360 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Transition 2.0: Re-establishing Constitutional Democracy in EU Member States
The central question of Transition 2.0 is this: what (and how) may a new government do to re-establish constitutional democracy, as well as repair membership within the European Union, without breaching the European rule of law? This volume demonstrates that EU law and international commitments impose constraints but also offer tools and assistance for facilitating the way back after rule of law and democratic backsliding. The various contributions explore the constitutional, legal, and social framework of 'Transition 2.0'.Dieser Band zeigt, dass das EU-Recht und die internationalen Verpflichtungen zwar ZwĂ€nge auferlegen, aber auch Instrumente und Hilfestellungen bieten, um den Weg zurĂŒck in die EuropĂ€ische Union nach Rechtsstaatlichkeitsdefiziten und demokratischen RĂŒckschritten zu erleichtern. Die verschiedenen BeitrĂ€ge untersuchen den verfassungsrechtlichen, rechtlichen und sozialen Rahmen des "Ăbergangs 2.0"
Learning Possibilistic Logic Theories
Vi tar opp problemet med Ä lÊre tolkbare maskinlÊringsmodeller fra usikker og manglende informasjon. Vi utvikler fÞrst en ny dyplÊringsarkitektur, RIDDLE: Rule InDuction with Deep LEarning (regelinduksjon med dyp lÊring), basert pÄ egenskapene til mulighetsteori. Med eksperimentelle resultater og sammenligning med FURIA, en eksisterende moderne metode for regelinduksjon, er RIDDLE en lovende regelinduksjonsalgoritme for Ä finne regler fra data. Deretter undersÞker vi lÊringsoppgaven formelt ved Ä identifisere regler med konfidensgrad knyttet til dem i exact learning-modellen. Vi definerer formelt teoretiske rammer og viser forhold som mÄ holde for Ä garantere at en lÊringsalgoritme vil identifisere reglene som holder i et domene. Til slutt utvikler vi en algoritme som lÊrer regler med tilhÞrende konfidensverdier i exact learning-modellen. Vi foreslÄr ogsÄ en teknikk for Ä simulere spÞrringer i exact learning-modellen fra data. Eksperimenter viser oppmuntrende resultater for Ä lÊre et sett med regler som tilnÊrmer reglene som er kodet i data.We address the problem of learning interpretable machine learning models from uncertain and missing information. We first develop a novel deep learning architecture, named RIDDLE (Rule InDuction with Deep LEarning), based on properties of possibility theory. With experimental results and comparison with FURIA, a state of the art method, RIDDLE is a promising rule induction algorithm for finding rules from data. We then formally investigate the learning task of identifying rules with confidence degree associated to them in the exact learning model. We formally define theoretical frameworks and show conditions that must hold to guarantee that a learning algorithm will identify the rules that hold in a domain. Finally, we develop an algorithm that learns rules with associated confidence values in the exact learning model. We also propose a technique to simulate queries in the exact learning model from data. Experiments show encouraging results to learn a set of rules that approximate rules encoded in data.Doktorgradsavhandlin
The great moving countering violent extremism show: An ethnography of CVE in the Canadian context
My dissertation critically examines through ethnographic fieldwork the rise of countering violent extremism [CVE] programs in Canada. CVE is an offshoot of counter-terrorism, with programs first taking hold in the mid-2000s following âhomegrown terrorismâ incidents in Madrid and London. CVE is based on the premise that a âradicalization processâ precedes terrorism. This allows for security and civil society-based interventions in the âpre-crimeâ space to interrupt terrorism before it happens. The most thorough and controversial example of this is the UKâs Prevent strategy, which legally mandates human services professionals to refer individuals showing signs of âradicalizationâ. In Canada, no such duty exists, though its national strategy nonetheless aims to harness âall of societyâ toward preventing violent extremism, enlisting the cooperation of teachers, artists, psychologists, social workers along with actors in the private sector.
My study is not about how individuals turn to âviolent extremismâ or âradicalizationâ but rather about examining that edifices that have created to respond to these perceived problems The implications of CVE as an âall of societyâ endeavour are manifold, particularly as the scope of CVE expands beyond âIslamismâ toward preventing âall typesâ of violent extremism, most recently on right-wing groups and violence against racial, ethnic, and gender minorities. Broadly, my research attempts to conceive of the implications of this expansion. What drives CVEâs growth in the face of sustained criticism over its deleterious impacts on Muslim communities? How do practitioners in CVE align their interests with the cause? What social functions does CVE take on? Moreover, can boundaries even be drawn around what constitutes CVE?
My study draws on interviews with 46 CVE practitioners and participant observation over a three-year period (2018-2020) with CVE entities operating in Canada. My findings indicate how an absence of knowledge over how to conduct CVE propels its encroachment into ever more diverse areas of social life. The paradigm operationalizes âuncertaintyâ to enroll actors with diverse interests and foster partnerships with communities including those (racialized, Indigenous, LGBTQ) that have had fraught relationships with security institutions.
In Chapter 1 - Searching for the CVE space I discuss my immersion in CVE and the type of fieldwork activities conducted. I also attempt to define my research object, outlining how CVE comprises a field of practice, a paradigm, a moral-social imperative, and lastly a space. Chapters 2 and 3 historicize CVEâs contemporary presence and disturb common understandings of its origins. I critique the explanation of CVEâs rise as a necessary and spontaneous reaction to evolving security threats to understand it as an outcome of performative security knowledge, where new security threats are discursively created rather than responded to. Chapters 4 and 5 focus on my fieldwork experience, examining how actors âenrollâ in the CVE cause through the open-ended, speculative quality of its activities. A distinction emerged with Muslim-identifying CVE practitioners, whose motivations to represent their communities in often hostile institutions and reduce the harm of CVE practices were typified by the repeated phrase âif youâre not at the table, youâre on the menuâ. In the conclusion chapter I connect the varying threads of preceding analysis and what they portend for CVEâs effects on societies. This includes examining how CVEâs efforts to redirect political grievances toward âpro-socialâ ends potentially disempowers social justice movements, reinforcing state hegemony and existing power inequities
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Jornadas Nacionales de InvestigaciĂłn en Ciberseguridad: actas de las VIII Jornadas Nacionales de InvestigaciĂłn en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de InvestigaciĂłn en Ciberseguridad (8ÂȘ. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernizaciĂłn tecnolĂłxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Formal Methods for Trustworthy Voting Systems : From Trusted Components to Reliable Software
Voting is prominently an important part of democratic societies, and its outcome may have a dramatic and broad impact on societal progress. Therefore, it is paramount that such a society has extensive trust in the electoral process, such that the systemâs functioning is reliable and stable with respect to the expectations within society. Yet, with or without the use of modern technology, voting is full of algorithmic and security challenges, and the failure to address these challenges in a controlled manner may produce fundamental flaws in the voting system and potentially undermine critical societal aspects.
In this thesis, we argue for a development process of voting systems that is rooted in and assisted by formal methods that produce transparently checkable evidence for the guarantees that the final system should provide so that it can be deemed trustworthy. The goal of this thesis is to advance the state of the art in formal methods that allow to systematically develop trustworthy voting systems that can be provenly verified. In the literature, voting systems are modeled in the following four comparatively separable and distinguishable layers: (1) the physical layer, (2) the computational layer, (3) the election layer, and (4) the human layer. Current research usually either mostly stays within one of those layers or lacks machine-checkable evidence, and consequently, trusted and understandable criteria often lack formally proven and checkable guarantees on software-level and vice versa.
The contributions in this work are formal methods that fill in the trust gap between the principal election layer and the computational layer by a reliable translation of trusted and understandable criteria into trustworthy software. Thereby, we enable that executable procedures can be formally traced back and understood by election experts without the need for inspection on code level, and trust can be preserved to the trustworthy system.
The works in this thesis all contribute to this end and consist in five distinct contributions, which are the following:
(I) a method for the generation of secure card-based communication schemes,
(II) a method for the synthesis of reliable tallying procedures,
(III) a method for the efficient verification of reliable tallying procedures,
(IV) a method for the computation of dependable election margins for reliable audits,
(V) a case study about the security verification of the GI voter-anonymization software.
These contributions span formal methods on illustrative examples for each of the three principal components, (1) voter-ballot box communication, (2) election method, and (3) election management, between the election layer and the computational layer.
Within the first component, the voter-ballot box communication channel, we build a bridge from the communication channel to the cryptography scheme by automatically generating secure card-based schemes from a small formal model with a parameterization of the desired security requirements. For the second component, the election method, we build a bridge from the election method to the tallying procedure by (1) automatically synthesizing a runnable tallying procedure from the desired requirements given as properties that capture the desired intuitions or regulations of fairness considerations, (2) automatically generating either comprehensible arguments or bounded proofs to compare tallying procedures based on user-definable fairness properties, and (3) automatically computing concrete election margins for a given tallying procedure, the collected ballots, and the computed election result, that enable efficient election audits. Finally, for the third and final component, the election management system, we perform a case study and apply state-of-the-art verification technology to a real-world e-voting system that has been used for the annual elections of the German Informatics Society (GI â âGesellschaft fĂŒr Informatikâ) in 2019. The case study consists in the formal implementation-level security verification that the voter identities are securely anonymized and the votersâ passwords cannot be leaked.
The presented methods assist the systematic development and verification of provenly trustworthy voting systems across traditional layers, i.e., from the election layer to the computational layer. They all pursue the goal of making voting systems trustworthy by reliable and explainable formal requirements. We evaluate the devised methods on minimal card-based protocols that compute a secure AND function for two different decks of cards, a classical knock-out tournament and several Condorcet rules, various plurality, scoring, and Condorcet rules from the literature, the Danish national parliamentary elections in 2015, and a state-of-the-art electronic voting system that is used for the German Informatics Societyâs annual elections in 2019 and following
Human History and Digital Future
Korrigierter Nachdruck. Im Kapitel "Wallace/Moullou: Viability of Production and Implementation of Retrospective Photogrammetry in Archaeology" wurden die Acknowledgemens enfternt.The Proceedings of the 46th Annual Conference on Computer Applications and Quantitative Methods in Archaeology, held between March 19th and 23th, 2018 at the University of TĂŒbingen, Germany, discuss the current questions concerning digital recording, computer analysis, graphic and 3D visualization, data management and communication in the field of archaeology. Through a selection of diverse case studies from all over the world, the proceedings give an overview on new technical approaches and best practice from various archaeological and computer-science disciplines
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