Reviewerly/exHarmony huggingface dataset:
Political ties and the yield curve

We examine the effect of political ties with the US on sovereign yields and ratings at various horizons. We find beneficial effects across both short- and long-term yields and ratings. Specifically, we find that stronger political ties with the US affect mainly the level of the yield curve of foreign sovereign bonds. These results imply that the market perceives political ties with the US as having both near- and long-term beneficial consequences.
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion

Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited. However, most anomaly localization research in machine learning focuses on non-medical datasets, and we find that these methods fall short when adapted to medical imaging datasets. The problem is alleviated when we solve the task from the image completion perspective, in which the presence of anomalies can be indicated by a discrepancy between the original appearance and its auto-completion conditioned on the surroundings. However, there are often many valid normal completions given the same surroundings, especially in the DBT dataset, making this evaluation criterion less precise. To address such an issue, we consider pluralistic image completion by exploring the distribution of possible completions instead of generating fixed predictions. This is achieved through our novel application of spatial dropout on the completion network during inference time only, which requires no additional training cost and is effective at generating diverse completions. We further propose minimum completion distance (MCD), a new metric for detecting anomalies, thanks to these stochastic completions. We provide theoretical as well as empirical support for the superiority over existing methods of using the proposed method for anomaly localization. On the DBT dataset, our model outperforms other state-of-the-art methods by at least 10% AUROC for pixel-level detection.
Toward a theory-driven ontological framework for the representation of inhabitants in building performance computing

Building performance computing focuses primarily on physical processes in buildings. As such, early practices in building simulation adopted a reductionist approach to the representation of buildings' inhabitants. More recently, efforts have been undertaken to enhance the representational realism of inhabitants in building modeling. However, progress in this area requires a robust ontological foundation, which in turn requires a theoretical understanding of the relevant domain. Based on the appraisal of past efforts, this paper identifies a gap between behavioral theories and occupant representations in building models. Consequently, a high-level occupant behavior theory is introduced and its relevance for ontological developments is illustrated through a derivative representational scheme (“otto”: occupants theory-tailored ontology). The established link between behavioral theory and the derivative data schema is suggested to provide the necessary conditions for the development of a comprehensive ontological framework toward representation of inhabitants' presence and behavior in computational building models.
Automated Generation, Verification, and Ranking of Secure SoC Access Control Policies

Modern System-on-chip (SoC) architectures are a heterogeneous mix of microprocessors, custom accelerators, memories, interfaces, peripherals, and other resources. These resources communicate using complex on-chip interconnect networks that attempt to quickly and efficiently arbitrate memory transactions whose behaviors can vary drastically depending on the current mode of operation and system operating state. Security- and safety-critical applications require access control policies that define how these resources interact to ensure that malicious and unsafe behaviors do not occur. The process of defining and then verifying the security of these access control policies relies heavily on manual effort. This paper describes an automated methodology to generate, verify, and rank secure SoC access control policies. Our methodology targets access control policies for AKER access control systems.
Automatic Abstraction Refinement in Neural Network Verification using Sensitivity Analysis

The formal verification of neural networks is essential for their application in safety-critical environments. However, the set-based verification of neural networks using linear approximations often obtains overly conservative results, while nonlinear approximations quickly become computationally infeasible in deep neural networks. We address this issue for the first time by automatically balancing between precision and computation time without splitting the propagated set. Our work introduces a novel automatic abstraction refinement approach using sensitivity analysis to iteratively reduce the abstraction error at the neuron level until either the specifications are met or a maximum number of iterations is reached. Our evaluation shows that we can tightly over-approximate the output sets of deep neural networks and that our approach is up to a thousand times faster than a naive approach. We further demonstrate the applicability of our approach in closed-loop settings.
Adversarial Attacks on Adaptive Cruise Control Systems

DNN-based Adaptive Cruise Control (ACC) systems are very convenient but also safety critical. Although prior work has explored physical adversarial attacks on DNN models, those attacks are mostly static and their effects on a real-world ACC system are not clear. In this work, we propose the first end-to-end attack on ACC systems, and we test the safety indication on the state-of-the-art ACC products. The experimental results show that our approach can make the vehicle driving with ACC accelerate unsafely and cause a rear-end collision.
Protocol-agnostic IoT Device Classification on Encrypted Traffic Using Link-Level Flows

Convenience is a strong driver for the evolution of technology. Such efforts have given rise to the Internet-of-Things (IoT), defined as the network of everyday devices (i.e., “things”) ranging from light bulbs to smart speakers, connected to the Internet and each other. IoT devices frequently transmit data wirelessly which can be passively collected by an adversary. In this work we present a methodology with which to perform device classification on encrypted traffic in a protocol-agnostic manner by applying network flow analysis to link-level data. Our evaluation demonstrates successful device classification for 15 device categories with an overall weighted F1-Score of 95% on a dataset consisting of Wi-Fi, Bluetooth, and Zigbee traffic. Furthermore, we explore model transferability between encrypted and decrypted datasets on these three networking protocols and present our flow generation tool, ProtoFlow.
Software Introspection for Signaling Social-Cyber Operations

Open-source software (OSS) is a critical element in the design and operation of complex cyber-physical systems. Contributions to OSS projects are typically the result of voluntary work and time allocation by researchers, software developers, hackers, and even opportunistic programmers. These communities often operate on a “trust” basis, and although they strive to evaluate the technical correctness and merits of contributed code, the processes they use are usually loosely supervised. Social rules, trust, reputation, and even arcane processes often govern these communities. While these components have undoubtedly contributed to the growth and expansion of OSS, they could also lead to opportunities for subversion [3], hindering the reliability of an OSS project. This, in turn, could not only compromise the integrity of cyber-physical systems depending on OSS but also affect their performance.
Software-Shaped Platforms

This paper outlines the vision for a new type of software-shaped platforms, or SOSH platforms for short, that can be implemented in commercial CPU+FPGA platforms. At the core of the SOSH paradigm is the idea of exposing direct control over the flow of data exchanged between hardware components in embedded System-on-Chips (SoC). Data flow manipulation primitives are synthesized in reprogrammable hardware and interposed between central processors, memory modules, and I/O devices. A new layer of system software is then introduced to leverage such primitives and to achieve fine-grained control and introspection over the interaction of SoC resources. By turning memory and I/O data flows into manageable entities, a new degree of internal awareness can be achieved in complex systems. We first review recent works that are well aligned with the concept of data flow manipulation primitives that can be deployed in SOSH platforms. Next, we outline future research avenues concerning the use of the SOSH paradigm for workload profiling and prediction, to implement advanced memory models, and to perform security threat identification and mitigation.
Cooperative Verification of PLC Programs Using CoVeriTeam: Towards a reliable and secure Industrial Control Systems

It is important that the programmed logic controller(PLC), the heart of industrial control systems (ICS), is formally verified to ensure that its safety and security requirements are met. However, the combination of the strengths of two or more verification tools to verify tasks has yet to be fully explored, especially in verification of safety-critical programs such as PLC programs. In this paper, we apply cooperative verification techniques by building a verifier validator (using Symbiotic as a verifier and the CPA checker as a validator). We investigate the possibilities of preventing false positives in verification results and discover how effective and efficient the (verifier-validator) is when compared with a single verification tool. The results show that the verifier validator was able to discover 3 properties that were not properly verified by a single verification tool in a shorter time span.
Physics-Informed Machine Learning Model Generalization in AIoT: Opportunites and Challenges

Recent advances in machine learning inspire the development of deep neural network-based smart sensing applications for the Artificial Intelligence of Things (AIoT). However, due to the nature of the AIoT sensing data, the machine learning models are in general subject to poor generalizability due to the scarcity of labeled training data and run-time domain shifts. The existing solutions rely on data-driven approaches and do not consider the physical laws that govern data generation or domain shifts. This paper discusses the potential of utilizing the known physical laws to improve the machine learning model generalizability for AIoT applications. Through three case studies, we demonstrate that physics-informed machine learning can (1) effectively assist the generalization of deep neural networks and (2) achieve better performance compared with conventional approaches. Our objective is to encourage more exploration into combining physical principles and machine learning algorithms in physics-rich AIoT.
Surrogate Modeling using Physics-guided Learning

Computer simulation models are used extensively in scientific and engineering problems for complex design tasks and decision processes. Surrogate models generated using data-driven techniques can approximate the behavior of complex simulation models with high fidelity and can accelerate the design process. This paper presents a physics-guided learning architecture that integrates parameters extracted from physics-based simulations into the intermediate layers of a neural network to constrain the learning process during the training of surrogate models and to improve their generalization. The proposed architecture is used to develop a surrogate model for evaluating the structural integrity of the hull of an unmanned underwater vehicle. It is shown that physics-guided learning can improve generalization in less explored regions of the design space compared to black-box models. In addition, the architecture improves the explainability of the model predictions using physics-based parameters and allows the designer to make decisions based on the input and physics-based intermediate parameters.
HackWrt: Network Traffic-Based Eavesdropping of Handwriting

The rapidly expanding world of IoT continues to produce new products that are a combination of familiar tools and concepts with new technology, such as the smart watch and the smart home. The smartpen is one such product. Smartpens are capable of recording and transmitting user content to a connected device. This allows users to effortlessly digitize their handwritten content, but this also means that users can—through the use of smart pens—inadvertently expose sensitive, handwritten information as it passes from the pen to the host device. For instance, a doctor could use a smart pen to quickly note down patient observations. More simply, a user could use one to record private information they would rather keep from prying eyes. This being the case, it is important that we evaluate the security around using a smart pen. In this paper, we present HackWrt, a method of recreating handwritten characters from raw, possibly encrypted traffic. This paper details the method by which HackWrt reconstructs handwritten text. We also evaluate its performance and accuracy on a dataset. The promising results we obtained show that this is a feasible method for exfiltrating data.
Scanning the Issue

This article reviews graph-based hazardous event detection methods for automated and autonomous vehicles. Traditional methods have often fallen short due to the complexity of events that involve a multitude of variables with intricate relationships. Therefore, this research explores the opportunity presented by graph-based methods for relational reasoning. Reasoning uses graph structures that can organize heterogeneous data about the scene and its relationships. The article reviews and categorizes state-of-the-art methods, datasets, and evaluation metrics to provide a comprehensive overview of the latest advancements in the field, as well as key research opportunities and open challenges.
Evaluating the Virtual Reference Services in a University Library

This study aims to evaluate the virtual reference service (VRS) queries made by library users and to determine whether there are any differences in the types of queries made by them. 1,472 email threads of VRS received from users of Global Library, O. P. Jindal Global University, India, during 2022 were collected and analyzed. The study’s findings reveal that the most frequently requested VRS queries are related to research articles, followed by database access and off-campus access issues. The authors suggest that libraries should prioritize providing training on accessing the online database and use chat, chatbot, and WhatsApp to provide virtual reference services to users.
Towards A Systematic Literature Review for Evaluating Smart Home Environments using light bulb sensors

As more homes become digitally connected the use of assistive technologies to support the operation in the home environment especially when one thinks of the elderly and the sick, our research shows that there is a gap in the literature with respect to the measure of the effectiveness of the use of these assistive technologies to support the home as a smart home. We conduct quantitative usability experiments supported by a conceptual human interaction framework to assess this effectiveness. We conduct as proof of concept these experiments with the use of a Google mini automated assistant and an internet ready light bulb sensor , to determine the responsiveness to the human voice, at different location proximities around the home, and the correctness to these responses to qualify the effectiveness argument. In this study the authors use their primary residence amidst the COVID19 pandemic to demonstrate the type of concerns. Our explicit contributions in this study are (i) A systematic review of the literature in this area (ii) the design of the smart home network environment with the google mini assistant and light ulb sensor that supports the human voice interactions (iii) Identify a suitable conceptual human interaction framework to support the use of the smart devices connected within the home users environment to support our experiments (iv) Provide critical case evaluation of the measure of effectiveness of the usability experiments supported by this conceptual framework.
IDARE – Intelligently Denying Authorization of Rogue-drone Entry

The advancement of drone system for smart agriculture have been supporting the essential farming product demands by the increasing world population. However, there is a high demand for anti-rogue drone system is rising. In this work, we propose the design and implementation of three different secure authorization protocols for different situations and requirements when authorizing drones to join drone network. These protocols can detect and deny authorization of rogue drones, so that illegal activities of rogue drones can be eliminated. In this work, we first focus on how in a multi-drone environment a rogue drone can learn details of genuine drone authentication approach to avoid detection as rogue drone, then secondly proposed different approaches to securely identify and authorize genuine drones as legitimate drones to join drone network while denying rogue drone. To demonstrate our approach, we validated it in real-world experimental environment setup and evaluate performance on user friendly, programmable educational drone (Tello) systems. In our initial results, we have discussed real-world test cases scenarios and pointed out pros and cons of each approach. Finally, we reported the best suitable approach for the application in agricultural environment.
Development of a Custom Wrist Wearable for Monitoring Motion, Temperature, SPO2, and Heart Rate

The wearable technology market is consistently growing, and many of these devices being developed for commercial applications and individual use are already incorporating sensors that measure vital information such as heart rate, blood oxygen, temperature, quality of sleep, and physical information such as calories burned, gait, or steps taken. It is very challenging for a small research team to have access to the custom integrated circuits (ICs) and software algorithms these systems demand. These wearables are wireless sensors and not monitors with user displays and controls. Typical sensing devices consist of Inertial Measurement Units (IMUs), temperature sensors, heart rate (HR) sensors, and blood SPO2 sensors along with wireless communication and a rechargeable battery. The key objective is to develop a custom wrist-mounted wearable with optimal hardware and software design for measurement and power efficiency for battery lifetimes up to 1 month.
Convolutional Neural Networks: An Analysis of Varying Factors

In computer vision, Convolutional Neural Networks (CNN) are regarded well for their ability to process data with grid-like topology, such as images. However, with the wide variety of models available, as well as the number of external factors associated with the problem at hand, it can be difficult to determine what model works best for a specific problem. To address this, the paper aims to analyze and compare the performances of different Convolutional Neural Networks on a fashion image dataset. Additionally, this research highlights factors to consider when building or deciding on a model, as well as showing an example of what models are the most suitable for a specific problem size.
UnIC-Net: Uncertainty Aware Involution-Convolution Hybrid Network for Two-level Disease Identification

Convolution is commonly used in deep learning models for image classification problems, and its primary purpose is to retrieve representations in the spatial domain that are concealed from view. However, since convolution only works on a single channel at a time, it often ignores the cross-channel correlations that may exist in an image. Involution is an inversed process of convolutions, resolving the issues convolutions create. Involution is a location-specific process that are skillfully coupled with the well-established design of convolutions, which results in an excellent efficiency for the network, especially in cell-like images. But ensuring and analyzing robust and reliable performance in medical images are also very important, since uncertainty aware models are helpful when it comes to reducing the risk factors. We utilize Monte Carlo dropout (MCD), for introducing uncertainty-aware feature to our model. In this paper, we propose an uncertainty aware involution-convolution hybrid network that achieves 98.79% accuracy in HAM10000 dataset and 96.81% accuracy in Malaria parasitized cells dataset.
Mobile Augmented Reality Shopping System

This document serves as an extended abstract to detail the architecture for an augmented reality shopping application under current development for a thesis. The system consists of a progressive web app client that presents the augmented view to the user and performs object tracking, and a cloud server that performs object detection, localization, and the necessary product information retrieval and analysis.
Prediction Model of Breast Cancer Survival Months: A Machine Learning Approach

Breast cancer is one of the most common cancers in women. Because of the importance of early diagnosis in treatment success, the number of routine check-ups has recently risen. The survival rate is one of the top concerns of patients following a diagnosis. Current methods are to find survival rates using traditional statistical approaches by examining people of similar conditions. This study is one of a handful that looked at forecasting breast cancer survival time using Machine Learning (ML) techniques. Using data from 4024 patients from the NIH SEER program and a Random Forest classifier, we were able to estimate breast cancer survival time within a two-year window with up to 72% accuracy. It was also discovered that the patient's age, race, and marital status are all strongly associated with anticipated survival time. This study is a step toward broader use of ML science in clinic-generated data processing, ultimately improving current practices and opening new avenues for challenging medical research problems.
Decoding the Encoder

Autoencoders are used in a variety of safety-critical applications. Uncertainty quantification is a key component to bolster the trustworthiness of such models. With the growing complexity of the autoencoder design and the dataset they are trained on, there is a dwindling correlation between the input and feature space representation. To address this latent space degeneracy, we propose a novel method of monotonically perturbing the encoded latent space to increase the entropy in the learned representations for every corresponding input. For every perturbation, we obtain a unique decoded signature corresponding to an evaluation metric in the continuous domain, which can be clustered to build a knowledge base and subsequently analyzed for outlier analysis. For the test cases, in the absence of ground truth, we can perturb the latent space representation and find the closest match of the test cases’ unique signatures to the existing knowledge base for uncertainty quantification and outlier detection. We evaluate our proposed novel method on glomeruli segmentation for frozen kidney donor section on whole slide imaging, a safety-critical application in digital pathology which serves as a precursor to kidney transplantation. We prove the proposed method’s effectiveness for outlier detection by ranking the test cases according to their associated uncertainties to leverage the attention of medical experts on boundary cases.