Reliability aspects of Hf-based capacitors: Breakdown and trapping effects

Dielectric breakdown and trapping effects are of serious concern for high-k dielectrics. In this paper acceleration models for dielectric breakdown and leakage current degradation in HfSiO films thicker 10 nm are introduced and the mechanism for leakage current increase during constant voltage stress is evaluated.     

Performers, trackers, lemmings and the lost: Sustained false optimism in forecasting project outcomes — Evidence from a quasi-experiment

The consistently successful delivery of projects remains an ambition that many organisations do not achieve. Whilst the reasons behind project failure are many, one recognised factor is the ‘planning fallacy’ – over-optimism in the planning phase of a project. Whilst the planning phase of a project may be a battle for acceptance and resource allocation, the execution phase is a battle for delivery. Based on both qualitative and quantitative data gathered from a project management simulation, this study set out to establish whether optimism bias persists beyond the planning phase and into the execution phase, and, if so, to explore the reasons why. The results confirm the extent and impact of optimism bias in initial project planning. More importantly, the contribution of this study is to demonstrate on-going or sustained false optimism.     

Das Forschungsfeld „Modellierung betrieblicher Informationssysteme“

The research field “Modeling business information systems” has a long tradition in the scientific discipline of Business and Information Systems Engineering (BISE). The present paper highlights research shaping the research field, discusses challenges impairing the development of the research field in the coming years, and outlines elements of a future research agenda.     

Kompetenzzentrum Wasserkraft

AktuellInnovatives Biomassekraftwerk

Erfolgreiches Pilotprojekt mit neuem Kraftwerkstyp     

Not everybody needs help to seek help: Surprising effects of metacognitive instructions to foster help-seeking in an online-learning environment

Offering help functions is a standard feature of computer-based interactive learning environments (ILE). Nevertheless, a number of recent studies indicate that learners are not using such help facilities effectively. We compared the effects of different metacognitive supports to foster learners’ help-seeking behavior in an ILE for plant identification. Four groups of university students (n = 51), each receiving a different metacognitive instruction, had to determine living plants. They had to think-aloud and were video recorded during the experiment. At the end of the session they completed a knowledge test. The surprising effect was that students in all groups were effective help-seekers. They adapted their help-seeking behavior to the complexity of the plants in an effective way. The results indicate that for students on university level effective help-seeking seems to depend largely on motivational factors.     

Automatic pronunciation scoring of words and sentences independent from the non-native’s first language

This paper describes an approach for automatic scoring of pronunciation quality for non-native speech. It is applicable regardless of the foreign language student’s mother tongue. Sentences and words are considered as scoring units. Additionally, mispronunciation and phoneme confusion statistics for the target language phoneme set are derived from human annotations and word level scoring results using a Markov chain model of mispronunciation detection. The proposed methods can be employed for building a part of the scoring module of a system for computer assisted pronunciation training (CAPT). Methods from pattern and speech recognition are applied to develop appropriate feature sets for sentence and word level scoring. Besides features well-known from and approved in previous research, e.g. phoneme accuracy, posterior score, duration score and recognition accuracy, new features such as high-level phoneme confidence measures are identified. The proposed method is evaluated with native English speech, non-native English speech from German, French, Japanese, Indonesian and Chinese adults and non-native speech from German school children. The speech data are annotated with tags for mispronounced words and sentence level ratings by native English teachers. Experimental results show, that the reliability of automatic sentence level scoring by the system is almost as high as the average human evaluator. Furthermore, a good performance for detecting mispronounced words is achieved. In a validation experiment, it could also be verified, that the system gives the highest pronunciation quality scores to 90% of native speakers’ utterances. Automatic error diagnosis based on a automatically derived phoneme mispronunciation statistic showed reasonable results for five non-native speaker groups. The statistics can be exploited in order to provide the non-native feedback on mispronounced phonemes.     

Modeling mobility in disaster area scenarios

This paper provides a model that realistically represents the movements in a disaster area scenario. The model is based on an analysis of tactical issues of civil protection. This analysis provides characteristics influencing network performance in public safety communication networks like heterogeneous area-based movement, obstacles, and joining/leaving of nodes. As these characteristics cannot be modeled with existing mobility models, we introduce a new disaster area mobility model. To examine the impact of our more realistic modeling, we compare it to existing ones (modeling the same scenario) using different pure movement and link-based metrics. The new model shows specific characteristics like heterogeneous node density. Finally, the impact of the new model is evaluated in an exemplary simulative network performance analysis. The simulations show that the new model discloses new information and has a significant impact on performance analysis.     

A study of structural and parametric learning in XCS

The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjusts parameters of existing rules, for example rule prediction and accuracy, in an evaluation step, which is not only important for applying the rules, but also for the genetic process. The two components interleave and in the case of XCS drive the population toward a minimal, fit, non-overlapping population. In this work we attempt to gain new insights as to the relative contributions of the two components. We find that the genetic component has an additional role when using the train/test approach which is not present in online learning. We compare XCS to a system in which the rule set is restricted to the initial random population (XCS-NGA, that is, XCS No Genetic Algorithm). For small Boolean functions we can give XCS-NGA all possible rules of a particular condition length. In online learning, XCS-NGA can, given sufficiently many rules, achieve a surprisingly high classification accuracy, comparable to that of XCS. In a train/test approach, however, XCS generalises better than XCS-NGA and there seem to be limitations of XCS-NGA which cannot be overcome simply by increasing the population size. This illustrates that the requirements of a function approximator tend to differ between reinforcement learning (which is typically online) and concept learning (which is typically train/test).