500 citations · 1.1k across the 25 of their papers we have counts for
28 papers
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +2
Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a pr…
Machine Learning for Microcontroller-Class Hardware: A Review
Swapnil Sayan Saha, Sandeep Singh Sandha, Mani Srivastava
The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning…
Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
Jeya Vikranth Jeyakumar, Ludmila Cherkasova, Saina Lajevardi +4
The IoT vision of a trillion connected devices over the next decade requires reliable end-to-end connectivity and automated device management platforms. While we have seen successf…
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
Marc Roig Vilamala, Tianwei Xing, Harrison Taylor +6
In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymboli…
Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss
Pengrui Quan, Ruiming Guo, Mani Srivastava
Recently, there has been a large amount of work towards fooling deep-learning-based classifiers, particularly for images, via adversarial inputs that are visually similar to the be…
An Experimentation Platform for Explainable Coalition Situational Understanding
Katie Barrett-Powell, Jack Furby, Liam Hiley +8
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML)…
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
Dave Braines, Federico Cerutti, Marc Roig Vilamala +3
Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the…
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
Marc Roig Vilamala, Harrison Taylor, Tianwei Xing +6
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is a…
Quick Question: Interrupting Users for Microtasks with Reinforcement Learning
Bo-Jhang Ho, Bharathan Balaji, Mehmet Koseoglu +3
Human attention is a scarce resource in modern computing. A multitude of microtasks vie for user attention to crowdsource information, perform momentary assessments, personalize se…
MANGO: A Python Library for Parallel Hyperparameter Tuning
Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov +1
Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started…
I Always Feel Like Somebody's Sensing Me! A Framework to Detect, Identify, and Localize Clandestine Wireless Sensors
Akash Deep Singh, Luis Garcia, Joseph Noor +1
The increasing ubiquity of low-cost wireless sensors has enabled users to easily deploy systems to remotely monitor and control their environments. However, this raises privacy con…
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations
Xijie Huang, Moustafa Alzantot, Mani Srivastava
Deep neural networks have achieved state-of-the-art performance on various tasks. However, lack of interpretability and transparency makes it easier for malicious attackers to inje…
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning
Moustafa Alzantot, Amy Widdicombe, Simon Julier +1
Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these mod…
Deep Residual Neural Networks for Audio Spoofing Detection
Moustafa Alzantot, Ziqi Wang, Mani B. Srivastava
The state-of-art models for speech synthesis and voice conversion are capable of generating synthetic speech that is perceptually indistinguishable from bonafide human speech. Thes…
Optimization vs. Reinforcement Learning for Wirelessly Powered Sensor Networks
Ayca Ozcelikkale, Mehmet Koseoglu, Mani Srivastava
We consider a sensing application where the sensor nodes are wirelessly powered by an energy beacon. We focus on the problem of jointly optimizing the energy allocation of the ener…
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty +3
Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches t…
Generating Natural Language Adversarial Examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary +3
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. In the image domain, th…
Realizing Uncertainty-Aware Timing Stack in Embedded Operating System
Amr Alanwar, Fatima M. Anwar, Joao P Hespanha +1
Time awareness is critical to a broad range of emerging applications -- in Cyber-Physical Systems and Internet of Things -- running on commodity platforms and operating systems. Tr…
SecSens: Secure State Estimation with Application to Localization and Time Synchronization
Amr Alanwar, Bernhard Etzlinger, Henrique Ferraz +2
Research evidence in Cyber-Physical Systems (CPS) shows that the introduced tight coupling of information technology with physical sensing and actuation leads to more vulnerability…
Did you hear that? Adversarial Examples Against Automatic Speech Recognition
Moustafa Alzantot, Bharathan Balaji, Mani Srivastava
Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate a…
D-SLATS: Distributed Simultaneous Localization and Time Synchronization
Amr Alanwar, Henrique Ferraz, Kevin Hsieh +4
Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Althou…
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao +4
State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution. Such networks strain the computati…
EmbedInsight: Automated Grading of Embedded Systems Assignments
Hao Li, Bo-Jhang Ho, Bharathan Balaji +3
Grading in embedded systems courses typically requires a face-to-face appointment between the student and the instructor because of experimental setups that are only available in l…
SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation
Moustafa Alzantot, Supriyo Chakraborty, Mani B. Srivastava
Our ability to synthesize sensory data that preserves specific statistical properties of the real data has had tremendous implications on data privacy and big data analytics. The s…