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See detailEstimating Urban Road Traffic States Using Mobile Network Signaling Data
Derrmann, Thierry UL; Frank, Raphaël UL; Viti, Francesco UL et al

in Derrmann, Thierry; Frank, Raphaël; Viti, Francesco (Eds.) et al Estimating Urban Road Traffic States Using Mobile Network Signaling Data (2017, October)

It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used ... [more ▼]

It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used in analyses, but also handovers between different locations that reflect mobility. In this work, we investigate if handovers can be used as a proxy metric for flows in the underlying road network, especially in urban environments. More precisely, we show that characteristic profiles of handovers within and between clusters of mobile network cells exist. We base these profiles on models from road traffic flow theory, and show that they can be used for traffic state estimation using floating-car data as ground truth. The presented model can be beneficial in areas with good mobile network coverage but low road traffic counting infrastructure, e.g. in developing countries, but also serve as an additional predictor for existing traffic state monitoring systems. [less ▲]

Detailed reference viewed: 28 (2 UL)
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See detailLIPS vs MOSA: a Replicated Em- pirical Study on Automated Test Case Generation
Panichella, Annibale UL; Kifetew, Fitsum; Tonella, Paolo

in International Symposium on Search Based Software Engineering (SSBSE) 2017 (2017, September 09)

Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The ... [more ▼]

Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The proponents of LIPS, a novel single-target test generator, conducted a preliminary empirical study to compare their approach with MOSA, an alternative many-objective test generator. However, their empirical investigation suffers from several external and internal validity threats, does not consider complex programs with many branches and does not include any qualitative analysis to interpret the results. In this paper, we report the results of a replication of the original study designed to address its major limitations and threats to validity. The new findings draw a completely different picture on the pros and cons of single-target vs many-objective approaches to test case generation. [less ▲]

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See detailMultiscale Modelling of Damage and Fracture in Discrete Materials Using a Variational Quasicontinuum Method
Rokos, Ondrej; Peerlings, Ron; Beex, Lars UL et al

Scientific Conference (2017, September 05)

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See detailIs Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Glauner, Patrick UL; Migliosi, Angelo UL; Meira, Jorge Augusto UL et al

in Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017) (2017, September)

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to ... [more ▼]

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection. [less ▲]

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See detailLegal Markup Generation in the Large: An Experience Report
Sannier, Nicolas UL; Adedjouma, Morayo UL; Sabetzadeh, Mehrdad UL et al

in the 25th International Requirements Engineering Conference (RE'17), Lisbon, 4-8 September 2017 (2017, September)

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See detailA Model-Driven Approach to Trace Checking of Pattern-based Temporal Properties
Dou, Wei; Bianculli, Domenico UL; Briand, Lionel UL

in Proceedings of the ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS 2017 ) (2017, September)

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See detailJoanAudit: A Tool for Auditing Common Injection Vulnerabilities
Thome, Julian UL; Shar, Lwin Khin UL; Bianculli, Domenico UL et al

in 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (2017, September)

JoanAudit is a static analysis tool to assist security auditors in auditing Web applications and Web services for common injection vulnerabilities during software development. It automatically identifies ... [more ▼]

JoanAudit is a static analysis tool to assist security auditors in auditing Web applications and Web services for common injection vulnerabilities during software development. It automatically identifies parts of the program code that are relevant for security and generates an HTML report to guide security auditors audit the source code in a scalable way. JoanAudit is configured with various security-sensitive input sources and sinks relevant to injection vulnerabilities and standard sanitization procedures that prevent these vulnerabilities. It can also automatically fix some cases of vulnerabilities in source code — cases where inputs are directly used in sinks without any form of sanitization — by using standard sanitization procedures. Our evaluation shows that by using JoanAudit, security auditors are required to inspect only 1% of the total code for auditing common injection vulnerabilities. The screen-cast demo is available at https://github.com/julianthome/joanaudit. [less ▲]

Detailed reference viewed: 62 (25 UL)
See detailThe relevance of verbal and visuo-spatial abilities for verbal number skills – what matters in 5 to 6 year olds?
Cornu, Véronique UL; Schiltz, Christine UL; Martin, Romain UL et al

Poster (2017, September)

The acquisition of verbal number skills, as defined by the meaningful use of number words, marks a milestone in numerical development. In the present study, we were particularly interested in the question ... [more ▼]

The acquisition of verbal number skills, as defined by the meaningful use of number words, marks a milestone in numerical development. In the present study, we were particularly interested in the question, whether verbal number skills are primarily verbal in nature, or if they call upon visuo-spatial processes, reflecting a spatial grounding of verbal number skills. 141 five- to six-year old children were tested on a range of verbal (i.e. vocabulary, phonological awareness and verbal working memory) and visuo-spatial abilities (i.e. spatial perception, visuo-motor integration and visuo-spatial working memory). We were particularly interested in the predictive role of these abilities for children’s verbal number skills (as measured by different counting and number naming tasks). In a latent regression model, basic visuo-spatial abilities, measured by spatial perception and visuo-motor integration, emerge as the most important predictor of verbal number skills. This gives raise to the assumption, that verbal number skills are, despite their verbal nature, spatially grounded in young children. [less ▲]

Detailed reference viewed: 27 (3 UL)
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See detailDas NEtzDG und die CPS Guidelines zur Verfolgung strafbarer Inhalte in sozialen Medien
Schmitz, Sandra UL; Robinson, Gavin UL

in Taeger, Jürgen (Ed.) Recht 4.0 - Innovationen aus den rechtswissenschaftlichen Laboren (2017)

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See detailMining AndroZoo: A Retrospect
Li, Li UL

in The International Conference on Software Maintenance and Evolution (ICSME) (2017, September)

This paper presents a retrospect of an Android app collection named AndroZoo and some research works conducted on top of the collection. AndroZoo is a growing collection of Android apps from various ... [more ▼]

This paper presents a retrospect of an Android app collection named AndroZoo and some research works conducted on top of the collection. AndroZoo is a growing collection of Android apps from various markets including the official Google Play. At the moment, over five million Android apps have been collected. Based on AndroZoo, we have explored several directions that mine Android apps for resolving various challenges. In this work, we summarize those resolved mining challenges in three research dimensions, including code analysis, app evolution analysis, malware analysis, and present in each dimension several case studies that experimentally demonstrate the usefulness of AndroZoo. [less ▲]

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See detailKomplexes Problemlösen und Intelligenz
Greiff, Samuel UL

Scientific Conference (2017, September)

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See detailDie Trainierbarkeit von komplexem Problemlösen im Rahmen eines Trainings für Experimentieren.
Stebner, Ferdinand; Kunze, Thiemo UL; Kemper, Christoph UL et al

Scientific Conference (2017, September)

Detailed reference viewed: 2 (0 UL)
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See detailA Deep Learning Approach for Optimizing Content Delivering in Cache-Enabled HetNet
Lei, Lei UL; You, Lei; Dai, Gaoyang et al

in IEEE International Symposium on Wireless Communication Systems (ISWCS), Bologna, Aug. 2017 (2017, August 31)

Detailed reference viewed: 12 (0 UL)