scholarly journals Goal Recognition for Deceptive Human Agents through Planning and Gaze

2021 ◽  
Vol 71 ◽  
pp. 697-732
Author(s):  
Thao Le ◽  
Ronal Singh ◽  
Tim Miller
Keyword(s):  
State Of The Art ◽  
Eye Gaze ◽  
Competitive Game ◽  
Gaze Patterns ◽  
Gaze Behaviour ◽  
Previous State ◽  
Goal Recognition ◽  
Recognition Systems ◽  
Using Data ◽  
Insight Into

Eye gaze has the potential to provide insight into the minds of individuals, and this idea has been used in prior research to improve human goal recognition by combining human's actions and gaze. However, most existing research assumes that people are rational and honest. In adversarial scenarios, people may deliberately alter their actions and gaze, which presents a challenge to goal recognition systems. In this paper, we present new models for goal recognition under deception using a combination of gaze behaviour and observed movements of the agent. These models aim to detect when a person is deceiving by analysing their gaze patterns and use this information to adjust the goal recognition. We evaluated our models in two human-subject studies: (1) using data collected from 30 individuals playing a navigation game inspired by an existing deception study and (2) using data collected from 40 individuals playing a competitive game (Ticket To Ride). We found that one of our models (Modulated Deception Gaze+Ontic) offers promising results compared to the previous state-of-the-art model in both studies. Our work complements existing adversarial goal recognition systems by equipping these systems with the ability to tackle ambiguous gaze behaviours.

scholarly journals Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

2022 ◽  
Author(s):  
Polianna Delfino-Pereira ◽  
Cláudio Moisés Valiense De Andrade ◽  
Virginia Mara Reis Gomes ◽  
Maria Clara Pontello Barbosa Lima ◽  
Maira Viana Rego Souza-Silva ◽  
...  
Keyword(s):  
Statistical Methods ◽  
State Of The Art ◽  
Prognostic Scores ◽  
Early Assessment ◽  
Meta Learning ◽  
Health Related ◽  
Previous State ◽  
Using Data ◽  
Selection Operator ◽  
Evaluation Metric

Abstract The majority prognostic scores proposed for early assessment of coronavirus disease 19 (COVID-19) patients are bounded by methodological flaws. Our group recently developed a new risk score - ABC2SPH - using traditional statistical methods (least absolute shrinkage and selection operator logistic regression - LASSO). In this article, we provide a thorough comparative study between modern machine learning (ML) methods and state-of-the-art statistical methods, represented by ABC2SPH, in the task of predicting in-hospital mortality in COVID-19 patients using data upon hospital admission. We overcome methodological and technological issues found in previous similar studies, while exploring a large sample (5,032 patients). Additionally, we take advantage of a large and diverse set of methods and investigate the effectiveness of applying meta-learning, more specifically Stacking, in order to combine the methods' strengths and overcome their limitations. In our experiments, our Stacking solutions improved over previous state-of-the-art by more than 26% in predicting death, achieving 87.1% of AUROC and MacroF1 of 73.9%. We also investigated issues related to the interpretability and reliability of the predictions produced by the most effective ML methods. Finally, we discuss the adequacy of AUROC as an evaluation metric for highly imbalanced and skewed datasets commonly found in health-related problems.

scholarly journals Complex facial emotion recognition and atypical gaze patterns in autistic adults

Autism ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 258-262 ◽  
Author(s):  
Melissa H Black ◽  
Nigel TM Chen ◽  
Ottmar V Lipp ◽  
Sven Bölte ◽  
Sonya Girdler
Keyword(s):  
Emotion Recognition ◽  
Visual Information ◽  
Eye Gaze ◽  
Recognition Task ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Expressions ◽  
Gaze Patterns ◽  
Gaze Behaviour ◽  
Autistic Adults

While altered gaze behaviour during facial emotion recognition has been observed in autistic individuals, there remains marked inconsistency in findings, with the majority of previous research focused towards the processing of basic emotional expressions. There is a need to examine whether atypical gaze during facial emotion recognition extends to more complex emotional expressions, which are experienced as part of everyday social functioning. The eye gaze of 20 autistic and 20 IQ-matched neurotypical adults was examined during a facial emotion recognition task of complex, dynamic emotion displays. Autistic adults fixated longer on the mouth region when viewing complex emotions compared to neurotypical adults, indicating that altered prioritization of visual information may contribute to facial emotion recognition impairment. Results confirm the need for more ecologically valid stimuli for the elucidation of the mechanisms underlying facial emotion recognition difficulty in autistic individuals.

scholarly journals Fair algorithms for selecting citizens’ assemblies

Nature ◽  
2021 ◽  
Author(s):  
Bailey Flanigan ◽  
Paul Gölz ◽  
Anupam Gupta ◽  
Brett Hennig ◽  
Ariel D. Procaccia
Keyword(s):  
Random Process ◽  
State Of The Art ◽  
Fair Division ◽  
High Impact ◽  
Participation Rates ◽  
The World ◽  
Previous State ◽  
Using Data ◽  
Selection Probabilities ◽  
Selection Algorithms

AbstractGlobally, there has been a recent surge in ‘citizens’ assemblies’1, which are a form of civic participation in which a panel of randomly selected constituents contributes to questions of policy. The random process for selecting this panel should satisfy two properties. First, it must produce a panel that is representative of the population. Second, in the spirit of democratic equality, individuals would ideally be selected to serve on this panel with equal probability2,3. However, in practice these desiderata are in tension owing to differential participation rates across subpopulations4,5. Here we apply ideas from fair division to develop selection algorithms that satisfy the two desiderata simultaneously to the greatest possible extent: our selection algorithms choose representative panels while selecting individuals with probabilities as close to equal as mathematically possible, for many metrics of ‘closeness to equality’. Our implementation of one such algorithm has already been used to select more than 40 citizens’ assemblies around the world. As we demonstrate using data from ten citizens’ assemblies, adopting our algorithm over a benchmark representing the previous state of the art leads to substantially fairer selection probabilities. By contributing a fairer, more principled and deployable algorithm, our work puts the practice of sortition on firmer foundations. Moreover, our work establishes citizens’ assemblies as a domain in which insights from the field of fair division can lead to high-impact applications.

Multi-Resolution Autoregressive Graph-to-Graph Translation for Molecules

2019 ◽  
Author(s):  
Wengong Jin ◽  
Regina Barzilay ◽  
Tommi S Jaakkola
Keyword(s):  
Drug Discovery ◽  
State Of The Art ◽  
Molecular Graph ◽  
Biochemical Properties ◽  
Large Margin ◽  
Previous State ◽  
Translation Methods ◽  
Atom Level ◽  
Precursor Molecules ◽  
Prior State

The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.

scholarly journals Dynamic scan paths investigations under manual and highly automated driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jordan Navarro ◽  
Otto Lappi ◽  
François Osiurak ◽  
Emma Hernout ◽  
Catherine Gabaude ◽  
...  
Keyword(s):  
Relative Frequency ◽  
Human Locomotion ◽  
Visual Scanning ◽  
Automated Driving ◽  
The Gaze ◽  
Visual Inputs ◽  
Gaze Patterns ◽  
Gaze Behaviour ◽  
Scan Paths ◽  
Highly Automated Driving

AbstractActive visual scanning of the scene is a key task-element in all forms of human locomotion. In the field of driving, steering (lateral control) and speed adjustments (longitudinal control) models are largely based on drivers’ visual inputs. Despite knowledge gained on gaze behaviour behind the wheel, our understanding of the sequential aspects of the gaze strategies that actively sample that input remains restricted. Here, we apply scan path analysis to investigate sequences of visual scanning in manual and highly automated simulated driving. Five stereotypical visual sequences were identified under manual driving: forward polling (i.e. far road explorations), guidance, backwards polling (i.e. near road explorations), scenery and speed monitoring scan paths. Previously undocumented backwards polling scan paths were the most frequent. Under highly automated driving backwards polling scan paths relative frequency decreased, guidance scan paths relative frequency increased, and automation supervision specific scan paths appeared. The results shed new light on the gaze patterns engaged while driving. Methodological and empirical questions for future studies are discussed.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

scholarly journals Significance of Mast Cell Formed Extracellular Traps in Microbial Defense

2021 ◽  
Author(s):  
Daniel Elieh Ali Komi ◽  
Wolfgang M. Kuebler
Keyword(s):  
State Of The Art ◽  
Extracellular Dna ◽  
Cellular Mechanisms ◽  
Extracellular Traps ◽  
Synthetic Chemicals ◽  
Dna Strands ◽  
Associated Proteins ◽  
Microbial Defense ◽  
And Function ◽  
Insight Into

AbstractMast cells (MCs) are critically involved in microbial defense by releasing antimicrobial peptides (such as cathelicidin LL-37 and defensins) and phagocytosis of microbes. In past years, it has become evident that in addition MCs may eliminate invading pathogens by ejection of web-like structures of DNA strands embedded with proteins known together as extracellular traps (ETs). Upon stimulation of resting MCs with various microorganisms, their products (including superantigens and toxins), or synthetic chemicals, MCs become activated and enter into a multistage process that includes disintegration of the nuclear membrane, release of chromatin into the cytoplasm, adhesion of cytoplasmic granules on the emerging DNA web, and ejection of the complex into the extracellular space. This so-called ETosis is often associated with cell death of the producing MC, and the type of stimulus potentially determines the ratio of surviving vs. killed MCs. Comparison of different microorganisms with specific elimination characteristics such as S pyogenes (eliminated by MCs only through extracellular mechanisms), S aureus (removed by phagocytosis), fungi, and parasites has revealed important aspects of MC extracellular trap (MCET) biology. Molecular studies identified that the formation of MCET depends on NADPH oxidase-generated reactive oxygen species (ROS). In this review, we summarize the present state-of-the-art on the biological relevance of MCETosis, and its underlying molecular and cellular mechanisms. We also provide an overview over the techniques used to study the structure and function of MCETs, including electron microscopy and fluorescence microscopy using specific monoclonal antibodies (mAbs) to detect MCET-associated proteins such as tryptase and histones, and cell-impermeant DNA dyes for labeling of extracellular DNA. Comparing the type and biofunction of further MCET decorating proteins with ETs produced by other immune cells may help provide a better insight into MCET biology in the pathogenesis of autoimmune and inflammatory disorders as well as microbial defense.

scholarly journals Estimation of the Actual Incidence of Coronavirus Disease (COVID-19) in Emergent Hotspots: The Example of Hokkaido, Japan during February–March 2020

2021 ◽  
Vol 10 (11) ◽  
pp. 2392
Author(s):  
Andrei R. Akhmetzhanov ◽  
Kenji Mizumoto ◽  
Sung-Mok Jung ◽  
Natalie M. Linton ◽  
Ryosuke Omori ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Surveillance System ◽  
Negative Binomial ◽  
Actual Number ◽  
Public Health Response ◽  
The Public ◽  
Health Response ◽  
Using Data ◽  
Insight Into ◽  
Actual Incidence

Following the first report of the coronavirus disease 2019 (COVID-19) in Sapporo city, Hokkaido Prefecture, Japan, on 14 February 2020, a surge of cases was observed in Hokkaido during February and March. As of 6 March, 90 cases were diagnosed in Hokkaido. Unfortunately, many infected persons may not have been recognized due to having mild or no symptoms during the initial months of the outbreak. We therefore aimed to predict the actual number of COVID-19 cases in (i) Hokkaido Prefecture and (ii) Sapporo city using data on cases diagnosed outside these areas. Two statistical frameworks involving a balance equation and an extrapolated linear regression model with a negative binomial link were used for deriving both estimates, respectively. The estimated cumulative incidence in Hokkaido as of 27 February was 2,297 cases (95% confidence interval (CI): 382–7091) based on data on travelers outbound from Hokkaido. The cumulative incidence in Sapporo city as of 28 February was estimated at 2233 cases (95% CI: 0–4893) based on the count of confirmed cases within Hokkaido. Both approaches resulted in similar estimates, indicating a higher incidence of infections in Hokkaido than were detected by the surveillance system. This quantification of the gap between detected and estimated cases helped to inform the public health response at the beginning of the pandemic and provided insight into the possible scope of undetected transmission for future assessments.

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