+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Dienstag, 4. October 2022, 10:00 Uhr
Ort: Raum 9222, Geb. E3, 2. Etage, Informatikzentrum, Ahornstr. 55
Referent: Shahid Khan M.Sc.
(Lehrstuhl Informatik 2)
Thema: Boolean-logic Driven Markov Processes - Explained. Analysed.
Verified.
Abstract:
Model-based dependability studies of engineering systems amount to
quantifying and improving dependability measures. These
measures include reliability, availability, maintainability and safety.
Two key ingredients of such studies are 1) models capturing the system
behaviour to an acceptable level of abstraction and 2) efficient and
accurate analysis techniques to quantify dependability measures.
Among the modelling techniques, static (or standard) fault trees
(SFTs) is a prominent dependability modelling language extensively used
by engineers to develop system models. However, it lacks expressive
power as it cannot model temporal dependencies of failures and has
limited support for repairs. Boolean logic-driven Markov processes
(BDMPs) and dynamic fault trees (DFTs) are two classical dynamic
extensions of SFTs that aim to mitigate the expressive power limitation
issue of SFTs. While DFTs are (generally) restricted to non-repairable
systems, BDMPs natively support repairs. The BDMP language has a
long-standing industrial usage history; the leading French electricity
utility company (EDF) extensively uses BDMPs to conduct dependability
studies.
Among the analysis techniques, probabilistic model checking is a
verification technique to determine the probability of a state-space
model satisfying or refuting a logical property. It combines efficient,
fully automated verification algorithms with numerical analysis. The
results of these automated verification procedures are numeric values
that dependability practitioners use to attain the reliability growth of
their systems.
The issue with BDMPs is that it lacks numerically exact analysis support and adequately
documented semantics. EDF provides two analysis tools for BDMPs: 1) a
Monte-Carlo simulator and 2) a sequence exploration
tool. Both tools provide approximate results, which may not be
acceptable for stringent dependability requirements of
safet-criticalsystems. The
semantics of BDMPs is essential for a comparative study with other
modelling languages, e.g., DFTs. This dissertation, developed in
collaboration with EDF, presents semantics and a model checker for BDMPs:
-- we propose Markov automaton- and generalised stochastic Petri
net-based semantics to BDMPs and empirically establish the accuracy,
-- we develop a probabilistic model checker for BDMPs and enhance the
scalability of the model checker using partial state-space exploration
techniques, and
-- we contrast BDMPs to DFTs and lift the repairable behaviour of BDMPs
to repairable DFTs.
This dissertation provides a holistic view of BDMPs. An exciting outcome
of this dissertation is that our model checker can analyse the Markovian
subset of the EDF-maintained Figaro language. This subset includes
capacity analysis diagrams, dynamic reliability block diagrams, electric
circuits, Petri nets, process diagrams, telecommunication networks, and
other EDF-developed Figaro-based modelling formalisms.
In this presentation, we provide: (1) a detailed introduction to
the BDMP formalism, (2) an insight of the BDMP model checker, and (3)
the semantics of BDMPs.
Es laden ein: die Dozentinnen und Dozenten der Informatik
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Dienstag, 20.09.2022, 10:00-11:00 Uhr
Der öffentliche Vortrag findet hybrid statt:
Raum: Raum 5053.2 (B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55
Zoom:
https://rwth.zoom.us/j/91824143381?pwd=dU5SNSt2TWtUMlZTOVFtRkczN1RvQT09
Meeting-ID: 918 2414 3381
Kenncode: 827047
Referent: Herr Thomas Osterland, M. Sc.
Lehrstuhl Informatik 5
Thema: Distributed Ledger Technology Processes
Abstract:
DLT offers new means to establish trust relationships within cooperative
processes among enterprises and thereby enables the implementation of use
cases with a lack of existing intermediaries. Prominent examples of use
cases are financial applications, provenance tracking and identity
management. All of them are contingent upon trust in business processes and
require the maintained security of the distributed ledger, that strongly
depends on the heterogeneity and robustness of the underlying ecosystem.
Participants need to trust the security of the ecosystem and the trust must
be maintained over time. Therefore, the distributed ledger technology is not
a simple component, that can be easily integrated into an existing
application unless the complex relationship between participants and their
trust into the underlying ecosystem is considered early in the engineering
process.
This thesis introduces a framework, that comprises a structured methodology
and tooling as a means to support conventional software processes that allow
software architects to address the specific requirements of distributed
ledger applications. We argue that these subjects are important to ensure
the quality and sustainability of an application, since 1) a distributed
ledger, that is not capable of handling future requirements of a use case
represents eventually a bottleneck and thus, harms the efficient and
reliable execution of business processes, 2) smart contracts with software
bugs, that either prevent the efficient execution of business processes or
favor one party over another will erode the trust that participants have
into the ecosystem, and 3) if the DLT is not capable of securing substantial
amounts of data use cases, that rely on the processing of data are
disadvantaged and the consequences for an ecosystem when handling large
amounts of data in the future are unforeseeable.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Freitag, 26. August 2022, 13.00 Uhr
Der öffentliche Vortrag findet hybrid statt:
Ort: Raum 9222, Ahornstr. 55
Zoom:
https://rwth.zoom.us/j/92249056004?pwd=cHkwRW1lNTlmS1RXQW95Wmw1MjhFQT09
Referent: Janosch Fuchs M.Sc.
Lehrstuhl Informatik 1
Thema: Graph Exploration with Advice (and Online Crossing Minimization)
Abstract:
Online problems reveal their input instance piece by piece and require
an irrevocable decision each time a new piece is revealed. This scenario
models critical applications where a decision, once it is made, cannot
be changed afterwards and also influences future decisions. Like for
classical offline problems, it is common to make a worst-case analysis
to give a guarantee on the performance of an online algorithm. A
worst-case analysis is achieved by assuming that a malicious adversary,
knowing the behavior of the online algorithm, creates the input. Due to
the nature of uncertainty online algorithms rarely compute an optimal
solution. Thus, their performance is measured in terms of the
competitive ratio, which compares the solution computed by the online
algorithm to the optimal solution achievable when the whole instance is
known beforehand.
To measure the impact of the missing knowledge about the future the
classical online setting was extended with a helpful oracle providing
information about the future input. The online algorithm receives the
information by accessing a tape containing a binary bit string. The
number of bits that the algorithm reads during its computation is called
its advice complexity. Bounds on the advice complexity of an online
algorithm that optimally solves an online problem show how much
information about the future is needed to avoid any mistake. An obvious
upper bound for the advice complexity of all online problems is the size
of the binary encoding of the input instance or the optimal solution.
But most of the time it is possible to derive better bounds by encoding
only critical decisions which then reveals the core difficulty of an
online problem. Thus, the advice complexity strongly correlates to the
difficulty of an online problem and it can be used to measure the
difficulty of different online problems.
In the main part, we analyze the advice complexity of the graph
exploration problem. Here, an algorithm has to move an agent through a
graph from vertex to vertex using the edges such that the agent
traverses the cheapest or shortest tour that visits every vertex at
least once. The algorithm does not know the graph beforehand and each
time the agent is located at a new vertex the reachable neighborhood is
revealed together with the cost to reach them from the current location.
Already seen or visited vertices are recognizable. Due to the already
known tight upper bound of n\log(n) for the advice complexity of the
graph exploration problem on dense graphs, we focus on sparse graphs. We
show that a linear amount of advice is sufficient and also necessary to
solve the graph exploration problem on directed and undirected graphs.
Es laden ein: die Dozentinnen und Dozenten der Informatik
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Mittwoch, 10.08.2022, 14:00-15:00 Uhr
Ort: Raum 5053.2 (großer B-IT-Raum)/Informatikzentrum, Ahornstraße
55
Referent: Herr Zhijian Li , M.Sc.
Institute for Computational Genomics
Thema: Computational Method for Single-cell ATAC-seq Imputation and
Dimensionality Reduction
Abstract:
Single-cell sequencing assay for transposase-accessible chromatin
(scATAC-seq) allows mapping of regulatory variation of thousands of cells at
the single-cell resolution. However, analyzing the scATAC-seq data is
computationally challenging due to the sparsity, high dimensionality, and
the nature of the binary signal. In this thesis, we proposed scOpen, a
computational approach for quantification of single-cell open chromatin
status and reduction of the dimensionality based on the non-negative matrix
factorization (NMF) topic modeling. We demonstrated that scOpen can improve
several crucial downstream analysis steps of scATAC-seq, such as clustering,
visualization, cis-regulatory DNA interactions, and delineation of
regulatory features. We also applied scOpen to investigate the regulatory
programs that drive the development of chronic kidney disease (CKD).
Altogether, these results demonstrate that scOpen is a useful computational
approach in single-cell open chromatin data analysis.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
Dear all,
this is a reminder for Gerhard Lakemeyer's talk on Tractable Reasoning
in First-Order Knowledge Bases
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Aktuel…>
taking place *today at 16:30* in room 5053.2 and on Zoom. Please find
the details below.
> In knowledge representation, obtaining a notion of belief which is
> tractable, expressive, and eventually complete has been a somewhat
> elusive goal. Expressivity here means that an agent should be able to
> hold arbitrary beliefs in a very expressive language like that of
> first-order logic, but without being required to perform full logical
> reasoning on those beliefs. Eventual completeness means that any
> logical consequence of what is believed will eventually come to be
> believed, given enough reasoning effort.
>
> Tractability in a first-order setting has been a research topic for
> many years, but in most cases limitations were needed on the form of
> what was believed, and eventual completeness was so far restricted to
> the propositional case. In this talk I present a novel logic of
> limited belief, which has all three desired properties.
Part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of "randomness" and "uncertainty"
in UnRAVeL’s research thrusts: Algorithms and complexity, verification,
logic and languages, and their application scenarios. The main aim is to
provide doctoral researchers as well as master students a broad overview
of the subjects of UnRAVeL.
Science undergoes continuous change and lives from the constant quest
for novel and better results, which are presented at conferences and in
journals. This year, 10 UnRAVeL professors will present some of their
most recent research successes.
Everyone interested, in particular doctoral researchers and master
students, are invited to attend the UnRAVeL lecture series 2022 and
engage in discussions with the researchers.
The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the
ground floor of building E2. All events are hybrid. To join remotely,
please use
https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09
/ Meeting ID: 960 0388 5007 / Passcode: 273710
We are looking forward to seeing many of you in the UnRAVeL survey
lecture "What's New in UnRAVeL?".
Best regards,
Andreas Klinger, Birgit Willms, and Tim Seppelt
Logo
Dear all,
this is a reminder for Britta Peis' talk on Stackelberg Network Pricing
Games
<https://www.unravel.rwth-aachen.de/go/id/tbciy?lidx=1#aaaaaaaaaatbcjr>
taking place *today at 16:30* in room 5053.2 and on Zoom. Please find
the details below.
> We study a variant of bilevel games, termed Stackelberg network
> pricing games, in which one distinguished player, the leader, can set
> prices on a subset of the edges or vertices in the underlying network.
> The remaining edges or vertices are assigned a fixed costs. Based on
> the leader’s decision, one or several followers optimize a
> polynomial-time solvable combinatorial optimization problem. That is,
> after the leader decided on the prices, each follower individually
> selects an optimal solution (e.g. a shortest path, a max weight
> matching, or a min cost spanning tree) based on the fixed costs and
> the leader’s prices. The leader’s goal is to select the prices such
> that the resulting revenue, which is determined by the sold items and
> their prices, is maximised.
>
> Briest et al. and Balcan et al. independently showed that the maximum
> revenue can be approximated to a factor of (roughly) log k, where k is
> the number of priceable elements. In the lecture, we discuss
> algorithms and complexity results for Stackelberg network pricing
> games in general, and Stackelberg Max Closure and Stackelberg
> Bipartite Min Weight Vertex Cover, in particular.
>
> Parts of the talk are based on joint work with Karsten Jungnitsch and
> Marc Schröder
Part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of "randomness" and "uncertainty"
in UnRAVeL’s research thrusts: Algorithms and complexity, verification,
logic and languages, and their application scenarios. The main aim is to
provide doctoral researchers as well as master students a broad overview
of the subjects of UnRAVeL.
Science undergoes continuous change and lives from the constant quest
for novel and better results, which are presented at conferences and in
journals. This year, 10 UnRAVeL professors will present some of their
most recent research successes.
Everyone interested, in particular doctoral researchers and master
students, are invited to attend the UnRAVeL lecture series 2022 and
engage in discussions with the researchers.
The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the
ground floor of building E2. All events are hybrid. To join remotely,
please use
https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09
/ Meeting ID: 960 0388 5007 / Passcode: 273710
Please find a list of all upcoming talks on the UnRAVeL website
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…>
and below:
* 28/06/2022 Britta Peis: Stackelberg Network Pricing Games
* 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order
Knowledge Bases
We are looking forward to seeing many of you in the UnRAVeL survey
lecture "What's New in UnRAVeL?".
Best regards,
Andreas Klinger, Birgit Willms, and Tim Seppelt
Logo
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Dienstag, 12. Juli 2022, 9.00 Uhr
Zoom: https://rwth.zoom.us/j/99072269440?pwd=bXNrOXlwSUhtd3IvVVlhNytURUY2UT09
Referent: Arvid Butting M.Sc.
Lehrstuhl Informatik 3
Thema: Systematic Composition of Language Components in MontiCore
Abstract:
In model-driven development (MDD), models are central software engineering artifacts. MDD is applied to various domains such as avionics, law, mechanical engineering, or robotics, in which the domain engineers are not always software engineers. To this end, modelers should specify models in a notation close to the application domain, which is achieved by employing domain-specific modeling languages (DSMLs). In complex modern software applications, different aspects of an application are modeled with numerous integrated models. The models conform to heterogeneous, integrated DSMLs that can assure consistency between the models of an application.
Ad-hoc development of DSMLs is a time-consuming and error-prone process. Systematic and "off-the-shelf" black-box reuse of DSMLs or parts of it supports engineering DSMLs faster and more reliably. In black-box reuse, unlike reuse via clone-and-own, the reused parts remain unchanged and do not result in co-existing clones. Such reuse requires language engineers to be able to integrate DSMLs through different forms of language composition. Current approaches for engineering DSMLs often rely on generic language infrastructure, which complicates compatibility checks between the infrastructures of languages that are to be composed. Approaches for modularization of DSMLs typically focus on the conceptual parts of a language rather than on their realizations.
This talk describes an approach for realizing modular language components that can be composed via their symbol tables to realize language product lines with the language workbench MontiCore. The proposed language components identify the entirety of source code artifacts that realize a DSML. The DSMLs rely on kind-typed symbol tables that assure language compatibility during language composition. Language composition via symbol tables is lightweight because language infrastructures are only loosely coupled. An approach for persisting symbol tables further decouples language infrastructures from another and increases the performance for type and consistency checking between models that conform to different DSMLs. With the approach for language product lines, language components can be composed systematically and undesired compositions can be avoided. Typed and persisted symbol tables, language components, and language product lines as presented in this talk aim to realize DSML engineering in the large.
Es laden ein: die Dozentinnen und Dozenten der Informatik
Sehr geehrte Damen und Herren,
leider hat sich ein Fehler im Zoom-Link eingeschlichen. Bitte beachten Sie
deshalb die nachfolgenden Informationen sowie den entsprechenden Link.
Vielen Dank!
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Dienstag, 28.06.2022, 10:00-11:00 Uhr
Der öffentliche Vortrag findet hybrid statt:
Raum: Raum 5053.1 (kleiner B-IT-Raum)/Informatikzentrum, Ahornstraße 55
Zoom: https://zoom.us/j/91338931025?pwd=SDN3TWh2c0ZTb2xjWGkwdHZYLzQ5QT09
Referent: Herr Md Rezaul Karim, Master of Engineering
Lehrstuhl Informatik 5
Thema: Interpreting Black-Box Machine Learning Models with Decision
Rules and Knowledge Graph Reasoning
Abstract:
Machine learning (ML) algorithms are increasingly used to solve complex
problems. However, due to high non-linear and higher-order interactions
between features, complex ML models become black-box methods - which means
it is not known how certain predictions are made. This may not be
acceptable in many situations (e.g., in clinical situations where AI may
significantly impact human lives). With the EU GDPR explainability has not
only become a desirable property of AI but also a legal requirement. An
interpretable ML model can outline how input instances are mapped into
certain outputs by identifying statistically significant features.
Literature pointed out that complex ML models tend to be less interpretable,
showing a trade-off between accuracy and interpretability. This thesis aims
to improve the interpretability and explainability of black-box ML models
without sacrificing significant predictive accuracy. As a starting point,
using a black-box multimodal neural network, representation learning is
performed on multimodal data in order to use the learned representation for
the classification task. To improve the interpretability of the learned
black-box model, different interpretable ML methods such as probing,
perturbing, and model surrogation techniques are applied. An interpretable
surrogate model is trained to approximate the behavior of the back-box
model. The surrogate model is used to generate explanations in terms of
decision rules and counterfactuals. To add symbolic reasoning capability to
the black-box model, a domain-specific knowledge graph (KG) is constructed
by integrating knowledge and facts from scientific literature. A semantic
reasoner is then used to validate the association of significant features
with different classes based on relations it learned from the KG.
Evidence-based decision rules are generated by combining the reasoning with
the predictions from the black-box model. The quantitative evaluation shows
that the proposed approach achieves an average accuracy of 96.25% on the
test dataset. It can also provide human-interpretable explanations of the
decisions in the form of counterfactual rules and evidence-based decision
rules. The quality of the explanations is evaluated in terms of
comprehensiveness and sufficiency.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
Romina Reddig
_______________________________
Romina Reddig
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21501
Fax: 0241-80-22321
E-Mail: reddig(a)dbis.rwth-aachen.de <mailto:reddig@dbis.rwth-aachen.de>
Von: Romina Reddig <reddig(a)dbis.rwth-aachen.de>
Gesendet: Montag, 20. Juni 2022 11:41
An: assistenten(a)informatik.rwth-aachen.de;
infprof(a)informatik.rwth-aachen.de; vortraege(a)informatik.rwth-aachen.de;
sekretariate(a)informatik.rwth-aachen.de; webteam(a)informatik.rwth-aachen.de;
biblio(a)informatik.rwth-aachen.de
Betreff: Einladung Promotionsverteidigung Rezaul Karim, 28. Juni 2022, 10:00
Uhr
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Dienstag, 28.06.2022, 10:00-11:00 Uhr
Der öffentliche Vortrag findet hybrid statt:
Raum: Raum 5053.1 (kleiner B-IT-Raum)/Informatikzentrum, Ahornstraße 55
Zoom: https://zoom.us/j/94726175294?pwd=QXJVeGVvZ09wV000OU53QkdrU0RRdz09
Referent: Herr Md Rezaul Karim, Master of Engineering
Lehrstuhl Informatik 5
Thema: Interpreting Black-Box Machine Learning Models with Decision
Rules and Knowledge Graph Reasoning
Abstract:
Machine learning (ML) algorithms are increasingly used to solve complex
problems. However, due to high non-linear and higher-order interactions
between features, complex ML models become black-box methods - which means
it is not known how certain predictions are made. This may not be
acceptable in many situations (e.g., in clinical situations where AI may
significantly impact human lives). With the EU GDPR explainability has not
only become a desirable property of AI but also a legal requirement. An
interpretable ML model can outline how input instances are mapped into
certain outputs by identifying statistically significant features.
Literature pointed out that complex ML models tend to be less interpretable,
showing a trade-off between accuracy and interpretability. This thesis aims
to improve the interpretability and explainability of black-box ML models
without sacrificing significant predictive accuracy. As a starting point,
using a black-box multimodal neural network, representation learning is
performed on multimodal data in order to use the learned representation for
the classification task. To improve the interpretability of the learned
black-box model, different interpretable ML methods such as probing,
perturbing, and model surrogation techniques are applied. An interpretable
surrogate model is trained to approximate the behavior of the back-box
model. The surrogate model is used to generate explanations in terms of
decision rules and counterfactuals. To add symbolic reasoning capability to
the black-box model, a domain-specific knowledge graph (KG) is constructed
by integrating knowledge and facts from scientific literature. A semantic
reasoner is then used to validate the association of significant features
with different classes based on relations it learned from the KG.
Evidence-based decision rules are generated by combining the reasoning with
the predictions from the black-box model. The quantitative evaluation shows
that the proposed approach achieves an average accuracy of 96.25% on the
test dataset. It can also provide human-interpretable explanations of the
decisions in the form of counterfactual rules and evidence-based decision
rules. The quality of the explanations is evaluated in terms of
comprehensiveness and sufficiency.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
Romina Reddig
_______________________________
Romina Reddig
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21501
Fax: 0241-80-22321
E-Mail: reddig(a)dbis.rwth-aachen.de <mailto:reddig@dbis.rwth-aachen.de>
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Freitag, 1. Juli 2022, 9:00 Uhr
Ort: Seminarraum Informatik 4 (COMSYS), E3, Ahornstr. 55
Zoom (hybrider Vortrag):
https://rwth.zoom.us/j/91545576628?pwd=d2JoNjVMKysvNU9lWWhzd200R3hJdz09
Referent: Jens Hiller M.Sc.
Lehrstuhl Informatik 4 (COMSYS)
Thema: Improving Functionality, Efficiency, and Trustworthiness of
Secure Communication on an Internet diversified by Mobile Devices
and the Internet of Things
Abstract:
Secure communication is essential for many use cases that exchange data
over the Internet. However, prevalently used security protocols, e.g.,
TLS 1.2, have been standardized many years ago. At that time, the
Internet was dominated by traditional devices and communication
scenarios, especially location-bound workstations communicating with
servers or cloud services. Since then, the advent of smartphones and the
Internet of Things (IoT) introduced new scenarios with more diverse
device types and use cases. The development to this evolved Internet
motivates the question for a likewise evolved secure communication that
fits new demands.
To examine the need for improvements, we analyze the state of the art of
secure communication for the different scenarios of the evolved
Internet. Our analysis reveals several open challenges, especially
missing advanced security and privacy features for secure communication
in the IoT, the need for increased efficiency of secure communication by
smartphones, and the demand for efficient secure low-latency
communication in the industrial IoT. Furthermore, also considering the
traditional Internet, we identify open problems in the PKI-based trust
infrastructure, and highlight the need to understand drivers and
obstacles of the roll-out of new security mechanisms to improve their
adoption and effective use.
We tackle these open challenges with four contributions. First, we
tailor the Tor anonymity network to resource-constrained IoT devices to
protect the metadata of IoT communication and also realize a
resource-efficient in-network access control. Additionally, we enable
IoT devices to use large and versatile secure communication stacks.
Second, we increase secure communication efficiency by realizing secure
low-latency communication for the industrial IoT. Moreover, we devise
more efficient best practices for the establishment of secure
connections by smartphones. Third, focusing on the trust infrastructure
of secure communication, we provide a detailed risk analysis of
cross-signing in the Web PKI, revealing that it can cause undesired
certificate trust paths. Fourth, we analyze drivers and obstacles for
the effective roll-out of adapted security protocols and procedures
based on measurements of TLS 1.3 and Certification Authority Authorization.
Overall, we show the need for adapting secure communication to the
evolved Internet and present corresponding improvements.
Es laden ein: die Dozentinnen und Dozenten der Informatik
***********************************************************************
*
* Informatik-Kolloquium / Computer Science Colloquium
*
***********************************************************************
Presenter: Hector Geffner (ICREA and Universitat Pompeu Fabra, Barcelona, Spain & Linköping University, Sweden)
Title: Language-based representation learning for acting and planning
Time: Monday 27-6-2022, 14.30
Location: Room 9222, E3 (Informatik-Zentrum, Erweiterungsbau E3,
https://www.informatik.rwth-aachen.de/cms/informatik/Fachgruppe/Informatik-…)
Abstract:
Recent breakthroughs in AI have shown the remarkable power of deep
learning and deep reinforcement learning. These developments, however,
have been tied to specific tasks, and progress in out-of-distribution
generalization has been limited. While it is assumed that these
limitations can be overcome by incorporating suitable inductive biases
in neural nets, this is left vague and informal, and does not provide
meaningful guidance. In this talk, I articulate a different learning
approach where representations are learned over domain-independent
target languages whose structure and semantics yield a meaningful and
strongly biased hypothesis space. The learned representations do not
emerge from biases in a low level architecture but from a general
preference for the simplest hypothesis that explain the data. I
illustrate this general idea by considering three concrete learning
problems in AI planning: learning general actions models, learning
general policies, and learning general subgoal structures ("intrinsic
rewards"). In all these cases, learning is formulated and solved as a
combinatorial optimization problem although nothing prevents the use of
deep learning techniques instead. Indeed, learning representations over
domain-independent languages with a known structure and semantics
provides an account of what is to be learned, while learning
representations with neural nets provides a complementary account of how
representations can be learned. The challenge and the opportunity is to
bring the two approaches together.
Reference: Target languages (vs. inductive biases) for learning to act
and plan. Hector Geffner. AAAI 2022. https://arxiv.org/abs/2109.07195
Short Bio:
Hector Geffner is an ICREA Research Professor at the Universitat Pompeu
Fabra (UPF) in Barcelona, Spain, and a Wallenberg Guest Professor at
Linköping University. He grew up in Buenos Aires and obtained a PhD in
Computer Science at UCLA in 1989. He then worked at the IBM T.J. Watson
Research Center in NY, USA, and at the Universidad Simon Bolivar, in
Caracas. Hector teaches courses on logic, AI, and social and
technological change, and is currently doing research on representations
learning for acting and planning as part of the ERC project RLeap 2020-2025.
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