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"title": "The Effect of Motivation on Learners' Performance and Satisfaction under Flipped Strategy in Discrete Math",
"normalizedTitle": "The Effect of Motivation on Learners' Performance and Satisfaction under Flipped Strategy in Discrete Math",
"abstract": "This Research Work in Progress paper presents preliminary data from an experiment study (N = 22) in investigating the effect of learners' motivation on learning outcomes, including learning performance and satisfaction. In STEM fields, learners are required to obtain declarative knowledge as well as apply them to solve real world problems. The flipped strategy has been a popular instructional approach and is used in many classrooms. Although many studies reported positive impact of the flipped strategy, the results are not consistent. The goal of this study is to explore whether learners' motivation in a flipped classroom affect their performance and their satisfaction. We use a quasi-experimental design with two groups based on the initial motivation scores. We find that our participants did not benefit from flipped strategy as much as the traditional strategy. We also found that the participants did not feel better (confident) about the subject. We conclude that students may not be comfortable with the flipped strategy because the flipped strategy is used for only about two weeks and that great instructional attentions may be needed to use the flipped strategy. In the end, we propose areas that are worthy of further investigation.",
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"content": "This Research Work in Progress paper presents preliminary data from an experiment study (N = 22) in investigating the effect of learners' motivation on learning outcomes, including learning performance and satisfaction. In STEM fields, learners are required to obtain declarative knowledge as well as apply them to solve real world problems. The flipped strategy has been a popular instructional approach and is used in many classrooms. Although many studies reported positive impact of the flipped strategy, the results are not consistent. The goal of this study is to explore whether learners' motivation in a flipped classroom affect their performance and their satisfaction. We use a quasi-experimental design with two groups based on the initial motivation scores. We find that our participants did not benefit from flipped strategy as much as the traditional strategy. We also found that the participants did not feel better (confident) about the subject. We conclude that students may not be comfortable with the flipped strategy because the flipped strategy is used for only about two weeks and that great instructional attentions may be needed to use the flipped strategy. In the end, we propose areas that are worthy of further investigation.",
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"title": "A Hybrid Approach to Administering a Spatial Skills Intervention",
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"abstract": "This full paper discusses the implementation and results of a hybrid spatial skills intervention in multiple introductory computing courses. Spatial skills abilities have been shown to have high correlation with students' success in STEM related fields. Studies have shown that running spatial skills interventions have increased student success in their corresponding fields. Additional research has found that spatial skills interventions are more successful when run in person with hands on activities. These studies saw that when spatial skills interventions are conducted online, there is no significant gain in a student's spatial skills. We discuss the implementation and results of a hybrid spatial skills intervention where the learning material is presented asynchronously online, and exercises and worksheets are completed by hand. We conducted our intervention across three universities at the same time in multiple introductory programming courses and found that our hybrid model resulted in significant gains in both students' spatial skills and programming ability. Students who did not participate in the intervention had an average decrease of -1.5 points (-5%) from pre to post scores on the Purdue Spatial Visualisation Test: Rotations (PSVT:R), used to measure spatial skills, and an average increase of 1.1 points (12 %) from pre to post on the Second Computer Science 1 Exam: Revised (SCS1R), used to measure students programming abilities. Students who participated in the intervention had an average increase of 1.2 points (4%) on the PSVT:R and an average increase in score of 1.6 points (18%) on the SCS1R. Students who participated in the intervention had an average total increase on the PSVT:R of 2.7 points (9%) and an average total gain on the SCS1R of 0.4 points (6%) compared to students who did not participate in the intervention. These results show that the use of a hybrid spatial skills intervention is a viable option to administering a spatial skills intervention and can be replicable at scale with little to no extra work from instructors.",
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{
"abstractType": "Regular",
"content": "This full paper discusses the implementation and results of a hybrid spatial skills intervention in multiple introductory computing courses. Spatial skills abilities have been shown to have high correlation with students' success in STEM related fields. Studies have shown that running spatial skills interventions have increased student success in their corresponding fields. Additional research has found that spatial skills interventions are more successful when run in person with hands on activities. These studies saw that when spatial skills interventions are conducted online, there is no significant gain in a student's spatial skills. We discuss the implementation and results of a hybrid spatial skills intervention where the learning material is presented asynchronously online, and exercises and worksheets are completed by hand. We conducted our intervention across three universities at the same time in multiple introductory programming courses and found that our hybrid model resulted in significant gains in both students' spatial skills and programming ability. Students who did not participate in the intervention had an average decrease of -1.5 points (-5%) from pre to post scores on the Purdue Spatial Visualisation Test: Rotations (PSVT:R), used to measure spatial skills, and an average increase of 1.1 points (12 %) from pre to post on the Second Computer Science 1 Exam: Revised (SCS1R), used to measure students programming abilities. Students who participated in the intervention had an average increase of 1.2 points (4%) on the PSVT:R and an average increase in score of 1.6 points (18%) on the SCS1R. Students who participated in the intervention had an average total increase on the PSVT:R of 2.7 points (9%) and an average total gain on the SCS1R of 0.4 points (6%) compared to students who did not participate in the intervention. These results show that the use of a hybrid spatial skills intervention is a viable option to administering a spatial skills intervention and can be replicable at scale with little to no extra work from instructors.",
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"abstract": "Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study.",
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"content": "Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study.",
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"abstract": "Bayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes' theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a deductive reasoning based approach for that. The concrete deductive diagram for a simple topic model is presented in the paper. The deductive reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.",
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"abstract": "Many bioinformatics studies aim to find features that differentiate between two or more classes. Recent work proposes a Bayesian framework for feature selection that places a prior on the label-conditioned feature distribution. Assuming independent features, the optimal Bayesian filter is obtained and has been solved for Gaussian features. Here we extend the optimal Bayesian filter for categorical data, compare it with several algorithms in synthetic simulations, and apply it to breast and colon cancer SNP datasets. For the real datasets we select the top SNP's, find the genes that map to them, and perform enrichment analysis. Literature review suggests many of the top genes and pathways are involved in cancer.",
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"abstract": "This paper integrates fuzzy Bayesian classifier with case-based reasoning (CBR) for improving case retrieval performance. In traditional retrieval process, CBR uses similarity metric to calculate similarity degree between all former cases and new case. Then, it retrieves the highest one. However, it would cause computation complexity and waste time. Our proposed approach uses Bayesian classifier to classify case base, then retrieve similar cases. In the real world problems, attributes may not always be discrete; they might be continuous as well. When users encounter continuous attributes, they must define an appropriate conditional likelihood density function. In fact, defining an appropriate conditional likelihood function is difficult. The issue of defining conditional likelihood density function of continuous or attributes is critical. Besides, the choice of likelihood density function would affect the accuracy of Bayesian network. Therefore, we apply fuzzy set theory to Bayesian network to define an appropriate conditional likelihood density function. An efficient fuzzy Bayesian case-based reasoning (FB-CBR) was built to solve car-diagnosing problems. The result shows that our proposed model is better than conventional models both in case matching accuracy and computational efficiency.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper integrates fuzzy Bayesian classifier with case-based reasoning (CBR) for improving case retrieval performance. In traditional retrieval process, CBR uses similarity metric to calculate similarity degree between all former cases and new case. Then, it retrieves the highest one. However, it would cause computation complexity and waste time. Our proposed approach uses Bayesian classifier to classify case base, then retrieve similar cases. In the real world problems, attributes may not always be discrete; they might be continuous as well. When users encounter continuous attributes, they must define an appropriate conditional likelihood density function. In fact, defining an appropriate conditional likelihood function is difficult. The issue of defining conditional likelihood density function of continuous or attributes is critical. Besides, the choice of likelihood density function would affect the accuracy of Bayesian network. Therefore, we apply fuzzy set theory to Bayesian network to define an appropriate conditional likelihood density function. An efficient fuzzy Bayesian case-based reasoning (FB-CBR) was built to solve car-diagnosing problems. The result shows that our proposed model is better than conventional models both in case matching accuracy and computational efficiency.",
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"fno": "28740344",
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"authors": [
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"affiliation": "National Taipei University of Technology",
"fullName": "Chui-Yu Chiu",
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"affiliation": "Fo Guang University",
"fullName": "Chih-Chung Lo",
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"affiliation": "National Taipei University of Technology",
"fullName": "Yan-Xin Hsu",
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"abstract": "In e-commerce websites, customers usually make comments, which include the properties of the product, the attitude to the vendor, express delivery information after buying the products. The information provides an important reference when others buy products in the website. In sentiment analysis, a finer-grained opinion mining approach focuses on not only the product itself as a whole but also product features, which can be a part or attribute of the product. Previous related research focuses on the explicit target mining but neglects the implicit ones. Whereas, the implicit features, which are implied by some words or phrases, are very significant and meaningful to express users' opinion. In this paper, we propose a new approach to uniformly extract explicit and implicit opinion features from the comments. Our experimental results prove the effectiveness of the approach. The recall is improved, suggesting that taking the implicit features into consideration can extract more useful information, meanwhile, the precision is stable, not decreases.",
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"abstract": "Euler diagrams form the basis of several visual modelling notations, including statecharts and constraint diagrams. Recently, various techniques for automated Euler diagram drawing have been proposed, contributing to the Euler diagram generation problem: given an abstract description, draw an Euler diagram with that description and which possesses certain properties. A common generation method is to find a dual graph from which an Euler diagram is subsequently created. In this paper we define transformations of the dual graph that allow us to alter the properties that the generated diagram possesses. In addition, because the dual graph of a previously generated diagram can be found, our transformations can be used to take such a diagram and produce a new diagram with the same abstract description, but with different properties. As a result, we can produce a variety of different diagrams for any given abstract description, allowing us to choose an Euler diagram that conforms to the properties that a user prefers.",
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"abstract": "Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels. M4L is more general and powerful than the typical Multi-view Multi-instance Multi-label Learning (M3L), which only accommodates single-typed bags and lacks the power to jointly model the naturally interconnected multi-typed objects in the physical world. To combat with this novel and challenging learning task, we develop a joint matrix factorization based solution (M4L-JMF). Particularly, M4L-JMF firstly encodes the diverse attributes and multiple inter(intra)-associations among multi-typed bags into respective data matrices, and then jointly factorizes these matrices into low-rank ones to explore the composite latent representation of each bag and its instances (if any). In addition, it incorporates a dispatch and aggregation term to distribute the labels of bags to individual instances and reversely aggregate the labels of instances to their affiliated bags in a coherent manner. Experimental results on benchmark datasets show that M4L-JMF achieves significantly better results than simple adaptions of existing M3L solutions on this novel problem.",
"abstracts": [
{
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"content": "Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels. M4L is more general and powerful than the typical Multi-view Multi-instance Multi-label Learning (M3L), which only accommodates single-typed bags and lacks the power to jointly model the naturally interconnected multi-typed objects in the physical world. To combat with this novel and challenging learning task, we develop a joint matrix factorization based solution (M4L-JMF). Particularly, M4L-JMF firstly encodes the diverse attributes and multiple inter(intra)-associations among multi-typed bags into respective data matrices, and then jointly factorizes these matrices into low-rank ones to explore the composite latent representation of each bag and its instances (if any). In addition, it incorporates a dispatch and aggregation term to distribute the labels of bags to individual instances and reversely aggregate the labels of instances to their affiliated bags in a coherent manner. Experimental results on benchmark datasets show that M4L-JMF achieves significantly better results than simple adaptions of existing M3L solutions on this novel problem.",
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"normalizedAbstract": "Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels. M4L is more general and powerful than the typical Multi-view Multi-instance Multi-label Learning (M3L), which only accommodates single-typed bags and lacks the power to jointly model the naturally interconnected multi-typed objects in the physical world. To combat with this novel and challenging learning task, we develop a joint matrix factorization based solution (M4L-JMF). Particularly, M4L-JMF firstly encodes the diverse attributes and multiple inter(intra)-associations among multi-typed bags into respective data matrices, and then jointly factorizes these matrices into low-rank ones to explore the composite latent representation of each bag and its instances (if any). In addition, it incorporates a dispatch and aggregation term to distribute the labels of bags to individual instances and reversely aggregate the labels of instances to their affiliated bags in a coherent manner. Experimental results on benchmark datasets show that M4L-JMF achieves significantly better results than simple adaptions of existing M3L solutions on this novel problem.",
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"Learning Artificial Intelligence",
"Matrix Algebra",
"Matrix Decomposition",
"Typical Multiview Multiinstance Multilabel Learning",
"Naturally Interconnected Multityped Objects",
"M 4 L JMF",
"Multityped Bags",
"Fuses",
"Conferences",
"Aggregates",
"Benchmark Testing",
"Data Mining",
"Task Analysis",
"Multi Typed Objects Multi Instance Learning",
"Multi View Learning",
"Multi Label Learning",
"Joint Matrix Factorization"
],
"authors": [
{
"affiliation": "Southwest University, China",
"fullName": "Yuanlin Yang",
"givenName": "Yuanlin",
"surname": "Yang",
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{
"affiliation": "Shandong University, China",
"fullName": "Guoxian Yu",
"givenName": "Guoxian",
"surname": "Yu",
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{
"affiliation": "Shandong University, China",
"fullName": "Jun Wang",
"givenName": "Jun",
"surname": "Wang",
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{
"affiliation": "George Mason University, USA",
"fullName": "Carlotta Domeniconi",
"givenName": "Carlotta",
"surname": "Domeniconi",
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{
"affiliation": "King Abdullah University of Science and Technology, Saudi Arabia",
"fullName": "Xiangliang Zhang",
"givenName": "Xiangliang",
"surname": "Zhang",
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"title": "2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
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"title": "Design through cognitive color theme: A new approach for fabric design",
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"abstract": "Fashion designers conceive color composition as an ensemble of tones, shades and tints that often resemble an abstract or natural theme. Color and tone adjustments are among the most frequent fabric design operations. Color and tone style adjustments are part of a generic process of cognition involved in the creation of new fabric designs. A challenging problem in this process is given a s et of natural images associated with a subject or a theme, the problem is to discover the underlying cognitive relationships that connect textures and color tones that are perceived by an observer or in our case, a fabric designer. These relationships culminate in optimal combinations of colors and tones that resemble the original theme in which a template of colors is defined with respect to an associated verbal description. We formulate the fabric design process as a cognitive process that simultaneously considers multiple color tones to form a desired theme as well as texture characteristics in the fabric. In this paper, we incorporate color assembly and texture knowledge in the fabric design process in order to automatically produce the color combinations learnt from a given natural or abstract theme. We demonstrate that the use of color theme associations can automatically generate new fabric designs that rival complex commercial designs that are otherwise difficult to generate even by experienced designers. Preliminary experiments confirm the effectiveness of our method.",
"abstracts": [
{
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"content": "Fashion designers conceive color composition as an ensemble of tones, shades and tints that often resemble an abstract or natural theme. Color and tone adjustments are among the most frequent fabric design operations. Color and tone style adjustments are part of a generic process of cognition involved in the creation of new fabric designs. A challenging problem in this process is given a s et of natural images associated with a subject or a theme, the problem is to discover the underlying cognitive relationships that connect textures and color tones that are perceived by an observer or in our case, a fabric designer. These relationships culminate in optimal combinations of colors and tones that resemble the original theme in which a template of colors is defined with respect to an associated verbal description. We formulate the fabric design process as a cognitive process that simultaneously considers multiple color tones to form a desired theme as well as texture characteristics in the fabric. In this paper, we incorporate color assembly and texture knowledge in the fabric design process in order to automatically produce the color combinations learnt from a given natural or abstract theme. We demonstrate that the use of color theme associations can automatically generate new fabric designs that rival complex commercial designs that are otherwise difficult to generate even by experienced designers. Preliminary experiments confirm the effectiveness of our method.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Fashion designers conceive color composition as an ensemble of tones, shades and tints that often resemble an abstract or natural theme. Color and tone adjustments are among the most frequent fabric design operations. Color and tone style adjustments are part of a generic process of cognition involved in the creation of new fabric designs. A challenging problem in this process is given a s et of natural images associated with a subject or a theme, the problem is to discover the underlying cognitive relationships that connect textures and color tones that are perceived by an observer or in our case, a fabric designer. These relationships culminate in optimal combinations of colors and tones that resemble the original theme in which a template of colors is defined with respect to an associated verbal description. We formulate the fabric design process as a cognitive process that simultaneously considers multiple color tones to form a desired theme as well as texture characteristics in the fabric. In this paper, we incorporate color assembly and texture knowledge in the fabric design process in order to automatically produce the color combinations learnt from a given natural or abstract theme. We demonstrate that the use of color theme associations can automatically generate new fabric designs that rival complex commercial designs that are otherwise difficult to generate even by experienced designers. Preliminary experiments confirm the effectiveness of our method.",
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"affiliation": "Department of Computing & Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong",
"fullName": "Dejun Zheng",
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"affiliation": "Department of Applied Mathematics, School of Science, Xidian University, Xi'an, China",
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"affiliation": "Department of Computing & Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong",
"fullName": "George Baciu",
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"affiliation": "Department of Computing & Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong",
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"title": "2010 International Conference on Measuring Technology and Mechatronics Automation",
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"title": "Scanner Color Chart Making and its Application in Printing Color Conversion",
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"abstract": "Digitizing a color image with a scanner means to represent the image’s colorimetric values with its RGB response signals. For one scanner, there always exists a determinate transform relation between the two set of values under a certain scanning condition. And the transform derivation process is called as scanner characterization. In ICC color management system, color chart generally used for characterizing scanners is the ANSI IT8 chart, i.e., IT8.7/2. But the IT8 chart is not well suitable for printing color conversion application, typically due to the fact that its colors have not covered the ordinarily printing color gamut. In this study, a proof printer, which could offer colors in a bigger color space than offset printing gamut, was used to output a new color chart throughout effectively controlled. The made new color chart contains 425 colors that more evenly space in CIELAB color space. Based on the new target, a color transform relation was derived by adopting a space-divided polynomial modeling method for an experimental scanner. The resulting average and maximal transformation color–differences within all the reference colors, in CIELAB unit, were 0.98 and 5.22 respectively, lower than 1.17 and 16.98 based on the profile transform, which was obtained by using the IT8 target and profile-making software.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Digitizing a color image with a scanner means to represent the image’s colorimetric values with its RGB response signals. For one scanner, there always exists a determinate transform relation between the two set of values under a certain scanning condition. And the transform derivation process is called as scanner characterization. In ICC color management system, color chart generally used for characterizing scanners is the ANSI IT8 chart, i.e., IT8.7/2. But the IT8 chart is not well suitable for printing color conversion application, typically due to the fact that its colors have not covered the ordinarily printing color gamut. In this study, a proof printer, which could offer colors in a bigger color space than offset printing gamut, was used to output a new color chart throughout effectively controlled. The made new color chart contains 425 colors that more evenly space in CIELAB color space. Based on the new target, a color transform relation was derived by adopting a space-divided polynomial modeling method for an experimental scanner. The resulting average and maximal transformation color–differences within all the reference colors, in CIELAB unit, were 0.98 and 5.22 respectively, lower than 1.17 and 16.98 based on the profile transform, which was obtained by using the IT8 target and profile-making software.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Digitizing a color image with a scanner means to represent the image’s colorimetric values with its RGB response signals. For one scanner, there always exists a determinate transform relation between the two set of values under a certain scanning condition. And the transform derivation process is called as scanner characterization. In ICC color management system, color chart generally used for characterizing scanners is the ANSI IT8 chart, i.e., IT8.7/2. But the IT8 chart is not well suitable for printing color conversion application, typically due to the fact that its colors have not covered the ordinarily printing color gamut. In this study, a proof printer, which could offer colors in a bigger color space than offset printing gamut, was used to output a new color chart throughout effectively controlled. The made new color chart contains 425 colors that more evenly space in CIELAB color space. Based on the new target, a color transform relation was derived by adopting a space-divided polynomial modeling method for an experimental scanner. The resulting average and maximal transformation color–differences within all the reference colors, in CIELAB unit, were 0.98 and 5.22 respectively, lower than 1.17 and 16.98 based on the profile transform, which was obtained by using the IT8 target and profile-making software.",
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"fullName": "Ge Guo",
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"abstract": "As visual equity contributes to brand recognition, reasoning and evolution of perception tends to empower brands. Research objectives are to examine the associations of consumer's perception of color and brand logo possible influences and the evolution of that perception. The results of data analyses shows that the three hypotheses in which consumers will apply conventional color associations to the color of a brand's logo; consumers will apply personality traits to a brand, using color in the brand's logo, and consumers view brand logo as more appropriate when brand personality and color associations are congruent. Data analyses further reveals that customers tend to associate colors with personality traits that would affect their perception towards a brand and its logo within the South-East Asian context.",
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"content": "As visual equity contributes to brand recognition, reasoning and evolution of perception tends to empower brands. Research objectives are to examine the associations of consumer's perception of color and brand logo possible influences and the evolution of that perception. The results of data analyses shows that the three hypotheses in which consumers will apply conventional color associations to the color of a brand's logo; consumers will apply personality traits to a brand, using color in the brand's logo, and consumers view brand logo as more appropriate when brand personality and color associations are congruent. Data analyses further reveals that customers tend to associate colors with personality traits that would affect their perception towards a brand and its logo within the South-East Asian context.",
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"affiliation": "College of Business American University of Malta,Malta",
"fullName": "Firend Al. Rasch",
"givenName": "Firend Al.",
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"affiliation": "College of Business American University of Malta,Malta",
"fullName": "Narcisa Roxana Moşteanu",
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"affiliation": "Universiti Putra Malaysia,Kuala Lumpur,Malaysia, Country",
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"abstract": "Fusiform shape of aneurysm is a common geometry of Abdominal Aorta Aneurysm (AAA). One of the ways to detect the presence of fusiform aneurysm is by performing computer tomography scan of the body. In this study, actual output of the CT-Scan was converted to 3D model in numerical modeling. The objective of this work is to study the effect of hypertensive blood pressure and the wall shear stress (WSS) since hypertension is reported to be a risk factor in rupture of fusiform aneurysms. The numerical prediction shows that the transient behavior of the interaction under hypertensive blood pressure is significantly different from that under normal blood pressure. The peak wall stress, peak WSS and peak deformation occur shortly after systolic peak pressure. The location of maximum WSS is not where the pressure is the highest but rather where the pressure gradient is the largest.",
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"content": "Fusiform shape of aneurysm is a common geometry of Abdominal Aorta Aneurysm (AAA). One of the ways to detect the presence of fusiform aneurysm is by performing computer tomography scan of the body. In this study, actual output of the CT-Scan was converted to 3D model in numerical modeling. The objective of this work is to study the effect of hypertensive blood pressure and the wall shear stress (WSS) since hypertension is reported to be a risk factor in rupture of fusiform aneurysms. The numerical prediction shows that the transient behavior of the interaction under hypertensive blood pressure is significantly different from that under normal blood pressure. The peak wall stress, peak WSS and peak deformation occur shortly after systolic peak pressure. The location of maximum WSS is not where the pressure is the highest but rather where the pressure gradient is the largest.",
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"abstract": "In this paper we present a novel class of so-called Radon-Like features, which allow for aggregation of spatially distributed image statistics into compact feature descriptors. Radon-Like features, which can be efficiently computed, lend themselves for use with both supervised and unsupervised learning methods. Here we describe various instantiations of these features and demonstrate there usefulness in context of neural connectivity analysis, i.e. Connectomics, in electron micrographs. Through various experiments on simulated as well as real data we establish the efficacy of the proposed features in various tasks like cell membrane enhancement, mitochondria segmentation, cell background segmentation, and vesicle cluster detection as compared to various other state-of-the-art techniques.",
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"content": "In this paper we present a novel class of so-called Radon-Like features, which allow for aggregation of spatially distributed image statistics into compact feature descriptors. Radon-Like features, which can be efficiently computed, lend themselves for use with both supervised and unsupervised learning methods. Here we describe various instantiations of these features and demonstrate there usefulness in context of neural connectivity analysis, i.e. Connectomics, in electron micrographs. Through various experiments on simulated as well as real data we establish the efficacy of the proposed features in various tasks like cell membrane enhancement, mitochondria segmentation, cell background segmentation, and vesicle cluster detection as compared to various other state-of-the-art techniques.",
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"Connectomics Applications",
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"affiliation": "School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA",
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"abstract": "In this contribution we demonstrate how a Multicut-based segmentation pipeline can be scaled up to datasets of hundreds of Gigabytes in size. Such datasets are prevalent in connectomics, where neuron segmentation needs to be performed across very large electron microscopy image volumes. We show the advantages of a hierarchical block-wise scheme over local stitching strategies and evaluate the performance of different Multicut solvers for the segmentation of the blocks in the hierarchy. We validate the accuracy of our algorithm on a small fully annotated dataset (5×5×5 μm) and demonstrate no significant loss in segmentation quality compared to solving the Multicut problem globally. We evaluate the scalability of the algorithm on a 95 x 60 x 60 μm image volume and show that solving the Multicut problem is no longer the bottleneck of the segmentation pipeline.",
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"abstract": "Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching. Here we introduce cross-classification clustering (3C), a technique that simultaneously tracks complex, interrelated objects in an image stack. The key idea in cross-classification is to efficiently turn a clustering problem into a classification problem by running a logarithmic number of independent classifications per image, letting the cross-labeling of these classifications uniquely classify each pixel to the object labels. We apply the 3C mechanism to achieve state-of-the-art accuracy in connectomics - the nanoscale mapping of neural tissue from electron microscopy volumes. Our reconstruction system increases scalability by an order of magnitude over existing single-object tracking methods (such as flood-filling networks). This scalability is important for the deployment of connectomics pipelines, since currently the best performing techniques require computing infrastructures that are beyond the reach of most laboratories. Our algorithm may offer benefits in other domains that require pixel-accurate tracking of multiple objects, such as segmentation of videos and medical imagery.",
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"content": "Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching. Here we introduce cross-classification clustering (3C), a technique that simultaneously tracks complex, interrelated objects in an image stack. The key idea in cross-classification is to efficiently turn a clustering problem into a classification problem by running a logarithmic number of independent classifications per image, letting the cross-labeling of these classifications uniquely classify each pixel to the object labels. We apply the 3C mechanism to achieve state-of-the-art accuracy in connectomics - the nanoscale mapping of neural tissue from electron microscopy volumes. Our reconstruction system increases scalability by an order of magnitude over existing single-object tracking methods (such as flood-filling networks). This scalability is important for the deployment of connectomics pipelines, since currently the best performing techniques require computing infrastructures that are beyond the reach of most laboratories. Our algorithm may offer benefits in other domains that require pixel-accurate tracking of multiple objects, such as segmentation of videos and medical imagery.",
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"keywords": [
"Computer Vision",
"Image Segmentation",
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"Neural Tissue",
"Electron Microscopy",
"Computing Infrastructures",
"Medical",
"Biological And Cell Microscopy",
"Deep Learning Motion And Tracking Recognition Detection",
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"Retrieval Segmentation"
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"affiliation": "MIT CSAIL",
"fullName": "Yaron Meirovitch",
"givenName": "Yaron",
"surname": "Meirovitch",
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{
"affiliation": "MIT",
"fullName": "Lu Mi",
"givenName": "Lu",
"surname": "Mi",
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{
"affiliation": "MIT",
"fullName": "Hayk Saribekyan",
"givenName": "Hayk",
"surname": "Saribekyan",
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{
"affiliation": "MIT",
"fullName": "Alexander Matveev",
"givenName": "Alexander",
"surname": "Matveev",
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{
"affiliation": "MIT",
"fullName": "David Rolnick",
"givenName": "David",
"surname": "Rolnick",
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{
"affiliation": "Massachusetts Institute of Technology",
"fullName": "Nir Shavit",
"givenName": "Nir",
"surname": "Shavit",
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"abstract": "One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biological objects often have tangled parts or complex branching patterns, which make it hard to abstract, categorize, and manipulate their morphology. In this paper, we develop a novel topological nomenclature system to name these objects like the appellation for chemical compounds to promote neuroscience analysis based on their skeletal structures. We first convert the volumetric representation into the topology-preserving reduced graph to untangle the objects. Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation. In ablation studies, we quantitatively show that graphs generated by our proposed method align with the perception of experts. On 3D shape retrieval and decomposition tasks, we qualitatively demonstrate that the encoded topological nomenclature features achieve better results than state-of-the-art shape descriptors. To advance neuroscience, we will release a 3D segmentation dataset of mitochondria and pyramidal neurons reconstructed from a 100μm cube electron microscopy volume with their reduced graph and topological nomenclature annotations. Code is publicly available at https://github.com/donglaiw/ibexHelper.",
"abstracts": [
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"content": "One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biological objects often have tangled parts or complex branching patterns, which make it hard to abstract, categorize, and manipulate their morphology. In this paper, we develop a novel topological nomenclature system to name these objects like the appellation for chemical compounds to promote neuroscience analysis based on their skeletal structures. We first convert the volumetric representation into the topology-preserving reduced graph to untangle the objects. Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation. In ablation studies, we quantitatively show that graphs generated by our proposed method align with the perception of experts. On 3D shape retrieval and decomposition tasks, we qualitatively demonstrate that the encoded topological nomenclature features achieve better results than state-of-the-art shape descriptors. To advance neuroscience, we will release a 3D segmentation dataset of mitochondria and pyramidal neurons reconstructed from a 100μm cube electron microscopy volume with their reduced graph and topological nomenclature annotations. Code is publicly available at https://github.com/donglaiw/ibexHelper.",
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"normalizedAbstract": "One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biological objects often have tangled parts or complex branching patterns, which make it hard to abstract, categorize, and manipulate their morphology. In this paper, we develop a novel topological nomenclature system to name these objects like the appellation for chemical compounds to promote neuroscience analysis based on their skeletal structures. We first convert the volumetric representation into the topology-preserving reduced graph to untangle the objects. Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation. In ablation studies, we quantitatively show that graphs generated by our proposed method align with the perception of experts. On 3D shape retrieval and decomposition tasks, we qualitatively demonstrate that the encoded topological nomenclature features achieve better results than state-of-the-art shape descriptors. To advance neuroscience, we will release a 3D segmentation dataset of mitochondria and pyramidal neurons reconstructed from a 100μm cube electron microscopy volume with their reduced graph and topological nomenclature annotations. Code is publicly available at https://github.com/donglaiw/ibexHelper.",
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"title": "Studying PPCA and Its Other Homologs in C7 Family towards the Binding with Deoxycholate Based on Unique Encoding of Amino Acids",
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"abstract": "Among all the proteins of Periplasmic C type cytochrome A (PPCA) family, only PPCA protein can interact with Deoxycholate (DXCA), while its other homologs can not, as observed from the crystal structures. This article presents a unique encoding scheme of amino acids which consists of six dimensional vectors where first three dimensions use the chemical and physical properties of amino acids and last three dimensions use one mathematical parameter \"Impression\" which has been previously very effective in explaining the degeneracy of Codon Table [14]. For bringing out the \"Impression\", the amino acids are denoted by ternary numbers which are done using molecular weights of amino acids in order. The use of chemical properties for the purpose of unique encoding of amino acids is our first agenda. Secondly we expose the reason of PPCA being able to interact alone among its homologs with regards to the embedded chemical properties along with graph theoretic model.",
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"content": "Among all the proteins of Periplasmic C type cytochrome A (PPCA) family, only PPCA protein can interact with Deoxycholate (DXCA), while its other homologs can not, as observed from the crystal structures. This article presents a unique encoding scheme of amino acids which consists of six dimensional vectors where first three dimensions use the chemical and physical properties of amino acids and last three dimensions use one mathematical parameter \"Impression\" which has been previously very effective in explaining the degeneracy of Codon Table [14]. For bringing out the \"Impression\", the amino acids are denoted by ternary numbers which are done using molecular weights of amino acids in order. The use of chemical properties for the purpose of unique encoding of amino acids is our first agenda. Secondly we expose the reason of PPCA being able to interact alone among its homologs with regards to the embedded chemical properties along with graph theoretic model.",
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