Privacy-preserving Non-negative Matrix Factorization with Outliers

Non-negative matrix factorization is a popular unsupervised machine learning algorithm for extracting meaningful features from data which are inherently non-negative. However, such data sets may often contain privacy-sensitive user data, and therefore, we may need to take necessary steps to ensure the privacy of the users while analyzing the data. In this work, we focus on developing a Non-negative matrix factorization algorithm in the privacy-preserving framework. More specifically, we propose a novel privacy-preserving algorithm for non-negative matrix factorisation capable of operating on private data, while achieving results comparable to those of the non-private algorithm. We design the framework such that one has the control to select the degree of privacy grantee based on the utility gap. We show our proposed framework's performance in six real data sets. The experimental results show that our proposed method can achieve very close performance with the non-private algorithm under some parameter regime, while ensuring strict privacy.Comment: 15 pages, 11 figures; additional explanations (in blue colours

Machine Learning for Microcontroller-Class Hardware -- A Review

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 requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. 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. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis

Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. These blocks can be identified from the spatial and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the brain blood vessels using machine learning methods. In this study, we propose several preprocessing schemes to improve the performance of these methods. Our method includes 3D-point cloud data extraction from image modality and their feature-space fusion to leverage complementary information inherent in different modalities. We also enforce the learned representation to be sequence-order invariant by utilizing bi-direction dataflow. Experimental results on The Clog Loss dataset show that our proposed method consistently outperforms the state-of-the-art preprocessing methods in stalled and non-stalled vessel classification.Comment: 5 pages, 4 figure

Nursing Personnel Planning for Rural Hospitals in Burdwan District, West Bengal, India, Using Workload Indicators of Staffing Needs

Lack of appropriate human resources planning is an important factor in the inefficient use of the public health facilities. Workforce projections can be improved by using objective methods of staffing needs based on the workload and actual work undertaken by workers, a guideline developed by Peter J. Shipp in collaboration with WHO\u2014Workload Indicators of Staffing Need (WISN). A cross-sectional study was carried out to estimate the nursing stuff requirement for the rural hospitals and provide a quantitative description of imbalances, if there is any, in the allocation at the district level during 2011. The average WISN turns out to be 0.35 for entire district, which means only 35% of the required nurses is available or 65% understaffed. So, there is an urgent need for more allocations and deployment of staff so that workload can be tackled and evenly distributed among all nursing personnel

Physics-Aware Tiny Machine Learning

Tiny machine learning has enabled Internet of Things platforms to make intelligent inferences for time-critical and remote applications from unstructured data. However, realizing edge artificial intelligence systems that can perform long-term high-level reasoning and obey the underlying system physics, rules, and constraints within the tight platform resource budget is challenging. This dissertation explores how rich, robust, and intelligent inferences can be made on extremely resource-constrained platforms in a platform-aware and automated fashion. Firstly, we introduce a robust training pipeline that handles sampling rate variability, missing data, and misaligned data timestamps through intelligent data augmentation techniques during training time. We use a controlled jitter in window length and add artificial misalignments in data timestamps between sensors, along with masking representations of missing data. Secondly, we introduce TinyNS, a platform-aware neurosymbolic architecture search framework for the automatic co-optimization and deployment of neural operators and physics-based process models. TinyNS exploits fast, gradient-free, and black-box Bayesian optimization to automatically construct the most performant learning-enabled, physics, and context-aware edge artificial intelligence program from a search space containing neural and symbolic operators within the platform resource constraints. To guarantee deployability, TinyNS receives hardware metrics directly from the target hardware during the optimization process. Thirdly, we introduce the concept of neurosymbolic tiny machine learning, where we showcase recipes for defining the physics-aware tiny machine learning program synthesis search space from five neurosymbolic program categories. Neurosymbolic artificial intelligence combines the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. We develop parsers to automatically write microcontroller code for neurosymbolic programs and showcase several previously unseen TinyML applications. These include onboard physics-aware neural-inertial navigation, on-device human activity recognition, on-chip fall detection, neural-Kalman filtering, and co-optimization of neural and symbolic processes. Finally, we showcase techniques to personalize and adapt tiny machine learning systems to the target domain and application. We illustrate the use of transfer learning, resource-efficient unsupervised template creation and matching, and foundation models as pathways to realize generalizable, domain-aware, and data-efficient edge artificial intelligence systems

Robust and Efficient Neural Inertial Localization and Complex Activity Recognition

Inertial complex activity recognition and neural inertial navigation are challenging due to missing samples, misaligned data timestamps across sensor channels, variations in sampling rates and high model deployment costs. In this thesis, we introduce a robust training pipeline for complex activity detection that handles sampling rate variability, missing data, and misaligned data timestamps using intelligent data augmentation techniques. Specifically, we use controlled jitter in window length and add artificial misalignments in data timestamps between sensors, along with masking representations of missing data. In addition, we exploit end-to-end sequential learning, alpha-beta filters, Madgwick filters, hardware and quantization-aware Bayesian neural architecture search and a temporal convolutional neural network backbone to form the basis of scalable, real-time and sub-meter GPS-free inertial localization on wide spectrum of target resource-constrained hardware. We also provide a compact, ultra-low-power, environmentally resilient and modular sensor tag configuration that pushes the state-of-the-art in inertial odometry hardware. On average, the network found via our efficient pipeline provided 3x peak activation and 6x memory savings over the state-of-the-art neural inertial algorithms and taking at most 24 hours to train and search pareto-optimal models in the backbone search space. Moreover, we evaluate the complex activity pipeline on state-of-the-art complex activity recognition dataset, achieving test accuracies of 88% and 72% respectively for coarse and granular-activity classification while ranking 3rd in the 2020 Cooking Activity Recognition Challenge out of 78 submissions

Machine Learning for Microcontroller-Class Hardware: A Review.

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 requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. 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. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward