Title
Topic
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‘HammerDodger: A Lightweight Defense Framework Against RowHammer Attack on DNNs’
“RowHammer attacks have become a serious security problem on deep neural networks (DNNs). Some carefully induced bit-flips degrade the prediction accuracy of DNN models to random guesses. This work proposes a lightweight defense framework that detects and mitigates adversarial bit-flip attacks. We employ a dynamic channel-shuffling obfuscation scheme to present moving targets to the attack, and develop a logits-based model integrity monitor with negligible performance loss.” Find the paper and full list of authors in the 2023 60th ACM/IEEE Design Automation Conference.
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‘ICML 2023 Topological Deep Learning Challenge : Design and Results’
“This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings.” Find the paper and full list of authors at ArXiv.
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Panel discussion for CSCW ’23: ‘Getting Data for CSCW Research’
“This panel will bring together a group of scholars from diverse methodological backgrounds to discuss critical aspects of data collection for CSCW research. This discussion will consider the rapidly evolving ethical, practical, and data access challenges, examine the solutions our community is currently deploying and envision how to ensure vibrant CSCW research going forward.” Find the full list of panelists in the Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing.
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‘An Example of (Too Much) Hyper-Parameter Tuning in Suicide Ideation Detection’
“This work starts with the TWISCO baseline, a benchmark of suicide-related content from Twitter. We find that hyper-parameter tuning can improve this baseline by 9%. We examined 576 combinations of hyper-parameters: learning rate, batch size, epochs and date range of training data. Reasonable settings of learning rate and batch size produce better results than poor settings.” Find the paper and full list of authors in the Proceedings of the International AAAI Conference on Web and Social Media.
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‘Predicting GPU Failures With High Precision Under Deep Learning Workloads’
“Graphics processing units (GPUs) are the de facto standard for processing deep learning (DL) tasks. In large-scale GPU clusters, GPU failures are inevitable and may cause severe consequences. For example, GPU failures disrupt distributed training, crash inference services, and result in service level agreement violations. In this paper, we study the problem of predicting GPU failures using machine learning (ML) models to mitigate their damages.” Find the paper and full list of authors in the Proceedings of the 16th ACM International Conference on Systems and Storage.
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‘NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers’
“Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to optimize deep neural network (DNN) models to meet performance, resource utilization and other requirements. Bugs in these compilers can result in models whose semantics differ from the original ones, producing incorrect results that corrupt the correctness of downstream applications. … In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers.” Find the paper and full list of authors at in the Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems.
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‘Semantic Encapsulation Using Linking Types’
“Interoperability pervades nearly all mainstream language implementations, as most systems leverage subcomponents written in different languages. And yet, such linking can expose a language to foreign behaviors that are internally inexpressible, which poses a serious threat to safety invariants and programmer reasoning. … In this paper, we outline an approach that encapsulates foreign code in a sound way — i.e., without disturbing the invariants promised by types of the core language.” Find the paper and full list of authors in the Proceedings of the 8th ACM SIGPLAN International Workshop on Type-Driven Development.
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‘A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops’
“As neural networks become more integrated into the systems that we depend on for transportation, medicine and security, it becomes increasingly important that we develop methods to analyze their behavior to ensure that they are safe to use within these contexts. The methods used in this paper seek to certify safety for closed-loop systems with neural network controllers, i.e., neural feedback loops, using backward reachability analysis.” Find the paper and full list of authors in the American Control Conference proceedings.
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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.”