466 research outputs found
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering
intelligent decision-making and a wide range of Artificial Intelligence (AI)
services across major corporations such as Google, Walmart, and AirBnb. KGs
complement Machine Learning (ML) algorithms by providing data context and
semantics, thereby enabling further inference and question-answering
capabilities. The integration of KGs with neuronal learning (e.g., Large
Language Models (LLMs)) is currently a topic of active research, commonly named
neuro-symbolic AI. Despite the numerous benefits that can be accomplished with
KG-based AI, its growing ubiquity within online services may result in the loss
of self-determination for citizens as a fundamental societal issue. The more we
rely on these technologies, which are often centralised, the less citizens will
be able to determine their own destinies. To counter this threat, AI
regulation, such as the European Union (EU) AI Act, is being proposed in
certain regions. The regulation sets what technologists need to do, leading to
questions concerning: How can the output of AI systems be trusted? What is
needed to ensure that the data fuelling and the inner workings of these
artefacts are transparent? How can AI be made accountable for its
decision-making? This paper conceptualises the foundational topics and research
pillars to support KG-based AI for self-determination. Drawing upon this
conceptual framework, challenges and opportunities for citizen
self-determination are illustrated and analysed in a real-world scenario. As a
result, we propose a research agenda aimed at accomplishing the recommended
objectives
Fundamental Approaches to Software Engineering
computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio
Computer Aided Verification
The open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency
Tools and Algorithms for the Construction and Analysis of Systems
This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers
Computer Aided Verification
The open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency
Tools and Algorithms for the Construction and Analysis of Systems
This open access two-volume set constitutes the proceedings of the 26th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The total of 60 regular papers presented in these volumes was carefully reviewed and selected from 155 submissions. The papers are organized in topical sections as follows: Part I: Program verification; SAT and SMT; Timed and Dynamical Systems; Verifying Concurrent Systems; Probabilistic Systems; Model Checking and Reachability; and Timed and Probabilistic Systems. Part II: Bisimulation; Verification and Efficiency; Logic and Proof; Tools and Case Studies; Games and Automata; and SV-COMP 2020
Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
Formal Verification of Industrial Software and Neural Networks
Software ist ein wichtiger Bestandteil unsere heutige Gesellschaft. Da Software vermehrt
in sicherheitskritischen Bereichen angewandt wird, mĂĽssen wir uns auf eine korrekte und
sichere Ausführung verlassen können. Besonders eingebettete Software, zum Beispiel in
medizinischen Geräten, Autos oder Flugzeugen, muss gründlich und formal geprüft werden.
Die Software solcher eingebetteten Systeme kann man in zwei Komponenten aufgeteilt.
In klassische (deterministische) Steuerungssoftware und maschinelle Lernverfahren
zum Beispiel fĂĽr die Bilderkennung oder Kollisionsvermeidung angewandt werden.
Das Ziel dieser Dissertation ist es den Stand der Technik bei der Verifikation von
zwei Hauptkomponenten moderner eingebetteter Systeme zu verbessern: in C/C++
geschriebene Software und neuronalen Netze. FĂĽr beide Komponenten wird das Verifikationsproblem
formal definiert und neue Verifikationsansätze werden vorgestellt
Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field
that aims to design computer agents with intelligent capabilities such as
understanding, reasoning, and learning through integrating multiple
communicative modalities, including linguistic, acoustic, visual, tactile, and
physiological messages. With the recent interest in video understanding,
embodied autonomous agents, text-to-image generation, and multisensor fusion in
application domains such as healthcare and robotics, multimodal machine
learning has brought unique computational and theoretical challenges to the
machine learning community given the heterogeneity of data sources and the
interconnections often found between modalities. However, the breadth of
progress in multimodal research has made it difficult to identify the common
themes and open questions in the field. By synthesizing a broad range of
application domains and theoretical frameworks from both historical and recent
perspectives, this paper is designed to provide an overview of the
computational and theoretical foundations of multimodal machine learning. We
start by defining two key principles of modality heterogeneity and
interconnections that have driven subsequent innovations, and propose a
taxonomy of 6 core technical challenges: representation, alignment, reasoning,
generation, transference, and quantification covering historical and recent
trends. Recent technical achievements will be presented through the lens of
this taxonomy, allowing researchers to understand the similarities and
differences across new approaches. We end by motivating several open problems
for future research as identified by our taxonomy
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