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Strabismus is a visual disorder characterized by eye misalignment. The effect of Panum’s Fusional Area (PFA) compensates for small misalignments. However, prominent misalignments affect binocular vision and when present in childhood it may lead to amblyopia, a developmental disorder of the visual system. With the advent of Virtual Reality (VR) technology, possibilities for novel binocular treatments to amblyopia arise in which the measurement of strabismus is crucial to correctly compensate for it. Thus, VR yields great potential due to the ability of displaying content to each eye independently. Major research in VR addresses this topic using eye-tracking while there is a paucity of research on image-based assessment methods. In this work, we propose a VR application for measuring strabismus in nine lines of sight. We conducted a study with 14 healthy participants to evaluate the system under two conditions: no strabismus and an artificial deviation induced by prism lenses. Further, we evaluated the effect of PFA on the system by measuring its extent in horizontal and vertical lines of sight. Results show significant difference between the expected deviation induced by prism lenses and the measured deviation. The existing difference within the measurements can be explained with the recorded extent of the PFA.

Objective Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned. Methods A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class. Results The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators. Conclusions The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.

Smart embedded systems often run sophisticated pattern recognition algorithms and are found in many areas like automotive, sports and medicine. The developer of such a system is often confronted with the accuracy---cost conflict as the resulting system should be as accurate as possible while being able to run on resource constraint hardware. This article introduces a method to support the solution of this design conflict with accuracy---cost reports. These reports compare classification systems regarding their classification rate (accuracy) and the mathematical operations and parameters of the working phase (cost). Our method is used to deduce the specific cost of various popular pattern recognition algorithms and to derive the overall cost of a classification system. We also show how our analysis can be used to estimate the computational cost for specific hardware architectures. A software toolbox to create accuracy---cost reports was implemented to facilitate the automatic classification system comparison with the presented methodology. The software is available for download and as supplementary material. We performed different experiments on synthetic and real-world data to underline the value of this analysis. Accurate and computationally cheap classification systems were easily identified. We were even able to find a better implementation candidate in an existing embedded classification problem. This work is the first step towards a comprehensive support tool for the design of embedded classification systems.

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.

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.

The amount of total body water (TBW) can be estimated based on bioimpedance measurements of the human body. In sports, TBW estimations are of importance because mild water losses can impair muscular strength and aerobic endurance. Severe water losses can even be life threatening. TBW estimations based on bioimpedance, however, fail during sports because the increased body temperature corrupts bioimpedance measurements. Therefore, this paper proposes a machine learning method that eliminates the effects of increased temperature on bioimpedance and, consequently, reveals the changes in bioimpedance that are due to TBW loss. This is facilitated by utilizing changes in skin and core temperature. The method was evaluated in a study in which bioimpedance, temperature, and TBW loss were recorded every 15 min during a 2-h running workout. The evaluation demonstrated that the proposed method is able to reduce the error of TBW loss estimation by up to 71%, compared to the state of art. In the future, the proposed method in combination with portable bioimpedance devices might facilitate the development of wearable systems for continuous and noninvasive TBW loss monitoring during sports.

Voice scrambling is widely used to add privacy to the radio communication of various authorities — but is also used by criminals to evade prosecution. In this article, we consider various analog voice scrambling techniques such as fixed frequency inversion, splitband inversion and rolling code scramblers. We explain how to break them using automatically extracted measures and scoring algorithms, and evaluate the proposed system using simulated data. While the simple inversion can be easily broken, the more advanced techniques require additional work prior to unsupervised automatization; the presented user interface allows the user to refine the automatic results to obtain a high quality solution.