OpenRaiser/Intern-Atlas huggingface dataset:
FreeEnricher: Enriching Face Landmarks without Additional Cost
Venue: arXiv cs.CV 2022
Year: 2022

Recent years have witnessed significant growth of face alignment. Though dense facial landmark is highly demanded in various scenarios, e.g., cosmetic medicine and facial beautification, most works only consider sparse face alignment. To address this problem, we present a framework that can enrich landmark density by existing sparse landmark datasets, e.g., 300W with 68 points and WFLW with 98 points. Firstly, we observe that the local patches along each semantic contour are highly similar in appearance. Then, we propose a weakly-supervised idea of learning the refinement ability on original sparse landmarks and adapting this ability to enriched dense landmarks. Meanwhile, several operators are devised and organized together to implement the idea. Finally, the trained model is applied as a plug-and-play module to the existing face alignment networks. To evaluate our method, we manually label the dense landmarks on 300W testset. Our method yields state-of-the-art accuracy not only in newly-constructed dense 300W testset but also in the original sparse 300W and WFLW testsets without additional cost.
Model Predictive Spherical Image-Based Visual Servoing On $SO(3)$ for Aggressive Aerial Tracking
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents an image-based visual servo control (IBVS) method for a first-person-view (FPV) quadrotor to conduct aggressive aerial tracking. There are three major challenges to maneuvering an underactuated vehicle using IBVS: (i) finding a visual feature representation that is robust to large rotations and is suited to be an optimization variable; (ii) keeping the target visible without sacrificing the robot's agility; and (iii) compensating for the rotational effects in the detected features. We propose a complete design framework to address these problems. First, we employ a rotation on $SO(3)$ to represent a spherical image feature on $S^{2}$ to gain singularity-free and second-order differentiable properties. To ensure target visibility, we formulate the IBVS as a nonlinear model predictive control (NMPC) problem with three constraints taken into account: the robot's physical limits, target visibility, and time-to-collision (TTC). Furthermore, we propose a novel attitude-compensation scheme to enable formulating the visibility constraint in the actual image plane instead of a virtual fix-orientation image plane. It guarantees that the visibility constraint is valid under large rotations. Extensive experimental results show that our method can track a fast-moving target stably and aggressively without the aid of a localization system.
Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media
Venue: arXiv cs.CL 2022
Year: 2022

In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. This set encompasses measures of syntactic complexity, lexical sophistication and diversity, readability, and register-specific ngram frequencies, as well as sentiment and emotion lexicons. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions.
Unsigned Play by Milan Kundera? An Authorship Attribution Study
Venue: arXiv cs.CL 2022
Year: 2022

In addition to being a widely recognised novelist, Milan Kundera has also authored three pieces for theatre: The Owners of the Keys (Majitelé klíčů, 1961), The Blunder (Ptákovina, 1967), and Jacques and his Master (Jakub a jeho pán, 1971). In recent years, however, the hypothesis has been raised that Kundera is the true author of a fourth play: Juro Jánošík, first performed in a 1974 production under the name of Karel Steigerwald, who was Kundera's student at the time. In this study, we make use of supervised machine learning to settle the question of authorship attribution in the case of Juro Jánošík, with results strongly supporting the hypothesis of Kundera's authorship.
Multi-Analyst Differential Privacy for Online Query Answering
Venue: arXiv cs.CY 2022
Year: 2022

Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflicting priorities that must share the same privacy loss budget. This motivates the problem of equitable budget-sharing for multi-analyst differential privacy. Our previous work defined desiderata that any mechanism in this space should satisfy and introduced methods for budget-sharing in the offline case where queries are known in advance. We extend our previous work on multi-analyst differentially private query answering to the case of online query answering, where queries come in one at a time and must be answered without knowledge of the following queries. We demonstrate that the unknown ordering of queries in the online case results in a fundamental limit in the number of queries that can be answered while satisfying the desiderata. In response, we develop two mechanisms, one which satisfies the desiderata in all cases but is subject to the fundamental limitations, and another that randomizes the input order ensuring that existing online query answering mechanisms can satisfy the desiderata.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
Venue: arXiv cs.CL 2022
Year: 2022

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.
Future Sight: Dynamic Story Generation with Large Pretrained Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Recent advances in deep learning research, such as transformers, have bolstered the ability for automated agents to generate creative texts similar to those that a human would write. By default, transformer decoders can only generate new text with respect to previously generated text. The output distribution of candidate tokens at any position is conditioned on previously selected tokens using a self-attention mechanism to emulate the property of autoregression. This is inherently limiting for tasks such as controllable story generation where it may be necessary to condition on future plot events when writing a story. In this work, we propose Future Sight, a method for finetuning a pretrained generative transformer on the task of future conditioning. Transformer decoders are typically pretrained on the task of completing a context, one token at a time, by means of self-attention. Future Sight additionally enables a decoder to attend to an encoded future plot event. This motivates the decoder to expand on the context in a way that logically concludes with the provided future. During inference, the future plot event can be written by a human author to steer the narrative being generated in a certain direction. We evaluate the efficacy of our approach on a story generation task with human evaluators.
Efficient Algorithms for the Bee-Identification Problem
Venue: arXiv cs.IT 2022
Year: 2022

The bee-identification problem, formally defined by Tandon, Tan and Varshney (2019), requires the receiver to identify "bees" using a set of unordered noisy measurements. In this previous work, Tandon, Tan, and Varshney studied error exponents and showed that decoding the measurements jointly results in a significantly smaller error exponent. In this work, we study algorithms related to this joint decoder. First, we demonstrate how to perform joint decoding efficiently. By reducing to the problem of finding perfect matching and minimum-cost matchings, we obtain joint decoders that run in time quadratic and cubic in the number of "bees" for the binary erasure (BEC) and binary symmetric channels (BSC), respectively. Next, by studying the matching algorithms in the context of channel coding, we further reduce the running times by using classical tools like peeling decoders and list-decoders. In particular, we show that our identifier algorithms when used with Reed-Muller codes terminate in almost linear and quadratic time for BEC and BSC, respectively. Finally, for explicit codebooks, we study when these joint decoders fail to identify the "bees" correctly. Specifically, we provide practical methods of estimating the probability of erroneous identification for given codebooks.
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Venue: arXiv cs.LG 2022
Year: 2022

Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions. However, current models typically rely on 2-dimensional molecular representations as input, and while utilization of 2\3- dimensional structural data has gained deserved traction in recent years as many of these models are still limited to static graph representations. We propose a novel approach based on the transformer model utilizing GNNs for characterizing dynamic features of protein-ligand interactions. Our message passing transformer pre-trains on a set of molecular dynamic data based off of physics-based simulations to learn coordinate construction and make binding probability and affinity predictions as a downstream task. Through extensive testing we compare our results with the existing models, our MDA-PLI model was able to outperform the molecular interaction prediction models with an RMSE of 1.2958. The geometric encodings enabled by our transformer architecture and the addition of time series data add a new dimensionality to this form of research.
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
Venue: arXiv cs.CL 2022
Year: 2022

The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications. Previous studies along this line primarily focused on perturbations in the natural language question side, neglecting the variability of tables. Motivated by this, we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm to measure the robustness of Text-to-SQL models. Following this proposition, we curate ADVETA, the first robustness evaluation benchmark featuring natural and realistic ATPs. All tested state-of-the-art models experience dramatic performance drops on ADVETA, revealing models' vulnerability in real-world practices. To defend against ATP, we build a systematic adversarial training example generation framework tailored for better contextualization of tabular data. Experiments show that our approach not only brings the best robustness improvement against table-side perturbations but also substantially empowers models against NL-side perturbations. We release our benchmark and code at: https://github.com/microsoft/ContextualSP.
Multi-head Uncertainty Inference for Adversarial Attack Detection
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. Using independent heads at different depths, the normalized predictions are assumed to follow the same Dirichlet distribution, and we estimate distribution parameter of it by moment matching. Cognitive uncertainty brought by the adversarial attacks will be reflected and amplified on the distribution. Experimental results show that the proposed MH-UI framework can outperform all the referred UI methods in the adversarial attack detection task with different settings.
Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning
Venue: arXiv cs.CL 2022
Year: 2022

A coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to tackle two main tasks: one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention. In this paper, we propose a hybrid rule-neural coreference resolution system based on actor-critic learning, such that it can achieve better coreference performance by leveraging the advantages from both the heuristic rules and a neural conference model. This end-to-end system can also perform both mention detection and resolution by leveraging a joint training algorithm. We experiment on the BERT model to generate input span representations. Our model with the BERT span representation achieves the state-of-the-art performance among the models on the CoNLL-2012 Shared Task English Test Set.
Narrative Visualization to Communicate Neurological Diseases
Venue: arXiv cs.HC 2022
Year: 2022

While narrative visualization has been used successfully in various applications to communicate scientific data in the format of a story to a general audience, the same has not been true for medical data. There are only a few exceptions that present tabular medical data to non-experts. However, a key component of medical visualization is the interactive analysis of 3D data, such as 3D models of anatomical structures, which were rarely included in narrative visualizations so far. In this design study, we investigate how neurological disease data can be communicated through narrative visualization techniques to a general audience in an understandable way. We designed a narrative visualization explaining cerebral small vessel disease. Learning about its avoidable risk factors serves to motivate the audience watching the resulting visual data story. Using this example, we discuss the adaption of basic narrative components. This includes the conflict and characters of a story, as well as the story's structure and content to address and communicate specific characteristics of medical data. Furthermore, we explore the extent to which complex medical relationships need to be simplified to be understandable to a general audience without distorting the underlying data and evidence. In particular, the data needs to be preprocessed for non-experts and appropriate forms of interaction must be found. We explore approaches to make the data more personally relatable, such as including a fictional patient. We evaluated our approach in a user study with 40 participants in a web-based implementation of the designed story. We found that the combination of a carefully thought-out storyline with a clear key message, appealing visualizations combined with easy-to-use interactions, and credible references are crucial for creating a narrative visualization about a neurological disease that engages an audience.
Approximate Query Processing via Tuple Bubbles
Venue: arXiv cs.DB 2022
Year: 2022

We propose a versatile approach to lightweight, approximate query processing by creating compact but tunably precise representations of larger quantities of original tuples, coined bubbles. Instead of working with tables of tuples, the query processing then operates on bubbles but leaves the traditional query processing paradigms conceptually applicable. We believe this is a natural and viable approach to render approximate query processing feasible for large data in disaggregated cloud settings or in resource-limited scenarios. Bubbles are tunable regarding the compactness of enclosed tuples as well as the granularity of statistics and the way they are instantiated. For improved accuracy, we put forward a first working solution that represents bubbles via Bayesian networks, per table, or along foreign-key joins. To underpin the viability of the approach, we report on an experimental evaluation considering the state-of-the-art competitors, where we show clear benefits when assessing the estimation accuracy, execution time, and required disk space.
Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets
Venue: arXiv cs.CL 2022
Year: 2022

Modal verbs, such as "can", "may", and "must", are commonly used in daily communication to convey the speaker's perspective related to the likelihood and/or mode of the proposition. They can differ greatly in meaning depending on how they're used and the context of a sentence (e.g. "They 'must' help each other out." vs. "They 'must' have helped each other out.") Despite their practical importance in natural language understanding, linguists have yet to agree on a single, prominent framework for the categorization of modal verb senses. This lack of agreement stems from high degrees of flexibility and polysemy from the modal verbs, making it more difficult for researchers to incorporate insights from this family of words into their work. This work presents Moverb dataset, which consists of 27,240 annotations of modal verb senses over 4,540 utterances containing one or more sentences from social conversations. Each utterance is annotated by three annotators using two different theoretical frameworks (i.e., Quirk and Palmer) of modal verb senses. We observe that both frameworks have similar inter-annotator agreements, despite having different numbers of sense types (8 for Quirk and 3 for Palmer). With the RoBERTa-based classifiers fine-tuned on \dataset, we achieve F1 scores of 82.2 and 78.3 on Quirk and Palmer, respectively, showing that modal verb sense disambiguation is not a trivial task. Our dataset will be publicly available with our final version.
A Differential Approach for Data and Classification Service based Privacy-Preserving Machine Learning Model in Cloud Environment
Venue: arXiv cs.CR 2022
Year: 2022

The massive upsurge in computational and storage has driven the local data and machine learning applications to the cloud environment. The owners may not fully trust the cloud environment as it is managed by third parties. However, maintaining privacy while sharing data and the classifier with several stakeholders is a critical challenge. This paper proposes a novel model based on differential privacy and machine learning approaches that enable multiple owners to share their data for utilization and the classifier to render classification services for users in the cloud environment. To process owners data and classifier, the model specifies a communication protocol among various untrustworthy parties. The proposed model also provides a robust mechanism to preserve the privacy of data and the classifier. The experiments are conducted for a Naive Bayes classifier over numerous datasets to compute the proposed model efficiency. The achieved results demonstrate that the proposed model has high accuracy, precision, recall, and F1-score up to 94%, 95%, 94%, and 94%, and improvement up to 16.95%, 20.16%, 16.95%, and 23.33%, respectively, compared with state-of-the-art works.
Automated Configuration and Usage of Strategy Portfolios for Bargaining
Venue: arXiv cs.MA 2022
Year: 2022

Bargaining can be used to resolve mixed-motive games in multi-agent systems. Although there is an abundance of negotiation strategies implemented in automated negotiating agents, most agents are based on single fixed strategies, while it is widely acknowledged that there is no single best-performing strategy for all negotiation settings. In this paper, we focus on bargaining settings where opponents are repeatedly encountered, but the bargaining problems change. We introduce a novel method that automatically creates and deploys a portfolio of complementary negotiation strategies using a training set and optimise pay-off in never-before-seen bargaining settings through per-setting strategy selection. Our method relies on the following contributions. We introduce a feature representation that captures characteristics for both the opponent and the bargaining problem. We model the behaviour of an opponent during a negotiation based on its actions, which is indicative of its negotiation strategy, in order to be more effective in future encounters. Our combination of feature-based methods generalises to new negotiation settings, as in practice, over time, it selects effective counter strategies in future encounters. Our approach is tested in an ANAC-like tournament, and we show that we are capable of winning such a tournament with a 5.6% increase in pay-off compared to the runner-up agent.