pwc-archive/papers-with-abstracts huggingface dataset:
Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography
Authors: José Manuel Hidalgo Rogel, Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Sergio López Bernal, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Date: 2022-09-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

- Background / Introduction: Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers' drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML). However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, and it is necessary to study the performance of scalable ML models suitable for groups of subjects. - Methods: To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. - Results: Results show that Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. - Conclusions: The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated. Thus, the proposed framework presents a novel approach for detecting drowsiness in driving scenarios using BCIs and ML.
Assessing Lower Limb Strength using Internet-of-Things Enabled Chair
Authors: Chelsea Yeh, Hanna Kaitlin Dy, Phillip Schodinger, Hudson Kaleb Dy
Date: 2022-09-08 00:00:00
Tasks: GPU, Time Series, Time Series Analysis

This project describes the application of the technologies of Machine Learning and Internet-of-Things to assess the lower limb strength of individuals undergoing rehabilitation or therapy. Specifically, it seeks to measure and assess the progress of individuals by sensors attached to chairs and processing the data through Google GPU Tensorflow CoLab. Pressure sensors are attached to various locations on a chair, including but not limited to the seating area, backrest, hand rests, and legs. Sensor data from the individual performing both sit-to-stand transition and stand-to-sit transition provides a time series dataset regarding the pressure distribution and vibratory motion on the chair. The dataset and timing information can then be fed into a machine learning model to estimate the relative strength and weakness during various phases of the movement.
Functional dimension of feedforward ReLU neural networks
Authors: J. Elisenda Grigsby, Kathryn Lindsey, Robert Meyerhoff, Chenxi Wu
Date: 2022-09-08 00:00:00

It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of piecewise linear functions with finitely many pieces. It is less well-known that for every fixed architecture of ReLU neural network, the parameter space admits positive-dimensional spaces of symmetries, and hence the local functional dimension near any given parameter is lower than the parametric dimension. In this work we carefully define the notion of functional dimension, show that it is inhomogeneous across the parameter space of ReLU neural network functions, and continue an investigation - initiated in [14] and [5] - into when the functional dimension achieves its theoretical maximum. We also study the quotient space and fibers of the realization map from parameter space to function space, supplying examples of fibers that are disconnected, fibers upon which functional dimension is non-constant, and fibers upon which the symmetry group acts non-transitively.
From Shapley Values to Generalized Additive Models and back
Authors: Sebastian Bordt, Ulrike Von Luxburg
Date: 2022-09-08 00:00:00
Tasks: Additive models

In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconciliation between the two by establishing a correspondence between Shapley Values and Generalized Additive Models (GAMs). We introduce $n$-Shapley Values, a parametric family of local post-hoc explanation algorithms that explain individual predictions with interaction terms up to order $n$. By varying the parameter $n$, we obtain a sequence of explanations that covers the entire range from Shapley Values up to a uniquely determined decomposition of the function we want to explain. The relationship between $n$-Shapley Values and this decomposition offers a functionally-grounded characterization of Shapley Values, which highlights their limitations. We then show that $n$-Shapley Values, as well as the Shapley Taylor- and Faith-Shap interaction indices, recover GAMs with interaction terms up to order $n$. This implies that the original Shapely Values recover GAMs without variable interactions. Taken together, our results provide a precise characterization of Shapley Values as they are being used in explainable machine learning. They also offer a principled interpretation of partial dependence plots of Shapley Values in terms of the underlying functional decomposition. A package for the estimation of different interaction indices is available at \url{https://github.com/tml-tuebingen/nshap}.
FedDAR: Federated Domain-Aware Representation Learning
Authors: Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li
Date: 2022-09-08 00:00:00
Tasks: Diversity, Federated Learning, Representation Learning

Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients. However, the latent relationships between clients' data are ignored. In this work, we focus on a special non-iid FL problem, called Domain-mixed FL, where each client's data distribution is assumed to be a mixture of several predefined domains. Recognizing the diversity of domains and the similarity within domains, we propose a novel method, FedDAR, which learns a domain shared representation and domain-wise personalized prediction heads in a decoupled manner. For simplified linear regression settings, we have theoretically proved that FedDAR enjoys a linear convergence rate. For general settings, we have performed intensive empirical studies on both synthetic and real-world medical datasets which demonstrate its superiority over prior FL methods.
Online Low Rank Matrix Completion
Authors: Prateek Jain, Soumyabrata Pal
Date: 2022-09-08 00:00:00
Tasks: Clustering, Collaborative Filtering, Low-Rank Matrix Completion, Matrix Completion

We study the problem of {\em online} low-rank matrix completion with $\mathsf{M}$ users, $\mathsf{N}$ items and $\mathsf{T}$ rounds. In each round, the algorithm recommends one item per user, for which it gets a (noisy) reward sampled from a low-rank user-item preference matrix. The goal is to design a method with sub-linear regret (in $\mathsf{T}$) and nearly optimal dependence on $\mathsf{M}$ and $\mathsf{N}$. The problem can be easily mapped to the standard multi-armed bandit problem where each item is an {\em independent} arm, but that leads to poor regret as the correlation between arms and users is not exploited. On the other hand, exploiting the low-rank structure of reward matrix is challenging due to non-convexity of the low-rank manifold. We first demonstrate that the low-rank structure can be exploited using a simple explore-then-commit (ETC) approach that ensures a regret of $O(\mathsf{polylog} (\mathsf{M}+\mathsf{N}) \mathsf{T}^{2/3})$. That is, roughly only $\mathsf{polylog} (\mathsf{M}+\mathsf{N})$ item recommendations are required per user to get a non-trivial solution. We then improve our result for the rank-$1$ setting which in itself is quite challenging and encapsulates some of the key issues. Here, we propose \textsc{OCTAL} (Online Collaborative filTering using iterAtive user cLustering) that guarantees nearly optimal regret of $O(\mathsf{polylog} (\mathsf{M}+\mathsf{N}) \mathsf{T}^{1/2})$. OCTAL is based on a novel technique of clustering users that allows iterative elimination of items and leads to a nearly optimal minimax rate.
Active Learning of Classifiers with Label and Seed Queries
Authors: Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen
Date: 2022-09-08 00:00:00
Tasks: Active Learning

We study exact active learning of binary and multiclass classifiers with margin. Given an $n$-point set $X \subset \mathbb{R}^m$, we want to learn any unknown classifier on $X$ whose classes have finite strong convex hull margin, a new notion extending the SVM margin. In the standard active learning setting, where only label queries are allowed, learning a classifier with strong convex hull margin $\gamma$ requires in the worst case $\Omega\big(1+\frac{1}{\gamma}\big)^{(m-1)/2}$ queries. On the other hand, using the more powerful seed queries (a variant of equivalence queries), the target classifier could be learned in $O(m \log n)$ queries via Littlestone's Halving algorithm; however, Halving is computationally inefficient. In this work we show that, by carefully combining the two types of queries, a binary classifier can be learned in time $\operatorname{poly}(n+m)$ using only $O(m^2 \log n)$ label queries and $O\big(m \log \frac{m}{\gamma}\big)$ seed queries; the result extends to $k$-class classifiers at the price of a $k!k^2$ multiplicative overhead. Similar results hold when the input points have bounded bit complexity, or when only one class has strong convex hull margin against the rest. We complement the upper bounds by showing that in the worst case any algorithm needs $\Omega\big(k m \log \frac{1}{\gamma}\big)$ seed and label queries to learn a $k$-class classifier with strong convex hull margin $\gamma$.
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
Authors: Taku Yamagata, Ahmed Khalil, Raul Santos-Rodriguez
Date: 2022-09-08 00:00:00
Tasks: D4RL, Offline RL, Q-Learning, Reinforcement Learning (RL)

Recent works have shown that tackling offline reinforcement learning (RL) with a conditional policy produces promising results. The Decision Transformer (DT) combines the conditional policy approach and a transformer architecture, showing competitive performance against several benchmarks. However, DT lacks stitching ability -- one of the critical abilities for offline RL to learn the optimal policy from sub-optimal trajectories. This issue becomes particularly significant when the offline dataset only contains sub-optimal trajectories. On the other hand, the conventional RL approaches based on Dynamic Programming (such as Q-learning) do not have the same limitation; however, they suffer from unstable learning behaviours, especially when they rely on function approximation in an off-policy learning setting. In this paper, we propose the Q-learning Decision Transformer (QDT) to address the shortcomings of DT by leveraging the benefits of Dynamic Programming (Q-learning). It utilises the Dynamic Programming results to relabel the return-to-go in the training data to then train the DT with the relabelled data. Our approach efficiently exploits the benefits of these two approaches and compensates for each other's shortcomings to achieve better performance. We empirically show these in both simple toy environments and the more complex D4RL benchmark, showing competitive performance gains.
$Δ$-PINNs: physics-informed neural networks on complex geometries
Authors: Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
Date: 2022-09-08 00:00:00

Physics-informed neural networks (PINNs) have demonstrated promise in solving forward and inverse problems involving partial differential equations. Despite recent progress on expanding the class of problems that can be tackled by PINNs, most of existing use-cases involve simple geometric domains. To date, there is no clear way to inform PINNs about the topology of the domain where the problem is being solved. In this work, we propose a novel positional encoding mechanism for PINNs based on the eigenfunctions of the Laplace-Beltrami operator. This technique allows to create an input space for the neural network that represents the geometry of a given object. We approximate the eigenfunctions as well as the operators involved in the partial differential equations with finite elements. We extensively test and compare the proposed methodology against traditional PINNs in complex shapes, such as a coil, a heat sink and a bunny, with different physics, such as the Eikonal equation and heat transfer. We also study the sensitivity of our method to the number of eigenfunctions used, as well as the discretization used for the eigenfunctions and the underlying operators. Our results show excellent agreement with the ground truth data in cases where traditional PINNs fail to produce a meaningful solution. We envision this new technique will expand the effectiveness of PINNs to more realistic applications.
Majority Vote for Distributed Differentially Private Sign Selection
Authors: Weidong Liu, Jiyuan Tu, Xiaojun Mao, Xi Chen
Date: 2022-09-08 00:00:00
Tasks: Privacy Preserving, regression

Privacy-preserving data analysis has become more prevalent in recent years. In this study, we propose a distributed group differentially private Majority Vote mechanism, for the sign selection problem in a distributed setup. To achieve this, we apply the iterative peeling to the stability function and use the exponential mechanism to recover the signs. For enhanced applicability, we study the private sign selection for mean estimation and linear regression problems, in distributed systems. Our method recovers the support and signs with the optimal signal-to-noise ratio as in the non-private scenario, which is better than contemporary works of private variable selections. Moreover, the sign selection consistency is justified by theoretical guarantees. Simulation studies are conducted to demonstrate the effectiveness of the proposed method.
Trigger Warnings: Bootstrapping a Violence Detector for FanFiction
Authors: Magdalena Wolska, Christopher Schröder, Ole Borchardt, Benno Stein, Martin Potthast
Date: 2022-09-09 00:00:00
Tasks: Binary Classification

We present the first dataset and evaluation results on a newly defined computational task of trigger warning assignment. Labeled corpus data has been compiled from narrative works hosted on Archive of Our Own (AO3), a well-known fanfiction site. In this paper, we focus on the most frequently assigned trigger type--violence--and define a document-level binary classification task of whether or not to assign a violence trigger warning to a fanfiction, exploiting warning labels provided by AO3 authors. SVM and BERT models trained in four evaluation setups on the corpora we compiled yield $F_1$ results ranging from 0.585 to 0.798, proving the violence trigger warning assignment to be a doable, however, non-trivial task.
Ranking-Enhanced Unsupervised Sentence Representation Learning
Authors: Yeon Seonwoo, Guoyin Wang, Changmin Seo, Sajal Choudhary, Jiwei Li, Xiang Li, Puyang Xu, Sunghyun Park, Alice Oh
Date: 2022-09-09 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding

Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking. Despite this progress, sentence encoders are still limited to using only an input sentence when predicting its semantic vector. In this work, we show that the semantic meaning of a sentence is also determined by nearest-neighbor sentences that are similar to the input sentence. Based on this finding, we propose a novel unsupervised sentence encoder, RankEncoder. RankEncoder predicts the semantic vector of an input sentence by leveraging its relationship with other sentences in an external corpus, as well as the input sentence itself. We evaluate RankEncoder on semantic textual benchmark datasets. From the experimental results, we verify that 1) RankEncoder achieves 80.07% Spearman's correlation, a 1.1% absolute improvement compared to the previous state-of-the-art performance, 2) RankEncoder is universally applicable to existing unsupervised sentence embedding methods, and 3) RankEncoder is specifically effective for predicting the similarity scores of similar sentence pairs.
Automatic Readability Assessment of German Sentences with Transformer Ensembles
Authors: Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger, Stephan Bialonski
Date: 2022-09-09 00:00:00
Proceeding: GermEval 2022 9
Tasks: Lexical Complexity Prediction, Text Complexity Assessment (GermEval 2022)

Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0.435.
F-coref: Fast, Accurate and Easy to Use Coreference Resolution
Authors: Shon Otmazgin, Arie Cattan, Yoav Goldberg
Date: 2022-09-09 00:00:00
Tasks: coreference-resolution, Coreference Resolution, GPU

We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode based on the LingMess architecture, providing state-of-the-art coreference accuracy, and a substantially faster model, F-coref, which is the focus of this work. F-coref allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover batching. Our code is available at https://github.com/shon-otmazgin/fastcoref
Deriving dynamical systems for language based on the Tolerance Principle
Authors: Fernando C. Alves
Date: 2022-09-09 00:00:00
Tasks: Language Acquisition

In this research note, I derive explicit dynamical systems for language within an acquisition-driven framework (Niyogi \& Berwick, 1997; Niyogi, 2006) assuming that children/learners follow the Tolerance Principle (Yang, 2016) to determine whether a rule is productive during the process of language acquisition. I consider different theoretical parameters such as population size (finite vs. infinite) and the number of previous generations that provide learners with data. Multiple simulations of the dynamics obtained here and applications to diacrhonic language data are in preparation, so they are not included in this first note.
Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling
Authors: Baohang Zhou, Ying Zhang, Xuhui Sui, Kehui Song, Xiaojie Yuan
Date: 2022-09-09 00:00:00
Tasks: Graph Attention, Intent Detection, Multi-Task Learning, Natural Language Understanding, Sentence, slot-filling, Slot Filling

Slot filling and intent detection are two fundamental tasks in the field of natural language understanding. Due to the strong correlation between these two tasks, previous studies make efforts on modeling them with multi-task learning or designing feature interaction modules to improve the performance of each task. However, none of the existing approaches consider the relevance between the structural information of sentences and the label semantics of two tasks. The intent and semantic components of a utterance are dependent on the syntactic elements of a sentence. In this paper, we investigate a multi-grained label refinement network, which utilizes dependency structures and label semantic embeddings. Considering to enhance syntactic representations, we introduce the dependency structures of sentences into our model by graph attention layer. To capture the semantic dependency between the syntactic information and task labels, we combine the task specific features with corresponding label embeddings by attention mechanism. The experimental results demonstrate that our model achieves the competitive performance on two public datasets.
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation
Authors: Zhi Qu, Taro Watanabe
Date: 2022-09-09 00:00:00
Proceeding: COLING 2022 10
Tasks: Machine Translation, Translation

Multilingual neural machine translation can translate unseen language pairs during training, i.e. zero-shot translation. However, the zero-shot translation is always unstable. Although prior works attributed the instability to the domination of central language, e.g. English, we supplement this viewpoint with the strict dependence of non-centered languages. In this work, we propose a simple, lightweight yet effective language-specific modeling method by adapting to non-centered languages and combining the shared information and the language-specific information to counteract the instability of zero-shot translation. Experiments with Transformer on IWSLT17, Europarl, TED talks, and OPUS-100 datasets show that our method not only performs better than strong baselines in centered data conditions but also can easily fit non-centered data conditions. By further investigating the layer attribution, we show that our proposed method can disentangle the coupled representation in the correct direction.
MaxMatch-Dropout: Subword Regularization for WordPiece
Authors: Tatsuya Hiraoka
Date: 2022-09-09 00:00:00
Proceeding: COLING 2022 10
Tasks: Machine Translation, Text Classification, Translation

We present a subword regularization method for WordPiece, which uses a maximum matching algorithm for tokenization. The proposed method, MaxMatch-Dropout, randomly drops words in a search using the maximum matching algorithm. It realizes finetuning with subword regularization for popular pretrained language models such as BERT-base. The experimental results demonstrate that MaxMatch-Dropout improves the performance of text classification and machine translation tasks as well as other subword regularization methods. Moreover, we provide a comparative analysis of subword regularization methods: subword regularization with SentencePiece (Unigram), BPE-Dropout, and MaxMatch-Dropout.