Title
Topic
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‘RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation’
“A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan in. … The RAMP pipeline proposed here solves these issues using new mapping and planning methods.” Find the paper and full list of authors at the IEEE International Conference on Robotics and Automation.
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‘Re-trainable Procedural Level Generation via Machine Learning (RT-PLGML) as Game Mechanic’
“We present re-trainable procedural level generation via machine learning (RT-PLGML), a game mechanic of providing in-game training examples for a PLGML system. We discuss opportunities and challenges, along with concept RT-PLGML games.” Find the paper and full list of authors at Proceedings of the 18th International Conference on the Foundations of Digital Games
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‘Solder: Retrofitting Legacy Code with Cross-Language Patches’
“Internet-of-things devices are widely deployed, and suffer from easy-to-exploit security issues. … Because patch deployments tend to be focused on server-side vulnerabilities, client software in large codebases such as Apache may remain largely unpatched, and hence, vulnerable. … In this paper, we address this issue of leaving latent vulnerabilities in legacy codebases. We propose Solder, a framework to patch or retrofit legacy C/C++ code by replacing any target function with a newly-implemented one in a safe language such as Rust.” Find the paper and full list of authors in the International Conference on Software Analysis, Evolution and Reengineering proceedings.
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‘On Regularity Lemma and Barriers in Streaming and Dynamic Matching’
“We present a new approach for finding matchings in dense graphs by building on Szemerédi’s celebrated Regularity Lemma. This allows us to obtain non-trivial albeit slight improvements over longstanding bounds for matchings in streaming and dynamic graphs.” Find the paper and full list of authors in the Proceedings of the 55th Annual ACM Symposium on Theory of Computing.
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‘SEIL: Simulation-Augmented Equivariant Imitation Learning’
“In robotic manipulation … traditional image-level data augmentation has shown the potential to improve sample efficiency in various machine learning tasks. However, image-level data augmentation is insufficient for an imitation learning agent to learn good manipulation policies in a reasonable amount of demonstrations. We propose Simulation-augmented Equivariant Imitation Learning (SEIL), a method that combines a novel data augmentation strategy of supplementing expert trajectories with simulated transitions and an equivariant model that exploits the O(2) symmetry in robotic manipulation.” Find the paper and full list of authors at the IEEE International Conference on Robotics and Automation proceedings.
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‘SNAP: Efficient Extraction of Private Properties with Poisoning’
“Property inference attacks allow an adversary to extract global properties of the training dataset from a machine learning model. … Several existing approaches for property inference attacks against deep neural networks have been proposed, but they all rely on the attacker training a large number of shadow models. … We consider the setting of property inference attacks in which the attacker can poison a subset of the training dataset and query the trained target model.” Find the paper and full list of authors at the IEEE Symposium on Security and Privacy proceedings.
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‘Layout Representation Learning With Spatial and Structural Hierarchies’
“We present a novel hierarchical modeling method for layout representation learning, the core of design documents (e.g., user interface, poster, template). Existing works on layout representation often ignore element hierarchies, which is an important facet of layouts, and mainly rely on the spatial bounding boxes for feature extraction. This paper proposes a Spatial-Structural Hierarchical Auto-Encoder (SSH-AE) that learns hierarchical representation by treating a hierarchically annotated layout as a tree format.” Find the paper and full list of authors at the Proceedings of the AAAI Conference on Artificial Intelligence.
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‘Improving Cross-Domain Detection with Self-Supervised Learning’
“Cross-Domain Detection (XDD) aims to train a domain-adaptive object detector using unlabeled images from a target domain and labeled images from a source domain. Existing approaches achieve this either by transferring the style of source images to that of target images, or by aligning the features of images from the two domains. In this paper, rather than proposing another method following the existing lines, we introduce a new framework complementary to existing methods.” Find the paper and full list of authors in the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
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‘Trainability Preserving Neural Pruning’
“Many recent works have shown trainability plays a central role in neural network pruning — unattended broken trainability can lead to severe under-performance and unintentionally amplify the effect of retraining learning rate, resulting in biased (or even misinterpreted) benchmark results. This paper introduces trainability preserving pruning (TPP), a scalable method to preserve network trainability against pruning, aiming for improved pruning performance and being more robust to retraining hyper-parameters (e.g., learning rate).” Find the paper and full list of authors at Open Review. Published at ICLR 2023.
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‘Sharing Speaker Heart Rate With the Audience Elicits Empathy and Increases Persuasion’
“Persuasion is a primary goal of public speaking, and eliciting audience empathy increases persuasion. In this research, we explore sharing a speaker’s heart rate as a social cue, to elicit empathy and increase persuasion in the audience. In particular, we developed two interfaces embedding the speaker’s heart rate over a recorded presentation video. … We observed that heart rate sharing significantly increased persuasion for participants with normal baseline empathy levels and increased empathic accuracy for all participants.” Find the paper and full list of authors in the journal of the International Conference on Persuasive Technology.
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‘Sturgeon-GRAPH: Constrained Graph Generation From Examples’
“Procedural level generation techniques that learn local neighborhoods from example levels (such as WaveFunctionCollapse) have risen in popularity. Usually the neighborhood structure (such as a regular grid) onto which a level is generated is fixed in advance and not generated. In this work, we present a constraint-based approach for graph generation that learns local neighborhood patterns (in the form of labeled nodes and edges) from example graphs. This allows the approach to generate graphs with varying structures that are still locally similar to the examples.”
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Dahiya speaks at AI for Good Global Summit
“Electrical and computer engineering professor Ravinder Dahiya was selected as a speaker for the AI for Good Global Summit: Accelerating the United Nations Sustainable Development Goals, which was held in Geneva, Switzerland, July 6-7, 2023. The AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure and other global development priorities.”
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Watch along with the Intellectual Property Awareness Summit
The Intellectual Property Awareness Summit, which “is a gathering of IP owners, creators, educators, lawyers, organizations and investors” took place on May 2nd “in conjunction with Northeastern University’s Center for Research Innovation.” It brought together individuals who shared “a common goal – to explore ways to make the benefits of IP rights, and the issues surrounding them, more apparent to people and society.” You can watch recordings of the summit’s panels and keynote address at YouTube.
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‘Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach’
“We develop a network of Bayesian agents that collectively model the mental states of teammates from the observed communication. Using a generative computational approach to cognition, we make two contributions. First, we show that our agent could generate interventions that improve the collective intelligence of a human-AI team beyond what humans alone would achieve. Second, we develop a real-time measure of human’s theory of mind ability and test theories about human cognition.” Find the paper and full list of authors in the Proceedings of the AAAI Conference on Artificial Intelligence.
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‘A Study of Multi-Factor and Risk-Based Authentication Availability’
“Password-based authentication (PBA) remains the most popular form of user authentication on the web despite its long-understood insecurity. Given the deficiencies of PBA, many online services support multi-factor authentication (MFA) and/or risk-based authentication (RBA) to better secure user accounts. … In this paper, we present a study of 208 popular sites in the Tranco top 5K that support account creation to understand the availability of MFA and RBA on the web … and how logging into sites through more secure SSO providers changes the landscape of user authentication security.” Find the paper and full list of authors at USENIX Security…
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‘Wrapped in Story: The Affordances of Narrative for Citizen Science Games’
“Citizen science games enable public participation in scientific research, yet these games often struggle to engage wide audiences. As a potential solution, some game developers look to narrative as an experience-enhancing feature. … We investigated the effects of wrapping a story around the tutorial puzzles of the citizen science game Foldit. We found that the narrative increased the time players spent engaging with the game’s tutorial and its scientific puzzles but did not substantially affect their progress through the tutorial.” Find the paper and full list of authors in the 18th International Conference on the Foundations of Digital Games proceedings.
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At the world’s largest conference of management scholars, Northeastern pulls out all the stops
Northeastern University faculty members presented research, won awards and hosted a reception for some of the 8,000 attendees who visited Boston for the 2023 Academy of Management Conference.
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Academy of Management 2023 Publication Awards
Northeastern faculty and post-docs were the recipients of numerous awards at the 2023 Academy of Management Conference.
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Department of Civil and Environmental Engineerign hosts 2023 FUNWAVE Workshop
“The Department of Civil and Environmental Engineering at Northeastern, alongside partners from The Center for Applied Coastal Research, University of Delaware and the US Army Engineer and Development Center hosted the fifth FUNWAVE-TVD Training Workshop.” As an open-source modeling program, FUNWAVE meant the workshop could cover “a variety of topics, ranging from wave theory to numerical modeling to coastal engineering applications, and included hands-on trainings and seminars on modeling development and case studies.” During the conference, “professor Qin Jim Chen gave a seminar on predicting hazardous rip currents using FUNWAVE-TVD.”
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Maheswaran speaks at ATINER2023 Round Table on ‘The Future of Science and Engineering Education’
Teaching professor in electrical and computer engineering, Bala Maheswaran presented “at the ATINER2023 Round Table Discussion on ‘The Future of Science and Engineering Education.’ This event took place on July 17-18 at the Athens Institute for Education and Research (ATINER) in Athens, Greece. During the roundtable discussion, Maheswaran spoke on the topic of ‘Sustainability in Engineering Education’ and shared the stage with presenters from various countries. The event fostered a diverse and dynamic exchange of ideas, shaping the future trajectory of science and engineering education.”
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‘Deep Bayesian Active Learning for Accelerating Stochastic Simulation’
“Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and with active learning approaches is an underexplored area. We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. INP consists of two components, a spatiotemporal surrogate model built upon Neural Process (NP) family and an acquisition function for active learning.” Find the paper and full list of authors in the SIGKDD Conference on Knowledge Discovery and Data Mining proceedings.
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‘Leveraging Structure for Improved Classification of Grouped Biased Data’
“We consider semi-supervised binary classification for applications in which data points are naturally grouped … and the labeled data is biased. … The groups overlap in the feature space and consequently the input-output patterns are related across the groups. To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups. … We demonstrate that under this assumption, the group carries additional information about the class, over the group-agnostic features, with provably improved area under the ROC curve.” Find the paper and full list of authors in the AAAI Conference on Artificial Intelligence proceedings.
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‘Accelerating Neural MCTS Algorithms Using Neural Sub-Net Structures’
“Neural MCTS algorithms are a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS) and have successfully trained Reinforcement Learning agents in a tabula-rasa way. … However, these algorithms … take a long time to converge, which requires high computational power and electrical energy. It also becomes difficult for researchers without cutting-edge hardware to pursue Neural MCTS research. We propose Step-MCTS, a novel algorithm that uses subnet structures, each of which simulates a tree that provides a lookahead for exploration.” Find the paper and full list of authors in the International Conference on Autonomous Agents and Multiagent Systems…
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‘OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization’
“For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. … We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools.” Find the paper and full list of authors in the Conference on Human Factors in Computing Systems proceedings.
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Understanding human decision-making during supply chains shortages
“Research conducted by mechanical and industrial engineering associate professor Jacqueline Griffin, professor Ozlem Ergun, and professor Stacy Marsella [in the Khoury College of Computer science, titled] ‘Agent-Based Modeling of Human Decision-Makers Under Uncertain Information During Supply Chain Shortages’ was published in the proceedings from the 2023 International Conference on Autonomous Agents and Multiagent Systems.”
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Minkara serves as moderator for intersection of disability panel
“Bioengineering assistant professor Mona Minkara served as a moderator for the ‘Intersection of Disability Panel’ at the Disrupting Ableism and Advancing STEM: A National Leadership Summit held on June 5, 2023.” Click on “Learn More” to watch the video.
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‘On Regularity Lemma and Barriers in Streaming and Dynamic Matching’
“We present a new approach for finding matchings in dense graphs by building on Szemerédi’s celebrated Regularity Lemma. This allows us to obtain non-trivial albeit slight improvements over longstanding bounds for matchings in streaming and dynamic graphs.” Find the paper and full list of authors in the Proceedings of the 55th Annual ACM Symposium on Theory of Computing.
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‘Speak Much, Remember Little: Cryptography in the Bounded Storage Model, Revisited’
“The goal of the bounded storage model (BSM) is to construct unconditionally secure cryptographic protocols, by only restricting the storage capacity of the adversary, but otherwise giving it unbounded computational power. Here, we consider a streaming variant of the BSM, where honest parties can stream huge amounts of data to each other so as to overwhelm the adversary’s storage, even while their own storage capacity is significantly smaller than that of the adversary.” Find the paper and full list of authors at Advances in Cryptology—EUROCRYPT 2023.
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Ramdin presents research on intergenerational families for improved health outcomes at ABNF
Valeria Ramdin, associate clinical professor and director of global health nursing, presented a talk on “Creating Intergenerational Family PODs for Improved Health Outcomes: Lessons Learned” at the 35th Annual Meeting & Scientific Conference for the Association of Black Nursing Faculty.
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Dewan discusses rising importance of nurse anesthetists at ICN
In a panel discussion at the International Council of Nurses 2023 conference, assistant clinical professor discussed the “importance in global healthcare” of nurse anesthetists. According to the panel description, “It is our hope that through the development of these guidelines, some of the barriers and walls that have hindered Nurse Anesthetists can be broken down. … The session will feature insights into the current challenges faced by nurse anesthetists, the innovative strategies being used to overcome these obstacles, and the transformative policies being adopted globally to support their role.”