Title
Topic
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Developing cutting-edge testing technology for 5G Open RAN
“Electrical and computer engineering principal research scientist Pedram Johari is leading a $2,000,000 project awarded by the Wireless Innovation Fund to develop a digital framework for testing 5G Open RAN systems called ‘DigiRAN: High-Fidelity Digital Twins for Interoperability, Security and Performance Testing of Open RAN Systems.’ DigiRAN is a digital framework that enables diverse, low-cost and automated testing of three core components for 5G Open RAN: interoperability, performance and security.”
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Ostadabbas receives NSF grant to integrate AR technologies into stroke rehabilitation
“Electrical and computer engineering associate professor Sarah Ostadabbas, in collaboration with the University of Pittsburgh and Myomo, Inc., has secured a $550,000 NSF grant for their project titled ‘PFI-RP: Augmented Reality and Electroencephalography for Detecting, Assessing and Rehabilitating Visual Unilateral Neglect in Stroke Patients.’ This project aims to create a comprehensive tool for detecting, assessing and rehabilitating neglect in stroke patients. It will use augmented reality (AR) and electroencephalography (EEG) to automatically detect neglect and stimulate the affected side of the body and environment.”
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Improving the efficiency of medical device communication
“Electrical and computer engineering William Lincoln Smith Professor Tommaso Melodia was awarded a patent for ‘Ultrasonic multiplexing network for implantable medical devices.'”
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Chowdhury and Jornet made IEEE Fellows
“Electrical and computer engineering professors Kaushik Chowdhury and Josep Jornet were elevated to IEEE Fellows. Chowdhury was elevated for contributions to the development of cognitive radio networks and applied machine learning for wireless systems. Jornet was recognized for contributions in terahertz communication and nanonetworking.”
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Connecting to medical devices through ultrasonic network
“Electrical and computer engineering William Lincoln Smith Professor Tommaso Melodia was awarded a patent for ‘Internet-linked ultrasonic network for medical devices.'”
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With new industrial ecology textbook, Matthew Eckelman suggests we treat industry ‘more like nature’
Associate professor of civil and environmental engineering Matthew Eckelman has co-authored “Industrial Ecology and Sustainability,” a new edition of a seminal textbook in the field of industrial ecology, which asks, “How can we make the industrial system act more like nature?”
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Protecting wireless systems from adversarial attacks
“Electrical and computer engineering William Lincoln Smith Professor Tommaso Melodia, assistant professor Francesco Restuccia and assistant research professor Salvatore D’oro were awarded a patent for ‘Neural network for adversarial deep learning in wireless systems.'”
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‘Exploring User Perceptions of Using An LLM-Based Conversational Assistant [as a] Cooking Partner’
Large language models (LLMs) have the ability to help in daily tasks. “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. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved.” Find the paper and authors list at ArXiv.
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‘Depredation Influences Anglers’ Perceptions on Soastal Shark Management and Conservation’
“Overfishing, habitat degradation, and climate change have caused declines in shark populations throughout the world’s oceans. … Reported increases in shark depredation within the last several years have begun to erode angler support for shark conservation, potentially undermining decades of previous work. To address these concerns, we implemented a GoM-wide online survey to characterize the impact of depredation on recreational reef fish anglers’ fishing satisfaction and perceptions of shark management and conservation.” Find the paper and full list of authors at Frontiers in Conservation Science.
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‘Design and Characterization of Model Systems that Promote and Disrupt Transparency of Vertebrate Crystallins In Vitro’
“Positioned within the eye, the lens supports vision by transmitting and focusing light onto the retina. As an adaptive glassy material, the lens is constituted primarily by densely-packed, polydisperse crystallin proteins that organize to resist aggregation and crystallization at high volume fractions, yet the details of how crystallins coordinate with one another to template and maintain this transparent microstructure remain unclear. The role of individual crystallin subtypes (α, β, and γ) and paired subtype compositions … is explored using combinations of spectrophotometry, hard-sphere simulations, and surface pressure measurements.” Find the paper and full list of authors in Advanced Science.
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Improving RF resonator technology
“Electrical and computer engineering associate professor Cristian Casella and electrical engineering student Xuanyi Zhao, PhD’23, were awarded a patent for ‘Two Dimensional Rod Resonator for RF Filtering.'”
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‘Behind the Scenes: Uncovering TLS and Server Certificate Practice of IoT Device Vendors in the Wild’
“IoT devices are increasingly used in consumer homes. Despite recent works in characterizing IoT TLS usage for a limited number of in-lab devices, there exists a gap in quantitatively understanding TLS behaviors from devices in the wild and server-side certificate management. To bridge this knowledge gap, we conduct a new measurement study by focusing on the practice of device vendors, through a crowdsourced dataset of network traffic. … Our study highlights potential concerns in the TLS/PKI practice by IoT device vendors.” Find the paper and full list of authors in the Proceedings of the 2023 ACM on Internet Measurement Conference.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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.
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‘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…
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‘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.
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‘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.