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"abstract": "Integrating novel feedback mechanisms into wearable devices could support medical rehabilitation applications and enhance existing interaction methods. One such feedback mechanism is layer jamming, which can be used to alter material stiffness in real time under computer control. Jamming can be integrated into smart garments, allowing clothing to adapt to environmental conditions or support a particular user task. For example, a person with a knee injury that requires restricted movement for their treatment would normally wear a knee brace. Layer jamming-enabled smart pants can be integrated as part of the treatment to restrict both speed and angular movement of the affected joint during recovery. This article explores the use of smart materials that combine low-profile jamming with clothing items for computer-controlled material stiffness. The authors present a current prototype system that employs jamming technology for this variable stiffness function and discuss future applications.",
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"content": "Image retargeting techniques adjust images into different sizes and have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods train a model using some benchmarks (e.g., RetargetMe), in which subjective scores evaluated by users are provided. Observing that it is challenging even for human subjects to give consistent scores for retargeting results of different source images (diff-source-results), in this paper we propose a learning-based OQA method that trains a General Regression Neural Network (GRNN) model based on relative scores - which preserve the ranking - of retargeting results of the same source image (same-source-results). In particular, we develop a novel training scheme with provable convergence that learns a common base scalar for same-source-results. With this source specific offset, our computed scores not only preserve the ranking of subjective scores for same-source-results, but also provide a reference to compare the diff-source-results. We train and evaluate our GRNN model using human preference data collected in RetargetMe. We further introduce a subjective benchmark to evaluate the generalizability of different OQA methods. Experimental results demonstrate that our method outperforms ten representative OQA methods in ranking prediction and has better generalizability to different datasets.",
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"title": "Ranking-Preserving Cross-Source Learning for Image Retargeting Quality Assessment",
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{
"givenName": "Yong-Jin",
"surname": "Liu",
"fullName": "Yong-Jin Liu",
"affiliation": "BNRist, Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, China",
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"givenName": "Yiheng",
"surname": "Han",
"fullName": "Yiheng Han",
"affiliation": "BNRist, Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, China",
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{
"givenName": "Zipeng",
"surname": "Ye",
"fullName": "Zipeng Ye",
"affiliation": "BNRist, Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, China",
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"givenName": "Yu-Kun",
"surname": "Lai",
"fullName": "Yu-Kun Lai",
"affiliation": "School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom",
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"abstract": "In the paper, we propose a class of dynamic conditional Gaussian graphical models (DCGGMs) based on a set of non-identical distribution observations, which changes smoothly with time or condition. Specifically, the DCGGMs model the dynamic output network influenced by conditioning input variables, which are encoded by a set of varying parameters. Moreover, we propose a joint smooth graphical Lasso to estimate the DCGGMs, which combines kernel smoother with sparse group Lasso penalty. At the same time, we design an efficient accelerated proximal gradient algorithm to solve this estimator. Theoretically, we establish the asymptotic properties of our model on consistency and sparsistency under the high-dimensional settings. In particular, we highlight a class of consistency theory for dynamic graphical models, in which the sample size can be seen as <inline-formula><tex-math notation=\"LaTeX\">Z_$n^{4/5}$_Z</tex-math></inline-formula> for estimating a local graphical model when the bandwidth parameter <inline-formula><tex-math notation=\"LaTeX\">Z_$h$_Z</tex-math></inline-formula> of kernel smoother is chosen as <inline-formula><tex-math notation=\"LaTeX\">Z_$h\\; \\asymp\\; n^{-1/5}$_Z</tex-math> </inline-formula> for describing the dynamic. Finally, the extensive numerical experiments on both synthetic and real datasets are provided to support the effectiveness of the proposed method.",
"abstracts": [
{
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"content": "In the paper, we propose a class of dynamic conditional Gaussian graphical models (DCGGMs) based on a set of non-identical distribution observations, which changes smoothly with time or condition. Specifically, the DCGGMs model the dynamic output network influenced by conditioning input variables, which are encoded by a set of varying parameters. Moreover, we propose a joint smooth graphical Lasso to estimate the DCGGMs, which combines kernel smoother with sparse group Lasso penalty. At the same time, we design an efficient accelerated proximal gradient algorithm to solve this estimator. Theoretically, we establish the asymptotic properties of our model on consistency and sparsistency under the high-dimensional settings. In particular, we highlight a class of consistency theory for dynamic graphical models, in which the sample size can be seen as <inline-formula><tex-math notation=\"LaTeX\">$n^{4/5}$ </tex-math><alternatives><inline-graphic xlink:href=\"huang-ieq1-2777462.gif\"/></alternatives></inline-formula> for estimating a local graphical model when the bandwidth parameter <inline-formula><tex-math notation=\"LaTeX\">$h$ </tex-math><alternatives><inline-graphic xlink:href=\"huang-ieq2-2777462.gif\"/></alternatives></inline-formula> of kernel smoother is chosen as <inline-formula><tex-math notation=\"LaTeX\">$h\\; \\asymp\\; n^{-1/5}$</tex-math> <alternatives><inline-graphic xlink:href=\"huang-ieq3-2777462.gif\"/></alternatives></inline-formula> for describing the dynamic. Finally, the extensive numerical experiments on both synthetic and real datasets are provided to support the effectiveness of the proposed method.",
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"title": "Learning Dynamic Conditional Gaussian Graphical Models",
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"authors": [
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"fullName": "Feihu Huang",
"affiliation": "College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China",
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"givenName": "Songcan",
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"affiliation": "College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China",
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"abstract": "Multi-source transfer regression is a practical and challenging problem where capturing the diverse relatedness of different domains is the key of adaptive knowledge transfer. In this article, we propose an effective way of explicitly modeling the domain relatedness of each domain pair through transfer kernel learning. Specifically, we first discuss the advantages and disadvantages of existing transfer kernels in handling the multi-source transfer regression problem. To cope with the limitations of the existing transfer kernels, we further propose a novel multi-source transfer kernel <inline-formula><tex-math notation=\"LaTeX\">Z_$k_{ms}$_Z</tex-math></inline-formula>. The proposed <inline-formula><tex-math notation=\"LaTeX\">Z_$k_{ms}$_Z</tex-math></inline-formula> assigns a learnable parametric coefficient to model the relatedness of each inter-domain pair, and simultaneously regulates the relatedness of the intra-domain pair to be 1. Moreover, to capture the heterogeneous data characteristics of multiple domains, <inline-formula><tex-math notation=\"LaTeX\">Z_$k_{ms}$_Z</tex-math></inline-formula> exploits different standard kernels for different domain pairs. We further provide a theorem that not only guarantees the positive semi-definiteness of <inline-formula><tex-math notation=\"LaTeX\">Z_$k_{ms}$_Z</tex-math></inline-formula> but also conveys a semantic interpretation to the learned domain relatedness. Moreover, the theorem can be easily used in the learning of the corresponding transfer Gaussian process model with <inline-formula><tex-math notation=\"LaTeX\">Z_$k_{ms}$_Z</tex-math></inline-formula>. Extensive empirical studies show the effectiveness of our proposed method on domain relatedness modelling and transfer performance.",
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"content": "Multi-source transfer regression is a practical and challenging problem where capturing the diverse relatedness of different domains is the key of adaptive knowledge transfer. In this article, we propose an effective way of explicitly modeling the domain relatedness of each domain pair through transfer kernel learning. Specifically, we first discuss the advantages and disadvantages of existing transfer kernels in handling the multi-source transfer regression problem. To cope with the limitations of the existing transfer kernels, we further propose a novel multi-source transfer kernel <inline-formula><tex-math notation=\"LaTeX\">$k_{ms}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"wei-ieq1-3184696.gif\"/></alternatives></inline-formula>. The proposed <inline-formula><tex-math notation=\"LaTeX\">$k_{ms}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"wei-ieq2-3184696.gif\"/></alternatives></inline-formula> assigns a learnable parametric coefficient to model the relatedness of each inter-domain pair, and simultaneously regulates the relatedness of the intra-domain pair to be 1. Moreover, to capture the heterogeneous data characteristics of multiple domains, <inline-formula><tex-math notation=\"LaTeX\">$k_{ms}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"wei-ieq3-3184696.gif\"/></alternatives></inline-formula> exploits different standard kernels for different domain pairs. We further provide a theorem that not only guarantees the positive semi-definiteness of <inline-formula><tex-math notation=\"LaTeX\">$k_{ms}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"wei-ieq4-3184696.gif\"/></alternatives></inline-formula> but also conveys a semantic interpretation to the learned domain relatedness. Moreover, the theorem can be easily used in the learning of the corresponding transfer Gaussian process model with <inline-formula><tex-math notation=\"LaTeX\">$k_{ms}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"wei-ieq5-3184696.gif\"/></alternatives></inline-formula>. Extensive empirical studies show the effectiveness of our proposed method on domain relatedness modelling and transfer performance.",
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"content": "The stable relationship between personality and EEG ensures the feasibility of personality inference from brain activities. In this paper, we recognize an individual's personality traits by analyzing brain waves when he or she watches emotional materials. Thirty-seven participants took part in this study and watched 7 standardized film clips that characterize real-life emotional experiences and target seven discrete emotions. Features extracted from EEG signals and subjective ratings enter the SVM classifier as inputs to predict five dimensions of personality traits. Our model achieves better classification performance for Extraversion (81.08 percent), Agreeableness (86.11 percent), and Conscientiousness (80.56 percent) when positive emotions are elicited than negative ones, higher classification accuracies for Neuroticism (78.38-81.08 percent) when negative emotions, except disgust, are evoked than positive emotions, and the highest classification accuracy for Openness (83.78 percent) when a disgusting film clip is presented. Additionally, the introduction of features from subjective ratings increases not only classification accuracy in all five personality traits (ranging from 0.43 percent for Conscientiousness to 6.3 percent for Neuroticism) but also the discriminative power of the classification accuracies between five personality traits in each category of emotion. These results demonstrate the advantage of personality inference from EEG signals over state-of-the-art explicit behavioral indicators in terms of classification accuracy.",
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"abstract": "Emotions are an inseparable part of human nature affecting our behavior in response to the outside world. Although most empirical studies have been dominated by two theoretical models including discrete categories of emotion and dichotomous dimensions, results from neuroscience approaches suggest a multi-processes mechanism underpinning emotional experience with a large overlap across different emotions. While these findings are consistent with the influential theories of emotion in psychology that emphasize a role for multiple component processes to generate emotion episodes, few studies have systematically investigated the relationship between discrete emotions and a full componential view. This article applies a componential framework with a data-driven approach to characterize emotional experiences evoked during movie watching. The results suggest that differences between various emotions can be captured by a few (at least 6) latent dimensions, each defined by features associated with component processes, including appraisal, expression, physiology, motivation, and feeling. In addition, the link between discrete emotions and component model is explored and results show that a componential model with a limited number of descriptors is still able to predict the level of experienced discrete emotion(s) to a satisfactory level. Finally, as appraisals may vary according to individual dispositions and biases, we also study the relationship between personality traits and emotions in our computational framework and show that the role of personality on discrete emotion differences can be better justified using the component model.",
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"content": "The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.",
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"title": "Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs",
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"givenName": "Carolina",
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"fullName": "Hilary Coon",
"affiliation": "University of Utah, Salt Lake City, UT",
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"fullName": "Alexander Lex",
"affiliation": "University of Utah, Salt Lake City, UT",
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"abstract": "Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model.",
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"title": "Knowledge Graph for China's Genealogy<xref rid=\"fn1\" ref-type=\"fn\"><sup>1</sup></xref><fn id=\"fn1\"><label>1.</label><p>A shorter version of this paper won the Best Paper Award at IEEE ICKG 2020 (the 11th IEEE International Conference on Knowledge Graph, ickg 2020.bigke.org).</p></fn>",
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"abstract": "Linear diagrams are used to visualize set systems by depicting set memberships as horizontal line segments in a matrix, where each set is represented as a row and each element as a column. Each such line segment of a set is shown in a contiguous horizontal range of cells of the matrix indicating that the corresponding elements in the columns belong to the set. As each set occupies its own row in the matrix, the total height of the resulting visualization is as large as the number of sets in the instance. Such a linear diagram can be visually sparse and intersecting sets containing the same element might be represented by distant rows. To alleviate such undesirable effects, we present LinSets.zip, a new approach that achieves a more space-efficient representation of linear diagrams. First, we minimize the total number of gaps in the horizontal segments by reordering columns, a criterion that has been shown to increase readability in linear diagrams. The main difference of LinSets.zip to linear diagrams is that multiple non-intersecting sets can be positioned in the same row of the matrix. Furthermore, we present several different rendering variations for a matrix-based representation that utilize the proposed row compression. We implemented the different steps of our approach in a visualization pipeline using integer-linear programming, and suitable heuristics aiming at sufficiently fast computations in practice. We conducted both a quantitative evaluation and a small-scale user experiment to compare the effects of compressing linear diagrams.",
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"abstract": "Opinion polling has been traditionally done via customer satisfaction studies in which questions are carefully designed to gather customer opinions about target products or services. This paper studies aspect-based opinion polling from unlabeled free-form textual customer reviews without requiring customers to answer any questions. First, a multi-aspect bootstrapping method is proposed to learn aspect-related terms of each aspect that are used for aspect identification. Second, an aspect-based segmentation model is proposed to segment a multi-aspect sentence into multiple single-aspect units as basic units for opinion polling. Finally, an aspect-based opinion polling algorithm is presented in detail. Experiments on real Chinese restaurant reviews demonstrated that our approach can achieve 75.5 percent accuracy in aspect-based opinion polling tasks. The proposed opinion polling method does not require labeled training data. It is thus easy to implement and can be applicable to other languages (e.g., English) or other domains such as product or movie reviews.",
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