pwc-archive/papers-with-abstracts huggingface dataset:
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
Authors: William Romano, Omar Sharif, Madhusudan Basak, Joseph Gatto, Sarah Preum
Date: 2023-01-27 00:00:00
Tasks: Keyphrase Extraction, Language Modelling, Large Language Model

Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase extraction is used to identify salient concepts in social media discourse without being constrained by predefined entity classes. This paper introduces a theme-driven keyphrase extraction framework tailored for social media, a pioneering approach designed to capture clinically relevant keyphrases from user-generated health texts. Themes are defined as broad categories determined by the objectives of the extraction task. We formulate this novel task of theme-driven keyphrase extraction and demonstrate its potential for efficiently mining social media text for the use case of treatment for opioid use disorder. This paper leverages qualitative and quantitative analysis to demonstrate the feasibility of extracting actionable insights from social media data and efficiently extracting keyphrases using minimally supervised NLP models. Our contributions include the development of a novel data collection and curation framework for theme-driven keyphrase extraction and the creation of MOUD-Keyphrase, the first dataset of its kind comprising human-annotated keyphrases from a Reddit community. We also identify the scope of minimally supervised NLP models to extract keyphrases from social media data efficiently. Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task.
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
Authors: Megan Leszczynski, Shu Zhang, Ravi Ganti, Krisztian Balog, Filip Radlinski, Fernando Pereira, Arun Tejasvi Chaganty
Date: 2023-01-27 00:00:00
Tasks: Language Modelling, Music Recommendation, Recommendation Systems, Retrieval, Synthetic Data Generation

Recommender systems are ubiquitous yet often difficult for users to control, and adjust if recommendation quality is poor. This has motivated conversational recommender systems (CRSs), with control provided through natural language feedback. However, as with most application domains, building robust CRSs requires training data that reflects system usage$\unicode{x2014}$here conversations with user utterances paired with items that cover a wide range of preferences. This has proved challenging to collect scalably using conventional methods. We address the question of whether it can be generated synthetically, building on recent advances in natural language. We evaluate in the setting of item set recommendation, noting the increasing attention to this task motivated by use cases like music, news, and recipe recommendation. We present TalkTheWalk, which synthesizes realistic high-quality conversational data by leveraging domain expertise encoded in widely available curated item collections, generating a sequence of hypothetical yet plausible item sets, then using a language model to produce corresponding user utterances. We generate over one million diverse playlist curation conversations in the music domain, and show these contain consistent utterances with relevant item sets nearly matching the quality of an existing but small human-collected dataset for this task. We demonstrate the utility of the generated synthetic dataset on a conversational item retrieval task and show that it improves over both unsupervised baselines and systems trained on a real dataset.
How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech
Authors: Aditya Yedetore, Tal Linzen, Robert Frank, R. Thomas McCoy
Date: 2023-01-26 00:00:00

When acquiring syntax, children consistently choose hierarchical rules over competing non-hierarchical possibilities. Is this preference due to a learning bias for hierarchical structure, or due to more general biases that interact with hierarchical cues in children's linguistic input? We explore these possibilities by training LSTMs and Transformers - two types of neural networks without a hierarchical bias - on data similar in quantity and content to children's linguistic input: text from the CHILDES corpus. We then evaluate what these models have learned about English yes/no questions, a phenomenon for which hierarchical structure is crucial. We find that, though they perform well at capturing the surface statistics of child-directed speech (as measured by perplexity), both model types generalize in a way more consistent with an incorrect linear rule than the correct hierarchical rule. These results suggest that human-like generalization from text alone requires stronger biases than the general sequence-processing biases of standard neural network architectures.
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
Authors: Zi Lin, Jeremiah Liu, Jingbo Shang
Date: 2023-01-26 00:00:00
Tasks: Semantic Parsing, Uncertainty Quantification

Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples. In comparison, symbolic parsers under-perform on population-level metrics, but exhibit unique strength in OOD and tail generalization. In this work, we study compositionality-aware approach to neural-symbolic inference informed by model confidence, performing fine-grained neural-symbolic reasoning at subgraph level (i.e., nodes and edges) and precisely targeting subgraph components with high uncertainty in the neural parser. As a result, the method combines the distinct strength of the neural and symbolic approaches in capturing different aspects of the graph prediction, leading to well-rounded generalization performance both across domains and in the tail. We empirically investigate the approach in the English Resource Grammar (ERG) parsing problem on a diverse suite of standard in-domain and seven OOD corpora. Our approach leads to 35.26% and 35.60% error reduction in aggregated Smatch score over neural and symbolic approaches respectively, and 14% absolute accuracy gain in key tail linguistic categories over the neural model, outperforming prior state-of-art methods that do not account for compositionality or uncertainty.
Beyond Arabic: Software for Perso-Arabic Script Manipulation
Authors: Alexander Gutkin, Cibu Johny, Raiomond Doctor, Brian Roark, Richard Sproat
Date: 2023-01-26 00:00:00
Tasks: Transliteration

This paper presents an open-source software library that provides a set of finite-state transducer (FST) components and corresponding utilities for manipulating the writing systems of languages that use the Perso-Arabic script. The operations include various levels of script normalization, including visual invariance-preserving operations that subsume and go beyond the standard Unicode normalization forms, as well as transformations that modify the visual appearance of characters in accordance with the regional orthographies for eleven contemporary languages from diverse language families. The library also provides simple FST-based romanization and transliteration. We additionally attempt to formalize the typology of Perso-Arabic characters by providing one-to-many mappings from Unicode code points to the languages that use them. While our work focuses on the Arabic script diaspora rather than Arabic itself, this approach could be adopted for any language that uses the Arabic script, thus providing a unified framework for treating a script family used by close to a billion people.
Task formulation for Extracting Social Determinants of Health from Clinical Narratives
Authors: Manabu Torii, Ian M. Finn, Son Doan, Paul Wang, Elly W. Yang, Daniel S. Zisook
Date: 2023-01-26 00:00:00
Tasks: Event Extraction, Language Modelling, Large Language Model, Relation Extraction

Objective: The 2022 n2c2 NLP Challenge posed identification of social determinants of health (SDOH) in clinical narratives. We present three systems that we developed for the Challenge and discuss the distinctive task formulation used in each of the three systems. Materials and Methods: The first system identifies target pieces of information independently using machine learning classifiers. The second system uses a large language model (LLM) to extract complete structured outputs per document. The third system extracts candidate phrases using machine learning and identifies target relations with hand-crafted rules. Results: The three systems achieved F1 scores of 0.884, 0.831, and 0.663 in the Subtask A of the Challenge, which are ranked third, seventh, and eighth among the 15 participating teams. The review of the extraction results from our systems reveals characteristics of each approach and those of the SODH extraction task. Discussion: Phrases and relations annotated in the task is unique and diverse, not conforming to the conventional event extraction task. These annotations are difficult to model with limited training data. The system that extracts information independently, ignoring the annotated relations, achieves the highest F1 score. Meanwhile, LLM with its versatile capability achieves the high F1 score, while respecting the annotated relations. The rule-based system tackling relation extraction obtains the low F1 score, while it is the most explainable approach. Conclusion: The F1 scores of the three systems vary in this challenge setting, but each approach has advantages and disadvantages in a practical application. The selection of the approach depends not only on the F1 score but also on the requirements in the application.
Using novel data and ensemble models to improve automated labeling of Sustainable Development Goals
Authors: Dirk U. Wulff, Dominik S. Meier, Rui Mata
Date: 2023-01-25 00:00:00
Tasks: Specificity

A number of labeling systems based on text have been proposed to help monitor work on the United Nations (UN) Sustainable Development Goals (SDGs). Here, we present a systematic comparison of systems using a variety of text sources and show that systems differ considerably in their specificity (i.e., true-positive rate) and sensitivity (i.e., true-negative rate), have systematic biases (e.g., are more sensitive to specific SDGs relative to others), and are susceptible to the type and amount of text analyzed. We then show that an ensemble model that pools labeling systems alleviates some of these limitations, exceeding the labeling performance of all currently available systems. We conclude that researchers and policymakers should care about the choice of labeling system and that ensemble methods should be favored when drawing conclusions about the absolute and relative prevalence of work on the SDGs based on automated methods.
From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions
Authors: Johannes Resin
Date: 2023-01-27 00:00:00
Tasks: Uncertainty Quantification

In the face of uncertainty, the need for probabilistic assessments has long been recognized in the literature on forecasting. In classification, however, comparative evaluation of classifiers often focuses on predictions specifying a single class through the use of simple accuracy measures, which disregard any probabilistic uncertainty quantification. I propose probabilistic top lists as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicitable through the use of strictly consistent evaluation metrics. The proposed evaluation metrics are based on symmetric proper scoring rules and admit comparison of various types of predictions ranging from single-class point predictions to fully specified predictive distributions. The Brier score yields a metric that is particularly well suited for this kind of comparison.
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Authors: Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol
Date: 2023-01-27 00:00:00
Tasks: Bayesian Inference, parameter estimation

Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously been used for approximate Bayesian computation, minimum distance estimation, generalised Bayesian inference, and within the nonparametric learning framework. The MMD is commonly estimated at a root-$m$ rate, where $m$ is the number of simulated samples. This can lead to significant computational challenges since a large $m$ is required to obtain an accurate estimate, which is crucial for parameter estimation. In this paper, we propose a novel estimator for the MMD with significantly improved sample complexity. The estimator is particularly well suited for computationally expensive smooth simulators with low- to mid-dimensional inputs. This claim is supported through both theoretical results and an extensive simulation study on benchmark simulators.
Multilayer hypergraph clustering using the aggregate similarity matrix
Authors: Kalle Alaluusua, Konstantin Avrachenkov, B. R. Vinay Kumar, Lasse Leskelä
Date: 2023-01-27 00:00:00
Tasks: Clustering, Stochastic Block Model

We consider the community recovery problem on a multilayer variant of the hypergraph stochastic block model (HSBM). Each layer is associated with an independent realization of a d-uniform HSBM on N vertices. Given the similarity matrix containing the aggregated number of hyperedges incident to each pair of vertices, the goal is to obtain a partition of the N vertices into disjoint communities. In this work, we investigate a semidefinite programming (SDP) approach and obtain information-theoretic conditions on the model parameters that guarantee exact recovery both in the assortative and the disassortative cases.
Exponential tail bounds and Large Deviation Principle for Heavy-Tailed U-Statistics
Authors: Milad Bakhshizadeh
Date: 2023-01-27 00:00:00

We study deviation of U-statistics when samples have heavy-tailed distribution so the kernel of the U-statistic does not have bounded exponential moments at any positive point. We obtain an exponential upper bound for the tail of the U-statistics which clearly denotes two regions of tail decay, the first is a Gaussian decay and the second behaves like the tail of the kernel. For several common U-statistics, we also show the upper bound has the right rate of decay as well as sharp constants by obtaining rough logarithmic limits which in turn can be used to develop LDP for U-statistics. In spite of usual LDP results in the literature, processes we consider in this work have LDP speed slower than their sample size $n$.
VulCNN: An Image-inspired Scalable Vulnerability Detection System
Authors: Yueming Wu, Deqing Zou, Shihan Dou, Wei Yang, Duo Xu, Hai Jin
Date: 2022-06-20 00:00:00
Proceeding: International Conference on Software Engineering 2022 6
Tasks: image-classification, Image Classification, Vulnerability Detection

Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intent to process the source code directly by treating them as text.To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and use them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming.

In this paper, we aim to achieve both scalability and accuracy on scanning large-scale source code vulnerabilities. Inspired by existing DL-based image classification which has the ability to analyze millions of images accurately, we prefer to use these techniques to accomplish our purpose. Specifically, we propose a novel idea that can efficiently convert the source code of a function into an image while preserving the program details. We implement VulCNN and evaluate it on a dataset of 13,687 vulnerable functions and 26,970 non-vulnerable functions. Experimental results report that VulCNN performs better than eight state-of-the-art vulnerability detectors (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, VulDeePecker, SySeVR, VulDeeLocator, and Devign). As for scalability, VulCNN is about four times faster than VulDeePecker and SySeVR, about 15 times faster than VulDeeLocator, and about six times faster than Devign. Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN has the ability to detect large-scale vulnerability. Through the scanning reports, we finally discover 73 vulnerabilities that are not reported in NVD.
Cost-aware Defense for Parallel Server Systems against Reliability and Security Failures
Authors: Qian Xie, Jiayi Wang, Li Jin
Date: 2023-01-27 00:00:00

Parallel server systems in transportation, manufacturing, and computing heavily rely on dynamic routing using connected cyber components for computation and communication. Yet, these components remain vulnerable to random malfunctions and malicious attacks, motivating the need for fault-tolerant dynamic routing that are both traffic-stabilizing and cost-efficient. In this paper, we consider a parallel server system with dynamic routing subject to reliability and stability failures. For the reliability setting, we consider an infinite-horizon Markov decision process where the system operator strategically activates protection mechanism upon each job arrival based on traffic state observations. We prove an optimal deterministic threshold protecting policy exists based on dynamic programming recursion of the HJB equation. For the security setting, we extend the model to an infinite-horizon stochastic game where the attacker strategically manipulates routing assignment. We show that both players follow a threshold strategy at every Markov perfect equilibrium. For both failure settings, we also analyze the stability of the traffic queues under control. Finally, we develop approximate dynamic programming algorithms to compute the optimal/equilibrium policies, supplemented with numerical examples and experiments for validation and illustration.
Exact hierarchical reductions of dynamical models via linear transformations
Authors: Alexander Demin, Elizaveta Demitraki, Gleb Pogudin
Date: 2023-01-27 00:00:00

Dynamical models described by ordinary differential equations (ODEs) are a fundamental tool in the sciences and engineering. Exact reduction aims at producing a lower-dimensional model in which each macro-variable can be directly related to the original variables, and it is thus a natural step towards the model's formal analysis and mechanistic understanding. We present an algorithm which, given a polynomial ODE model, computes a longest possible chain of exact linear reductions of the model such that each reduction refines the previous one, thus giving a user control of the level of detail preserved by the reduction. This significantly generalizes over the existing approaches which compute only the reduction of the lowest dimension subject to an approach-specific constraint. The algorithm reduces finding exact linear reductions to a question about representations of finite-dimensional algebras. We provide an implementation of the algorithm, demonstrate its performance on a set of benchmarks, and illustrate the applicability via case studies. Our implementation is freely available at https://github.com/x3042/ExactODEReduction.jl
On granularity of prosodic representations in expressive text-to-speech
Authors: Mikolaj Babianski, Kamil Pokora, Raahil Shah, Rafal Sienkiewicz, Daniel Korzekwa, Viacheslav Klimkov
Date: 2023-01-26 00:00:00
Tasks: Expressive Speech Synthesis, Speech Synthesis, text-to-speech, Text to Speech

In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training. Same text may correspond to various acoustic realizations, which is known as a one-to-many mapping problem in text-to-speech. Utterance, word, or phoneme-level representations are extracted from target signal in an auto-encoding setup, to complement phonetic input and simplify that mapping. This paper compares prosodic embeddings at different levels of granularity and examines their prediction from text. We show that utterance-level embeddings have insufficient capacity and phoneme-level tend to introduce instabilities when predicted from text. Word-level representations impose balance between capacity and predictability. As a result, we close the gap in naturalness by 90% between synthetic speech and recordings on LibriTTS dataset, without sacrificing intelligibility.
Quantized Deep Path-following Control on a Microcontroller
Authors: Pablo Zometa, Timm Faulwasser
Date: 2023-01-27 00:00:00
Tasks: Quantization

Model predictive Path-Following Control (MPFC) is a viable option for motion systems in many application domains. However, despite considerable progress on tailored numerical methods for predictive control, the real-time implementation of predictive control and MPFC on small-scale autonomous platforms with low-cost embedded hardware remains challenging. While usual stabilizing MPC formulations lead to static feedback laws, the MPFC feedback turns out to be dynamic as the path parameter acts as an internal controller variable. In this paper, we leverage deep learning to implement predictive path-following control on microcontrollers. We show that deep neural networks can approximate the dynamic MPFC feedback law accurately. Moreover, we illustrate and tackle the challenges that arise if the target platform employs limited precision arithmetic. Specifically, we draw upon a post-stabilization with an additional feedback law to attenuate undesired quantization effects. Simulation examples underpin the efficacy of the proposed approach.
Optimization of a Commercial Injection-Moulded component by Using DOE and Simulation
Authors: Mandana Kariminejad, David Tormey, Saif Huq, Jim Morrison, Jeff Redmond, Carlos Souto, Marion McAfee
Date: 2023-01-27 00:00:00

Injection moulding is an important industry, providing a significant percentage of the demand for plastic products throughout the world. The process consists of many variables which directly or indirectly influence the part quality and cycle time. The first step in optimizing the process parameters is identifying the most significant variables affecting the desired output. For this purpose, various Design of Experiments methods (DOE) have been developed to investigate the effect of the experimental variables on the output and predict the required settings to achieve the optimal value of the output. In this study we investigate the application of DOE for a commercial injection moulded component which suffers from a long cycle time and high shrinkage. The Taguchi method has been used to analyze the effect of four input variables on the two output variables: cycle time and shrinkage. The component has been simulated in the Moldflow software to validate the predicted output and optimized settings of the variables from the DOE. A comparison of the simulation result and the predicted value from the DOE illustrated good accordance. The calculated optimal setting with the Taguchi method reduced the cycle time from the 40s to about 23s and met the shrinkage criteria for this commercial part.