Title
Topic
-
Northeastern welcomes researchers from around the globe for Genome Interpretation Workshop
“After years of involvement with the Critical Assessment of Genome Interpretation (CAGI), and after co-chairing the group’s most recent conference in 2022, Predrag Radivojac, professor of computer science and associate dean of research, finally got his chance to welcome the group to Boston and to Northeastern. Beginning on Friday, September 29 and continuing through the weekend, nearly 100 computational genomics and biomedical researchers flocked to the top floor of Northeastern’s East Village to catch up on the latest research and evaluate the challenges that remain.”
-
‘The Apocalyptic Nature of Rivalry Violence and the Need for a New Research Agenda’
Claudio Lanza, assistant professor in international Relations and sociology at Northeastern University London, gave a talk on Tuesday, Nov. 7, 2023. The talk was hosted at the Brudnick Center for Violence and Conflict.
-
‘From 5G Sniffing to Harvesting Leakages of Privacy-Preserving Messengers’
“We present the first open-source tool capable of efficiently sniffing 5G control channels, 5GSniffer and demonstrate its potential to conduct attacks on users privacy. 5GSniffer builds on our analysis of the 5G RAN control channel exposing side-channel leakage. We note that decoding the 5G control channels is significantly more challenging than in LTE. … We devise a set of techniques to achieve real-time control channels sniffing (over three orders of magnitude faster than brute-forcing).” Find the paper and full list of authors at the 2023 IEEE Symposium on Security and Privacy.
-
‘An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit With Low Regret’
“Recently a multi-agent variant of the classical multi-armed bandit was proposed to tackle fairness issues in online learning. Inspired by a long line of work in social choice and economics, the goal is to optimize the Nash social welfare instead of the total utility. Unfortunately previous algorithms either are not efficient or achieve sub-optimal regret in terms of the number of rounds. We propose a new efficient algorithm with lower regret than even previous inefficient ones.” Find the paper and full list of authors in the Proceedings of the AAAI Conference on Artificial Intelligence.
-
‘8th International Workshop on Mental Health and Well-being: Sensing and Intervention’
“Mental health and well-being are critical components of overall health. … Detecting symptoms of mental illness early-on and delivering interventions to prevent and/or manage symptoms can improve health and well-being outcomes. Ubiquitous systems are increasingly playing a central role in uncovering clinically relevant contextual information on mental health. … The goal of this workshop is to bring together researchers, practitioners, and industry professionals interested in identifying, articulating, and addressing such issues and opportunities.” Find the paper and authors list in the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing…
-
‘Black-box Attacks Against Neural Binary Function Detection’
“Binary analyses based on deep neural networks (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in recent years. DNNs have been wildly successful at pushing the performance and accuracy envelopes in the natural language and image processing domains. … [However,] in this paper, we empirically demonstrate that the current state of the art in neural function boundary detection is vulnerable to both inadvertent and deliberate adversarial attacks.” Find the paper and full list of authors in the Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses.
-
‘StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks’
“Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. … Our approach extracts the compact obstacle distribution based on volumetric representations.” Find the paper and full list of authors in the 2023 IEEE International Conference on Robotics and Automation proceedings.
-
‘High-Throughput Microscopy Image Deblurring With Graph Reasoning Attention Network’
“High-quality (HQ) microscopy images afford more detailed information for modern life science research and quantitative image analyses. However, in practice, HQ microscopy images are not commonly available or suffer from blurring artifacts. Compared with natural images, such low-quality (LQ) microscopy ones often share some visual characteristics: more complex structures, less informative background, and repeating patterns. … To address those problems, we collect HQ electron microscopy and histology datasets and propose a graph reasoning attention network (GRAN).” Find the paper and full list of authors in the 2023 IEEE 20th International Symposium on Biomedical Imaging proceedings.
-
‘NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-Shot Real Image Animation’
“Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. Despite successful synthesis of fake identity images randomly sampled from latent space, adopting these models for generating face images of real subjects is still a challenging task due to its so-called inversion issue. In this paper, we propose a universal method to surgically finetune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image.” Find the paper and full list of authors in the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition proceedings.
-
‘Viper: A Fast Snapshot Isolation Checker’
“Snapshot isolation (SI) is supported by most commercial databases and is widely used by applications. However, checking SI today — given a set of transactions, checking if they obey SI — is either slow or gives up soundness. We present viper, an SI checker that is sound, complete and fast. Viper checks black-box databases and hence is transparent to both users and databases. To be fast, viper introduces BC-polygraphs, a new representation of transaction dependencies.” Find the paper and full list of authors in the Proceedings of the Eighteenth European Conference on Computer Systems.
-
‘Framing Frames: Bypassing Wi-Fi Encryption by Manipulating Transmit Queues’
“Wi-Fi devices routinely queue frames at various layers of the network stack before transmitting, for instance, when the receiver is in sleep mode. In this work, we investigate how Wi-Fi access points manage the security context of queued frames. By exploiting power-save features, we show how to trick access points into leaking frames in plaintext, or encrypted using the group or an all-zero key. We demonstrate resulting attacks against several open-source network stacks.” Find the paper and full list of authors in the 32nd USENIX Security Symposium proceedings.
-
‘La Independiente: Designing Ubiquitous Systems for Latin American and Caribbean Women Crowdworkers’
“Since 2018, Venezuelans have contributed to 75% of leading AI crowd work platforms’ total workforce. … Few initiatives have investigated the impact of such work in the Global South through the lens of feminist theory. … We surveyed 55 LAC women on the crowd work platform Toloka to understand their personal goals, professional values and hardships. … Most participants shared a desire to hear the experiences of other women crowdworkers.” Find the paper and full list of authors in the Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the International Symposium on Wearable…
-
‘SCORE: A Second-Order Conic Initialization for Range-Aided SLAM’
“We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem.” Find the paper and full list of authors in the IEEE International Conference on Robotics and Automation proceedings.
-
‘Towards Automated Pain Assessment Using Embodied Conversational Agents’
“Narrative accounts are the ultimate authoritative source for pain assessment, and face-to-face encounters provide a rich context in which nonverbal conversational behavior can be used to enrich the detail in these descriptions. Embodied Conversational Agents—animated characters that simulate face-to-face conversation—can provide a medium for automated pain assessment in which multimodal pain narratives are elicited, clarified and grounded. … We describe work towards a conversational agent that elicits various aspects of a pain experience, followed by an empathic summary.” Find the paper and full list of authors in the Companion Publication of the 25th International Conference on Multimodal Interaction.
-
‘Improving Multiparty Interactions With a Robot Using Large Language Models’
“Speaker diarization is a key component of systems that support multiparty interactions of co-located users, such as meeting facilitation robots. The goal is to identify who spoke what, often to provide feedback, moderate participation, and personalize responses by the robot. … We leverage large language models (LLMs) to identify speaker labels from transcribed text and observe an exact match of 77% and a word level accuracy of 90%.” Find the paper and full list of authors in the Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems.
-
‘Sublinear Time Algorithms and Complexity of Approximate Maximum Matching’
“Sublinear time algorithms for approximating maximum matching size have long been studied. Much of the progress over the last two decades on this problem has been on the algorithmic side. … A more recent algorithm by [Behnezhad, Roghani, Rubinstein, and Saberi; SODA’23] obtains a slightly-better-than-1/2 approximation in O(n1+є) time (for arbitrarily small constant ε>0). … Proving any super-linear in n lower bound, even for (1−є)-approximations, has remained elusive. … In this paper, we prove the first super-linear in n lower bound for this problem.” Find the paper and authors list in the Proceedings of the 55th Annual ACM Symposium on…
-
‘Persistent Memory Research in the Post-Optane Era’
“After over a decade of researcher anticipation for the arrival of persistent memory (PMem), the first shipments of 3D XPoint-based Intel Optane Memory in 2019 were quickly followed by its cancellation in 2022. Was this another case of an idea quickly fading from future to past tense, relegating work in this area to the graveyard of failed technologies? … Without persistent memory itself, is future PMem research doomed? We offer two arguments for why reports of the death of PMem research are greatly exaggerated.” Find the paper and authors list in the Proceedings of the 1st Workshop on Disruptive Memory…
-
‘The Digital-Safety Risks of Financial Technologies for Survivors of Intimate Partner Violence’
“Digital technologies play a growing role in exacerbating financial abuse for survivors of intimate partner violence (IPV). … Scant research has examined how consumer-facing financial technologies can facilitate or obstruct IPV-related attacks on a survivor’s financial well-being. … We simulated both close-range and remote attacks commonly used by IPV adversaries. We discover that mobile banking and peer-to-peer payment applications are generally ill-equipped to deal with user-interface bound (UI-bound) adversaries, permitting unauthorized access to logins, surreptitious surveillance and harassing messages and system prompts.” Find the paper and full list of authors in the 32nd USENIX Security Symposium proceedings.
-
‘Discovering Informative and Robust Positives for Video Domain Adaptation’
Unsupervised domain adaptation for video recognition is challenging where the domain shift includes both spatial variations and temporal dynamics. Previous works have focused on exploring contrastive learning for cross-domain alignment. However, limited variations in intra-domain positives, false cross-domain positives, and false negatives hinder contrastive learning from fulfilling intra-domain discrimination and cross-domain closeness. This paper presents a non-contrastive learning framework without relying on negative samples for unsupervised video domain adaptation.” Find the paper and full list of authors at ICLR 2023.
-
‘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.
-
‘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.
-
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.
-
‘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.
-
‘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.
-
‘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.
-
‘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.
-
‘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.