Reviewerly/exHarmony huggingface dataset:
Novel data fusion strategy for human gait analysis using multiple kinect sensors

Human gait analysis using microsoft Kinect sensor is an intriguing area of research. Fusing multiple sensors' data for clinical gait analysis using calibrated instruments helps in producing more accurate results. A novel technique for spatial calibration is proposed and implemented in this paper. This paper emphasizes on a novel technique of using five Kinect V2 sensors (four clients and one server) and data fusion methods to create unified skeletons for subject-wise gait analysis. These eradicate the problems of inaccurate skeleton poses caused due to occlusions or tracking failures from a single Kinect which otherwise remains a problem in most vision-based sensing systems. Two methods are compared for estimating states of discrete time linear systems. The first one is classical Kalman filtering, which gives accurate results when state disturbances are assumed to be Gaussian white noises and measurement noises and the statistical properties are procurable. Secondly, a Set-Membership filter is used which relies upon the principle of prediction and correction as well. A novel Set-Membership filtering approach is proposed where the measurement noise is modelled by multivariate Gaussian probability density function bounded by −1 to +1. Based on our observations of the linear frameworks joined with interval investigation, the two phases of the estimator are done in a productive way. Both the fusion techniques are tested on overground and treadmill data and the results are validated and compared with ground truth obtained using Qualisys motion capture systems. The proposed approach is also compared quantitatively with state-of-the-art methods.
No-reference light field image quality assessment based on depth, structural and angular information

Light field imaging can capture more abundant scene information, including angular and spatial information, compared with traditional imaging technologies. However, light field image (LFI) processing will inevitably introduce distortion which is different from traditional imaging process. Therefore, LFI quality assessment has become one of important research issues on LFI processing. In this paper, we propose a depth, structural and angular information based no-reference LFI quality assessment method. Firstly, the horizontal and vertical mean difference images (MDIs) are defined to integrate the difference images of LFI's sub-aperture images (SAIs) and reflect the horizontal and vertical depth and structural information of LFI. Considering the multi-channel characteristics of human visual system, an effective feature extraction scheme with Curvelet decomposition is designed for MDIs and SAIs of the distorted LFI. As one of the representations of LFI, epipolar plane images (EPIs) contain LFI's angular and depth information, which reflect the angular consistency of LFI more intuitively. Therefore, an algorithm, namely local maximum similarity index statistics, is designed to extract the directional features from horizontal and vertical EPIs. Finally, the quality of distorted LFI is predicted by pooling these extracted features. Experimental results on three LFI datasets show that the proposed method can achieve better performance, compared with the representative traditional image quality assessment methods as well as the state-of-the-art LFI quality assessment methods.
Geometry-Guided Street-View Panorama Synthesis From Satellite Imagery

This paper presents a new approach for synthesizing a novel street-view panorama given a satellite image, as if captured from the geographical location at the center of the satellite image. Existing works approach this as an image generation problem, adopting generative adversarial networks to implicitly learn the cross-view transformations, but ignore the geometric constraints. In this paper, we make the geometric correspondences between the satellite and street-view images explicit so as to facilitate the transfer of information between domains. Specifically, we observe that when a 3D point is visible in both views, and the height of the point relative to the camera is known, there is a deterministic mapping between the projected points in the images. Motivated by this, we develop a novel satellite to street-view projection (S2SP) module which learns the height map and projects the satellite image to the ground-level viewpoint, explicitly connecting corresponding pixels. With these projected satellite images as input, we next employ a generator to synthesize realistic street-view panoramas that are geometrically consistent with the satellite images. Our S2SP module is differentiable and the whole framework is trained in an end-to-end manner. Extensive experimental results on two cross-view benchmark datasets demonstrate that our method generates more accurate and consistent images than existing approaches.
Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification

Few-shot image classification with graph neural network (GNN) is a hot topic in recent years. Most GNN-based approaches have achieved promising performance. These methods utilize node features or one-dimensional edge feature for classification ignoring rich edge featues between nodes. In this letter, we propose a novel graph neural network exploiting multi-dimensional edge features (MDE-GNN) based on edge-labeling graph neural network (EGNN) and transductive neural network for few-shot learning. Unlike previous GNN-based approaches, we utilize multi-dimensional edge features information to construct edge matrices in graph. After layers of node and edge feautres updating, we generate a similarity score matrix by the mulit-dimensional edge features through a well-designed edge aggregation module. The parameters in our network are iteratively learnt by episode training with an edge similarity loss. We apply our model to supervised few-shot image classification tasks. Compared with previous GNNs and other few-shot learning approaches, we achieve state-of-the-art performance with two benchmark datasets.
Joint exploring of risky labeled and unlabeled samples for safe semi-supervised clustering

In the past few years, Safe Semi-Supervised Learning (S3L) has become an emerging research topic. A few studies have been investigated in the S3L field and obtained desired performance. However, these studies mainly focus on classification problems which cause less attention on clustering. Meanwhile, there is no study takes both risky labeled and unlabeled samples into consideration(e.g., mislabeled samples and outliers). Therefore, we propose a novel Safe Semi-Supervised clustering method to safely explore the labeled an unlabeled samples. Firstly, we apply an effective approach to compute Safe Degree (SD) by estimating local density and minimum distance of each labeled and unlabeled sample. If a sample has large local density and small minimum distance, it can be safe, and correspondingly SD should be high. Otherwise, the sample should be risky and SD is low. Then the SD is introduced into a model-based semi-supervised clustering method to reduce the negative influences of risky labeled and unlabeled samples. Additionally, we construct a graph-based regularization term to limit the outputs of risky labeled samples to be those of nearest unlabeled neighbors. In this case, it is expected to further reduce the harm of risky labeled samples. At the same time, an illustration on an artificial dataset is given to explain the usefulness of the defined SD. Finally, the results which conducted on ten UCI datasets show that our algorithm is effective enough to achieve good clustering performance.
Unsupervised domain adaptation based on cluster matching and Fisher criterion for image classification

Transferring knowledge learned from a labeled domain (source domain) to an unlabeled domain (target domain) is challenging when the two domains have different distributions. The key to the problem is to reduce the distribution shift between the two domains. To align the distributions, most existing works first learn a classifier on the source domain to obtain pseud-labels for target samples, then calculate the target domain distribution based on the pseud-labels. However, the classifier may not meet the target domain because it loses sight of the target distribution during the learning procedure. The mislabeled samples will cause large errors in the calculation of the target domain distribution. To address this issue, we propose a novel method, named cluster matching and Fisher criterion (CMFC), to generate an accurate pseudo-label for each target sample in a latent discriminative subspace by considering both domain distributions. Specifically, we first cluster the samples in both domains, respectively, in the latent subspace and then match the cluster centroid in the target domain with the class centroid in the source domain. Both domain distributions are taken into consideration via cluster matching to assign more accurate pseud-labels. Moreover, we leverage the Fisher criterion to minimize intra-class variances while maximizing inter-class variances, which is conducive to further reducing the distribution shift. We incorporate cluster matching and the Fisher criterion into a united model and design an ADMM algorithm to effectively solve the proposed method. Extensive experiments on five datasets for classification tasks demonstrate the superiority of CMFC.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding

Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving. Previous works studied each perception task separately, and lacked a collective quantitative analysis. In this work, we show that it is possible to perform all perception tasks via a simple and efficient multi-task network. Our proposed network, LidarMTL, takes raw LiDAR point cloud as inputs, and predicts six perception outputs for 3D object detection and road understanding. The network is based on an encoder-decoder architecture with 3D sparse convolution and deconvolution operations. Extensive experiments verify the proposed method with competitive accuracies compared to state-of-the-art object detectors and other task-specific networks. LidarMTL is also leveraged for online localization. Code and pre-trained model have been made available at https://github.com/frankfengdi/LidarMTL.
Introduction to Supervised Machine Learning

The common conception and criticism of machine learning (ML) in medicine is that it centers around a “black box,” an inscrutable series of mathematical calculations that take in data and spit out predictions, lacking the pathobiological explanatory rigor to which medical researchers are accustomed. Although this is oversimplified, it is not altogether untrue. It is also not really a problem. The true place for ML in medicine is not in explanation, but in prediction.The broadest definitions of ML describe processes that take in historical data, learn salient relationships, and use that knowledge to make predictions on new data. To that end, ML algorithms have been present in medicine for decades—hiding in plain sight in the form of, for example, logistic regression and Cox proportional hazard models. Although these algorithms fall within the category of supervised ML (there is a single target outcome, such as death, to predict), they are somewhat special in that they are readily interpretable and assign a “weight” to the various parameters in the model. Indeed, researchers often over interpret these weights, suggesting they imply a causal mechanism as opposed to a mere association (1). Modern ML algorithms reorient the central goal to flexibly predicting outcomes for new data as closely as possible. This allows us to ease many of the strong assumptions behind classic models, permitting the connection between covariates and an outcome to be mediated by any black-box algorithm, saving considerations of interpretability and plausibility for post-hoc discussions (2) In this communication, we hope to orient readers to the techniques, mechanisms, and purpose of supervised ML, which has a goal of predicting outcomes.For ML strategies to work, the entire data lifecycle must be carefully considered. The pipeline naturally begins with data selection, which includes cohort selection, but further aims to clarify which aspects …
Anti-Spoofing Door Lock Using Face Recognition and Blink Detection

Computer vision has become a highly evolving field in recent years, dealing with methods for obtaining, processing, examining, and understanding digital images. Human face recognition in computer vision has a vital role to play in security and surveillance, and the mechanisms for increasing the security levels are strengthening day by day. The existing human face recognition system has been enhanced by introducing an anti-spoofing mechanism which will help to stop a nefarious person to intentionally get around with the system. This article focuses on an approach to detect a human face using texture analysis which includes computing a Histogram of Gradients (HOG) over a region of the face and then uses Support Vector Machines (SVMs) to recognize a face. A blink detection mechanism used in this article ensures the liveliness of the person, making the system more reliable. A Raspberry Pi module is used in implementing the work involved in this paper and the programming is done in Python using libraries like OpenCV and NumPy. This model can achieve a maximum accuracy of 92.68% and achieves optimal results during the afternoon, taking a total of 9.89 seconds for face recognition and blink detection.
UAV Trajectory Planning With Probabilistic Geo-Fence via Iterative Chance-Constrained Optimization

Chance-constrained optimization provides a promi- sing framework for solving control and planning problems with uncertainties, due to its modeling capability to capture randomness in real-world applications. In this paper, we consider a UAV trajectory planning problem with probabilistic geo-fence, building on the chance-constrained optimization approach. In the considered problem, randomness of the model, such as the uncertain boundaries of geo-fences, is incorporated in the formulation. By solving the formulated chance-constrained optimization with a novel sampling based solution method, the optimal UAV trajectory is achieved while limiting the probability of collision with geo-fences to a prefixed threshold. Furthermore, to obtain a totally collision-free trajectory, i.e., avoiding the collision not only at the discrete time-steps but also within the entire time horizon, we build on the idea of an iterative scheme. That is, to iterate the solving of the chance-constrained optimization until the collision with probabilistic geo-fence is avoided at any time within the time horizon. At last, we validate the effectiveness of our method via numerical simulations.
Exploiting Spatio-Temporal Properties of I/Q Signal Data Using 3D Convolution for RF Transmitter Identification

In this work we implement an automatic emitter identification system using three dimensional convolutional neural networks (3D-CNN). We show that it is possible to leverage the short term spatio-temporal properties of the raw I/Q signal data using 3D-CNN and apply this to the task of transmitter identification. The time series of raw I/Q signal data exhibits a “helical” structure which encodes the intrinsic properties of the emitter as short term variations. These features are exploited by our system to create unique “fingerprints” for the emitters, which are finally used for the task of identification. Our system is end-to-end, works with the raw I/Q signal data and is able to automatically discriminate between different transmitters without using a recurrent structure, thus reducing the complexity of the system. We experimented with four novel RF transmitter datasets that we curated in an uncalibrated and uncontrolled indoor environment. The first dataset consists of four identical transmitters, the second eight identical transmitters, the third four heterogeneous transmitters and the last eight heterogeneous transmitters. We use Ettus Research USRP Software Defined Radios (USRP B210, B200, X310), ADLM Pluto and BladeRF for transmission and collect the raw signal data using a RTL Software Defined Radio (RTL SDR) receiver dongle, from each of the four and eight emitters at a time. We use the data to train and test the 3D convolutional neural network for discerning intrinsic transmitter characteristics for the task of classification. Through our methods and implementation, we show that we can identify the transmitters with ~99 % accuracy.
Toward Confidential Cloud Computing

Although largely driven by economies of scale, the development of the modern cloud also enables increased security. Large data centers provide aggregate availability, reliability, and security assurances. The operational cost of ensuring that operating systems, databases, and other services have secure configurations can be amortized among all tenants, allowing the cloud provider to employ experts who are responsible for security; this is often unfeasible for smaller businesses, where the role of systems administrator is often conflated with many others.
Non-Markovianity and the Landauer principle in composite thermal environments

We study non-Markovianity and the Landauer principle in a composite thermal environment consisting of a memory dissipated to a Markovian reservoir. We consider non-Markovianity with respect to four different combinations of two parts of the environment regarding their bosonic and fermionic natures represented by a quantum harmonic oscillator and a qubit, respectively. It is found that non-Markovianity can be suppressed, kept invariant, or enhanced by increasing environmental temperature conditioned on different combinations. Focusing on our model, we establish a modified Landauer principle which holds in the non-Markovian process. We then generalize the model to the case with multiple environments to explore scaling effects of the environments on non-Markovianity. We find that non-Markovianity can be increased by enlarging the size of the environments. We also derive a Landauer bound in these multiple non-Markovian thermal environments. Our results provide strategies for acquiring and enhancing non-Markoviaity in the thermal environments and show the possibility to generalize the conventional Landauer principle to the non-Markovian process.
Designing Substitution Box Based on the 1D Logistic Map Chaotic System

Abstract Many researchers have tried to use the chaotic systems in generating different cryptographic tools, in order to provide high level of security to the data passing through network. In modern symmetric cryptographic systems, one of the most essential functions is a nonlinear mapping Substitution process which is responsible for creating the confusion between plaintext and ciphertext. It replaces each input bits by other bits using the lookup table S-Box. In this paper a new S-Box based on a 1D logistic map chaotic system was designed. The chaotic sequence generated from the 1D logistic map was processed to generate the hexa code values, and after that this hexa code also processed to construct the new good S-Box. This proposal was evaluated using the S-Box tests criteria including avalanche, balanced, completeness, strict avalanche, and invertibility. The analysis results show that the proposed S-Box passes all these statistical tests, has large avalanche effect and therefore the S-Box can avoid many attacks. The construction of both Substitution-Box and its inverse take only few milliseconds, so it can be used in many lightweight block ciphers.
Image super-resolution using multi-granularity perception and pyramid attention networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of multimedia and computer vision communities and obtained remarkable performance. However, most current methods ignore to utilize multi-granularity features of low-resolution (LR) image to further improve the SISR performance. And the channel and spatial features obtained from original LR images are treated equally, resulting in unnecessary computations for abundant uninformative features, thereby hindering the representational ability of super-resolution (SR) models. In this paper, we present a novel Multi-Granularity Pyramid Attention Network (MGPAN) which fully exploits the multi-granularity perception and attention mechanisms to improve the quality of reconstructed images. We design a multi-branch dilated convolution layer with varied kernels corresponding to receptive fields of different sizes to modulate multi-granularity features for adaptively capturing more important information. Moreover, a novel spatial pyramid pooling attention (SPPA) module is constructed to integrate the channel-wise and multi-scale spatial information, which is beneficial to compute the response values from the multi-scale regions of each neuron, and then establish the accurate mapping from low to high dimensional solution space. Besides, for long-short-term information preservation and information flow enhancement, we adopt the short, long, and global skip connection structures to concatenate and fuse the states of each module, which can improve the SR network performance effectively. Extensive experiments on several standard benchmark datasets show that the proposed MGPAN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.
Evaluation of an Inquiry-Based Virtual Lab for Junior High School Science Classes

The study aims to evaluate the effectiveness of an inquiry-based virtual reality (VR) science lab used in junior high school science classes. The Scientific Investigation VR Lab (SIVRLAB) is designed for 9th-grade students to learn about electrochemical cells. It is situated in a guided problem-solving context, where learners need to review the concept of oxidation-reduction reactions and assemble a voltaic cell to save a robot. The SIVRLAB features several cognitive supports and guides for students to plan and record experiments and resolve the problem. It has both a head-mounted display (HMD) version and a desktop VR version. The study recruited 66 9th graders from three classes to evaluate the two versions of the SIVRLAB. The students were assigned to one of three conditions, namely, (1) using immersive HMD SIVRLAB individually, (2) using desktop SIVRLAB individually, and (3) observing one student use immersive HMD SIVRLAB. The students were briefly introduced to the concept of electrochemical cells in the first class and were instructed to use the SIVRLAB sessions in the next class. The results from knowledge pre- and post-tests, a user experience survey, and students’ reflections were collected and analyzed qualitatively. The findings revealed that students who used the desktop VR obtained the highest test scores among the three groups. However, in the follow-up physical laboratory test, the performance of the students in the original HMD VR experimental group was better than those in the desktop VR experimental group. The paper also discusses student feedback and teacher observations regarding the design and interaction with immersive VR. Lastly, the implications of the study and recommendations for future studies are presented.