Reviewerly/exHarmony huggingface dataset:
Dance Movement Recognition Technology Based on Multifeature Information Fusion

Multifeature data integration technology uses computer technology to automatically analyze and process the observation data obtained by multiple sensors in chronological order according to a defined threshold. And it can complete the necessary decision-making and evaluation tasks in the data processing process. This article is aimed at studying the effect of multifeature information fusion on dance movement recognition technology. The article mainly adopts the principle of structural risk mitigation and the image thresholding method to effectively obtain the binarized image of the dancer’s moving image, so as to select an appropriate threshold, use the threshold as its distinguishing standard, and then segment the pixel threshold. The method of multifeature information fusion and dance movement recognition technology is introduced. The performance of image classification algorithms is compared, and the experiment of dance video movement, human behavior recognition, and gait phase recognition are carried out. The experimental data show that in the power identification algorithm of this article, when the peak value is 1000 bits, the experimental data is 256 bits, the analysis and comparison data is 457 bits, and the experimental environment is 812 bits. When the peak value is 2000 bits, the experimental data is 451 bits, the analysis and comparison data is 635 bits, and the experimental environment is 947 bits. The experimental data is 562 bits, the analysis and comparison data is 546 bits, and the experimental environment is 774 bits. The experimental results show that the dance action recognition technique based on multifeature information fusion is studied well.
Two-photon interferences of weak coherent lights

Multiphoton interference is an important phenomenon in modern quantum mechanics and experimental quantum optics, and it is fundamental for the development of quantum information science and technologies. Over the last three decades, several theoretical and experimental studies have been performed to understand the essential principles underlying such interference and to explore potential applications. Recently, the two-photon interference (TPI) of phase-randomized weak coherent states has played a key role in the realization of long-distance quantum communication based on the use of classical light sources. In this context, we investigated TPI experiments with weak coherent pulses at the single-photon level and quantitatively analyzed the results in terms of the single- and coincidence-counting rates and one- and two-photon interference-fringe shapes. We experimentally examined the Hong-Ou-Mandel-type TPI of phase-randomized weak coherent pulses to compare the TPI effect with that of correlated photons. Further experiments were also performed with two temporally- and spatially separated weak coherent pulses. Although the observed interference results, including the results of visibility and fringe shape, can be suitably explained by classical intensity correlation, the physics underlying the TPI effect needs to be interpreted as the interference between the two-photon states at the single-photon level within the utilized interferometer. The results of this study can provide a more comprehensive understanding of the TPI of coherent light at the single-photon level.
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modeling

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, a factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained Internet of Things (IoT) and edge devices. In this article, we move beyond the traditional real space where most deep FM-based models are defined and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes' parameter size reduction in comparison to state-of-the-art peer methods.
Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation

Three-dimensional human pose and shape estimation is an important problem in the computer vision community, with numerous applications such as augmented reality, virtual reality, human computer interaction, and so on. However, training accurate 3D human pose and shape estimators based on deep learning approaches requires a large number of images and corresponding 3D ground-truth pose pairs, which are costly to collect. To relieve this constraint, various types of weakly or self-supervised pose estimation approaches have been proposed. Nevertheless, these methods still involve supervision signals, which require effort to collect, such as unpaired large-scale 3D ground truth data, a small subset of 3D labeled data, video priors, and so on. Often, they require installing equipment such as a calibrated multi-camera system to acquire strong multi-view priors. In this paper, we propose a self-supervised learning framework for 3D human pose and shape estimation that does not require other forms of supervision signals while using only single 2D images. Our framework inputs single 2D images, estimates human 3D meshes in the intermediate layers, and is trained to solve four types of self-supervision tasks (i.e., three image manipulation tasks and one neural rendering task) whose ground-truths are all based on the single 2D images themselves. Through experiments, we demonstrate the effectiveness of our approach on 3D human pose benchmark datasets (i.e., Human3.6M, 3DPW, and LSP), where we present the new state-of-the-art among weakly/self-supervised methods.
Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network

Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time.
Distilling Relation Embeddings from Pretrained Language Models

Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities.Among others, this makes it possible to distill high-quality word vectors from pre-trained language models.However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words.Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more finegrained way than is possible with knowledge graphs.To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors.We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. 1
A scalable and fast artificial neural network syndrome decoder for surface codes

Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations consist of a syndrome decoding step where measured stabilizer operators are used to determine appropriate corrections for errors in physical qubits. Decoding algorithms have undergone substantial development, with recent work incorporating machine learning (ML) techniques. Despite promising initial results, the ML-based syndrome decoders are still limited to small scale demonstrations with low latency and are incapable of handling surface codes with boundary conditions and various shapes needed for lattice surgery and braiding. Here, we report the development of an artificial neural network (ANN) based scalable and fast syndrome decoder capable of decoding surface codes of arbitrary shape and size with data qubits suffering from the depolarizing error model. Based on rigorous training over 50 million random quantum error instances, our ANN decoder is shown to work with code distances exceeding 1000 (more than 4 million physical qubits), which is the largest ML-based decoder demonstration to-date. The established ANN decoder demonstrates an execution time in principle independent of code distance, implying that its implementation on dedicated hardware could potentially offer surface code decoding times of O($\mu$sec), commensurate with the experimentally realisable qubit coherence times. With the anticipated scale-up of quantum processors within the next decade, their augmentation with a fast and scalable syndrome decoder such as developed in our work is expected to play a decisive role towards experimental implementation of fault-tolerant quantum information processing.
A Proposed Framework for Secure Data Storage in a Big Data Environment Based on Blockchain and Mobile Agent

The sum of Big Data generated from different sources is increasing significantly with each passing day to extent that it is becoming challenging for traditional storage methods to store this massive amount of data. For this reason, most organizations have resolved to use third-party cloud storage to store data. Cloud storage has advanced in recent times, but it still faces numerous challenges with regard to security and privacy. This paper discusses Big Data security and privacy challenges and the minimum requirements that must be provided by future solutions. The main objective of this paper is to propose a new technical framework to control and manage Big Data security and privacy risks. A design science research methodology is used to carry out this project. The proposed framework takes advantage of Blockchain technology to provide secure storage of Big Data by managing its metadata and policies and eliminating external parties to maintain data security and privacy. Additionally, it uses mobile agent technology to take advantage of the benefits related to system performance in general. We present a prototype implementation for our proposed framework using the Ethereum Blockchain in a real data storage scenario. The empirical results and framework evaluation show that our proposed framework provides an effective solution for secure data storage in a Big Data environment.
Conversational Agents in Organisations

Conversational agents (CAs) promise to create significant organisational value, by transforming how organisations operate and serve customers. Yet, the malleability of this technology poses challenges to both researchers and practitioners because of the wide range of strategic applications they can enable. Drawing on the lens of routine capability, this study investigates strategic applications of CAs, and their associated implementation enablers and challenges. Via an exploratory case study of eight organisations that have successfully implemented CAs, this paper contributes to the literature on the value and implementation of conversational agents in particular and cognitive technologies in general by developing a typology of CA strategic applications and their implementation considerations. For practitioners, the findings highlight the interplay between technology, user, and project management factors that need to be addressed to ensure the successful delivery of the value of CAs.
Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 2017 were included. ML models for operative mortality and major morbidity were previously developed using extreme gradient boosting. Concordance and discordance in predicted risk between ML and STS models were defined using equal-size tertile-based thresholds of risk. Calibration metrics and discriminatory capability were compared between concordant and discordant patients. A total of 243 142 patients were included. Nearly all calibration metrics were improved in cases of concordance. Similarly, concordance indices improved substantially in cases of concordance for all models with the exception of deep sternal wound infection. The greatest improvements in concordant versus discordant cases were in renal failure: ML model (concordance index, 0.660 [95% CI, 0.632-0.687] discordant versus 0.808 [95% CI, 0.794-0.822] concordant) and STS model (concordance index, 0.573 [95% CI, 0.549-0.576] discordant versus 0.797 [95% CI, 0.782-0.811] concordant) (each P<0.001). Excluding deep sternal wound infection, the concordance indices ranged from 0.549 to 0.660 for discordant cases and 0.674 to 0.808 for concordant cases. Conclusions Supplementing ML models with existing STS models for surgical aortic valve replacement may have an important role in risk prediction and should be explored further. In particular, for the roughly 25% to 50% of patients demonstrating discordance in estimated risk between ML and STS, there appears to be a substantial decline in predictive performance suggesting vulnerability of the existing models in these patient subsets.
Analyzing Dynamic Adversarial Training Data in the Limit

To create models that are robust across a wide range of test inputs, training datasets should include diverse examples that span numerous phenomena.Dynamic adversarial data collection (DADC), where annotators craft examples that challenge continually improving models, holds promise as an approach for generating such diverse training sets.Prior work has shown that running DADC over 1-3 rounds can help models fix some error types, but it does not necessarily lead to better generalization beyond adversarial test data.We argue that running DADC over many rounds maximizes its training-time benefits, as the different rounds can together cover many of the task-relevant phenomena.We present the first study of longer-term DADC, where we collect 20 rounds of NLI examples for a small set of premise paragraphs, with both adversarial and non-adversarial approaches.Models trained on DADC examples make 26% fewer errors on our expert-curated test set compared to models trained on non-adversarial data.Our analysis shows that DADC yields examples that are more difficult, more lexically and syntactically diverse, and contain fewer annotation artifacts compared to non-adversarial examples.
Deep Learning Models for Automatic Makeup Detection

Makeup can disguise facial features, which results in degradation in the performance of many facial-related analysis systems, including face recognition, facial landmark characterisation, aesthetic quantification and automated age estimation methods. Thus, facial makeup is likely to directly affect several real-life applications such as cosmetology and virtual cosmetics recommendation systems, security and access control, and social interaction. In this work, we conduct a comparative study and design automated facial makeup detection systems leveraging multiple learning schemes from a single unconstrained photograph. We have investigated and studied the efficacy of deep learning models for makeup detection incorporating the use of transfer learning strategy with semi-supervised learning using labelled and unlabelled data. First, during the supervised learning, the VGG16 convolution neural network, pre-trained on a large dataset, is fine-tuned on makeup labelled data. Secondly, two unsupervised learning methods, which are self-learning and convolutional auto-encoder, are trained on unlabelled data and then incorporated with supervised learning during semi-supervised learning. Comprehensive experiments and comparative analysis have been conducted on 2479 labelled images and 446 unlabelled images collected from six challenging makeup datasets. The obtained results reveal that the convolutional auto-encoder merged with supervised learning gives the best makeup detection performance achieving an accuracy of 88.33% and area under ROC curve of 95.15%. The promising results obtained from conducted experiments reveal and reflect the efficiency of combining different learning strategies by harnessing labelled and unlabelled data. It would also be advantageous to the beauty industry to develop such computational intelligence methods.
Petrogenesis of the Palaeoarchean Keonjhar Granite, Singhbhum Craton, India: Product of Crustal Reworking or Subduction?

The early Archean represents an important eon in the evolution of the earth's continental crust and could provide insights into the nature of geodynamic processes that operated during that period.The Singhbhum Craton from the Indian Shield is the only major archive of Palaeo-Mesoarchean geological processes.The Palaeoarchean granitoids from the Keonjhar area of Singhbhum Craton are potassic granites and granodiorites of calc-alkaline affinity.Their age and elemental concentrations resemble the low Al 2 O 3 granites reported from the Eastern Pilbara Craton of Australia.The geochemical systematics of these granitoids suggests their derivation due to crustal reworking involving partial melting of a tonalitic source, possibly older metamorphic tonalitic gneiss (OMTG).The OMTG could have been derived due to the melting of an enriched basaltic source at the base of an oceanic plateau.In the second stage, the resultant underplating at crustal levels caused the reworking that led to intracrustal melting and differentiation of OMTG to form potassic granites, similar to that of Keonjhar pluton.Consolidating the evidences from the available geochemical and isotopic studies with our own data and correlating them with the geophysical evidences, we interpret that the Keonjhar granitoids are the product of intracrustal melting in an oceanic plateau setting.
An Image Encryption Algorithm Based on the Improved Sine-Tent Map

This paper makes use of the characteristics of initial sensitivity and randomness of the chaotic map to design an image encryption algorithm based on the sine map and the tent map. The sine map is used to improve the tent map; then, the improved sine-tent map is proposed. The traditional tent map proposed in this paper has an expanded control parameter range and better chaos. In this algorithm, bit rearrangement is adopted to further improve the improved sine-tent map, which can reconstruct the output value and expand the chaotic characteristics of the map. In this algorithm, the image parameters are connected with the algorithm to generate the key. In the encryption step, a method of replacing the most significant bit and scrambling-diffusion algorithm is designed to encrypt the plaintext image. Finally, the algorithm is simulated with the experiment and evaluated with analysis; then, the experimental results are given. The evaluation results show that the ciphertext of the algorithm has high randomness, strong robustness, and better resistance to differential attacks after comparison. The correlation of the ciphertext image pixels is very low, and the algorithm is highly secure as a conclusion.
The Cultural Differences in Etiquette and Customs Between China and Britain in Nonverbal Communication

Nonverbal behavior as well as verbal behavior, is closely related to culture when expressing ideas. Due to the huge differences between Chinese and English culture, there are also a lot of differences in nonverbal communication. By comparing the common etiquette and customs in nonverbal communication activities between China and Britain, meanwhile the cultural differences between them are figured out.
WAFP-Net: Weighted Attention Fusion Based Progressive Residual Learning for Depth Map Super-Resolution

Despite the remarkable progresses achieved in depth map super-resolution (DSR), it remains a major challenge to tackle with real-world degradation of low-resolution (LR) depth maps. Synthetic datasets are mainly used in existing DSR approaches, which is quite different from what would get from a real depth sensor. Besides, the enhancements of features in existing DSR approaches are not sufficiently enough, which also limit the performance. To alleviate these problems, we first propose two types of degradation models to describe the generation of LR depth maps, including bi-cubic down-sampling with noise and interval down-sampling, and different DSR models are learned correspondingly. Then, we propose a weighted attention fusion strategy that is embedded into a progressive residual learning framework, which guarantees that the high-resolution (HR) depth maps can be well recovered in a coarse-to-fine manner. The weighted attention fusion strategy can enhance the features with abundant high-frequency components in both global and local manners, thus better HR depth maps can be expected. Besides, to re-use the effective information in the progressive process sufficiently, a multi-stage fusion module is combined into the proposed framework, and the Total Generalized Variation (TGV) regularization and input loss are exploited to further improve the performance of our method. Extensive experiments of different benchmarks demonstrate the superiority of our approach over the state-of-the-art (SOTA) approaches.