Reviewerly/exHarmony huggingface dataset:
On Compositional Generalization of Neural Machine Translation

Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT.However, there still exist significant issues such as robustness, domain generalization, etc.In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs.We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization, although it performs remarkably well under traditional metrics.
Adding Binary Search Connections to Improve DenseNet Performance

Convolutional neural networks have significantly improved the field of computer vision. Most of the standard deep learning models for vision are based on convolutional neural networks. One such model is DenseNet, which is well known for providing comparable results to ResNet with fewer trainable variables. This paper proposes a mechanism to improve the performance of DenseNet architecture. We have compared our proposed approach with the original DenseNet architecture on CIFAR100 dataset. Effects of applying dropout mechanism with our proposed mechanism is also studied on DenseNet model and the obtained results showed that our approach helps DenseNet to significantly improve the performance and helping the model to learn faster. Even in situations where original DenseNet-121 would have overfit, our mechanism helped DenseNet-121 to keep improving the accuracy without overfitting on the dataset assuring better accuracy on testing data.
Modeling Multiple Temporal Scales of Full-Body Movements for Emotion Classification

This work investigates classification of emotions from full-body movements by using a novel Convolutional Neural Network-based architecture.The model is composed of two shallow networks processing in parallel when the 8-bit RGB images obtained from time intervals of 3D-positional data are the inputs.One network performs a coarse-grained modelling in the time domain while the other one applies a fine-grained modelling.We show that combining different temporal scales into a single architecture improves the classification results of a dataset composed of short excerpts of the performances of professional dancers who interpreted four affective states: anger, happiness, sadness, and insecurity.Additionally, we investigate the effect of data chunk duration, overlapping, the size of the input images and the contribution of several data augmentation strategies for our proposed method.Better recognition results were obtained when the duration of a data chunk was longer, and this was further improved by applying balanced data augmentation.Moreover, we test our method on other existing motion capture datasets and compare the results with prior art.In all experiments, our results surpassed the state-of-the-art approaches, showing that this method generalizes across diverse settings and contexts.
Perlin Random Erasing for Data Augmentation

In the last decade, Deep Learning is applied in a wide range of problems with tremendous success. Large data, increased computational resources, and theoretical improvements are main reasons for this success. As the dataset grows, the realworld is better represented, allows developing a model that can generalize. However, creating a labeled dataset is expensive, timeconsuming, or sometimes even challenging. Therefore, researchers proposed data augmentation methods to increase dataset size by creating variations of the existing data. This study proposes an extension to Random Erasing data augmentation method by introducing smoothness. The proposed method provides better performance compared to Random Erasing data augmentation method, which is shown using a transfer learning scenario on the UC Merced Land-use image dataset.
Role of COVIDsafe app and control measures in Australia in combating COVID-19 pandemic

Purpose COVID-19 (C19) has been destroying the world's health and emergency response system for almost the past year. Policymakers and health practitioners are trying their best to save the public through various policy development and initiatives in this regard. This study aims to examine the containment measures and their impacts on Australia's C19 situation in Australia's COVIDsafe app background. Design/methodology/approach This study investigates the role of the Australian Government's (AG) Health Containment (HC) and Stringency response (SR) in combating the C19 situation in Australia. The time horizon has been taken from January to October 2020 and applied Linear Regression with graphical demonstration analysis by STATA-18 version and MS Word chart features. Findings By applying linear regression and graphical demonstration, statistics revealed that AG made various policy developments during the C19 pandemic. However, due to inconsistent and unsustainable measures, the second C19 wave hit Australia much harder than the first wave. COVIDsafe app has been a vital AG in this regard; however, it did not show its progress during the second wave due to privacy issues. After the more focused and aggressive research and development measures, AG overcame the App drawbacks and controlled the situation, demonstrating 92% recovered statistics from C19. Practical implications The study concludes that AG should enforce many prudent policy measures and distinct E-government features in the COVIDsafe app and make it secure so people will use it in probable forthcoming C19 waves. Originality/value This study has examined the Government of Australia's containment measures in the background discussion of the COVIDsafe app.
WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans

In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms.In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content.While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive.This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants with a dataset containing toxic spans annotation in English posts.In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our best performing neural transformer model achieves an 0.68 F1-Score.Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.
Multi-GRU Based Automated Image Captioning for Smartphones

Image captioning is the description of an image with natural language expressions using computer vision and natural language processing fields. Recent advances in hardware and processing power in smartphones lead the development of many image captioning applications. In this study, a novel automatic image captioning system based on the encoder-decoder approach that can be applied in smartphones is proposed. While high-level visual information is extracted with the ResNet152V2 convolutional neural network in the encoder part, the proposed decoder transforms the extracted visual information into natural expressions of the images. The proposed decoder with the multilayer gated recurrent unit structure allows generating more meaningful captions using the most relevant visual information. The proposed system has been evaluated using different performance metrics on the MSCOCO dataset and it outperforms the state-ofthe-art approaches. The proposed system is also integrated with our custom-designed Android application, named IMECA, which generates captions in offline mode unlike similar applications. Thus, image captioning is intended to be practical for more people.
The search for causality: A comparison of different techniques for causal inference graphs.

Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Self-Guided Curriculum Learning for Neural Machine Translation

In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible.Inspired by this, we formulate a difficulty criterion based on the recovery degrees of training examples.Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model "knows" how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees.Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree.Experimental results on translation benchmarks including WMT14 English⇒German and WMT17 Chinese⇒English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.
Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter

In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios.
Explaining Deep Neural Network Models with Adversarial Gradient Integration

Deep neural networks (DNNs) have became one of the most high performing tools in a broad range of machine learning areas. However, the multilayer non-linearity of the network architectures prevent us from gaining a better understanding of the models’ predictions. Gradient based attribution methods (e.g., Integrated Gradient (IG)) that decipher input features’ contribution to the prediction task have been shown to be highly effective yet requiring a reference input as the anchor for explaining model’s output. The performance of DNN model interpretation can be quite inconsistent with regard to the choice of references. Here we propose an Adversarial Gradient Integration (AGI) method that integrates the gradients from adversarial examples to the target example along the curve of steepest ascent to calculate the resulting contributions from all input features. Our method doesn’t rely on the choice of references, hence can avoid the ambiguity and inconsistency sourced from the reference selection. We demonstrate the performance of our AGI method and compare with competing methods in explaining image classification results. Code is available from https://github.com/pd90506/AGI.
LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model

Recently proposed pre-trained language models can be easily fine-tuned to a wide range of downstream tasks.However, a large-scale labelled task-specific dataset is required for fine-tuning creating a bottleneck in the development process of machine learning applications.To foster a fast development by reducing manual labelling efforts, we propose a Label-Efficient Training Scheme (LETS).The proposed LETS consists of three elements: (i) task-specific pre-training to exploit unlabelled task-specific corpus data, (ii) label augmentation to maximise the utility of labelled data, and (iii) active learning to label data strategically.In this paper, we apply LETS to a novel aspect-based sentiment analysis (ABSA) use-case for analysing the reviews of the health-related program supporting people to improve their sleep quality.We validate the proposed LETS on a custom health-related program-reviews dataset and another ABSA benchmark dataset.Experimental results show that the LETS can reduce manual labelling efforts 2-3 times compared to labelling with random sampling on both datasets.The LETS also outperforms other state-ofthe-art active learning methods.Furthermore, the experimental results show that LETS can contribute to better generalisability with both datasets compared to other methods thanks to the task-specific pre-training and the proposed label augmentation.We expect this work could contribute to the natural language processing (NLP) domain by addressing the issue of the high cost of manually labelling data.Also, our work could contribute to the healthcare domain by introducing a new potential application of NLP techniques.
Awareness, use and attitudes of students towards e-books: differences based on gender, discipline and degree level

Purpose The purpose of this study was to explore the level of awareness, use and attitudes of students regarding e-books. Design/methodology/approach This study used a quantitative research design based on the survey method. A structured questionnaire was designed and used for data collection from four different universities of Pakistan. A total of 305 responses were collected from a sample of 400, and 303 of those responses were used for data analysis. Findings The results of this study indicated that students were aware of e-books and used them in their daily lives. Most of the respondents had a positive attitude towards e-books and indicated that they considered e-books are easy to find and support them in their assignments. Online features of e-books were important to them. There was a significant difference of opinion found among students of different disciplines regarding the use of e-books. Research limitations/implications This study was limited to four disciplines in four universities, one discipline per university. Therefore, its findings may not be applied to other universities or other disciplines. Practical implications This paper offers a new idea to academic libraries related to purchasing e-books. This study will provide guidelines to information professionals for development of their e-book collection and implementation of innovative approaches in library services or e-books services to facilitate their users according to their need. Originality/value To the best of the authors’ knowledge, this is the first study on the level of awareness, use and attitudes of university students towards e-books in Pakistan.
Advancing Imbalanced Domain Adaptation: Cluster-Level Discrepancy Minimization With a Comprehensive Benchmark

Unsupervised domain adaptation methods have been proposed to tackle the problem of covariate shift by minimizing the distribution discrepancy between the feature embeddings of source domain and target domain. However, the standard evaluation protocols assume that the conditional label distributions of the two domains are invariant, which is usually not consistent with the real-world scenarios such as long-tailed distribution of visual categories. In this article, the imbalanced domain adaptation (IDA) is formulated for a more realistic scenario where both label shift and covariate shift occur between the two domains. Theoretically, when label shift exists, aligning the marginal distributions may result in negative transfer. Therefore, a novel cluster-level discrepancy minimization (CDM) is developed. CDM proposes cross-domain similarity learning to learn tight and discriminative clusters, which are utilized for both feature-level and distribution-level discrepancy minimization, palliating the negative effect of label shift during domain transfer. Theoretical justifications further demonstrate that CDM minimizes the target risk in a progressive manner. To corroborate the effectiveness of CDM, we propose two evaluation protocols according to the real-world situation and benchmark existing domain adaptation approaches. Extensive experiments demonstrate that negative transfer does occur due to label shift, while our approach achieves significant improvement on imbalanced datasets, including Office-31, Image-CLEF, and Office-Home.
A new chaotic signal based on deep learning and its application in image encryption

To improve the security of image encryption in singular chaotic systems, an encryption algorithm based on deep-learning is proposed in this paper. To begin with, the chaos sequence is generated by using a hyperchaotic Lorenz system, prior to creating new chaotic signals based on chaotic characteristics obtained from he simulations of the powerful complex network structure of long-short term memory artificial neural network (LSTM-ANN). Then, dynamic characteristics of the new signals are analyzed with the largest Lyapunov exponent, 0-1 test, power spectral analysis, phase diagrams and NIST test. In the end, the new signals are applied to image encryption, the results of which verify the expected increased difficulty in attacking the encrypted system. This is attributable to the differences of the new signals generated using the proposed method from the original chaotic signals, as well as arises from the high complexity and nonlinearity of the system. Considering its ability to withstand common encryption attacks, it is hence reasonable to conclude that the proposed method exhibits higher safety and security than other traditional methods.
Music Emotion Analysis Based on PSO-BP Neural Network and Big Data Analysis

The current music teaching can effectively improve students' music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.
Glow-WaveGAN: Learning Speech Representations from GAN-Based Variational Auto-Encoder for High Fidelity Flow-Based Speech Synthesis

Current two-stage TTS framework typically integrates an acoustic model with a vocoder -- the acoustic model predicts a low resolution intermediate representation such as Mel-spectrum while the vocoder generates waveform from the intermediate representation. Although the intermediate representation is served as a bridge, there still exists critical mismatch between the acoustic model and the vocoder as they are commonly separately learned and work on different distributions of representation, leading to inevitable artifacts in the synthesized speech. In this work, different from using pre-designed intermediate representation in most previous studies, we propose to use VAE combining with GAN to learn a latent representation directly from speech and then utilize a flow-based acoustic model to model the distribution of the latent representation from text. In this way, the mismatch problem is migrated as the two stages work on the same distribution. Results demonstrate that the flow-based acoustic model can exactly model the distribution of our learned speech representation and the proposed TTS framework, namely Glow-WaveGAN, can produce high fidelity speech outperforming the state-of-the-art GAN-based model.
Compensated Fuzzy Neural Network-Based Music Teaching Ability Assessment Model

College is the main place to carry out music teaching, and it is important to assess the music teaching ability in college effectively. Based on this, this paper firstly analyzes the necessity of music teaching ability assessment and briefly summarizes the application of neural network and deep learning technology in music teaching ability assessment and secondly designs an assessment model based on compensated fuzzy neural network algorithm and analyzes the accuracy of the model, finds out the causes of forming abnormal output by analysing the general dimensional conditions of the algorithm of the model, and proposes corresponding correction. Finally, the reliability and feasibility of the music teaching ability assessment model were experimentally verified by combining with teaching practice. The research results confirm the feasibility of the compensated fuzzy neural network algorithm in music teaching ability assessment, which has important reference significance for improving the quality of music teaching in colleges and universities.
InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization

We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features. The core idea of our work is to leverage recent progress in self-supervised image representation learning. Representation learning methods compute a single high-level feature capturing an entire image. In contrast, we compute multiple high-level features, each capturing image segments of one particular semantic class. To this end, we propose a novel two-step learning procedure comprising a segmentation and a mutual information maximization step. In the first step, we segment images based on local and global features. In the second step, we maximize the mutual information between local features and high-level features of their respective class. For training, we provide solely unlabeled images and start from random network initialization. For quantitative and qualitative evaluation, we use established benchmarks, and COCO-Persons, whereby we introduce the latter in this paper as a challenging novel benchmark. InfoSeg significantly outperforms the current state-of-the-art, e.g., we achieve a relative increase of \(26\%\) in the Pixel Accuracy metric on the COCO-Stuff dataset.