Effects of prior knowledge and joint attention on learning from eye movement modelling examples

Eye movement modelling examples (EMMEs) are instructional videos of a model's demonstration and explanation of a task that also show where the model is looking. EMMEs are expected to synchronize students' visual attention with the model's, leading to better learning than regular video modelling examples (MEs). However, synchronization is seldom directly tested. Moreover, recent research suggests that EMMEs might be more effective than ME for low prior knowledge learners. We therefore used a 2 × 2 between-subjects design to investigate if the effectiveness of EMMEs (EMMEs/ME) is moderated by prior knowledge (high/low, manipulated by pretraining), applying eye tracking to assess synchronization. Contrary to expectations, EMMEs did not lead to higher learning outcomes than ME, and no interaction with prior knowledge was found. Structural equation modelling shows the mechanism through which EMMEs affect learning: Seeing the model's eye movements helped learners to look faster at referenced information, which was associated with higher learning outcomes.     

Support Vector Machine State Estimation

The power system state estimator based on the support vector machine (SVM) and the weighted least squares (WLS) method is presented in the paper. The WLS provides state estimations necessary for creating SVM model which is then used for state estimation. The developed algorithm was tested on the IEEE systems, and the performance indicators were calculated in order to compare the accuracy of estimation and the measurement error filtering. The results indicate that the proposed hybrid model outperforms the classical WLS-based state estimation in terms of accuracy and improves measurement error filtering in comparison to the classical estimator.     

Annotated news corpora and a lexicon for sentiment analysis in Slovene

In this study, we introduce Slovene web-crawled news corpora with sentiment annotation on three levels of granularity: sentence, paragraph and document levels. We describe the methodology and tools that were required for their construction. The corpora contain more than 250,000 documents with political, business, economic and financial content from five Slovene media resources on the web. More than 10,000 of them were manually annotated as negative, neutral or positive. All corpora are publicly available under a Creative Commons copyright license. We used the annotated documents to construct a Slovene sentiment lexicon, which is the first of its kind for Slovene, and to assess the sentiment classification approaches used. The constructed corpora were also utilised to monitor within-the-document sentiment dynamics, its changes over time and relations with news topics. We show that sentiment is, on average, more explicit at the beginning of documents, and it loses sharpness towards the end of documents.     

User bias in online trust systems: aligning the system designers’ intentions with the users’ expectations

The advent of social networks opened a myriad opportunities for merging the social character of trust with the technical possibilities offered by the Internet and its availability as a mobile service. While most of the computational trust models aim to detect trustworthy entities, much less attention is paid to how these models are perceived by the users who are the core of the system. This paper delves into the workings of online trust systems under user bias and analyses the user behaviour through biases defined by Prospect theory. By performing empirical study on an existing system, we are able to demonstrate that there is a huge discrepancy between the aim of implementation of the online trust models and the users’ perception of those models. Understanding of this relation by the system designers can reduce complexity and improve the user experience and the system performance. The results imply that the tendency of the users to exhibit cognitive biases is not only the cause, but also the effect from the trust system design. These results and the analysis are then used to propose to the system designers a methodology for user bias identification and mitigation in the form of a Choice architecture for trust systems.     

DimSUM: Dimension and Scale Unifying Map for Visual Abstraction of DNA Origami Structures

We present a novel visualization concept for DNA origami structures that integrates a multitude of representations into a Dimension and Scale Unifying Map (DimSUM). This novel abstraction map provides means to analyze, smoothly transition between, and interact with many visual representations of the DNA origami structures in an effective way that was not possible before. DNA origami structures are nanoscale objects, which are challenging to model in silico. In our holistic approach we seamlessly combine three‐dimensional realistic shape models, two‐dimensional diagrammatic representations, and ordered alignments in one‐dimensional arrangements, with semantic transitions across many scales. To navigate through this large, two‐dimensional abstraction map we highlight locations that users frequently visit for certain tasks and datasets. Particularly interesting viewpoints can be explicitly saved to optimize the workflow. We have developed DimSUM together with domain scientists specialized in DNA nanotechnology. In the paper we discuss our design decisions for both the visualization and the interaction techniques. We demonstrate two practical use cases in which our approach increases the specialists’ understanding and improves their effectiveness in the analysis. Finally, we discuss the implications of our concept for the use of controlled abstraction in visualization in general.     

Tablet game-supported speech therapy embedded in children’s popular practices

Modern speech-language pathology practices (SLPPs) have adopted tablet games in recent years. Research is needed to take advantage of the potential benefits tablets can offer and to explore the factors that influence its introduction. This paper discusses a survey that was conducted to investigate the factors that influence speech-language pathologists’ (SLPs) beliefs and the actual use of tablets in SLPPs. The results of the survey, conducted among Slovenian SLPs, indicated that their most positive beliefs concerning tablets relate to social influence. Specifically, the beliefs relate to children’s interest and practice with tablets and have a significant impact on the decision-making of SLPs concerning introducing tablets in therapy. Conversely, SLPs’ most negative beliefs relate to the conditions for facilitating tablet use, particularly the lack of Slovenian applications. This paper discusses a separate case study that was conducted in which tablet game-supported therapy was introduced to a group of 44 children. The case study results indicated that the children accepted the use of tablet games for therapy purposes and the games had a positive impact on the children’s motivation and satisfaction.     

Combinational illumination estimation method based on image-specific PCA filters and support vector regression

Accurate illuminant estimation from digital image data is a fundamental step of practically every image colour correction. Combinational illuminant estimation schemes have been shown to improve estimation accuracy significantly compared to other colour constancy algorithms. These schemes combine individual estimates of simpler colour constancy algorithms in some ‘intelligent’ manner into a joint and, usually, more efficient illuminant estimation. Among them, a combinational method based on Support Vector Regression (SVR) was proposed recently, demonstrating the more accurate illuminant estimation (Li et al. IEEE Trans. Image Process. 23(3), 1194–1209, 2014). We extended this method by our previously introduced convolutional framework, in which the illuminant was estimated by a set of image-specific filters generated using a linear analysis. In this work, the convolutional framework was reformulated, so that each image-specific filter obtained by principal component analysis (PCA) produced one illuminant estimate. All these individual estimates were then combined into a joint illuminant estimation by using SVR. Each illuminant estimation by using a single image-specific PCA filter within the convolutional framework actually represented one base algorithm for the combinational method based on SVR. The proposed method was validated on the well-known Gehler image dataset, reprocessed and prepared by author Shi, and, as well, on the NUS multi-camera dataset. It was shown that the median and trimean angular errors were (non-significantly) lower for our proposed method compared to the original combinational method based on SVR for which our method utilized just 6 image-specific PCA filters, while the original combinational method required 12 base algorithms for similar results. Nevertheless, a proposed method unified grey edge framework, PCA analysis, linear filtering theory, and SVR regression formally for the combinational illuminant estimation.     

Determination of dynamic contact angles within microfluidic devices

Here, we report a single-point detection method for the determination of dynamic surface conditions inside microfluidic channels. The proposed method is based on monitoring fluorescence amplitude as a function of the convolution of a laser beam with segmented flow consisting of two immiscible liquids, one containing fluorescent dye. The fluorescence amplitude is determined by the flow rate and the droplet shape, which is affected by the channel surface properties. We modeled the interaction of a droplet and a laser beam via computer-aided design software, using the laser beam location in relation to the droplet shape as a parameter. The method was applied to fused silica capillaries with both unmodified and modified surfaces, with segmented flow exhibiting water contact angles of ≈?30° and ≈?100°, respectively. The method allows discrimination between hydrophillic and hydrophobic surfaces, as well as the quality of the treatment. The results were verified using fluorescence imaging of the droplets via a stroboscopic technique. We also applied this method to the analysis of microfabricated channels with non-circular cross sections. We demonstrated that the technique enables the determination of the hydrophobicity of channel surfaces, a crucial property required for the generation of segmented flow or emulsions for applications such as digital PCR.     

Novel swirl flow-focusing microfluidic device for the production of monodisperse microbubbles

A novel swirl flow-focusing microfluidic axisymmetric device for the generation of monodisperse microbubbles at high production rates to be used as in-line contrast agents for medical applications is presented. The swirl effect is induced upstream of the discharge orifice by a circular array of microblades which form a given angle with the radial direction. The induced vortical component on the focusing liquid stabilizes the gas meniscus by the vorticity amplification due to vortex stretching as the liquid is forced through the discharge orifice. The stabilized meniscus tapers into a steady gas ligament that breaks into monodisperse microbubbles. A reduction up to \(57\%\) in the microbubble diameter is accomplished when compared to conventional axisymmetric flow-focusing microdevices. An exhaustive experimental study is performed for various blade angles and numerous gas to liquid flow rate ratios, validating previous VoF numerical simulations. The microbubbles issued from the stabilized menisci verify prior scaling law of flow-focusing.     

An expressive dissimilarity measure for relational clustering using neighbourhood trees

Clustering is an underspecified task: there are no universal criteria for what makes a good clustering. This is especially true for relational data, where similarity can be based on the features of individuals, the relationships between them, or a mix of both. Existing methods for relational clustering have strong and often implicit biases in this respect. In this paper, we introduce a novel dissimilarity measure for relational data. It is the first approach to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph. We experimentally evaluate the proposed dissimilarity measure on both clustering and classification tasks using data sets of very different types. Considering the quality of the obtained clustering, the experiments demonstrate that (a) using this dissimilarity in standard clustering methods consistently gives good results, whereas other measures work well only on data sets that match their bias; and (b) on most data sets, the novel dissimilarity outperforms even the best among the existing ones. On the classification tasks, the proposed method outperforms the competitors on the majority of data sets, often by a large margin. Moreover, we show that learning the appropriate bias in an unsupervised way is a very challenging task, and that the existing methods offer a marginal gain compared to the proposed similarity method, and can even hurt performance. Finally, we show that the asymptotic complexity of the proposed dissimilarity measure is similar to the existing state-of-the-art approaches. The results confirm that the proposed dissimilarity measure is indeed versatile enough to capture relevant information, regardless of whether that comes from the attributes of vertices, their proximity, or connectedness of vertices, even without parameter tuning.