pwc-archive/papers-with-abstracts huggingface dataset:
A Privacy Preserved and Cost Efficient Control Scheme for Coronavirus Outbreak Using Call Data Record and Contact Tracing
Authors: Shibli Nisar, Syed Muhammad Ali Zuhaib, Abasin Ulasyar, Muhammad Tariq
Date: 2020-10-04 00:00:00

Coronavirus or COVID-19, which has been declared pandemic by the World Health Organization, has incurred huge losses to the lives of people throughout the world. Although, the scientists, researchers and doctors are working round the clock to develop a vaccine for COVID-19, it may take a year or two to make a safe and effective vaccine available for the world. In current circumstances, a solution must be developed to control or stop the spread of the virus. For this purpose, a novel technique based on call data record analysis (CDRA)and contact tracing is proposed that can effectively control the coronavirus outbreak. A positive coronavirus patient can be traced through CDRA and contact tracing. The technique can track the path traversed by the patient and collect the cell numbers of all those people who have met with the patient. Keeping in tact the privacy of this group of people, who are contacted through their cell numbers so that they can isolate themselves till the result of their coronavirus test arrives. If a test result of a person comes positive among the group, then he/she must be isolated and same CDRA and contact tracing procedures are adopted for that person. A COVID-19 patient is geo tagged and alerts are sent if any violation of isolation is done by the patient. Moreover, the general public is informed in advance to avoid the path followed by the patients. This cost effective mechanism is not only capable to control the coronavirus outbreak but also helps in isolating the patient in his/her house.
A Multi-task Learning Framework for Opinion Triplet Extraction
Authors: Chen Zhang, Qiuchi Li, Dawei Song, Benyou Wang
Date: 2020-10-04 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Sentiment Triplet Extraction, Extract Aspect, Multi-Task Learning, Sentiment Analysis, Triplet

The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.
A Study for Universal Adversarial Attacks on Texture Recognition
Authors: Yingpeng Deng, Lina J. Karam
Date: 2020-10-04 00:00:00
Tasks: Adversarial Attack, Deep Learning, General Classification, image-classification, Image Classification, Object, Object Recognition

Given the outstanding progress that convolutional neural networks (CNNs) have made on natural image classification and object recognition problems, it is shown that deep learning methods can achieve very good recognition performance on many texture datasets. However, while CNNs for natural image classification/object recognition tasks have been revealed to be highly vulnerable to various types of adversarial attack methods, the robustness of deep learning methods for texture recognition is yet to be examined. In our paper, we show that there exist small image-agnostic/univesal perturbations that can fool the deep learning models with more than 80\% of testing fooling rates on all tested texture datasets. The computed perturbations using various attack methods on the tested datasets are generally quasi-imperceptible, containing structured patterns with low, middle and high frequency components.
Multi-turn Response Selection using Dialogue Dependency Relations
Authors: Qi Jia, Yizhu Liu, Siyu Ren, Kenny Q. Zhu, Haifeng Tang
Date: 2020-10-04 00:00:00
Proceeding: EMNLP 2020 11

Multi-turn response selection is a task designed for developing dialogue agents. The performance on this task has a remarkable improvement with pre-trained language models. However, these models simply concatenate the turns in dialogue history as the input and largely ignore the dependencies between the turns. In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Each thread can be regarded as a self-contained sub-dialogue. We also propose Thread-Encoder model to encode threads and candidates into compact representations by pre-trained Transformers and finally get the matching score through an attention layer. The experiments show that dependency relations are helpful for dialogue context understanding, and our model outperforms the state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results on UbuntuV2.
A New Mask R-CNN Based Method for Improved Landslide Detection
Authors: Silvia Liberata Ullo, Amrita Mohan, Alessandro Sebastianelli, Shaik Ejaz Ahamed, Basant Kumar, Ramji Dwivedi, G. R. Sinha
Date: 2020-10-04 00:00:00
Tasks: Transfer Learning

This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model. A data set of 160 elements is created containing landslide and non-landslide images. The proposed method consists of three steps: (i) augmenting training image samples to increase the volume of the training data, (ii) fine tuning with limited image samples, and (iii) performance evaluation of the algorithm in terms of precision, recall and F1 measure, on the considered landslide images, by adopting ResNet-50 and 101 as backbone models. The experimental results are quite encouraging as the proposed method achieves Precision equals to 1.00, Recall 0.93 and F1 measure 0.97, when ResNet-101 is used as backbone model, and with a low number of landslide photographs used as training samples. The proposed algorithm can be potentially useful for land use planners and policy makers of hilly areas where intermittent slope deformations necessitate landslide detection as prerequisite before planning.
Explaining Deep Neural Networks
Authors: Oana-Maria Camburu
Date: 2020-10-04 00:00:00
Tasks: Decision Making, speech-recognition, Speech Recognition, Superpixels

Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. However, the decision-making processes of these models are generally not interpretable to users. In various domains, such as healthcare, finance, or law, it is critical to know the reasons behind a decision made by an artificial intelligence system. Therefore, several directions for explaining neural models have recently been explored. In this thesis, I investigate two major directions for explaining deep neural networks. The first direction consists of feature-based post-hoc explanatory methods, that is, methods that aim to explain an already trained and fixed model (post-hoc), and that provide explanations in terms of input features, such as tokens for text and superpixels for images (feature-based). The second direction consists of self-explanatory neural models that generate natural language explanations, that is, models that have a built-in module that generates explanations for the predictions of the model.
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning
Authors: Yifan Gao, Piji Li, Wei Bi, Xiaojiang Liu, Michael R. Lyu, Irwin King
Date: 2020-10-04 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Dialogue Generation, Informativeness, Meta-Learning, Sentence

Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown improvements in the quality of generated responses. However, the number of utterances for different types of fine-grained sentence functions is extremely imbalanced. Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent. Consequently, dialogue generation conditioned on these infrequent sentence functions suffers from data deficiency. In this paper, we investigate a structured meta-learning (SML) approach for dialogue generation on infrequent sentence functions. We treat dialogue generation conditioned on different sentence functions as separate tasks, and apply model-agnostic meta-learning to high-resource sentence functions data. Furthermore, SML enhances meta-learning effectiveness by promoting knowledge customization among different sentence functions but simultaneously preserving knowledge generalization for similar sentence functions. Experimental results demonstrate that SML not only improves the informativeness and relevance of generated responses, but also can generate responses consistent with the target sentence functions.
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, Xing Xie
Date: 2020-10-04 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

User modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these methods cannot exploit useful information in unlabeled user behavior data, and their performance may be not optimal when labeled data is scarce. Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data. We propose two self-supervision tasks for user model pre-training. The first one is masked behavior prediction, which can model the relatedness between historical behaviors. The second one is next $K$ behavior prediction, which can model the relatedness between past and future behaviors. The pre-trained user models are finetuned in downstream tasks to learn task-specific user representations. Experimental results on two real-world datasets validate the effectiveness of our proposed user model pre-training method.
Learning Compositional Structures for Deep Learning: Why Routing-by-agreement is Necessary
Authors: Sai Raam Venkatraman, Ankit Anand, S. Balasubramanian, R. Raghunatha Sarma
Date: 2020-10-04 00:00:00

A formal description of the compositionality of neural networks is associated directly with the formal grammar-structure of the objects it seeks to represent. This formal grammar-structure specifies the kind of components that make up an object, and also the configurations they are allowed to be in. In other words, objects can be described as a parse-tree of its components -- a structure that can be seen as a candidate for building connection-patterns among neurons in neural networks. We present a formal grammar description of convolutional neural networks and capsule networks that shows how capsule networks can enforce such parse-tree structures, while CNNs do not. Specifically, we show that the entropy of routing coefficients in the dynamic routing algorithm controls this ability. Thus, we introduce the entropy of routing weights as a loss function for better compositionality among capsules. We show by experiments, on data with a compositional structure, that the use of this loss enables capsule networks to better detect changes in compositionality. Our experiments show that as the entropy of the routing weights increases, the ability to detect changes in compositionality reduces. We see that, without routing, capsule networks perform similar to convolutional neural networks in that both these models perform badly at detecting changes in compositionality. Our results indicate that routing is an important part of capsule networks -- effectively answering recent work that has questioned its necessity. We also, by experiments on SmallNORB, CIFAR-10, and FashionMNIST, show that this loss keeps the accuracy of capsule network models comparable to models that do not use it .
Paragraph-level Commonsense Transformers with Recurrent Memory
Authors: Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz, Ronan Le Bras, Maxwell Forbes, Yejin Choi
Date: 2020-10-04 00:00:00
Tasks: Sentence, World Knowledge

Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text. A recent model, COMET, can generate such implicit commonsense inferences along several dimensions such as pre- and post-conditions, motivations, and mental states of the participants. However, COMET was trained on commonsense inferences of short phrases, and is therefore discourse-agnostic. When presented with each sentence of a multi-sentence narrative, it might generate inferences that are inconsistent with the rest of the narrative. We present the task of discourse-aware commonsense inference. Given a sentence within a narrative, the goal is to generate commonsense inferences along predefined dimensions, while maintaining coherence with the rest of the narrative. Such large-scale paragraph-level annotation is hard to get and costly, so we use available sentence-level annotations to efficiently and automatically construct a distantly supervised corpus. Using this corpus, we train PARA-COMET, a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives. PARA-COMET captures both semantic knowledge pertaining to prior world knowledge, and episodic knowledge involving how current events relate to prior and future events in a narrative. Our results show that PARA-COMET outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
Improving Lesion Detection by exploring bias on Skin Lesion dataset
Authors: Anusua Trivedi, Sreya Muppalla, Shreyaan Pathak, Azadeh Mobasher, Pawel Janowski, Rahul Dodhia, Juan M. Lavista Ferres
Date: 2020-10-04 00:00:00
Tasks: Deep Learning, Lesion Detection

All datasets contain some biases, often unintentional, due to how they were acquired and annotated. These biases distort machine-learning models' performance, creating spurious correlations that the models can unfairly exploit, or, contrarily destroying clear correlations that the models could learn. With the popularity of deep learning models, automated skin lesion analysis is starting to play an essential role in the early detection of Melanoma. The ISIC Archive is one of the most used skin lesion sources to benchmark deep learning-based tools. Bissoto et al. experimented with different bounding-box based masks and showed that deep learning models could classify skin lesion images without clinically meaningful information in the input data. Their findings seem confounding since the ablated regions (random rectangular boxes) are not significant. The shape of the lesion is a crucial factor in the clinical characterization of a skin lesion. In that context, we performed a set of experiments that generate shape-preserving masks instead of rectangular bounding-box based masks. A deep learning model trained on these shape-preserving masked images does not outperform models trained on images without clinically meaningful information. That strongly suggests spurious correlations guiding the models. We propose use of general adversarial network (GAN) to mitigate the underlying bias.
Reproducible Science with LaTeX
Authors: Haim Bar, Haiying Wang
Date: 2020-10-04 00:00:00

This paper proposes a procedure to execute external source codes from a LaTeX document and include the calculation outputs in the resulting Portable Document Format (pdf) file automatically. It integrates programming tools into the LaTeX writing tool to facilitate the production of reproducible research. In our proposed approach to a LaTeX-based scientific notebook the user can easily invoke any programming language or a command-line program when compiling the LaTeX document, while using their favorite LaTeX editor in the writing process. The required LaTeX setup, a new Python package, and the defined preamble are discussed in detail, and working examples using R, Julia, and MatLab to reproduce existing research are provided to illustrate the proposed procedure. We also demonstrate how to include system setting information in a paper by invoking shell scripts when compiling the document.
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
Authors: Junyi Li, Siqing Li, Wayne Xin Zhao, Gaole He, Zhicheng Wei, Nicholas Jing Yuan, Ji-Rong Wen
Date: 2020-10-04 00:00:00
Tasks: Graph Neural Network, Review Generation, Sentence, Text Generation

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.
Explanation Ontology: A Model of Explanations for User-Centered AI
Authors: Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A. Foreman, Amar K. Das, Deborah L. McGuinness
Date: 2020-10-04 00:00:00

Explainability has been a goal for Artificial Intelligence (AI) systems since their conception, with the need for explainability growing as more complex AI models are increasingly used in critical, high-stakes settings such as healthcare. Explanations have often added to an AI system in a non-principled, post-hoc manner. With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system's AI capabilities. We design an explanation ontology to model both the role of explanations, accounting for the system and user attributes in the process, and the range of different literature-derived explanation types. We indicate how the ontology can support user requirements for explanations in the domain of healthcare. We evaluate our ontology with a set of competency questions geared towards a system designer who might use our ontology to decide which explanation types to include, given a combination of users' needs and a system's capabilities, both in system design settings and in real-time operations. Through the use of this ontology, system designers will be able to make informed choices on which explanations AI systems can and should provide.
Explanation Ontology in Action: A Clinical Use-Case
Authors: Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A. Foreman, Amar K. Das, Deborah L. McGuinness
Date: 2020-10-04 00:00:00

We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.
Generalized Two-Dimensional Quaternion Principal Component Analysis with Weighting for Color Image Recognition
Authors: Zhi-Gang Jia, Zi-Jin Qiu, Qian-Yu Wang, Mei-Xiang Zhao, Dan-Dan Zhu
Date: 2020-10-04 00:00:00
Tasks: Face Recognition

One of the most powerful methods of color image recognition is the two-dimensional principle component analysis (2DQPCA) approach, which is based on quaternion representation and preserves color information very well. However, the current versions of 2DQPCA are still not feasible to extract different geometric properties of color images according to practical data analysis requirements and they are vulnerable to strong noise. In this paper, a generalized 2DQPCA approach with weighting is presented with imposing $L_{p}$ norms on both constraint and objective functions. As a unit 2DQPCA framework, this new version makes it possible to choose adaptive regularizations and constraints according to actual applications and can extract both geometric properties and color information of color images. The projection vectors generated by the deflating scheme are required to be orthogonal to each other. A weighting matrix is defined to magnify the effect of main features. This overcomes the shortcomings of traditional 2DQPCA that the recognition rate decreases as the number of principal components increases. The numerical results based on the real face databases validate that the newly proposed method is robust to noise and performs better than the state-of-the-art 2DQPCA-based algorithms and four prominent deep learning methods.
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
Authors: Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou
Date: 2020-10-04 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Data Augmentation, Machine Reading Comprehension, Natural Language Inference, QNLI, Question Answering, Question Generation, Question-Generation, Question Rewriting, Reading Comprehension

In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA