Auswirkungen kulturspezifischer Verhaltensmuster auf das Sozialkapital in multinationalen IT-Projektteams

This paper contributes to a better understanding and to mitigate negative consequences of cultural diversity in multinational IT project teams. Our research explores how culture-specific behaviors impact social capital among team members and how firms can manage the strains. In the existing IS culture literature, culture-specific behaviors are ?C if at all ?C traced back to single culture dimensions. In contrast, the approach proposed in this article goes one step further suggesting that it is necessary to combine several culture dimensions to better understand a certain culture-specific behavior and consequently be able to better manage resulting relationship problems in multinational settings. Conducting exploratory case studies in six multinational IT projects, two exemplary cultural behavior patterns (face maintenance in India and post-communism in the Czech Republic) are identified, and management actions to avoid project performance problems are derived. The results contribute to a better understanding and management of the negative impact of culture-specific behaviors in IT project teams and corroborate that research based on culture dimensions, such as those conceptualized by Hofstede or House et al., is valuable for understanding multi-country IS projects. The findings in particular suggest that aggregating these dimensions to cultural behavior patterns improves their explanatory power and consequently the management??s capability to mitigate the negative consequences of cultural diversity.     

IPSEP-COLA: an incremental procedure for separation constraint layout of graphs

Existing information-visualization techniques that target small screens are usually limited to exploring a few hundred items. In this article we present a scatterplot tool for personal digital assistants that allows the handling of many thousands of items. The application's scalability is achieved by incorporating two alternative interaction techniques: a geometric-semantic zoom that provides smooth transition between overview and detail, and a fisheye distortion that displays the focus and context regions of the scatterplot in a single view. A user study with 24 participants was conducted to compare the usability and efficiency of both techniques when searching a book database containing 7500 items. The study was run on a pen-driven Wacom board simulating a PDA interface. While the results showed no significant difference in task-completion times, a clear majority of 20 users preferred the fisheye view over the zoom interaction. In addition, other dependent variables such as user satisfaction and subjective rating of orientation and navigation support revealed a preference for the fisheye distortion. These findings partly contradict related research and indicate that, when using a small screen, users place higher value on the ability to preserve navigational context than they do on the ease of use of a simplistic, metaphor-based interaction style     

Executing Formal Specifications with Concurrent Constraint Programming

We have implemented a technique for execution of formal, model-based specifications. The specifications we can execute are written at a level of abstraction that is close to that used in nonexecutable specifications. The specification abstractions supported by our execution technique include using quantified assertions to directly construct post-state values, and indirect definitions of post-state values (definitions that do not use equality). Our approach is based on translating specifications to the concurrent constraint programming language AKL. While there are, of course, expressible assertions that are not executable, our technique is amenable to any formal specification language based on a finite number of intrinsic types and pre- and postcondition assertions.     

Reprint of a process model for developing usable cross-cultural websites

In this paper we present a process model for developing usable cross-cultural websites. Compatible with ISO 13407, the process model documents an abstraction of the design process focusing on cultural issues in development. It provides a framework in which a variety of user-based and expert-based techniques for analysis and design are placed within the life-cycle of website development. In developing the model, we relate practical approaches to design with theories and models of culture and discuss the relevance of such theories to the practical design process. In particular we focus on four key concerns: how an audit of local website attractors can inform the design process; the concept of a cultural fingerprint to contrast websites with the cultural needs of local users; the problems associated with user evaluation; and cross-cultural team development. We then show their relation to our process model. We conclude by summarising our contribution to date within the field.     

Tracking Motion, Deformation, and Texture Using Conditionally Gaussian Processes

We present a generative model and inference algorithm for 3D nonrigid object tracking. The model, which we call G-flow, enables the joint inference of 3D position, orientation, and nonrigid deformations, as well as object texture and background texture. Optimal inference under G-flow reduces to a conditionally Gaussian stochastic filtering problem. The optimal solution to this problem reveals a new space of computer vision algorithms, of which classic approaches such as optic flow and template matching are special cases that are optimal only under special circumstances. We evaluate G-flow on the problem of tracking facial expressions and head motion in 3D from single-camera video. Previously, the lack of realistic video data with ground truth nonrigid position information has hampered the rigorous evaluation of nonrigid tracking. We introduce a practical method of obtaining such ground truth data and present a new face video data set that was created using this technique. Results on this data set show that G-flow is much more robust and accurate than current deterministic optic-flow-based approaches.     

Motion Blur for EWA Surface Splatting

This paper presents a novel framework for elliptical weighted average (EWA) surface splatting with time‐varying scenes. We extend the theoretical basis of the original framework by replacing the 2D surface reconstruction filters by 3D kernels which unify the spatial and temporal component of moving objects. Based on the newly derived mathematical framework we introduce a rendering algorithm that supports the generation of high‐quality motion blur for point‐based objects using a piecewise linear approximation of the motion. The rendering algorithm applies ellipsoids as rendering primitives which are constructed by extending planar EWA surface splats into the temporal dimension along the instantaneous motion vector. Finally, we present an implementation of the proposed rendering algorithm with approximated occlusion handling using advanced features of modern GPUs and show its capability of producing motion‐blurred result images at interactive frame rates.     

Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry

ContextBuilding defect prediction models in large organizations has many challenges due to limited resources and tight schedules in the software development lifecycle. It is not easy to collect data, utilize any type of algorithm and build a permanent model at once. We have conducted a study in a large telecommunications company in Turkey to employ a software measurement program and to predict pre-release defects. Based on our prior publication, we have shared our experience in terms of the project steps (i.e. challenges and opportunities). We have further introduced new techniques that improve our earlier results.ObjectiveIn our previous work, we have built similar predictors using data representative for US software development. Our task here was to check if those predictors were specific solely to US organizations or to a broader class of software.MethodWe have presented our approach and results in the form of an experience report. Specifically, we have made use of different techniques for improving the information content of the software data and the performance of a Naïve Bayes classifier in the prediction model that is locally tuned for the company. We have increased the information content of the software data by using module dependency data and improved the performance by adjusting the hyper-parameter (decision threshold) of the Naïve Bayes classifier. We have reported and discussed our results in terms of defect detection rates and false alarms. We also carried out a cost–benefit analysis to show that our approach can be efficiently put into practice.ResultsOur general result is that general defect predictors, which exist across a wide range of software (in both US and Turkish organizations), are present. Our specific results indicate that concerning the organization subject to this study, the use of version history information along with code metrics decreased false alarms by 22%, the use of dependencies between modules further reduced false alarms by 8%, and the decision threshold optimization for the Naïve Bayes classifier using code metrics and version history information further improved false alarms by 30% in comparison to a prediction using only code metrics and a default decision threshold.ConclusionImplementing statistical techniques and machine learning on a real life scenario is a difficult yet possible task. Using simple statistical and algorithmic techniques produces an average detection rate of 88%. Although using dependency data improves our results, it is difficult to collect and analyze such data in general. Therefore, we would recommend optimizing the hyper-parameter of the proposed technique, Naïve Bayes, to calibrate the defect prediction model rather than employing more complex classifiers. We also recommend that researchers who explore statistical and algorithmic methods for defect prediction should spend less time on their algorithms and more time on studying the pragmatic considerations of large organizations.     

Effects of age and body mass index on breast characteristics: a cluster analysis

Limited research has quantified variation in the characteristics of the breasts among women and determined how these breast characteristics are influenced by age and body mass. The aim of this study was to classify the breasts of women in the community into different categories based on comprehensive and objective measurements of the characteristics of their breasts and torsos, and to determine the effect of age and body mass index on the prevalence of these breast categories. Four breast characteristic clusters were identified (X-Large, Very-ptotic & Splayed; Large, Ptotic & Splayed; Medium & Mildly-ptotic; and Small & Non-ptotic), with age and BMI shown to significantly affect the breast characteristic clusters. These results highlight the difference in breast characteristics exhibited among women and how these clusters are affected by age and BMI. The breast characteristic clusters identified in this study could be used as a basis for future bra designs and sizing systems in order to improve bra fit for women.

Practitioner summary: This original research provides evidence for bra designers and manufacturers on the diverse breast characteristics exhibited by women within the population and the significant effect that both body mass index and age have on the breast characteristic clusters. Future bra designs should consider the variation in breast characteristics among women.

Abbreviations: BMI: Body Mass Index; UBCC: Under-bust chest circumference     

Why Risk Assessments Fail

Over the past decades there has been a series of major corporate governance crises. After each wave post-mortems were convened and efforts made by regulators to identify root causes. The good news—or bad, depending on your perspective—for the internal audit profession is that rarely were questions raised by those commissions and regulators about the role internal audit should have played to avoid the current crisis being reviewed.

What the commissions did call for was a massive global focus on the need for boards of directors to better oversee risk in their organizations. As pressure on directors mounts globally to improve risk oversight, their dissatisfaction with traditional internal audit services is also growing. This article suggests the root cause of the mounting internal audit customer dissatisfaction globally is internal audit “paradigm paralysis”¹—a strong attachment to traditional ways of doing internal audits that no longer meet the needs of key customers. Specific recommendations are made to help internal auditors transition past the paradigm paralysis and adopt new methods that better meet the needs of its key customers. *This article first appeared in ACCA UK’s Internal Audit e-bulletin.     

COMPARING ANNs AND GENETIC PROGRAMMING FOR VOICE QUALITY ASSESSMENT POST-TREATMENT

In the U.K., the rehabilitation of a patient's voice following treatment for cancer of the larynx is managed by Speech and Language Therapists (SALT), who listen to a patient's stylized speech and then use their experience and domain knowledge to make an assessment of the current quality of the patient's voice. This process is very subjective and time consuming, and could benefit from using AI techniques to provide objective, reproducible assessments of voice quality. A comparative study of voice quality assessment post-treatment using Artificial Neural Networks (ANN), the preferred AI technique in this application area, and Genetic Programming (GP) is described, using the same dataset, training, and verification procedures. The GP approach was found to give more accurate classifications of bad quality (immediately post-treatment) and good quality (recovered) voicings than the ANN, and in addition, gave indication of the most significant parameters in the input dataset.