Reviewerly/exHarmony huggingface dataset:
bwsTools: An R package for case 1 best-worst scaling

Case 1 best-worst scaling, also known as best-worst scaling or MaxDiff, is a popular method for examining the relative ratings and ranks of a series of items in various disciplines in academia and industry. The method involves a survey respondent indicating the "best" and "worst" from a sample of items across a series of trials. Many methods exist for calculating scores at the individual and aggregate levels. I introduce the bwsTools package, a free and open-source set of tools for the R statistical programming language, to aid researchers and practitioners in the construction and analysis of best-worst scaling designs. This package is designed to work seamlessly with tidy data, does not require design matrices, and employs various published individual- and aggregate-level scoring methods that have yet to be employed in free software.
Extending Parrotron: An End-to-End, Speech Conversion and Speech Recognition Model for Atypical Speech

We present an extended Parrotron model: a single, end-to-end network that enables voice conversion and recognition simultaneously. Input spectrograms are transformed to output spectrograms in the voice of a predetermined target speaker while also generating hypotheses in a target vocabulary. We study the performance of this novel architecture, which jointly predicts speech and text, on atypical (e.g. dysarthric) speech. We show that with as little as an hour of atypical speech, speaker adaptation can yield a 77% relative reduction in Word Error Rate (WER), measured by ASR performance on the converted speech. We also show that data augmentation using a customized synthesizer built on atypical speech can provide an additional 10% relative improvement over the best speaker-adapted model. Finally, we show how these methods generalize across 8 types of atypical speech for a range of speech impairment severities.
Improvements to Prosodic Alignment for Automatic Dubbing

Automatic dubbing is an extension of speech-to-speech translation such that the resulting target speech is carefully aligned in terms of duration, lip movements, timbre, emotion, prosody, etc. of the speaker in order to achieve audiovisual coherence. Dubbing quality strongly depends on isochrony, i.e., arranging the translation of the original speech to optimally match its sequence of phrases and pauses. To this end, we present improvements to the prosodic alignment component of our recently introduced dubbing architecture. We present empirical results for four dubbing directions – English to French, Italian, German and Spanish – on a publicly available collection of TED Talks. Compared to previous work, our enhanced prosodic alignment model significantly improves prosodic alignment accuracy and provides segmentation perceptibly better or on par with manually annotated reference segmentation.
InMap: Automated Interactive Code-to-Architecture Mapping Recommendations

Reflexion Modelling is a popular method used in industry for Software Architectural Consistency Checking (SACC). However, it involves a mapping step that is manual and tedious. There exist techniques and tools that attempt to automate mapping, yet they are either limited in their approach or they require an initial set of manually pre-mapped entities. This study proposes a novel technique, InMap, that improves the mapping process in reflexion modelling by both providing versatility and eliminating the constraint of needing a set of manually pre-mapped entities in order to automate mapping. Using a software’s architecture descriptions, InMap applies information retrieval concepts to the software’s source code to interactively provide mapping recommendations to an architect. For the six systems InMap was evaluated on, the recommendations it provided achieved an average recall of 0.97, and an average precision of 0.82. InMap also achieved higher, f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -scores in comparison to existing techniques that require premapping. This provides a basis for improving industry tools that use reflexion modelling or similar SACC methods.
Exploring the Potential of Unsupervised Image Synthesis for SAR-Optical Image Matching

We consider SAR-optical image matching problems, where correspondences are acquired from a pair of SAR and optical images. Recent methods for such a problem typically simplify the SAR-optical image matching to the SAR-SAR or optical-optical image matchings using supervised-image-synthesis methods. However, training supervised-image-synthesis needs plenty of aligned SAR-optical image pairs while gathering sufficient amounts of aligned multi-modal image pairs is challenging in remote sensing. In this work, we investigate the applicability of unsupervised-image-synthesis for SAR-optical image matching such that the unaligned SAR-optical images could be used. To this end, we apply feature matching loss to a well known unsupervised-image-synthesis method, i.e., CycleGAN, to enforce the feature matching consistency. Moreover, we develop a shared-matching-strategy to improve the results of SAR-optical image matching further. Qualitative comparisons against CycleGAN, StarGAN, and DualGAN demonstrate the superiority of our approach. Quantitative results show that, compared with CycleGAN, StarGAN, and DualGAN, our method obtains at least 2.6 times more qualified SAR-optical matchings.
Chaotic Reversible Watermarking Method Based on IWT with Tamper Detection for Transferring Electronic Health Record

Health IoT deals with sensitive medical information of patients, therefore security concerns need to be addressed. Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for IoT based healthcare systems. Recently, watermarking algorithms as an efficient response to these requirements is in the spotlight. Further, as smart city-based applications have to react to real-time situations, efficient computation is a demand for them. In this paper, a secure, lightweight, reversible, and high capacity watermarking algorithm with tamper detection capability is proposed for IoT based healthcare systems. The scheme has applied Integer Wavelet Transform (IWT) and chaotic map for efficiency and increasing security. EHR is encrypted and then embedded into the Least Significant Bits (LSB) of wavelet coefficients of medical images. The proposed method has been extensively tested for various color and grayscale commonly used medical and general images. Investigations on experimental results and criterions such as Peak Signal to Noise Ratio (PSNR) and Bit Error Ratio (BER) above 45.41 dB and 0.04, respectively, for payload of 432,538 bits indicate that the proposed method, besides providing security, being reversible, tamper detection capability, and high embedding capacity, has high imperceptibility and adequate resistance against different types of attacks.
A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios

Research in autonomous driving for unstructured environments suffers from a lack of semantically labeled datasets compared to its urban counterpart. Urban and unstructured outdoor environments are challenging due to the varying lighting and weather conditions during a day and across seasons. In this paper, we introduce TAS500, a novel semantic segmentation dataset for autonomous driving in unstructured environments. TAS500 offers fine-grained vegetation and terrain classes to learn drivable surfaces and natural obstacles in outdoor scenes effectively. We evaluate the performance of modern semantic segmentation models with an additional focus on their efficiency. Our experiments demonstrate the advantages of fine-grained semantic classes to improve the overall prediction accuracy, especially along the class boundaries. The dataset and pretrained model are available at mucar3.de/icpr2020-tas500.
SynAug: Synthesis-Based Data Augmentation for Text-Dependent Speaker Verification

Text-dependent speaker verification systems trained on large amount of labelled data exhibit remarkable performance. However, collecting the speech from a lot of speakers with target transcript is a lengthy and expensive process. In this work, we propose a synthesis based data augmentation method (SynAug) to expand the training set with more speakers and text-controlled synthesized speech. The performance of SynAug is evaluated on the RSR2015 dataset. Experimental results show that for i-vector framework, the proposed methods can boost the system performance significantly, especially for the low-resource condition where the amount of genuine speech is extremely limited. Moreover, combined with traditional data augmentation methods such as adding noises and reverberation, the systems could be further strengthened in extremely limited resource situation.
Understanding Emotions of Developer Community Towards Software Documentation

The availability of open-source projects facilitates developers to contribute and collaborate on a wide range of projects. As a result, the developer community contributing to such open-source projects is also increasing. Many of the projects involve frequent updates and extensive reuses. A well-updated documentation helps in a better understanding of the software project and also facilitates efficient contribution and reuse. Though software documentation plays an important role in the development and maintenance of software, it also suffers from various issues that include insufficiency, inconsistency, ill-maintainability, and so on. Exploring the perception of developers towards documentation could help in understanding the reasons behind prevalent issues in software documentation. It could further aid in deciding on training that could be given to the developer community towards building more sustainable projects for society. Analyzing sentiments of contributors to a project could provide insights on understanding developer perceptions. Hence, as the first step towards this direction, we analyze sentiments of commit messages specific to the documentation of a software project. To this end, we considered the commit history of 998 GitHub projects from the GHTorrent dataset and identified 10,996 commits that correspond to the documentation of repositories. Further, we apply sentiment analysis techniques to obtain insights on the type of sentiment being expressed in commit messages of the selected commits. We observe that around 45% of the identified commit messages express trust emotion.
Improving RNN Transducer Modeling for Small-Footprint Keyword Spotting

The recurrent neural network transducer (RNN-T) model has been proved effective for keyword spotting (KWS) recently. However, compared with cross-entropy (CE) or connectionist temporal classification (CTC) based models, the additional prediction network in the RNN-T model increases the model size and computational cost. Besides, since the keyword training data usually only contain the keyword sequence, the prediction network might has over-fitting problems. In this paper, we improve the RNN-T modeling for small-footprint keyword spotting in three aspects. First, to address the over-fitting issue, we explore multi-task training where a CTC loss is added to the encoder. The CTC loss is calculated with both KWS data and ASR data, while the RNN-T loss is calculated with ASR data so that only the encoder is augmented with KWS data. Second, we use the feed-forward neural network to replace the LSTM for prediction network modeling. Thus all possible prediction network outputs could be pre-computed for decoding. Third, we further improve the model with transfer learning, where a model trained with 160 thousand hours of ASR data is used to initialize the KWS model. On a self-collected far-field wake-word testset, the proposed RNN-T system greatly improves the performance comparing with a strong "keyword-filler" baseline.
Comparative study between machine and deep learning methods for age, gender and ethnicity identification

Facial recognition is becoming increasingly used in real-world applications, such as video surveillance, human-computer interaction applications. One of the most popular applications is the automatic age, gender, and ethnicity (race) classification. The extraction of these facial attributes has an important role in social interactions this study aims to compare different machine and deep learning techniques and propose a novel deep learning architecture to automatically classify age, gender, and ethnicity from a person's face image. We used UTKFace dataset, each one is labeled with age, gender, and ethnicity. Machine learning methods did not give more than 58.04%, 86.25%, and 72.78% as test accuracy for age, gender, and race respectively. Transfer learning gave 63.53%, 89.14% and 72.39% as best results when our proposed CNN architectures outperform the previous results, they gave 65.92%, 90.3% and 78.88 % with a modified version of age prediction that gave 80.46 % using three classes (Child, Teenager, and Adult) instead of five.
Recommendation System With Hierarchical Recurrent Neural Network for Long-Term Time Series

Nowadays, recommendation systems are widely used on various services.This system predicts what item a user will use next using large amounts of stored user history.Recommendation systems are commonly applied in various fields as movies, e-commerce, and social services.However, previous researches on recommendation systems commonly overlooked the importance of item usage sequence and time intervals of the time series data from users.We provide a novel recommendation system that incorporates these temporal properties of the user history.We design a recurrent neural network (RNN) model with a hierarchical structure so that the sequence and time intervals of the user's item usage history can be considered.The model is divided into two layers: a layer for a long time and a layer for a short time.We conduct experiments on real-world data such as Movielens and Steam datasets, which has a long-time range, and show that our new model outperforms the previous widely used recommendation methods, including RNN-based models.We also conduct experiments to find out the influence of the length and time interval of sequences in our model.These experimental results show that both sequence length and time interval are influential, indicating that it is important to consider the temporal properties for long-term sequences.
Towards Understanding Perceptual Differences between Genuine and Face-Swapped Videos

In this paper, we report on perceptual experiments indicating that there are distinct and quantitatively measurable differences in the way we visually perceive genuine versus face-swapped videos.
The Usability (or Not) of Refactoring Tools

Although software developers typically have access to numerous refactoring tools, most developers avoid using these tools despite their benefits. Researchers have identified many reasons for the disuse of refactoring tools, including a lack of awareness by the developers, a lack of predictability of the tools, and a lack of need for the tools. In this paper, we build on this earlier work and employ the ISO 9241-11 definition of usability to develop a theory of usability for refactoring tools. We investigate existing refactoring tools using this theory by analyzing how 17 developers experience refactoring tools in three software change tasks we asked them to perform. We analyze qualitatively the resulting interview transcripts based on our theory and report on a number of observations that can inform tool designers interested in improving the usability of refactoring tools. For instance, we found a desire for developers to guide how a refactoring tool changes the code and a need for refactoring tools to describe changes made to developers. Refactoring tools are currently expected to preserve program behavior. These observations indicate that it may be necessary to give developers more control over this property, including the ability to relax it, for the tools to be usable; that is, for the tools to be effective, efficient and satisfying for the developer to employ.
Boundary-aware Graph Convolution for Semantic Segmentation

Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution N et-work(BGCN et), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.
A Survey of Data Pricing Methods

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“I Am Iron Man”

Priming is used as a way of increasing the diversity of proposals in end-user elicitation studies, but priming has not been investigated thoroughly in this context. We conduct a distributed end-user elicitation study with 167 participants, which had three priming groups: a no-priming control group, sci-fi priming, and a creative mindset group. We evaluated the gestures proposed by these groups in a distributed learnability and memorability study with 18 participants. We found that the user-elicited gestures from the sci-fi group were significantly faster to learn, requiring an average of 1.22 viewings to learn compared to 1.60 viewings required to learn the control gestures, and 1.56 viewings to learn the gestures elicited from the creative mindset group. In addition, both primed gesture groups had higher memorability with 80% of the sci-fi-primed gestures and 73% of the creative mindset group gestures were recalled correctly after one week without practice compared to 43% of the control group gestures.
DO as I Mean, Not as I Say: Sequence Loss Training for Spoken Language Understanding

Spoken language understanding (SLU) systems extract transcriptions, as well as semantics of intent or named entities from speech, and are essential components of voice activated systems. SLU models, which either directly extract semantics from audio or are composed of pipelined automatic speech recognition (ASR) and natural language understanding (NLU) models, are typically trained via differentiable cross-entropy losses, even when the relevant performance metrics of interest are word or semantic error rates. In this work, we propose non-differentiable sequence losses based on SLU metrics as a proxy for semantic error and use the REINFORCE trick to train ASR and SLU models with this loss. We show that custom sequence loss training is the state-of-the-art on open SLU datasets and leads to 6% relative improvement in both ASR and NLU performance metrics on large proprietary datasets. We also demonstrate how the semantic sequence loss training paradigm can be used to update ASR and SLU models without transcripts, using semantic feedback alone.
Meta-Adapter: Efficient Cross-Lingual Adaptation With Meta-Learning

Transfer learning from a multilingual model has shown favorable results on low-resource automatic speech recognition (ASR). However, full-model fine-tuning generates a separate model for every target language and is not suitable for deploying and maintaining in production. The key challenge lies in how to efficiently extend the pre-trained model with fewer parameters. In this paper, we propose to combine the adapter module with meta-learning algorithms to achieve high recognition performance under low-resource settings and improve the parameter-efficiency of the model. Extensive experiments show that our methods can achieve comparable or even superior recognition rates than the state-of-the-art baselines on low-resource languages, especially under very-low-resource conditions, with a significantly smaller model profile.
A Sequential Contrastive Learning Framework for Robust Dysarthric Speech Recognition

Dysarthria is a manifestation of disruption in the neuromuscular physiology resulting in uneven, slow, slurred, harsh, or quiet speech. Despite the remarkable progress of automatic speech recognition (ASR), it poses great challenges in developing stable ASR for dysarthric individuals due to the high intra- and inter-speaker variations and data deficiency. In this paper, we propose a contrastive learning framework for robust dysarthric speech recognition (DSR) by capturing the dysarthric speech variability. Several speech data augmentation strategies are explored to form two branches of the framework, meanwhile alleviating the scarcity of dysarthria data. We also develop an efficient projection head acting on a sequence of learned hidden representations for defining contrastive loss. Experiment results on DSR demonstrate that the model is better than or comparable to the supervised baseline.
Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech

In this paper, we introduce Kathaka, a model trained with a novel two-stage training process for neural speech synthesis with contextually appropriate prosody. In Stage I, we learn a prosodic distribution at the sentence level from mel-spectrograms available during training. In Stage II, we propose a novel method to sample from this learnt prosodic distribution using the contextual information available in text. To do this, we use BERT on text, and graph-attention networks on parse trees extracted from text. We show a statistically significant relative improvement of 13.2% in naturalness over a strong baseline when compared to recordings. We also conduct an ablation study on variations of our sampling technique, and show a statistically significant improvement over the baseline in each case.
UAV 3D environment obstacle avoidance trajectory planning based on improved artificial potential field method

Abstract To address the inefficiency of the traditional artificial potential field method in complex environment for obstacle avoidance, the basic potential field function of the traditional artificial potential field method is improved, and the traditional spherical potential field is proposed to be improved to ellipsoidal potential field, and the improved algorithm is compared and simulated in MATLAB. The results show that the improved artificial potential field method satisfies the UAV to have high efficiency of safety and passability in obstacle avoidance trajectory planning in complex 3D environment.
A data classification method based on particle swarm optimisation and kernel function extreme learning machine

Aiming at the problem of data classification in enterprise cloud data, this article proposes a data classifier based on Particle Swarm Optimisation and Kernel Function Extreme Learning Machine (PSO-KELM) is proposed. PSO is utilised to get optimal parameters and the parameters of KELM are obtained accurately, which improves the accuracy of data classification. Through the cloud data test and a comparison with Kernel Function Extreme Learning Machine (KELM), Kernel Function Support Vector Machine (KSVM) and Semi-Supervised and Transfer Learning (SSTL), the better classification effect is obtained. Moreover, this article proposes an effective method for data classification.