pwc-archive/papers-with-abstracts huggingface dataset:
Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms
Authors: Md. Maniruzzaman, Md. Jahanur Rahman, Benojir Ahammed, Md. Menhazul Abedin, Harman S. Suri, Mainak Biswas, Ayman El-Baz, Petros Bangeas, Georgios Tsoulfas, Jasjit S. Suri
Date: 2019-04-08 00:00:00
Proceeding: Computer Methods and Programs in Biomedicine 2019 4
Tasks: BIG-bench Machine Learning, Cancer Classification, General Classification

Objective: A colon microarray data is a repository of thousands of gene expressions with different
strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth.
This study presents an exhaustive comparative study of different machine learning (ML) systems which
serves two major purposes: (a) identification of high risk differential genes using statistical tests and (b)
development of a ML strategy for predicting cancer genes.
Methods: Four statistical tests namely: Wilcoxon sign rank sum (WCSRS), t test, Kruskal–Wallis (KW), and
F-test were adapted for cancerous gene identification using their p-values. The extracted gene set was
used to classify cancer patients using ten classifiers namely: linear discriminant analysis (LDA), quadratic
discriminant analysis (QDA), naïve Bayes (NB), Gaussian process classification (GPC), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), Adaboost (AB),
and random forest (RF). Performance was then evaluated using cross-validation protocols and standardized metrics viz. accuracy (ACC) and area under the curve (AUC).
Results: The colon cancer dataset consists of 2000 genes from 62 patients (40 cancer vs. 22 control). The
overall mean ACC of our ML system using all four statistical tests and all ten classifiers was 90.50%. The
ML system showed an ACC of 99.81% using a combination WCSRS test and RF-based classifier. This is an
improvement of 8% over previously published values in literature.
Conclusions: RF-based model with statistical tests for detection of high risk genes showed the best performance for accurate cancer classification in multi-center clinical trials
ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders
Authors: Yu Gu, Xiang Yin, Yonghui Rao, Yuan Wan, Benlai Tang, Yang Zhang, Jitong Chen, Yuxuan Wang, Zejun Ma
Date: 2020-04-23 00:00:00
Tasks: Decoder, Prediction, Singing Voice Synthesis

This paper presents ByteSing, a Chinese singing voice synthesis (SVS) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders. Different from the conventional SVS models, the proposed ByteSing employs Tacotron-like encoder-decoder structures as the acoustic models, in which the CBHG models and recurrent neural networks (RNNs) are explored as encoders and decoders respectively. Meanwhile an auxiliary phoneme duration prediction model is utilized to expand the input sequence, which can enhance the model controllable capacity, model stability and tempo prediction accuracy. WaveRNN neural vocoders are also adopted as neural vocoders to further improve the voice quality of synthesized songs. Both objective and subjective experimental results prove that the SVS method proposed in this paper can produce quite natural, expressive and high-fidelity songs by improving the pitch and spectrogram prediction accuracy and the models using attention mechanism can achieve best performance.
An Empirical Investigation into Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Authors: Michele Tufano College of William and Mary Williamsburg, USA Cody Watson College of William and Mary Williamsburg, USA Gabriele Bavota Università della Svizzera italiana (USI) Lugano, Switzerland Massimiliano Di Penta University of Sannio Benevento, Italy Martin White College of William and Mary Williamsburg, VA, USA Denys Poshyvanyk College of William and Mary
Date: 2018-09-07 00:00:00
Proceeding: Accepted to the ACM Transactions on Software Engineering and Methodology 2018 9
Tasks: Bug fixing, Decoder, Machine Translation, Translation

Millions of open-source projects with numerous bug fixes are available
in code repositories. This proliferation of software development
histories can be leveraged to learn how to fix common programming
bugs. To explore such a potential, we perform an empirical
study to assess the feasibility of using Neural Machine Translation
techniques for learning bug-fixing patches for real defects.We mine
millions of bug-fixes from the change histories of GitHub repositories
to extract meaningful examples of such bug-fixes. Then, we
abstract the buggy and corresponding fixed code, and use them to
train an Encoder-Decoder model able to translate buggy code into
its fixed version. Our model is able to fix hundreds of unique buggy
methods in the wild. Overall, this model is capable of predicting
fixed patches generated by developers in 9% of the cases.
TeamTat: a collaborative text annotation tool
Authors: Rezarta Islamaj, Dongseop Kwon, Sun Kim, Zhiyong Lu
Date: 2020-04-24 00:00:00
Tasks: Management, text annotation

Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for image display, project management, and multi-user team annotation. In response, we developed TeamTat (teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC, (uploaded locally or automatically retrieved from PubMed or PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotators convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus-quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
Authors: Alexander R. Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
Date: 2020-04-24 00:00:00
Proceeding: ACL 2020 6
Tasks: Language Modeling, Language Modelling, Question Answering, Question Generation, Question-Generation, Sentence

Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. This paradigm, however, relies on scarce, and costly to obtain, large-scale human-labeled data. We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to learn more complex context-question relationships. Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.
Lite Transformer with Long-Short Range Attention
Authors: Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han
Date: 2020-04-24 00:00:00
Proceeding: ICLR 2020 1
Tasks: Abstractive Text Summarization, AutoML, GPU, Language Modeling, Language Modelling, Machine Translation, Quantization, Question Answering, Translation

Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve high performance, which makes it not suitable for mobile applications that are tightly constrained by the hardware resources and battery. In this paper, we present an efficient mobile NLP architecture, Lite Transformer to facilitate deploying mobile NLP applications on edge devices. The key primitive is the Long-Short Range Attention (LSRA), where one group of heads specializes in the local context modeling (by convolution) while another group specializes in the long-distance relationship modeling (by attention). Such specialization brings consistent improvement over the vanilla transformer on three well-established language tasks: machine translation, abstractive summarization, and language modeling. Under constrained resources (500M/100M MACs), Lite Transformer outperforms transformer on WMT'14 English-French by 1.2/1.7 BLEU, respectively. Lite Transformer reduces the computation of transformer base model by 2.5x with 0.3 BLEU score degradation. Combining with pruning and quantization, we further compressed the model size of Lite Transformer by 18.2x. For language modeling, Lite Transformer achieves 1.8 lower perplexity than the transformer at around 500M MACs. Notably, Lite Transformer outperforms the AutoML-based Evolved Transformer by 0.5 higher BLEU for the mobile NLP setting without the costly architecture search that requires more than 250 GPU years. Code has been made available at https://github.com/mit-han-lab/lite-transformer.
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
Authors: Duy-Kien Nguyen, Vedanuj Goswami, Xinlei Chen
Date: 2020-04-24 00:00:00
Proceeding: ICLR 2021 1
Tasks: Object Counting, Question Answering, Visual Question Answering (VQA)

This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category). Unlike most prior works that use explicit, symbolic models which can be computationally expensive and limited in generalization, we propose a simple and effective alternative by revisiting modulated convolutions that fuse the query and the image locally. Following the design of residual bottleneck, we call our method MoVie, short for Modulated conVolutional bottlenecks. Notably, MoVie reasons implicitly and holistically and only needs a single forward-pass during inference. Nevertheless, MoVie showcases strong performance for counting: 1) advancing the state-of-the-art on counting-specific VQA tasks while being more efficient; 2) outperforming prior-art on difficult benchmarks like COCO for common object counting; 3) helped us secure the first place of 2020 VQA challenge when integrated as a module for 'number' related questions in generic VQA models. Finally, we show evidence that modulated convolutions such as MoVie can serve as a general mechanism for reasoning tasks beyond counting.
CQE in Description Logics Through Instance Indistinguishability (extended version)
Authors: Gianluca Cima, Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo
Date: 2020-04-24 00:00:00
Tasks: Privacy Preserving

We study privacy-preserving query answering in Description Logics (DLs). Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability. We derive data complexity results for query answering over DL-Lite$_{\mathcal{R}}$ ontologies, through a comparison with an alternative, existing confidentiality-preserving approach to CQE. Finally, we identify a semantically well-founded notion of approximated query answering for CQE, and prove that, for DL-Lite$_{\mathcal{R}}$ ontologies, this form of CQE is tractable with respect to data complexity and is first-order rewritable, i.e., it is always reducible to the evaluation of a first-order query over the data instance.
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
Authors: Biao Zhang, Philip Williams, Ivan Titov, Rico Sennrich
Date: 2020-04-24 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, Translation

Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations. In this paper, we explore ways to improve them. We argue that multilingual NMT requires stronger modeling capacity to support language pairs with varying typological characteristics, and overcome this bottleneck via language-specific components and deepening NMT architectures. We identify the off-target translation issue (i.e. translating into a wrong target language) as the major source of the inferior zero-shot performance, and propose random online backtranslation to enforce the translation of unseen training language pairs. Experiments on OPUS-100 (a novel multilingual dataset with 100 languages) show that our approach substantially narrows the performance gap with bilingual models in both one-to-many and many-to-many settings, and improves zero-shot performance by ~10 BLEU, approaching conventional pivot-based methods.
Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs
Authors: Tarcísio Souza Costa, Simon Gottschalk, Elena Demidova
Date: 2020-04-24 00:00:00
Tasks: Knowledge Graphs, Question Answering

Semantic Question Answering (QA) is a crucial technology to facilitate intuitive user access to semantic information stored in knowledge graphs. Whereas most of the existing QA systems and datasets focus on entity-centric questions, very little is known about these systems' performance in the context of events. As new event-centric knowledge graphs emerge, datasets for such questions gain importance. In this paper, we present the Event-QA dataset for answering event-centric questions over knowledge graphs. Event-QA contains 1000 semantic queries and the corresponding English, German and Portuguese verbalizations for EventKG - an event-centric knowledge graph with more than 970 thousand events.
Impact of different belief facets on agents' decision -- a refined cognitive architecture to model the interaction between organisations' institutional characteristics and agents' behaviour
Authors: Amir Hosein Afshar Sedigh, Martin K. Purvis, Bastin Tony Roy Savarimuthu, Christopher K. Frantz, Maryam A. Purvis
Date: 2020-04-24 00:00:00
Tasks: Fairness

This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules.
Learning Gaussian Maps for Dense Object Detection
Authors: Sonaal Kant
Date: 2020-04-24 00:00:00
Tasks: Decoder, Dense Object Detection, Multi-Task Learning, Object, object-detection, Object Detection

Object detection is a famous branch of research in computer vision, many state of the art object detection algorithms have been introduced in the recent past, but how good are those object detectors when it comes to dense object detection? In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other. We also show that, multi-task learning of gaussian maps along with classification and bounding box regression gives us a significant boost in accuracy over the baseline. We introduce Gaussian Layer and Gaussian Decoder in the existing RetinaNet network for better accuracy in dense scenes, with the same computational cost as the RetinaNet. We show the gain of 6\% and 5\% in mAP with respect to baseline RetinaNet. Our method also achieves the state of the art accuracy on the SKU110K \cite{sku110k} dataset.
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
Authors: Biao Zhang, Ivan Titov, Rico Sennrich
Date: 2020-04-24 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Decoder, Document Summarization, Machine Translation

Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation. In this work, by contrast, we hypothesize that these encoder outputs can be compressed to shorten the sequence delivered for decoding. We take Transformer as the testbed and introduce a layer of stochastic gates in-between the encoder and the decoder. The gates are regularized using the expected value of the sparsity-inducing L0penalty, resulting in completely masking-out a subset of encoder outputs. In other words, via joint training, the L0DROP layer forces Transformer to route information through a subset of its encoder states. We investigate the effects of this sparsification on two machine translation and two summarization tasks. Experiments show that, depending on the task, around 40-70% of source encodings can be pruned without significantly compromising quality. The decrease of the output length endows L0DROP with the potential of improving decoding efficiency, where it yields a speedup of up to 1.65x on document summarization tasks against the standard Transformer. We analyze the L0DROP behaviour and observe that it exhibits systematic preferences for pruning certain word types, e.g., function words and punctuation get pruned most. Inspired by these observations, we explore the feasibility of specifying rule-based patterns that mask out encoder outputs based on information such as part-of-speech tags, word frequency and word position.
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Authors: Bill Cassidy, Neil D. Reeves, Pappachan Joseph, David Gillespie, Claire O'Shea, Satyan Rajbhandari, Arun G. Maiya, Eibe Frank, Andrew Boulton, David Armstrong, Bijan Najafi, Justina Wu, Moi Hoon Yap
Date: 2020-04-24 00:00:00
Tasks: 2D Object Detection, Diabetic Foot Ulcer Detection

Every 20 seconds, a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The MICCAI challenge discussed in this paper, which concerns the automated detection of diabetic foot ulcers using machine learning techniques, will accelerate the development of innovative healthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app that patients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance of a diabetic foot ulcer (DFU). Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospital and the Manchester University NHS Foundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. Based on a joint effort involving the lead scientists of the UK, US, India and New Zealand, this challenge will solicit original work, and promote interactions between researchers and interdisciplinary collaborations. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluation results. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research. This grand challenge takes on even greater urgency in a peri and post-pandemic period, where stresses on resource utilization will increase the need for technology that allows people to remain active, healthy and intact in their home.
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
Authors: Hamed Jelodar, Yongli Wang, Rita Orji, Hucheng Huang
Date: 2020-04-24 00:00:00
Tasks: Decision Making, Sentiment Analysis, Sentiment Classification

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.
A survey on domain adaptation theory: learning bounds and theoretical guarantees
Authors: Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani
Date: 2020-04-24 00:00:00
Tasks: BIG-bench Machine Learning, Domain Adaptation, Transfer Learning

All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from newly collected data, which for some applications can be costly or impossible to obtain. Therefore, it has become necessary to develop approaches that reduce the need and the effort to obtain new labeled samples by exploiting data that are available in related areas, and using these further across similar fields. This has given rise to a new machine learning framework known as transfer learning: a learning setting inspired by the capability of a human being to extrapolate knowledge across tasks to learn more efficiently. Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation. In this sub-field, the data distribution is assumed to change across the training and the test data, while the learning task remains the same. We provide a first up-to-date description of existing results related to domain adaptation problem that cover learning bounds based on different statistical learning frameworks.