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Peer Reviewed
See detailDignity as Discursive Enactment of Tradition: A Narrative Approach on Tradition in Family Business
Adiguna, Rocky UL

Scientific Conference (2017, August 06)

Our current understanding of tradition in organizations remain very limited. The lack of studies that take tradition as the main focus have made this concept overlooked as an important organizational ... [more ▼]

Our current understanding of tradition in organizations remain very limited. The lack of studies that take tradition as the main focus have made this concept overlooked as an important organizational feature. In this paper, I set out to address this issue by exploring how tradition is (re)produced and (re)interpreted in a century-old family-owned hotel. By adopting a narrative approach as an interpretive lens, I found that the reproduction and reinterpretation of tradition is discursively mediated through the notion of dignity. In particular, this paper argues for three forms of 'doing' dignity: first, dignity-by-category that is enacted through the discursive use of category making; second, dignity-by-sanctity that is enacted through sanctifying particular relations; and third, dignity-by-authority that is enacted through the exercise of authority to compel others to acknowledge one's dignity. To extend it further, the possibility of conceptual relations between tradition, dignity, and narrative identity is discussed. Drawing from the broader fields of social sciences, this study contributes to the scarce literature on tradition theory and dignity in organizations. [less ▲]

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See detailReconceptualising Global Finance and Its Regulation
Nabilou, Hossein UL

in Banking & Finance Law Review (2017), 32(3), 579-602

Detailed reference viewed: 29 (7 UL)
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See detailPanel participant
Greiff, Samuel UL

Scientific Conference (2017, August)

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See detailCollaborative problem solving behavior. A deep dive into log files
Schweitzer, Nick UL; Herborn, Katharina UL; Mustafic, Maida UL et al

Scientific Conference (2017, August)

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See detailQuantitative systems pharmacology and the personalized drug–microbiota–diet axis
Thiele, Ines UL; Clancy, Catherine UL; Heinken, Almut Katrin UL et al

in Current Opinion in Systems Biology (2017), 4

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity ... [more ▼]

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug–microbiota–diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine. [less ▲]

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See detailIndoor localization using ambient FM radio RSS fingerprinting: A 9-month study
Popleteev, Andrei UL

in 17th IEEE International Conference on Computer and Information Technology (CIT-2017) (2017, August)

While indoor positioning systems aspire for higher accuracy, their coverage is typically limited to buildings with dedicated hardware. A possible alternative is offered by infrastructure-free positioning ... [more ▼]

While indoor positioning systems aspire for higher accuracy, their coverage is typically limited to buildings with dedicated hardware. A possible alternative is offered by infrastructure-free positioning methods. In particular, several studies have demonstrated feasibility of indoor positioning using broadcast FM radio signals, which are available in most populated areas worldwide. However, previous work provides little information about long-term performance of FM-based indoor localization. This paper presents a longitudinal study of FM indoor positioning based on received signal strength (RSS) fingerprinting. We evaluate system's performance on a large dataset of real-world FM signals, systematically collected in several large-scale multi-floor testbeds over the course of 9 months. We also investigate the impact of different classifiers, training schedules and fingerprint sizes on localization accuracy. The results demonstrate that well-trained FM-based system can provide reliable indoor positioning even several months after deployment. [less ▲]

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See detailHuman in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms
Hammerschmidt, Christian UL; State, Radu UL; Verwer, Sicco

Poster (2017, August)

We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse ... [more ▼]

We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively. [less ▲]

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See detailQuery-able Kafka: An agile data analytics pipeline for mobile wireless networks
Falk, Eric UL; Gurbani, Vijay K.; State, Radu UL

in Proceedings of the VLDB Endowment (2017, August), 10

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond ... [more ▼]

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond sentiment and trend analysis to include real-time detection of security attacks and prediction of network state (i.e., is the network transitioning towards an outage). Current streaming analytics platforms operate under the assumption that arriving traffic is to the order of kilobytes produced at very high frequencies. However, communications networks, especially the telecommunication networks, challenge this assumption because some of the arriving traffic in these networks is to the order of gigabytes, but produced at medium to low velocities. Furthermore, these large datasets may need to be ingested in their entirety to render network insights in real-time. Our interest is to subject today's streaming analytics platforms --- constructed from state-of-the art software components (Kafka, Spark, HDFS, ElasticSearch) --- to traffic densities observed in such communications networks. We find that filtering on such large datasets is best done in a common upstream point instead of being pushed to, and repeated, in downstream components. To demonstrate the advantages of such an approach, we modify Apache Kafka to perform limited \emph{native} data transformation and filtering, relieving the downstream Spark application from doing this. Our approach outperforms four prevalent analytics pipeline architectures with negligible overhead compared to standard Kafka. [less ▲]

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See detailAn Extensible and Lightweight Modular Ontology for Programming Education
Grevisse, Christian UL; Botev, Jean UL; Rothkugel, Steffen UL

in Advances in Computing - 12th Colombian Conference, CCC 2017, Cali, Colombia, September 19-22, 2017, Proceedings (2017, August)

Semantic web technologies such as ontologies can foster the reusability of learning material by introducing common sets of concepts for annotation purposes. However, suggesting learning material from an ... [more ▼]

Semantic web technologies such as ontologies can foster the reusability of learning material by introducing common sets of concepts for annotation purposes. However, suggesting learning material from an open, heterogeneous corpus is a nontrivial problem. In this paper, we propose an extensible and lightweight modular ontology for programming education. Its main purpose is to integrate annotated learning material related to programming into an IDE such as Eclipse. Our ontology is based on a modular architecture, which is extensible with respect to different programming languages. Aligning language-specific concepts with user-specific tags allows us to suggest learning resources for code elements in a fine-grained and cross-curricular way. Our concrete implementation establishes relations between learning aspects in Java or C code and annotated resources such as articles on online question-and-answer sites. [less ▲]

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See detailAn Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications through Security Slicing and Hybrid Constraint Solving
Thome, Julian UL; Shar, Lwin Khin UL; Bianculli, Domenico UL et al

Report (2017)

Malicious users can attack Web applications by exploiting injection vulnerabilities in the source code. This work addresses the challenge of detecting injection vulnerabilities in a scalable and effective ... [more ▼]

Malicious users can attack Web applications by exploiting injection vulnerabilities in the source code. This work addresses the challenge of detecting injection vulnerabilities in a scalable and effective way. We propose an integrated approach that seamlessly combines security slicing with hybrid constraint solving, i.e., constraint solving based on a combination of automata-based solving and meta-heuristic search. We use static analysis to extract minimal program slices relevant to security from Web programs and to generate attack conditions. We then apply hybrid constraint solving to determine the satisfiability of attack conditions and thus detect vulnerabilities. The experimental results, using a benchmark suite comprising nine diverse and representative Web applications, show that our approach (implemented in the JOACO tool) is significantly more effective at detecting injection vulnerabilities than state-of-the-art approaches, achieving 98% recall, without producing any false alarm. We also compared the constraint solving module of our approach with state-of-the-art constraint solvers, using five different benchmark suites; our approach correctly solved the highest number of constraints (43177 out of 43184), without producing any incorrect result, and was the one with the least number of time-out/failing cases. In both scenarios, the execution time was practically acceptable, given the offline nature of vulnerability detection. [less ▲]

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See detailBelief Change in a Preferential Non-Monotonic Framework
Casini, Giovanni UL; Meyer, Thomas

in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (2017, August)

Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this paper we show that we can ... [more ▼]

Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this paper we show that we can also integrate the two formalisms by studying belief change within a (preferential) non-monotonic framework. This integration relies heavily on the identification of the monotonic core of a non-monotonic framework. We consider belief change operators in a non-monotonic propositional setting with a view towards preserving consistency. These results can also be applied to the preservation of coherence—an important notion within the field of logic-based ontologies. We show that the standard AGM approach to belief change can be adapted to a preferential non-monotonic framework, with the definition of expansion, contraction, and revision operators, and corresponding representation results. Surprisingly, preferential AGM belief change, as defined here, can be obtained in terms of classical AGM belief change. [less ▲]

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See detailKoszul-Tate resolutions as cofibrant replacements of algebras over differential operators
Di Brino, Gennaro; Pistalo, Damjan UL; Poncin, Norbert UL

E-print/Working paper (2017)

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See detailFeedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR
Blount, Drew; Banda, Peter UL; Teuscher, Christof et al

in Artificial Life (2017), 23(3), 295-317

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel ... [more ▼]

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system. [less ▲]

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See detailOn Koszul-Tate resolutions and Sullivan models
Pistalo, Damjan UL; Poncin, Norbert UL

E-print/Working paper (2017)

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See detailReal-time Error Control for Surgical Simulation: Application to Percutaneous Interventions
Bui, Huu Phuoc UL; Tomar, Satyendra UL; Courtecuisse, Hadrien et al

Presentation (2017, August)

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See detailExperimental and numerical assessment of the mechanics of keloid-skin composites undergoing large deformations
Sensale, Marco UL; Chambert, Jerome; Chouly, Franz et al

Scientific Conference (2017, August)

Detailed reference viewed: 27 (6 UL)
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See detailThe territorial principalities in Lotharingia
Pauly, Michel UL; Margue, Michel UL

in Loud, Graham A.; Schenk, Jochen (Eds.) The Origins of the German Principalities, 1100-1350 (2017)

The creation of the territorial principalities was a lengthy process that was still far from over in the mid-fourteenth century. It is best not to speak of ‘states’ even in this period. But by this time ... [more ▼]

The creation of the territorial principalities was a lengthy process that was still far from over in the mid-fourteenth century. It is best not to speak of ‘states’ even in this period. But by this time the great lords of old Lotharingia had, by and large, succeeded in replacing the old face-to-face mode of lordship, and in integrating the regional nobles and burghers, and a majority of the newer religious foundations, into a ‘land’. All the different powers, both noble and burgher, as well as the prince, benefited from the establishment of a large territory with common peace and law, which protected them against outside threats and internal fragmentation, and from the encouragement of economic development. The integration of knightly vassals and burghers into the structures of government made the territorial lordships more efficient thanks to their differing areas of expertise. [less ▲]

Detailed reference viewed: 28 (3 UL)