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Beyond Surveys
(2018)
What Stays in Mind?
(2018)
Beacon in the Dark
(2018)
The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations.
We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations based on rearranged and aggregated extracted information to reveal salient patterns.
CurEx
(2018)
The integration of diverse structured and unstructured information sources into a unified, domain-specific knowledge base is an important task in many areas. A well-maintained knowledge base enables data analysis in complex scenarios, such as risk analysis in the financial sector or investigating large data leaks, such as the Paradise or Panama papers. Both the creation of such knowledge bases, as well as their continuous maintenance and curation involves many complex tasks and considerable manual effort. With CurEx, we present a modular system that allows structured and unstructured data sources to be integrated into a domain-specific knowledge base. In particular, we (i) enable the incremental improvement of each individual integration component; (ii) enable the selective generation of multiple knowledge graphs from the information contained in the knowledge base; and (iii) provide two distinct user interfaces tailored to the needs of data engineers and end-users respectively. The former has curation capabilities and controls the integration process, whereas the latter focuses on the exploration of the generated knowledge graph.
Scrum2kanban
(2018)
Using university capstone courses to teach agile software development methodologies has become commonplace, as agile methods have gained support in professional software development. This usually means students are introduced to and work with the currently most popular agile methodology: Scrum. However, as the agile methods employed in the industry change and are adapted to different contexts, university courses must follow suit. A prime example of this is the Kanban method, which has recently gathered attention in the industry. In this paper, we describe a capstone course design, which adds the hands-on learning of the lean principles advocated by Kanban into a capstone project run with Scrum. This both ensures that students are aware of recent process frameworks and ideas as well as gain a more thorough overview of how agile methods can be employed in practice. We describe the details of the course and analyze the participating students' perceptions as well as our observations. We analyze the development artifacts, created by students during the course in respect to the two different development methodologies. We further present a summary of the lessons learned as well as recommendations for future similar courses. The survey conducted at the end of the course revealed an overwhelmingly positive attitude of students towards the integration of Kanban into the course.
An energy consumption model for multiModal wireless sensor networks based on wake-up radio receivers
(2018)
Energy consumption is a major concern in Wireless Sensor Networks. A significant waste of energy occurs due to the idle listening and overhearing problems, which are typically avoided by turning off the radio, while no transmission is ongoing. The classical approach for allowing the reception of messages in such situations is to use a low-duty-cycle protocol, and to turn on the radio periodically, which reduces the idle listening problem, but requires timers and usually unnecessary wakeups. A better solution is to turn on the radio only on demand by using a Wake-up Radio Receiver (WuRx). In this paper, an energy model is presented to estimate the energy saving in various multi-hop network topologies under several use cases, when a WuRx is used instead of a classical low-duty-cycling protocol. The presented model also allows for estimating the benefit of various WuRx properties like using addressing or not.
Recently blockchain technology has been introduced to execute interacting business processes in a secure and transparent way. While the foundations for process enactment on blockchain have been researched, the execution of decisions on blockchain has not been addressed yet. In this paper we argue that decisions are an essential aspect of interacting business processes, and, therefore, also need to be executed on blockchain. The immutable representation of decision logic can be used by the interacting processes, so that decision taking will be more secure, more transparent, and better auditable. The approach is based on a mapping of the DMN language S-FEEL to Solidity code to be run on the Ethereum blockchain. The work is evaluated by a proof-of-concept prototype and an empirical cost evaluation.
Several areas in Southeast Asia are very vulnerable to climate change and unable to take immediate/effective actions on countermeasures due to insufficient capabilities. Malaysia, in particular the east coast of peninsular Malaysia and Sarawak, is known as one of the vulnerable regions to flood disaster. Prolonged and intense rainfall, natural activities and increase in runoff are the main reasons to cause flooding in this area. In addition, topographic conditions also contribute to the occurrence of flood disaster. Kuching city is located in the northwest of Borneo Island and part of Sarawak river catchment. This area is a developing state in Malaysia experiencing rapid urbanization since 2000s, which has caused the insufficient data availability in topography and hydrology. To deal with these challenging issues, this study presents a flood modelling framework using the remote sensing technologies and machine learning techniques to acquire the digital elevation model (DEM) with improved accuracy for the non-surveyed areas. Intensity–duration–frequency (IDF) curves were derived from climate model for various scenario simulations. The developed flood framework will be beneficial for the planners, policymakers, stakeholders as well as researchers in the field of water resource management in the aspect of providing better ideas/tools in dealing with the flooding issues in the region.
PlAnalyzer
(2018)
In this work we propose PIAnalyzer, a novel approach to analyze PendingIntent related vulnerabilities. We empirically evaluate PIAnalyzer on a set of 1000 randomly selected applications from the Google Play Store and find 1358 insecure usages of Pendinglntents, including 70 severe vulnerabilities. We manually inspected ten reported vulnerabilities out of which nine correctly reported vulnerabilities, indicating a high precision. The evaluation shows that PIAnalyzer is efficient with an average execution time of 13 seconds per application.
We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.