Reviewerly/exHarmony huggingface dataset:
NoSQL: Future of BigData Analytics Characteristics and Comparison with RDBMS

The growth of digital world has become so fast and the data volume turned to more composite in relations to size from terabyte and higher, the variation of data including structured, unstructured and hybrid, pace has gone so high in growth of data. This is called 'Big Data' as a worldwide phenomenon. Typically, this is measured as the collection of data from multiple sources has become so huge that it can't be well exploited or maintained by using regular methods used for data management: e.g., RDBMS the conventional relational database management systems or search engines used conventionally. The experts have been working to solve handle these problems, the traditional relational database management systems are modified by precisely designed a set of substitutes DBMS's; which are search based systems, New SQL and NoSQL. This goal of this study is to provide characteristics, classification and evaluation of database management system called NoSQL in Big Data Analytics. The study is planned to help people, specifically the companies to gain. The current computation power also falls less against the massive extended storage volume. The fast growth of unstructured data requires a complete model change for the new age to meet the incoming challenges and observe the progress of new proficient techniques for data engineering. The explanation of BigData, RDBMS and NoSQL have also been described in this study to put light on difference of data and requirement of new technology. This study will highlight the differences, capabilities of both RDBMS and NoSQL database management systems in accordance with BigData. With the help of qualitative and quantitative approach the study has emphasized on the limitation of RDBMS and requirement of new technology NoSQL used for unstructured BigData used for analytics.
Perspectives of Online Education in Pakistan: Post-covid Scenario

The outbreak of the famous Corona Virus in the world at the end of the year 2019 has triggered the world to bring major changes to the everyday walks of life. This virus entered Pakistan in February 2020 and resulted in the lockdown to be enforced throughout the country. Just as many other fields of life, the education sector also had to tolerate a massive hit of this closing down. The traditional approach of education based on the face-to-face interactions between the students and instructors went dependent on online platforms. This was a major and sudden switch, which was found extremely difficult throughout the world as well as in Pakistan. The concept of online education was not anything new. It prevailed earlier also, but that was the one conducted after proper planning, which was not at all in the case Covid-19. This research paper first discusses the perspectives of parents and teachers on the effectiveness of online education by qualitative approach. Secondly, the results of the conducted surveys are evaluated in the quantitative part of the paper. Further, the results of both the research types are compared to discuss and reason the overall scenario of online education in Pakistan.
Meta Multi-Instance Multi-Label learning by heterogeneous network fusion

Multi-Instance Multi-Label Learning (MIML) models complex objects (bags), each of which is composed with a set of instances and associated with a set of labels. Current MIML solutions still focus on a single-type of bags and assume an independent and identically distribution (IID) of training data. But these bags are linked with objects of other types, which also encode the semantics of bags. In addition, they generally need abundant labeled data for training. To effectively mine MIML objects linked with objects of other types, we propose a heterogeneous network embedding and meta learning based approach (MetaMIML). MetaMIML introduces the context learner with network embedding to learn context representations of bags for structure information extraction, the task learner to extract the meta knowledge for fast adapting to new tasks with scarce training objects, and finally fuses the structural and attribute information to predict the labels of bags/instances. In this way, MetaMIML can not only naturally deal with MIML objects at data level improving, but also exploit the power of meta-learning at the model enhancing. Experiments on benchmark datasets demonstrate that MetaMIML achieves a significantly better performance than state-of-the-art algorithms.
BiT-MAC: Mortality prediction by bidirectional time and multi-feature attention coupled network on multivariate irregular time series

Mortality prediction is crucial to evaluate the severity of illness and assist in improving the prognosis of patients. In clinical settings, one way is to analyze the multivariate time series (MTSs) of patients based on their medical data, such as heart rates and invasive mean arterial blood pressure. However, this suffers from sparse, irregularly sampled, and incomplete data issues. These issues can compromise the performance of follow-up MTS-based analytic applications. Plenty of existing methods try to deal with such irregular MTSs with missing values by capturing the temporal dependencies within a time series, yet in-depth research on modeling inter-MTS couplings remains rare and lacks model interpretability. To this end, we propose a bidirectional time and multi-feature attention coupled network (BiT-MAC) to capture the temporal dependencies (i.e., intra-time series coupling) and the hidden relationships among variables (i.e., inter-time series coupling) with a bidirectional recurrent neural network and multi-head attention, respectively. The resulting intra- and inter-time series coupling representations are then fused to estimate the missing values for a more robust MTS-based prediction. We evaluate BiT-MAC by applying it to the missing-data corrupted mortality prediction on two real-world clinical datasets, i.e., PhysioNet'2012 and COVID-19. Extensive experiments demonstrate the superiority of BiT-MAC over cutting-edge models, verifying the great value of the deep and hidden relations captured by MTSs. The interpretability of features is further demonstrated through a case study.
A Diachronic Cross-Platforms Analysis of Violent Extremist Language in the Incel Online Ecosystem

The emergence and growth of incel subculture online has triggered a considerable body of research to date, most of which analyzing its worldview or mapping its position and connections within the broader manosphere. While this research has considerably enhanced our understanding of the incel phenomenon, it tends to offer a somewhat static, one-dimensional portrayal of what is—like all online subcultures and communities—a highly dynamic and multi-layered environment. Consequently, we lack sufficiently nuanced answers to what is arguably a critical question for law enforcement and academics alike: is this a violent extremist ideology? Using a uniquely extensive corpus covering a range of online spaces constitutive of the incelosphere spanning several years, we analyze the evolution of incel language across both time and platforms. Specifically, we test whether this language has grown more extreme over time as online spaces shutdown and others emerged. Our findings demonstrate that, while levels of violent extremist language do vary across the incelosphere, they have steadily increased in the main online spaces over the past 6 years. Further, we demonstrate that, while activity on these online spaces is responsive to offline events, the impact of these on violent extremist ideation is not uniform.
Robocentric Model-Based Visual Servoing for Quadrotor Flights

This article proposes a robocentric formulation for quadrotor visual servoing. This formulation presents the task-specific state dynamics of the quadrotor in its body reference frame. Compared with other visual servoing methods, our method allows tightly and integrated state estimation and control on the same robocentric model, and allows a faster system response in aggressive quadrotor flights. On the theory level, we prove the controllability and observability of the proposed robocentric model. Then, we design an on-manifold Kalman filter for the estimation and an on-manifold iterative model predictive control for motion planning and control. We verify our proposed formulation and controller in two crucial quadrotor flight tasks: 1) hovering; and 2) dynamic obstacle avoidance. Experiment results show that the quadrotor is able to resist large external disturbances and recover its position and orientation from two reference visual features. Moreover, the quadrotor is able to avoid dynamic obstacles reliably at a relative speed up to 7.4 m/s, demonstrating the effectiveness of our visual servoing methods in agile quadrotor flights.
Static Gesture Recognition Algorithm Based on Improved YOLOv5s

With the government’s increasing support for the virtual reality (VR)/augmented reality (AR) industry, it has developed rapidly in recent years. Gesture recognition, as an important human-computer interaction method in VR/AR technology, is widely used in the field of virtual reality. The current static gesture recognition technology has several shortcomings, such as low recognition accuracy and low recognition speed. A static gesture recognition algorithm based on improved YOLOv5s is proposed to address these issues. The content-aware re-assembly of features (CARAFE) is used to replace the nearest neighbor up-sampling method in YOLOv5s to make full use of the semantic information in the feature map and improve the recognition accuracy of the model for gesture regions. The adaptive spatial feature fusion (ASFF) method is introduced to filter out useless information and retain useful information for efficient feature fusion. The bottleneck transformer method is initially introduced into the gesture recognition task, reducing the number of model parameters and increasing the accuracy while accelerating the inference speed. The improved algorithm achieved an mAP(mean average precision) of 96.8%, a 3.1% improvement in average accuracy compared with the original YOLOv5s algorithm; the confidence level of the actual detection results was higher than the original algorithm.
<scp>Privacy‐preserving</scp> data mining and machine learning in healthcare: Applications, challenges, and solutions

Abstract Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy‐preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state‐of‐the‐art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions. This article is categorized under: Application Areas &gt; Health Care Technologies &gt; Machine Learning Commercial, Legal, and Ethical Issues &gt; Security and Privacy
Bertraffic: Bert-Based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications

Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech activity detection (SAD) and speaker diarization (SD). In the failure condition, two or more segments remain in the same recording, jeopardizing the overall performance. We propose a system that combines SAD and a BERT model to perform speaker change detection and speaker role detection (SRD) by chunking ASR transcripts, i.e., SD with a defined number of speakers together with SRD. The proposed model is evaluated on real-life public ATC databases. Our BERT SD model baseline reaches up to 10% and 20% token-based Jaccard error rate (JER) in public and private ATC databases. We also achieved relative improvements of 32% and 7.7% in JERs and SD error rate (DER), respectively, compared to VBx, a well-known SD system. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is stored in the following public GitHub repository: https://github.com/idiap/bert-text-diarization-atc
It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems

We are witnessing the emergence of an “AI economy and society” where AI technologies and applications are increasingly impacting health care, business, transportation, defense and many aspects of everyday life. Many successes have been reported where AI systems even surpassed the accuracy of human experts. However, AI systems may produce errors, can exhibit bias, may be sensitive to noise in the data, and often lack technical and judicial transparency resulting in reduction in trust and challenges to their adoption. These recent shortcomings and concerns have been documented in both the scientific and general press such as accidents with self-driving cars, biases in healthcare or hiring and face recognition systems for people of color, and seemingly correct decisions later found to be made due to wrong reasons etc. This has resulted in the emergence of many government and regulatory initiatives requiring trustworthy and ethical AI to provide accuracy and robustness, some form of explainability, human control and oversight, elimination of bias, judicial transparency and safety. The challenges in delivery of trustworthy AI systems have motivated intense research on explainable AI systems (XAI). The original aim of XAI is to provide human understandable information of how AI systems make their decisions in order to increase user trust. In this paper we first very briefly summarize current XAI work and then challenge the recent arguments that present “accuracy vs. explainability” as being mutually exclusive and for focusing mainly on deep learning with its limited XAI capabilities. We then present our recommendations for the broad use of XAI in all stages of delivery of high stakes trustworthy AI systems, e.g., development; validation/certification; and trustworthy production and maintenance.
Technology Solutions to Reduce Anxiety and Increase Cognitive Availability in Students

Recent research indicates that the majority of postsecondary students in North America “feel overwhelming anxiety,” an issue that has also been observed in many other regions and is negatively impacting students’ academic performance and overall well-being. This article reports the results of our interdisciplinary analysis of innovative ideas in cognitive science, learning models and mindsets, and current technological approaches that are aimed at understanding and addressing these anxiety and academic engagement issues. We identify the knowledge gaps and propose critical insights and research directions, such as the use of virtual reality (VR) and computer games, to fill those gaps and deal with the anxiety problem.
On convergence rates of Kaczmarz-type methods with different selection rules of working rows

The Kaczmarz method is a classical while effective iteration method for solving very large-scale consistent systems of linear equations, and the randomized Kaczmarz method is an important and valuable variant of the Kaczmarz method. By theoretically analyzing and numerically experimenting several criteria typically adopted in the non-randomized and the randomized Kaczmarz method for selecting the working row, we derive sharper upper bounds for the convergence rates of some of the correspondingly induced Kaczmarz-type methods including those with respect to the maximal residual, maximal distance, and distance selection rules of the working row, and, for this whole suite of iteration methods consisting of the Kaczmarz methods with respect to the uniform, non-uniform, residual, distance, maximal residual, and maximal distance selection rules of the working row, we reveal their comparable relationships in terms of both mean-squared distance and mean-squared error, and show their computational effectiveness and numerical robustness based upon implementing a large number of test examples. Here the mean-squared distance is defined as the mean-value of the squared Euclidean norm of the current update increment of the iteration, and the mean-squared error is defined as the mean-value of the squared Euclidean norm of the current error that is the difference between the current iterate and the true solution of the target linear system.
Unsupervised Domain Adaptation Augmented by Mutually Boosted Attention for Semantic Segmentation of VHR Remote Sensing Images

This work investigates unsupervised domain adaptation (UDA)-based semantic segmentation of very high-resolution (VHR) remote sensing (RS) images from different domains. Most existing UDA methods resort to generative adversarial networks (GANs) to cope with the domain shift problem caused by the discrepancies across different domains. However, these GAN-based UDA methods directly align two domains in the appearance, latent, or output space based on convolutional neural networks (CNNs), making them ineffective in exploiting long-range dependencies across the high-level feature maps derived from different domains. Unfortunately, such high-level features play an essential role in characterizing RS images with complex content. To circumvent this obstacle, a mutually boosted attention transformer (MBATrans) is proposed to capture cross-domain dependencies of semantic feature representations in this work. Compared with conventional UDA methods, MBATrans can significantly reduce domain discrepancies by capturing transferable features using global attention. More specifically, MBATrans utilizes a novel mutually boosted attention (MBA) module to align cross-domain feature maps while enhancing domain-general features. Furthermore, a novel GAN-based network with improved discriminative capability is devised by integrating an additional discriminator to learn domain-specific features. Extensive experiments on two large-scale VHR RS datasets, namely, International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Vaihingen, confirm the superior performance of the proposed MBATrans-augmented GAN (MBATA-GAN) architecture. The source code in this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sstary/SSRS</uri> .
Gradient-Feedback Zhang Neural Network for Unconstrained Time-Variant Convex Optimization and Robot Manipulator Application

Optimization problems are frequently encountered in various fields. In this article, the unconstrained time-variant convex optimization (UTVCO) problem is investigated. Generally, gradient neural network (GNN) is a traditional and effective method for solving time-invariant problems by making use of the gradient information. However, GNN is less effective on time-variant problems. On the other hand, Zhang neural network (ZNN) performs well on time-variant problems by exploiting the time-derivative information. In order to solve the UTVCO problem effectively and quickly with the help of the gradient information, inspired by the two methods, gradient-feedback ZNN (GZNN) is presented by taking both advantages of GNN and ZNN to solve the UTVCO problem. The main contributions are presented as follows. 1) The GZNN model for solving the UTVCO problem is proposed and analyzed theoretically. 2) Comparisons among GZNN, ZNN, GNN, and other models are presented with detailed discussions. Suggestions are provided on how to choose a model for solving the UTVCO problem better. 3) Tracking control of the robot manipulator is formulated as a UTVCO problem and studied with the GZNN model. According to the simulative and physical experiments, the task of tracking control is accomplished excellently by using the GZNN model.
A Highly Compressed Accelerator With Temporal Optical Flow Feature Fusion and Tensorized LSTM for Video Action Recognition on Terminal Device

Deep learning-based action recognition has become ubiquitous in the video analysis area; however, large neural networks require enormous computations to achieve high performance, which hinder them from mobile applications that are tightly constrained by hardware resources. In this work, we introduce a highly compact and fast neural network-based action recognition accelerator named ARA on the terminal device. We build an LSTM-based spatio-temporal action recognition model with extracted time-series features from RGB frames and flow features from optical flow fields. Then the LSTM-based spatio-temporal model is deeply compressed with tensor decomposition to further reduce redundant parameters and lessen computation overhead. Based on the datasets UCF-11, UCF-101, and HMDB51, our proposed method achieves 95.87%, 94.08%, and 75.71% classification accuracy, being comparable with other state-of-the-art methods. In particular, our proposed method significantly compresses the parameter of the LSTM model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$215\times $ </tex-math></inline-formula> on the UCF-101 dataset. The proposed system can also achieve a fast running speed of 157.7 FPS on GPU. Furthermore, we validate the performance of the proposed system on an ARM-based terminal device; the results show it only has 0.017-s latency and 4.73-W power consumption.
An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

Automated vehicles are a revolutionary step in mobility, providing a safe and convenient riding experience while keeping the human-driving task minimal to none. Therefore, these intelligent vehicles are equipped with sophisticated perception sensors (e.g., cameras and radars), high-performance computers, artificial intelligence (AI)-driven algorithms, and connectivity with other internet-of-things (IoT) devices. This makes autonomous vehicles (AVs) a special kind of cyber-physical system (CPS) that is moving at speed in highly interactive and dynamic environments (e.g., public roads). Thus, AV is a potential target for cyber attackers to weaponize, compromising safety and mobility on the road. The first step in addressing this problem is to have a robust threat modeling framework that can address the evolving cyber-physical threats, especially to AV applications. In this regard, two areas are studied in this paper: the common practice of threat modeling in automotive and the ISO/SAE 21434 standard, and sensors and machine learning (ML) algorithms for AV perception systems and potential cyber-physical attacks. A comparative threat analysis for an AV perception system with the ISO/SAE 21434 standard and a system-theoretic process analysis for security (STPA-Sec) approach is also demonstrated in this paper. Based on the analysis, this paper proposes a robust threat analysis and risk assessment framework with mathematical modeling to identify cyber-physical threats to AV perception systems that are critical for the driving behaviors and complex interactions of AVs in their operational design domain.
AI Maintenance: A Robustness Perspective

In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.