Reviewerly/exHarmony huggingface dataset:
Multi-Source Survival Domain Adaptation

Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the partial information held by censored cases. A good analysis can, for example, determine whether one medical treatment for a group of patients is better than another. With the rise of machine learning, survival analysis can be modeled as learning a function that maps studied patients to their survival times. To succeed with that, there are three crucial issues to be tackled. First, some patient data is censored: we do not know the true survival times for all patients. Second, data is scarce, which led past research to treat different illness types as domains in a multi-task setup. Third, there is the need for adaptation to new or extremely rare illness types, where little or no labels are available. In contrast to previous multi-task setups, we want to investigate how to efficiently adapt to a new survival target domain from multiple survival source domains. For this, we introduce a new survival metric and the corresponding discrepancy measure between survival distributions. These allow us to define domain adaptation for survival analysis while incorporating censored data, which would otherwise have to be dropped. Our experiments on two cancer data sets reveal a superb performance on target domains, a better treatment recommendation, and a weight matrix with a plausible explanation.
Assessing Learned Representations under Open-World Novelty

My dissertation research focuses on sequential decision-making (SDM) in complex environments, and how agents can perform well even when novelty is introduced to those environments. The problem of how agents can respond intelligently to novelty has been a long-standing challenge in AI, and poses unique problems across approaches to SDM. This question has been studied in various formulations, including open-world learning and reasoning, transfer learning, concept drift, and statistical relational learning. Classical and modern approaches in agent design offer tradeoffs in human effort for feature encoding, ease of deployment in new domains, and the development of both provably and empirically reliable policies. I propose a formalism for studying open-world novelty in SDM processes with feature-rich observations. I study the conditions under which causal-relational queries can be estimated from non-novel observations, and empirically examine the effects of open-world novelty on agent behavior.
Does Knowing When Help Is Needed Improve Subgoal Hint Performance in an Intelligent Data-Driven Logic Tutor?

The assistance dilemma is a well-recognized challenge to determine when and how to provide help during problem solving in intelligent tutoring systems. This dilemma is particularly challenging to address in domains such as logic proofs, where problems can be solved in a variety of ways. In this study, we investigate two data-driven techniques to address the when and how of the assistance dilemma, combining a model that predicts when students need help learning efficient strategies, and hints that suggest what subgoal to achieve. We conduct a study assessing the impact of the new pedagogical policy against a control policy without these adaptive components. We found empirical evidence which suggests that showing subgoals in training problems upon predictions of the model helped the students who needed it most and improved test performance when compared to their control peers. Our key findings include significantly fewer steps in posttest problem solutions for students with low prior proficiency and significantly reduced help avoidance for all students in training.
Embodied, Intelligent Communication for Multi-Agent Cooperation

High-performing human teams leverage intelligent and efficient communication and coordination strategies to collaboratively maximize their joint utility. Inspired by teaming behaviors among humans, I seek to develop computational methods for synthesizing intelligent communication and coordination strategies for collaborative multi-robot systems. I leverage both classical model-based control and planning approaches as well as data-driven methods such as Multi-Agent Reinforcement Learning (MARL) to provide several contributions towards enabling emergent cooperative teaming behavior across both homogeneous and heterogeneous (including agents with different capabilities) robot teams.
ANALYTICAL SCHEME IN FILM CRITICS’ WORK. EVGENY GUSYATINSKY

The article is devoted to identifying the analytical scheme used by film critic Yevgeny Gusyatinsky in reviews for the printed version of Cinema Art Journal. The review of domestic publications, which are focused on various aspects of the film review genre, is followed by a constructive analysis of the texts by E. Gusyatinsky, carried out in accordance with the methodology developed by S.Yu. Schtein. The film critic's reviews of feature films published in Cinema Art Journal are successively analyzed, and each text's key principles of construction are formulated. The systematization of the analytical models used allows us not only to conclude that there is a more or less unified scheme for analyzing the film used by the author but also to continue the study on a larger scale, identifying the schemes used by other film critics and comparing them.
Toward Robust Uncertainty Estimation with Random Activation Functions

Deep neural networks are in the limelight of machine learning with their excellent performance in many data-driven applications. However, they can lead to inaccurate predictions when queried in out-of-distribution data points, which can have detrimental effects especially in sensitive domains, such as healthcare and transportation, where erroneous predictions can be very costly and/or dangerous. Subsequently, quantifying the uncertainty of the output of a neural network is often leveraged to evaluate the confidence of its predictions, and ensemble models have proved to be effective in measuring the uncertainty by utilizing the variance of predictions over a pool of models. In this paper, we propose a novel approach for uncertainty quantification via ensembles, called Random Activation Functions (RAFs) Ensemble, that aims at improving the ensemble diversity toward a more robust estimation, by accommodating each neural network with a different (random) activation function. Extensive empirical study demonstrates that RAFs Ensemble outperforms state-of-the-art ensemble uncertainty quantification methods on both synthetic and real-world datasets in a series of regression tasks.
GAN-Based Domain Inference Attack

Model-based attacks can infer training data information from deep neural network models. These attacks heavily depend on the attacker's knowledge of the application domain, e.g., using it to determine the auxiliary data for model-inversion attacks. However, attackers may not know what the model is used for in practice. We propose a generative adversarial network (GAN) based method to explore likely or similar domains of a target model -- the model domain inference (MDI) attack. For a given target (classification) model, we assume that the attacker knows nothing but the input and output formats and can use the model to derive the prediction for any input in the desired form. Our basic idea is to use the target model to affect a GAN training process for a candidate domain's dataset that is easy to obtain. We find that the target model may distort the training procedure less if the domain is more similar to the target domain. We then measure the distortion level with the distance between GAN-generated datasets, which can be used to rank candidate domains for the target model. Our experiments show that the auxiliary dataset from an MDI top-ranked domain can effectively boost the result of model-inversion attacks.
The Role of Public Spaces in Disaster Mitigation-Based Urban Planning in Southeast Asia

The continent of Asia is one of the continents crossed by the Pacific Ring of Fire (Ring of Fire).The Pacific Ring of Fire is an area that experiences frequent earthquakes and volcanic eruptions that burn the Pacific Ocean basin.The Pacific Ring of Fire causes the countries it passes through to experience frequent disasters.Various ways have been done to anticipate disasters by countries in Southeast Asia and East Asia.This research will move forward by deepening the possibilities of the form of conceptualization and the contributable potential of public space to be included as a fundamental part of disaster-based urban planning, especially in countries in the Southeast Asia and East Asia.This study uses descriptive analysis techniques because descriptive research is a simple analytical model and very easy to apply.This study also uses qualitative analysis because researchers want to interpret and describe non-numeric data.The Southeast Asia and East Asia Regions were chosen as the research location, namely the countries in these regions have similarities in terms of geographical location, traversed by the Pacific Ring of Fire.The source of data in this research is secondary data in the form of additional data such as literature data, documents related to data needed in further research.The instrument in this study was a computer device.Most of the conceptualization of public space in several countries in Southeast Asia and East Asia has implemented a disaster mitigation system.The use of public space in Southeast Asia and East Asia is more in the use of green open spaces for catchment areas and gathering places as well as mitigation in accordance with the characteristics of disasters that often occur in each country.The potential that can be contributed from disaster mitigation-based urban planning can prevent and reduce the impact of the disaster itself.
A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)

User demand mining aims to identify the implicit demand from the e-commerce reviews, which are always irregular, vague and diverse. Existing sentiment analysis research mainly focuses on aspect-opinion-sentiment triplet extraction, while the deeper user demands remain unexplored. In this paper, we formulate a novel research question of jointly mining aspect-opinion-sentiment-demand, and propose a Mutually Enhanced Bidirectional Extraction (MEMB) framework for capturing the dynamic interaction among different types of information. Finally, experiments on Chinese e-commerce data demonstrate the efficacy of the proposed model.
DISPUTool 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Political Debates

Political debates are one of the most salient moments of an election campaign, where candidates are challenged to discuss the main contemporary and historical issues in a country. These debates represent a natural ground for argumentative analysis, which has always been employed to investigate political discourse structure and strategy in philosophy and linguistics. In this paper, we present DISPUTool 2.0, an automated tool which relies on Argument Mining methods to analyse the political debates from the US presidential campaigns to extract argument components (i.e., premise and claim) and relations (i.e., support and attack), and highlight fallacious arguments. DISPUTool 2.0 allows also for the automatic analysis of a piece of a debate proposed by the user to identify and classify the arguments contained in the text. A REST API is provided to exploit the tool's functionalities.
Towards Safe Reinforcement Learning via OOD Dynamics Detection in Autonomous Driving System (Student Abstract)

Deep reinforcement learning (DRL) has proven effective in training agents to achieve goals in complex environments. However, a trained RL agent may exhibit, during deployment, unexpected behavior when faced with a situation where its state transitions differ even slightly from the training environment. Such a situation can arise for a variety of reasons. Rapid and accurate detection of anomalous behavior appears to be a prerequisite for using DRL in safety-critical systems, such as autonomous driving. We propose a novel OOD detection algorithm based on modeling the transition function of the training environment. Our method captures the bias of model behavior when encountering subtle changes of dynamics while maintaining a low false positive rate. Preliminary evaluations on the realistic simulator CARLA corroborate the relevance of our proposed method.
Clustered Federated Learning for Heterogeneous Data (Student Abstract)

Federated Learning (FL) aims to achieve a global model via aggregating models from all devices. However, it can diverge when the data on the users’ devices are heterogeneous. To address this issue, we propose a novel clustered FL method (FPFC) based on a nonconvex pairwise fusion penalty. FPFC can automatically identify clusters without prior knowledge of the number of clusters and the set of devices in each cluster. Our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform inexact computation. We also provide convergence guarantees of FPFC for general nonconvex losses. Experiment results demonstrate the advantages of FPFC over existing methods.
Planning and Learning with Adaptive Lookahead

Some of the most powerful reinforcement learning frameworks use planning for action selection. Interestingly, their planning horizon is either fixed or determined arbitrarily by the state visitation history. Here, we expand beyond the naive fixed horizon and propose a theoretically justified strategy for adaptive selection of the planning horizon as a function of the state-dependent value estimate. We propose two variants for lookahead selection and analyze the trade-off between iteration count and computational complexity per iteration. We then devise a corresponding deep Q-network algorithm with an adaptive tree search horizon. We separate the value estimation per depth to compensate for the off-policy discrepancy between depths. Lastly, we demonstrate the efficacy of our adaptive lookahead method in a maze environment and Atari.
Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model

It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainly resolve this issue by retraining the entire model to impose the uncertainty quantification capability so that the learned model can achieve desired performance in accuracy and uncertainty prediction simultaneously. However, training the model from scratch is computationally expensive, and a trade-off might exist between prediction accuracy and uncertainty quantification. To this end, we consider a more practical post-hoc uncertainty learning setting, where a well-trained base model is given, and we focus on the uncertainty quantification task at the second stage of training. We propose a novel Bayesian uncertainty learning approach using the Dirichlet meta-model, which is effective and computationally efficient. Our proposed method requires no additional training data and is flexible enough to quantify different uncertainties and easily adapt to different application settings, including out-of-domain data detection, misclassification detection, and trustworthy transfer learning. Finally, we demonstrate our proposed meta-model approach's flexibility and superior empirical performance on these applications over multiple representative image classification benchmarks.
Meta Learning in Decentralized Neural Networks: Towards More General AI

Meta-learning usually refers to a learning algorithm that learns from other learning algorithms. The problem of uncertainty in the predictions of neural networks shows that the world is only partially predictable and a learned neural network cannot generalize to its ever-changing surrounding environments. Therefore, the question is how a predictive model can represent multiple predictions simultaneously. We aim to provide a fundamental understanding of learning to learn in the contents of Decentralized Neural Networks (Decentralized NNs) and we believe this is one of the most important questions and prerequisites to building an autonomous intelligence machine. To this end, we shall demonstrate several pieces of evidence for tackling the problems above with Meta Learning in Decentralized NNs. In particular, we will present three different approaches to building such a decentralized learning system: (1) learning from many replica neural networks, (2) building the hierarchy of neural networks for different functions, and (3) leveraging different modality experts to learn cross-modal representations.
Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract)

Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora.
Development of a Human-Agent Interaction System including Norm and Emotion in an Evacuation Situation (Student Abstract)

Agent-based modeling and simulation can provide a powerful test environment for crisis management scenarios. Human agent interaction has limitations in representing norms issued by an agent to a human agent that has emotions. In this study, we present an approach to the interaction between a virtual normative agent and a human agent in an evacuation scenario. Through simulation comparisons, it is shown that the method used in this study can more fully simulate the real-life out come of an emergency situation and also improves the au thenticity of the agent interaction.
ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction (Student Abstract)

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods.
LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility (Student Abstract)

Learning to recover clear images from images having a combination of degrading factors is a challenging task. That being said, autonomous surveillance in low visibility conditions caused by high pollution/smoke, poor air quality index, low light, atmospheric scattering, and haze during a blizzard, etc, becomes even more important to prevent accidents. It is thus crucial to form a solution that can not only result in a high-quality image but also which is efficient enough to be deployed for everyday use. However, the lack of proper datasets available to tackle this task limits the performance of the previous methods proposed. To this end, we generate the LowVis-AFO dataset, containing 3647 paired dark-hazy and clear images. We also introduce a new lightweight deep learning model called Low-Visibility Restoration Network (LVRNet). It outperforms previous image restoration methods with low latency, achieving a PSNR value of 25.744 and an SSIM of 0.905, hence making our approach scalable and ready for practical use.
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)

This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.
Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions

Communicating the predictive uncertainty of deep neural networks transparently and reliably is important in many safety-critical applications such as medicine. However, modern neural networks tend to be poorly calibrated, resulting in wrong predictions made with a high confidence. While existing post-hoc calibration methods like temperature scaling or isotonic regression yield strongly calibrated predictions in artificial experimental settings, their efficiency can significantly reduce in real-world applications, where scarcity of labeled data or domain drifts are commonly present. In this paper, we first investigate the impact of these characteristics on post-hoc calibration and introduce an easy-to-implement extension of common post-hoc calibration methods based on test time augmentation. In extensive experiments, we demonstrate that our approach results in substantially better calibration on various architectures. We demonstrate the robustness of our proposed approach on a real-world application for skin cancer classification and show that it facilitates safe decision-making under real-world uncertainties.
Knowledge-Embedded Narrative Construction from Open Source Intelligence

Storytelling is an innate part of language-based communication. Today, current events are reported via Open Source Intelligence (OSINT) sources like news websites, blogs, and discussion forums. Scattered and fragmented sources such as these can be better understood when organized as chains of event plot points, or narratives, that have the ability to communicate end-end stories. Though search engines can retrieve aggregated event information, they lack the ability to sequence relevant events together to form narratives about different topics. I propose an AI system inspired by Gustav Freytag’s narrative theory called the Plot Element Pyramid and use knowledge graphs to represent, chain, and reason over narratives from disparately sourced event details to better comprehend convoluted, noisy information about critical events during intelligence analysis.