Lösung: Man kann die Ebene mit Sechsecken überdecken. Nimmt man die Mittelpunkte der Sechsecke als Punkte für ein Voronoidiagramm, dann haben alle genau 6 Nachbarn, und die Voronoi Polygone sind gerade die Sechsecke. Gäbe es eine Punkteverteilung, bei der jeder Punkt mehr als sechs Nachbarn hätte, dann könnte man die Punkte so hinschieben, dass die Voronoi Polygone regelmässige n-Ecke mit n > 6 wären. Eine solche Überdeckung der Ebene gibt es aber nicht (wie man sich leicht aus einer Betrachtung der Winkel überlegen kann).

Computational geometry

Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a state-of-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and black-box, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications.

/pdf/black-box-vs-white-box-understanding-their-advantages-and-3rj32l77ou.pdf

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

The world’s population is aging, and developed countries are engaged in developing a new aged-care paradigm to reduce spiraling healthcare costs. Assistive technologies like Socially Assistive Robots (SAR) are being considered as enablers to support the process of care giving or keep elderly at home longer. This article reports a mixed-method systematic review of SAR in elderly care and recognizes its impact on elderly well-being, integrating evidence from qualitative and quantitative studies. It follows the principles explained in Cochrane Handbook for Systematic Reviews of Interventions and classifies interventions, measures, and outcomes of field trials of SAR in elderly care. Eighty-six studies in 37 study groups have been included. The findings imply positive effects of SAR on elderly well-being. Ten significant recommendations are made to help avoid the current limitations of existing research and to improve future research and its applicability. This review revealed that SAR can potentially enhance...

Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Most software quality research has focused on identifying faults (i.e., information is incorrectly recorded in an artifact). Because software still exhibits incorrect behavior, a different approach is needed. This paper presents a systematic literature review to develop taxonomy of errors (i.e., the sources of faults) that may occur during the requirements phase of software lifecycle. This taxonomy is designed to aid developers during the requirement inspection process and to improve overall software quality. The review identified 149 papers from the software engineering, psychology and human cognition literature that provide information about the sources of requirements faults. A major result of this paper is a categorization of the sources of faults into a formal taxonomy that provides a starting point for future research into error-based approaches to improving software quality.

/pdf/a-systematic-literature-review-to-identify-and-classify-2jnc13hk01.pdf

A systematic literature review to identify and classify software requirement errors

In our software development group we have faced difficulties in improving software quality. In order to find the causes of this problem we have analyzed software bugs that were found in the credit authorization terminal software that our group developed, and found the real causes of the bug. As a result, it was found that about 50% of the real causes were made by the designer's carelessness. Based on this finding, guidelines were established to improve the designers’ behavior. Furthermore, we made clear the causes of software bugs, the phase in which they were made, the relation between the software bugs and their real causes. We also made the measures to prevent the software bugs and implemented them. And we refer to the effect that the substantial amount of software bugs can be decreased by the implementation.

Analysis of software bug causes and its prevention

A pet robot, similar to the popular AIBO (Artificial Intelligence robot) is emerging onto the domestic scene. These robots exist primarily for in-home entertainment, and they are known to create healing effects through their interactions with humans. However, there is a lack of practical research into what kinds of relationships exist between these robots and human beings, what kinds of influence the robots have, and other such questions. This paper discusses the kinds of effects that robots can have on human beings, and examines whether humans’ lives can be enriched if these robots play the role of not only a robotic pet but also a life partner of sorts. It also investigates how robots are useful for residents of elderly-care facilities, using a commercially available pet robot that looks like a real cat.

Healing Effects of Pet Robots at an Elderly-Care Facility

The text classification is an important subject in the data mining. For the text classification, several methods have been developed up to now, as the nearest neighbor analysis, the latent semantic analysis, etc. The k-nearest neighbor (kNN) classification is a well-known simple and effective method for the classification of data in many domains. In the use of the kNN, the distance function is important to measure the distance and the similarity between data. To improve the performance of the classifier by the kNN, a new approach to combine multiple distance functions is proposed here. The weighting factors of elements in the distance function, are computed by GA for the effectiveness of the measurement. Further, an ensemble processing was developed for the improvement of the classification accuracy. Finally, it is shown by experiments that the methods, developed here, are effective in the text classification.

Text Classification by Combining Different Distance Functions with Weights

In this paper, we establish an effort prediction modelusing an artificial neural network (ANN) for complementingmissing values. We add missing values to the data viacollaborative filtering using the method of Tsunoda et al.’smethod[14]. In addition, we perform an evaluation experimentto compare the accuracy of the ANN model with that ofthe MRA model using Welch’s t-test[16]. The results showthat the ANN model is more accurate than the MRA model,since the mean errors of the ANN are statistically significantlylower.

Using an Artificial Neural Network for Predicting Embedded Software Development Effort

In this paper, we outline the effort prediction model and the evaluation experiment. In addition we explore the parameters in the model. The model predicts effort of embedded software developments via multiple regression analysis using the collaborative filtering[5]. Because companies, recently, focus on methods to predict effort of projects, which prevent project failures such as exceeding deadline and cost, due to more complex embedded software, which brings the evolution of the performance and function enhancement [3, 7, 4]. In the model, we have fixed two parameters named k and ampmax, which would infuence the accuracy of predicting effort. Hence, we investigate a tendency of them in the model and find the optimum value.

Improving Accuracy of Multiple Regression Analysis for Effort Prediction Model

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