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28th ACM SIGSAC Conference on computer and Communications Security, CCS 2022

作者： Xie, Mengyao Wu, Chenggang Zhang, Yinqian Xu, Jiali Lai, Yuanming Kang, Yan Wang, Wei Wang, Zhe Sklp Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Engineering SUSTech Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Sklp Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9781450394505

Intel control-flow enforcement technology (CET) is a new hardware feature available in recent Intel processors. It supports the coarse-grained control-flow integrity for software to defeat memory corruption attacks. In this paper, we retrofit CET, particularly the write-protected shadow pages of CET used for implementing shadow stacks, to develop a generic and efficient intra-process memory isolation mechanism, dubbed CETIS. To provide user-friendly interfaces, a CETIS framework was developed, which provides memory file abstraction for the isolated memory regions and a set of APIs to access said regions. CETIS also comes with a compiler-assisted tool chain for users to build secure applications easily. The practicality of using CETIS to protect CPI, CFIXX, and JIT-compilers was demonstrated, and the evaluation reveals that CETIS is performed better than state-of-the-art intra-memory isolation mechanisms, such as MPK. © 2022 Owner/Author.

关键词： Retrofitting

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Navigating the Edge: UAS Boundary Tracing for Efficient Volcanic Plume Monitoring

Navigating the Edge: UAS Boundary Tracing for Efficient Volc...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： John Ericksen Abir Islam Carter Frost Kevin Aubert G. Matthew Fricke Varsha Dani Rafael Fierro Tobias Fischer Scott Nowicki Jared Saia Melanie Moses Department of Computer Science University of New Mexico Department of Electrical Engineering University of New Mexico Center for Advanced Research Computing University of New Mexico Computer Science Department Rochester Institute of Technology Earth and Planetary Sciences University of New Mexico Santa Fe Institute Santa Fe

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

We present the implementation and validation of Sketch, an algorithm that uses two Unpiloted Aerial Systems (UASs) to trace the boundary of volcanic plumes. Sketchguarantees asymptotically optimal flight distance and turning by maintaining a sandwich invariant where one UAS stays inside the plume boundary (defined by a $\textbf{CO}_{2}$ concentration threshold) and the other UAS stays outside. The UASs adjust their flight paths based on real-time CO 2 measurements to maintain this invariant. This paper details the implementation of Sketchon a real-world UAS platform, the Dragonfly drone. We evaluate the efficacy of Sketchthrough extensive testing in physics-based simulations and real-world outdoor environments using virtual plumes. The algorithm is compared to a single-UAS baseline algorithm called Zigzag. Results show that Sketchmeets the expectations set by theory, and it is more efficient than Zigzag, achieving shorter flight paths, less turning, and faster mapping times. While Zigzagexhibits slightly higher accuracy in estimating plume area and boundary, Sketchoffers a more efficient real-time volcanic plume monitoring approach, especially in time-sensitive situations. These results demonstrate the feasibility and efficacy of Sketchfor real-time volcanic plume monitoring, paving the way for accurate $\textbf{CO}_2$ emission estimation in hazardous and challenging environments.

关键词： Accuracy Navigation Volcanic activity Estimation Turning Real-time systems Safety Security Monitoring Testing

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Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Nobile, Nicholas A. Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Mills, Brian Popescu, Cosmin Constantin Callahan, Dennis Hu, Juejun Ríos Ocampo, Carlos A. Youngblood, Nathan Electrical & Computer Engineering Department University of Pittsburgh PittsburghPA15213 United States Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Draper Scholar Program The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States

Sb2Se3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop- and pass-band functionalities. Experimental results highlight fabrication challenges and efficacy of the designs. © Optica Publishing Group 2024, © 2024 The Author(s)

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LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments

LANCAR: Leveraging Language for Context-Aware Robot Locomoti...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chak Lam Shek Xiyang Wu Wesley A. Suttle Carl Busart Erin Zaroukian Dinesh Manocha Pratap Tokekar Amrit Singh Bedi Department of Electrical and Computer Engineering University of Maryland College Park MD USA DEVCOM Army Research Laboratory Adelphi MD USA Department of Computer Science University of Maryland College Park MD USA Department of Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context from their observations, creating a similar context-aware navigation system for robots is difficult. The essence of the issue lies in the acquisition and interpretation of context information, a task complicated by the inherent ambiguity of human language. In this work, we introduce LANCAR, which addresses this issue by combining a context translator with reinforcement learning (RL) agents for context-aware locomotion. LANCAR allows robots to comprehend context information through Large Language Models (LLMs) sourced from human observers and convert this information into actionable context embeddings. These embeddings, combined with the robot’s sensor data, provide a complete input for the RL agent’s policy network. We provide an extensive evaluation of LANCAR under different levels of context ambiguity and compare with alternative methods. The experimental results showcase the superior generalizability and adaptability across different terrains. Notably, LANCAR shows at least a 7.4% increase in episodic reward over the best alternatives, highlighting its potential to enhance robotic navigation in unstructured environments. More details and experiment videos could be found in this link.

关键词： Visualization Translation Navigation Foundation models Reinforcement learning Observers Robot sensing systems Robustness Robots Videos

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HyperScore: A unified measure to model hypertension progression using multi-modality measurements and semi-supervised learning

HyperScore: A unified measure to model hypertension progress...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alkhodari, Mohanad Lapidaire, Winok Xiong, Zhaohan Kart, Turkay Iturria-Medina, Yasser Hadjileontiadis, Leontios Khandoker, Ahsan Lewandowski, Adam J. Banerjee, Abhirup Leeson, Paul University of Oxford Cardiovascular Clinical Research Facility Radcliffe Department of Medicine Oxford United Kingdom Khalifa University Healthcare Engineering Innovation Center Department of Biomedical Engineering Abu Dhabi United Arab Emirates Imperial College London Biomedical Image Analysis Group Department of Computing London United Kingdom McGill University Neuroinformatics for Personalized Medicine Lab Montreal Canada Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece University of Oxford Institute of Biomedical Engineering Department of Engineering Science Oxford United Kingdom

ISBN: (纸本)9798350337488

Hypertension is a serious medical condition that affects over a billion people worldwide. The proper management of disease progression requires an extended knowledge of the overall functional and structural changes in the whole body in response to the hypertension. Here, we propose HyperScore, an integrative and unified measure of hypertension progression relative to multi-organ and multi-modality clinical measurements and based on a semi-supervised machine learning (ML) approach. We developed the measure based on a large participating cohort from the UK Biobank database (n=27,099) with over 500 imaging and clinical variables from multiple modalities. The semi-supervised approach was developed based on the contrastive trajectory inference mechanism to provide a score that reflects the proximity of a participant to the disease state (range: 0-1). Modelling revealed that majority of hypertensive participants had scores above 0.25, whereas normotensives had scores below this threshold. The sensitivity and specificity were above 89%, with an area under the receiver operating characteristics of 96.4%. The modelling showed a stable performance when evaluating hidden testing sets on a 10-fold cross-validation scheme with nearly 0.1 error. There was a strong association (r2>0.6) between HyperScore and organs' phenotypic patterns, especially for variables such as white matter hyperintensity and body mass index. This study is the first to potentiate ML-based modelling of hypertension progression from a multi-organ perspective, which could significantly aid in clinical decision making to save lives. © 2023 IEEE.

关键词： blood pressure contrastive PCA Hypertension machine learning semi-supervised learning

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Universal Optics with Programmable Multimode Interference

Universal Optics with Programmable Multimode Interference

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Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Larocque, Hugo Englund, Dirk Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

We show that multimode interference programmable optics benefit from the simple algorithmic construction and length scaling of state-of-the-art programmable photonic circuits, while featuring orders of magnitude reduc...

ISBN: (纸本)9781557528209

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Optimizing Vehicle Positioning via Multi-Model Image and Radio Frequency Fusion

Optimizing Vehicle Positioning via Multi-Model Image and Rad...

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IEEE International Conference on Communications (ICC)

作者： Ouwen Huan Mingzhe Chen Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

关键词： Training Deep learning Base stations Simulation Fingerprint recognition Cameras Data models

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Forecasting

eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Fo...

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Biomedical engineering International Conference (BMEiCON)

作者： Divya Syal Juan Zuluaga Navid Shaghaghi Department of Computer Science & Engineering (CSEN) and Information Systems & Analytics (ISA) Ethical Pragmatic and Intelligent Computing (EPIC) Research Laboratory Santa Clara University (SCU) Santa Clara California USA

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

Respiratory Syncytial Virus (RSV) is a flu-like illness annually responsible for 100K-160K hospitalizations and 6K-10K deaths in adults above the age of 65 as well as 58K-80K hospitalizations and 100–300 deaths in children under 5 years of age in the United States. A live prediction of the rate of growth of RSV cases during the RSV season can empower healthcare organizations to best prepare for RSV peaks and ultimately reduce the RSV mortality rate without over-budgeting, be it pharmaceutical companies producing too many perishable testing kits, vaccines and medications or hospitals keeping too many beds and staffing in reserve and thus away from other needed patients. This study adds a new RSV forecasting model to eVision, the collection of disease-forecasting Long Short-Term Memory (LSTM) neural networks, developed by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent computing) research lab. This model leverages data from the Centers of Disease Control and Prevention (CDC) and Google Trends between 2021–2024 to predict the trend of RSV cases through the typical RSV season and beyond in the United States. Given the lack of extensive RSV data - which has only been collected by the CDC on a national level since 2021 - and how easily RSV is mistaken for other diseases, this model has a limitation on the number of weeks ahead in which its prediction can maintain a high accuracy for. This model successfully predicts percent positive RSV Polymerase Chain Reaction (PCR) tests with 94.45 % forecasting accuracy three weeks in advance at the national level, for the 2023–2024 RSV season.

关键词： Accuracy computer viruses Computational modeling Biological system modeling Buildings Predictive models Market research Data models Forecasting Long short term memory

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Integrating Generative Artificial Intelligence Into Future Internet of Vehicles [From the Guest Editors]

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IEEE Vehicular Technology Magazine 2025年第2期20卷 40-42页

作者： Gang Sun Hongyang Du Abbas Jamalipour Gabriel-Miro Muntean Lin Cai Jiawen Kang Dusit Niyato University of Electronic Science and Technology of China Chengdu China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China University of Sydney Sydney NSW Australia Performance Engineering Laboratory Dublin City University Dublin Ireland Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada Guangdong University of Technology Guangzhou China College of Computing and Data Science Nanyang Technological University Singapore Singapore

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