Reviewerly/exHarmony huggingface dataset:
Non-autoregressive Translation with Layer-Wise Prediction and Deep Supervision

How do we perform efficient inference while retaining high translation quality? Existing neural machine translation models, such as Transformer, achieve high performance, but they decode words one by one, which is inefficient. Recent non-autoregressive translation models speed up the inference, but their quality is still inferior. In this work, we propose DSLP, a highly efficient and high-performance model for machine translation. The key insight is to train a non-autoregressive Transformer with Deep Supervision and feed additional Layer-wise Predictions. We conducted extensive experiments on four translation tasks (both directions of WMT'14 EN-DE and WMT'16 EN-RO). Results show that our approach consistently improves the BLEU scores compared with respective base models. Specifically, our best variant outperforms the autoregressive model on three translation tasks, while being 14.8 times more efficient in inference.
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

How can we generalize to a new prediction task at test time when it also uses a new modality as input? More importantly, how can we do this with as little annotated data as possible? This problem of cross-modal generalization is a new research milestone with concrete impact on real-world applications. For example, can an AI system start understanding spoken language from mostly written text? Or can it learn the visual steps of a new recipe from only text descriptions? In this work, we formalize cross-modal generalization as a learning paradigm to train a model that can (1) quickly perform new tasks (from new domains) while (2) being originally trained on a different input modality. Such a learning paradigm is crucial for generalization to low-resource modalities such as spoken speech in rare languages while utilizing a different high-resource modality such as text. One key technical challenge that makes it different from other learning paradigms such as meta-learning and domain adaptation is the presence of different source and target modalities which will require different encoders. We propose an effective solution based on meta-alignment, a novel method to align representation spaces using strongly and weakly paired cross-modal data while ensuring quick generalization to new tasks across different modalities. This approach uses key ideas from cross-modal learning and meta-learning, and presents strong results on the cross-modal generalization problem. We benchmark several approaches on 3 real-world classification tasks: few-shot recipe classification from text to images of recipes, object classification from images to audio of objects, and language classification from text to spoken speech across 100 languages spanning many rare languages. Our results demonstrate strong performance even when the new target modality has only a few (1-10) labeled samples and in the presence of noisy labels, a scenario particularly prevalent in low-resource modalities.
Can Explanations Be Useful for Calibrating Black Box Models?

NLP practitioners often want to take existing trained models and apply them to data from new domains.While fine-tuning or few-shot learning can be used to adapt a base model, there is no single recipe for making these techniques work; moreover, one may not have access to the original model weights if it is deployed as a black box.We study how to improve a black box model's performance on a new domain by leveraging explanations of the model's behavior.Our approach first extracts a set of features combining human intuition about the task with model attributions generated by black box interpretation techniques, then uses a simple calibrator, in the form of a classifier, to predict whether the base model was correct or not.We experiment with our method on two tasks, extractive question answering and natural language inference, covering adaptation from several pairs of domains with limited target-domain data.The experimental results across all the domain pairs show that explanations are useful for calibrating these models, boosting accuracy when predictions do not have to be returned on every example.We further show that the calibration model transfers to some extent between tasks. 1 1 Code available:
Real Additive Margin Softmax for Speaker Verification

The additive margin softmax (AM-Softmax) loss has delivered remarkable performance in speaker verification. A supposed behavior of AM-Softmax is that it can shrink within-class variation by putting emphasis on target logits, which in turn improves margin between target and non-target classes. In this paper, we conduct a careful analysis on the behavior of AM-Softmax loss, and show that this loss does not implement real max-margin training. Based on this observation, we present a Real AM-Softmax loss which involves a true margin function in the softmax training. Experiments conducted on VoxCeleb1, SITW and CNCeleb demonstrated that the corrected AM-Softmax loss consistently outperforms the original one. The code has been released at https://gitlab.com/csltstu/sunine.
Semi-Supervised Machine Condition Monitoring by Learning Deep Discriminative Audio Features

In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operates on raw audio, by supervising the formation of salient audio embeddings in latent states of a deep temporal convolutional neural network. By fusing the supervised feature learning approach with an unsupervised deep one-class neural network, we are able to model the characteristics of each source and implicitly detect anomalies in different operational states of industrial machines. Moreover, we enable the exploitation of spatial audio information in the learning process, by formulating a novel front-end processing strategy for circular microphone arrays. Experimental results on the MIMII dataset demonstrate the effectiveness of the proposed method, reaching a state-of-the-art mean AUC score of 91.0%. Anomaly detection performance is significantly improved by incorporating multi-channel audio data in the feature extraction process, as well as training the convolutional neural network on the spatially invariant front-end. Finally, the proposed semi-supervised approach allows the concise modeling of normal machine conditions and accurately detects system anomalies, compared to existing anomaly detection methods.
A Generative Model for Texture Synthesis based on Optimal Transport Between Feature Distributions

We propose GOTEX, a general framework for texture synthesis by optimization that constrains the statistical distribution of local features. While our model encompasses several existing texture models, we focus on the case where the comparison between feature distributions relies on optimal transport distances. We show that the semi-dual formulation of optimal transport allows to control the distribution of various possible features, even if these features live in a high-dimensional space. We then study the resulting minimax optimization problem, which corresponds to a Wasserstein generative model, for which the inner concave maximization problem can be solved with standard stochastic gradient methods. The alternate optimization algorithm is shown to be versatile in terms of applications, features and architecture; in particular, it allows to produce high-quality synthesized textures with different sets of features. We analyze the results obtained by constraining the distribution of patches or the distribution of responses to a pre-learned VGG neural network. We show that the patch representation can retrieve the desired textural aspect in a more precise manner. We also provide a detailed comparison with state-of-the-art texture synthesis methods. The GOTEX model based on patch features is also adapted to texture inpainting and texture interpolation. Finally, we show how to use our framework to learn a feed-forward neural network that can synthesize on-the-fly new textures of arbitrary size in a very fast manner. Experimental results and comparisons with the mainstream methods from the literature illustrate the relevance of the generative models learned with GOTEX.
Coregulation Supervision Strategy of Drug Enterprises under the Government Reward and Punishment Mechanism

Considering the government reward and punishment mechanism and the collusion behavior between third-party testing agencies and drug enterprises, based on the coregulation information platform, this paper constructs an evolutionary game model of coregulation supervision, which involves the participation of local government, drug enterprises, and third-party testing agencies. The stable equilibrium points of each participant’s strategic choices are solved. The stability of the strategic combination is analyzed by Lyapunov’s first method, and MATLAB 2020b is used for simulation analysis to verify the influence of each decision variable on different players’ strategic choices. The results show that, firstly, the government-increased awards and penalties will promote the integrity of drug enterprises and noncollusion of third-party testing agencies, but it is not conducive to strict performance of regulatory responsibilities by the local government. Secondly, the provision of real drug test reports by third-party testing agencies to the coregulation information platform can supervise drug enterprises and restrict local government to perform its duty. Thirdly, the central government’s punishment to the local government’s dereliction of duty is significant to enhancing the robustness of drug enterprises’ integrity operation. Furthermore, reasonably setting rewards and punishments and perfecting the coregulation information platform will help form a coregulation pattern of government supervision, self-discipline of drug enterprises, and social supervision. Finally, drug quality is highly related to whether drug enterprises operate with integrity. Standardizing coregulation supervision of drug enterprises’ integrity operation is the key to ensuring the safety of the source of drug quality. Therefore, this paper enriches and expands the theoretical basis of the coregulation supervision of drug enterprises’ integrity operation and proposes corresponding countermeasures and suggestions.
Educational use of Voice Assistants and Smart Speakers

Recently, voice assistants have become very popular since smart speakers incorporate them.This technology is very userfriendly since interaction is carried out using natural language.Voice assistants are being used in everyday life and their technology is constantly evolving, providing new services for the users.Since this technology is used in everyday life by many users, perhaps voice assistants can be used as educational tools.However, there are significant privacy concerns and researchers have discovered serious security risks, although there are studies that provide solutions for these issues.In this study, we focus on the educational use of voice assistants in different levels of education starting from primary school to higher education.Although the popularity of voice assistants is high, there are not many studies regarding their educational potential.
Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or network becomes very large due to iteration and many participants. Although edge computing can reduce latency to some extent, there are significant challenges in further reducing system latency. Federated learning is an emerging paradigm that has recently attracted great interest in academia and industry. The basic idea is to train a globally optimal machine learning model among all participating collaborators. In this paper, a gradient reduction algorithm based on federated random variance is proposed to reduce the number of iterations between the participant and the server from the perspective of the system while ensuring the accuracy, and the corresponding convergence analysis is given. Finally, the method is verified by linear regression and logistic regression. Experimental results show that the proposed method can significantly reduce the communication cost compared with the general stochastic gradient descent federated learning.
Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model

Estimation of probability density functions (pdf) is considered an essential part of statistical modelling. Heteroskedasticity and outliers are the problems that make data analysis harder. The Cauchy mixture model helps us to cover both of them. This paper studies five different significant types of non-parametric multivariate density estimation techniques algorithmically and empirically. At the same time, we do not make assumptions about the origin of data from any known parametric families of distribution. The method of the inversion formula is made when the cluster of noise is involved in the general mixture model. The effectiveness of the method is demonstrated through a simulation study. The relationship between the accuracy of evaluation and complicated multidimensional Cauchy mixture models (CMM) is analyzed using the Monte Carlo method. For larger dimensions (d ~ 5) and small samples (n ~ 50), the adaptive kernel method is recommended. If the sample is n ~ 100, it is recommended to use a modified inversion formula (MIDE). It is better for larger samples with overlapping distributions to use a semi-parametric kernel estimation and more isolated distribution-modified inversion methods. For the mean absolute percentage error, it is recommended to use a semi-parametric kernel estimation when the sample has overlapping distributions. In the smaller dimensions (d = 2) and a sample is with overlapping distributions, it is recommended to use the semi-parametric kernel method (SKDE) and for isolated distributions, it is recommended to use modified inversion formula (MIDE). The inversion formula algorithm shows that with noise cluster, the results of the inversion formula improved significantly.
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks.However, the efficacy of meta-learning crucially depends on the distribution of tasks available for training, and this is often assumed to be known a priori or constructed from limited supervised datasets.In this work, we aim to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text, to enable large-scale meta-learning in NLP.We design multiple distributions of self-supervised tasks by considering important aspects of task diversity, difficulty, type, domain, and curriculum, and investigate how they affect meta-learning performance.Our analysis shows that all these factors meaningfully alter the task distribution, some inducing significant improvements in downstream few-shot accuracy of the metalearned models.Empirically, results on 20 downstream tasks show significant improvements in few-shot learning -adding up to +4.2% absolute accuracy (on average) to the previous unsupervised meta-learning method, and perform comparably to supervised methods on the FewRel 2.0 benchmark.
Impact of Youtube and Video Podcast on Listening Comprehension Among Young Learners

&lt;p class="HRPUB-Abstract"&gt;Even though listening comprehension is an important language skill in second language learning, it is considered to be the most difficult skill to learn. Besides, the teaching of listening comprehension among primary pupils is often neglected even though the role of listening comprehension in language teaching has been repeatedly emphasized. Using an action research design, this study explores the teaching of listening comprehension utilizing YouTube and Video-Podcast involving 40 Primary Year 4 pupils from one of the cluster schools of excellence in the northern region of Malaysia. This study attempts to investigate to what extent did the use of YouTube and Video-Podcast enhance Primary Year 4 pupils’ listening comprehension. This study adopted multiple data collection methods including pre-post tests, reflection, observation, and focused group interviews. However, the data analysis section was mostly pre and post tests data and observation. Only these data collection and analysis tools will be expanded in this study, and not the reflection and focus group interviews. The quantitative data were analyzed statistically using paired sample t-test while thematic analysis was used for analyzing the qualitative data. The findings revealed that YouTube and Video-Podcast yielded positive significant impact on listening comprehension skills among Primary ESL pupils. There was statistically significant increase in listening comprehension scores from pre-test and post-test. Four themes emerged from the observation data. It&lt;em&gt; &lt;/em&gt;increased learners’ participation and understanding, developed team spirit as well as enhanced interest and motivation of the young learners.&lt;/p&gt;
Disentangled Representations for Short-Term and Long-Term Person Re-Identification

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons’ appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and -unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">identity shuffle GAN</i> (IS-GAN). It disentangles identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features, or encourage the identity-related and -unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03 and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset. Our code and models are available online: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cvlab-yonsei.github.io/projects/ISGAN/</uri> .
Human Action Recognition Technology in Dance Video Image

In order to effectively improve the recognition rate of human action in dance video image, shorten the recognition time of human action, and ensure the recognition effect of dance motion, this study proposes a human motion recognition method of dance video image. This recognition method uses neural network theory to transform and process the human action posture in the dance video image, constructs the hybrid model of human motion feature pixels according to the feature points of human action in the image coordinate system, and extracts the human motion features in dance video image. This study uses the background probability model of human action image to sum the variance of human action feature function and update the human action feature function. It can also use Kalman filter to detect human action in dance video image. In the research process, it gets the human multiposture action image features according to the linear combination of human action features. Combined with the feature distribution matrix, it processes the human action features through pose transformation and obtains the human action feature model in the dance video image to accurately identify the human action in the dance video image. The experimental results show that the dance motion recognition effect of the proposed method is good, which can effectively improve the recognition rate of human action in dance video image and shorten the recognition time.
A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.
U-LanD: Uncertainty-Driven Video Landmark Detection

This paper presents U-LanD, a framework for automatic detection of landmarks on key frames of the video by leveraging the uncertainty of landmark prediction. We tackle a specifically challenging problem, where training labels are noisy and highly sparse. U-LanD builds upon a pivotal observation: a deep Bayesian landmark detector solely trained on key video frames, has significantly lower predictive uncertainty on those frames vs. other frames in videos. We use this observation as an unsupervised signal to automatically recognize key frames on which we detect landmarks. As a test-bed for our framework, we use ultrasound imaging videos of the heart, where sparse and noisy clinical labels are only available for a single frame in each video. Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly outperform the state-of-the-art non-Bayesian counterpart by a noticeable absolute margin of 42% in R2 score, with almost no overhead imposed on the model size.