pwc-archive/papers-with-abstracts huggingface dataset:
Easter2.0: Improving convolutional models for handwritten text recognition
Authors: Kartik Chaudhary, Raghav Bali
Date: 2022-05-30 00:00:00
Tasks: Data Augmentation, Few-Shot Learning, Handwritten Text Recognition, HTR, Optical Character Recognition (OCR)

Convolutional Neural Networks (CNN) have shown promising results for the task of Handwritten Text Recognition (HTR) but they still fall behind Recurrent Neural Networks (RNNs)/Transformer based models in terms of performance. In this paper, we propose a CNN based architecture that bridges this gap. Our work, Easter2.0, is composed of multiple layers of 1D Convolution, Batch Normalization, ReLU, Dropout, Dense Residual connection, Squeeze-and-Excitation module and make use of Connectionist Temporal Classification (CTC) loss. In addition to the Easter2.0 architecture, we propose a simple and effective data augmentation technique 'Tiling and Corruption (TACO)' relevant for the task of HTR/OCR. Our work achieves state-of-the-art results on IAM handwriting database when trained using only publicly available training data. In our experiments, we also present the impact of TACO augmentations and Squeeze-and-Excitation (SE) on text recognition accuracy. We further show that Easter2.0 is suitable for few-shot learning tasks and outperforms current best methods including Transformers when trained on limited amount of annotated data. Code and model is available at: https://github.com/kartikgill/Easter2
Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning
Authors: Glenda Tan Hui En, Koay Tze Erhn, Shen Bingquan
Date: 2022-05-30 00:00:00

More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve vaccine design, this project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants from an existing virus (exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as a "virus" attacking a host cell. To increase infectivity, the "virus" mutates to bind better to the host's receptor. 2 algorithms were attempted - greedy search and beam search. The strength of this variant-host binding was then assessed by a transformer network we developed, with a high accuracy of 90%. With both components, beam search eventually proposed more infectious variants. Therefore, this approach can potentially enable researchers to develop vaccines that provide protection against future infectious variants before they emerge, pre-empting outbreaks and saving lives.
Most Equitable Voting Rules
Authors: Lirong Xia
Date: 2022-05-30 00:00:00
Tasks: Fairness, Open-Ended Question Answering

In social choice theory, anonymity (all agents being treated equally) and neutrality (all alternatives being treated equally) are widely regarded as ``minimal demands'' and ``uncontroversial'' axioms of equity and fairness. However, the ANR impossibility -- there is no voting rule that satisfies anonymity, neutrality, and resolvability (always choosing one winner) -- holds even in the simple setting of two alternatives and two agents. How to design voting rules that optimally satisfy anonymity, neutrality, and resolvability remains an open question. We address the optimal design question for a wide range of preferences and decisions that include ranked lists and committees. Our conceptual contribution is a novel and strong notion of most equitable refinements that optimally preserves anonymity and neutrality for any irresolute rule that satisfies the two axioms. Our technical contributions are twofold. First, we characterize the conditions for the ANR impossibility to hold under general settings, especially when the number of agents is large. Second, we propose the most-favorable-permutation (MFP) tie-breaking to compute a most equitable refinement and design a polynomial-time algorithm to compute MFP when agents' preferences are full rankings.
Enhancing Sequential Recommendation with Graph Contrastive Learning
Authors: Yixin Zhang, Yong liu, Yonghui Xu, Hao Xiong, Chenyi Lei, wei he, Lizhen Cui, Chunyan Miao
Date: 2022-05-30 00:00:00
Tasks: Auxiliary Learning, Contrastive Learning, Recommendation Systems, Sequential Recommendation

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.
Temporal Multiresolution Graph Neural Networks For Epidemic Prediction
Authors: Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh, Risi Kondor
Date: 2022-05-30 00:00:00
Tasks: Graph Learning, Prediction, Time Series, Time Series Analysis

In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreading of epidemic and pandemic based on the historical time-series data collected from the actual COVID-19 pandemic and chickenpox epidemic in several European countries, and have obtained competitive results in comparison to other previous state-of-the-art temporal architectures and graph learning algorithms. We have shown that capturing the multiscale and multiresolution structures of graphs is important to extract either local or global information that play a critical role in understanding the dynamic of a global pandemic such as COVID-19 which started from a local city and spread to the whole world. Our work brings a promising research direction in forecasting and mitigating future epidemics and pandemics.
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Authors: Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang, Weiran Huang
Date: 2022-05-30 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Data Visualization, Out-of-Distribution Generalization

Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to $t$-SNE can also be adopted in the SSCL setting, achieving significant improvement in both in-distribution and out-of-distribution generalization.
Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism
Authors: Haris Aziz, Alexander Lam, Mashbat Suzuki, Toby Walsh
Date: 2022-05-30 00:00:00
Tasks: Fairness

Proportionality is an attractive fairness concept that has been applied to a range of problems including the facility location problem, a classic problem in social choice. In our work, we propose a concept called Strong Proportionality, which ensures that when there are two groups of agents at different locations, both groups incur the same total cost. We show that although Strong Proportionality is a well-motivated and basic axiom, there is no deterministic strategyproof mechanism satisfying the property. We then identify a randomized mechanism called Random Rank (which uniformly selects a number $k$ between $1$ to $n$ and locates the facility at the $k$'th highest agent location) which satisfies Strong Proportionality in expectation. Our main theorem characterizes Random Rank as the unique mechanism that achieves universal truthfulness, universal anonymity, and Strong Proportionality in expectation among all randomized mechanisms. Finally, we show via the AverageOrRandomRank mechanism that even stronger ex-post fairness guarantees can be achieved by weakening universal truthfulness to strategyproofness in expectation.
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Authors: Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh B. Gundavarapu, Alex Lamb, Nan Rosemary Ke, Yoshua Bengio
Date: 2022-05-30 00:00:00
Tasks: Decision Making, Inductive Bias, Sequential Decision Making

Recurrent neural networks have a strong inductive bias towards learning temporally compressed representations, as the entire history of a sequence is represented by a single vector. By contrast, Transformers have little inductive bias towards learning temporally compressed representations, as they allow for attention over all previously computed elements in a sequence. Having a more compressed representation of a sequence may be beneficial for generalization, as a high-level representation may be more easily re-used and re-purposed and will contain fewer irrelevant details. At the same time, excessive compression of representations comes at the cost of expressiveness. We propose a solution which divides computation into two streams. A slow stream that is recurrent in nature aims to learn a specialized and compressed representation, by forcing chunks of $K$ time steps into a single representation which is divided into multiple vectors. At the same time, a fast stream is parameterized as a Transformer to process chunks consisting of $K$ time-steps conditioned on the information in the slow-stream. In the proposed approach we hope to gain the expressiveness of the Transformer, while encouraging better compression and structuring of representations in the slow stream. We show the benefits of the proposed method in terms of improved sample efficiency and generalization performance as compared to various competitive baselines for visual perception and sequential decision making tasks.
End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment
Authors: Yongli Zhu, Chanan Singh
Date: 2022-05-30 00:00:00
Tasks: BIG-bench Machine Learning

Conventional power system reliability suffers from the long run time of Monte Carlo simulation and the dimension-curse of analytic enumeration methods. This paper proposes a preliminary investigation on end-to-end machine learning for directly predicting the reliability index, e.g., the Loss of Load Probability (LOLP). By encoding the system admittance matrix into the input feature, the proposed machine learning pipeline can consider the impact of specific topology changes due to regular maintenances of transmission lines. Two models (Support Vector Machine and Boosting Trees) are trained and compared. Details regarding the training data creation and preprocessing are also discussed. Finally, experiments are conducted on the IEEE RTS-79 system. Results demonstrate the applicability of the proposed end-to-end machine learning pipeline in reliability assessment.
Unfooling Perturbation-Based Post Hoc Explainers
Authors: Zachariah Carmichael, Walter J Scheirer
Date: 2022-05-29 00:00:00
Tasks: Adversarial Attack, Anomaly Detection, Decision Making, Natural Questions

Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary about the lack of transparency of its decision-making processes. Perturbation-based post hoc explainers offer a model agnostic means of interpreting these systems while only requiring query-level access. However, recent work demonstrates that these explainers can be fooled adversarially. This discovery has adverse implications for auditors, regulators, and other sentinels. With this in mind, several natural questions arise - how can we audit these black box systems? And how can we ascertain that the auditee is complying with the audit in good faith? In this work, we rigorously formalize this problem and devise a defense against adversarial attacks on perturbation-based explainers. We propose algorithms for the detection (CAD-Detect) and defense (CAD-Defend) of these attacks, which are aided by our novel conditional anomaly detection approach, KNN-CAD. We demonstrate that our approach successfully detects whether a black box system adversarially conceals its decision-making process and mitigates the adversarial attack on real-world data for the prevalent explainers, LIME and SHAP.
UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification
Authors: Andrei Paraschiv, Mihai Dascalu, Dumitru-Clementin Cercel
Date: 2022-05-29 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Abusive Language, Hate Speech Detection

In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub
A Framework for Generating Informative Benchmark Instances
Authors: Nguyen Dang, Özgür Akgün, Joan Espasa, Ian Miguel, Peter Nightingale
Date: 2022-05-29 00:00:00
Tasks: Benchmarking

Benchmarking is an important tool for assessing the relative performance of alternative solving approaches. However, the utility of benchmarking is limited by the quantity and quality of the available problem instances. Modern constraint programming languages typically allow the specification of a class-level model that is parameterised over instance data. This separation presents an opportunity for automated approaches to generate instance data that define instances that are graded (solvable at a certain difficulty level for a solver) or can discriminate between two solving approaches. In this paper, we introduce a framework that combines these two properties to generate a large number of benchmark instances, purposely generated for effective and informative benchmarking. We use five problems that were used in the MiniZinc competition to demonstrate the usage of our framework. In addition to producing a ranking among solvers, our framework gives a broader understanding of the behaviour of each solver for the whole instance space; for example by finding subsets of instances where the solver performance significantly varies from its average performance.
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
Authors: YiRong Chen, Weiquan Fan, Xiaofen Xing, Jianxin Pang, Minlie Huang, Wenjing Han, Qianfeng Tie, Xiangmin Xu
Date: 2022-05-29 00:00:00
Tasks: Chinese Sentiment Analysis, Conversational Response Generation, Dialog Act Classification, Dialogue Generation, Emotional Dialogue Acts, Emotion Recognition, Emotion Recognition in Conversation, Open-Domain Dialog, Personality Recognition in Conversation, Personality Trait Recognition, Personalized and Emotional Conversation

Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the process of conversation generation, we propose CPED, a large-scale Chinese personalized and emotional dialogue dataset, which consists of multi-source knowledge related to empathy and personal characteristic. These knowledge covers gender, Big Five personality traits, 13 emotions, 19 dialogue acts and 10 scenes. CPED contains more than 12K dialogues of 392 speakers from 40 TV shows. We release the textual dataset with audio features and video features according to the copyright claims, privacy issues, terms of service of video platforms. We provide detailed description of the CPED construction process and introduce three tasks for conversational AI, including personality recognition, emotion recognition in conversations as well as personalized and emotional conversation generation. Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation. Our motivation is to propose a dataset to be widely adopted by the NLP community as a new open benchmark for conversational AI research. The full dataset is available at https://github.com/scutcyr/CPED.
Evaluating Automated Driving Planner Robustness against Adversarial Influence
Authors: Andres Molina-Markham, Silvia G. Ionescu, Erin Lanus, Derek Ng, Sam Sommerer, Joseph J. Rushanan
Date: 2022-05-29 00:00:00

Evaluating the robustness of automated driving planners is a critical and challenging task. Although methodologies to evaluate vehicles are well established, they do not yet account for a reality in which vehicles with autonomous components share the road with adversarial agents. Our approach, based on probabilistic trust models, aims to help researchers assess the robustness of protections for machine learning-enabled planners against adversarial influence. In contrast with established practices that evaluate safety using the same evaluation dataset for all vehicles, we argue that adversarial evaluation fundamentally requires a process that seeks to defeat a specific protection. Hence, we propose that evaluations be based on estimating the difficulty for an adversary to determine conditions that effectively induce unsafe behavior. This type of inference requires precise statements about threats, protections, and aspects of planning decisions to be guarded. We demonstrate our approach by evaluating protections for planners relying on camera-based object detectors.
Learning Security Strategies through Game Play and Optimal Stopping
Authors: Kim Hammar, Rolf Stadler
Date: 2022-05-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve through reinforcement learning and self-play. The game-theoretic perspective allows us to find defender strategies that are effective against dynamic attackers. The optimal stopping formulation gives us insight into the structure of optimal strategies, which we show to have threshold properties. To obtain the optimal defender strategies, we introduce T-FP, a fictitious self-play algorithm that learns Nash equilibria through stochastic approximation. We show that T-FP outperforms a state-of-the-art algorithm for our use case. Our overall method for learning and evaluating strategies includes two systems: a simulation system where defender strategies are incrementally learned and an emulation system where statistics are produced that drive simulation runs and where learned strategies are evaluated. We conclude that this approach can produce effective defender strategies for a practical IT infrastructure.
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
Authors: Zuxin Liu, Zijian Guo, Zhepeng Cen, huan zhang, Jie Tan, Bo Li, Ding Zhao
Date: 2022-05-29 00:00:00
Tasks: Adversarial Attack, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL problems are not robust and safe against carefully designed observational perturbations. We formally analyze the unique properties of designing effective observational adversarial attackers in the safe RL setting. We show that baseline adversarial attack techniques for standard RL tasks are not always effective for safe RL and propose two new approaches - one maximizes the cost and the other maximizes the reward. One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward. We further propose a robust training framework for safe RL and evaluate it via comprehensive experiments. This paper provides a pioneer work to investigate the safety and robustness of RL under observational attacks for future safe RL studies. Code is available at: \url{https://github.com/liuzuxin/safe-rl-robustness}
Saliency Map Based Data Augmentation
Authors: Jalal Al-Afandi, Bálint Magyar, András Horváth
Date: 2022-05-29 00:00:00
Tasks: Classification, Data Augmentation

Data augmentation is a commonly applied technique with two seemingly related advantages. With this method one can increase the size of the training set generating new samples and also increase the invariance of the network against the applied transformations. Unfortunately all images contain both relevant and irrelevant features for classification therefore this invariance has to be class specific. In this paper we will present a new method which uses saliency maps to restrict the invariance of neural networks to certain regions, providing higher test accuracy in classification tasks.