Reviewerly/exHarmony huggingface dataset:
Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS

Neural TTS has demonstrated strong capabilities to generate human-like speech with high quality and naturalness, while its generalization to out-of-domain texts is still a challenging task, with regard to the design of attention-based sequence-to-sequence acoustic modeling. Various errors occur in those inputs with unseen context, including attention collapse, skipping, repeating, etc., which limits the broader applications. In this paper, we propose a novel stepwise monotonic attention method in sequence-to-sequence acoustic modeling to improve the robustness on out-of-domain inputs. The method utilizes the strict monotonic property in TTS with constraints on monotonic hard attention that the alignments between inputs and outputs sequence must be not only monotonic but allowing no skipping on inputs. Soft attention could be used to evade mismatch between training and inference. The experimental results show that the proposed method could achieve significant improvements in robustness on out-of-domain scenarios for phoneme-based models, without any regression on the in-domain naturalness test.
HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking

Recent works on end-to-end trainable neural network based approaches have demonstrated state-of-the-art results on dialogue state tracking. The best performing approaches estimate a probability distribution over all possible slot values. However, these approaches do not scale for large value sets commonly present in real-life applications and are not ideal for tracking slot values that were not observed in the training set. To tackle these issues, candidate-generation-based approaches have been proposed. These approaches estimate a set of values that are possible at each turn based on the conversation history and/or language understanding outputs, and hence enable state tracking over unseen values and large value sets however, they fall short in terms of performance in comparison to the first group. In this work, we analyze the performance of these two alternative dialogue state tracking methods, and present a hybrid approach (HyST) which learns the appropriate method for each slot type. To demonstrate the effectiveness of HyST on a rich-set of slot types, we experiment with the recently released MultiWOZ-2.0 multi-domain, task-oriented dialogue-dataset. Our experiments show that HyST scales to multi-domain applications. Our best performing model results in a relative improvement of 24% and 10% over the previous SOTA and our best baseline respectively.
A New Meta-Baseline for Few-Shot Learning

Meta-learning has become a popular framework for few-shot learning in recent years, with the goal of learning a model from collections of few-shot classification tasks. While more and more novel meta-learning models are being proposed, our research has uncovered simple baselines that have been overlooked. We present a Meta-Baseline method, by pre-training a classifier on all base classes and meta-learning on a nearest-centroid based few-shot classification algorithm, it outperforms recent state-of-the-art methods by a large margin. Why does this simple method work so well? In the meta-learning stage, we observe that a model generalizing better on unseen tasks from base classes can have a decreasing performance on tasks from novel classes, indicating a potential objective discrepancy. We find both pre-training and inheriting a good few-shot classification metric from the pre-trained classifier are important for Meta-Baseline, which potentially helps the model better utilize the pre-trained representations with stronger transferability. Furthermore, we investigate when we need meta-learning in this Meta-Baseline. Our work sets up a new solid benchmark for this field and sheds light on further understanding the phenomenons in the meta-learning framework for few-shot learning.
Opinion Mining, Sentiment Analysis and Emotion Understanding in Advertising: A Bibliometric Analysis

In the last decade, the advertising industry has experienced a quantum leap, powered by recent advances in neuroscience, a large investment in artificial intelligence, and a high degree of consumer expertise. Within this context, opinion mining, sentiment analysis, and emotion understanding bring us closer to one of the most sought-after objectives of advertising: to offer relevant ads at scale. The importance of studies about opinion mining, sentiment analysis, and emotion understanding in advertising has been rising exponentially over the last years. The peak of this new situation has been the interest of the research community in studying the relationship between such innovations and the spread of smart and contextual advertising. This article analyzes those works that address the relationship between sentiment analysis, opinion mining, and emotion understanding in advertising. The main objective is to clarify the current state of these studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within the current convergence advertising research scenario. To reach such objectives, a bibliometric analysis was conducted, retrieving and analyzing 919 research works published between 2010 and 2019 based on results from Web of Science (WoS).
Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images

We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI). Multiple instance learning (MIL) approaches, typically used for the analysis of WSIs, divide the image (bag) into patches (instances), which are used to predict a single bag-level label. These approaches perform poorly in cytopathology slides due to a unique bag structure: sparsely located informative instances with varying characteristics of abnormality. We address these challenges by considering multiple types of labels: bag-level malignancy and ordered diagnostic scores, as well as instance-level informativeness and abnormality labels. We study their contribution beyond the MIL setting by proposing a maximum likelihood estimation (MLE) framework, from which we derive a two-stage deep-learning-based algorithm. The algorithm identifies informative instances and assigns them local malignancy scores that are incorporated into a global malignancy prediction. We derive a lower bound of the MLE, leading to an improved training strategy based on weak supervision, that we motivate through statistical analysis. The lower bound further allows us to extend the proposed algorithm to simultaneously predict multiple bag and instance-level labels from a single output of a neural network. Experimental results demonstrate that the proposed algorithm provides competitive performance compared to several competing methods, achieves (expert) human-level performance, and allows augmentation of human decisions.
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples.Data augmentation is a common method used to prevent overfitting and improve OOD generalization.However, in natural language, it is difficult to generate new examples that stay on the underlying data manifold.We introduce SSMBA, a data augmentation method for generating synthetic training examples by using a pair of corruption and reconstruction functions to move randomly on a data manifold.We investigate the use of SSMBA in the natural language domain, leveraging the manifold assumption to reconstruct corrupted text with masked language models.In experiments on robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently outperforms existing data augmentation methods and baseline models on both in-domain and OOD data, achieving gains of 0.8% accuracy on OOD Amazon reviews, 1.8% accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English. 1
A Benchmark for Edge-Preserving Image Smoothing

Edge-preserving image smoothing is an important step for many low-level vision problems. Though many algorithms have been proposed, there are several difficulties hindering its further development. First, most existing algorithms cannot perform well on a wide range of image contents using a single parameter setting. Second, the performance evaluation of edge-preserving image smoothing remains subjective, and there lacks a widely accepted datasets to objectively compare the different algorithms. To address these issues and further advance the state of the art, in this work we propose a benchmark for edge-preserving image smoothing. This benchmark includes an image dataset with groundtruth image smoothing results as well as baseline algorithms that can generate competitive edge-preserving smoothing results for a wide range of image contents. The established dataset contains 500 training and testing images with a number of representative visual object categories, while the baseline methods in our benchmark are built upon representative deep convolutional network architectures, on top of which we design novel loss functions well suited for edge-preserving image smoothing. The trained deep networks run faster than most state-of-the-art smoothing algorithms with leading smoothing results both qualitatively and quantitatively. The benchmark is publicly accessible via https://github.com/zhufeida/Benchmark_EPS.
SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer

Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to determine what effective treatment we can take to overcome it. Therefore, the novel Machine Learning (ML) techniques come to a place that predicts cervical cancer before it becomes too serious. Furthermore, four common diagnosis testing namely, Hinselmann, Schiller, Cytology, and Biopsy have been compared and predicted with four common ML models, namely Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NNs), and Extreme Gradient Boosting (XGB). Additionally, to enhance the better performance of ML models, the Stratified k-fold cross-validation (SKCV) method has been implemented over here. The findings of the experiments demonstrate that utilizing an RF classifier for analyzing the cervical cancer risk, could be a good alternative for assisting clinical specialists in classifying this disease in advance.
Feature Selection and Classification – A Probabilistic Wrapper Approach

Feature selection is defined as a problem to find a minimum set of M features for an inductive algorithm to achieve the highest predictive accuracy from the data described by the original N features where M ≤ N. A probabilistic wrapper model is proposed as another method besides the exhaustive search and the heuristic approach. The aim of this model is to avoid local minima and exhaustive search. The highest predictive accuracy is the criterion in search of the smallest M. Analysis and experiments show that this model can effectively find relevant features and remove irrelevant ones in the context of improving the predictive accuracy of an induction algorithm. It is simple, straightforward, and providing fast solutions while searching for the optimal. The applications of such a model, its future work and some related issues are also discussed.
Two-Stage Trajectory Optimization for Autonomous Ground Vehicles Parking Maneuver

This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multilayer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework.
Learning Controllable Fair Representations

Learning data representations that are transferable and are fair with respect to certain protected attributes is crucial to reducing unfair decisions while preserving the utility of the data. We propose an information-theoretically motivated objective for learning maximally expressive representations subject to fairness constraints. We demonstrate that a range of existing approaches optimize approximations to the Lagrangian dual of our objective. In contrast to these existing approaches, our objective allows the user to control the fairness of the representations by specifying limits on unfairness. Exploiting duality, we introduce a method that optimizes the model parameters as well as the expressiveness-fairness trade-off. Empirical evidence suggests that our proposed method can balance the trade-off between multiple notions of fairness and achieves higher expressiveness at a lower computational cost.
Zero-Shot Knowledge Distillation in Deep Networks

Knowledge distillation deals with the problem of training a smaller model (Student) from a high capacity source model (Teacher) so as to retain most of its performance. Existing approaches use either the training data or meta-data extracted from it in order to train the Student. However, accessing the dataset on which the Teacher has been trained may not always be feasible if the dataset is very large or it poses privacy or safety concerns (e.g., bio-metric or medical data). Hence, in this paper, we propose a novel data-free method to train the Student from the Teacher. Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation. We, therefore, dub our method Zero-Shot Knowledge Distillation and demonstrate that our framework results in competitive generalization performance as achieved by distillation using the actual training data samples on multiple benchmark datasets.
Dense Temporal Convolution Network for Sign Language Translation

The sign language translation (SLT) which aims at translating a sign language video into natural language is a weakly supervised task, given that there is no exact mapping relationship between visual actions and textual words in a sentence label. To align the sign language actions and translate them into the respective words automatically, this paper proposes a dense temporal convolution network, termed DenseTCN which captures the actions in hierarchical views. Within this network, a temporal convolution (TC) is designed to learn the short-term correlation among adjacent features and further extended to a dense hierarchical structure. In the kth TC layer, we integrate the outputs of all preceding layers together: (1) The TC in a deeper layer essentially has larger receptive fields, which captures long-term temporal context by the hierarchical content transition. (2) The integration addresses the SLT problem by different views, including embedded short-term and extended longterm sequential learning. Finally, we adopt the CTC loss and a fusion strategy to learn the featurewise classification and generate the translated sentence. The experimental results on two popular sign language benchmarks, i.e. PHOENIX and USTCConSents, demonstrate the effectiveness of our proposed method in terms of various measurements.
A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA

Abstract Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with &gt;93.5% sensitivity and &gt;96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.
Assessing the Impact of OCR Quality on Downstream NLP Tasks

A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR).Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis.However, the process of creating text through OCR introduces varying degrees of error to the text.The impact of these errors on natural language processing (NLP) tasks has only been partially studied.We perform a series of extrinsic assessment tasks -sentence segmentation, named entity recognition, dependency parsing, information retrieval, topic modelling and neural language model fine-tuning -using popular, out-of-the-box tools in order to quantify the impact of OCR quality on these tasks.We find a consistent impact resulting from OCR errors on our downstream tasks with some tasks more irredeemably harmed by OCR errors.Based on these results, we offer some preliminary guidelines for working with text produced through OCR.
An integrated fuzzy-ANP and fuzzy-ISM approach using blockchain for sustainable supply chain

Purpose The main objective of this paper is to justify the implementation of blockchain (BC) over the traditional method deployed in the supply chain (SC) after using the fuzzy–analytic network process (fuzzy-ANP) application. Over the past two decades, the overall product cost is affected by the SC at a global level. Organizations are working on their existing SC for improving their performance. BC technology is a newly emerging technology and magnetizes the attention of researchers and industrialists. This technology is still at the initial stage, and only little investigation is available in the literature and it has not been much investigated by researchers. Design/methodology/approach Literature and expert opinion interpretation in BC characteristics are further analyzed and modeled using fuzzy–interpretive structural modeling (fuzzy-ISM), fuzzy-MICMAC and fuzzy-ANP. The combined approach of both fuzzy-ISM and fuzzy-MICMAC is applied to identify the common drivers to integrate the BC technology in the light of efficient supply chain management (SCM). Findings Comparative analysis between traditional and BC-based supply chain (BCSC) using fuzzy-ANP is carried out, considering the common driving characteristics. The proposed integrated (combined) approach of fuzzy-ISM, fuzzy-MICMAC and Fuzzy-ANP found that integration of BC with SCM is better prioritized than traditional supply chain management (TSCM). The findings in the article endorse that the TSCM can be made efficient by integrating the BC technology considering five most driving characteristics, namely, data safety and decentralization, accessibility, documentation, data management and quality. Originality/value The current proposed research work identifies 12 characteristics after studying numerous literature reviews and having a discussion with SC experts with knowledge of BC. The integrated approach of fuzzy-ISM and fuzzy-MICMAC is implemented here. After that, fuzzy-ANP is used to give ranking among BCSCM and TSCM. The study carried out in this article motivates industries to implement BC in their SC system. It will reduce the transaction cost, documentation work, save time and eliminate human error at the national and international levels. The common characteristics identified in this proposed work would help in managerial decisions for the adoption of BC to ensure that the system becomes more transparent, easily traceable and finally improve the performance.
Contextualized Weak Supervision for Text Classification

Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers.Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlooked.In this paper, we propose a novel framework ConWea, providing contextualized weak supervision for text classification.Specifically, we leverage contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of the same word, and thus create a contextualized corpus.This contextualized corpus is further utilized to train the classifier and expand seed words in an iterative manner.This process not only adds new contextualized, highly label-indicative keywords but also disambiguates initial seed words, making our weak supervision fully contextualized.Extensive experiments and case studies on real-world datasets demonstrate the necessity and significant advantages of using contextualized weak supervision, especially when the class labels are fine-grained.
CASNet: A Cross-Attention Siamese Network for Video Salient Object Detection

Recent works on video salient object detection have demonstrated that directly transferring the generalization ability of image-based models to video data without modeling spatial-temporal information remains nontrivial and challenging. Considering both intraframe accuracy and interframe consistency of saliency detection, this article presents a novel cross-attention based encoder-decoder model under the Siamese framework (CASNet) for video salient object detection. A baseline encoder-decoder model trained with Lovász softmax loss function is adopted as a backbone network to guarantee the accuracy of intraframe salient object detection. Self- and cross-attention modules are incorporated into our model in order to preserve the saliency correlation and improve intraframe salient detection consistency. Extensive experimental results obtained by ablation analysis and cross-data set validation demonstrate the effectiveness of our proposed method. Quantitative results indicate that our CASNet model outperforms 19 state-of-the-art image- and video-based methods on six benchmark data sets.