Reviewerly/exHarmony huggingface dataset:
Detecting Simpson’s Paradox: A Step Towards Fairness in Machine Learning

In the last two decades, artificial intelligence (AI) and machine learning (ML) have grown tremendously. However, understanding and assessing the impacts of causality and statistical paradoxes are still some of the critical challenges in their domains. Currently, these terms are widely discussed within the context of explainable AI (XAI) and algorithmic fairness. However, they are still not in the mainstream AI and ML application development scenarios. In this paper, first, we discuss the impact of Simpson's paradox on linear trends, i.e., on continuous values, and then we demonstrate its effects via three benchmark training datasets used in ML. Next, we provide an algorithm for detecting Simpson's paradox. The algorithm has experimented with the three datasets and appears beneficial in detecting the cases of Simpson's paradox in continuous values. In future, the algorithm can be utilized in designing a certain next-generation platform for fairness in ML.
Large‐scale knowledge distillation with elastic heterogeneous computing resources

Abstract Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time. In addition, it is difficult to afford long training time and inference time of big models even in high performance servers, as well. As an efficient approach to compress a large deep model (a teacher model) to a compact model (a student model), knowledge distillation emerges as a promising approach to deal with the big models. Existing knowledge distillation methods cannot exploit the elastic available computing resources and correspond to low efficiency. In this paper, we propose an Elastic Deep Learning framework for knowledge Distillation, that is, EDL‐Dist. The advantages of EDL‐Dist are threefold. First, the inference and the training process is separated. Second, elastic available computing resources can be utilized to improve the efficiency. Third, fault‐tolerance of the training and inference processes is supported. We take extensive experimentation to show that the throughput of EDL‐Dist is up to 3.125 times faster than the baseline method (online knowledge distillation) while the accuracy is similar or higher.
Compression Domain Reversible Robust Watermarking Based on Multilayer Embedding

The integration of data sensing, communication, and computing (SCC) is a requirement for the IoT to provide a high quality of service. However, there are still data security issues in SCC integration, where data are vulnerable to leakage during sensing, communication, and computing, and the leaked data are difficult to be copyrighted and traced to the source of leakage. In this study, we consider the copyright protection problem in multimedia data transmission and propose a robust reversible watermarking algorithm based on multilayer embedding in the compression domain. In the first stage, the robust watermark is embedded into the mid-frequency coefficients, and then, the auxiliary information to revert the robust embedding is embedded into the high-frequency coefficients. To improve robustness and reduce embedding distortion, we propose a coefficient selection method, by which the watermark and the recovery information are embedded in different DCT-quantized coefficients according to the texture complexity. Experiment results indicate that the proposed method performs better than some state-of-the-art RRW methods.
Online Learning in Mathematics Higher Education during the COVID-19 Pandemic: A Survey Study of Portuguese Students

Coronavirus pandemic has impacted the whole education system. Suddenly, presential classrooms are replaced by online learning, leaving students and teachers to deal with various problems without any specific preparation. This research study examines the perceptions of 78 students from a Portuguese Higher Education (HE) institution towards online learning during the Coronavirus disease 19 (COVID-19) pandemic. Two months after the implementation of the online learning, they responded to a survey whose responses were analyzed by using descriptive statistics and independent Chi-squared tests. The findings of this study reveal the difficulties that the students encountered, like the lack of familiar/spatial conditions at home, the failures of Internet connection, and the fear of clarifying doubts in this context. The preference for face-to-face classes was also expressed. However, the selected platform used for the online classes, the appropriate development of the explanations and the digital contents made available by the teachers were appreciated by the students. The present research also investigates the factors that affected the study motivation of the students and the future plans regarding their studies.
DeepHistory: A convolutional neural network for automatic animation of museum paintings

Abstract Deep learning models have shown that it is possible to train neural networks to dispense, to a lesser or greater extent, with the need for human intervention for the task of image animation, which helps to reduce not only the production time of these audiovisual pieces, but also presents benefits with respect to the economic investment they require to be made. However, these models suffer from two common problems: the animations they generate are of very low resolution and they require large amounts of training data to generate good results. To deal with these issues, this article introduces the architectural modification of a state‐of‐the‐art image animation model integrated with a video super‐resolution model to make the generated videos more visually pleasing to viewers. Although it is possible to train the animation models with higher resolution images, the time it would take to train them would be much longer, which does not necessarily benefit the quality of the animation, so it is more efficient to complement it with another model focused on improving the animation resolution of the generated video as we demonstrate in our results. We present the design and implementation of a convolutional neural network based on an state‐of‐art model focused on the image animation task, which is trained with a set of facial data from videos extracted from the YouTube platform. To determine which of all the modifications to the selected state‐of‐the‐art model architecture is better, the results are compared with different metrics that evaluate the performance in image animation and video quality enhancement tasks. The results show that modifying the architecture of the model focused on the detection of characteristic points significantly helps to generate more anatomically and visually attractive videos. In addition, perceptual testing with users shows that using a super‐resolution video model as a plugin helps generate more visually appealing videos.
Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning

In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolutional neural network (CNN) based methods are developed to perform vehicle re-identification to address key challenges such as occlusion, illumination change, scale, etc. The advancement of transformers in computer vision has opened an opportunity to explore the re-identification process further to enhance performance. In this paper, a framework is developed to perform the re-identification of vehicles across CCTV cameras. To perform re-identification, the proposed framework fuses the vehicle representation learned using a CNN and a transformer model. The framework is tested on a dataset that contains 81 unique vehicle identities observed across 20 CCTV cameras. From the experiments, the fused vehicle re-identification framework yields an mAP of 61.73% which is significantly better when compared with the standalone CNN or transformer model.
Modulation and signal class labelling with active learning and classification using machine learning

Supervised learning in machine learning (ML) requires labelled data set. Further real-time data classification requires an easily available methodology for labelling. Wireless modulation and signal classification find their application in plenty of areas such as military, commercial and electronic reconnaissance and cognitive radio. This paper mainly aims to solve the problem of real-time wireless modulation and signal class labelling with an active learning framework. Further modulation and signal classification is performed with machine learning algorithms such as KNN, SVM, Naive Bayes. Active learning helps in labelling the data points belonging to different classes with the least amount of data samples trained. An accuracy of 86 percent is obtained by the active learning algorithm for the signal with SNR 18 dB. Further, KNN based model for modulation and signal classification perform well over a range of SNR, and an accuracy of 99.8 percent is obtained for 18 dB signal. The novelty of this work exists in applying active learning for wireless modulation and signal class labelling. Both modulation and signal classes are labelled at a given time with help of couplet formation from the data samples.
A Novel Deep Architecture for Multi-Task Crowd Analysis

Recently, crowd analysis has become an essential tool for crowd disaster management. The crowd analysis is not a single task but is a collective implication of several related tasks like crowd behavior analysis, crowd counting, and crowd flow analysis. Although different models for individual tasks have been proposed in the literature, there is a lack of a multitasking framework for crowd analysis. One of the main reasons could be the availability of the multitasking crowd analysis dataset. To this end, this paper created a multitask crowd analysis dataset using two publicly available crowd behavior datasets (MED, GTA) and proposed a novel deep architecture for multitasking crowd analysis. Two different crowd analysis tasks are considered, i.e., crowd behavior (normal and panic) and crowd counting. Around 89,000 frames were annotated for obtaining ground-truth crowd counts. In addition to this, a two-stage learning mechanism is proposed. In the first stage, a novel deep model is proposed that extracts high-level spatial-temporal features from the multiscale low-level spatial-temporal features and is used to learn normal crowd behavior and counting. The second stage utilized the features of the deep model and inputted them to the one-class support vector machine (OC-SVM) for crowd panic detection. The obtained results are compared with state-of-the-art and show its effectiveness in multitasking crowd analysis.
Computer Vision-Based Signature Forgery Detection System Using Deep Learning: A Supervised Learning Approach

Authentication is a crucial aspect of data security. It is one of the most important issues of our time. As technology advances, our interactions with machines are becoming increasingly automated. As a result, for a variety of security concerns, the demand for authentication is rapidly expanding. As a result, biometric-based authentication has become extremely popular. It has a significant edge over other approach. However, because different ways are utilized to verify people, this incidence is not a substitute for a problem. Signatures were one of the first commonly utilized biometric traits for identifying people. This paper describes a method for simplifying signature verification by preprocessing signatures. It also included a novel deep learning-based method for detecting faked signatures. With an accuracy of 85-95 %, the proposed method detects forgeries.
Intruder Detection and Tracking using 77GHz FMCW Radar and Camera Data

Target detection and tracking using optical and radar sensors have many applications in surveillance. As the optical sensor helps to visualize the target and the radar can provide its range and velocity, their combination results in useful information for continuous monitoring and coherence. This paper presents a radar-camera experimental setup to detect and track intruders in a restricted area. A real-time experiment with different target speeds and various radar cross-sections(RCS) (1. A person running, 2. A cyclist, and 3. A motorcyclist). We deployed a 77GHz IWR1642BOOST FMCW (Frequency Modulated Continuous Wave) radar module as a radar unit and a phone camera with an aperture of f/1.79 as an optical sensor. The data collected from the radar and camera sensor are applied to detection and tracking modules to obtain target tracks. The radar provides the observations of range, Doppler, and angle information. These observations are used to estimate the state of the target via extended Kalman filtering(EKF), dBscan clustering, and global nearest neighbor(GNN) association, followed by track maintenance. The optical sensor provides video frames as input and output tracks via foreground detection, blob analysis, motion-based detection, Kalman filtering, and track maintenance. The experimental result shows that combining radar and optical sensors accomplishes tracking accuracy and coherence in target detection and tracking.
ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing Mechanism

Convolutional Neural Networks need the construction of informative features, which are determined by channel-wise and spatial-wise information at the network’s layers. In this research, we focus on introducing a novel solution that uses sophisticated optimization to improve both the spatial and channel components within the receptive field of each layer. Capsule networks were used to understand the spatial association between features on the feature map. Standalone capsule networks have shown good results in comparatively simple datasets than in complex datasets as a result of the inordinate amount of feature information. Thus, to tackle this issue, we have proposed ME-CapsNet by introducing deeper convolutional layers to extract important features before passing through modules of capsule layers strategically to improve the performance of the network significantly. The deeper convolutional layer includes blocks of Squeeze-Excitation networks which use a stochastic sampling approach for progressively reducing the spatial size, thereby dynamically recalibrating the channels by reconstructing their interdependencies without much loss of important feature information. Extensive experimentation was done using commonly used datasets, demonstrating the efficiency of the proposed ME-CapsNet, which clearly outperforms various research works by achieving higher accuracy with minimal model complexity in complex datasets.
Machine Learning Approaches to Assess Mood of the News Editorial

Social media and news channels have always been vital sources for spreading information and raising awareness about recent occurrences. As reported by a survey, 82 percent of respondents from India stated that they sourced their news online, which included social media, as of the year 2021, making it a popular form of accessing news1. For a long time, information about COVID - 19 has been one of the most popular topics. News channel networks and editorials were one of the first places where knowledge regarding COVID - 19 was widely disseminated. In this study, sentiment analysis models have been developed to categorize tweets by some of India’s most well-known news stations into positive and negative during the COVID - 19 virus was new in India from June 2020 to July 2020. We attempted to do so by developing nine various models based on different datasets and classification algorithms to investigate the news channels' tweets more thoroughly. According to our findings, the model that provided us with the highest accuracy and performance has been trained using the NLTK Dataset and the Logistic Regression Classifier.
Optimal placement of IMU sensor for the detection of children activity

In this paper an investigation to determine the optimal placement of IMU sensors for the purpose of children characteristic activity detection is presented. The article compares four different placement of two IMU sensors on human body. Ten healthy volunteers participated within the study. Data were collected firstly from two wireless 9-axial IMU sensors placed at the left and right wrists, then sensors were placed at lower back and hip (dominant hand side). Activities included jumping, rotating, walking, walking on tiptoe, running, clapping hands, standing still, sitting still and dancing. Several parameters such as mean, standard deviation, skewness, kurtosis, energy, correlations, Hjorth parameters (activity, mobility and complexity) and spectra purity index, were calculated from measured data. Data from all locations provided similar levels of accuracy in differentiate analyzed activities.
Human System Interaction in Review: Advancing the Artificial Intelligence Transformation

The industrial advancement of human society has been fundamentally driven by diverse ‘systems’ that facilitate ‘human interaction’ within physical, digital, virtual, social and artificial environments, and upon the hyper-connected layers of system-system interactions across these environments. The research and practice of Human System Interaction (HSI) has undergone exponential development due to the enhanced capabilities, increased efficiencies and decreased costs of digitalization. Primarily driven by its unique capacity for information persistence, digitalization is now leading us into a nexus of transition where HSI is being transformed by Artificial Intelligence (AI). AI has leveraged the data and information amassed by digitalization to learn, reason, predict, optimize and thereby augment both human-system interaction and system-system interaction, within and across all hyper-connected environments noted above. In this paper, we review this evolution of HSI and contribute towards its future directions by articulating the AI transformation strategy for this nexus of transition into a Human-AI-System Interaction. The paper begins with a review of HSI that focuses on developments in the past 15 years, followed by the AI transformation strategy which comprises of the primary configurations for Human-AI-System Interaction, the current capabilities of AI, a lifecycle approach for the design, development and deployment of an AI solution and the ethical implications of AI in HSI.
How Virtual Body Continuity with Different Hand Representations Influence on User Perceptions and Task Performance

Virtual avatars or hands in virtual reality connect users’ physical bodies and virtual worlds. Changes in the virtual hand representations (body continuity and hand realism) may affect user perceptions and task performance. However, there is no agreed conclusion on how they influence user perceptions (the sense of embodiment and presence) and limited evidence of task performance. Therefore, this paper investigates the impact of body continuity (connected and disconnected virtual hand) with three hand realism levels on user perceptions and task performance by self-report and objective performance data in virtual reality. The results revealed no significant results about body continuity on user perceptions, while a significant effect of hand realism levels on sense of embodiment and presence was found. Moreover, the abstract disconnected and connected hands reported lower task scores than those realistic hands from task performance data. Overall, this study provides new insights into further understanding user perceptions and task performance under the connected and disconnected hands, and it has practical reference value for exploring the later research on virtual hand representations.
Pedestrian detection in low-resolution thermal images

Over one million people die in car accidents worldwide each year. A solution that will be able to reduce situations in which pedestrian safety is at risk has been sought for a long time. One of the techniques for detecting pedestrians on the road is the use of artificial intelligence in connection with thermal imaging. The purpose of this work was to design a system to assist the safety of people and car intelligence with the use of automatic detection of pedestrians in low-resolution thermal image sequences. The data acquisition system was designed and used to collect thermal images for the needs of training of machine learning methods. The created new dataset consists of 9178 annotated, low-resolution images of pedestrians in different traffic conditions. Several deep, object detection models were adapted and trained using the new dataset together with public datasets. The best model turned out to be the adapted Faster R-CNN ResNet50 FPN (Faster Region-based Convolutional, Neural Networks Residual network50, Feature Pyramid Network) model with mean Average Precision (mAP) equal to 94.00%. It was also shown that the use of transfer learning based on the features learned from the RGB images results in mAP greater than 85.00% for all investigated algorithms. The designed system finds practical application in increasing road safety through the potential use of autonomous cars and city monitoring.
Attention feature fusion network for small traffic sign detection

Abstract Object detection has made great progress with the rise of convolutional neural networks in recent years. Traffic sign detection is a research hotspot for object detection tasks. The existing detection models have the problems of inaccurate positioning and low classification accuracy when detecting small traffic signs. To address these issues, in this paper, we propose a small traffic sign detection method based on YOLOv4. Specifically, we design an attention-based feature fusion module including attention spatial pyramid pooling (ASPP) and attention path aggregation networks (APAN). ASPP highlights useful small object information and suppresses invalid interference information in the background. APAN reduces information loss during feature fusion. A large number of experimental results on public datasets show that the method in this paper improves the detection performance of the model. In terms of small traffic sign detection, the method improves YOLOv4 by 12 mAP, and meets the real-time requirements of automatic driving detection (more than 50 FPS).
Robust self-adaptive Kalman filter with application in target tracking

Kalman filter has been applied extensively to the target tracking. The estimation performance of Kalman filter is closely resulted by the quality of prior information about the process noise covariance (Q) and the measurement noise covariance (R). Therefore, the development of adaptive Kalman filter is mainly to reduce the estimation errors produced by the uncertainty of Q and R. In this paper, the proposed self-adaptive Kalman filter algorithm has solved the problems of covariance-matching method about the determination of the width of the window and the addition of storage burden and that can update Q and R simultaneously. Simulation results confirm that the proposed method outperforms the traditional Kalman filter and has the better estimation performance than the other two adaptive Kalman filters in the target tracking. The developed filtering algorithm has the following characteristics: high robustness, low computing load, easy operation and tuning Q, R simultaneously.