Enhancing ReViCe for Mitigating Cache Side-Channel Attacks: A Secure and Practical Approach

With more sensitive data being stored on computers, the cyber-attack risk is increasing globally. Therefore, it’s crucial to address security vulnerabilities before new threats emerge promptly and potentially cause significant harm in the future. Recently, Spectre and Meltdown attacks known as cache side-channel attacks exploit modern processor characteristics. Despite the seriousness of these attacks, they are under-researched and need more attention to safeguard our data. This thesis addresses how to optimize and improve the ReViCe, the solution for mitigating vul-nerabilities of cache side-channel attacks, a problem caused by characteristics of modern proces-sors. ReViCe enables speculative loads to refresh caches ahead of time while saving any removed line within the victim cache. If mis-speculation occurs, the replaced lines from the victim cache can be returned to the cache for undoing the cache changes, which can effectively isolate the cache changes for protecting us against cache-based Spectre and Meltdown attacks. We also introduce a more realistic, secure design for ReViCe. We enhance the security design by conducting experiments, tackling related work CacheRewinder, identifying the appropriate size of victim cache and buffer, and incorporating a deadlock-free algorithm. These changes allow us to implement a safer and more practical version of ReViCe, requiring less memory

Power Capability Estimation Accounting for Thermal and Electrical Constraints of Lithium-Ion Batteries.

Lithium-ion (Li-ion) batteries have become one of the most critical components in vehicle electrification due to their high specific power and energy density. The performance and longevity of these batteries rely on constraining their operation such that voltage and temperature are regulated within prescribed intervals. Enforcement of constraints on the power capability is a viable solution to protect Li-ion batteries from overheating as well as over-charge/discharge. Moreover, the ability to estimate power capability is vital in formulating power management strategies that account for battery performance limitations while minimizing fuel consumption and emissions. To estimate power capability accounting for thermal and electrical constraints, the characterization of thermal and electrical system behavior is required. In the course of addressing this problem, first, a computationally efficient thermal model for a cylindrical battery is developed. The solution of the convective heat transfer problem is approximated by polynomials with identifiable parameters that have physical meaning. The parameterized thermal model is shown to accurately predict the measured core and surface temperatures. The model-based thermal estimation methodology is augmented for cases of unknown cooling conditions. The proposed method is shown with experimental data to accurately provide estimates of the core temperature even under faults in the cooling system. To jointly account for the thermal and electrical constraints, we utilize time scale separation, and propose a real-time implementable method to predict power capability of a Li-ion battery. The parameterized battery thermal model and estimation algorithms are integrated into a power management system for a series hybrid electric vehicle. An algorithm for sequential estimation of coupled model parameters and states is developed using sensitivity-based parameter grouping. The fully integrated co-simulation of the battery electro-thermal behavior and the on-line adaptive estimators reveal that the power management system can effectively determine power flow among hybrid powertrain components without violating operational constraints.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107128/1/youngki_1.pd

Mondrian: On-Device High-Performance Video Analytics with Compressive Packed Inference

In this paper, we present Mondrian, an edge system that enables high-performance object detection on high-resolution video streams. Many lightweight models and system optimization techniques have been proposed for resource-constrained devices, but they do not fully utilize the potential of the accelerators over dynamic, high-resolution videos. To enable such capability, we devise a novel Compressive Packed Inference to minimize per-pixel processing costs by selectively determining the necessary pixels to process and combining them to maximize processing parallelism. In particular, our system quickly extracts ROIs and dynamically shrinks them, reflecting the effect of the fast-changing characteristics of objects and scenes. It then intelligently combines such scaled ROIs into large canvases to maximize the utilization of inference accelerators such as GPU. Evaluation across various datasets, models, and devices shows Mondrian outperforms state-of-the-art baselines (e.g., input rescaling, ROI extractions, ROI extractions+batching) by 15.0-19.7% higher accuracy, leading to $\times$6.65 higher throughput than frame-wise inference for processing various 1080p video streams. We will release the code after the paper review

Hierarchical Climate Control Strategy for Electric Vehicles with Door-Opening Consideration

This study proposes a novel climate control strategy for electric vehicles (EVs) by addressing door-opening interruptions, an overlooked aspect in EV thermal management. We create and validate an EV simulation model that incorporates door-opening scenarios. Three controllers are compared using the simulation model: (i) a hierarchical non-linear model predictive control (NMPC) with a unique coolant dividing layer and a component for cabin air inflow regulation based on door-opening signals; (ii) a single MPC controller; and (iii) a rule-based controller. The hierarchical controller outperforms, reducing door-opening temperature drops by 46.96% and 51.33% compared to single layer MPC and rule-based methods in the relevant section. Additionally, our strategy minimizes the maximum temperature gaps between the sections during recovery by 86.4% and 78.7%, surpassing single layer MPC and rule-based approaches, respectively. We believe that this result opens up future possibilities for incorporating the thermal comfort of passengers across all sections within the vehicle.Comment: This paper, intended for presentation at the IEEE Intelligent Vehicles Symposium (IV) 2024, comprises six pages and includes eight figure

Human mesangial cell production of monocyte chemoattractant protein-1: Modulation by lovastatin

Human mesangial cell production of monocyte chemoattractant protein-1: Modulation by lovastatin. Macrophages play a critical role in the progression of clinical and experimental glomerular injury. Serum-stimulated human fetal mesangial cells in culture produce a chemotactic factor that is monocyte-selective. This chemotactic factor is most likely monocyte chemoattractant protein-1 (MCP-1) as a monoclonal antibody directed against MCP-1, but not an irrelevant antibody, suppressed the mesangial cell-derived chemotactic activity. Inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase by lovastatin resulted in a reduction of the mesangial cell-derived chemotactic activity as well as MCP-1 mRNA expression. The inhibitory effects of lovastatin in the presence of exogenous cholesterol were reversed by mevalonate, suggesting a role for isoprenoid intermediates of the mevalonate pathway and/or isoprenylated proteins in mesangial cell MCP-1 regulation. These findings suggest an additional mechanism by which HMG-CoA reductase inhibition in vivo may reduce glomerular injury

High-resolution embedding extractor for speaker diarisation

Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each speech segment. However, because of the sliding window approach, a segment easily includes two or more speakers owing to speaker change points. This study proposes a novel embedding extractor architecture, referred to as a high-resolution embedding extractor (HEE), which extracts multiple high-resolution embeddings from each speech segment. Hee consists of a feature-map extractor and an enhancer, where the enhancer with the self-attention mechanism is the key to success. The enhancer of HEE replaces the aggregation process; instead of a global pooling layer, the enhancer combines relative information to each frame via attention leveraging the global context. Extracted dense frame-level embeddings can each represent a speaker. Thus, multiple speakers can be represented by different frame-level features in each segment. We also propose an artificially generating mixture data training framework to train the proposed HEE. Through experiments on five evaluation sets, including four public datasets, the proposed HEE demonstrates at least 10% improvement on each evaluation set, except for one dataset, which we analyse that rapid speaker changes less exist.Comment: 5pages, 2 figure, 3 tables, submitted to ICASS

Absolute decision corrupts absolutely: conservative online speaker diarisation

Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early phase of an input session can lead to catastrophic results. We hypothesise that cautiously increasing the number of estimated speakers is of paramount importance among many other factors. Thus, our proposed framework includes decreasing the number of speakers by one when the system judges that an increase in the past was faulty. We also adopt dual buffers, checkpoints and centroids, where checkpoints are combined with silhouette coefficients to estimate the number of speakers and centroids represent speakers. Again, we believe that more than one centroid can be generated from one speaker. Thus we design a clustering-based label matching technique to assign labels in real-time. The resulting system is lightweight yet surprisingly effective. The system demonstrates state-of-the-art performance on DIHARD 2 and 3 datasets, where it is also competitive in AMI and VoxConverse test sets.Comment: 5pages, 2 figure, 4 tables, submitted to ICASS

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N