Publications

Correction to: Why public health matters today and tomorrow: the role of applied public health research
Lindsay McLaren
Paula Braitstein
David L Buckeridge
Damien Contandriopoulos
Maria I. Creatore
Guy Faulkner
David Hammond
Steven J. Hoffman
Yan Kestens
Scott Leatherdale
Jonathan McGavock
Wendy V. Norman
Candace Nykiforuk
Valéry Ridde
Janet Smylie
The article “Why public health matters today and tomorrow: the role of applied public health research,” written by Lindsay McLaren et al… (voir plus)., was originally published Online First without Open Access.
2020-09-01
Canadian Journal of Public Health = Revue Canadienne de Santé Publique (publié)
doi.org
Learning General Transformations of Data for Out-of-Sample Extensions
Matthew Amodio
David van Dijk
Guy Wolf
Smita Krishnaswamy
While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from on… (voir plus)e distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new distribution not seen in training. Thus, they memorize the domain of the transformation, and cannot generalize the transformation out of sample. To address this, we propose a new neural network called a Neuron Transformation Network (NTNet) that isolates the signal representing the transformation itself from the other signals representing internal distribution variation. This signal can then be removed from a new dataset distributed differently from the original one trained on. We demonstrate the effectiveness of our NTNet on more than a dozen synthetic and biomedical single-cell RNA sequencing datasets, where the NTNet is able to learn the data transformation performed by genetic and drug perturbations on one sample of cells and successfully apply it to another sample of cells to predict treatment outcome.
2020-08-31
Europe PubMed Central (accepté)
doi.org
SENET: A Semantic Web for Supporting Automation of Software Engineering Tasks
Yalin Liu
Jinfeng Lin
Jane Cleland-Huang
Michael Vierhauser
Jin L.C. Guo
Sugandha Lohar
The use of Natural Language (NL) interfaces to allow devices and applications to respond to verbal commands or free-form textual queries is … (voir plus)becoming increasingly prevalent in our society. To a large extent, their success in interpreting and responding to a request is dependent upon rich underlying ontologies and conceptual models that understand the technical or domain specific vocabulary of diverse users. The effective use of NL interfaces in the Software Engineering (SE) domains requires its own ontology models focusing upon software related terms and concepts. While many SE glossaries exist, they are often incomplete and tend to define the vocabulary for specific sub-fields without capturing associations between terms and phrases. This limits their usefulness for supporting NL-related tasks. In this paper we propose an approach for constructing and evolving a semantic network of software engineering concepts and phrases. Our approach starts with a set of existing SE glossaries, uses the existing glossary terms and explicitly defined associations as a starting point, uses machine learning-based techniques to dynamically identify and document additional associations between terms, leverages the network to interpret NL queries in the SE domain, and finally augments the resulting semantic network with feedback provided by users. We evaluate the viability of our approach within the sub-domain of Agile Software Development, focusing on requirements related queries, and show that the semantic network enhances the ability of an NL interface to correctly interpret and execute user queries.
2020-08-31
International Workshop on Artificial Intelligence for Requirements Engineering (publié)
doi.org
On the Effectiveness of Two-Step Learning for Latent-Variable Models
Cem Subakan
Maxime Gasse
Laurent Charlin
Latent-variable generative models offer a principled solution for modeling and sampling from complex probability distributions. Implementing… (voir plus) a joint training objective with a complex prior, however, can be a tedious task, as one is typically required to derive and code a specific cost function for each new type of prior distribution. In this work, we propose a general framework for learning latent variable generative models in a two-step fashion. In the first step of the framework, we train an autoencoder, and in the second step we fit a prior model on the resulting latent distribution. This two-step approach offers a convenient alternative to joint training, as it allows for a straightforward combination of existing models without the hustle of deriving new cost functions, and the need for coding the joint training objectives. Through a set of experiments, we demonstrate that two-step learning results in performances similar to joint training, and in some cases even results in more accurate modeling.
2020-08-31
International Workshop on Machine Learning for Signal Processing (publié)
doi.org
Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems
Alexander Nicholson
Deeksha M. Arya
Jin L.C. Guo
Traceability links between software artifacts serve as an invaluable resource for reasoning about software products and their development pr… (voir plus)ocess. Most conventional methods for capturing traceability are based on pair-wise artifact relations such as trace matrices or navigable links between two directly related artifacts. However, this limited view of trace links ignores the propagating effect of artifact connections as well as the trace link properties at a project level. In this work, we propose the use of network structures to provide another perspective from which reasoning on a collective of trace events is possible. We explore various network analysis techniques in the issue tracking system of sixty-six open source projects. Our observation reveals two salient properties of the traceability network, i.e. scale free and triadic closure. These properties provide a strong indication of the applicability of network analysis tools and can be used to identify and examine important "hub" issues. As a stepping stone, these properties can further support project status analysis and link type prediction. As a proof-of-concept, we demonstrate the effectiveness of applying the triadic closure property to link type prediction.
2020-08-31
International Workshop on Artificial Intelligence for Requirements Engineering (publié)
doi.org
Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach
Julian Tillmann
M. Uljarevic
Daisy Crawley
G. Dumas
Eva Loth
D. Murphy
J. Buitelaar
Tony Charman
Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Daniel Claudia Yvette Bhismadev Ineke Flavio Dell’ Guillaume Christine Jessica Vincent Pilar David Hannah Joerg Mark H. Emily J. H. Prantik Meng-Chuan Xavier Liogier Michael David J. René Luke Andreas Carolin Nico Laurence Marianne Bob Gahan Antonio M. Barbara Amber Jessica Roberto Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Ahmad Ambrosino Auyeung Baumeister Beckmann Bourge
Jumana Ahmad
Sara Ambrosino
Bonnie Auyeung
Sarah Baumeister
Christian Beckmann
Thomas Bourgeron
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna … (voir 39 de plus)
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Flavio Dell’Acqua
Guillaume Dumas
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Hannah Hayward
Joerg F. Hipp
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Luke Mason
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Mueller
Larry O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2020-08-30
Molecular Autism (publié)
doi.org
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
Travis Manderson
Stefan Wapnick
David Meger
Gregory Dudek
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructure… (voir plus)d outdoor environments using only visual inputs. Our approach applies a hybrid model-based and model-free reinforcement learning method that is entirely self-supervised in labeling terrain roughness and collisions using on-board sensors. Notably, we provide both first-person and overhead aerial image inputs to our model. We find that the fusion of these complementary inputs improves planning foresight and makes the model robust to visual obstructions. Our results show the ability to generalize to environments with plentiful vegetation, various types of rock, and sandy trails. During evaluation, our policy attained 90% smooth terrain traversal and reduced the proportion of rough terrain driven over by 6.1 times compared to a model using only first-person imagery.
2020-08-30
2020 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to b… (voir plus)etter understand and analyze systems under development. Domain modelling is used during requirements analysis or the early stages of design to transform informal requirements written in natural language to domain models which are analyzable and more concise. Since domain modelling is time-consuming and requires modelling skills and experience, many approaches have been proposed to extract domain concepts and relationships automatically using extraction rules. However, relationships and patterns are often hidden in the sentences of a problem description. Automatic recognition of relationships or patterns in those cases requires context information and external knowledge of participating domain concepts, which goes beyond what is possible with extraction rules. In this paper, we draw on recent work on domain model extraction and envision a novel technique where sentence boundaries are customized and clusters of sentences are created for domain concepts. The technique further exploits a BiLSTM neural network model to identify relationships and patterns among domain concepts. We also present a classification strategy for relationships and patterns and use it to instantiate our technique. Preliminary results indicate that this novel idea is promising and warrants further research.
2020-08-30
2020 IEEE Tenth International Model-Driven Requirements Engineering (MoDRE) (publié)
doi.org
View-Invariant Loop Closure with Oriented Semantic Landmarks
Jimmy Li
Karim Koreitem
David Meger
Gregory Dudek
Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving lo… (voir plus)calization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.
2020-08-30
2020 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
Information correspondence between types of documentation for APIs
Deeksha M. Arya
Jin L.C. Guo
Martin P. Robillard
2020-08-25
Empirical Software Engineering (publié)
doi.org
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Marc-Andre Schulz
B. T. Thomas Yeo
Joshua T. Vogelstein
Janaina Mourao-Miranada
Jakob N. Kather
Konrad Kording
Blake Richards
Danilo Bzdok
Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep lear… (voir plus)ning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.
2020-08-24
Nature Communications (publié)
doi.org
Dynamic planning of redundant robots within a set-based task-priority inverse kinematics framework.
Daniele Di Vito
Mathieux Bergeron
David Meger
Gregory Dudek
Gianluca Antonelli
This work presents the dynamic planning of redundant robots by merging a global and local planner. The global planner is implemented as a sa… (voir plus)mpling-based algorithm which works in the reduced-dimensionality of the robot workspace applying the Cartesian constraints only. The output trajectory is then checked within a framework of set-based task priority inverse kinematics verifying the fulfillment of the other task constraints. The inverse kinematics framework is used also in real-time as local motion control to ensure a reactive behaviour to address, e.g., mismatch between the apriori information and on-line perception acquisition. During the movement, the motion planner runs in background to adapt to changes in the environment or, in general, to continuously optimize the path. The proposed method is experimentally validated with a Kinova Jaco2 7 degrees of freedom manipulator.
2020-08-23
2020 IEEE Conference on Control Technology and Applications (CCTA) (publié)
doi.org