Reviewerly/exHarmony huggingface dataset:
Octonion continuous orthogonal moments and their applications in color stereoscopic image reconstruction and zero-watermarking

Continuous orthogonal moments (COMs) are a type of effective image features widely used in various fields of image processing. However, most of the existing COMs are used for processing flat images and are not suitable for color stereoscopic images. For this reason, this paper first proposes an octonion theory applicable to color stereoscopic images, all color components of color stereoscopic images are coded by using the imaginary part of octonion, and all color components are processed as a whole, and the internal relations among all components are preserved. Then this paper combines the octonion theory with COMs to propose the octonion continuous orthogonal moments (OCOMs). The OCOMs fully reflect and retain the specific correlations between the left- and right-view components of color stereoscopic images, and provide good image description capability. Experimental results show that OCOMs have strong stability and good reconstruction performance when processing color stereoscopic images. Compared with other zero-watermarking methods, the zero-watermarking method embedded by OCOMs has stronger robustness.
Attention-guided chained context aggregation for semantic segmentation

The way features propagate in Fully Convolutional Networks is of momentous importance to capture multi-scale contexts for obtaining precise segmentation masks. This paper proposes a novel series-parallel hybrid paradigm called the Chained Context Aggregation Module (CAM) to diversify feature propagation. CAM gains features of various spatial scales through chain-connected ladder-style information flows and fuses them in a two-stage process, namely pre-fusion and re-fusion. The serial flow continuously increases receptive fields of output neurons and those in parallel encode different region-based contexts. Each information flow is a shallow encoder-decoder with appropriate down-sampling scales to sufficiently capture contextual information. We further adopt an attention model in CAM to guide feature re-fusion. Based on these developments, we construct the Chained Context Aggregation Network (CANet), which employs an asymmetric decoder to recover precise spatial details of prediction maps. We conduct extensive experiments on six challenging datasets, including Pascal VOC 2012, Pascal Context, Cityscapes, CamVid, SUN-RGBD and GATECH. Results evidence that CANet achieves state-of-the-art performance.
A Visual-Based Gesture Prediction Framework Applied in Social Robots

In daily life, people use their hands in various ways for most daily activities. There are many applications based on the position, direction, and joints of the hand, including gesture recognition, gesture prediction, robotics and so on. This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures. The model is applied to the NAO robot to verify the effectiveness of the proposed method. First of all, in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion, the Kalman filter is applied to the original data. Then some new feature descriptors are introduced. The length feature, angle feature and angular velocity feature are extracted from the filtered data. These features are fed into the long-short time memory recurrent neural network (LSTM-RNN) with different combinations. Experimental results show that the combination of coordinate, length and angle features achieves the highest accuracy of 99.31%, and it can also run in real time. Finally, the trained model is applied to the NAO robot to play the finger-guessing game. Based on the predicted gesture, the NAO robot can respond in advance.
MLVSNet: Multi-level Voting Siamese Network for 3D Visual Tracking

Benefiting from the excellent performance of Siamese-based trackers, huge progress on 2D visual tracking has been achieved. However, 3D visual tracking is still under-explored. Inspired by the idea of Hough voting in 3D object detection, in this paper, we propose a Multi-level Voting Siamese Network (MLVSNet) for 3D visual tracking from outdoor point cloud sequences. To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the fi-nal level feature as in previous methods. We also design an efficient and lightweight Target-Guided Attention (TGA) module to transfer the target information and highlight the target points in the search area. Moreover, we propose a Vote-cluster Feature Enhancement (VFE) module to exploit the relationships between different vote clusters. Extensive experiments on the 3D tracking benchmark of KITTI dataset demonstrate that our MLVSNet outperforms state-of-the-art methods with significant margins. Code will be available at https://github.com/CodeWZT/MLVSNet.
Quantum Computers for High-Performance Computing

Quantum computing systems are developing rapidly as powerful solvers for a variety of real-world calculations.Traditionally, many of these same applications are solved using conventional high-performance computing (HPC) systems, which have progressed sharply through decades of hardware and software improvements.Here, we present a perspective on the motivations and challenges of pairing quantum computing systems with modern HPC infrastructure.We outline considerations and requirements for the use cases, macroarchitecture, microarchitecture, and programming models needed to integrate near-term quantum computers with HPC system, and we conclude with the expectation that such efforts are well within reach of current technology.
A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

As the population worldwide continues to age and the percentage of elderly people continues to increase, falls have been become the second leading cause of death from accidental or unintentional injuries. Although many imaging sensing devices have been used to detect falls for elderly people, most involve using the Internet to transfer images taken by a camera to a large back-end server, which performs the necessary calculations; however, algorithm limitations and computational complexity may cause bottlenecks in image outflow, and the image transfer can result in privacy problems. To address these problems, in this paper, an artificial intelligence (AI) fall detection method is proposed that operates using an edge computing architecture, called the pose estimation-based fall detection methodology (PEFDM), which is based on a human body posture recognition technique. The proposed PEFDM can effectively reduce the computational load, runs smoothly on mainstream edge computing systems and possesses artificial intelligence computing capabilities. By using edge computing, the privacy and upload bandwidth issues caused by image outflow can be eliminated. Experiments with real humans show that the PEFDM can detect falls for elderly people with a recognition accuracy of up to 98.1%.
Deep features based convolutional neural network model for text and non-text region segmentation from document images

A deep convolutional neural network model is presented here which uses deep learning features for text and non-text region segmentation from document images. The key objective is to extract text regions from the complex layout document images without any prior knowledge of segmentation. In a real-world scenario, a document or magazine images contain various text information along with non-text regions such as symbols, logos, pictures, and graphics. Extraction of text regions from non-text regions is challenging. To mitigate these issues, an efficient and robust segmentation technique has been proposed in this paper. The implementation of the proposed model is divided into three phases: (a) a method for pre-processing of document images using different patch sizes is employed to handle the situations for variants of text fonts and sizes in mage; (b) a deep convolutional neural network model is proposed to predict the text or non-text or ambiguous region within the image; (c) a method for post-processing of document image is proposed to handle the situation where the image has complex ambiguous regions by utilizing the recursive partitioning of those regions into their proper classes (i.e. text or non-text) and then the system accumulates the responses of those predictive patches with varying resolutions for handling the situation of text fonts variations within the image. Extensive computer simulations have been conducted using a collection of complex layout magazine images from Google sites and the ICDAR 2015 database. Results are collected and compared with state-of-the-art methods. It reveals that the proposed model is robust and more effective as compared to state-of-the-art methods. • A method to analyze the complex layout document images using a deep neural network architecture is proposed. • The proposed model deals with the case of ambiguous regions of the document images. • Extensive computer simulations are presented to demonstrate the performance. • Results and findings are presented in a comprehensive manner.
Online Multi-Label Streaming Feature Selection With Label Correlation

Multi-label streaming feature selection has attracted extensive attention in diverse big data applications. However, most existing works focused on the scenarios where labels are independent, while ignoring the real scenarios that they may be interdependent and correlated with each other. This paper aims to fill this gap by developing a novel online multi-label streaming feature selection scheme by taking into account the existence of label correlation, known as (OMSFS <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sub>LC</sub></i> ). In our design, we first calculate the correlation degree between labels to obtain the label weight. Then, we integrate the mutual information and the label weight to evaluate the correlation between features and labels. In particular, it consists of three stages: 1) online significance analysis, which can determine the significant features via the correlation degree between the newly arriving features and labels; 2) online relevance analysis, which can obtain relevant features via the mutual information; and 3) online redundancy analysis, which can filter the redundant features for removal via pairwise comparison. We implement our solution and conduct extensive experiments on benchmark datasets for performance evaluations. The experimental results exhibit that OMSFS <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sub>LC</sub></i> significantly outperforms the state-of-the-art methods in terms of effectiveness and efficiency.
Online Distributed Trajectory Planning for Quadrotor Swarm With Feasibility Guarantee Using Linear Safe Corridor

This letter presents a new online multi-agent trajectory planning algorithm that guarantees to generate safe, dynamically feasible trajectories in a cluttered environment. The proposed algorithm utilizes a linear safe corridor (LSC) to formulate the distributed trajectory optimization problem with only feasible constraints, so it does not resort to slack variables or soft constraints to avoid optimization failure. We adopt a priority-based goal planning method to prevent the deadlock without an additional procedure to decide which robot to yield. The proposed algorithm can compute the trajectories for 60 agents on average 15.5 ms per agent with an Intel i7 laptop and shows a similar flight distance and distance compared to the baselines based on soft constraints. We verified that the proposed method can reach the goal without deadlock in both the random forest and the indoor space, and we validated the safety and operability of the proposed algorithm through a real flight test with ten quadrotors in a maze-like environment.
Range, Endurance, and Optimal Speed Estimates for Multicopters

Multicopters are among the most versatile mobile robots. Their applications range from inspection and mapping tasks to providing vital reconnaissance in disaster zones and to package delivery. The range, endurance, and speed a multirotor vehicle can achieve while performing its task is a decisive factor not only for vehicle design and mission planning, but also for policy makers deciding on the rules and regulations for aerial robots. To the best of the authors’ knowledge, this work proposes the first approach to estimate the range, endurance, and optimal flight speed for a wide variety of multicopters. This advance is made possible by combining a state-of-the-art first-principles aerodynamic multicopter model based on blade-element-momentum theory with an electric-motor model and a graybox battery model. This model predicts the cell voltage with only 1.3% relative error ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$43.1\, {\mathrm mV}$</tex-math></inline-formula> ), even if the battery is subjected to non-constant discharge rates. Our approach is validated with real-world experiments on a test bench as well as with flights at speeds up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$65\, {\mathrm km}/{\mathrm h}$</tex-math></inline-formula> in one of the world’s largest motion-capture systems. We also present an accurate pen-and-paper algorithm to estimate the range, endurance and optimal speed of multicopters to help future researchers build drones with maximal range and endurance, ensuring that future multirotor vehicles are even more versatile.
Deep Generative Model for Image Inpainting With Local Binary Pattern Learning and Spatial Attention

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based image inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially in the boundary and highly textured regions. To tackle this challenge, in this work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining a local binary pattern (LBP) learning network with an actual inpainting network. Specifically, the first LBP learning network using U-Net architecture is designed to accurately predict the structural information of the missing region, which subsequently guides the second image inpainting network for better filling the missing pixels. Furthermore, an improved spatial attention mechanism is integrated into the image inpainting network, by considering the consistency not only between the known region with the generated one, but also within the generated region itself. Extensive experiments on public datasets including <monospace>CelebA-HQ</monospace>, <monospace>Places</monospace> and <monospace>Paris StreetView</monospace> demonstrate that our model generates better inpainting results than the state-of-the-art competing algorithms, both quantitatively and qualitatively. The source code and trained models are available at <uri>https://github.com/HighwayWu/ImageInpainting</uri>.
A Deep Ensemble-Based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. We begin by evaluating the architecture uncertainty environment, where we show that adversarial attacks crafted to fool specific AMC DL architectures are not directly transferable to different DL architectures. Next, we consider the domain uncertainty environment, where we show that adversarial attacks crafted on time domain and frequency domain features to not directly transfer to the altering domain. Using these insights, we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of deep learning architectures trained on time and frequency domain representations of received signals. Through evaluation on two wireless signal datasets under different sources of uncertainty, we demonstrate that our ADE obtains substantial improvements in AMC classification performance compared with baseline defenses across different adversarial attacks and potencies.
An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet

Convolutional Neural Networks (CNN) continue to dominate research in the area of hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its effectiveness in a variety of computer vision applications such as object segmentation, image classification, face detection, and traffic signs recognition, among others. However, there are numerous constraints for deploying CNNs on FPGA, including limited on-chip memory, CNN size, and configuration parameters. This paper introduces Ad-MobileNet, an advanced CNN model inspired by the baseline MobileNet model. The proposed model uses an Ad-depth engine, which is an improved version of the depth-wise separable convolution unit. Moreover, we propose an FPGA-based implementation model that supports the Mish, TanhExp, and ReLU activation functions. The experimental results using the CIFAR-10 dataset show that our Ad-MobileNet has a classification accuracy of 88.76% while requiring little computational hardware resources. Compared to state-of-the-art methods, our proposed method has a fairly high recognition rate while using fewer computational hardware resources. Indeed, the proposed model helps to reduce hardware resources by more than 41% compared to that of the baseline model.
Hey Alexa, why do we use voice assistants? The driving factors of voice assistant technology use

This study identified three factors to predict the use of the artificial intelligence communication devices known as voice assistants such as Amazon’s Alexa or Apple’s Siri: technological factors (usefulness and perceived ease of use), social influence factors (personal and societal norms), and risk factors (privacy concerns and perceived security). Considering these factors as significant antecedents, this study proposed an extended Technology Acceptance Model. To examine the proposed model, an online survey was conducted with 558 respondents. The findings indicate that perceived usefulness, personal norms, and perceived security have positive relationships with attitudes toward using voice assistants. Additionally, privacy concerns had a negative relationship with attitudes toward using voice assistants, which in turn has a positive relationship with the behavioral intention. Practical and theoretical implications are discussed.
Characterization of nonlinear spin-wave interference by reservoir-computing metrics

We study the computational potential of a spin-wave (SW) substrate by applying two metrics known from reservoir computing. At low intensities, SW scatterers can perform linear operations, while at higher intensities, nonlinear phenomena dominate, possibly enabling high-function, general-purpose computing. The transition between the linear and nonlinear regimes can be quantified by the intensity-dependent kernel rank (KR) and generalization rank (GR). The KR and GR metrics prove that the SW substrate displays the nonlinearities required for computing and give recipes for device designs that utilize nonlinearity.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels Using Tissue Graphs

Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant regions in scanned histology slides. However, the techniques necessitate task-specific large datasets of annotated pixels, which is tedious, time-consuming, expensive, and infeasible to acquire for many histology tasks. Thus, weakly-supervised semantic segmentation techniques are proposed to leverage weak supervision which is cheaper and quicker to acquire. In this paper, we propose \(\textsc {SegGini}\), a weakly-supervised segmentation method using graphs, that can utilize weak multiplex annotations, i.e., inexact and incomplete annotations, to segment arbitrary and large images, scaling from tissue microarray (TMA) to whole slide image (WSI). Formally, \(\textsc {SegGini}\) constructs a tissue-graph representation for an input image, where the graph nodes depict tissue regions. Then, it performs weakly-supervised segmentation via node classification by using inexact image-level labels, incomplete scribbles, or both. We evaluated \(\textsc {SegGini}\) on two public prostate cancer datasets containing TMAs and WSIs. Our method achieved state-of-the-art segmentation performance on both datasets for various annotation settings while being comparable to a pathologist baseline. Code and models are available at: https://github.com/histocartography/seg-gini.
Full-Body Motion from a Single Head-Mounted Device: Generating SMPL Poses from Partial Observations

The increased availability and maturity of head-mounted and wearable devices opens up opportunities for remote communication and collaboration. However, the signal streams provided by these devices (e.g., head pose, hand pose, and gaze direction) do not represent a whole person. One of the main open problems is therefore how to leverage these signals to build faithful representations of the user. In this paper, we propose a method based on variational autoencoders to generate articulated poses of a human skeleton based on noisy streams of head and hand pose. Our approach relies on a model of pose likelihood that is novel and theoretically well-grounded. We demonstrate on publicly available datasets that our method is effective even from very impoverished signals and investigate how pose prediction can be made more accurate and realistic.
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?

Geometric constraints are shown to enforce scale consistency and remedy the scale ambiguity issue in self-supervised monocular depth estimation. Meanwhile, scale-invariant losses focus on learning relative depth, leading to accurate relative depth prediction. To combine the best of both worlds, we learn scale-consistent self-supervised depth in a scale-invariant manner. Towards this goal, we present a scale-aware geometric (SAG) loss, which enforces scale consistency through point cloud alignment. Compared to prior arts, SAG loss takes relative scale into consideration during relative motion estimation, enabling more precise alignment and explicit supervision for scale inference. In addition, a novel two-stream architecture for depth estimation is designed, which disentangles scale from depth estimation and allows depth to be learned in a scale-invariant manner. The integration of SAG loss and two-stream network enables more consistent scale inference and more accurate relative depth estimation. Our method achieves state-of-the-art performance under both scale-invariant and scale-dependent evaluation settings.