pwc-archive/papers-with-abstracts huggingface dataset:
Automation Of Transiting Exoplanet Detection, Identification and Habitability Assessment Using Machine Learning Approaches
Authors: Pawel Pratyush, Akshata Gangrade
Date: 2021-12-06 00:00:00
Tasks: BIG-bench Machine Learning

We are at a unique timeline in the history of human evolution where we may be able to discover earth-like planets around stars outside our solar system where conditions can support life or even find evidence of life on those planets. With the launch of several satellites in recent years by NASA, ESA, and other major space agencies, an ample amount of datasets are at our disposal which can be utilized to train machine learning models that can automate the arduous tasks of exoplanet detection, its identification, and habitability determination. Automating these tasks can save a considerable amount of time and minimize human errors due to manual intervention. To achieve this aim, we first analyze the light intensity curves from stars captured by the Kepler telescope to detect the potential curves that exhibit the characteristics of an existence of a possible planetary system. For this detection, along with training conventional models, we propose a stacked GBDT model that can be trained on multiple representations of the light signals simultaneously. Subsequently, we address the automation of exoplanet identification and habitability determination by leveraging several state-of-art machine learning and ensemble approaches. The identification of exoplanets aims to distinguish false positive instances from the actual instances of exoplanets whereas the habitability assessment groups the exoplanet instances into different clusters based on their habitable characteristics. Additionally, we propose a new metric called Adequate Thermal Adequacy (ATA) score to establish a potential linear relationship between habitable and non-habitable instances. Experimental results suggest that the proposed stacked GBDT model outperformed the conventional models in detecting transiting exoplanets. Furthermore, the incorporation of ATA scores in habitability classification enhanced the performance of models.
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder
Authors: Sangkeum Lee, Hojun Jin, Sarvar Hussain Nengroo, Yoonmee Doh, Chungho Lee, Taewook Heo, Dongsoo Har
Date: 2021-12-06 00:00:00
Tasks: Anomaly Detection, Event Detection, Fault Detection

Anomaly detection is concerned with a wide range of applications such as fault detection, system monitoring, and event detection. Identifying anomalies from metering data obtained from smart metering system is a critical task to enhance reliability, stability, and efficiency of the power system. This paper presents an anomaly detection process to find outliers observed in the smart metering system. In the proposed approach, bi-directional long short-term memory (BiLSTM) based autoencoder is used and finds the anomalous data point. It calculates the reconstruction error through autoencoder with the non-anomalous data, and the outliers to be classified as anomalies are separated from the non-anomalous data by predefined threshold. Anomaly detection method based on the BiLSTM autoencoder is tested with the metering data corresponding to 4 types of energy sources electricity/water/heating/hot water collected from 985 households.
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Authors: Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng
Date: 2021-12-05 00:00:00
Tasks: BIG-bench Machine Learning, Translation

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. We need evidence to support that the resulting decisions are well-founded. To aid development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of available training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) in the use of a particular code implementation of the chosen algorithm. We detail how trust can fail at each step and illustrate our taxonomy with two case studies: an analysis of the efficacy of microcredit and The Economist's predictions of the 2020 US presidential election. Finally, we describe a wide variety of methods that can be used to increase trust at each step of our taxonomy. The use of our taxonomy highlights steps where existing research work on trust tends to concentrate and also steps where establishing trust is particularly challenging.
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Authors: Xuesong Wang, Zhihang Hu, Tingyang Yu, Ruijie Wang, Yumeng Wei, Juan Shu, Jianzhu Ma, Yu Li
Date: 2021-12-05 00:00:00

Muilti-modality data are ubiquitous in biology, especially that we have entered the multi-omics era, when we can measure the same biological object (cell) from different aspects (omics) to provide a more comprehensive insight into the cellular system. When dealing with such multi-omics data, the first step is to determine the correspondence among different modalities. In other words, we should match data from different spaces corresponding to the same object. This problem is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Secondly, matched single-cell multi-omics data are rare and hard to collect. Furthermore, due to the limitations of the experimental environment, the data are usually highly noisy. To promote the single-cell multi-omics research, we overcome the above challenges, proposing a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward. Compared with the other state-of-the-art methods, our method performs better in both simulated and real single-cell data. The proposed method is helpful for the single-cell multi-omics research. The improvement for integration on the simulated data is significant.
A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems
Authors: Yang Li, Meng Zhang, Chen Chen
Date: 2021-12-05 00:00:00
Tasks: Data Augmentation, Deep Learning, valid

Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem. Although a big enough dataset can be directly generated by contingency simulation, this data generation process is usually cumbersome and inefficient; while data augmentation provides a low-cost and efficient way to artificially inflate the representative and diversified training datasets with label preserving transformations. In this respect, this paper proposes a novel deep-learning intelligent system incorporating data augmentation for STVSA of power systems. First, due to the unavailability of reliable quantitative criteria to judge the stability status for a specific power system, semi-supervised cluster learning is leveraged to obtain labeled samples in an original small dataset. Second, to make deep learning applicable to the small dataset, conditional least squares generative adversarial networks (LSGAN)-based data augmentation is introduced to expand the original dataset via artificially creating additional valid samples. Third, to extract temporal dependencies from the post-disturbance dynamic trajectories of a system, a bi-directional gated recurrent unit with attention mechanism based assessment model is established, which bi-directionally learns the significant time dependencies and automatically allocates attention weights. The test results demonstrate the presented approach manages to achieve better accuracy and a faster response time with original small datasets. Besides classification accuracy, this work employs statistical measures to comprehensively examine the performance of the proposal.
Modeling and Predicting Blood Flow Characteristics through Double Stenosed Artery from CFD simulation using Deep Learning Models
Authors: Ishat Raihan Jamil, Mayeesha Humaira
Date: 2021-12-04 00:00:00

Establishing patient-specific finite element analysis (FEA) models for computational fluid dynamics (CFD) of double stenosed artery models involves time and effort, restricting physicians' ability to respond quickly in time-critical medical applications. Such issues might be addressed by training deep learning (DL) models to learn and predict blood flow characteristics using a dataset generated by CFD simulations of simplified double stenosed artery models with different configurations. When blood flow patterns are compared through an actual double stenosed artery model, derived from IVUS imaging, it is revealed that the sinusoidal approximation of stenosed neck geometry, which has been widely used in previous research works, fails to effectively represent the effects of a real constriction. As a result, a novel geometric representation of the constricted neck is proposed which, in terms of a generalized simplified model, outperforms the former assumption. The sequential change in artery lumen diameter and flow parameters along the length of the vessel presented opportunities for the use of LSTM and GRU DL models. However, with the small dataset of short lengths of doubly constricted blood arteries, the basic neural network model outperforms the specialized RNNs for most flow properties. LSTM, on the other hand, performs better for predicting flow properties with large fluctuations, such as varying blood pressure over the length of the vessels. Despite having good overall accuracies in training and testing across all the properties for the vessels in the dataset, the GRU model underperforms for an individual vessel flow prediction in all cases. The results also point to the need of individually optimized hyperparameters for each property in any model rather than aiming to achieve overall good performance across all outputs with a single set of hyperparameters.
Multi-scale Graph Convolutional Networks with Self-Attention
Authors: Zhilong Xiong, Jia Cai
Date: 2021-12-04 00:00:00
Tasks: Computational Efficiency, Graph Classification, Node Classification

Graph convolutional networks (GCNs) have achieved remarkable learning ability for dealing with various graph structural data recently. In general, deep GCNs do not work well since graph convolution in conventional GCNs is a special form of Laplacian smoothing, which makes the representation of different nodes indistinguishable. In the literature, multi-scale information was employed in GCNs to enhance the expressive power of GCNs. However, over-smoothing phenomenon as a crucial issue of GCNs remains to be solved and investigated. In this paper, we propose two novel multi-scale GCN frameworks by incorporating self-attention mechanism and multi-scale information into the design of GCNs. Our methods greatly improve the computational efficiency and prediction accuracy of the GCNs model. Extensive experiments on both node classification and graph classification demonstrate the effectiveness over several state-of-the-art GCNs. Notably, the proposed two architectures can efficiently mitigate the over-smoothing problem of GCNs, and the layer of our model can even be increased to $64$.
EmTract: Extracting Emotions from Social Media
Authors: Domonkos F. Vamossy, Rolf Skog
Date: 2021-12-07 00:00:00

We develop an open-source tool (EmTract) that extracts emotions from social media text tailed for financial context. To do so, we annotate ten thousand short messages from a financial social media platform (StockTwits) and combine it with open-source emotion data. We then use a pre-tuned NLP model, DistilBERT, augment its embedding space by including 4,861 tokens (emojis and emoticons), and then fit it first on the open-source emotion data, then transfer it to our annotated financial social media data. Our model outperforms competing open-source state-of-the-art emotion classifiers, such as Emotion English DistilRoBERTa-base on both human and chatGPT annotated data. Compared to dictionary based methods, our methodology has three main advantages for research in finance. First, our model is tailored to financial social media text; second, it incorporates key aspects of social media data, such as non-standard phrases, emojis, and emoticons; and third, it operates by sequentially learning a latent representation that includes features such as word order, word usage, and local context. Using EmTract, we explore the relationship between investor emotions expressed on social media and asset prices. We show that firm-specific investor emotions are predictive of daily price movements. Our findings show that emotions and market dynamics are closely related, and we provide a tool to help study the role emotions play in financial markets.
Reducing Target Group Bias in Hate Speech Detectors
Authors: Darsh J Shah, Sinong Wang, Han Fang, Hao Ma, Luke Zettlemoyer
Date: 2021-12-07 00:00:00
Tasks: text-classification, Text Classification

The ubiquity of offensive and hateful content on online fora necessitates the need for automatic solutions that detect such content competently across target groups. In this paper we show that text classification models trained on large publicly available datasets despite having a high overall performance, may significantly under-perform on several protected groups. On the \citet{vidgen2020learning} dataset, we find the accuracy to be 37\% lower on an under annotated Black Women target group and 12\% lower on Immigrants, where hate speech involves a distinct style. To address this, we propose to perform token-level hate sense disambiguation, and utilize tokens' hate sense representations for detection, modeling more general signals. On two publicly available datasets, we observe that the variance in model accuracy across target groups drops by at least 30\%, improving the average target group performance by 4\% and worst case performance by 13\%.
Automated Story Generation as Question-Answering
Authors: Louis Castricato, Spencer Frazier, Jonathan Balloch, Nitya Tarakad, Mark Riedl
Date: 2021-12-07 00:00:00
Tasks: Generative Question Answering, Language Modeling, Language Modelling, Question Answering, Story Generation

Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work toward a given goal or ending. Second, they often lose coherence as the story gets longer. We propose a novel approach to automated story generation that treats the problem as one of generative question-answering. Our proposed story generation system starts with sentences encapsulating the final event of the story. The system then iteratively (1) analyzes the text describing the most recent event, (2) generates a question about "why" a character is doing the thing they are doing in the event, and then (3) attempts to generate another, preceding event that answers this question.
A pragmatic account of the weak evidence effect
Authors: Samuel A. Barnett, Thomas L. Griffiths, Robert D. Hawkins
Date: 2021-12-07 00:00:00
Tasks: Decision Making

Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker's "hidden agenda" when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a pragmatic account for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect. Critically, this model predicts a systematic relationship between belief updates and expectations about the information source: weak evidence should only backfire when speakers are expected to act under persuasive goals and prefer the strongest evidence. We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations, and show that a pragmatic listener model accounts for the empirical data better than alternative models. Our findings suggest further avenues for rational models of social reasoning to illuminate classical decision-making phenomena.
UCD-CS at TREC 2021 Incident Streams Track
Authors: Congcong Wang, David Lillis
Date: 2021-12-07 00:00:00
Tasks: Humanitarian, Multi-Task Learning, Text Augmentation

In recent years, the task of mining important information from social media posts during crises has become a focus of research for the purposes of assisting emergency response (ES). The TREC Incident Streams (IS) track is a research challenge organised for this purpose. The track asks participating systems to both classify a stream of crisis-related tweets into humanitarian aid related information types and estimate their importance regarding criticality. The former refers to a multi-label information type classification task and the latter refers to a priority estimation task. In this paper, we report on the participation of the University College Dublin School of Computer Science (UCD-CS) in TREC-IS 2021. We explored a variety of approaches, including simple machine learning algorithms, multi-task learning techniques, text augmentation, and ensemble approaches. The official evaluation results indicate that our runs achieve the highest scores in many metrics. To aid reproducibility, our code is publicly available at https://github.com/wangcongcong123/crisis-mtl.
GKS: Graph-based Knowledge Selector for Task-oriented Dialog System
Authors: Jen-Chieh Yang, Jia-Yan Wu, Sung-Ping Chang, Ya-Chieh Huang
Date: 2021-12-07 00:00:00
Tasks: Decision Making, Graph Attention, Language Modeling, Language Modelling

In previous research, knowledge-selection tasks mostly rely on language model-based methods or knowledge ranking. However, while approaches that rely on the language models take all knowledge as sequential input, knowledge does not contain sequential information in most circumstances. On the other hand, the knowledge-ranking methods leverage dialog history and each given knowledge snippet separately, but they do not consider information between knowledge snippets. In the Tenth Dialog System Technology Challenges (DSTC10), we participated in the second Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations. To deal with the problems mentioned above, we modified training methods based on state-of-the-art (SOTA) models for the first and third sub-tasks. As for the second sub-task of knowledge selection, we proposed Graph-Knowledge Selector (GKS), utilizing a graph-attention base model incorporated with the language model. GKS makes knowledge-selection decisions in the dialog by simultaneously considering each knowledge embedding generated from the language model without sequential features. Moreover, GKS leverages considerable knowledge in decision-making and takes relations across knowledge as part of the selection process. As a result, GKS outperforms several SOTA models proposed in the data-set on knowledge selection from the Ninth Dialog System Technology Challenges (DSTC9).
Change Summarization of Diachronic Scholarly Paper Collections by Semantic Evolution Analysis
Authors: Naman Paharia, Muhammad Syafiq Mohd Pozi, Adam Jatowt
Date: 2021-12-07 00:00:00
Tasks: Articles

The amount of scholarly data has been increasing dramatically over the last years. For newcomers to a particular science domain (e.g., IR, physics, NLP) it is often difficult to spot larger trends and to position the latest research in the context of prior scientific achievements and breakthroughs. Similarly, researchers in the history of science are interested in tools that allow them to analyze and visualize changes in particular scientific domains. Temporal summarization and related methods should be then useful for making sense of large volumes of scientific discourse data aggregated over time. We demonstrate a novel approach to analyze the collections of research papers published over longer time periods to provide a high-level overview of important semantic changes that occurred over the progress of time. Our approach is based on comparing word semantic representations over time and aims to support users in a better understanding of large domain-focused archives of scholarly publications. As an example dataset we use the ACL Anthology Reference Corpus that spans from 1979 to 2015 and contains 22,878 scholarly articles.
Parsing with Pretrained Language Models, Multiple Datasets, and Dataset Embeddings
Authors: Rob van der Goot, Miryam de Lhoneux
Date: 2021-12-07 00:00:00
Proceeding: ACL (TLT, SyntaxFest) 2021 12

With an increase of dataset availability, the potential for learning from a variety of data sources has increased. One particular method to improve learning from multiple data sources is to embed the data source during training. This allows the model to learn generalizable features as well as distinguishing features between datasets. However, these dataset embeddings have mostly been used before contextualized transformer-based embeddings were introduced in the field of Natural Language Processing. In this work, we compare two methods to embed datasets in a transformer-based multilingual dependency parser, and perform an extensive evaluation. We show that: 1) embedding the dataset is still beneficial with these models 2) performance increases are highest when embedding the dataset at the encoder level 3) unsurprisingly, we confirm that performance increases are highest for small datasets and datasets with a low baseline score. 4) we show that training on the combination of all datasets performs similarly to designing smaller clusters based on language-relatedness.
Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets
Authors: Kofi Arhin, Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh
Date: 2021-12-07 00:00:00

The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise. For toxic text identification, task-specific fine-tuning of these models are performed using datasets labeled by annotators who provide ground-truth labels in an effort to distinguish between offensive and normal content. These projects have led to the development, improvement, and expansion of large datasets over time, and have contributed immensely to research on natural language. Despite the achievements, existing evidence suggests that ML models built on these datasets do not always result in desirable outcomes. Therefore, using a design science research (DSR) approach, this study examines selected toxic text datasets with the goal of shedding light on some of the inherent issues and contributing to discussions on navigating these challenges for existing and future projects. To achieve the goal of the study, we re-annotate samples from three toxic text datasets and find that a multi-label approach to annotating toxic text samples can help to improve dataset quality. While this approach may not improve the traditional metric of inter-annotator agreement, it may better capture dependence on context and diversity in annotators. We discuss the implications of these results for both theory and practice.