Publications

Video Game Bad Smells: What They Are and How Developers Perceive Them
Vittoria Nardone
Biruk Asmare Muse
Mouna Abidi
Foutse Khomh
Massimiliano Di Penta
Video games represent a substantial and increasing share of the software market. However, their development is particularly challenging as i… (see more)t requires multi-faceted knowledge, which is not consolidated in computer science education yet. This article aims at defining a catalog of bad smells related to video game development. To achieve this goal, we mined discussions on general-purpose and video game-specific forums. After querying such a forum, we adopted an open coding strategy on a statistically significant sample of 572 discussions, stratified over different forums. As a result, we obtained a catalog of 28 bad smells, organized into five categories, covering problems related to game design and logic, physics, animation, rendering, or multiplayer. Then, we assessed the perceived relevance of such bad smells by surveying 76 game development professionals. The survey respondents agreed with the identified bad smells but also provided us with further insights about the discussed smells. Upon reporting results, we discuss bad smell examples, their consequences, as well as possible mitigation/fixing strategies and trade-offs to be pursued by developers. The catalog can be used not only as a guideline for developers and educators but also can pave the way toward better automated tool support for video game developers.
2023-05-25
ACM Transactions on Software Engineering and Methodology (published)
doi.org
Formalizing theories of storage versus computation with Tree-based Grammars
Emily Morgan
Rabia Ergin
Timothy J. O'Donnell

In this paper, we will explore why Tree-based Grammars (e.g. Johnson et al., 2007; Joshi & Schabes, 1997; O’Donnell, 2015; Scha… (see more) et al., 1999) are useful to formalize and test theories of storage versus composition in language. In particular, we highlight how different learning frameworks for these grammars formalize different predictions about which preconstructed chunks will be stored holistically versus which sequences will be constructed compositionally. These frameworks thus provide a principled way of exploring the space of possibilities between full-decomposition theories (in which only the smallest possible units are stored) and full-storage theories (in which anything and everything is stored).

2023-05-24
PsyArXiv (preprint)
doi.org
Formalizing theories of storage versus computation with Tree-based Grammars
Emily Morgan
Rabia Ergin
Timothy J. O'Donnell

In this paper, we will explore why Tree-based Grammars (e.g. Johnson et al., 2007; Joshi & Schabes, 1997; O’Donnell, 2015; Scha… (see more) et al., 1999) are useful to formalize and test theories of storage versus composition in language. In particular, we highlight how different learning frameworks for these grammars formalize different predictions about which preconstructed chunks will be stored holistically versus which sequences will be constructed compositionally. These frameworks thus provide a principled way of exploring the space of possibilities between full-decomposition theories (in which only the smallest possible units are stored) and full-storage theories (in which anything and everything is stored).

2023-05-24
PsyArXiv (preprint)
doi.org
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Yoshua Bengio
Paul F. Christiano … (see 1 more)
Allan Dafoe
2023-05-23
ArXiv (preprint)
doi.org
arxiv.org
De novo motor learning creates structure in neural activity space that shapes adaptation
Joanna C. Chang
Matthew G Perich
Lee Miller
Juan A. Gallego
Claudia Clopath
2023-05-23
bioRxiv (preprint)
doi.org
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Setareh Dabiri
Vasileios Lioutas
Berend Zwartsenberg
Yunpeng Liu
Matthew Niedoba
Xiaoxuan Liang
Dylan Green
Justice Sefas
Jonathan Wilder Lavington
Frank N. Wood
Adam Ścibior
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to … (see more)the distribution of real data. We investigate specifically the impact of object placement distribution, keeping all other aspects of synthetic data fixed. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.
2023-05-23
ArXiv (preprint)
doi.org
arxiv.org
Fast D
<sub>M,M</sub> calculation in LDR brachytherapy using deep learning methods
Francisco Berumen
S. Enger
Luc Beaulieu
2023-05-22
Physics in Medicine & Biology (published)
doi.org
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning
Florian Bordes
Randall Balestriero
Quentin Garrido
Adrien Bardes
P Vincent
One unexpected technique that emerged in recent years consists in training a Deep Network (DN) with a Self-Supervised Learning (SSL) method,… (see more) and using this network on downstream tasks but with its last few projector layers entirely removed. This trick of throwing away the projector is actually critical for SSL methods to display competitive performances on ImageNet for which more than 30 percentage points can be gained that way. This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream. But it seems not to be, and this study sheds some light on why. This trick, which we name Guillotine Regularization (GR), is in fact a generically applicable method that has been used to improve generalization performance in transfer learning scenarios. In this work, we identify the underlying reasons behind its success and show that the optimal layer to use might change significantly depending on the training setup, the data or the downstream task. Lastly, we give some insights on how to reduce the need for a projector in SSL by aligning the pretext SSL task and the downstream task.
2023-05-22
TMLR (accepted)
openreview.net
openreview.net
Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming
Mostafa Elaraby
Guy Wolf
Margarida Carvalho
2023-05-22
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (published)
doi.org
arxiv.org
Hybrid GRAND Sphere Decoding: Accelerated GRAND for Low-Rate Codes
Huayi Zhou
Warren J. Gross
Guessing random additive noise decoding (GRAND) and sphere decoding (SD) are two algorithms that can achieve maximum likelihood decoding. In… (see more) this paper, a hybrid GRAND-SD (HGRAND) scheme is proposed to extend GRAND to low-rate codes. An accelerated GRAND decoder, assisted by a sphere decoder running in parallel and giving hints to it to allow skipping of certain candidates allows HGRAND to achieve a latency below the minimum latency of the individual component decoders while guaranteeing error-correction performance.
2023-05-20
International Symposium on Circuits and Systems (published)
doi.org
ToxBuster: In-game Chat Toxicity Buster with BERT
Zachary Yang
Yasmine Maricar
M. Davari
Nicolas Grenon-Godbout
Reihaneh Rabbany
Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumpti… (see more)on. We introduce ToxBuster, a simple and scalable model trained on a relatively large dataset of 194k lines of game chat from Rainbow Six Siege and For Honor, carefully annotated for different kinds of toxicity. Compared to the existing state-of-the-art, ToxBuster achieves 82.95% (+7) in precision and 83.56% (+57) in recall. This improvement is obtained by leveraging past chat history and metadata. We also study the implication towards real-time and post-game moderation as well as the model transferability from one game to another.
2023-05-20
ArXiv (preprint)
doi.org
arxiv.org
Training Acceleration of Frequency Domain CNNs Using Activation Compression
Seyyed Hasan Mozafari
James J. Clark
Warren J. Gross
Brett Meyer
Reducing the complexity of training convolutional neural networks results in lower energy consumption expended during training, or higher ac… (see more)curacy by admitting a greater number of training epochs within a training time budget. During backpropagation, a considerable amount of temporary data is offloaded from GPU memory to CPU memory, increasing training time. In this paper, we address this training time overhead by introducing an activation compression technique for frequency domain convolutional neural networks. Applying this compression technique on frequency domain AlexNet results in activation compression of 57.7%, and a reduction of training time by 23%, with a negligible effect on classification accuracy.
2023-05-20
International Symposium on Circuits and Systems (published)
doi.org