Indoor Positioning System

The purpose of our project is to develop a complementary system to the current GPS with a focus on indoor localization and navigation. The current need for localization extends beyond what GPS can provide in today’s state of technology. Radio signals used in the global system are vast but weak, unable to penetrate obstacles and buildings in high density, populous areas of the world. Our system is designed to solve this problem by implementing an Indoor Localization System using a stronger ultra-wideband signal in the frequency spectrum. At a high level, the system is modeled after the architecture of the global positioning system by utilizing anchors as the satellites and tags as the receivers. With the use of up to date cloud technology, an end-to-end system is created through the Internet of Things with the inclusion of information security and a fully developed front-end user interface. The packaging is encapsulated within a miniature PCB design at a low cost, aimed as a plug-and-play integration within other systems in need of indoor detection. Applications of our IPS design include domains such as navigation (room-to-room assistance in a building), national defense (search and rescue operations, underground tracking, surveillance), commercialized zones (indicators for specific products on shelf, asset tracking in warehouses), and robotics (autonomous vehicles, drone detection). We demonstrate that all the components mentioned are essential to effectively carry out successful indoor localization with a focus on user flexibility and efficiency in response. We are able to use the system to enable an indoor drone show

Corporate Demand for Reinsurance: Evidence from the United Kingdom Motor Reinsurance Market

A substantial number of studies have researched the corporate demand for reinsurance but very few of them have looked into the motor reinsurance market. This dissertation employs a panel data analysis to undertake an empirical investigation into the UK motor reinsurance market in attempt to test whether certain determinants, expressed in related hypotheses, have influences over the demand for motor reinsurance. This dissertation finds that the motor reinsurance price is negatively unit elastic to the demand for the motor reinsurance whereas the motor insurance gross claim, the group membership, and the total asset are positively correlated to the demand for the motor reinsurance

DIME-FM: DIstilling Multimodal and Efficient Foundation Models

Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and Florence, are trained on large-scale datasets of image-caption pairs and achieve superior transferability and robustness on downstream tasks, but they are difficult to use in many practical applications due to their large size, high latency and fixed architectures. Unfortunately, recent work shows training a small custom VLFM for resource-limited applications is currently very difficult using public and smaller-scale data. In this paper, we introduce a new distillation mechanism (DIME-FM) that allows us to transfer the knowledge contained in large VLFMs to smaller, customized foundation models using a relatively small amount of inexpensive, unpaired images and sentences. We transfer the knowledge from the pre-trained CLIP-ViTL/14 model to a ViT-B/32 model, with only 40M public images and 28.4M unpaired public sentences. The resulting model "Distill-ViT-B/32" rivals the CLIP-ViT-B/32 model pre-trained on its private WiT dataset (400M image-text pairs): Distill-ViT-B/32 achieves similar results in terms of zero-shot and linear-probing performance on both ImageNet and the ELEVATER (20 image classification tasks) benchmarks. It also displays comparable robustness when evaluated on five datasets with natural distribution shifts from ImageNet

SoK: Fully Homomorphic Encryption Accelerators

Fully Homomorphic Encryption~(FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the underlying polynomial computations with high computation complexity and extremely time-consuming ciphertext maintenance operations. To tackle this challenge, various FHE accelerators have recently been proposed by both research and industrial communities. This paper takes the first initiative to conduct a systematic study on the 14 FHE accelerators -- cuHE/cuFHE, nuFHE, HEAT, HEAX, HEXL, HEXL-FPGA, 100$\times$, F1, CraterLake, BTS, ARK, Poseidon, FAB and TensorFHE. We first make our observations on the evolution trajectory of these existing FHE accelerators to establish a qualitative connection between them. Then, we perform testbed evaluations of representative open-source FHE accelerators to provide a quantitative comparison on them. Finally, with the insights learned from both qualitative and quantitative studies, we discuss potential directions to inform the future design and implementation for FHE accelerators

Lightweight and privacy-aware fine-grained access control for IoT-oriented smart health

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