Reviewerly/exHarmony huggingface dataset:
An effectual classical dance pose estimation and classification system employing Convolution Neural Network –Long ShortTerm Memory (CNN-LSTM) network for video sequences

Automatic human action recognition remains an intricate issue that includes extractions as well as classifying spatial features of individual pose on video remains as difficult problem for computer vision specialists. Human activity is described as temporal variation of the human body. Capturing and assessing multimedia content of dance remains beneficial for the conservation of cultural heritage, for creating video recommendation systems, for aiding learners to employ tutoring systems , and so on. Indian classical dance (ICD) classification remains a fascinating discipline due to its intricate body posture . This gives a stage for examining diverse computer vision alongside deep learning notions. Through modification in learning styles, automated teaching solutions became unavoidable over all disciplines, from classical to online forums. Furthermore, ICD becomes a necessary portion of a prosperous cultural and heritage that at any means should be updated and conserved. Although several classification approaches prevail for bidimensional dance images, in this study, we define the issue of dance style classification for classifying Indian dance. The dance consists of intricate poses like full-body rotation and self-occlusion, and we proffer three-phase deep learning techniques . Initially, we excerpted necessary joint positions of the dance out of every video frame by pre-trained paradigm, TensorFlow Mobile Net Architecture, that assists us in assessing whatsoever body position within the frame. Next, correlative or identification of the determined action factors by Cosine Similarity was employed. Lastly, we classified the dance pose by excerpted salient details and trained this employing Convolution Neural Network – Long Short Term Memory (CNN-LSTM) Network structure utilizing training dataset of system for classification. We incorporate a feature vector of flow, angles linking anchor joint, and regularised remoteness vectors that catch proximity configuration in skeletal design rather than providing an informal chain of joints in sequence learner that ordinarily obstructs network execution. Therefore, kinematic association amidst body joints through frames employing pose assessment aids in finer showing of the Spatio-temporal reliance. The proffered CNN-LSTM structure for dance classification is compared with Multilayer Perceptron (MLP), Convolutional LSTM (LRCN), Multilayer Perceptron (MLP), CONB3D and LSTM concerning criteria like F1-score, accuracy, recall, AUC curve and precision. Therefore, determined that the proffered CNN-LSTM classification structure attains 98.53% of accuracy, 99.04% of precision, 98.47% of recall, 99.12% of AUC score, and 98.72% of F1-score during the examining procedure. During the training procedure, this attains 94.01% of accuracy, 93.11% of precision, 94.76% of recall, 96.06% of AUC score, and 93.51% of F1-score.
Designing an Illumination-Aware Network for Deep Image Relighting

Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images. Creating or finding satisfying lighting conditions, in reality, is laborious and time-consuming, so it is of great value to develop a technology to manipulate illumination in an image as post-processing. Although previous works have explored techniques based on the physical viewpoint for relighting images, extensive supervisions and prior knowledge are necessary to generate reasonable images, restricting the generalization ability of these works. In contrast, we take the viewpoint of image-to-image translation and implicitly merge ideas of the conventional physical viewpoint. In this paper, we present an Illumination-Aware Network (IAN) which follows the guidance from hierarchical sampling to progressively relight a scene from a single image with high efficiency. In addition, an Illumination-Aware Residual Block (IARB) is designed to approximate the physical rendering process and to extract precise descriptors of light sources for further manipulations. We also introduce a depth-guided geometry encoder for acquiring valuable geometry- and structure-related representations once the depth information is available. Experimental results show that our proposed method produces better quantitative and qualitative relighting results than previous state-of-the-art methods. The code and models are publicly available on https://github.com/NK-CS-ZZL/IAN.
Investigating a Combination of Input Modalities, Canvas Geometries, and Inking Triggers on On-Air Handwriting in Virtual Reality

Humans communicate by writing, often taking notes that assist thinking. With the growing popularity of collaborative Virtual Reality (VR) applications, it is imperative that we better understand aspects that affect writing in these virtual experiences. On-air writing in VR is a popular writing paradigm due to its simplicity in implementation without any explicit needs for specialized hardware. A host of factors can affect the efficacy of this writing paradigm and in this work, we delved into investigating the same. Along these lines, we investigated the effects of a combination of factors on users’ on-air writing performance, aiming to understand the circumstances under which users can both effectively and efficiently write in VR. We were interested in studying the effects of the following factors: (1) input modality: brush vs. near-field raycast vs. pointing gesture, (2) inking trigger method: haptic feedback vs. button based trigger, and (3) canvas geometry: plane vs. hemisphere. To evaluate the writing performance, we conducted an empirical evaluation with thirty participants, requiring them to write the words we indicated under different combinations of these factors. Dependent measures including the writing speed, accuracy rates, perceived workloads, and so on, were analyzed. Results revealed that the brush based input modality produced the best results in writing performance, that haptic feedback was not always effective over button based triggering, and that there are trade-offs associated with the different types of canvas geometries used. This work attempts at laying a foundation for future investigations that seek to understand and further improve the on-air writing experience in immersive virtual environments.
Semantic-aware network embedding via optimized random walk and paragaraph2vec

The Deepwalk-based network embedding algorithm, which was inspired by the word2vec model, can quickly and efficiently learn a low-dimensional vector representation for each node in a complex network. However, the success of word2vec in natural language environments relies heavily on ubiquitous contextual semantics, and the Deepwalk model is less accurate than other deep models due to ignoring contextual semantics in complex networks. To solve this challenge, a semantic-aware network embedding algorithm is proposed in this paper, simply called SANE, via optimized random walk and paragaraph2vec. In the SANE, a semantic-aware random walk strategy is designed to unsupervised generate many semantic-bearing node sequences from a complex network as a training set. Then, based on the training set, the paragraph2vec model is optimized by considering both global semantics and local semantics for learning more accurate low-dimensional node feature representations. Experiments on multiple tasks and multiple networks show the advantages of our SANE algorithm.
Information Maximizing Adaptation Network With Label Distribution Priors for Unsupervised Domain Adaptation

Unsupervised domain adaptation, which transfers knowledge from the source domain to the target domain, has still been a challenging problem. However, previous domain adaptation methods typically minimize the domain discrepancy by using the pseudo target labels. Since the pseudo labels can be noisy, which may cause misalignment and unsatisfying adaptation performance. To address the above challenges, we propose an information maximization adaptation network with label distribution priors. We revisit feature alignment in unsupervised domain adaptation from the perspective of distribution alignment, and find that learning discriminant feature representation requires to minimizing distribution discrepancy and maximizing source mutual information between the outputs of the classifier and feature representations. Due to domain shift, maximizing target mutual information may align features to incorrect class directly. We propose a weighted target mutual information by re-weighting the estimated mutual information via the mean prediction confidence in mini-batch, which can eliminate the negative impact of inaccurate estimation. In addition, we introduce a regularization term of label priors distribution to encourage the similarity to the real label distribution. Extensive experimental results on three benchmark datasets show that our proposed method can achieve remarkable results compared with previous methods.
Model-Free Authoring by Demonstration of Assembly Instructions in Augmented Reality

Among the most compelling applications of Augmented Reality are spatially registered tutorials. The effort of creating such instructions remains one of the obstacles precluding a wider use. We propose a system that is capable of extracting 3D instructions in a completely model-free manner from demonstrations, based on volumetric changes. The instructions are visualised later in an interactive Augmented Reality guidance application, on a mobile head-mounted display. We enable a technology that can be used by anyone in an ad-hoc tabletop setup for assemblies with rigid components.
BiFuse++: Self-Supervised and Efficient Bi-Projection Fusion for $360^{\circ }$ Depth Estimation

Due to the rise of spherical cameras, monocular 360 ° depth estimation becomes an important technique for many applications (e.g., autonomous systems). Thus, state-of-the-art frameworks for monocular 360 ° depth estimation such as bi-projection fusion in BiFuse are proposed. To train such a framework, a large number of panoramas along with the corresponding depth ground truths captured by laser sensors are required, which highly increases the cost of data collection. Moreover, since such a data collection procedure is time-consuming, the scalability of extending these methods to different scenes becomes a challenge. To this end, self-training a network for monocular depth estimation from 360 ° videos is one way to alleviate this issue. However, there are no existing frameworks that incorporate bi-projection fusion into the self-training scheme, which highly limits the self-supervised performance since bi-projection fusion can leverage information from different projection types. In this paper, we propose BiFuse++ to explore the combination of bi-projection fusion and the self-training scenario. To be specific, we propose a new fusion module and Contrast-Aware Photometric Loss to improve the performance of BiFuse and increase the stability of self-training on real-world videos. We conduct both supervised and self-supervised experiments on benchmark datasets and achieve state-of-the-art performance.
Weighted Pointer: Error-aware Gaze-based Interaction through Fallback Modalities

Gaze-based interaction is a fast and ergonomic type of hands-free interaction that is often used with augmented and virtual reality when pointing at targets. Such interaction, however, can be cumbersome whenever user, tracking, or environmental factors cause eye tracking errors. Recent research has suggested that fallback modalities could be leveraged to ensure stable interaction irrespective of the current level of eye tracking error. This work thus presents Weighted Pointer interaction, a collection of error-aware pointing techniques that determine whether pointing should be performed by gaze, a fallback modality, or a combination of the two, depending on the level of eye tracking error that is present. These techniques enable users to accurately point at targets when eye tracking is accurate and inaccurate. A virtual reality target selection study demonstrated that Weighted Pointer techniques were more performant and preferred over techniques that required the use of manual modality switching.
Effect of network strategic capabilities on digital transformation in Jordanian universities

The study aims to explore the effect of network strategic capabilities (NSCs) with its dimension of artificial intelligence (AI) and blockchain on digital transformation (DT) in Jordanian universities. The paper used the analytical-descriptive approach to analyze and interpret the results. The study population includes Jordanian universities, and the sample consists of top management. Out of 400, 304 questionnaires were completed and returned. The results show that AI (β = 1.219, t = 1.175, p &amp;amp;lt; 0.00) and blockchain (β = –0.773, t = 0.437, p &amp;amp;lt; 0.00) have a significant effect on DT. The first sub-hypothesis results concerning leadership revealed that AI (β = 0.525, t = 0.360, p &amp;amp;lt; 0.03) and blockchain (β = –0.538, t = 0.186, p &amp;amp;lt; 0.04) have a significant effect on leadership. The second sub-hypothesis result concerning strategic planning revealed that AI (β = 4.031, t = 3.050, p &amp;amp;lt; 0.002) and blockchain (β = –5.150, t = 2.334, p &amp;amp;lt; 0.020) have a significant effect on strategic planning. While for third sub-hypothesis concerning infrastructure, the results of AI were β = 0.818, t = 1.011, p &amp;amp;lt; 0.032 and for blockchain β = 0.159, t = 0.121, p &amp;amp;lt; 0.904. This result shows that AI has a significant effect on infrastructure, while blockchain does not have any effect. Therefore, NSCs must be integrated into the business process to enhance and boost DT efficiently and effectively.
Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution

The accurate segmentation of multi-organ based on computed tomography (CT) images is important for the diagnosis of abdominal diseases, such as cancer staging, and for surgical planning, such as reducing damage to healthy tissues surrounding the target organ. This task is extremely challenging due to the complexity of background in CT and the variable sizes and shapes of different organs. In this paper, a segmentation model based on U-Net is proposed for five organs related to hepato-biliary-pancreatic surgery, including the pancreas, duodenum, gallbladder, liver and stomach. The proposed model has deformable receptive fields and utilizes the structure of organs in terms of locations and sizes to reduce the interference of complex backgrounds, which makes it an efficient and accurate segmentation method. A spatial attention block is proposed to highlight the organ regions of interest during feature extraction by learning spatial attention maps through explicit external supervision. Moreover, a deformable convolution block is set up to deal with variations in shapes and sizes by producing reasonable receptive fields for different organs through additional trainable offsets. In addition, the skip-connection structure of U-Net is improved by using multi-scale attention maps and high-level semantic information. The proposed model is compared with U-Net and several improved variants on the TCIA multi-organ segmentation dataset, including segmentation performance, time consumption and model parameters. The results show that the proposed model can effectively improve the overall segmentation performance with an average DICE of 80.46% at the cost of a 7.86% increase in model parameters. Compared to U-Net, the average DICE is increased by 1.65%, the average JSC is increased by 1.79% and the average 95% HD is reduced by 4.08. It is a competitive multi-organ segmentation method with better application potential.
Peculiarities of cloud computing use in the process of the first-year students’ adaptive potential development

Technologies based on cloud computing is one of the demanded and actively developing areas of the modern information world. Cloud computing refers to an innovative technology that allows you to combine IT resources of various hardware platforms into a single whole and provide the user with access to them via a local network or the global Internet. Cloud services from various providers offer users access to their resources via the Internet via free or shareware cloud applications, the hardware and software requirements of which do not imply that the user has high-performance and resource-consuming computers. Cloud technologies represent a new way of organizing the educational process and offers an alternative to traditional methods of organizing the educational process, creates an opportunity for personal learning, collective teaching, interactive classes, and the organization of psychological support. The scientific article is devoted to the problem of integrating cloud technologies not only in the process of training highly qualified specialists, but also in the formation of professionally important personality traits. The article describes the experience of introducing cloud technologies into the process of forming the adaptive potential of students in conditions of social constraints caused by the COVID-19 pandemic.
AutoML Loss Landscapes

As interest in machine learning and its applications becomes more widespread, how to choose the best models and hyper-parameter settings becomes more important. This problem is known to be challenging for human experts, and consequently, a growing number of methods have been proposed for solving it, giving rise to the area of automated machine learning (AutoML). Many of the most popular AutoML methods are based on Bayesian optimization, which makes only weak assumptions about how modifying hyper-parameters effects the loss of a model. This is a safe assumption that yields robust methods, as the AutoML loss landscapes that relate hyper-parameter settings to loss are poorly understood. We build on recent work on the study of one-dimensional slices of algorithm configuration landscapes by introducing new methods that test n -dimensional landscapes for statistical deviations from uni-modality and convexity, and we use them to show that a diverse set of AutoML loss landscapes are highly structured. We introduce a method for assessing the significance of hyper-parameter partial derivatives, which reveals that most (but not all) AutoML loss landscapes only have a small number of hyper-parameters that interact strongly. To further assess hyper-parameter interactions, we introduce a simplistic optimization procedure that assumes each hyper-parameter can be optimized independently, a single time in sequence, and we show that it obtains configurations that are statistically tied with optimal in all of the n -dimensional AutoML loss landscapes that we studied. Our results suggest many possible new directions for substantially improving the state of the art in AutoML.
Automatic Analysis of Constructional Diversity as a Predictor of EFL Students’ Writing Proficiency

Abstract One of the important components in second language (L2) development is to produce clause-level units of form–meaning pairings or argument structure constructions. Based on the usage-based constructionist approach that language development entails an ability to use more diverse, more complex, and less frequent constructions, this study tested whether constructional diversity and complexity predict L2 learners’ writing proficiency. Using a natural language processing tool called the Constructional Diversity Analyzer (CDA), we analyzed 3,284 essays produced by college EFL students in terms of the proportion of individual constructions and their diversity. Results from regression analyses showed that constructional diversity reliably predicted learner proficiency: essays with higher scores contained more diverse constructions. We also found that less frequent and more complex constructions made a stronger contribution to predicting the written proficiency levels. Based on the findings, we argue for the validity of constructional diversity and the use of individual constructions as reliable predictors of L2 writing proficiency and propose the application of the CDA for L2 writing assessment and instruction.
Radical Creative Semantic Anchoring: Creative-action metaphors and timbral interaction

This paper explores participatory and socially engaged practices in ubiquitous music (ubimus). We discuss recent advances that target timbre as their focus while incorporating semantic strategies for knowledge transfer among participants. Creative Semantic Anchoring (ASC from the original in Portuguese) is a creative-action metaphor that shows promising preliminary results in collaborative asynchronous activities. Given its grounding in local resources and its support for explicit knowledge, ASC features a good potential to boost socially distributed knowledge. We discuss three strategies that consolidate and expand this approach within ubiquitous music and propose the label Radical ASC. We investigate the implications of this framework through the analysis of two artistic projects: Atravessamentos and Ntrallazzu .
Sentiment analysis techniques, challenges, and opportunities: Urdu language-based analytical study

Sentiment analysis in research involves the processing and analysis of sentiments from textual data. The sentiment analysis for high resource languages such as English and French has been carried out effectively in the past. However, its applications are comparatively few for resource-poor languages due to a lack of textual resources. This systematic literature explores different aspects of Urdu-based sentiment analysis, a classic case of poor resource language. While Urdu is a South Asian language understood by one hundred and sixty-nine million people across the planet. There are various shortcomings in the literature, including limitation of large corpora, language parsers, and lack of pre-trained machine learning models that result in poor performance. This article has analyzed and evaluated studies addressing machine learning-based Urdu sentiment analysis. After searching and filtering, forty articles have been inspected. Research objectives have been proposed that lead to research questions. Our searches were organized in digital repositories after selecting and screening relevant studies. Data was extracted from these studies. Our work on the existing literature reflects that sentiment classification performance can be improved by overcoming the challenges such as word sense disambiguation and massive datasets. Furthermore, Urdu-based language constructs, including language parsers and emoticons, context-level sentiment analysis techniques, pre-processing methods, and lexical resources, can also be improved.
Sociotechnical imaginaries of remote personal touch before and during COVID-19: An analysis of UK newspapers

This article considers newspapers’ role in shaping the sociotechnical imaginaries of touch, and emerging technologies that digitally mediate touch. It examines the discourses of touch and personal relationships at a distance that circulated in major British broadsheet newspapers during the 2020 outbreak of coronavirus disease-19, alongside dominant narratives of touch and remote communication in the previous 5 years. In doing so, the article demonstrates how existing discourses of touch and remote communication intensified during the pandemic, while imaginations of remote touch narrowed. The sociotechnical imaginaries of digital touch matter because they illuminate the kinds of social relations touch technologies are perceived to forge, maintain or deny.