3,832 research outputs found
Language Design for Reactive Systems: On Modal Models, Time, and Object Orientation in Lingua Franca and SCCharts
Reactive systems play a crucial role in the embedded domain. They continuously interact with their environment, handle concurrent operations, and are commonly expected to provide deterministic behavior to enable application in safety-critical systems. In this context, language design is a key aspect, since carefully tailored language constructs can aid in addressing the challenges faced in this domain, as illustrated by the various concurrency models that prevent the known pitfalls of regular threads. Today, many languages exist in this domain and often provide unique characteristics that make them specifically fit for certain use cases. This thesis evolves around two distinctive languages: the actor-oriented polyglot coordination language Lingua Franca and the synchronous statecharts dialect SCCharts. While they take different approaches in providing reactive modeling capabilities, they share clear similarities in their semantics and complement each other in design principles. This thesis analyzes and compares key design aspects in the context of these two languages. For three particularly relevant concepts, it provides and evaluates lean and seamless language extensions that are carefully aligned with the fundamental principles of the underlying language. Specifically, Lingua Franca is extended toward coordinating modal behavior, while SCCharts receives a timed automaton notation with an efficient execution model using dynamic ticks and an extension toward the object-oriented modeling paradigm
Sweep-UC: Swapping Coins Privately
Fair exchange (also referred to as atomic swap) is a fundamental operation in any cryptocurrency that allows users to atomically exchange coins.
While a large body of work has been devoted to this problem, most solutions lack on-chain privacy. Thus, coins retain a public transaction history which is known to degrade the fungibility of a currency. This has led to a flourishing line of related research on fair exchange with privacy guarantees. Existing protocols either rely on heavy scripting (which also degrades fungibility and leads to high transaction fees), do not support atomic swaps across a wide range of currencies, or come with incomplete security proofs.
To overcome these limitations, we introduce Sweep-UC (Read as Sweep Ur Coins.), the first fair exchange protocol that simultaneously is efficient, minimizes scripting, and is compatible with a wide range of currencies (more than the state of the art). We build Sweep-UC from modular sub-protocols and give a rigorous security analysis in the UC framework. Many of our tools and security definitions can be used in standalone fashion and may serve as useful components for future constructions of fair exchange
In principle vs in practice: User, expert and policymaker attitudes towards the right to data portability in the internet of things
The right to data portability (RtDP) was enshrined in law with the introduction of the EU's General Data Orotection Regulation (GDPR, Article 20) in 2018. RtDP gives a user the right to obtain and transfer their data to a different service, and the data controller the obligation to facilitate this transfer. Since GDPR's implementation, RtDP has been highlighted in the Digital Markets Act (DMA; 2022) and the proposed Data Act. Despite these reinforcements, there are gaps in understanding of RtDP amongst digital service users. Additionally, many organisations struggle to facilitate data transfer, particularly when it comes to the Internet of Things (IoT). This study examines the attitudes towards IoT data portability by conducting semi-structured interviews with users of consumer IoT devices (n = 28), academics/industry experts (n = 11) and policymakers (n = 8). Results indicate that whilst policymakers and consumers value this right in principle, it is rendered meaningless without a data subject's ability to exercise it in practice. A lack of guidance for data controllers and consumers has created an atmosphere of uncertainty which urgently needs to be addressed
Language integrated relational lenses
Relational databases are ubiquitous. Such monolithic databases accumulate large
amounts of data, yet applications typically only work on small portions of the data
at a time. A subset of the database defined as a computation on the underlying
tables is called a view. Querying views is helpful, but it is also desirable to update
them and have these changes be applied to the underlying database. This view
update problem has been the subject of much previous work before, but support
by database servers is limited and only rarely available.
Lenses are a popular approach to bidirectional transformations, a generalization
of the view update problem in databases to arbitrary data. However, perhaps surprisingly, lenses have seldom actually been used to implement updatable views in
databases. Bohannon, Pierce and Vaughan propose an approach to updatable views called relational lenses. However, to the best of our knowledge this
proposal has not been implemented or evaluated prior to the work reported in
this thesis.
This thesis proposes programming language support for relational lenses. Language integrated relational lenses support expressive and efficient view updates,
without relying on updatable view support from the database server. By integrating relational lenses into the programming language, application development
becomes easier and less error-prone, avoiding the impedance mismatch of having
two programming languages. Integrating relational lenses into the language poses
additional challenges. As defined by Bohannon et al. relational lenses completely
recompute the database, making them inefficient as the database scales. The
other challenge is that some parts of the well-formedness conditions are too general for implementation. Bohannon et al. specify predicates using possibly infinite
abstract sets and define the type checking rules using relational algebra.
Incremental relational lenses equip relational lenses with change-propagating semantics that map small changes to the view into (potentially) small changes
to the source tables. We prove that our incremental semantics are functionally
equivalent to the non-incremental semantics, and our experimental results show
orders of magnitude improvement over the non-incremental approach. This thesis introduces a concrete predicate syntax and shows how the required checks
are performed on these predicates and show that they satisfy the abstract predicate specifications. We discuss trade-offs between static predicates that are fully
known at compile time vs dynamic predicates that are only known during execution and introduce hybrid predicates taking inspiration from both approaches.
This thesis adapts the typing rules for relational lenses from sequential composition to a functional style of sub-expressions. We prove that any well-typed
functional relational lens expression can derive a well-typed sequential lens.
We use these additions to relational lenses as the foundation for two practical implementations: an extension of the Links functional language and a library written
in Haskell. The second implementation demonstrates how type-level computation can be used to implement relational lenses without changes to the compiler.
These two implementations attest to the possibility of turning relational lenses
into a practical language feature
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Performance Anomalies in Concurrent Data Structure Microbenchmarks
Recent decades have witnessed a surge in the development of concurrent data structures with an increasing interest in data structures implementing concurrent sets (CSets). Microbenchmarking tools are frequently utilized to evaluate and compare the performance differences across concurrent data structures. The underlying structure and design of the microbenchmarks themselves can play a hidden but influential role in performance results. However, the impact of microbenchmark design has not been well investigated. In this work, we illustrate instances where concurrent data structure performance results reported by a microbenchmark can vary 10-100x depending on the microbenchmark implementation details. We investigate factors leading to performance variance across three popular microbenchmarks and outline cases in which flawed microbenchmark design can lead to an inversion of performance results between two concurrent data structure implementations. We further derive a set of recommendations for best practices in the design and usage of concurrent data structure microbenchmarks and explore advanced features in the Setbench microbenchmark
Differentially-Private Decision Trees with Probabilistic Robustness to Data Poisoning
Decision trees are interpretable models that are well-suited to non-linear
learning problems. Much work has been done on extending decision tree learning
algorithms with differential privacy, a system that guarantees the privacy of
samples within the training data. However, current state-of-the-art algorithms
for this purpose sacrifice much utility for a small privacy benefit. These
solutions create random decision nodes that reduce decision tree accuracy or
spend an excessive share of the privacy budget on labeling leaves. Moreover,
many works do not support or leak information about feature values when data is
continuous. We propose a new method called PrivaTree based on private
histograms that chooses good splits while consuming a small privacy budget. The
resulting trees provide a significantly better privacy-utility trade-off and
accept mixed numerical and categorical data without leaking additional
information. Finally, while it is notoriously hard to give robustness
guarantees against data poisoning attacks, we prove bounds for the expected
success rates of backdoor attacks against differentially-private learners. Our
experimental results show that PrivaTree consistently outperforms previous
works on predictive accuracy and significantly improves robustness against
backdoor attacks compared to regular decision trees
Digital Innovations for a Circular Plastic Economy in Africa
Plastic pollution is one of the biggest challenges of the twenty-first century that requires innovative and varied solutions. Focusing on sub-Saharan Africa, this book brings together interdisciplinary, multi-sectoral and multi-stakeholder perspectives exploring challenges and opportunities for utilising digital innovations to manage and accelerate the transition to a circular plastic economy (CPE).
This book is organised into three sections bringing together discussion of environmental conditions, operational dimensions and country case studies of digital transformation towards the circular plastic economy. It explores the environment for digitisation in the circular economy, bringing together perspectives from practitioners in academia, innovation, policy, civil society and government agencies. The book also highlights specific country case studies in relation to the development and implementation of different innovative ideas to drive the circular plastic economy across the three sub-Saharan African regions. Finally, the book interrogates the policy dimensions and practitioner perspectives towards a digitally enabled circular plastic economy.
Written for a wide range of readers across academia, policy and practice, including researchers, students, small and medium enterprises (SMEs), digital entrepreneurs, non-governmental organisations (NGOs) and multilateral agencies, policymakers and public officials, this book offers unique insights into complex, multilayered issues relating to the production and management of plastic waste and highlights how digital innovations can drive the transition to the circular plastic economy in Africa.
The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
- …