pwc-archive/papers-with-abstracts huggingface dataset:
Simulation-based inference methods for particle physics
Authors: Johann Brehmer, Kyle Cranmer
Date: 2020-10-13 00:00:00
Tasks: Probabilistic Programming

Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters with observed data. We explain why the likelihood function of high-dimensional LHC data cannot be explicitly evaluated, why this matters for data analysis, and reframe what the field has traditionally done to circumvent this problem. We then review new simulation-based inference methods that let us directly analyze high-dimensional data by combining machine learning techniques and information from the simulator. Initial studies indicate that these techniques have the potential to substantially improve the precision of LHC measurements. Finally, we discuss probabilistic programming, an emerging paradigm that lets us extend inference to the latent process of the simulator.
RuSemShift: a dataset of historical lexical semantic change in Russian
Authors: Julia Rodina, Andrey Kutuzov
Date: 2020-10-13 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentence

We present RuSemShift, a large-scale manually annotated test set for the task of semantic change modeling in Russian for two long-term time period pairs: from the pre-Soviet through the Soviet times and from the Soviet through the post-Soviet times. Target words were annotated by multiple crowd-source workers. The annotation process was organized following the DURel framework and was based on sentence contexts extracted from the Russian National Corpus. Additionally, we report the performance of several distributional approaches on RuSemShift, achieving promising results, which at the same time leave room for other researchers to improve.
Multilingual Argument Mining: Datasets and Analysis
Authors: Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim
Date: 2020-10-13 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Argument Mining, Machine Translation, Natural Language Understanding, Transfer Learning, Translation

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.
Temporal Collaborative Filtering with Graph Convolutional Neural Networks
Authors: Esther Rodrigo Bonet, Duc Minh Nguyen, Nikos Deligiannis
Date: 2020-10-13 00:00:00
Tasks: Collaborative Filtering, Graph Neural Network, Recommendation Systems

Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF-based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF-based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.
Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions
Authors: Chang Li, Hideyoshi Yanagisawa
Date: 2020-10-13 00:00:00

With the growing utility of today's conversational virtual assistants, the importance of user motivation in human-AI interaction is becoming more obvious. However, previous studies in this and related fields, such as human-computer interaction and human-robot interaction, scarcely discussed intrinsic motivation and its affecting factors. Those studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation. The current study aims to cover intrinsic motivation by taking an affective-engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments are conducted where these two factors are manipulated by making participants believe they are interacting with the smart speaker "Amazon Echo". Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter's absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. The results also suggested suppressive effects by uncertainty on intrinsic motivation, though we had not hypothesized before experiments. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of uncertainty's effects. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.
Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface
Authors: Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis
Date: 2020-10-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.
Procedural 3D Terrain Generation using Generative Adversarial Networks
Authors: Emmanouil Panagiotou, Eleni Charou
Date: 2020-10-13 00:00:00
Tasks: Colorization, Generative Adversarial Network, Image-to-Image Translation

Procedural 3D Terrain generation has become a necessity in open world games, as it can provide unlimited content, through a functionally infinite number of different areas, for players to explore. In our approach, we use Generative Adversarial Networks (GAN) to yield realistic 3D environments based on the distribution of remotely sensed images of landscapes, captured by satellites or drones. Our task consists of synthesizing a random but plausible RGB satellite image and generating a corresponding Height Map in the form of a 3D point cloud that will serve as an appropriate mesh of the landscape. For the first step, we utilize a GAN trained with satellite images that manages to learn the distribution of the dataset, creating novel satellite images. For the second part, we need a one-to-one mapping from RGB images to Digital Elevation Models (DEM). We deploy a Conditional Generative Adversarial network (CGAN), which is the state-of-the-art approach to image-to-image translation, to generate a plausible height map for every randomly generated image of the first model. Combining the generated DEM and RGB image, we are able to construct 3D scenery consisting of a plausible height distribution and colorization, in relation to the remotely sensed landscapes provided during training.
Penalized model-based clustering of fMRI data
Authors: Andrew DiLernia, Karina Quevedo, Jazmin Camchong, Kelvin Lim, Wei Pan, Lin Zhang
Date: 2020-10-13 00:00:00
Tasks: Clustering, Functional Connectivity, model

Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia.
Detecting Anomalies from Video-Sequences: a Novel Descriptor
Authors: Giulia Orrù, Davide Ghiani, Maura Pintor, Gian Luca Marcialis, Fabio Roli
Date: 2020-10-13 00:00:00
Tasks: Anomaly Detection

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named "trit" (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the "no-anomaly" one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).
It's the Journey Not the Destination: Building Genetic Algorithms Practitioners Can Trust
Authors: Jakub Vincalek, Sean Walton, Ben Evans
Date: 2020-10-13 00:00:00
Tasks: Survey

Genetic algorithms have been developed for decades by researchers in academia and perform well in engineering applications, yet their uptake in industry remains limited. In order to understand why this is the case, the opinions of users of engineering design tools were gathered. The results from a survey showing the attitudes of engineers and students with design experience with respect to optimisation algorithms are presented. A survey was designed to answer two research questions: To what extent is there a pre-existing sentiment (negative or positive) among students, engineers, and managers towards genetic algorithm-based design? and What are the requirements of practitioners with regards to design optimisation and the design optimisation process? A total of 23 participants (N = 23) took part in the 3-part mixed methods survey. Thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards genetic algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Participants have expressed a desire to continue to remain in the design loop. This is at odds with the motivation of a portion of the genetic algorithms community of removing humans from the loop. It is clear we need to take a different approach to increase industrial uptake. Based on this, the following recommendations have been made to increase their use in industry: an increase of transparency and explainability of genetic algorithms, an increased focus on user experience, better communication between developers and engineers, and visualising algorithm behaviour.
Which Model to Transfer? Finding the Needle in the Growing Haystack
Authors: Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic
Date: 2020-10-13 00:00:00
Proceeding: CVPR 2022 1
Tasks: Transfer Learning

Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. We provide a formalization of this problem through a familiar notion of regret and introduce the predominant strategies, namely task-agnostic (e.g. ranking models by their ImageNet performance) and task-aware search strategies (such as linear or kNN evaluation). We conduct a large-scale empirical study and show that both task-agnostic and task-aware methods can yield high regret. We then propose a simple and computationally efficient hybrid search strategy which outperforms the existing approaches. We highlight the practical benefits of the proposed solution on a set of 19 diverse vision tasks.
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Authors: Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson
Date: 2020-10-13 00:00:00
Tasks: Deep Learning, Fairness

The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectrum allocation, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson's 1981 work characterizing single-item optimal auctions, there has been limited progress outside of restricted settings. A recent paper by D\"utting et al. circumvents analytic difficulties by applying deep learning techniques to, instead, approximate optimal auctions. In parallel, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension
Authors: Ekta Sood, Simon Tannert, Diego Frassinelli, Andreas Bulling, Ngoc Thang Vu
Date: 2020-10-13 00:00:00
Proceeding: CONLL 2020
Tasks: Machine Reading Comprehension, Reading Comprehension

While neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures. We find that higher similarity to human attention and performance significantly correlates to the LSTM and CNN models. However, we show this relationship does not hold true for the XLNet models -- despite the fact that the XLNet performs best on this challenging task. Our results suggest that different architectures seem to learn rather different neural attention strategies and similarity of neural to human attention does not guarantee best performance.
Aspect-based Document Similarity for Research Papers
Authors: Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, Georg Rehm
Date: 2020-10-13 00:00:00
Proceeding: COLING 2020 8
Tasks: Document Classification, Recommendation Systems

Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like recommender systems that rely on document similarity. In this paper, we extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity for research papers. Paper citations indicate the aspect-based similarity, i.e., the section title in which a citation occurs acts as a label for the pair of citing and cited paper. We apply a series of Transformer models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. Our results show SciBERT as the best performing system. A qualitative examination validates our quantitative results. Our findings motivate future research of aspect-based document similarity and the development of a recommender system based on the evaluated techniques. We make our datasets, code, and trained models publicly available.
Projection techniques to update the truncated SVD of evolving matrices
Authors: Vassilis Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh, Kenneth L. Clarkson
Date: 2020-10-13 00:00:00
Tasks: Recommendation Systems

This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such matrix problems represent an important computational kernel in applications such as Latent Semantic Indexing and Recommender Systems. Nonetheless, the proposed framework is purely algebraic and targets general updating problems. The algorithm presented in this paper undertakes a projection view-point and focuses on building a pair of subspaces which approximate the linear span of the sought singular vectors of the updated matrix. We discuss and analyze two different choices to form the projection subspaces. Results on matrices from real applications suggest that the proposed algorithm can lead to higher accuracy, especially for the singular triplets associated with the largest modulus singular values. Several practical details and key differences with other approaches are also discussed.
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures
Authors: Jinting Wu, Yujia Zhang, Xiaoguang Zhao, Wenbin Gao
Date: 2020-10-13 00:00:00
Tasks: Emotion Classification, Emotion Recognition, Generalized Zero-Shot Learning, Gesture Recognition, Multi-Task Learning, Zero-Shot Learning

Although automatic emotion recognition from facial expressions and speech has made remarkable progress, emotion recognition from body gestures has not been thoroughly explored. People often use a variety of body language to express emotions, and it is difficult to enumerate all emotional body gestures and collect enough samples for each category. Therefore, recognizing new emotional body gestures is critical for better understanding human emotions. However, the existing methods fail to accurately determine which emotional state a new body gesture belongs to. In order to solve this problem, we introduce a Generalized Zero-Shot Learning (GZSL) framework, which consists of three branches to infer the emotional state of the new body gestures with only their semantic descriptions. The first branch is a Prototype-Based Detector (PBD) which is used to determine whether an sample belongs to a seen body gesture category and obtain the prediction results of the samples from the seen categories. The second branch is a Stacked AutoEncoder (StAE) with manifold regularization, which utilizes semantic representations to predict samples from unseen categories. Note that both of the above branches are for body gesture recognition. We further add an emotion classifier with a softmax layer as the third branch in order to better learn the feature representations for this emotion classification task. The input features for these three branches are learned by a shared feature extraction network, i.e., a Bidirectional Long Short-Term Memory Networks (BLSTM) with a self-attention module. We treat these three branches as subtasks and use multi-task learning strategies for joint training. The performance of our framework on an emotion recognition dataset is significantly superior to the traditional method of emotion classification and state-of-the-art zero-shot learning methods.