Reviewerly/exHarmony huggingface dataset:
RETRACTED: Structure and status quo in the promotion of dance education and teaching based on digital network communication

The advancement of information digitization has accelerated the digital transformation of the industry. Dance education belongs to the field of art and involves the education industry. Its digital development and application research must conform to the general laws of dance art and the special laws of dance education. Digital scientific development and application need to address the common features of technology and art, not blind research and development. This paper introduces the structure and current analysis of dance education and teaching promotion based on digital network communication through the analysis and construction of the system. This paper proposes a digital network communication method to construct an Internet communication mode, which realizes the information exchange between users' computers, and makes dance education and teaching spread on the Internet. In addition, it also uses a diversification method to analyze the structure of dance education. So as to achieve the effect of improving the integrity of teaching and enhancing the degree of knowledge. The experimental results of this article show that under the spread of digital networks, the quality of dance education has increased by 50%, and students’ enthusiasm and interest in dance education courses have increased by 18%. Among them, the information dissemination of dance education on the Internet has also increased greatly. With the advancement, people will pay more and more attention to dance teaching.
Automating user-feedback driven requirements prioritization

Feedback from end users of software applications is a valuable resource in understanding what users request, what they value, and what they dislike. Information derived from user-feedback can support software evolution activities, such as requirements prioritization. User-feedback analysis is still mostly performed manually by practitioners, despite growing research in automated analysis. We address two issues in automated user-feedback analysis: (i) most of the existing automated analysis approaches that exploit linguistic analysis assume that the vocabulary adopted by users (when expressing feedback) and developers (when formulating requirements) are the same; and (ii) user-feedback analysis techniques are usually experimentally evaluated only on some user-feedback dataset, not involving assessment by potential software developers. We propose an approach, ReFeed, that computes, for each requirement, the set of related user-feedback, and from such user-feedback extracts quantifiable properties which are relevant for prioritizing the requirement. The extracted properties are propagated to the related requirements, based on which ranks are computed for each requirement. ReFeed relies on domain knowledge, in the form of an ontology, helping mitigate the gap in the vocabulary of end users and developers. The effectiveness of ReFeed is evaluated on a realistic requirements prioritization scenario in two experiments involving graduate students from two different universities. ReFeed is able to synthesize reasonable priorities for a given set of requirements based on properties derived from user-feedback. The implementation of ReFeed and related resources are publicly available. The results from our studies are encouraging in that using only three properties of user-feedback, ReFeed is able to prioritize requirements with reasonable accuracy. Such automatically determined prioritization could serve as a good starting point for requirements experts involved in the task of prioritizing requirements Future studies could explore additional user-feedback properties to improve the effectiveness of computed priorities.
Sports Dance Movement Assessment Method Using Augment Reality and Mobile Edge Computing

AR (Augment Reality) is an emerging technology that combines computer technology and simulation technology. It uses a computer to generate a simulation environment to immerse users in the environment. AR can simulate the real environment or things and present it to users by virtue of its multiperceptual, interactive, immersive, and other characteristics, to achieve an immersive effect. For sports dance, the same can be used to enhance the effect of teaching and learning through the use of AR technology. Aiming at the problems of delay and terminal equipment energy consumption caused by high-speed data transmission and calculation of virtual technology, this paper proposes a sports dance movement transmission scheme that uses equal power distribution on the uplink. Firstly, based on the collaborative attributes of the AR sports dance business, a system model for AR characteristics is established; secondly, the system frame structure is analyzed in detail, and the constraint conditions are established to minimize the total energy consumption of the system; finally, the mathematical model of Mobile Edge Computing (MEC) based on convex optimization is established under the condition that the delay and power consumption meet the constraints, so as to obtain the optimal communication and computing resource allocation scheme of sports dance in AR. The experimental results reveal that the proposed sports dance movement assessment method based on AR and MEC is efficient.
Online Education in the Russian Arctic: Employers’ Confidence and Educational Institutions’ Readiness

The rapid spread of online learning demonstrates that it is becoming one of the trends in the development of vocational education in the modern world. Along with the obvious advantages of online learning such as cost reduction, cross-border opportunities for receiving it, and adaptability for students, educational institutions encounter specific difficulties: a lack of optimal teaching methods, inflexibility of the institutional environment to the use of new teaching technologies, the transformation of communication between teachers and students, and technological unpreparedness for the development of online learning. At the same time, the need to solve the problem of accessibility of education and fill the shortage of labor resources in Russia, in particular its Arctic zone (AZRF), will contribute to the spread of online learning practices. To consider developing online education, this article, on the one hand, presents the results of a study of the regional employers’ confidence in education in a non-traditional format and, on the other hand, shows the readiness of vocational educational institutions to implement training programs in a distance format. The main research method was a questionnaire survey, in which 2240 organizations and 344 professional educational institutions located in the Russian Arctic took part. The survey results indicate that more than half of employers (58%) declared the applicability of online learning in the Russian Arctic, but about 40.6% of companies do not consider applicants with a diploma from online education. At the same time, employers’ confidence in distance learning in higher education is lower than in vocational secondary education. Additionally, the majority of institutions of higher education (62.5%) believe in the possibility of using distance education in the Russian Arctic, while organizations of vocational secondary education (64.98%) have the opposite opinion. Based on the results of the study, recommendations for federal and regional authorities were prepared.
Thermal Facial Expression Recognition Using Modified ResNet152

Prabhakaran, Aiswarya K. Nair, Jyothisha J. Sarath, S.Facial expression for emotion detection has taken wide popularity with visible images using machine learning techniques and convolutional neural networks. However, emotion recognition from visible images is not much plausible as they are sensitive to light conditions and people can easily fake expression. In this paper, we propose a method for facial expression recognition with thermal images using ResNet152. Residual networks are easier to optimize, and can gain accuracy from considerably increased depth. The objective of this paper is to use a pre-trained modified ResNet152 to train thermal facial images in order to predict different emotions. We use natural visible and infrared facial expression (NVIE) dataset for emotion classification.
Gradient Regularized Contrastive Learning for Continual Domain Adaptation

Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algorithms remains a challenge. To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains. The obstacles in this problem are both domain shift and catastrophic forgetting. We propose Gradient Regularized Contrastive Learning (GRCL) to solve the obstacles. At the core of our method, gradient regularization plays two key roles: (1) enforcing the gradient not to harm the discriminative ability of source features which can, in turn, benefit the adaptation ability of the model to target domains; (2) constraining the gradient not to increase the classification loss on old target domains, which enables the model to preserve the performance on old target domains when adapting to an in-coming target domain. Experiments on Digits, DomainNet and Office-Caltech benchmarks demonstrate the strong performance of our approach when compared to the state-of-the-art.
Learning to Explain: Generating Stable Explanations Fast

The importance of explaining the outcome of a machine learning model, especially a blackbox model, is widely acknowledged.Recent approaches explain an outcome by identifying the contributions of input features to this outcome.In environments involving large blackbox models or complex inputs, this leads to computationally demanding algorithms.Further, these algorithms often suffer from low stability, with explanations varying significantly across similar examples.In this paper, we propose a Learning to Explain (L2E) approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples.Once the explanation algorithm is distilled into an explainer network, it can be used to explain new instances.Our experiments on three classification tasks, which compare our approach to six explanation algorithms, show that L2E is between 5 and 7.5 × 10 4 times faster than these algorithms, while generating more stable explanations, and having comparable faithfulness to the black-box model.
Answer Generation for Retrieval-based Question Answering Systems

Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results.While generally effective, these models fail to provide a satisfying answer when all retrieved candidates are of poor quality, even if they contain correct information.In AS2, models are trained to select the best answer sentence among a set of candidates retrieved for a given question.In this work, we propose to generate answers from a set of AS2 top candidates.Rather than selecting the best candidate, we train a sequence to sequence transformer model to generate an answer from a candidate set.Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan

Multilingual language models have been a crucial breakthrough as they considerably reduce the need of data for under-resourced languages.Nevertheless, the superiority of language-specific models has already been proven for languages having access to large amounts of data.In this work, we focus on Catalan with the aim to explore to what extent a medium-sized monolingual language model is competitive with state-of-the-art large multilingual models.For this, we: (1) build a clean, high-quality textual Catalan corpus (CaText), the largest to date (but only a fraction of the usual size of the previous work in monolingual language models), (2) train a Transformerbased language model for Catalan (BERTa), and (3) devise a thorough evaluation in a diversity of settings, comprising a complete array of downstream tasks, namely, Part of Speech Tagging, Named Entity Recognition and Classification, Text Classification, Question Answering, and Semantic Textual Similarity, with most of the corresponding datasets being created ex novo.The result is a new benchmark, the Catalan Language Understanding Benchmark (CLUB), which we publish as an open resource, together with the clean textual corpus, the language model, and the cleaning pipeline.Using state-of-the-art multilingual models and a monolingual model trained only on Wikipedia as baselines, we consistently observe the superiority of our model across tasks and settings.
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text

Generating code-switched text is a problem of growing interest, especially given the scarcity of corpora containing large volumes of real code-switched text.In this work, we adapt a state-of-the-art neural machine translation model to generate Hindi-English codeswitched sentences starting from monolingual Hindi sentences.We outline a carefully designed curriculum of pretraining steps, including the use of synthetic code-switched text, that enable the model to generate high-quality codeswitched text.Using text generated from our model as data augmentation, we show significant reductions in perplexity on a language modeling task, compared to using text from other generative models of CS text.We also show improvements using our text for a downstream code-switched natural language inference task.Our generated text is further subjected to a rigorous evaluation using a human evaluation study and a range of objective metrics, where we show performance comparable (and sometimes even superior) to codeswitched text obtained via crowd workers who are native Hindi speakers.
Region-aware Global Context Modeling for Automatic Nerve Segmentation from Ultrasound Images

We present a novel deep learning model equipped with a new region-aware global context modeling technique for automatic nerve segmentation from ultrasound images, which is a challenging task due to (1) the large variation and blurred boundaries of targets, (2) the large amount of speckle noise in ultrasound images, and (3) the inherent real-time requirement of this task. It is essential to efficiently capture long-range dependencies by global context modeling for a segmentation network to overcome these challenges. Traditional global context modeling techniques usually explore pixel-aware correlations to establish long-range dependencies, which are usually computation-intensive and greatly degrade time performance. In addition, in this application, pixel-aware modeling may inevitably introduce much speckle noise in the computation and potentially degrade segmentation performance. In this paper, we propose a novel region-aware modeling technique to establish long-range dependencies based on different regions to improve segmentation accuracy while maintaining real-time performance; we call it region-aware pyramid aggregation (RPA) module. In order to adaptively divide the feature maps into a set of semantic-independent regions, we develop an attention mechanism and integrate it into the spatial pyramid network to evaluate the semantic similarity of different regions. We further develop an adaptive pyramid fusion (APF) module to dynamically fuse the multi-level features generated from the decoder to refining the segmentation results. We conducted extensive experiments on a famous public ultrasound nerve image segmentation dataset. Experimental results demonstrate that our method consistently outperforms our rivals in terms of segmentation accuracy. The code is available at https://github.com/jsonliu-szu/RAGCM.
Multidimensional CNN-LSTM Network for Automatic Modulation Classification

Automatic modulation classification (AMC) is the premise for signal detection and demodulation applications, especially in non-cooperative communication scenarios. It has been a popular topic for decades and has gained significant progress with the development of deep learning methods. To further improve classification accuracy, a hierarchical multifeature fusion (HMF) based on a multidimensional convolutional neural network (CNN)-long short-term memory (LSTM) network is proposed in this paper. First, a multidimensional CNN module (MD-CNN) is proposed for feature compensation between interactive features extracted by two-dimensional convolutional filters and respective features extracted by one-dimensional filters. Second, learnt features of the MD-CNN module are fed into an LSTM layer for further exploitation of temporal features. Finally, classification results are obtained by the Softmax classifier. The effectiveness of the proposed method is verified by abundant experimental results on two public datasets, RadioML.2016.10a and RadioML.2016.10b. Satisfying results are obtained as compared with state-of-the-art methods.
Privacy and Utility of X-Vector Based Speaker Anonymization

We study the scenario where individuals (<i>speakers</i>) contribute to the publication of an anonymized speech corpus. Data <i>users</i> leverage this public corpus for downstream tasks, e.g., training an automatic speech recognition (ASR) system, while <i>attackers</i> may attempt to de-anonymize it using auxiliary knowledge. Motivated by this scenario, speaker anonymization aims to conceal speaker identity while preserving the quality and usefulness of speech data. In this article, we study x-vector based speaker anonymization, the leading approach in the VoicePrivacy Challenge, which converts the speaker&#x2019;s voice into that of a random pseudo-speaker. We show that the strength of anonymization varies significantly depending on how the pseudo-speaker is chosen. We explore four design choices for this step: the distance metric between speakers, the region of speaker space where the pseudo-speaker is picked, its gender, and whether to assign it to one or all utterances of the original speaker. We assess the quality of anonymization from the perspective of the three actors involved in our threat model, namely the speaker, the user and the attacker. To measure privacy and utility, we use respectively the linkability score achieved by the attackers and the decoding word error rate achieved by an ASR model trained on the anonymized data. Experiments on LibriSpeech show that the best combination of design choices yields state-of-the-art performance in terms of both privacy and utility. Experiments on Mozilla Common Voice further show that it guarantees the same anonymization level against re-identification attacks among 50 speakers as original speech among 20,000 speakers.
Connecting the dots: Kerala’s use of digital technology during the COVID-19 response

Digital tools are increasingly being applied to support the response to the ongoing COVID-19 pandemic in India and elsewhere globally. This article draws from global frameworks to explore the use of digital tools in the state of Kerala across the domains of communication, surveillance, clinical management, non-clinical support, and core health system readiness and response. Kerala is considered India’s first digital state, with the highest percentage of households with computers (24%) and the internet (51%) in India, 95% mobile phone penetration, 62% smartphone penetration and 75% digital literacy. Kerala has long been a model for the early adoption of digital technology for education and health. As part of the pandemic response, technology has been used across private and public sectors, including law enforcement, health, information technology and education. Efforts have sought to ensure timely access to health information, facilitate access to entitlements, monitor those under quarantine and track contacts, and provide healthcare services though telemedicine. Kerala’s COVID-19 pandemic response showcases the diverse potential of digital technology, the importance of building on a strong health system foundation, the value of collaboration, and the ongoing challenges of data privacy and equity in digital access.
Where do DeFi stablecoins go? A closer look at what DeFi composability really means.

One of the benefits of decentralized finance (DeFi) – an alternative financial system built on blockchain – is composability, which means the system’s building blocks (tokens) can freely interact with one another to form new services. One example is stablecoin, a token with fixed exchange rate, which is backed by token collaterals. While stablecoins can be used to facilitate payments and exchanges, in DeFi they can be used to earn returns (“yield farming”), potentially multiplicatively. We use transaction-level blockchain data to analyze a stablecoin’s flows between protocols and provide suggestive evidence of DeFi yield-chasing behavior. We shed light on what DeFi total value locked might really measure and highlight the complexity in DeFi analysis and market surveillance.
CIMON: Towards High-quality Hash Codes

Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning. Moreover, few of the methods consider the robustness of models, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness.
The Future of Marketing Analytics and Public Policy

The purpose of this special issue is to encourage the emerging role of analytics in marketing and public policy research. We draw attention to a multitude of comprehensive data sources and analytical techniques that tackle important public policy and marketing issues. We highlight six key domains that provide fruitful avenues for such pursuit: retail analytics, social media analytics, marketing mix analytics, services including healthcare, nonprofits and politics, and artificial intelligence and robotics. We also offer an overview of the various articles and commentaries that are included in this special issue, and we encourage future research building on the underlying analytics approaches, substantive findings, and theoretical discoveries.