Search Results for author: Mani Srivastava

Found 33 papers, 11 papers with code

Principles of Designing Robust Remote Face Anti-Spoofing Systems

no code implementations6 Jun 2024 Xiang Xu, Tianchen Zhao, Zheng Zhang, Zhihua Li, Jon Wu, Alessandro Achille, Mani Srivastava

Protecting digital identities of human face from various attack vectors is paramount, and face anti-spoofing plays a crucial role in this endeavor.

Face Anti-Spoofing Face Swapping

On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study

no code implementations3 Apr 2024 Tomoyoshi Kimura, Jinyang Li, Tianshi Wang, Denizhan Kara, Yizhuo Chen, Yigong Hu, Ruijie Wang, Maggie Wigness, Shengzhong Liu, Mani Srivastava, Suhas Diggavi, Tarek Abdelzaher

This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications.

LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces

no code implementations28 Mar 2024 Xiaomin Ouyang, Mani Srivastava

To answer this question, we design an effective prompting framework for LLMs on high-level reasoning tasks, which can handle traces from the raw sensor data as well as the low-level perception results.

Data Summarization World Knowledge

FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space

1 code implementation NeurIPS 2023 Shengzhong Liu, Tomoyoshi Kimura, Dongxin Liu, Ruijie Wang, Jinyang Li, Suhas Diggavi, Mani Srivastava, Tarek Abdelzaher

Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics.

Contrastive Learning Time Series

Penetrative AI: Making LLMs Comprehend the Physical World

no code implementations14 Oct 2023 Huatao Xu, Liying Han, Qirui Yang, Mo Li, Mani Srivastava

Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks.

Common Sense Reasoning World Knowledge

Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations

no code implementations24 Jul 2023 Yi Han, Matthew Chan, Eric Wengrowski, Zhuohuan Li, Nils Ole Tippenhauer, Mani Srivastava, Saman Zonouz, Luis Garcia

We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks.

Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras

1 code implementation10 Apr 2023 Sandeep Singh Sandha, Bharathan Balaji, Luis Garcia, Mani Srivastava

Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms.

object-detection Object Detection +2

Depth Estimation From Camera Image and mmWave Radar Point Cloud

no code implementations CVPR 2023 Akash Deep Singh, Yunhao Ba, Ankur Sarker, Howard Zhang, Achuta Kadambi, Stefano Soatto, Mani Srivastava, Alex Wong

To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map.

Depth Estimation

On the amplification of security and privacy risks by post-hoc explanations in machine learning models

no code implementations28 Jun 2022 Pengrui Quan, Supriyo Chakraborty, Jeya Vikranth Jeyakumar, Mani Srivastava

A variety of explanation methods have been proposed in recent years to help users gain insights into the results returned by neural networks, which are otherwise complex and opaque black-boxes.

Model extraction

Automatic Concept Extraction for Concept Bottleneck-based Video Classification

no code implementations21 Jun 2022 Jeya Vikranth Jeyakumar, Luke Dickens, Luis Garcia, Yu-Hsi Cheng, Diego Ramirez Echavarria, Joseph Noor, Alessandra Russo, Lance Kaplan, Erik Blasch, Mani Srivastava

CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts.

Classification Video Classification

Machine Learning for Microcontroller-Class Hardware: A Review

no code implementations29 May 2022 Swapnil Sayan Saha, Sandeep Singh Sandha, Mani Srivastava

We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it.

BIG-bench Machine Learning

PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings

no code implementations25 Apr 2022 Moustafa Alzantot, Luis Garcia, Mani Srivastava

Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data that preserves statistical properties and utility of real-world datasets, especially in the context of image and natural language text.

Activity Recognition Classification +4

Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics

no code implementations7 Mar 2022 Jeya Vikranth Jeyakumar, Ludmila Cherkasova, Saina Lajevardi, Moray Allan, Yue Zhao, John Fry, Mani Srivastava

In this work, we design a novel, scalable approach, where a general demand forecasting model is built using the combined data of all the companies with a normalization factor.


Using DeepProbLog to perform Complex Event Processing on an Audio Stream

no code implementations15 Oct 2021 Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.

Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss

no code implementations31 Jan 2021 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 benign examples.


An Experimentation Platform for Explainable Coalition Situational Understanding

no code implementations27 Oct 2020 Katie Barrett-Powell, Jack Furby, Liam Hiley, Marc Roig Vilamala, Harrison Taylor, Federico Cerutti, Alun Preece, Tianwei Xing, Luis Garcia, Mani Srivastava, Dave Braines

We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing.

BIG-bench Machine Learning Explainable artificial intelligence

Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams

no code implementations23 Oct 2020 Dave Braines, Federico Cerutti, Marc Roig Vilamala, Mani Srivastava, Lance Kaplan Alun Preece, Gavin Pearson

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 human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes.

A Hybrid Neuro-Symbolic Approach for Complex Event Processing

no code implementations7 Sep 2020 Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.


NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations

no code implementations18 Nov 2019 Xijie Huang, Moustafa Alzantot, Mani Srivastava

NeuronInspect first identifies the existence of backdoor attack targets by generating the explanation heatmap of the output layer.

Backdoor Attack Outlier Detection +1

NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning

no code implementations5 Aug 2019 Moustafa Alzantot, Amy Widdicombe, Simon Julier, Mani Srivastava

When applied to image classification models, NeuroMask identifies the image parts that are most important to classifier results by applying a mask that hides/reveals different parts of the image, before feeding it back into the model.

General Classification Image Classification

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

3 code implementations28 May 2018 Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, huan zhang, Cho-Jui Hsieh, Mani Srivastava

Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches.

Adversarial Attack Adversarial Robustness +1

Generating Natural Language Adversarial Examples

5 code implementations EMNLP 2018 Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.

Diversity Natural Language Inference +1

Did you hear that? Adversarial Examples Against Automatic Speech Recognition

1 code implementation2 Jan 2018 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 automatic speech recognition to enable natural interaction between humans and machines.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

no code implementations10 Nov 2017 Amr Alanwar, Henrique Ferraz, Kevin Hsieh, Rohit Thazhath, Paul Martin, Joao Hespanha, Mani Srivastava

Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion.

Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration

no code implementations15 Jul 2017 Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani Srivastava, Zhuowen Tu, Rajesh K. Gupta

State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.

Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets

2 code implementations20 Sep 2014 Jack Kelly, Nipun Batra, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava

In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.

Other Computer Science

NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

2 code implementations15 Apr 2014 Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava

We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.


Cannot find the paper you are looking for? You can Submit a new open access paper.