University of Mannheim

About: University of Mannheim is a education organization based out in Mannheim, Germany. It is known for research contribution in the topics: Context (language use) & Politics. The organization has 4448 authors who have published 12918 publications receiving 446557 citations. The organization is also known as: Uni Mannheim & UMA.

Comparison of automatic shot boundary detection algorithms

Abstract: Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. Although the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic components, the shots, only few comparative investigations on early shot boundary detection algorithms have been published. These investigations mainly concentrate on measuring the edit detection performance, however, do not consider the algorithms? ability to classify the types and to locate the boundaries of the edits correctly. This paper extends these comparative investigations. More recent algorithms designed explicitly to detect specific complex editing operations such as fades and dissolves are taken into account, and their ability to classify the types and locate the boundaries of such edits are examined. The algorithms? performance is measured in terms of hit rate, number of false hits, and miss rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments show that while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rate for dis-solves is usually unacceptably high, ranging from 50% up to over 400%. Moreover, all algorithms seem to fail under roughly the same conditions.

The nepenthes platform: an efficient approach to collect malware

Abstract: Up to now, there is little empirically backed quantitative and qualitative knowledge about self-replicating malware publicly available. This hampers research in these topics because many counter-strategies against malware, e.g., network- and host-based intrusion detection systems, need hard empirical data to take full effect. We present the nepenthes platform, a framework for large-scale collection of information on self-replicating malware in the wild. The basic principle of nepenthes is to emulate only the vulnerable parts of a service. This leads to an efficient and effective solution that offers many advantages compared to other honeypot-based solutions. Furthermore, nepenthes offers a flexible deployment solution, leading to even better scalability. Using the nepenthes platform we and several other organizations were able to greatly broaden the empirical basis of data available about self-replicating malware and provide thousands of samples of previously unknown malware to vendors of host-based IDS/anti-virus systems. This greatly improves the detection rate of this kind of threat.

Why the Rigour-Relevance Gap in Management Research Is Unbridgeable

Abstract: In this paper we discuss, on the basis of system theory, the rigour–relevance gap in management research and the proposal to overcome it. From a system theory perspective, social systems are self-referential or autopoietic, which means that communication elements of one system, such as science, cannot be authentically integrated into communication of other systems, such as the system of a business organization. Social systems can only irritate – provoke – each other, i.e. alter conditions in such a way that other systems are forced to respond. Because of the differences between management science and practice it is impossible to assess relevance of research output within the system of science. On the basis of our analysis we show that neither action research nor Mode 2 research nor recent approaches to collaborative research can succeed in producing research that is rigorous as well as relevant. Researchers and practitioners cannot collaboratively produce research, they can only irritate each other. However, sometimes irritations or provocations turn out inspiring.

Dabigatran for Prevention of Stroke after Embolic Stroke of Undetermined Source

Abstract: BACKGROUND: Cryptogenic strokes constitute 20 to 30% of ischemic strokes, and most cryptogenic strokes are considered to be embolic and of undetermined source. An earlier randomized trial showed that rivaroxaban is no more effective than aspirin in preventing recurrent stroke after a presumed embolic stroke from an undetermined source. Whether dabigatran would be effective in preventing recurrent strokes after this type of stroke was unclear. METHODS: We conducted a multicenter, randomized, double-blind trial of dabigatran at a dose of 150 mg or 110 mg twice daily as compared with aspirin at a dose of 100 mg once daily in patients who had had an embolic stroke of undetermined source. The primary outcome was recurrent stroke. The primary safety outcome was major bleeding. RESULTS: A total of 5390 patients were enrolled at 564 sites and were randomly assigned to receive dabigatran (2695 patients) or aspirin (2695 patients). During a median follow-up of 19 months, recurrent strokes occurred in 177 patients (6.6%) in the dabigatran group (4.1% per year) and in 207 patients (7.7%) in the aspirin group (4.8% per year) (hazard ratio, 0.85; 95% confidence interval [CI], 0.69 to 1.03; P = 0.10). Ischemic strokes occurred in 172 patients (4.0% per year) and 203 patients (4.7% per year), respectively (hazard ratio, 0.84; 95% CI, 0.68 to 1.03). Major bleeding occurred in 77 patients (1.7% per year) in the dabigatran group and in 64 patients (1.4% per year) in the aspirin group (hazard ratio, 1.19; 95% CI, 0.85 to 1.66). Clinically relevant nonmajor bleeding occurred in 70 patients (1.6% per year) and 41 patients (0.9% per year), respectively. CONCLUSIONS: In patients with a recent history of embolic stroke of undetermined source, dabigatran was not superior to aspirin in preventing recurrent stroke. The incidence of major bleeding was not greater in the dabigatran group than in the aspirin group, but there were more clinically relevant nonmajor bleeding events in the dabigatran group. (Funded by Boehringer Ingelheim; RE-SPECT ESUS ClinicalTrials.gov number, NCT02239120.).

Efficient design with interdependent valuations

Abstract: We study efficient, Bayes-Nash incentive compatible mechanisms in a social choice setting that allows for informational and allocative externalities. We show that such mechanisms exist only if a congruence condition relating private and social rates of information substitution is satisfied. If signals are multi-dimensional, the congruence condition is determined by an integrability constraint, and it can hold only in nongeneric cases where values are private or a certain symmetry assumption holds. If signals are one-dimensional, the congruence condition reduces to a monotonicity constraint and it can be generically satisfied. We apply the results to the study of multi-object auctions, and we discuss why such auctions cannot be reduced to one-dimensional models without loss of generality.