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Cryptocurrency introduces usability challenges by requiring users to manage signing keys. Popular signing key management services (e.g., custodial wallets), however, either introduce a trusted party or burden users with managing signing key shares, posing the same usability challenges. TEE (Trusted Execution Environment) is a promising technology to avoid both, but practical implementations of TEEs suffer from various side-channel attacks that have proven hard to eliminate. This paper explores a new approach to side-channel mitigation through economic incentives for TEE-based cryptocurrency wallet solutions. By taking the cost and profit of side-channel attacks into consideration, we designed a Stick-and-Carrot-based cryptocurrency wallet, CrudiTEE, that leverages penalties (the stick) and rewards (the carrot) to disincentivize attackers from exfiltrating signing keys in the first place. We model the attacker’s behavior using a Markov Decision Process (MDP) to evaluate the effectiveness of the bounty and enable the service provider to adjust the parameters of the bounty’s reward function accordingly. 

This empirical case study utilizes conjecture mapping to capture and systematically map conjectures about the support needed for K-12 teachers to incorporate computational thinking into teaching. The case analysis highlighted a teacher’s year-long professional development experience focused on integrating computational thinking. The evolving conjecture map provides a framework to trace and understand relationships between the learning designs, activities, and teacher outcomes. Using rich data from the teacher's experience, the study tests and refines the hypothesized connections laid out in the original conjecture map to build an understanding of effective computational thinking professional development design. 

The rate at which humanity is producing data has increased sig- nificantly over the last decade. As organizations generate unprece- dented amounts of data, storing, cleaning, integrating, and ana- lyzing this data consumes significant (human and computational) resources. At the same time organizations extract significant value from their data. In this work, we present our vision for develop- ing an objective metric for the value of data based on the recently introduced concept of data relevance, outline proposals for how to efficiently compute and maintain such metrics, and how to utilize data value to improve data management including storage organi- zation, query performance, intelligent allocation of data collection and curation efforts, improving data catalogs, and for making pric- ing decisions in data markets. While we mostly focus on tabular data, the concepts we introduce can also be applied to other data models such as semi-structure data (e.g., JSON) or property graphs. Furthermore, we discuss strategies for dealing with data and work- loads that evolve and discuss how to deal with data that is currently not relevant, but has potential value (we refer to this as dark data). Furthermore, we sketch ideas for measuring the value that a query / workload has for an organization and reason about the interaction between query and data value. 

The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption. 

We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning augmented algorithm achieves both consistency and robustness.