2,226 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Risk and threat mitigation techniques in internet of things (IoT) environments: a survey
Security in the Internet of Things (IoT) remains a predominant area of concern. Although several other surveys have been published on this topic in recent years, the broad spectrum that this area aims to cover, the rapid developments and the variety of concerns make it impossible to cover the topic adequately. This survey updates the state of the art covered in previous surveys and focuses on defences and mitigations against threats rather than on the threats alone, an area that is less extensively covered by other surveys. This survey has collated current research considering the dynamicity of the IoT environment, a topic missed in other surveys and warrants particular attention. To consider the IoT mobility, a life-cycle approach is adopted to the study of dynamic and mobile IoT environments and means of deploying defences against malicious actors aiming to compromise an IoT network and to evolve their attack laterally within it and from it. This survey takes a more comprehensive and detailed step by analysing a broad variety of methods for accomplishing each of the mitigation steps, presenting these uniquely by introducing a “defence-in-depth” approach that could significantly slow down the progress of an attack in the dynamic IoT environment. This survey sheds a light on leveraging redundancy as an inherent nature of multi-sensor IoT applications, to improve integrity and recovery. This study highlights the challenges of each mitigation step, emphasises novel perspectives, and reconnects the discussed mitigation steps to the ground principles they seek to implement
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Many deep learning applications benefit from using large models with billions
of parameters. Training these models is notoriously expensive due to the need
for specialized HPC clusters. In this work, we consider alternative setups for
training large models: using cheap "preemptible" instances or pooling existing
resources from multiple regions. We analyze the performance of existing
model-parallel algorithms in these conditions and find configurations where
training larger models becomes less communication-intensive. Based on these
findings, we propose SWARM parallelism, a model-parallel training algorithm
designed for poorly connected, heterogeneous and unreliable devices. SWARM
creates temporary randomized pipelines between nodes that are rebalanced in
case of failure. We empirically validate our findings and compare SWARM
parallelism with existing large-scale training approaches. Finally, we combine
our insights with compression strategies to train a large Transformer language
model with 1B shared parameters (approximately 13B before sharing) on
preemptible T4 GPUs with less than 200Mb/s network.Comment: Accepted to International Conference on Machine Learning (ICML) 2023.
25 pages, 8 figure
Towards trustworthy computing on untrustworthy hardware
Historically, hardware was thought to be inherently secure and trusted due to its
obscurity and the isolated nature of its design and manufacturing. In the last two
decades, however, hardware trust and security have emerged as pressing issues.
Modern day hardware is surrounded by threats manifested mainly in undesired
modifications by untrusted parties in its supply chain, unauthorized and pirated
selling, injected faults, and system and microarchitectural level attacks. These threats,
if realized, are expected to push hardware to abnormal and unexpected behaviour
causing real-life damage and significantly undermining our trust in the electronic and
computing systems we use in our daily lives and in safety critical applications. A
large number of detective and preventive countermeasures have been proposed in
literature. It is a fact, however, that our knowledge of potential consequences to
real-life threats to hardware trust is lacking given the limited number of real-life
reports and the plethora of ways in which hardware trust could be undermined. With
this in mind, run-time monitoring of hardware combined with active mitigation of
attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed
as the last line of defence. This last line of defence allows us to face the issue of live
hardware mistrust rather than turning a blind eye to it or being helpless once it occurs.
This thesis proposes three different frameworks towards trustworthy computing
on untrustworthy hardware. The presented frameworks are adaptable to different
applications, independent of the design of the monitored elements, based on
autonomous security elements, and are computationally lightweight. The first
framework is concerned with explicit violations and breaches of trust at run-time,
with an untrustworthy on-chip communication interconnect presented as a potential
offender. The framework is based on the guiding principles of component guarding,
data tagging, and event verification. The second framework targets hardware elements
with inherently variable and unpredictable operational latency and proposes a
machine-learning based characterization of these latencies to infer undesired latency
extensions or denial of service attacks. The framework is implemented on a DDR3
DRAM after showing its vulnerability to obscured latency extension attacks. The
third framework studies the possibility of the deployment of untrustworthy hardware
elements in the analog front end, and the consequent integrity issues that might arise
at the analog-digital boundary of system on chips. The framework uses machine
learning methods and the unique temporal and arithmetic features of signals at this
boundary to monitor their integrity and assess their trust level
Intelligent embedded systems platform for vehicular cyber-physical systems
Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems
Anpassen verteilter eingebetteter Anwendungen im laufenden Betrieb
The availability of third-party apps is among the key success factors for software ecosystems: The users benefit from more features and innovation speed, while third-party solution vendors can leverage the platform to create successful offerings.
However, this requires a certain decoupling of engineering activities of the different parties not achieved for distributed control systems, yet.
While late and dynamic integration of third-party components would be required, resulting control systems must provide high reliability regarding real-time requirements, which leads to integration complexity.
Closing this gap would particularly contribute to the vision of software-defined manufacturing, where an ecosystem of modern IT-based control system components could lead to faster innovations due to their higher abstraction and availability of various frameworks.
Therefore, this thesis addresses the research question:
How we can use modern IT technologies and enable independent evolution and easy third-party integration of software components in distributed control systems, where deterministic end-to-end reactivity is required, and especially, how can we apply distributed changes to such systems consistently and reactively during operation?
This thesis describes the challenges and related approaches in detail and points out that existing approaches do not fully address our research question.
To tackle this gap, a formal specification of a runtime platform concept is presented in conjunction with a model-based engineering approach.
The engineering approach decouples the engineering steps of component definition, integration, and deployment.
The runtime platform supports this approach by isolating the components, while still offering predictable end-to-end real-time behavior.
Independent evolution of software components is supported through a concept for synchronous reconfiguration during full operation, i.e., dynamic orchestration of components.
Time-critical state transfer is supported, too, and can lead to bounded quality degradation, at most.
The reconfiguration planning is supported by analysis concepts, including simulation of a formally specified system and reconfiguration, and analyzing potential quality degradation with the evolving dataflow graph (EDFG) method.
A platform-specific realization of the concepts, the real-time container architecture, is described as a reference implementation.
The model and the prototype are evaluated regarding their feasibility and applicability of the concepts by two case studies.
The first case study is a minimalistic distributed control system used in different setups with different component variants and reconfiguration plans to compare the model and the prototype and to gather runtime statistics.
The second case study is a smart factory showcase system with more challenging application components and interface technologies.
The conclusion is that the concepts are feasible and applicable, even though the concepts and the prototype still need to be worked on in future -- for example, to reach shorter cycle times.Eine große Auswahl von Drittanbieter-Lösungen ist einer der Schlüsselfaktoren für Software Ecosystems:
Nutzer profitieren vom breiten Angebot und schnellen Innovationen, während Drittanbieter über die Plattform erfolgreiche Lösungen anbieten können.
Das jedoch setzt eine gewisse Entkopplung von Entwicklungsschritten der Beteiligten voraus, welche für verteilte Steuerungssysteme noch nicht erreicht wurde.
Während Drittanbieter-Komponenten möglichst spät -- sogar Laufzeit -- integriert werden müssten, müssen Steuerungssysteme jedoch eine hohe Zuverlässigkeit gegenüber Echtzeitanforderungen aufweisen, was zu Integrationskomplexität führt.
Dies zu lösen würde insbesondere zur Vision von Software-definierter Produktion beitragen, da ein Ecosystem für moderne IT-basierte Steuerungskomponenten wegen deren höherem Abstraktionsgrad und der Vielzahl verfügbarer Frameworks zu schnellerer Innovation führen würde.
Daher behandelt diese Dissertation folgende Forschungsfrage:
Wie können wir moderne IT-Technologien verwenden und unabhängige Entwicklung und einfache Integration von Software-Komponenten in verteilten Steuerungssystemen ermöglichen, wo Ende-zu-Ende-Echtzeitverhalten gefordert ist, und wie können wir insbesondere verteilte Änderungen an solchen Systemen konsistent und im Vollbetrieb vornehmen?
Diese Dissertation beschreibt Herausforderungen und verwandte Ansätze im Detail und zeigt auf, dass existierende Ansätze diese Frage nicht vollständig behandeln.
Um diese Lücke zu schließen, beschreiben wir eine formale Spezifikation einer Laufzeit-Plattform und einen zugehörigen Modell-basierten Engineering-Ansatz.
Dieser Ansatz entkoppelt die Design-Schritte der Entwicklung, Integration und des Deployments von Komponenten.
Die Laufzeit-Plattform unterstützt den Ansatz durch Isolation von Komponenten und zugleich Zeit-deterministischem Ende-zu-Ende-Verhalten.
Unabhängige Entwicklung und Integration werden durch Konzepte für synchrone Rekonfiguration im Vollbetrieb unterstützt, also durch dynamische Orchestrierung.
Dies beinhaltet auch Zeit-kritische Zustands-Transfers mit höchstens begrenzter Qualitätsminderung, wenn überhaupt.
Rekonfigurationsplanung wird durch Analysekonzepte unterstützt, einschließlich der Simulation formal spezifizierter Systeme und Rekonfigurationen und der Analyse der etwaigen Qualitätsminderung mit dem Evolving Dataflow Graph (EDFG).
Die Real-Time Container Architecture wird als Referenzimplementierung und Evaluationsplattform beschrieben.
Zwei Fallstudien untersuchen Machbarkeit und Nützlichkeit der Konzepte.
Die erste verwendet verschiedene Varianten und Rekonfigurationen eines minimalistischen verteilten Steuerungssystems, um Modell und Prototyp zu vergleichen sowie Laufzeitstatistiken zu erheben.
Die zweite Fallstudie ist ein Smart-Factory-Demonstrator, welcher herausforderndere Applikationskomponenten und Schnittstellentechnologien verwendet.
Die Konzepte sind den Studien nach machbar und nützlich, auch wenn sowohl die Konzepte als auch der Prototyp noch weitere Arbeit benötigen -- zum Beispiel, um kürzere Zyklen zu erreichen
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