Open Access

In parcel delivery, the “last mile” from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens (“the crowd”) perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers’ smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.

Aim/Purpose: We explore impressions and experiences of Information Systems graduates during their first years of employment in the IT field. The results help to understand work satisfaction, career ambition, and motivation of junior employees. This way, the attractiveness of working in the field of IS can be increased and the shortage of junior employees reduced. Background: Currently IT professions are characterized by terms such as “shortage of professionals” and “shortage of junior employees”. To attract more people to work in IT detailed knowledge about experiences of junior employees is necessary. Methodology: Data from a large survey of 193 graduates of the degree program “Information Systems” at University of Applied Sciences and Arts Hannover (Germany) show characteristics of their professional life like work satisfaction, motivation, career ambition, satisfaction with opportunities, development and career advancement, satisfaction with work-life balance. It is also asked whether men and women gain the same experiences when entering the job market and have the same perceptions. Findings: The participants were highly satisfied with their work, but limitations or restrictions due to gender are noteworthy. Recommendations for Practitioners: The results provide information on how human resource policies can make IT professions more attractive and thus convince graduates to seek jobs in the field. For instance, improving the balance between work and various areas of private life seems promising. Also, restrictions with respect to the work climate and improving communication along several dimensions need to be considered. Future Research: More detailed research on ambition and achievement is necessary to understand gender differences.

Der Tagungsband der Teaching Trends 2018 bietet allen Leser*innen spannende Einblicke in Präsenzhochschulen, die in geschickten Szenarien verschiedene digitale Medien für den Kompetenzerwerb ihrer Studierenden nutzen. In einer breiten Sicht auf die Digitalisierung beschäftigen sich die Tagungsbeiträge mit neuen Lernformaten wie Blended Learning und Inverted Classroom, deren aktuellen rechtlichen Rahmenbedingungen in DSGVO und Urheberrecht und technischen Grundlagen, z.B. in Augmented / Virtual Reality oder Audience Response. Darüber hinaus jedoch kommen übergreifende Strategien und Entwicklungskonzepte zu Wort, die die Hochschule in eine digitale Zukunft führen. In allen Bereichen berichteten die Vortragenden sowohl direkt aus ihrer Lehrpraxis als auch aus der begleitenden Forschung. Zur Abrundung der Tagung haben die Herausgeber*innen das einleitende Streitgespräch zur Bedeutung der digitalen Transformation für Universitäten, die Podiumsdiskussion zu Herausforderungen, die sich daraus für das Studium ergeben, sowie eine Keynote zur Architektur von Lernräumen zu Papier gebracht.

In the present paper we sketch an automated procedure to compare different versions of a contract. The contract texts used for this purpose are structurally differently composed PDF files that are converted into structured XML files by identifying and classifying text boxes. A classifier trained on manually annotated contracts achieves an accuracy of 87% on this task. We align contract versions and classify aligned text fragments into different similarity classes that enhance the manual comparison of changes in document versions. The main challenges are to deal with OCR errors and different layout of identical or similar texts. We demonstrate the procedure using some freely available contracts from the City of Hamburg written in German. The methods, however, are language agnostic and can be applied to other contracts as well.

Our work is motivated primarily by the lack of standardization in the area of Event Processing Network (EPN) models. We identify general requirements for such models. These requirements encompass the possibility to describe events in the real world, to establish temporal and causal relationships among the events, to aggregate the events, to organize the events into a hierarchy, to categorize the events into simple or complex, to create an EPN model in an easy and simple way and to use that model ad hoc. As the major contribution, this paper applies the identified requirements to the RuleCore model.

Dieser Beitrag adressiert einleitend die aktuelle Bedrohungslage aus Sicht der Industrie mit einem Fokus auf das Feld und die Feldgeräte. Zentral wird dann die Frage behandelt, welchen Beitrag Feldgeräte im Kontext von hoch vernetzten Produktionsanlagen für die künftige IT-Sicherheit leisten können und müssen. Unter anderem werden auf Basis der bestehenden Standards wie IEC 62443-4-1, IEC 62443-4-2 oder der VDI 2182-1 und VDI 2182-4 ausgewählte Methoden und Maßnahmen am Beispiel eines Durchflussmessgerätes vorgestellt, die zur künftigen Absicherung von Feldgeräten notwendig sind.

Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific exts outperform word embeddings learned from much larger general corpora.

NOA is a search engine for scientific images from open access publications based on full text indexing of all text referring to the images and filtering for disciplines and image type. Images will be annotated with Wikipedia categories for better discoverability and for uploading to WikiCommons. Currently we have indexed approximately 2,7 Million images from over 710 000 scientific papers from all fields of science.

The reuse of scientific raw data is a key demand of Open Science. In the project NOA we foster reuse of scientific images by collecting and uploading them to Wikimedia Commons. In this paper we present a text-based annotation method that proposes Wikipedia categories for open access images. The assigned categories can be used for image retrieval or to upload images to Wikimedia Commons. The annotation basically consists of two phases: extracting salient keywords and mapping these keywords to categories. The results are evaluated on a small record of open access images that were manually annotated.

Automatisch bewertete, variable Programmieraufgaben stellen besondere Schnittstellenanforderungen an Autobewerter (Grader) und Lernmanagementsysteme (LMS). Um Wiederverwendung von Aufgaben über Systemgrenzen hinweg zu begünstigen, schlagen wir vor, Aufgabenschablonen durch eine von allen beteiligten Systemen genutzte Middleware zu instanziieren und dabei Variabilitätsinformationen in einem Schnittstellen-Datenmodell zu transportieren. Wir stellen ein solches Datenmodell vor, welches für die Grader-unabhängige Kommunikation mit LMS ausgelegt ist und beispielhaft im Autobewerter Graja implementiert wurde. Zudem wird eine Dialogkomponente für die manuelle Werteauswahl vorgestellt, die auch bei großen Wertemengen effizient und Grader-unabhängig einsetzbar ist. Die Eignung des Dialogs und des Datenmodells wird anhand eines typischen Bewertungsszenarios diskutiert.