Reviewerly/exHarmony huggingface dataset:
An ingenious face recognition system based on HRPSM_CNN under unrestrained environmental condition

Face recognition is an emerging technology that divulges various applications in diverse fields like medical image analysis, surveillance, personal identification, and security related cases. In order to effectively recognize the images from the known data sets, there are a number of face recognition algorithms which are in practice. However, a few problems are encountered in effective recognition with a satisfied parameter. Even though there are various algorithms like Local Binary pattern(LBP), Directional Binary Code(DBC), Multi Support Vector Machine(Multi- SVM), and Convolutional Neural Network(CNN)which are being used for face recognition, still the face recognition is not achieved satisfactorily especially for the large databases as the images are affected due to poor lighting and also owing to occlusion occurring in the stagnant pictures. Hence, a new approach called Hybrid Robust Point Set Matching Convolutional Neural Network(HRPSM_CNN) is proposed to effectively recognize the faces from the data sets over the unconstrained situations. This proposed method shows enhanced receiver operating characteristics when compared to the traditional algorithms. This HRPSM_CNN provides 97 % of accuracy rate for ORL and AR database and 96 % for LFW face database which are significantly higher than the existing traditional algorithms. The proposed algorithm is implemented in visually impaired assistive device and the results show better recognition under difficult situations like various lighting and weather conditions.
Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-person Human Pose Estimation

In keypoint estimation tasks such as human pose estimation, heatmap-based regression is the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer from quantization error and require excessive computation to generate and post-process. Motivated to find a more efficient solution, we propose to model individual keypoints and sets of spatially related keypoints (i.e., poses) as objects within a dense single-stage anchor-based detection framework. Hence, we call our method KAPAO (pronounced “Ka-Pow”), for Keypoints And Poses As Objects. KAPAO is applied to the problem of single-stage multi-person human pose estimation by simultaneously detecting human pose and keypoint objects and fusing the detections to exploit the strengths of both object representations. In experiments we observe that KAPAO is faster and more accurate than previous methods, which suffer greatly from heatmap post-processing. The accuracy-speed trade-off is especially favourable in the practical setting when not using test-time augmentation. Source code: https://github.com/wmcnally/kapao .
One-Step Implementation of Time-Optimal-Control Three-Qubit Nonadiabatic Holonomic Controlled Gates in Rydberg Atoms

We propose a one-step scheme to implement the time-optimal nonadiabatic holonomic three-qubit controlled gates in Rydberg atoms by modulating Rabi frequencies of driving fields on the target atom. We perform the time-optimal operations following the brachistochrone curve, after which the scheme manifests completely different characteristics in contrast to the conventional nonadiabatic holonomic quantum computation (NHQC) that all operations require exactly the same evolution time, even for small angular rotations. Since the present three-qubit controlled-gate scheme significantly reduces the evolution time, the influence of decoherence can be greatly reduced. On the other hand, the robustness on the systematic error can also be guaranteed by the feature of geometric phase. The simulation through master equation verifies the high fidelity and robustness of our scheme. Although we use the Rydberg-atom system to verify the feasibility of our scheme, any quantum platforms capable of achieving the same form of effective Hamiltonian is feasible, which means the proposed three-qubit time-optimal-control nonadiabatic holonomic gates is independent of the particular physical platform and may have the potential for wider application.
Digital Proxemics: Designing Social and Collaborative Interaction in Virtual Environments

Behaviour in virtual environments might be informed by our experiences in physical environments, but virtual environments are not constrained by the same physical, perceptual, or social cues. Instead of replicating the properties of physical spaces, one can create virtual experiences that diverge from reality by dynamically manipulating environmental, aural, and social properties. This paper explores digital proxemics, which describe how we use space in virtual environments and how the presence of others influences our behaviours, interactions, and movements. First, we frame the open challenges of digital proxemics in terms of activity, social signals, audio design, and environment. We explore a subset of these challenges through an evaluation that compares two audio designs and two displays with different social signal affordances: head-mounted display (HMD) versus desktop PC. We use quantitative methods using instrumented tracking to analyse behaviour, demonstrating how personal space, proximity, and attention compare between desktop PC and HMDs.
Cloud-based XR Services: A Survey on Relevant Challenges and Enabling Technologies

In recent years, the emergence of XR (eXtended Reality) applications, including Holography, Augmented, Virtual and Mixed Reality, has resulted in the creation of rather demanding requirements for Quality of Experience (QoE) and Quality of Service (QoS). In order to cope with requirements such as ultra-low latency and increased bandwidth, it is of paramount importance to leverage certain technological paradigms. The purpose of this paper is to identify these QoE and QoS requirements and then to provide an extensive survey on technologies that are able to facilitate the rather demanding requirements of Cloud-based XR Services. To that end, a wide range of enabling technologies are explored. These technologies include e.g. the ETSI (European Telecommunications Standards Institute) Multi-Access Edge Computing (MEC), Edge Storage, the ETSI Management and Orchestration (MANO), the ETSI Zero touch network &amp; Service Management (ZSM), Deterministic Networking, the 3GPP (3rd Generation Partnership Project) Media Streaming, MPEG’s (Moving Picture Experts Group) Mixed and Augmented Reality standard, the Omnidirectional MediA Format (OMAF), ETSI’s Augmented Reality Framework etc.
A comparative survey on lightweight block ciphers for resource constrained applications

Information security for low-resource devices has received a fair amount of attention in recent years. These devices have physical constraint in terms of area, power, and memory. Therefore, lightweight cryptographic algorithms are providing sufficient security to such devices. In this paper, many lightweight block ciphers have been comprehensively discussed with their characteristics. These ciphers are broadly categorised based on structural design of round function. In addition, the analysis indicates that few lightweight ciphers are well-suited for hardware implementation. Few are healthy compatible with software implementation. However, some of the block ciphers are designed well for both hardware and software implementations. Moreover, there are various performance metrics used for comparison in resource constrained environments (RCEs). More specifically, the objective of this survey is to compare various modern symmetric cryptographic solutions employed to secure small-scale communication devices.
Design of internet of things online oral English teaching platform based on long-term and short-term memory network

In order to overcome the problems of low fluency and operability in the current online oral English teaching platform, an online oral English teaching platform based on internet of things is proposed. This platform uses the internet of things to design the architecture of online oral English teaching platform. The virtual teaching environment was constructed and the oral English teaching system was used to correct the pronunciation and mouth movements of the users. A part-of-speech labelling model based on the long and short time memory network is constructed, and an attention mechanism is introduced into the long and short time memory network. Experimental results show that the network delay of the platform designed in this paper varies between 0.26 s and 0.37 s, the required cost and development time are reduced by about 50%, and the user-friendly performance of the man-machine interface is improved by 13.20%.
Monofacial vs bifacial solar photovoltaic systems in snowy environments

There has been a recent surge in interest in the more accurate snow loss estimates for solar photovoltaic (PV) systems as large-scale deployments move into northern latitudes. Preliminary results show bifacial modules may clear snow faster than monofacial PV. This study analyzes snow losses on these two types of systems using empirical hourly data including energy, solar irradiation and albedo, and open-source image processing methods from images of the arrays in a northern environment in the winter. Projection transformations based on reference anchor points and snowless ground truth images provide reliable masking and optical distortion correction with fixed surveillance cameras. This allows individual PV module-level snow shedding ratio determination as well as average cumulative snow load by employing grayscale segmentation. The data is used to determine the no-snow losses of two systems during summer and snow losses during winter. The results found monofacial snow losses are in average 33% for winter period, and 16% on an annual basis. Bifacial systems perform better than monofacial in severe winter conditions as average winter snow losses was 16% and the annual losses were 2% in the worst-case scenario. In addition, there was a bifacial gain of 19% compared to monofacial system during winter.
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation

A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity. To address these problems, we present a Dynamic lightweight High-Resolution Network (Dite-HRNet), which can efficiently extract multi-scale contextual information and model long-range spatial dependency for human pose estimation. Specifically, we propose two methods, dynamic split convolution and adaptive context modeling, and embed them into two novel lightweight blocks, which are named dynamic multi-scale context block and dynamic global context block. These two blocks, as the basic component units of our Dite-HRNet, are specially designed for the high-resolution networks to make full use of the parallel multi-resolution architecture. Experimental results show that the proposed network achieves superior performance on both COCO and MPII human pose estimation datasets, surpassing the state-of-the-art lightweight networks. Code is available at: https://github.com/ZiyiZhang27/Dite-HRNet.
RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification

Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning. The imbalanced class distribution misleads classifiers from correctly classifying the minor class. This paper introduces Reduced Noise-SMOTE (RN-SMOTE) for pre-processing imbalanced data. RN-SMOTE firstly, oversamples the training data using SMOTE which introduces noisy oversampled synthetic instances in the minority class. Then, applying DBSCAN to detect and remove noise. Next, the clean artificial instances are combined with the original data. Finally, RN-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. RN-SMOTE is evaluated using 9 different classifiers and 9 different imbalanced datasets with different imbalance ratios and five of them are used for outlier detection. The results proved that the performance of the classifiers has been improved with RN-SMOTE and outperformed the performance with original data and SMOTE with percentage based on the classifier, dataset and evaluation metric. Also, performance of RN-SMOTE has been compared to the performance of the current state of art and resulted in an increase up to 37.41%, 23.28%, 13.95% and 9.07% in terms of Recall, F1, Precision and Accuracy for RN-SMOTE.
Echo state network with multiple delayed outputs for multiple delayed time series prediction

In this paper, considering the influence of multiple delayed output items on the prediction accuracy of echo state network, a novel echo state network with multiple delayed outputs (MDO-ESN) is proposed for time series prediction with multiple delayed outputs. Firstly, for a given learning task, through studying the autocorrelation of output signal, its delayed characteristics can be determined, and then the corresponding delayed item of output equation of the MDO-ESN can be adjusted adaptively. Secondly, in order to improve the adaptability of the MDO-ESN in different learning tasks, a sufficient condition is given to satisfy the stability of the MDO-ESN. Thirdly, a parameter optimization method is given to reduce the dependence of the prediction accuracy of the MDO-ESN on the reservoir parameters of the MDO-ESN. Finally, two numerical simulation examples and one actual simulation example are used for verifying the effectiveness of the MDO-ESN.
Sensor-Based Human Activity and Behavior Research: Where Advanced Sensing and Recognition Technologies Meet

Human activity recognition (HAR) and human behavior recognition (HBR) have been playing increasingly important roles in the digital age [...]
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search a sub-structure from the original model end-to-end across multiple dimensions, including the input tokens, MHSA and MLP modules with state-of-the-art performance. Our method is based on a learnable and unified ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> sparsity constraint with pre-defined factors to reflect the global importance in the continuous searching space of different dimensions. The searching process is highly efficient through a single-shot training scheme. For instance, on DeiT-S, ViT-Slim only takes ~43 GPU hours for the searching process, and the searched structure is flexible with diverse dimensionalities in different modules. Then, a budget threshold is employed according to the requirements of accuracy-FLOPs trade-off on running devices, and a retraining process is performed to obtain the final model. The extensive experiments show that our ViT-Slim can compress up to 40% of parameters and 40% FLOPs on various vision transformers while increasing the accuracy by ~0.6% on ImageNet. We also demonstrate the advantage of our searched models on several downstream datasets. Our code is available at https://github.com/Arnav0400/ViT-Slim.
Collective radiance of giant atoms in non-Markovian regime

We investigate the non-Markovian dynamics of two giant artificial atoms interacting with a continuum of bosonic modes in a one-dimensional (1D) waveguide. Based on the diagrammatic method, we present the exact analytical solutions, which predict the rich phenomena of collective radiance. For the certain collective states, the decay rates are found to be far beyond that predicted in the the Dicke model and standard Markovian framework, which indicates the occurrence of super-superradiance. The superadiance-to-subradiance transition could be realized by adjusting the exchange symmetry of giant atoms. Moreover, there exists multiple bound states in continuum (BICs), with photons/phonons bouncing back and forth in the cavity-like geometries formed by the coupling points. The trapped photons/phonons in the BICs can also be re-released conveniently by changing the energy level splitting of giant atoms. The mechanism relies on the joint effects of the coherent time-delayed feedback and the interference between the coupling points of giant atoms. This work fundamentally broadens the fields of giant atom collective radiance by introducing non-Markovianity. It also paves the way for a clean analytical description of nonlinear open quantum system with more complex retardation.
Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion

Underwater images typically suffer from various quality degradation issues due to the scattering and absorption of light, but these degraded-quality underwater images are unbeneficial for analysis and applications. To effectively solve these quality degradation issues, an underwater image enhancement method via weighted wavelet visual perception fusion is introduced, called WWPF. Concretely, we first present an attenuation-map-guided color correction strategy to correct the color distortion of an underwater image. Subsequently, we employ the maximum information entropy optimized global contrast strategy to the color-corrected image to obtain a global contrast-enhanced image. Meanwhile, we apply a fast integration optimized local contrast strategy to the color-corrected image to get a local contrast-enhanced image. To exploit the complementary of the global contrast-enhanced image and the local contrast-enhanced image, we introduce a weighted wavelet visual perception fusion strategy to obtain a high-quality underwater image by fusing the high-frequency and low-frequency components of images at different scales. Our extensive experiments on three benchmarks validate that our WWPF outperforms the state-of-the-art methods in qualitative and quantitative. Besides, the underwater images processed by our WWPF also benefit practical underwater applications. The code is available https://github.com/Li-Chongyi/WWPF_code.
Applying Bayesian Network Approach to Determine the Association Between Morphological Features Extracted from Prostate Cancer Images

Cancer is a major public health problem across the globe due to which millions of deaths occur every year. In the United States, prostate cancer is the second leading cause of cancer- related deaths in men. The major causes of prostate cancer include increasing age, family history, diet, sexual behavior, and geographic location. Early detection of prostate cancer can effectively reduce the mortality rate. In the past, researchers have adopted various multimodal feature extracting strategies to extract diverse and comprehensive quantitative imaging features and employed machine learning methods to detect prostate cancer. However, existing techniques lack detailed analysis of the magnitude of relationship among different individual discriminatory features, which is very important to understand the dynamics of the disease. In this study, we extracted diverse morphological features to summarize the imaging profile of patients of prostate cancer imaging database and employed Bayesian network analysis approach to quantify the association between different features and the strength of the association. The features and the association between the features were, respectively, modeled as the nodes and the edges of the network. The strength of association between the nodes was computed using Pearson’s correlation, mutual Information and Kullback–Liebler methods. The strongest associations were found between multiple features: (Area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Equidiameter), (Area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Circulatory 2), (Circulatory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\to $ </tex-math></inline-formula> (Elongatedness), (Circulatory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\to $ </tex-math></inline-formula> Entropy), (Circulatory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\to $ </tex-math></inline-formula> Max. Radius), and (Min. Radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Eccentricity). Moreover, interaction impact among nodes and node force was also computed. This analysis will help in finding the features that are more dominant to establish the relationship and can further increase the detection performance.
Outlier Detection Using Structural Scores in a High-Dimensional Space

Outlier detection has drawn significant interest from both academia and industry, such as network intrusion detection. Most existing methods implicitly or explicitly rely on distances in Euclidean space. However, the Euclidean distance may be incapable of measuring the similarity among high-dimensional data due to the curse of dimensionality, thus leading to inferior performance in practice. This paper presents an innovative approach for outlier detection from the view of meaningful structure scores. If two points have similar features, the difference between their structural scores is small and vice versa. The scores are calculated by measuring the variance of angles weighted by data representation, which takes the global data structure into the measurement. Thus, it could consistently rank more similar points. Compared with existing methods, our structural scores could be better to reflect the characteristics of data in a high-dimensional space. The proposed method consistently ranks more similar points. Experiments on synthetic and several real-world datasets have demonstrated the effectiveness and efficiency of our proposed methods.
A Parameter-Selection-Based Chaotic System

Control parameters play a key role in a chaotic system, where they directly determine its chaotic performance. In this brief, we propose a novel parameter-selection-based chaotic system (PSCS), where a new sequence of control parameters is generated using Kolmogorov entropy (KE). KE measures the chaotic behaviors of a chaotic system, and the positive values indicate that the system exhibits chaotic characteristics. Hence, the new sequence is formed by the parameters that yield positive KE values, which always maintain the chaotic behaviors in each iteration. Coupling the obtained sequence into a basic chaotic map, PSCS achieves a simple structure and lower computation cost. Experiments on the time complexity, NIST SP800-22 test suit, and information entropy demonstrate the good performance of the proposed PSCS.