gaussian state. The main innovation is to use auxiliary conditioning variables to reduce the problem of sampling to the computation of the pure-state probability amplitudes, for which the most computationally expensive step is the calculation of a loop hafnian. We implement and benchmark an improved loop-hafnian algorithm and show that it can be used to compute pure-state probabilities, the dominant step in the sampling algorithm, of events involving up to 50 photons in a single workstation, i.e., without the need of a supercomputer.Received 23 February 2021Accepted 8 November 2021DOI:https://doi.org/10.1103/PRXQuantum.3.010306Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBoson samplingQuantum information processing with continuous variablesQuantum opticsQuantum InformationAtomic, Molecular & Optical
iReTADS: An Intelligent Real-Time Anomaly Detection System for Cloud Communications Using Temporal Data Summarization and Neural Network

A new distributed environment at less financial expenditure on communication over the Internet is presented by cloud computing. In recent times, the increased number of users has made network traffic monitoring a difficult task. Although traffic monitoring and security problems are rising in parallel, there is a need to develop a new system for providing security and reducing network traffic. A new method, iReTADS, is proposed to reduce the network traffic using a data summarization technique and also provide network security through an effective real-time neural network. Although data summarization plays a significant role in data mining, still no real methods are present to assist the summary evaluation. Thus, it is a serious endeavor to present four metrics for data summarization with temporal features such as conciseness, information loss, interestingness, and intelligibility. In addition, a new metric time is also introduced for effective data summarization. Finally, a new neural network known as Modified Synergetic Neural Network (MSNN) on summarized datasets for detecting the real-time anomaly-behaved nodes in network and cloud is introduced. Experimental results reveal that the iReTADS can effectively monitor traffic and detect anomalies. It may further drive studies on controlling the outbreaks and controlling pandemics while studying medical datasets, which results in smart healthy cities.