Publications

How Useful Are Educational Questions Generated by Large Language Models?
Sabina Elkins
Ekaterina Kochmar
Jackie CK Cheung
Iulian V. Serban
2022-12-31
AIED (Posters/Late Breaking Results/...) (publié)
doi.org
arxiv.org
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
Mohammad Tahaei
Marios Constantinides
Daniele Quercia
Sean Kennedy
Michael Muller
Simone Stumpf
Q. Vera Liao
Ricardo Baeza-Yates
Lora Aroyo
Jess Holbrook
Ewa Luger
Michael Madaio
Ilana Golbin Blumenfeld
Maria De-Arteaga
Jessica Vitak
A.R. Olteanu
2022-12-31
CHI Extended Abstracts (publié)
doi.org
arxiv.org
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
Eric Nguyen
Michael Poli
Marjan Faizi
Armin W Thomas
Callum Birch-Sykes
Michael Wornow
Aman Patel
Clayton M. Rabideau
Stefano Massaroli
Yoshua Bengio
Stefano Ermon
Stephen Baccus
Christopher Re
2022-12-31
Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (publié)
doi.org
openreview.net
Identification of Substitutable Context-Free Languages over Infinite Alphabets from Positive Data
Yutaro Numaya
Diptarama Hendrian
Ryo Yoshinaka
Ayumi Shinohara
François Coste
Faissal Ouardi
Guillaume Rabusseau
This paper is concerned with the identification in the limit from positive data of sub-stitutable context-free languages cfl s) over infinit… (voir plus)e alphabets. Clark and Eyraud (2007) showed that substitutable cfl s over finite alphabets are learnable in this learning paradigm. We show that substitutable cfl s generated by grammars whose production rules may have predicates that represent sets of potentially infinitely many terminal symbols in a compact manner are learnable if the terminal symbol sets represented by those predicates are learnable, under a certain condition. This can be seen as a result parallel to Argyros and D’Antoni’s work (2018) that amplifies the query learnability of predicate classes to that of symbolic automata classes. Our result is the first that shows such amplification is possible for identifying some cfl s in the limit from positive data.
2022-12-31
ICGI (publié)
dblp.uni-trier.de
Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies
Hosna Hooshyar
Eduardo Guerra
Jorge Melegati
Dron Khanna
Abdullah Aldaeej
Gerardo Matturro
Luciana Zaina
Des Greer
Usman Rafiq
Rafael Chanin
Xiaofeng Wang
Juan Garbajosa
Pekka Abrahamsson
Foutse Khomh
Anh Nguyen-Duc
The restrictions imposed by the COVID-19 pandemic required software development teams to adapt, being forced to work remotely and adjust the… (voir plus) software engineering activities accordingly. In the studies evaluating these effects, a few have assessed the impact on software engineering activities from a broader perspective and after a period of time when teams had time to adjust to the changes. No studies have been found comparing software startups and established companies either. This paper aims to investigate the impacts of COVID-19 on software development activities after one year of the pandemic restrictions, comparing the results between startups and established companies. Our approach was to design a cross-sectional survey and distribute it online among software development companies worldwide. The participants were asked about their perception of COVID-19’s pandemic impact on different software engineering activities: requirements engineering, software architecture, user experience design, software implementation, and software quality assurance. The survey received 170 valid answers from 29 countries, and for all the software engineering activities, we found that most respondents did not observe a significant impact. The results also showed that software startups and established companies were affected differently since, in some activities, we found a negative impact in the former and a positive impact in the latter. Regarding the time spent on each software engineering activity, most of the answers reported no change, but on those that did, the result points to an increase in time. Thus, we cannot find any relation between the change in time of effort and the reported positive or negative impact.
2022-12-31
IEEE Access (publié)
doi.org
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar
Sumner Magruder
Edward De Brouwer
Matheo Morales
Aarthi Venkat
Frederik Wenkel
Guy Wolf
Smita Krishnaswamy
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these … (voir plus)dynamic relationships is crucial for understanding and predicting system behavior. In this paper, we propose Regulatory Temporal Interaction Network Inference (RiTINI) for inferring time-varying interaction graphs in complex systems using a novel combination of space-and-time graph attentions and graph neural ordinary differential equations (ODEs). RiTINI leverages time-lapse signals on a graph prior, as well as perturbations of signals at various nodes in order to effectively capture the dynamics of the underlying system. This approach is distinct from traditional causal inference networks, which are limited to inferring acyclic and static graphs. In contrast, RiTINI can infer cyclic, directed, and time-varying graphs, providing a more comprehensive and accurate representation of complex systems. The graph attention mechanism in RiTINI allows the model to adaptively focus on the most relevant interactions in time and space, while the graph neural ODEs enable continuous-time modeling of the system's dynamics. We evaluate RiTINI's performance on various simulated and real-world datasets, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.
2022-12-31
LoG (publié)
doi.org
proceedings.mlr.press
Inferring multiple consensus trees and supertrees using clustering: a review
Vladimir Makarenkov
Gayane S. Barseghyan
Nadia Tahiri
2022-12-31
ArXiv (prépublication)
doi.org
arxiv.org
An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems
Ahmed Haj Yahmed
Rached Bouchoucha
Houssem Ben Braiek
Foutse Khomh
Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven sys… (voir plus)tems, DRL systems can exhibit undesirable behaviors due to environmental drifts, which often occur in constantly-changing production settings. Continual Learning (CL) is the inherent self-healing approach for adapting the DRL agent in response to the environment's conditions shifts. However, successive shifts of considerable magnitude may cause the production environment to drift from its original state. Recent studies have shown that these environmental drifts tend to drive CL into long, or even unsuccessful, healing cycles, which arise from inefficiencies such as catastrophic forgetting, warm-starting failure, and slow convergence. In this paper, we propose Dr. DRL, an effective self-healing approach for DRL systems that integrates a novel mechanism of intentional forgetting into vanilla CL (i.e., standard CL) to overcome its main issues. Dr. DRL deliberately erases the DRL system's minor behaviors to systematically prioritize the adaptation of the key problem-solving skills. Using well-established DRL algorithms, Dr. DRL is compared with vanilla CL on various drifted environments. Dr. DRL is able to reduce, on average, the healing time and fine-tuning episodes by, respectively, 18.74% and 17.72%. Dr. DRL successfully helps agents to adapt to 19.63% of drifted environments left unsolved by vanilla CL while maintaining and even enhancing by up to 45% the obtained rewards for drifted environments that are resolved by both approaches.
2022-12-31
2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (publié)
doi.org
arxiv.org
Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Yihui Li
David Earl Hostallero
Amin Emad
Motivation: Recent advances in deep learning model development have enabled more accurate prediction of drug response in cancer. However, th… (voir plus)e black-box nature of these models still remains a hurdle in their adoption for precision cancer medicine. Recent efforts have focused on making these models interpretable by incorporating signaling pathway information in model architecture. While these models improve interpretability, it is unclear whether this higher interpretability comes at the cost of less accurate predictions, or a prediction improvement can also be obtained. Results: In this study, we comprehensively and systematically assessed four state-of-the-art interpretable models developed for drug response prediction to answer this question using three pathway collections. Our results showed that models that explicitly incorporate pathway information in the form of a latent layer perform worse compared to models that incorporate this information implicitly. Moreover, in most evaluation setups the best performance is achieved using a simple black-box model. In addition, replacing the signaling pathways with randomly generated pathways shows a comparable performance for the majority of these interpretable models. Our results suggest that new interpretable models are necessary to improve the drug response prediction performance. In addition, the current study provides different baseline models and evaluation setups necessary for such new models to demonstrate their superior prediction performance. Availability and Implementation: Implementation of all methods are provided inhttps://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP. Generated uniform datasets are inhttps://zenodo.org/record/7101665#.YzS79HbMKUk. Contact:amin.emad@mcgill.caSupplementary Information: Online-only supplementary data is available at the journal’s website.
2022-12-31
Bioinform. (publié)
doi.org
Invasion of Ukraine Discourse on TikTok Dataset
Benjamin D. Steel
Sara J. Parker
Derek Ruths
We present a dataset of videos and comments from the social media platform TikTok, centred around the invasion of Ukraine in 2022, an event … (voir plus)that launched TikTok into the geopolitical arena. The discourse around the invasion exposed myriad political behaviours and dynamics that are unexplored on this platform. To this end we provide a mass scale language and interaction dataset for further research into these processes. An initial investigation of language and social interaction dynamics are explored in this paper. The dataset and the library used to collect it are open sourced to the public.
2022-12-31
arXiv.org (prépublication)
doi.org
Invited commentary on Stoehr J et al: The personal impact of involvement in international global health outreach: A national survey of former operation smile student volunteers.
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org
Iorl: Inductive-Offline-Reinforcement-Learning for Traffic Signal Control Warmstarting
François-Xavier Devailly
Denis Larocque
Laurent Charlin
2022-12-31
Social Science Research Network (publié)
doi.org