Effects of Transcendental Meditation versus resting on physiological and subjective arousal.

On 4 successive days, 10 highly trained and experienced meditators (aged 26–54 yrs) were asked to relax for 5 min, meditate for 20 min, and then relax for 5 min. In contrast, 10 other Ss (aged 22–32 yrs) who had no training or experience with meditation were asked to relax for 5 min, rest for 20 min, and then relax for 5 min. Physiological arousal (heart and respiration rates, skin resistance, and blood pressure) and subjective arousal (cognitive, somatic, and relaxation) were measured throughout the experiment. Prior to meditating or resting, meditators tended to have higher heart rates and diastolic blood pressure than did nonmeditators. Meditation was associated with generally reduced arousal; but while meditating, meditators did not evidence lower levels of arousal than nonmeditators did while resting. Results place qualifications on previous studies of the effects of meditation on arousal. Implications for the study of personality functioning, stress management, and psychotherapy are discussed. (27 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

TSTL: the template scripting testing language

International Journal on Software Tools for Technology Transfer - A test harness, in automated test generation, defines the set of valid tests for a system, as well as their correctness properties....     

Investigating the impact of video games on high school students’ engagement and learning about genetics

The popularity of video games has transcended entertainment crossing into the world of education. While the literature base on educational gaming is growing, there is still a lack of systematic study of this emerging technology’s efficacy. This quasi-experimental study evaluated a teacher created video game on genetics in terms of its affective and cognitive impact on student users. While statistical results indicated no differences (p > .05) in student learning as measured by our instrument, there were significant differences (p < .05) found in the participants’ level of engagement while interfacing with the video game. Implications on this emerging line of inquiry are discussed.     

DASS Good: Explainable Data Mining of Spatial Cohort Data

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.     

Web-log-driven business activity monitoring

Business process transformation defines a new level of business optimization that manifests as a range of industry-specific initiatives that bring processes, people, and information together to optimize efficiency. This new optimization level is possible because the Web has assumed the role of a common infrastructure. To examine how BPT can optimize an organization's processes, we describe a corporate initiative that was developed within IBM's supply chain organization to transform the import compliance process that supports the company's global logistics. The initiative sought to give IBM greater awareness of regulatory compliance exceptions - information critical to the corporation and its importing partners.     

Domain structures due to induced anisotropy and stresses in filmheads

The local anisotropy near the backgap closure of inductive-film heads is generally the result of two superimposed anisotropies. One is the anisotropy induced during deposition of the magnetic yoke in the presence of a magnetic field, and the other is a stress anisotropy whose magnitude and direction depend on the sign and magnitude of the stress-magnetostriction product. The authors present a simple model for these combined anisotropies and the associated domain wall patterns, assuming that the stress-magnetostriction product falls off as r^-2, where r is the distance from the center of the backgap closure, which in this case is assumed to be circular. One of the features of the predicted domain wall patterns is a three-wall intersection occurring where the two anisotropies cancel, i.e. where the yoke material is isotropic. Examples of domain walls in the vicinity of a backgap closure of an experimental film head are shown and discussed     

Benchmarking attribute selection techniques for discrete class data mining

Data engineering is generally considered to be a central issue in the development of data mining applications. The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant, and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Attribute selection generally involves a combination of search and attribute utility estimation plus evaluation with respect to specific learning schemes. This leads to a large number of possible permutations and has led to a situation where very few benchmark studies have been conducted. This paper presents a benchmark comparison of several attribute selection methods for supervised classification. All the methods produce an attribute ranking, a useful devise for isolating the individual merit of an attribute. Attribute selection is achieved by cross-validating the attribute rankings with respect to a classification learner to find the best attributes. Results are reported for a selection of standard data sets and two diverse learning schemes C4.5 and naive Bayes.     

Adaptive random forests for evolving data stream classification

Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.     

The Farmer, the Fairies, and the Computers

Using a temporary substitution of format, the author offers a fairy tale to demonstrate that we can’t expect understanding when computers take the place of humans in our interactions with service providers.     

ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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