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Computing and networking are increasingly implemented in software. We design and build a software build assurance scheme detecting if there have been injections or modifications in the various steps in the software supply chain, including the source code, compiling, and distribution. Building on the reproducible build and software bill of materials (SBOM), our work is distinguished from previous research in assuring multiple software artifacts across the software supply chain. Reproducible build, in particular, enables our scheme, as our scheme requires the software materials/artifacts to be consistent across machines with the same operating system/specifications. Furthermore, we use blockchain to deliver the proof reference, which enables our scheme to be distributed so that the assurance beneficiary and verifier are the same, i.e., the node downloading the software verifies its own materials, artifacts, and outputs. Blockchain also significantly improves the assurance efficiency. We first describe and explain our scheme using abstraction and then implement our scheme to assure Ethereum as the target software to provide concrete proof-of-concept implementation, validation, and experimental analyses. Our scheme enables more significant performance gains than relying on a centralized server thanks to the use of blockchain (e.g., two to three orders of magnitude quicker in verification) and adds small overheads (e.g., generating and verifying proof have an overhead of approximately one second, which is two orders of magnitude smaller than the software download or build processes). 

Cryptocurrency software implements the cryptocurrency operations. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. More specifically, we focus on Bitcoin for implementation and integration and advance its Version-message based hand-shaking and thus call our scheme Version++, The Version++ protocol provides software assurance, which is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Utilizing Merkle Tree for the verification efficiency, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. This paper for the conference demonstration supplements our technical paper at CCNC 2023 for synergy but highlights the prototyping and demonstration components of our research. 

Cryptocurrency software implements the cryptocurrency operations, including the distributed consensus protocol and the peer-to-peer networking. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight due to the dynamic nature of the peer connections and the large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. In addition, the Version++ verification overhead is small compared to the overall handshaking process; our measured overhead of 2.22% with minimal networking latency between the virtual machines provides an upper bound in the real-world networking with greater handshaking duration, i.e., the relative Version++ overhead in the real world with physically separate machines will be smaller. 

Computer-generated holography (CGH) holds transformative potential for a wide range of applications, including direct-view, virtual and augmented reality, and automotive display systems. While research on holographic displays has recently made impressive progress, image quality and eye safety of holographic displays are fundamentally limited by the speckle introduced by coherent light sources. Here, we develop an approach to CGH using partially coherent sources. For this purpose, we devise a wave propagation model for partially coherent light that is demonstrated in conjunction with a camera-in-the-loop calibration strategy. We evaluate this algorithm using light-emitting diodes (LEDs) and superluminescent LEDs (SLEDs) and demonstrate improved speckle characteristics of the resulting holograms compared with coherent lasers. SLEDs in particular are demonstrated to be promising light sources for holographic display applications, because of their potential to generate sharp and high-contrast two-dimensional (2D) and 3D images that are bright, eye safe, and almost free of speckle. 

Holographic near-eye displays promise unprecedented capabilities for virtual and augmented reality (VR/AR) systems. The image quality achieved by current holographic displays, however, is limited by the wave propagation models used to simulate the physical optics. We propose a neural network-parameterized plane-to-multiplane wave propagation model that closes the gap between physics and simulation. Our model is automatically trained using camera feedback and it outperforms related techniques in 2D plane-to-plane settings by a large margin. Moreover, it is the first network-parameterized model to naturally extend to 3D settings, enabling high-quality 3D computer-generated holography using a novel phase regularization strategy of the complex-valued wave field. The efficacy of our approach is demonstrated through extensive experimental evaluation with both VR and optical see-through AR display prototypes.