Women Traders of the Viking Age: An Analysis of Grave Goods

The image of Viking culture that most of us carry in our heads is largely masculine and testosterone-driven. Where are women in the Scandinavian past? Examining grave goods associated with female burials—including items of personal adornment—this article paints a richer and more balanced picture of the Viking world

Making Indefinite Kernel Learning Practical

In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems. We then embed evolutionary computation into the most prominent representative of this class of learning methods, namely into Support Vector Machines (SVM). In contrast to former applications of evolutionary algorithms to SVM we do not only optimize the method or kernel parameters. We rather use evolution strategies in order to directly solve the posed constrained optimization problem. Transforming the problem into the Wolfe dual reduces the total runtime and allows the usage of kernel functions just as for traditional SVM. We will show that evolutionary SVM are at least as accurate as their quadratic programming counterparts on eight real-world benchmark data sets in terms of generalization performance. They always outperform traditional approaches in terms of the original optimization problem. Additionally, the proposed algorithm is more generic than existing traditional solutions since it will also work for non-positive semidefinite or indefinite kernel functions. The evolutionary SVM variants frequently outperform their quadratic programming competitors in cases where such an indefinite Kernel function is used. --

Structuring music collections by exploiting peers' processing

Music collections are structured in very different ways by different useres. There is not one general taxonomy, but individual, user-specific structures exist. Most users appreciate some support in structering their collection. A large variety of methods has been developed for textual collections. However, audio data are completely different. In this paper, we present a peer to peer scenario where a music collection is enhanced a set of audio data in a node of the user's taxonomy by retrieving (partial) taxonomies of peers. In order to classify audio data into a taxonomy features need to be extracted. Adopting feature extraction to a particular set of classes is effective but not efficient. Hence, we propose again to exploit what has allready been done. Wellsuited feature extraction for one classification task is transferred to similar tasks using a new distance measures. --

Learning feature extraction for learning from audio data

Today, large collections of digital music plays are available. These audio data are time series which need to be indexed and classified for diverse applications. Indexing and classification differs from time series analysis, in that it generalises several series, whereas time series analysis handles just one series a time. The classification of audio data cannot use similarity measures defined on the raw data, e.g. using time warping, or generalise the shape of the series. The appropriate similarity or generalisation for audio data requires feature extraction before classification can successfully be applied to the transformed data. Methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires to tailor the feature set anew. Hence, we consider the construction of feature extraction methods from elementary operators itself a first learning step. We use a genetic programming approach. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences --

Employability Skills Training for Displaced Homemakers Measured via the Practice Interview

Two groups of females participated in a practice interview designed to measure the effectiveness of assertive communication taught during a two week program for Displaced Homemakers. The Experimental Group, selected according to CETA criteria, were interviewed after receiving training. The Control Group were women similar in all relevant respects with the exception of having worked for pay outside the home within the past three years. The structured interview was designed to incorporate the same areas for evaluation as would potential employers in a real selection process. Instructions to provide motivation, or demand characteristics, for both groups were contained in a letter given to all participants. A Posttest Only Control Group research design was utilized. Content of interview was not measured. Rather, the 17 basic questions asked by the researcher were used as the instrument to measure verbal rate of communicating job-relevant and/or transferable volunteer experience. Six (6) tapes were randomly selected and scored for inter-rater reliability. Nonverbal communication behaviors which were rated by both the Interviewer and an Independent Observer were: Eye Contact, Posture and Appearance. Findings indicate that both verbal and nonverbal behavior were significantly (p\u3c .001 and p.\u3c .05) greater for the Experimental Group, i.e., Displaced Homemakers, who received training

Parameters of low back pain chronicity among athletes: associations with physical and mental stress

Objective: In the general population, physical and mental stress factors are linked to chronic low back pain (LBP). The aim of the present study was to examine this association among athletes

Self-configuring data mining for ubiquitous computing

Ubiquitous computing software needs to be autonomous so that essential decisions such as how to configure its particular execution are self-determined. Moreover, data mining serves an important role for ubiquitous computing by providing intelligence to several types of ubiquitous computing applications. Thus, automating ubiquitous data mining is also crucial. We focus on the problem of automatically configuring the execution of a ubiquitous data mining algorithm. In our solution, we generate configuration decisions in a resource aware and context aware manner since the algorithm executes in an environment in which the context often changes and computing resources are often severely limited. We propose to analyze the execution behavior of the data mining algorithm by mining its past executions. By doing so, we discover the effects of resource and context states as well as parameter settings on the data mining quality. We argue that a classification model is appropriate for predicting the behavior of an algorithm?s execution and we concentrate on decision tree classifier. We also define taxonomy on data mining quality so that tradeoff between prediction accuracy and classification specificity of each behavior model that classifies by a different abstraction of quality, is scored for model selection. Behavior model constituents and class label transformations are formally defined and experimental validation of the proposed approach is also performed