Reviewerly/exHarmony huggingface dataset:
Skeleton-based Human Keypoints Detection and Action Similarity Assessment for Fitness Assistance

Human pose detection refers to finding the position of important joints of human body such as head, hands and feet in image or video, which is a frontier topic in computer vision and is widely used in many fields such as human activity analysis, advanced human-computer interaction and virtual reality. The human pose similarity metric refers to the measurement of similarity between different human poses by metric, which is crucial for the research of human pose recognition based on database retrieval. Currently, in the online fitness situation, video movement instructions are commonly used in a lecture-style with limited teaching effect. In this paper, we propose to use OpenPose and BPE algorithms to analyze and compare fitness movements based on human pose estimation and movement similarity assessment, which can provide real-time movement modification for learners and obtain movement comparison analysis and evaluation results. The experimental results show that the method has higher accuracy, shorter time consumption and better robustness.
Application of Hadoop-Based Cloud Computing in Teaching Platform Research

This article explains the knowledge properties for characterization and research outcomes. It deals with developing and integrating the Hadoop cloud computing platform. The platform utilized in this paper is a one-piece learning algorithm, a statistical model and a cloud-based selection model. Hadoop supports this model, which is suitable for data size computing. We develop Cloud Computing integrated Autocorrelation Function (CCiACF) computation model based on Hadoop learning and implement an efficient teaching platform research data processing and correlation system. Multiple simulations are conducted on the Hadoop platform under various operating conditions to check the training ability’s exactness and characteristics. The Spark Structure of this research is to successfully and effectively develop computational system performance and improve teaching platform research models using Hadoop based cloud computing. Various experimental studies have been performed, and findings indicate that the method proposed is highly successful in data collection, governance and analysis.
A robust and automated method for geometric modelling of thick laminates with multiple and asymmetric ply wrinkles

Ply wrinkles are often found in thick laminates and they are known to be a major failure cause of composite structures. To assess the criticality of the ply wrinkles accurately and efficiently, the complex wrinkle geometries - which are commonly simplified as a single sinusoidal, Gaussian or combined function, should be described precisely and preferably in an automated manner. This study presents a robust method for automated geometric reconstruction and finite element meshing of entire thick laminates with multiple, asymmetric out-of-plane ply wrinkles. We extract coordinate information of each data point along the plies from inspection images, subsequently, multiple Gaussian functions are used to best fit the complex wrinkle geometry. To reconstruct the entire laminate, we interpolate ply geometries of the intermediate layers based on the fitted layers. Finite element models of thick laminates with the concerned ply wrinkles are subsequently generated and analysed to assess structural criticality. The entire procedure is automatised and is implemented in MATLAB codes. The demonstration studies show the robustness and efficiency of the proposed method which can contribute to closing the gap between non-destructive testing and criticality assessment of thick laminates with ply wrinkles.
Geometric Parameter Identification of Medical Robot Based on Improved Beetle Antennae Search Algorithm

To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add the random wandering behavior of the wolf colony algorithm to the search process of the beetle antennae search algorithm to strengthen its capability for local search. Following this, we improve the global convergence ability of the beetle antennae search algorithm by using the simulated annealing algorithm. We compare the accuracy of end positioning of the proposed algorithm with the frog-jumping algorithm and the beetle antennae search algorithm with variable step length through simulations. The results show that the proposed algorithm has a higher accuracy of convergence, and can significantly improve the accuracy of end positioning of the medical robot.
Sign, Attend and Tell: Spatial Attention for Sign Language Recognition

Sign Language Recognition (SLR) has witnessed a boost in recent years, particularly with the surge of deep learning techniques. However, most existing methods do not exploit the concept of attention mechanisms, despite their success in several computer vision tasks. In this paper, we propose a novel method for isolated SLR which utilizes spatial attention to focus the processing on the informative, discriminating parts of the input. This is particularly important for SLR, since the RGB image contains several distracting information such as background and signer's clothes, which are irrelevant for the task. We investigate three ways for incorporating spatial attention: a) pre-focused attention, which uses optical-flow-based motion as a prior b) learned attention, where the network learns where to focus during training, and c) hybrid attention, which combines both approaches by initializing the attention layer in the learned attention with the motion-based attention masks used in the pre-focused attention. We show, first, that all three approaches outperform state-of-the-art methods on one of the largest isolated SLR datasets, validating the effectiveness of attention mechanisms on the SLR task, and second, that the best performing approach is the hybrid attention, combining both ideas.
Decoupling Recognition and Transcription in Mandarin ASR

Much of the recent literature on automatic speech recognition (ASR) is taking an end-to-end approach. Unlike English where the writing system is closely related to sound, Chinese characters (Hanzi) represent meaning, not sound. We propose factoring audio → Hanzi into two sub-tasks: (1) audio → Pinyin and (2) Pinyin → Hanzi, where Pinyin is a system of phonetic transcription of standard Chinese. Factoring the audio → Hanzi task in this way achieves 3.9% CER (character error rate) on the Aishell-1 corpus, the best result reported on this dataset so far.
Adversarial Neural Networks for Error Correcting Codes

Error correcting codes are a fundamental component in modern day communication systems, demanding extremely high throughput, ultra-reliability and low latency. Recent approaches using machine learning (ML) models as decoders offer both improved performance and great adaptability to unknown environments, where traditional decoders struggle. We introduce a general framework to further boost the performance and applicability of ML models. We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words, and, hence, guides the decoding models to recover transmitted codewords. Our framework is game-theoretic, motivated by generative adversarial networks (GANs), with the decoder and discriminator competing in a zero-sum game. The decoder learns to simultaneously decode and generate codewords while the discriminator learns to tell the difference between decoded outputs and codewords. Thus, the decoder is able to decode noisy received signals into codewords, increasing the probability of successful decoding. We show a strong connection of our framework with the optimal maximum likelihood decoder by proving that this decoder defines a Nash equilibrium point of our game. Hence, training to equilibrium has a good possibility of achieving the optimal maximum likelihood performance. Moreover, our framework does not require training labels, which are typically unavailable during communications, and, thus, seemingly can be trained online and adapt to channel dynamics. To demonstrate the performance of our framework, we combine it with recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
Certificateless Proxy Signcryption in the Standard Model for a UAV Network

As we all know, the random oracle model (ROM) is a simulation of the hash function and cannot replace the actual computation of hash function and the public-key cryptography scheme, which was proved to be secure in ROM may be insecure when instantiating random oracles with concrete hash functions in practical applications. So far as we know, certificateless proxy signcryption (CLPSC) scheme’s security is only proved by applying ROM. Therefore, it is quite important to construct a CLPSC scheme in the standard model (SM). In this article, we propose the first CLPSC scheme in SM, which can achieve public verifiability and we prove that this scheme is EUF-CMA-CLPSC security and IND-CCA-CLPSC security against type I adversary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {A}_{I}$ </tex-math></inline-formula> and Type II adversary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {A}_{II}$ </tex-math></inline-formula> . Additionally, we compare our scheme’s performance with several previous schemes’ performance in terms of calculation, communication, and security. The results show that our CLPSC scheme has better security performance, which is suitable for a unmanned aerial vehicle network.
Speckle Reduction in Matrix-Log Domain for Synthetic Aperture Radar Imaging

Synthetic aperture radar (SAR) images are widely used for Earth observation to complement optical imaging. By combining information on the polarization and the phase shift of the radar echos, SAR images offer high sensitivity to the geometry and materials that compose a scene. This information richness comes with a drawback inherent to all coherent imaging modalities: a strong signal-dependent noise called “speckle.” This paper addresses the mathematical issues of performing speckle reduction in a transformed domain: the matrix-log domain. Rather than directly estimating noiseless covariance matrices, recasting the denoising problem in terms of the matrix-log of the covariance matrices stabilizes noise fluctuations and makes it possible to apply off-the-shelf denoising algorithms. We refine the method MuLoG by replacing heuristic procedures with exact expressions and improving the estimation strategy. This corrects a bias of the original method and should facilitate and encourage the adaptation of general-purpose processing methods to SAR imaging.
Disentangling Style and Speaker Attributes for TTS Style Transfer

End-to-end neural TTS has shown improved performance in speech style transfer. However, the improvement is still limited by the available training data in both target styles and speakers. Additionally, degenerated performance is observed when the trained TTS tries to transfer the speech to a target style from a new speaker with an unknown, arbitrary style. In this paper, we propose a new approach to seen and unseen style transfer training on disjoint, multi-style datasets, i. e., datasets of different styles are recorded, one individual style by one speaker in multiple utterances. An inverse autoregressive flow (IAF) technique is first introduced to improve the variational inference for learning an expressive style representation. A speaker encoder network is then developed for learning a discriminative speaker embedding, which is jointly trained with the rest neural TTS modules. The proposed approach of seen and unseen style transfer is effectively trained with six specifically-designed objectives: reconstruction loss, adversarial loss, style distortion loss, cycle consistency loss, style classification loss, and speaker classification loss. Experiments demonstrate, both objectively and subjectively, the effectiveness of the proposed approach for seen and unseen style transfer tasks. The performance of our approach is superior to and more robust than those of four other reference systems of prior art.
Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets

Wireless sensor networks (WSNs) have been applied in networking devices, and a new problem has emerged called source-location privacy (SLP) in critical security systems. In wireless sensor networks, hiding the location of the source node from the hackers is known as SLP. The WSNs have limited battery capacity and low computational ability. Many state-of-the-art protocols have been proposed to address the SLP problems and other problems such as limited battery capacity and low computational power. One of the popular protocols is random path routing (RPR), and in random path routing, the system keeps sending the message randomly along all the possible paths from a source node to a sink node irrespective of the path’s distance. The problem arises when the system keeps sending a message via the longest route, resulting because of high battery usage and computational costs. This research paper presents a novel networking model referred to as calculated random path routing (CRPR). CRPR first calculates the top three shortest paths, and then randomly sends a token to any of the top three shortest calculated paths, ensuring the optimal tradeoff between computational cost and SLP. The proposed methodology includes the formal modeling of the CRPR in Colored Petri Nets. We have validated and verified the CRPR, and the results depict the optimal tradeoff.
An Analysis of Initialization Techniques of Particle Swarm Optimization Algorithm for Global Optimization

Artificial intelligence (AI) has made its way in almost every field of life. From education and health care, it has its application in expert systems, defense, speech recognition, game development, and many more. Frequently many meta-heuristic techniques have been successfully implied for the detection of medical diseases and promises for accurate perception. A well-known swarm-based intelligent stochastic search technique namely Particle Swarm optimization (PSO) is driven from the key behavior of bee swarm during the process of searching of their food source. Consequently, due to the ease of numerical experimentation, PSO is applied to address various kinds of optimization problems. Population initialization play an important role in PSO algorithm. To improve the convergence and population diversity factors, rather than using the random distribution for initialization of population, quasi random sequences can be used effectively. This study presents a survey of various initialization approaches implied in the PSO that are based on quasi-random sequences (Halton, Torus) and pseudo-random sequence (LCG, MCG) strategies. In this study, the state of the art population initialization techniques are used. The systematic analysis unveils the most potential research areas of population initialization and also existing research gaps, although, the main focus is to provide the directions for future work and development in this particular area. This study gives a systematic view for the state of the-art of research, which is discussed in the specified literature to till date. It is anticipated that this study would be helpful to study the PSO algorithm in more depth and detail. This study also finds the efficiency of many QRS (Halton, Torus) and PRNGs (LCG, MCG) based initialization approaches by comparing their performance observed for nine famous benchmark test problems.
A Text Classification Method Based on Graph Attention Networks

With the rapid generation and dissemination of information data in modern society, intelligent processing of text classification is becoming more and more important. The Sequential and Graph-based deep learning models are often used in Natural Language Processing (NLP). The Sequential model usually uses Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) The model performs natural language processing. The graph-based depth model uses the Co-occurrence relationship between texts to learn the characteristics of texts and texts for classification. In this paper, we use RNN to preliminarily calculate the features in the text as the node of the graph, construct a graph with the help of the modification relationship between texts, and then use the graph model to obtain the final text features used to predict the text category. The experiment was compared with a variety of methods through a variety of data sets, and the results showed that the method in this paper achieved better results on the text data set used for emotion classification, and the accuracy rate reached 82.03%.
Federated Learning for Data Mining in Healthcare

Vast developments in technology have occurred in both hardware and software, and now increasing amounts of data related to healthcare is readily available, for example, the data of insurance companies, medical institutions, pharmaceutical industries, patients, and through the personal devices used by people to monitor their health. Data science researchers are taking advantage of this opportunity to use all this data to improve the quality of care delivery. However, it is difficult to generate robust results in healthcare data because the data is private and usually fragmented. It is well known that the patient records owned by the hospitals in electronic health records (EHR) cannot be shared because the data is considered to be sensitive. This makes it difficult to develop effective and best approaches that can be applied to such diverse and sensitive data. Federated learning is a training mechanism for the shared global model that has a centralized server, and it can maintain sensitivity of data at local places where the data originates. It can safeguard the security and can likewise associate the healthcare data sources that are divided. The objective of this overview is to give a survey of federated learning advancements, particularly data mining in medical services. Federated learning empowers preparing a worldwide AI model from information conveyed across different locales, without moving the data. This is especially pertinent in healthcare applications, where data comprises individual, highly sensitive data. Furthermore, data analysis strategies must agree with regulatory guidelines. Although federated learning prevents sharing raw data, it is conceivable to launch privacy attacks on the model parameters that are uncovered during the training process, or on the generated machine learning model.
<i>XR Ulysses</i>: addressing the disappointment of cancelled site-specific re-enactments of Joycean literary cultural heritage on Bloomsday

Site-specific performances are shows created for a specific location and can occur in one or more areas outside the traditional theatre. Social gathering restrictions during the Covid-19 lockdown demanded that these shows be shut down. However, site-specific performances that apply emergent and novel mobile digital technologies have been afforded a compelling voice in showing how performance practitioners and audiences might proceed under the stifling constraints of lockdown and altered live performance paradigms, however they may manifest. Although extended reality (XR) technologies have been in development for a long time, their recent surge in sophistication presents renewed potentialities for site-specific performers to explore ways of bringing the physical world into the digital to recreate real-world places in shared digital spaces. In this research, we explore the potential role of digital XR technologies, such as volumetric video, social virtual reality (VR) and photogrammetry, for simulating site-specific theatre, thereby assessing the potential of these content creation techniques to support future remote performative events. We report specifically on adapting a real-world site-specific performance for VR. This case study approach provides examples and opens dialogues on innovative approaches to site-specific performance in the post-Covid-19 era.
Voting Heterogeneous Ensemble for Code Smell Detection

Code smells are poor design and implementation choices that hinders the overall software quality. Code smells detection using machine learning models has been an active research area to assist software engineers in identifying smelly code. In this paper, we empirically investigate the detection performance of Voting ensemble in detecting class-level and method-level code smells. We built our Voting ensemble in a heterogeneous manner using five different base models: Decision Trees, Logistic Regression, Support Vector Machines, Multi-Layer Perceptron, and Stochastic Gradient Descent models. Predictions output were aggregated using the Soft voting to form the final ensemble prediction output. Voting ensemble detection performance was evaluated against each base model and within the context of five code smells: God Class, Data Class, Long Method, Feature Envy, Long Parameter List, and Switch Statements smells. Statistical pairwise comparison results indicates the superior performance of Voting ensemble in detecting all code smells, while base models had varying detection performance across code smells.
Out of sight but not out of mind: on the North–South academic knowledge spillovers and human capital nexus

This article investigates whether student flows significantly channel academic knowledge from developed countries in the North to less developed ones in the South. It also examines if human capital absorbs and materialises this academic knowledge into technological development. Empirical analysis on a panel of 55 developing countries over 2003–2016 period indicates that student flows from the South to the North is an important vehicle for the North–South knowledge spillovers and human capital in the South strongly facilitates this diffusion process. These findings convey important implications for developing countries' migration and education strategies that aim to promote innovation, technological development and growth performance. They are of special interests to policymakers and educational practitioners.
MILP Based Differential Attack on Round Reduced WARP

WARP is a 128-bit lightweight block cipher presented by S. Banik et al. at SAC 2020. It is based on 32-nibble type-2 Generalised Feistel Network (GFN) structure and uses a permutation over nibbles to optimize the security and efficiency. The designers provided a lower bound on the number of active S-boxes but they did not provide the differential characteristics against these bounds. In this paper, we model the MILP problem for WARP and present the 18-round and 19-round differential characteristics with the probability of $$2^{-122}$$ and $$2^{-132}$$ respectively. We also present a key recovery attack on 21 rounds with the data complexity of $$2^{113}$$ chosen plaintexts. To the best of our knowledge, this is the first key recovery attack against 21-round WARP using differential cryptanalysis.