pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Course Recommendation System
Authors: Yuanguo Lin, Shibo Feng, Fan Lin, Wenhua Zeng, Yong liu, Pengcheng Wu
Date: 2021-07-19 00:00:00
Proceeding: journal 2021 7
Tasks: Hierarchical Reinforcement Learning

In the process of course learning, users incline to change their interests with the improvements of their cognition. Existing course recommendation methods usually assume that users’ preferences are static. They fail to capture the user’s dynamic interests in sequential learning behaviours. In this respect, the recommendations show low accuracy and adaptivity, especially when users have diverse interests in many different courses. Thus, they may not be suitable for applying in the online course recommendation scenario. In this paper, we propose a novel course recommendation framework, named Dynamic Attention and hierarchical Reinforcement Learning (DARL), to improve the adaptivity of the recommendation model. DARL automatically captures the user’s preferences in each interaction between a profile reviser and a recommendation model, and thereby enhances the effectiveness of course recommendation. For tracking the changes in users’ preferences, DARL adaptively updates the attention weight of the corresponding course at different sessions to improve the recommendation accuracy. We perform empirical experiments on two real-world MOOCs (i.e., Massive Open Online Courses) datasets. Experimental results demonstrate that DARL significantly outperforms state-of-the-art course recommendation methods in terms of major evaluation metrics.
Wav2vec2 Base Vietnamese 160h
Authors: khanhld
Date: 2022-05-08 00:00:00
Proceeding: Huggingface 2022 5
Tasks: Speech Recognition

Wav2vec2 Base Vietnamese 160h
Cross-Domain Authorship Attribution Combining Instance Based and Profile-Based Features
Authors: Andrea Bacciu, Massimo La Morgia, Alessandro Mei, Eugenio Nerio Nemmi, Valerio Neri, Julinda Stefa
Date: 2019-05-05 00:00:00
Proceeding: CLEF 2019 5
Tasks: Authorship Attribution

Being able to identify the author of an unknown text is crucial. Although it is a well-studied field, it is still an open problem, since a standard approach has yet to be found. In this notebook, we propose our model for the Authorship Attribution task of PAN 2019, that focuses on cross-domain setting covering 4 different languages: French, Italian, English, and Spanish. We use ngrams of characters, words, stemmed words, and distorted text. Our model has an SVM for each feature and an ensemble architecture. Our final results outperform the baseline given by PAN in almost every problem. With this model, we reach the second place in the task with an F1-score of 68%.
TLMOTE: A Topic-based Language Modelling Approach for Text Oversampling
Authors: Arjun Choudhry, Seba Susan, Anmol Bansal, Anubhav Sharma
Date: 2022-05-04 00:00:00
Proceeding: The 35th International Florida Artificial Intelligence Research Society (FLAIRS) Conference 2022 5
Tasks: Language Modelling, Sentiment Analysis, Spam detection, Suggestion mining

Training machine learning and deep learning models on unbalanced datasets can lead to a bias portrayed by the models towards the majority classes. To tackle the problem of bias towards majority classes, researchers have presented various techniques to oversample the minority class data points. Most of the available state-of-the-art oversampling techniques generate artificial data points which cannot be comprehensibly understood by the reader, despite the synthetic data points generated being similar to the original minority class data points. In this work, we present Topic-based Language Modelling Approach for Text Oversampling (TLMOTE), a novel text oversampling technique for supervised learning from unbalanced datasets. TLMOTE improves upon previous approaches by generating data points which can be intelligibly understood by the reader, can relate to the main topics of the minority class, and introduces more variations to the synthetic data generated. We evaluate the efficacy of our approach on various tasks like Suggestion Mining SemEval 2019 Task 9 Subtasks A and B, SMS Spam Detection, and Sentiment Analysis. Experimental results verify that oversampling unbalanced datasets using TLMOTE yields a higher macro F1 score than with other oversampling techniques.
Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction
Authors: Junyong Park, Daekyum Kim, Sejoon Huh, Sungho Jo
Date: 2022-05-02 00:00:00
Proceeding: Pattern Recognition 2022 5
Tasks: Action Segmentation, Segmentation

Action segmentation aims to split videos into segments of different actions. Recent work focuses on dealing with long-range dependencies of long, untrimmed videos, but still suffers from over-segmentation and performance saturation due to increased model complexity. This paper addresses the aforementioned issues through a divide-and-conquer strategy that first maximizes the frame-wise classification accuracy of the model and then reduces the over-segmentation errors. This strategy is implemented with the Dilation Passing and Reconstruction Network, composed of the Dilation Passing Network, which primarily aims to increase accuracy by propagating information of different dilations, and the Temporal Reconstruction Network, which reduces over-segmentation errors by temporally encoding and decoding the output features from the Dilation Passing Network. We also propose a weighted temporal mean squared error loss that further reduces over-segmentation. Through evaluations on the 50Salads, GTEA, and Breakfast datasets, we show that our model achieves significant results compared to existing state-of-the-art models.
Transcripts per million ratio: applying distribution-aware normalisation over the popular TPM method
Authors: Hilbert Lam Yuen In, Robbe Pincket
Date: 2022-05-05 00:00:00

Current popular methods in literature of RNA sequencing normalisation do not account for gene length when compared across samples, whilst adjusting for count biases in the data. This creates a gap in the normalisation as bigger genes in RNA sequencing accumulate more reads due to shotgun sequencing methods. As a result, the proportions of these reads inter-sample are not properly accounted for in current normalisation methods. Alternatively, methods which account for gene length do not account for the pan-sample biases in the data by accounting for a central read average. Thus, in order to fill in the gap in the literature, we propose a novel method of Transcripts Per Million Ratio and its relatives in RNA-sequencing differential expression normalisation that can be used in different conditions, which takes into account the gene length as well as relative expression in normalisation.
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks
Authors: Rambod Mojgani, Maciej Balajewicz, Pedram Hassanzadeh
Date: 2022-05-05 00:00:00

Physics-informed neural networks (PINNs) leverage neural-networks to find the solutions of partial differential equation (PDE)-constrained optimization problems with initial conditions and boundary conditions as soft constraints. These soft constraints are often considered to be the sources of the complexity in the training phase of PINNs. Here, we demonstrate that the challenge of training (i) persists even when the boundary conditions are strictly enforced, and (ii) is closely related to the Kolmogorov n-width associated with problems demonstrating transport, convection, traveling waves, or moving fronts. Given this realization, we describe the mechanism underlying the training schemes such as those used in eXtended PINNs (XPINN), curriculum regularization, and sequence-to-sequence learning. For an important category of PDEs, i.e., governed by non-linear convection-diffusion equation, we propose reformulating PINNs on a Lagrangian frame of reference, i.e., LPINNs, as a PDE-informed solution. A parallel architecture with two branches is proposed. One branch solves for the state variables on the characteristics, and the second branch solves for the low-dimensional characteristics curves. The proposed architecture conforms to the causality innate to the convection, and leverages the direction of travel of the information in the domain. Finally, we demonstrate that the loss landscapes of LPINNs are less sensitive to the so-called "complexity" of the problems, compared to those in the traditional PINNs in the Eulerian framework.
Collective Relevance Labeling for Passage Retrieval
Authors: Jihyuk Kim, Minsoo Kim, Seung-won Hwang
Date: 2022-05-06 00:00:00
Proceeding: NAACL 2022 7
Tasks: Information Retrieval, Knowledge Distillation, Passage Retrieval, Retrieval

Deep learning for Information Retrieval (IR) requires a large amount of high-quality query-document relevance labels, but such labels are inherently sparse. Label smoothing redistributes some observed probability mass over unobserved instances, often uniformly, uninformed of the true distribution. In contrast, we propose knowledge distillation for informed labeling, without incurring high computation overheads at evaluation time. Our contribution is designing a simple but efficient teacher model which utilizes collective knowledge, to outperform state-of-the-arts distilled from a more complex teacher model. Specifically, we train up to x8 faster than the state-of-the-art teacher, while distilling the rankings better. Our code is publicly available at https://github.com/jihyukkim-nlp/CollectiveKD
Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations
Authors: Sangwon Seo, Vaibhav V. Unhelkar
Date: 2022-05-05 00:00:00
Tasks: Imitation Learning

We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to existing multi-agent imitation learning techniques, BTIL explicitly models and infers the time-varying mental states of team members, thereby enabling learning of decentralized team policies from demonstrations of suboptimal teamwork. Further, to allow for sample- and label-efficient policy learning from small datasets, BTIL employs a Bayesian perspective and is capable of learning from semi-supervised demonstrations. We demonstrate and benchmark the performance of BTIL on synthetic multi-agent tasks as well as a novel dataset of human-agent teamwork. Our experiments show that BTIL can successfully learn team policies from demonstrations despite the influence of team members' (time-varying and potentially misaligned) mental states on their behavior.
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
Authors: Alexander Kathan, Andreas Triantafyllopoulos, Xiangheng He, Manuel Milling, Tianhao Yan, Srividya Tirunellai Rajamani, Ludwig Küster, Mathias Harrer, Elena Heber, Inga Grossmann, David D. Ebert, Björn W. Schuller
Date: 2022-05-06 00:00:00

Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the results of self-assessed Patient-Health-Questionnaires (PHQ), indicating current symptom severity of depressive individuals. In this work, we explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12, using leave-one-subject-out cross-validation, as well as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the last 7 days. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.
Summary Markov Models for Event Sequences
Authors: Debarun Bhattacharjya, Saurabh Sihag, Oktie Hassanzadeh, Liza Bialik
Date: 2022-05-06 00:00:00
Tasks: Time Series, Time Series Analysis

Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora. We propose a family of models for such event sequences -- summary Markov models -- where the probability of observing an event type depends only on a summary of historical occurrences of its influencing set of event types. This Markov model family is motivated by Granger causal models for time series, with the important distinction that only one event can occur in a position in an event sequence. We show that a unique minimal influencing set exists for any set of event types of interest and choice of summary function, formulate two novel models from the general family that represent specific sequence dynamics, and propose a greedy search algorithm for learning them from event sequence data. We conduct an experimental investigation comparing the proposed models with relevant baselines, and illustrate their knowledge acquisition and discovery capabilities through case studies involving sequences from text.
Quantifying Synthesis and Fusion and their Impact on Machine Translation
Authors: Arturo Oncevay, Duygu Ataman, Niels van Berkel, Barry Haddow, Alexandra Birch, Johannes Bjerva
Date: 2022-05-06 00:00:00
Proceeding: NAACL 2022 7
Tasks: Diversity, Machine Translation, Translation

Theoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level. We consider Payne (2017)'s approach to classify morphology using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). For computing synthesis, we test unsupervised and supervised morphological segmentation methods for English, German and Turkish, whereas for fusion, we propose a semi-automatic method using Spanish as a case study. Then, we analyse the relationship between machine translation quality and the degree of synthesis and fusion at word (nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment level (previous language pairs plus English-German in both directions). We complement the word-level analysis with human evaluation, and overall, we observe a consistent impact of both indexes on machine translation quality.
Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
Authors: Yu-Shun Hsiao, Siva Kumar Sastry Hari, Michał Filipiuk, Timothy Tsai, Michael B. Sullivan, Vijay Janapa Reddi, Vasu Singh, Stephen W. Keckler
Date: 2022-05-06 00:00:00
Tasks: Autonomous Vehicles

The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort. This paper proposes a sensor frame processing rate (FPR) estimation model, Zhuyi, that quantifies the minimum safe FPR continuously in a driving scenario. Zhuyi can be employed post-deployment as an online safety check and to prioritize work. Experiments conducted using a multi-camera state-of-the-art industry AV system show that Zhuyi's estimated FPRs are conservative, yet the system can maintain safety by processing only 36% or fewer frames compared to a default 30-FPR system in the tested scenarios.
UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models
Authors: Omid Esrafilian, Rajeev Gangula, David Gesbert
Date: 2022-05-06 00:00:00

This paper considers the problem of ground user localization based on received signal strength (RSS) measurements obtained by an unmanned aerial vehicle (UAV). We treat UAV-user link channel model parameters and antenna radiation pattern of the UAV as unknowns that need to be estimated. A hybrid channel model is proposed that consists of a traditional path loss model combined with a neural network approximating the UAV antenna gain function. With this model and a set of offline RSS measurements, the unknown parameters are estimated. We then employ the particle swarm optimization (PSO) technique which utilizes the learned hybrid channel model along with a 3D map of the environment to accurately localize the ground users. The performance of the developed algorithm is evaluated through simulations and also real-world experiments.
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Authors: Jiaqi Gao, Jingqi Li, Hongming Shan, Yanyun Qu, James Z. Wang, Fei-Yue Wang, Junping Zhang
Date: 2022-05-06 00:00:00
Tasks: Crowd Counting, Lifelong learning

Crowd counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the new incoming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks when handling multiple domains: (1) the models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies of intrinsic data distributions from various domains, which is called catastrophic forgetting; (2) the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of the domain shift; and (3) it leads to linearly increasing storage overhead, either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigated a new crowd counting task in the incremental domains training setting called Lifelong Crowd Counting. Its goal is to alleviate the catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains. Specifically, we propose a self-distillation learning framework as a benchmark (Forget Less, Count Better, or FLCB) for lifelong crowd counting, which helps the model sustainably leverage previous meaningful knowledge for better crowd counting to mitigate the forgetting when the new data arrive. In addition, a new quantitative metric, normalized backward transfer (nBwT), is developed to evaluate the forgetting degree of the model in the lifelong learning process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Authors: Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi
Date: 2022-05-06 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc explainability methods where a simple, human-interpretable model imitates the behavior of these blackbox models are often proposed to help users trust model predictions. In this work, we audit the quality of such explanations for different protected subgroups using real data from four settings in finance, healthcare, college admissions, and the US justice system. Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups. We also demonstrate that pairing explainability methods with recent advances in robust machine learning can improve explanation fairness in some settings. However, we highlight the importance of communicating details of non-zero fidelity gaps to users, since a single solution might not exist across all settings. Finally, we discuss the implications of unfair explanation models as a challenging and understudied problem facing the machine learning community.
Vehicle management in a modular production context using Deep Q-Learning
Authors: Lucain Pouget, Timo Hasenbichler, Jakob Auer, Klaus Lichtenegger, Andreas Windisch
Date: 2022-05-06 00:00:00
Tasks: Deep Reinforcement Learning, Job Shop Scheduling, Management, Q-Learning, Scheduling

We investigate the feasibility of deploying Deep-Q based deep reinforcement learning agents to job-shop scheduling problems in the context of modular production facilities, using discrete event simulations for the environment. These environments are comprised of a source and sink for the parts to be processed, as well as (several) workstations. The agents are trained to schedule automated guided vehicles to transport the parts back and forth between those stations in an optimal fashion. Starting from a very simplistic setup, we increase the complexity of the environment and compare the agents' performances with well established heuristic approaches, such as first-in-first-out based agents, cost tables and a nearest-neighbor approach. We furthermore seek particular configurations of the environments in which the heuristic approaches struggle, to investigate to what degree the Deep-Q agents are affected by these challenges. We find that Deep-Q based agents show comparable performance as the heuristic baselines. Furthermore, our findings suggest that the DRL agents exhibit an increased robustness to noise, as compared to the conventional approaches. Overall, we find that DRL agents constitute a valuable approach for this type of scheduling problems.
Alternating Good-for-MDP Automata
Authors: Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak
Date: 2022-05-06 00:00:00
Tasks: Reinforcement Learning (RL), Translation

When omega-regular objectives were first proposed in model-free reinforcement learning (RL) for controlling MDPs, deterministic Rabin automata were used in an attempt to provide a direct translation from their transitions to scalar values. While these translations failed, it has turned out that it is possible to repair them by using good-for-MDPs (GFM) B\"uchi automata instead. These are nondeterministic B\"uchi automata with a restricted type of nondeterminism, albeit not as restricted as in good-for-games automata. Indeed, deterministic Rabin automata have a pretty straightforward translation to such GFM automata, which is bi-linear in the number of states and pairs. Interestingly, the same cannot be said for deterministic Streett automata: a translation to nondeterministic Rabin or B\"uchi automata comes at an exponential cost, even without requiring the target automaton to be good-for-MDPs. Do we have to pay more than that to obtain a good-for-MDP automaton? The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDP property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDP automata we produce from deterministic Streett automata are bi-linear in the the size of the deterministic automaton and its index, and can therefore be exponentially more succinct than minimal nondeterministic B\"uchi automata.