Defense Notices

This project explores the adaptation of the U-Net convolutional neural network, renowned for its medical image segmentation prowess, to the analysis of Printed Circuit Boards (PCBs). By utilizing the Fine-Printed Circuit Board Image Collection (FPIC) dataset, we address key challenges in PCB inspection, such as the precise segmentation of complex components, handling class imbalances, and capturing minute details. The U-Net model has been finely tuned with an encoding-decoding architecture, enhanced by convolutional layers, batch normalization, and dropout techniques to extract and reconstruct high-quality features from PCB images effectively. The Dice coefficient, used as the loss function, significantly improves boundary accuracy, and manages class diversity. Throughout extensive training and validation phases, the model has demonstrated superior performance metrics compared to traditional methods, making substantial advancements in automated PCB inspection. During the rigorous training and validation stages, the U-Net model demonstrated excellent performance metrics, eclipsing traditional inspection methods. For capacitors, the model achieved a training accuracy of 95.03% and a validation accuracy of 95.92%. For resistors, training using transfer learning techniques resulted in even more remarkable performance, with training accuracy reaching 98% and validation accuracy hitting 98.23%. These metrics highlight the model's robustness and accuracy, marking a significant advancement in automated PCB inspection and suggesting the model's potential for wider industrial applications in multiclass component segmentation within complex PCB.

In this dissertation our goal is to evaluate how the loss of information at binary-level affects the performance of existing compiler-level techniques in terms of both efficiency and effectiveness. Binary analysis is difficult, as most of semantic and syntactic information available at source-level gets lost during the compilation process. If the binary is stripped and/ or optimized, then it negatively affects the efficacy of binary analysis frameworks. Moreover, handwritten assembly, obfuscation, excessive indirect calls or jumps, etc. further degrade the accuracy of binary analysis. Challenges to precise binary analysis have implications on the effectiveness, accuracy, and performance, of security and program hardening techniques implemented at the binary level. While these challenges are well-known, their respective impacts on the effectiveness and performance of program hardening techniques are less well-studied.

In this dissertation, we employ classes of defense mechanisms to protect software from the most common software attacks, like buffer overflows and control flow attacks, to determine how this loss of program information at the binary-level affects the effectiveness and performance of defense mechanisms. Additionally, we aim to tackle an important problem of type recovery from binary executables that in turn help bolster the software protection mechanisms.

Advancements in web technologies have significantly expanded access to diverse cultural narratives, yet black literature remains underrepresented in digital domains. The BlackLitNetwork addresses this oversight by harnessing Elasticsearch, MongoDB, React, Python, CSS, HTML, and Node.js, to enhance the discoverability and engagement with black novels. A major component of the platform is a novel generator built with Elasticsearch, which employs powerful full-text search capabilities, essential for users to navigate an extensive literary database effectively.

MongoDB supports the archives platform with a flexible data schema for managing varied literary content efficiently, while Python facilitates robust data cleaning and preprocessing to ensure data integrity and usability. The user interface, created using React, transforms Figma designs from our design team into a dynamic web presence, integrating HTML and CSS to ensure both aesthetic appeal and accessibility.

To further enhance security and manageability, we've implemented a Node.js backend. This layer acts as a middleware, managing and processing requests between our frontend and Elasticsearch. This not only secures our data interactions but also allows for request handling before querying Elasticsearch. This architecture ensures that BlackLitNetwork remains scalable and maintainable.

BlackLitNetwork also features specialized pages for podcasts, briefs, and interactive data visualizations, each designed to highlight historical, and contextual elements of black literature. These components aid in fostering a deeper understanding, establishing BlackLitNetwork as a tool for scholars. This project not only enriches the field of humanities but also promotes a broader understanding of the black literary heritage, making it a resource for researchers, educators, and readers keen on exploring the richness of black literature.

This project aims to enhance predictive models for forecasting healthcare resource demand, particularly focusing on hospital bed occupancy and emergency room visits while considering external factors such as disease outbreaks and weather conditions. Utilizing a range of machine learning techniques, the research seeks to improve the accuracy and reliability of these forecasts, essential for optimizing healthcare resource management. The project involves multiple phases, starting with the collection and preparation of historical data from public health databases and hospital records, enriched with external variables such as weather patterns and epidemiological data. Advanced feature engineering is key, transforming raw data into a machine learning-friendly format, including temporal and lag features to identify patterns and trends. The study explores various machine learning methods, from traditional models like ARIMA to advanced techniques such as LSTM networks and GRU models, incorporating rigorous training and validation protocols to ensure robust performance. Model effectiveness is evaluated using metrics like MAE, RMSE, and MAPE, with a strong focus on model interpretability and explainability through techniques like SHAP and LIME. The project also addresses practical implementation challenges and ethical considerations, aiming to bridge academic research with practical healthcare applications. Findings are intended for dissemination through academic papers and conferences, ensuring that the models developed meet both the ethical standards and practical needs of the healthcare industry.

This paper introduces a novel approach to manage collections of artifacts through smart contract access control, rooted in on-chain role-based property-level access control. This smart contract facilitates the lifecycle of these artifacts including allowing for the creation, modification, removal, and historical auditing of the artifacts through both direct and suggested actions. This method introduces a collection object designed to store role privileges concerning state object properties. User roles are defined within an on-chain entity that maps users' signed identities to roles across different collections, enabling a single user to assume varying roles in distinct collections. Unlike existing key-level endorsement mechanisms, this approach offers finer-grained privileges by defining them on a per-property basis, not at the key level. The outcome is a more flexible and fine-grained access control system seamlessly integrated into the smart contract itself, empowering administrators to manage access with precision and adaptability across diverse organizational contexts. This has the added benefit of allowing for the auditing of not only the history of the artifacts, but also for the permissions granted to the users.  

The explosion of social media, while fostering connection, inadvertently exposes personal details that heighten password vulnerability. This thesis tackles this critical link, aiming to raise public awareness of the dangers of weak passwords and excessive online sharing. We introduce a novel password guessing algorithm, SocGuess, which capitalizes on the rich trove of information on social media profiles. SocGuess leverages Named Entity Recognition (NER) to identify key data points within this information, such as dates, locations, and names. To further enhance its accuracy, SocGuess is trained on the rockyou dataset, a large collection of leaked passwords. By identifying different kinds of entities within these passwords, SocGuess can calculate the probability of these entities appearing in passwords. Armed with this knowledge, SocGuess dynamically generates password guesses in order of probability by filling these entity placeholders with the corresponding data points harvested from the target’s social media profiles. This targeted approach shows SocGuess to crack 33% more passwords than existing algorithms during experimentation, demonstrably surpassing traditional methods.

Writing algorithms for a parallel or distributed environment has always been plagued with a variety of challenges, from supervising synchronous reads and writes, to managing job queues and avoiding deadlock. While many languages have libraries or language constructs to mitigate these obstacles, very few attempt to remove those challenges entirely, and even fewer do so while divorcing the means of handling those problems from the means of parallelization or distribution. This project introduces a language called Swarm, which attempts to do just that.

Swarm is a first-class parallel/distributed programming language with modular, swappable parallel drivers. It is intended for everything from multi-threaded local computation on a single machine to large scientific computations split across many nodes in a cluster.

Swarm contains next to no explicit syntax for typical parallel logic, only containing keywords for declaring which variables should reside in shared memory, and describing what code should be parallelized. The remainder of the logic (such as waiting for the results from distributed jobs or locking shared accesses) are added in when compiling to a custom bytecode called Swarm Virtual Instructions (SVI). SVI is then executed by a virtual machine whose parallelization logic is abstracted out, such that the same SVI bytecode can be executed in any parallel/distributed environment.

Full-system simulation of computer systems is critical for capturing the complex interplay between various hardware and software components in future systems. Modeling the network subsystem is indispensable for the fidelity of full-system simulations due to the increasing importance of scale-out systems. Over the last decade, the network software stack has undergone major changes, with userspace networking stacks and data-plane networks rapidly replacing the conventional kernel network stack. Nevertheless, the current state-of-the-art architectural simulator, gem5, still employs kernel networking, which precludes realistic network application scenarios.

First, we perform a comprehensive profiling study to identify and propose architectural optimizations to accelerate a state-of-the-art architectural simulator. We choose gem5 as the representative architectural simulator, run several simulations with various configurations, perform a detailed architectural analysis of the gem5 source code on different server platforms, tune both system and architectural settings for running simulations, and discuss the future opportunities in accelerating gem5 as an important application. Our detailed profiling of gem5 reveals that its performance is extremely sensitive to the size of the L1 cache. Our experimental results show that a RISC-V core with 32KB data and instruction cache improves gem5’s simulation speed by 31%∼61% compared with a baseline core with 8KB L1 caches. Second, this work extends gem5’s networking capabilities by integrating kernel-bypass/user-space networking based on the DPDK framework, significantly enhancing network throughput and reducing latency. By enabling user-space networking, the simulator achieves a substantial 6.3× improvement in network bandwidth compared to traditional Linux software stacks. Our hardware packet generator model (EtherLoadGen) provides up to a 2.1× speedup in simulation time. Additionally, we develop a suite of networking micro-benchmarks for stress testing the host network stack, allowing for efficient evaluation of gem5’s performance. Through detailed experimental analysis, we characterize the performance differences when running the DPDK network stack on both real systems and gem5, highlighting the sensitivity of DPDK performance to various system and microarchitecture parameters.

Coupled line couplers are used as directional couplers to enable measurement of forward and reverse power in RF transmitters. These measurements provide valuable feedback to the control loops regulating transmitter power output levels. This project seeks to synthesize, simulate, build, and test a broadband, five-stage coupled line coupler with a 20 dB coupling factor. The coupler synthesis is evaluated against ideal coupler components in Keysight ADS.  Fabrication of coupled line couplers is typically accomplished with a stripline topology, but a microstrip topology is additionally evaluated. Measurements from the fabricated coupled line couplers are then compared to the Keysight ADS EM simulations, and some explanations for the differences are provided. Additionally, measurements from a commercially available broadband directional coupler are provided to show what can be accomplished with the right budget.

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.