Reviewerly/exHarmony huggingface dataset:
A lightweight and efficient neural network for modulation recognition

This paper proposes a lightweight neural network for automatic modulation recognition, entitled combined, comprising a lightweight backbone network and an optional network. The former network is applied to recognize 24 types of signal modulation and relies on a multi-staged process that involves multiple convolution kernels of different characteristics in the depth direction to generate spliced feature maps. The network parameters are reduced by adjusting the number of convolution kernels and superimposing large and small convolution kernels after adjusting the feature map size by up and downsampling. The additional network effectively increases the recognition accuracy of the amplitude modulation-single side band-suppressed carrier (AM-SSB-SC) and amplitude modulation-single side band-with carrier (AM-SSB-WC), complementing the backbone network's low recognition accuracy on AM-SSB-SC. Trials on the DeepSig dataset demonstrate a recognition accuracy of 98.9% at 16 dB signal-to-noise ratio (SNR) for the combined network and 98.2% at 26 dB SNR for the backbone network, the highest recognition and single-model accuracy reported yet. Experiments of our backbone network on the Hisarmod dataset present a recognition accuracy of 99.8% for five modulation families while affording the least trainable parameters among similar networks.
Enhanced graph recommendation with heterogeneous auxiliary information

Abstract The boom in the field of movies and TV programs, which is a kind of information overload, may lead to poor user experience and are detrimental to the healthy development of the industry, hence personalized program recommendation is crucial. Since program names, labels, and synopsis are highly condensed languages, to enable better semantic representations for personalized recommendations and enrich the completeness requirements of data resources, we propose an enhanced graph recommendation with heterogeneous auxiliary information (EGR-HA), focusing on auxiliary information knowledge representations, and graph neural network-based node updates. Firstly, multi-source heterogeneous auxiliary information knowledge is fused to supplement semantics of program and user to obtain initial representations that contain rich semantics, then user and program node embedding representations are aggregated in multiple layers through graph neural networks to model higher-order interaction history information and realize user and program representation update; finally, user viewing prediction is performed based on deep networks to realize personalized program recommendation. The final experiment results in indicators, such as normalized discounted cumulative gain (NDCG), hit rate (HR) and root mean square error (RMSE), verified the effectiveness of this method by comparing with various methods.
Federated Principal Component Analysis for Genome-Wide Association Studies

Federated learning (FL) has emerged as a privacy-aware alternative to centralized data analysis, especially for biomedical analyses such as genome-wide association studies (GWAS). The data remains with the owner, which enables studies previously impossible due to privacy protection regulations. Principal component analysis (PCA) is a frequent preprocessing step in GWAS, where the eigenvectors of the sample-by-sample covariance matrix are used as covariates in the statistical tests. Therefore, a federated version of PCA suitable for vertical data partitioning is required for federated GWAS. Existing federated PCA algorithms exchange the complete sample eigenvectors, a potential privacy breach. In this paper, we present a federated PCA algorithm for vertically partitioned data which does not exchange the sample eigenvectors and is hence suitable for federated GWAS. We show that it outperforms existing federated solutions in terms of convergence behavior and scalability. Additionally, we provide a user-friendly privacy-aware web tool to promote acceptance of federated PCA among GWAS researchers.
Broad-learning recurrent Hermite neural control for unknown nonlinear systems

The broad-learning systems (BLS) with advance control theories have been studied, but found to have two disadvantages: one is that the calculations are too complicated and the other is that the convergence time cannot be guaranteed. In order to mitigate the high computational loading, this study proposes a broad-learning Hermite neural network (BHNN), which has the capability of dynamic mapping and reduces the structural complexity of neural network. Meanwhile, a broad-learning recurrent Hermite neural control (BRHNC) system is proposed while maintaining finite-time stability to speed up the tracking error convergence. The proposed BRHNC system comprises two controllers: a recurrent broad controller that utilizes a BHNN to approximate on-line an ideal finite-time controller and a robust exponential controller that ensures system stability through a Lyapunov function. Meanwhile, the BHNN’s full-tuned parameter learning laws are developed to increase the approximating capacity, learning capacity and accuracy using gradient descent method. Finally, simulation and experimental results show that the BRHNC system has good control, tracking and disturbance rejection properties, while the BRHNC system requires no prior knowledge about the system dynamics.
A dense background representation method for traffic surveillance based on roadside LiDAR

The performance of traditional video-based traffic surveillance systems is susceptible to illumination variation and perspective distortion. This has been a significant motivation in recent years for research into Light Detection and Ranging (LiDAR)-based traffic surveillance systems, as LiDAR is insensitive to both factors. The first step in LiDAR data processing involves effective extraction of moving foreground objects from a referenced background. However, existing methods only detect a static background based on LiDAR point density or relative distance. In this research, we develop a novel dense background representation model (DBRM) for stationary roadside LiDAR sensors to detect both static and dynamic backgrounds, for freeway traffic surveillance purposes. Background objects tend to be stationary in space and time. DBRM utilizes this property to detect two types of background: both static and dynamic. While the static background is represented by fixed structures, the dynamic background – which may be characterized by quasi-static objects such as tree foliage – is modeled by mixtures of Gaussian probability distributions. Experiments were carried out in two different scenarios to compare the proposed model with two other state-of-the-art models. The results demonstrate the effectiveness, robustness, and detail-preserving advantages of the proposed model.
Enriching teachers’ assessments of rhythmic Forró dance skills by modelling motion sensor data

Rhythmic skills are fundamental in social partner dance. Yet, large class sizes and time constraints often make it challenging for teachers to assess learners’ rhythmic skills to provide constructive feedback. As motion sensors are becoming widely available and embedded in most smartphones, it is also becoming feasible to create models of anatomical movements of the human body that could be used to support such teachers’ assessments. Current solutions aimed at modelling motor skills using motion sensors and artificial intelligence (AI) have enabled the detection of some dimensions of rhythm, but they are either technically too complex to be used ‘in-the-wild’ or have not been designed based on dance teachers’ assessment needs. This paper presents an approach for modelling rhythmic movements, via a single smartphone, while learners learn how to dance Forró. The purpose is to enrich teachers’ rhythmic assessments with data. Following a user-centred approach, we first elicited from teachers the dance elements that they commonly focus their rhythm assessments on (i.e., tempo, pause, step size and weight transfer). Then, selected features were extracted from raw motion sensor data related to the rhythmic patterns of learners dancing, their synchronisation with the beat of the music, and particular characteristics of the song being played. Finally, machine learning (ML) algorithms were used to create predictive computational models using these features. The modelling approach was validated through two studies: (1) a quantitative comparison between the ML outputs and dance teachers assessments of learners’ dance performance; and (2) a qualitative analysis of the potential pedagogical uses of the outputs of the ML models envisioned by dance teachers.
Text Representations and Word Embeddings

Today, a vast amount of unstructured text data is consistently at our disposal. Owing to the rapid increase in user-generated content on the one hand and powerful, ready-to-use machine learning algorithms on the other hand, automated text analysis can now be carried out in a way that would have been unimaginable a few years ago. However, in order to be able to analyze textual data further, it first becomes necessary to extract features and transform the text into numerical values, which is required by many algorithms. As such, this chapter will present the basics of vectorizing text, beginning with the most simple approaches of text representation and increasing in complexity and performance as each subsequent algorithm is presented. The aim of this chapter is, thus, to convey the intuition behind each approach in a straightforward manner and to focus on the elements that are relevant for practical application.
Eye-Tracking Feature Extraction for Biometric Machine Learning

Eye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.We performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.Out of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.The features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.
A Multi-Stream Sequence Learning Framework for Human Interaction Recognition

Human interaction recognition (HIR) is challenging due to multiple humans’ involvement and their mutual interaction in a single frame, generated from their movements. Mainstream literature is based on three-dimensional (3-D) convolutional neural networks (CNNs), processing only visual frames, where human joints data play a vital role in accurate interaction recognition. Therefore, this article proposes a multistream network for HIR that intelligently learns from skeletons’ key points and spatiotemporal visual representations. The first stream localises the joints of the human body using a pose estimation model and transmits them to a 1-D CNN and bidirectional long short-term memory to efficiently extract the features of the dynamic movements of each human skeleton. The second stream feeds the series of visual frames to a 3-D convolutional neural network to extract the discriminative spatiotemporal features. Finally, the outputs of both streams are integrated via fully connected layers that precisely classify the ongoing interactions between humans. To validate the performance of the proposed network, we conducted a comprehensive set of experiments on two benchmark datasets, UT-interaction and TV human interaction, and found 1.15% and 10.0% improvement in the accuracy.
Predicting the severity of <scp>COVID</scp>‐19 patients using a multi‐threaded evolutionary feature selection algorithm

Abstract The COVID‐19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID‐19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi‐threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG‐ELM) to predict the severity level of the COVID‐19 patients. We conduct a set of experiments on a recently published real‐world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi‐threaded implementation with statistical analysis. In order to verify the efficiency of MG‐ELM, we compare our results with traditional and state‐of‐the‐art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.
Skeleton-based Action Recognition for Human-Robot Interaction using Self-Attention Mechanism

Motion prediction and action recognition play an influential role in the enhancement of interactions between humans and robots. We aim to predict motions and recognize actions for an interaction-based supermarket assistance scenario. Skeleton-based prediction of human motion and action recognition methods gained a lot of attention with the help of recurrent neural networks, convolutional neural networks, and graph convolutions. For recognition of actions, most of the proposed architectures rely on the predefined structure of the skeleton. In this work, we introduce a new small-scale dataset with actions that are possible in a supermarket interaction scenario. we propose two different self-attention-based models for recognition of actions for learning long-range correlations that do not rely on a predefined skeleton structure. We evaluate the models with extensive experiments containing specific input feature encodings that enhances the motion or trajectory features for accurate prediction and recognition of actions. We validate the effectiveness of the models on the actions in supermarket dataset and a standard benchmark dataset for action recognition known as the NTU RGB+D dataset.
Adaptive neural network control for image‐based visual servoing of robot manipulators

This paper presents a novel adaptive neural network control strategy for image-based visual servoing (IBVS) of robotic manipulators with both eye-in-hand and eye-to-hand camera configurations in the presence of unknown dynamics and external disturbances. The IBVS method is combined with the adaptive neural network to construct the proposed adaptive neural network controller to solve the visual servoing control problem of robots. The adaptive neural network based IBVS controller is designed based on the depth-independent interaction matrix, which can be trained on-line to identify the visual servoing robotic system modeling errors. Moreover, the proposed method can approach the unknown nonlinear dynamics for both eye-in-hand and eye-to-hand camera configurations without requiring the robot dynamics to be linearly parameterizable, and the exact knowledge of the robot structure is not needed. On the basis of the nonlinear robot dynamics, the Lyapunov stability analysis is given to prove the asymptotical convergence of the image position and velocity errors. Simulation results for both camera configurations are provided to demonstrate the performance of the proposed adaptive neural network based approach.
Design of Iterative Fuzzy Radial Basis Function Neural Networks Based on Iterative Weighted Fuzzy C-Means Clustering and Weighted LSE Estimation

In this article, a reinforced iterative fuzzy radial basis function neural networks (IFRBFNN) is introduced as an augmented FRBFNN architecture generated through the iterative refinement process of weighted fuzzy C-means (WFCM) clustering and weighted least square error estimation. It is well known that the location of the clusters have an effect on the classification performance of the FRBFNN. The underlying idea behind this article is how to define the center points of clusters based on the data distribution analysis as well as the improvement of classification performance. In a nutshell, while the fuzzy C-means clustering to define the positions of radial basis functions (RBFs) based in the unsupervised learning manner is usually used in a FRBFNN, the parameters (cluster centers and their ensuing polynomial coefficients) refinement of FRBFNN through the proposed iterative method lead to superb classification performance by relocating the positions of RBFs over the input space implied by supervised learning. The idea of the proposed approach is to relocate the centers (prototypes) of the fuzzy clusters by using WFCM clustering algorithm and to re-estimate the coefficients by weighted least square estimation with the aid of the cross-entropy loss values of data so that the classification performance is improved. The weight related to each data is determined by some auxiliary information (i.e., the modified version of the cross-entropy loss value of each data). When it comes to the estimation of the coefficients of the consequent polynomial in IFRBFNN, they are estimated by the weighted least square error estimation technique, where the weights of the coefficients are defined based on the cross-entropy loss values. The auxiliary information such as the weights for relocation of the centers of RBFs and the weights for the re-estimation of the polynomial coefficients should be defined from the viewpoint of enhancement of the classification performance. The cross-entropy loss value of each data is able to meet the necessity of the required supervision signal. Several numerical experiments are provided to demonstrate the usefulness of the proposed design method for the FRBFNN for classification problems.
SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction

The Synthetic Aperture Radar (SAR) target recognition model usually needs to be retrained with all the samples when there are new-coming samples of new targets. Incremental learning emerges to continuously obtain new knowledge from new data while preserving most previously learned knowledge, saving both time and storage. There are still three problems in the existing incremental learning methods: (1) the recognition performance of old target classes degrades significantly during the incremental process; (2) the target classes are easily confused when similar target classes increase; (3) the model is inclined to new target classes due to class imbalance. Regarding the three problems, firstly, the old sample preservation and knowledge distillation were introduced to preserve both old representative knowledge and knowledge structure. Secondly, a class separation loss function was designed to reduce the intra-class distance and increase the inter-class distance, effectively avoiding the confusion between old and new classes. Thirdly, a bias correction layer and a linear model was designed, which enabled the model to treat the old and new target classes more fairly and eliminate the bias. The experimental results on the MSTAR dataset verified the superior performance compared with the other incremental learning methods.
Sagnac Interferometer Based Digital Shearography for Simultaneous Dual Sensitivity Measurement

This paper presents a single camera digital shearography system with dual sensitivity to enable simultaneous measurement of minor and large shearograms in a single field measurement. Digital shearography has many benefits, such as its non-contact measurement, high sensitivity and resistance to turbulence. It has been successfully used for testing of minor deformation gradients in nondestructive testing (NDT). Most digital shearography systems can only work in a single measurement field. As a result, minor shearograms may be overlooked, resulting in incomplete detection in a full-field measurement. The application must allow for multiple measurements in different fields of view or overloading to reveal small defects. The former increases costs, while the latter can result in huge surface deformation that may mask important information. A novel dual sensitivity shearographic system that can simultaneously identify minor and large shearograms in a single field measurement has been developed. The setup is based on the modified Sagnac Interferometer, which uses a polarized Michelson interferometer as a substitute for one of its three mirrors to create additional shearing channels. Two pairs of orthogonally polarized waves carry the large and small field of view information individually without cross-interference on the target screen. The carrier frequency spatial phase shift method is adopted to separate the signal on the frequency domain. The system's performance for dynamic imaging of an object with defects was experimentally investigated. Theory derivation and description of the setup are shown in detail in this paper.
Simplified Denoising for Robust Specific Emitter Identification of Preamble-based Waveforms

Internet of Things (IoT) deployments continue to grow at an accelerated rate, thus presenting a growing surface over which nefarious actors can conduct attacks. This disturbing revelation is exacerbated by the fact that roughly 70% of all IoT devices employ weak or no encryption. Deep learning (DL)-based Specific Emitter Identification (SEI) has been put forward as a possible approach by which to secure IoT devices and related infrastructures. This work presents a DL-based SEI approach that remains robust under degrading signal-to-noise ratio (SNR) conditions while greatly reducing the complexity that is typically associated with DL-based approaches. The presented approach achieves an average percent classification performance of 97% or higher for SNR values greater than or equal to 6 dB.
Multi-feature Fusion VoteNet for 3D Object Detection

In this article, we propose a Multi-feature Fusion VoteNet (MFFVoteNet) framework for improving the 3D object detection performance in cluttered and heavily occluded scenes. Our method takes the point cloud and the synchronized RGB image as inputs to provide object detection results in 3D space. Our detection architecture is built on VoteNet with three key designs. First, we augment the VoteNet input with point color information to enhance the difference of various instances in a scene. Next, we integrate an image feature module into the VoteNet to provide a strong object class signal that can facilitate deterministic detections in occlusion. Moreover, we propose a Projection Non-Maximum Suppression (PNMS) method in 3D object detection to eliminate redundant proposals and hence provide more accurate positioning of 3D objects. We evaluate the proposed MFFVoteNet on two challenging 3D object detection datasets, i.e., ScanNetv2 and SUN RGB-D. Extensive experiments show that our framework can effectively improve the performance of 3D object detection.