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Found 31 papers, 10 papers with code
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
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
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.
GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
Constantly locating moving objects, i. e., geospatial tracking, is essential for autonomous building infrastructure.
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
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.
Penetrative AI: Making LLMs Comprehend the Physical World
Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks.
Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations
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.
Heteroskedastic Geospatial Tracking with Distributed Camera Networks
Visual object tracking has seen significant progress in recent years.
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras
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.
Depth Estimation From Camera Image and mmWave Radar Point Cloud
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.
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
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.
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
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.
Machine Learning for Microcontroller-Class Hardware: A Review
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.
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings
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.
Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
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
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
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.
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
Explaining the inner workings of deep neural network models have received considerable attention in recent years.
An Experimentation Platform for Explainable Coalition Situational Understanding
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
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
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
Quick Question: Interrupting Users for Microtasks with Reinforcement Learning
Human attention is a scarce resource in modern computing.
MANGO: A Python Library for Parallel Hyperparameter Tuning
Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually.
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations
NeuronInspect first identifies the existence of backdoor attack targets by generating the explanation heatmap of the output layer.
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning
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.
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
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.
Generating Natural Language Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.
Did you hear that? Adversarial Examples Against Automatic Speech Recognition
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
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
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
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
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.
Applications
