Mani B. Srivastava's research while affiliated with University of California, Los Angeles and other places
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Publications (547)
Smart ear-worn devices (called earables) are being equipped with various onboard sensors and algorithms, transforming earphones from simple audio transducers to multi-modal interfaces making rich inferences about human motion and vital signals. However, developing sensory applications using earables is currently quite cumbersome with several barrie...
Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bou...
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. However, explanations give rise to potential side-channels that can be leveraged by an adversary for mounting attacks on the system. In particular, post-hoc...
Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-level visual concepts from images, e.g., identifying the wing color and beak length for bird-species classification. However, t...
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 deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requir...
Printers have become ubiquitous in modern office spaces, and their placement in these spaces been guided more by accessibility than security. Due to the proximity of printers to places with potentially high-stakes information, the possible misuse of these devices is concerning. We present a previously unexplored covert channel that effectively uses...
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. Nevertheless, until now, there ha...
Neural networks have been shown to provide rich and complicated inferences from time-series data over first-principle approaches. However, with inference moving to the edge, and IoT platforms shrinking, realizing AI-based inference on-board is challenging. While communication bandwidth, energy budget, and form factor of these platforms have gone do...
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 successful efforts for maintaining small IoT testbeds, there are multiple challenges for the efficient management of large-scale device deployments. With Industrial IoT, incorpor...
Personalized IoT adapts their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapts to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions ta...
![Fig. 1: A Bug Hunting Workflow [17] is part of the design verification...](profile/Sandeep-Sandha/publication/356911955/figure/fig1/AS:1126888726630403@1645682617876/A-Bug-Hunting-Workflow-17-is-part-of-the-design-verification-of-integrated-circuits-at_Q320.jpg)
Machine learning (ML) classifiers are widely adopted in the learning-enabled components of intelligent Cyber-physical Systems (CPS) and tools used in designing integrated circuits. Due to the impact of the choice of hyperparameters on an ML classifier performance, hyperparameter tuning is a crucial step for application success. However, the practic...
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 subsymbolic data as an input, (ii) retaining the flexibility and modularity on the definitions of complex event rules, (iii) allowing the system to be trained in an end-to-end mann...
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. However, researchers usually use Lp-norm minimization as a proxy for imperceptibility, which oversimplifies the diversity and richness of real-world images...
Deep Reinforcement Learning (RL) has demonstrated to be useful for a wide variety of robotics applications. To address sample efficiency and safety during training, it is common to train Deep RL policies in a simulator and then deploy to the real world, a process called simToReal transfer. For robotics applications, the deployment heterogeneities a...
Explaining the inner workings of deep neural network models have received considerable attention in recent years. Researchers have attempted to provide human parseable explanations justifying why a model performed a specific classification. Although many of these toolkits are available for use, it is unclear which style of explanation is preferred...
An ability to detect, classify, and locate complex acoustic events can be a powerful tool to help smart systems build context-awareness, e.g., to make rich inferences about human behaviors in physical spaces. Conventional methods to measure acoustic signals employ microphones as sensors. As signals from multiple acoustic sources are blended during...
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. The Situational Understanding Explorer (SUE) platform is designed to be lightweight, t...
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. In such a setting it is essential that the hum...
This paper appeared in media: "AI research strengthens certainty in battlefield decision-making [https://www.army.mil/article/249169]"
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The resurgen...
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 available. We propose a hybrid neuro-symbolic architecture based on Event Calculus that can perform Complex Event Processing (CEP). It leverages both a neural network to i...
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 successful efforts for maintaining small IoT testbeds, there are multiple challenges for the efficient management of large-scale device deployments. With Industrial IoT, incorpor...
Complex activity recognition using multiple on-body sensors is challenging due to missing samples, misaligned data timestamps across sensors, and variations in sampling rates. In this paper, we introduce a robust training pipeline that handles sampling rate variability, missing data and misaligned data timestamps using intelligent data augmentation...
Human attention is a scarce resource in modern computing. A multitude of microtasks vie for user attention to crowdsource information, perform momentary assessments, personalize services, and execute actions with a single touch. A lot gets done when these tasks take up the invisible free moments of the day. However, an interruption at an inappropri...
Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the par...
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 to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps re...
We propose a new clock synchronization architecture for systems under time transfer attacks. Facilitated by a feedforward control with feedback trim --based clock adjustment, coupled with packet filtering and frequency shaping techniques, our proposed architecture bounds the clock errors in the presence of a powerful network attacker capable of att...
The increasing ubiquity of low-cost wireless sensors in smart homes and buildings has enabled users to easily deploy systems to remotely monitor and control their environments. However, this raises privacy concerns for third-party occupants, such as a hotel room guest who may be unaware of deployed clandestine sensors. Previous methods focused on s...
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 to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps re...
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 inject trojan backdoor into the neural networks, which will make the model behave abnormally when a backdoor sample with a specific trigger is input. In this paper, we propos...
The increasing complexity and ubiquity of using IoT devices exacerbate the existing programming challenges in smart environments such as smart homes, smart buildings, and smart cities. Recent works have focused on detecting conflicts for the safety and utility of IoT applications, but they usually do not emphasize any means for conflict resolution...
Modern smartphones and smartwatches are equipped with inertial sensors (accelerometer, gyroscope, and magnetometer) that can be used for Human Activity Recognition (HAR) to infer tasks such as daily activities, transportation modes and, gestures. HAR requires collecting raw inertial sensor values and training a machine learning model on the collect...
Achieving precise time synchronization across a collection of smartphones poses unique challenges due to their limited hardware support, exclusively wireless networking interface, and restricted timing stack control. Given the ubiquity and popularity of smartphones in modern distributed applications, clock discrepancies often lead to degraded appli...
Accurate human activity recognition (HAR) is the key to enable emerging context-aware applications that require an understanding and identification of human behavior, e.g., monitoring disabled or elderly people who live alone. Traditionally, HAR has been implemented either through ambient sensors, e.g., cameras, or through wearable devices, e.g., a...
Biologging is a scientific endeavor that studies the environment and animals within it by outfitting the latter with sensors of their dynamics as they roam freely in their natural habitats. As wearable technologies advance for the monitoring of human health, it may be instructive to reflect on the successes and failures of biologging in field biolo...
Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these models are black-boxes and it is hard to understand how they make their decisions. Over the past few years, researchers have studied the problem of providing explanations of...
Machine learning has enabled many interesting applications and is extensively being used in big data systems. The popular approach - training machine learning models in frameworks like Tensorflow, Pytorch and Keras - requires movement of data from database engines to analytical engines, which adds an excessive overhead on data scientists and become...
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 to generating adversarial examples typically require a significant number of queries, either for training a substitute network or performing gradient estimation. We introd...
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. These methods represent a threat to the automatic speaker verification (ASV) systems. Additionally, replay attacks where the attacker uses a speaker to replay a previously re...
Offloading techniques enable many emerging computer vision applications on mobile platforms by executing compute-intensive tasks on resource-rich servers. Although there have been a significant amount of research efforts devoted in optimizing mobile offloading frameworks, most previous works are evaluated in a single-tenant setting, that is, a serv...
Thanks to the adoption of more sensors in the automotive industry, context-aware Advanced Driver Assistance Systems (ADAS) become possible. On one side, a common thread in ADAS applications is to focus entirely on the context of the vehicle and its surrounding vehicles leaving the human (driver) context out of consideration. On the other side, and...
IoT devices are permeating every corner of our lives today paving the road for more substantial smart systems. Despite their ability to collect and analyze a significant amount of sensory data, traditional IoT typically depends on fixed policies and schedules to enhance user experience. However, fixed policies that do not account for variations in...
The Internet of Battlefield Things (IoBT) might be one of the most expensive cyber-physical systems of the next decade, yet much research remains to develop its fundamental enablers. A challenge that distinguishes the IoBT from its civilian counterparts is resilience to a much larger spectrum of threats.
Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use...
User interaction is an essential part of many mobile devices such as smartphones and wrist bands. Only by interacting with the user can these devices deliver services, enable proper configurations, and learn user preferences. Push notifications are the primary method used to attract user attention in modern devices. However, these notifications can...
Traditional machine learning approaches for recognizing modes of transportation rely heavily on hand-crafted feature extraction methods which require domain knowledge. So, we propose a hybrid deep learning model: Deep Convolutional Bidirectional-LSTM (DCBL) which combines convolutional and bidirectional LSTM layers and is trained directly on raw se...
1.The development of multi‐sensor animal‐attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. 2.The high volumes of data, are pushing us towards machine‐learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more lik...
