Matthias Ring

Bio: Matthias Ring is an academic researcher from University of Erlangen-Nuremberg. The author has contributed to research in topics: Feature (computer vision) & Kernel method. The author has an hindex of 7, co-authored 19 publications receiving 197 citations.

Automatic clustering of code changes

Abstract: Several research tools and projects require groups of similar code changes asinput. Examples are recommendation and bug finding tools that can providevaluable information to developers based on such data. With the help ofsimilar code changes they can simplify the application of bug fixes and codechanges to multiple locations in a project. But despite their benefit, thepractical value of existing tools is limited, as users need to manually specifythe input data, i.e., the groups of similar code changes.To overcome this drawback, this paper presents and evaluates two syntacticalsimilarity metrics, one of them is specifically designed to run fast, incombination with two carefully selected and self-tuning clustering algorithmsto automatically detect groups of similar code changes.We evaluate the combinations of metrics and clustering algorithms by applyingthem to several open source projects and also publish the detected groups ofsimilar code changes online as a reference dataset. The automatically detectedgroups of similar code changes work well when used as input for LASE, arecommendation system for code changes.

Software-based performance and complexity analysis for the design of embedded classification systems

Abstract: Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true for example for the fields of sports, automotive and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources. In this paper, we present a solution to the two main challenges, namely obtaining a classification system with low computational complexity and at the same time high classification accuracy. For the first challenge, we propose complexity measures on the mathematical operation and parameter level, because the abstraction level of the commonly used Landau notation is too high in the context of embedded system implementation. For the second challenge, we present a software toolbox that trains different classification systems, compares their classification rate, and finally analyzes the complexity of the trained system. To give an impression of the importance of such complexity measures when dealing with limited hardware resources, we present the example analysis of the popular Pima Indians Diabetes data set, where considerable complexity differences between classification systems were revealed.

Privacy Implications of Room Climate Data

Abstract: Smart heating applications promise to increase energy efficiency and comfort by collecting and processing room climate data. While it has been suspected that the sensed data may leak crucial personal information about the occupants, this belief has up until now not been supported by evidence.

Effects of stimulation frequency, amplitude, and impulse width on muscle fatigue.

Abstract: Introduction: We investigated the effect of stimulation intensity (in percent of maximal tolerated stimulation current, mTSC), frequency, and impulse width on muscle fatigue. Methods: Using a randomized crossover design, 6 parameter combinations (80% mTSC, 80 Hz, 400 μs; 60% mTSC, 80 Hz, 400 μs; 80% mTSC, 20 Hz, 400 μs; 60% mTSC, 20 Hz, 400 μs; 80% mTSC, 80 Hz, 150 μs; 60% mTSC, 80 Hz, 150 μs) were tested in both legs of 13 athletic men (age 26 ± 2.3). The slope of the linear regression line over all tetani (FIS) and the number of tetani whose force was above 50% of the initial tetanus (FIN) were used to quantify fatigue. Results: FIS and FIN were significantly lower in high-frequency protocols. No effects on FIS and FIN were found for intensity and impulse width. Conclusions: Stimulation frequency, but not impulse width or intensity, affected fatigue kinetics. Muscle Nerve 53: 608–616, 2016

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.