Title
Topic
-
‘BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models’
“Smart home IoT platforms are typically closed systems, meaning that there is poor visibility into device behavior. Understanding device behavior is important not only for determining whether devices are functioning as expected, but also can reveal implications for privacy, [security and safety]. … In this work, we demonstrate that the vast majority of IoT behavior can indeed be modeled, using a novel multi-dimensional approach that relies only on the (often encrypted) network traffic exchanged by IoT devices.” Find the paper and full list of authors in the Proceedings of the 2023 ACM on Internet Measurement Conference.
-
‘Detection of Sexism on Social Media With Multiple Simple Transformers’
“Social media platforms have become virtual communication channels, allowing users to voice their thoughts and opinions. However, this openness and features of anonymity have also given rise to the proliferation of harmful and offensive content, including sexism. This research aims at proposing a methodology and explores the use of different simple transformers. …The proposed approach has great scope for the efficient detection of sexist content on social media, aiding in the development of effective content moderation systems.” Find the paper and full list of authors at the CEUR Workshop Proceedings.
-
‘Localizing Traffic Differentiation’
“Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. … In prior work, WeHe detects differentiation via end-to-end throughput measurements between a client and server but does not isolate the network responsible for it. Differentiation can occur anywhere on the network path between endpoints. … We present a system, WeHeY, built atop WeHe, that can localize traffic differentiation, i.e., obtain concrete evidence that the differentiation happened within the client’s ISP.” Find the paper and full list of authors in the 2023 ACM on Internet Measurement Conference proceedings.
-
‘Decoupled Fitness Criteria for Reactive Systems’
“The correctness problem for reactive systems has been thoroughly explored and is well understood. Meanwhile, the efficiency problem for reactive systems has not received the same attention. Indeed, one correct system may be less fit than another correct system and determining this manually is challenging and often done ad hoc. We propose a novel and general framework which automatically assigns comparable fitness scores to reactive systems using interpretable parameters that are decoupled from the system being evaluated.” Find the paper and full list of authors in Software Engineering and Formal Methods.
-
‘Pyramid Attention Network for Image Restoration’
“Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales. However, recent advanced deep convolutional neural network-based methods for image restoration do not take full advantage of self-similarities by relying on self-attention neural modules that only process information at the same scale. To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid.” Find the paper and full list of authors at the International Journal of Computer Vision.
-
‘Layout Sequence Prediction From Noisy Mobile Modality’
“Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. Nevertheless, real-world situations often involve obstructed cameras, missed objects or objects out of sight due to environmental factors, leading to incomplete or noisy trajectories. To overcome these limitations, we propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories.” Find the paper and full list of authors at ArXiv.
-
‘When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations’
“We study the fairness of dimensionality reduction methods for recommendations. We focus on the established method of principal component analysis (PCA), which identifies latent components and produces a low-rank approximation via the leading components while discarding the trailing components. Prior works have defined notions of “fair PCA”; however, these definitions do not answer the following question: what makes PCA unfair? We identify two underlying mechanisms of PCA that induce unfairness at the item level.” Find the paper and full list of authors at ArXiv.
-
‘An Energy-Efficient Neural Network Accelerator With Improved Protections Against Fault-Attacks’
“Embedded neural network (NN) implementations are susceptible to misclassification under fault attacks. Injecting strong electromagnetic (EM) pulses is a non-invasive yet detrimental attack that affects the NN operations by (i) causing faults in the NN model/inputs while being read by the NN computation unit and (ii) corrupting NN computations results to cause misclassification eventually. This paper presents the first ASIC demonstration of an energy-efficient NN accelerator with inbuilt fault attack detection.” Find the paper and full list of authors in the European Conference on Solid-State Circuits proceedings.
-
‘CAFA-Evaluator: A Python Tool for Benchmarking Ontological Classification Methods’
“We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. … Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software.” Find the paper and full list of authors at ArXiv.
-
‘FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning’
Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. … Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. … In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL.” Find the paper and full list of authors at ACM Transactions on Internet of Things.
-
‘A Unified Dynamic Model for the Decomposition of Skin Conductance and the Inference of Sudomotor Nerve Activities’
“In behavioral health informatics, inferring an individual’s psychological state from physiological and behavioral data is fundamental. A key physiological signal in this endeavor is electrodermal activity (EDA), often quantified as skin conductance (SC), known for its sensitivity to a variety of psychological stimuli. Traditional methods to analyze skin conductance, such as the trough-to-peak method, often result in imprecise estimations due to overlapping skin conductance responses. … This paper introduces a novel fourth order dynamic system to model the temporal dynamics of skin conductance, unifying both the tonic level and phasic response.” Find the paper and full list of authors at…
-
‘KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting’
“Knitting machines can fabricate complex fabric structures using robust industrial fabrication machines. However, machine knitting’s full capabilities are only available through low-level programming languages that operate on individual machine operations. We present KnitScript, a domain-specific machine knitting scripting language that supports computationally driven knitting designs. KnitScript provides a comprehensive virtual model of knitting machines, giving access to machine-level capabilities as they are needed while automating a variety of tedious and error-prone details.” Find the paper and full list of authors in the Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology.
-
‘Demonstration of Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AI’
“With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. … We introduce Style2Fab, a system for automatically segmenting 3D models into functional and aesthetic elements, and selectively modifying the aesthetic segments, without affecting the functional segments.” Find the paper and full list of authors in the Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology.
-
‘A Feasibility Study on the Use of Audio-Based Ecological Momentary Assessment With Persons With Aphasia’
“We describe a smartphone/smartwatch system to evaluate anomia in individuals with aphasia by using audio-recording-based ecological momentary assessments. The system delivers object-naming assessments to a participant’s smartwatch, whereby a prompt signals the availability of images of these objects on the watch screen. Participants attempt to speak the names of the images that appear on the watch display out loud and into the watch as they go about their lives.” Find the paper and full list of authors in the Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility.
-
‘Memory Efficient Multithreaded Incremental Segmented Sieve Algorithm’
“Prime numbers are fundamental in number theory and play a significant role in various areas, from pure mathematics to practical applications, including cryptography. In this contribution, we introduce a multithreaded implementation of the Segmented Sieve algorithm. In our implementation, instead of handling large prime ranges in one iteration, the sieving process is broken down incrementally, which theoretically eliminates the challenges of working with large numbers and can reduce memory usage, providing overall more efficient multi-core utilization over extended computations.” Find the paper and full list of authors at ArXiv.
-
‘OmniControl: Control Any Joint at Any Time for Human Motion Generation’
“We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process. Unlike previous methods that can only control the pelvis trajectory, OmniControl can incorporate flexible spatial control signals over different joints at different times with only one model. Specifically, we propose analytic spatial guidance that ensures the generated motion can tightly conform to the input control signals. At the same time, realism guidance is introduced to refine all the joints to generate more coherent motion.” Find the paper and full list of authors at ArXiv.
-
Ergun elected INFORMS fellow
“Ozlem Ergun, College of Engineering distinguished professor in mechanical and industrial engineering, was elected a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS) for her applications of O.R. methods to humanitarian and health systems, emergency response and transportation and logistics problems and establishing a community of O.R. professionals with interest in public programs.”
-
‘A Time-Dependent Momentum-Resolved Scattering Approach to Core-Level Spectroscopies’
While new light sources allow for unprecedented resolution in experiments with X-rays, a theoretical understanding of the scattering cross-section is lacking. … This requires a knowledge of a full set of eigenstates in order to account for all intermediate processes away from equilibrium. … In this work, we present an alternative paradigm, recasting the problem in the time domain and explicitly solving the time-dependent Schrödinger equation without the limitations of perturbation theory: a faithful simulation of the scattering processes taking place in actual experiments, including photons and core electrons.” Find the paper and full list of authors at SciPost Physics.
-
‘Contrasting Ocean — Atmosphere Dynamics Mediate Flood Hazard Across the Mississippi River Basin’
“The Mississippi River basin drains nearly one-half of the contiguous United States. … Here, we use climate reanalysis and river gauge data to document the evolution of floods on the Missouri and Ohio Rivers—the two largest tributaries of the Mississippi River—and how they are influenced by major modes of climate variability centered in the Pacific and Atlantic Oceans. We show that the largest floods on these tributaries are preceded by the advection and convergence of moisture from the Gulf of Mexico following distinct atmospheric mechanisms.” Find the paper and full list of authors at Earth Interactions.
-
‘Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis’
“We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences. … This paper constructs a large-scale, unified dataset of traffic accident records from official reports of various states in the US, totaling 9 million records, accompanied by road networks and traffic volume reports. Using this new dataset, we evaluate existing deep-learning methods for predicting the occurrence of accidents on road networks.” Find the paper and full list of authors at ArXiv.
-
‘E4UnityIntegration-MIT: An Open-Source Unity Plug-in for Collecting Physiological Data Using Empatica E4 During Gameplay’
“Physiological measurement of player experience during gameplay has been of increasing interest within game research circles. A commonly-used non-invasive wearable device for physiological measurement is the Empatica E4 wristband. … [However,] the E4’s integration with popular game engines such as Unity 3D presents certain challenges due to non-obvious critical bugs in the library and limited documentation applicability within the Unity context. … We present an open-source Unity plug-in designed to mitigate the challenges associated with integrating the E4 into Unity projects.” Find the paper and authors list at the 36th Annual ACM Symposium on User Interface Software and Technology adjunct…
-
‘Vision and Language Navigation in the Real World via Online Visual Language Mapping’
“Navigating in unseen environments is crucial for mobile robots. Enhancing them with the ability to follow instructions in natural language will further improve navigation efficiency in unseen cases. However, state-of-the-art vision-and-language navigation (VLN) methods are mainly evaluated in simulation, neglecting the complex and noisy real world. … In this work, we propose a novel navigation framework to address the VLN task in the real world. … The proposed framework includes four key components: (1) an LLMs-based instruction parser … (2) an online visual-language mapper … (3) a language indexing-based localizer … and (4) a DD-PPO-based local controller.” Find the paper…
-
‘”Mango Mango, How To Let the Lettuce Dry Without a Spinner?”: … An LLM-Based … Cooking Partner’
“The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. … In this research, we chose cooking, a complex daily task, as a scenario to investigate people’s successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system’s ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task.” Find the paper and full list of authors at ArXiv.
-
‘Modeling Dynamics over Meshes With Gauge Equivariant Nonlinear Message Passing’
“Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partial differential equations (PDEs) over manifolds depend critically on the underlying geometry. While graph neural networks have been successfully applied to PDEs, they do not incorporate surface geometry and do not consider local gauge symmetries of the manifold. … To address these issues, we introduce a new gauge equivariant architecture using nonlinear message passing.” Find the paper and full list of authors at ArXiv.
-
‘Will the Prince Get True Love’s Kiss? On the Model Sensitivity to Gender Perturbation Over Fairytale Texts’
“Recent studies show that traditional fairytales are rife with harmful gender biases. To help mitigate these gender biases in fairytales, this work aims to assess learned biases of language models by evaluating their robustness against gender perturbations. Specifically, we focus on Question Answering (QA) tasks in fairytales. Using counterfactual data augmentation to the FairytaleQA dataset, we evaluate model robustness against swapped gender character information, and then mitigate learned biases by introducing counterfactual gender stereotypes during training time.” Find the paper and full list of authors at ArXiv.
-
‘Sample Complexity of Opinion Formation on Networks’
“Consider public health officials aiming to spread awareness about a new vaccine in a community interconnected by a social network. How can they distribute information with minimal resources, ensuring community-wide understanding that aligns with the actual facts? … In this paper, we initialize the study of sample complexity in opinion formation to solve this problem. … Intriguingly, we discover optimal strategies often allocate samples inversely to the degree, hinting at vital policy implications.” Find the paper and full list of authors at ArXiv.
-
‘Inclusive Portraits: Race-Aware Human-in-the-Loop Technology’
“AI has revolutionized the … automatic facial verification of people. Automated approaches … can face challenges when processing content from certain communities, including communities of people of color. This challenge has prompted the adoption of ‘human-in-the-loop’ (HITL) approaches, where human workers collaborate with the AI to minimize errors. However, most HITL approaches do not consider workers’ individual characteristics and backgrounds. This paper proposes a new approach … that connects with social theories around race to design a racially-aware human-in-the-loop system.” Find the paper and authors list at the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms and Optimization…
-
‘User Inference Attacks on Large Language Models’
“In this paper, we study the privacy implications of fine-tuning LLMs on user data. To this end, we define a realistic threat model, called user inference, wherein an attacker infers whether or not a user’s data was used for fine-tuning. We implement attacks for this threat model that require only a small set of samples from a user (possibly different from the samples used for training). … We find that LLMs are susceptible to user inference attacks across a variety of fine-tuning datasets, at times with near perfect attack success rates.” Find the paper and full list of authors at…
-
‘What’s Next in Affective Modeling? Large Language Models’
“Large Language Models (LLM) have recently been shown to perform well at various tasks from language understanding, reasoning, storytelling, and information search to theory of mind. In an extension of this work, we explore the ability of GPT-4 to solve tasks related to emotion prediction. GPT-4 performs well across multiple emotion tasks; it can distinguish emotion theories and come up with emotional stories. We show that by prompting GPT-4 to identify key factors of an emotional experience, it is able to manipulate the emotional intensity of its own stories.” Find the paper and full list of authors at ArXiv.