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"abstract": "Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), which controls lip movements, head poses, and facial expressions of a talking head in a granular manner. Our insight is to decouple the audio-visual driving sources through prior-based pre-processing designs. Detailedly, we disassemble the driving image into three complementary parts including: 1) a cropped mouth that facilitates lip-sync; 2) a masked head that implicitly learns pose; and 3) the upper face which works corporately and complementarily with a time-shifted mouth to contribute the expression. Interestingly, the encoded features from the three sources are integrally balanced through reconstruction training. Extensive experiments show that our method generates expressive faces with not only synced mouth shapes, controllable poses, but precisely animated emotional expressions as well.",
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"affiliation": "University of Toronto",
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"abstract": "The recent availability of consumer-level head-mounted displays and omnidirectional cameras has been driving an explosion of 360 video content. Transforming the original recorded 360 video content in meaningful interactive multimedia presentations that support viewers in tasks such as learning, entertainment, and telepresence, however, is not trivial and requires new tools. Such tools must provide an easy-to-use authoring model for the integration of different media objects and active user interface elements.In this paper, based on real-world scenarios for interactive 360 videos, we gather a set of requirements and propose a declarative authoring model to support authors in the process of designing and creating 360-degree interactive video presentations. As a case study, we implement different applications showing the expressiveness and completeness of the model in the scope of the target scenarios.",
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"abstract": "We present Net EvViz, a visualization tool for analysis and exploration of a dynamic social network. There are plenty of visual social network analysis tools but few provide features for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the code base of the Node XL template for Microsoft Excel, a popular network visualization tool. The key features of this work are (1) The ability of the user to specify and edit temporal annotations to the network components in an Excel sheet, (2) See the dynamics of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, and (3) Temporal exploration of the network layout using an edge coloring scheme and a dynamic Time slider. The objectives of the new features presented in this paper are to let the data analysts, computer scientists and others to observe the dynamics or evolution in a network interactively. We presented Net EvViz to five users of Node XL and received positive responses.",
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"normalizedAbstract": "Computational screening of databases have gained immense popularity in the pharmaceutical research and development. To do such screening tests the technique used is Virtual Screening. It uses computer based algorithms and methods which takes into consideration a lot of parameters to discover new ligands on the basis of biological structures. The process of discovering new drugs has now become a crucial factor for all the Pharmaceutical Industries. Acceleration of Virtual screening would provide an edge to save the resources as well as time required. Here, the effectual implementation of parallel architecture of CUDA and GP-GPU for the acceleration of Virtual screening will be discussed. The implementation is in CUDA programming models. This implementation tries to take maximum advantage of a GPU to give better solution in the process of drug discovery. The result which would be 213× speedup when implemented on CUDA GPU architecture to provide the better solution considering performance & cost ratio.",
"fno": "07380432",
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"CUDA",
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"affiliation": "Department of Computer Engineering, Sandip Institute of Technology and Research Centre, Savitribai Phule Pune University, India",
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"affiliation": "Department of Computer Engineering, Sandip Institute of Technology and Research Centre, Savitribai Phule Pune University, India",
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{
"affiliation": "Department of Computer Engineering, Sandip Institute of Technology and Research Centre, Savitribai Phule Pune University, India",
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"affiliation": "Department of Computer Engineering, Sandip Institute of Technology and Research Centre, Savitribai Phule Pune University, India",
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"abstract": "Study of spatial relations between biological objects is crucial for understanding biological processes. Monitoring drug or particle delivery inside cells and studying the dynamics of subcellular proteins are some of the examples. Biological applications have varying demands in terms of speed and accuracy. While accuracy may be the most important factor for small-scale biology, speed is also a concern for high-content/high-throughput screening applications. In this paper we present a variety of algorithms for inter-object spatial relations in two-and three-dimensional space. These algorithms provide trade-off between speed and accuracy, depending on the requirements of the application. Results for speed and accuracy are reported for real as well as synthetic data sets.",
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{
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"content": "Study of spatial relations between biological objects is crucial for understanding biological processes. Monitoring drug or particle delivery inside cells and studying the dynamics of subcellular proteins are some of the examples. Biological applications have varying demands in terms of speed and accuracy. While accuracy may be the most important factor for small-scale biology, speed is also a concern for high-content/high-throughput screening applications. In this paper we present a variety of algorithms for inter-object spatial relations in two-and three-dimensional space. These algorithms provide trade-off between speed and accuracy, depending on the requirements of the application. Results for speed and accuracy are reported for real as well as synthetic data sets.",
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"normalizedAbstract": "Study of spatial relations between biological objects is crucial for understanding biological processes. Monitoring drug or particle delivery inside cells and studying the dynamics of subcellular proteins are some of the examples. Biological applications have varying demands in terms of speed and accuracy. While accuracy may be the most important factor for small-scale biology, speed is also a concern for high-content/high-throughput screening applications. In this paper we present a variety of algorithms for inter-object spatial relations in two-and three-dimensional space. These algorithms provide trade-off between speed and accuracy, depending on the requirements of the application. Results for speed and accuracy are reported for real as well as synthetic data sets.",
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"affiliation": "School of Electrical and Computer Engineering, 465 Northwestern Avenue, Purdue University, West Lafayette, IN 47907",
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"title": "Using Chemoinformatics and Rough Set Rule Induction for HIV Drug Discovery",
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"abstract": "This paper presents a computational approach to HIV Drug discovery using rough set based rule induction. Since conventional drug discovery is a time consuming process in which drugs are discovered either by chance or by screening the natural products, alternative methods were required to hasten the process in order to abridge the demand and supply gap. Chemoinformatics, providing novel methodologies to alleviate the problem, helps chemists to make sense of the data, attempting to predict the properties of chemical substances from a sample of data which involves lesser amount of time as compared to discovering new drugs. In this paper we make use of rough based rule induction to compare rule sets from two categories of drug databases; HIV and General. Upon comparison drugs were discovered which shared common properties with HIV drugs. These selected drugs will then be passed for clinical testing.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper presents a computational approach to HIV Drug discovery using rough set based rule induction. Since conventional drug discovery is a time consuming process in which drugs are discovered either by chance or by screening the natural products, alternative methods were required to hasten the process in order to abridge the demand and supply gap. Chemoinformatics, providing novel methodologies to alleviate the problem, helps chemists to make sense of the data, attempting to predict the properties of chemical substances from a sample of data which involves lesser amount of time as compared to discovering new drugs. In this paper we make use of rough based rule induction to compare rule sets from two categories of drug databases; HIV and General. Upon comparison drugs were discovered which shared common properties with HIV drugs. These selected drugs will then be passed for clinical testing.",
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"normalizedAbstract": "This paper presents a computational approach to HIV Drug discovery using rough set based rule induction. Since conventional drug discovery is a time consuming process in which drugs are discovered either by chance or by screening the natural products, alternative methods were required to hasten the process in order to abridge the demand and supply gap. Chemoinformatics, providing novel methodologies to alleviate the problem, helps chemists to make sense of the data, attempting to predict the properties of chemical substances from a sample of data which involves lesser amount of time as compared to discovering new drugs. In this paper we make use of rough based rule induction to compare rule sets from two categories of drug databases; HIV and General. Upon comparison drugs were discovered which shared common properties with HIV drugs. These selected drugs will then be passed for clinical testing.",
"fno": "05460741",
"keywords": [
"Chemistry Computing",
"Database Management Systems",
"Diseases",
"Drugs",
"Medical Computing",
"Rough Set Theory",
"Supply And Demand",
"Chemoinformatics",
"Rough Set Rule Induction",
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"Rough Sets",
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"fullName": "Gurpreet Singh Mohaar",
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"abstract": "The purpose is to study a multiplex PCR method for screening qualified Chinese herbal medicine granules for pharmaceutical enterprises. In this experiment, the uidA gene in Escherichia coli genome, inv A gene in Salmonella genome and 16S rDNA were used as target genes. Three pairs of primers were selected to establish and optimize the multiplex PCR system for the detection of bacteria in drugs. The amplified products were 194 bp, 362 bp and 1374 bp, respectively. The specificity of the multiplex PCR was verified by 11 strains of bacteria. The results showed that the sensitivity of multiplex PCR detection could reach Z_$10^{3}-10^{4}$_Z CFU/ml at the cell level; bacteria were found in two of the six drugs tested in the market; bacteria were found in all the four drugs that did not pass the drug test, but none of the tested drugs had Escherichia coli and Salmonella. The results showed that the multiplex PCR detection method could detect Escherichia coli and Salmonella in Chinese herbal medicine granules rapidly, accurately and efficiently, which provided reference for the preliminary screening of Chinese herbal medicine granules.",
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"content": "The purpose is to study a multiplex PCR method for screening qualified Chinese herbal medicine granules for pharmaceutical enterprises. In this experiment, the uidA gene in Escherichia coli genome, inv A gene in Salmonella genome and 16S rDNA were used as target genes. Three pairs of primers were selected to establish and optimize the multiplex PCR system for the detection of bacteria in drugs. The amplified products were 194 bp, 362 bp and 1374 bp, respectively. The specificity of the multiplex PCR was verified by 11 strains of bacteria. The results showed that the sensitivity of multiplex PCR detection could reach $10^{3}-10^{4}$ CFU/ml at the cell level; bacteria were found in two of the six drugs tested in the market; bacteria were found in all the four drugs that did not pass the drug test, but none of the tested drugs had Escherichia coli and Salmonella. The results showed that the multiplex PCR detection method could detect Escherichia coli and Salmonella in Chinese herbal medicine granules rapidly, accurately and efficiently, which provided reference for the preliminary screening of Chinese herbal medicine granules.",
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"abstract": "There are more than 20 targets related to the action of lipid-lowering drugs. These targets have important reference value in the development of lipid-lowering drugs and the selection of clinical drugs. To perform molecular docking and virtual screening, we should first prepare the three-dimensional structure of the target protein. This article analyzes the three-dimensional structure of the key target of hyperlipidemia, and selects the target protein with high resolution and clear active cavity, it provides the basis for structure-based drug design.",
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"content": "There are more than 20 targets related to the action of lipid-lowering drugs. These targets have important reference value in the development of lipid-lowering drugs and the selection of clinical drugs. To perform molecular docking and virtual screening, we should first prepare the three-dimensional structure of the target protein. This article analyzes the three-dimensional structure of the key target of hyperlipidemia, and selects the target protein with high resolution and clear active cavity, it provides the basis for structure-based drug design.",
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"authors": [
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"affiliation": "Guangdong Pharmaceutical University,College of Medical Information Engineering,Guangzhou,Guangdong,China",
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"abstract": "Prostate cancer is a complex disease which advances in stages. While clinical failure (including metastasis) is a significant endpoint following a radical prostatectomy, it can often take years to manifest, usually too late to be optimistically treated. Instead the earlier endpoint of PSA Recurrence is frequently used as a surrogate in prognostic modeling. The central issue in these models is managing censored observations which challenge traditional regression techniques. The true target times of a majority of instances are unknown, what is known is a censored target representing some earlier indeterminate time. In this work we apply a novel transduction approach for semi-supervised survival analysis which has previously been shown to be powerful in medical prognosis. The approach considers censored samples as semi-supervised regression targets leveraging the partial nature of unsupervised information. In this work, the approach leads to a significant increase in performance for predicting advanced prostate cancer from earlier endpoints and may also be useful in other diseases for predicting advanced endpoints from earlier stages.",
"abstracts": [
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"content": "Prostate cancer is a complex disease which advances in stages. While clinical failure (including metastasis) is a significant endpoint following a radical prostatectomy, it can often take years to manifest, usually too late to be optimistically treated. Instead the earlier endpoint of PSA Recurrence is frequently used as a surrogate in prognostic modeling. The central issue in these models is managing censored observations which challenge traditional regression techniques. The true target times of a majority of instances are unknown, what is known is a censored target representing some earlier indeterminate time. In this work we apply a novel transduction approach for semi-supervised survival analysis which has previously been shown to be powerful in medical prognosis. The approach considers censored samples as semi-supervised regression targets leveraging the partial nature of unsupervised information. In this work, the approach leads to a significant increase in performance for predicting advanced prostate cancer from earlier endpoints and may also be useful in other diseases for predicting advanced endpoints from earlier stages.",
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"abstract": "Computer-aided detection (CAD) algorithms have been proposed for auto-detection of different types of cancer. CAD algorithms rely on machine learning methods to classify regions of interest in images into cancerous and healthy regions. In cancer screening, the foremost problem to solve is whether a patient has cancer, regardless of the location of cancerous regions in the organ. This allows early detection of the disease leading to a right course of action in terms of treatment to be taken. In machine learning, this problem has been formulated as multi-instance learning (MIL) where bags of instances are classified rather than the individual instances. In this paper, we propose a bag of bags (BoB) nested MIL algorithm where high-level bags (or parent bags), each contains multiple smaller bags of instances. We applied the proposed BoB MIL algorithm to prostate cancer detection problem using magnetic resonance imaging data to first detect which patients have cancer and consequently, to detect which slices in the 3D volume imaging data of the detected patients contain cancerous regions. Experimental results obtained from the imaging data of 30 patients with ground-truth data based on biopsy results show that the proposed algorithm is not only capable of detecting prostate cancer at patient level, it is also able to detect the cancerous regions at slice level of imaging data with high accuracy.",
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"content": "Computer-aided detection (CAD) algorithms have been proposed for auto-detection of different types of cancer. CAD algorithms rely on machine learning methods to classify regions of interest in images into cancerous and healthy regions. In cancer screening, the foremost problem to solve is whether a patient has cancer, regardless of the location of cancerous regions in the organ. This allows early detection of the disease leading to a right course of action in terms of treatment to be taken. In machine learning, this problem has been formulated as multi-instance learning (MIL) where bags of instances are classified rather than the individual instances. In this paper, we propose a bag of bags (BoB) nested MIL algorithm where high-level bags (or parent bags), each contains multiple smaller bags of instances. We applied the proposed BoB MIL algorithm to prostate cancer detection problem using magnetic resonance imaging data to first detect which patients have cancer and consequently, to detect which slices in the 3D volume imaging data of the detected patients contain cancerous regions. Experimental results obtained from the imaging data of 30 patients with ground-truth data based on biopsy results show that the proposed algorithm is not only capable of detecting prostate cancer at patient level, it is also able to detect the cancerous regions at slice level of imaging data with high accuracy.",
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"abstract": "Facing the phenomenon of \"ageing population\", the diagnosis and treatment of prostate cancer has become a serious men’s health issue in Taiwan. This study aimed to provide new scientific and quantitative information for traditional Chinese medicine (TCM) physicians in clinical practice of prostate cancer. In this study, data mining techniques were employed to explore the hidden knowledge among meridian energy of prostate cancer from 213 patients’ health examination data including patient demographics and evaluations for the prostate-specific antigen (PSA) blood test as well as the meridian energy. Through the construction of decision tree, totally ten classification rules for predicting prostate cancer are extracted. The average correction classification accuracy is close to 80%. The findings are considered as helpful reference in diagnosis and treatment of prostate cancer for TCM physicians.",
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"content": "Facing the phenomenon of \"ageing population\", the diagnosis and treatment of prostate cancer has become a serious men’s health issue in Taiwan. This study aimed to provide new scientific and quantitative information for traditional Chinese medicine (TCM) physicians in clinical practice of prostate cancer. In this study, data mining techniques were employed to explore the hidden knowledge among meridian energy of prostate cancer from 213 patients’ health examination data including patient demographics and evaluations for the prostate-specific antigen (PSA) blood test as well as the meridian energy. Through the construction of decision tree, totally ten classification rules for predicting prostate cancer are extracted. The average correction classification accuracy is close to 80%. The findings are considered as helpful reference in diagnosis and treatment of prostate cancer for TCM physicians.",
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"normalizedAbstract": "Facing the phenomenon of \"ageing population\", the diagnosis and treatment of prostate cancer has become a serious men’s health issue in Taiwan. This study aimed to provide new scientific and quantitative information for traditional Chinese medicine (TCM) physicians in clinical practice of prostate cancer. In this study, data mining techniques were employed to explore the hidden knowledge among meridian energy of prostate cancer from 213 patients’ health examination data including patient demographics and evaluations for the prostate-specific antigen (PSA) blood test as well as the meridian energy. Through the construction of decision tree, totally ten classification rules for predicting prostate cancer are extracted. The average correction classification accuracy is close to 80%. The findings are considered as helpful reference in diagnosis and treatment of prostate cancer for TCM physicians.",
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"title": "A Multi-Omics Classifier For Prediction Of Androgen Deprivation Treatment Response In Prostate Cancer Patients",
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"abstract": "Despite the advancement in the management of prostate cancer in recent years, treatment strategies are only efficient against localized disease while managing metastatic cancer remains a challenge. As a result, the global burden of the disease has remained significant. Efficient and personalized management of the disease before metastasis is therefore of prime importance. In this study, we developed a classifier to predict the response of prostate cancer patients to treatment leveraging on multi-omics datasets provided by The Cancer Genome Atlas (TCGA). Our investigation using ten machine learning algorithms reveals that tree-based algorithms had better predictive performance than probabilistic models such as Naive Bayes and kernel-based methods such as Support Vector Machines. We also investigated the performance of all possible omics combinations. Our results show that there is an overall increase in performance when multiple omics datasets are used in contrast to single omics strategies. We have predicted for the first time, possible androgen deprivation treatment response outcomes for 68 prostate cancer patients with missing phenotype values in the TCGA dataset.",
"abstracts": [
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"content": "Despite the advancement in the management of prostate cancer in recent years, treatment strategies are only efficient against localized disease while managing metastatic cancer remains a challenge. As a result, the global burden of the disease has remained significant. Efficient and personalized management of the disease before metastasis is therefore of prime importance. In this study, we developed a classifier to predict the response of prostate cancer patients to treatment leveraging on multi-omics datasets provided by The Cancer Genome Atlas (TCGA). Our investigation using ten machine learning algorithms reveals that tree-based algorithms had better predictive performance than probabilistic models such as Naive Bayes and kernel-based methods such as Support Vector Machines. We also investigated the performance of all possible omics combinations. Our results show that there is an overall increase in performance when multiple omics datasets are used in contrast to single omics strategies. We have predicted for the first time, possible androgen deprivation treatment response outcomes for 68 prostate cancer patients with missing phenotype values in the TCGA dataset.",
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"Cancer",
"Genomics",
"Learning Artificial Intelligence",
"Medical Computing",
"Patient Treatment",
"Pattern Classification",
"Androgen Deprivation Treatment Response Outcomes",
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"affiliation": "Covenant University Bioinformatics Research (CUBRe), Covenant University, P.M.B. 1023,Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE),Department of Computer and Information Sciences,Ota,Ogun State,Nigeria",
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"abstract": "Early detection of cancer can significantly increase the chance of successful treatment. This research performs a study on early cancer detection for prostate cancer patients from whom cancer tissue was analyzed with Illumina Hi-Seq ribonucleic acid (RNA) Sequencing (RNA-Seq). Cancer relevant genes with the most significant correlations with the clinical outcome of the sample type (cancer /non-cancer) and the overall survival (OS) were assessed. Traditional cancer diagnosis primarily depends on physicians' experience to identify morphological abnormalities. Gene expression level data can assist physicians in detecting cancer cases at a much earlier stage and thus can significantly improve the potential of patient treatment. In this research, for the classification task, we applied machine learning and data mining approaches to detect cancer versus non-cancer based on gene expression data. Our goal was to detect cancer at the earliest stage. Besides, for the regression task, survival outcomes in prostate cancer patients were performed. Regression trees were built using cancer-sensitive genes along with clinical attribute `Gleason score' as predictors, and the clinical variable `overall survival' as the target variable. Knowledge in the form of rules is one of the vital tasks in data mining as it provides concise statements of easily understandable and potentially valuable information. For the classification model, we derived rules from a decision tree and interpreted these rules for cancer and non-cancer patients. For the regression or survival model, we generated rules for predicting or estimating the survival time of cancer patients. In this study, cancer-relevant genes were analyzed as predictors, although various genes may interact with genes currently known to contribute to cancer. These findings have implications for assessing gene-gene interactions and gene-environment interactions of prostate cancer as well as for other types of cancer.",
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"content": "Early detection of cancer can significantly increase the chance of successful treatment. This research performs a study on early cancer detection for prostate cancer patients from whom cancer tissue was analyzed with Illumina Hi-Seq ribonucleic acid (RNA) Sequencing (RNA-Seq). Cancer relevant genes with the most significant correlations with the clinical outcome of the sample type (cancer /non-cancer) and the overall survival (OS) were assessed. Traditional cancer diagnosis primarily depends on physicians' experience to identify morphological abnormalities. Gene expression level data can assist physicians in detecting cancer cases at a much earlier stage and thus can significantly improve the potential of patient treatment. In this research, for the classification task, we applied machine learning and data mining approaches to detect cancer versus non-cancer based on gene expression data. Our goal was to detect cancer at the earliest stage. Besides, for the regression task, survival outcomes in prostate cancer patients were performed. Regression trees were built using cancer-sensitive genes along with clinical attribute `Gleason score' as predictors, and the clinical variable `overall survival' as the target variable. Knowledge in the form of rules is one of the vital tasks in data mining as it provides concise statements of easily understandable and potentially valuable information. For the classification model, we derived rules from a decision tree and interpreted these rules for cancer and non-cancer patients. For the regression or survival model, we generated rules for predicting or estimating the survival time of cancer patients. In this study, cancer-relevant genes were analyzed as predictors, although various genes may interact with genes currently known to contribute to cancer. These findings have implications for assessing gene-gene interactions and gene-environment interactions of prostate cancer as well as for other types of cancer.",
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"title": "2020 Workshop on Visual Analytics in Healthcare (VAHC)",
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"title": "Visualization Co-Design with Prostate Cancer Survivors who have Limited Graph Literacy",
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"abstract": "Visualizing patient-reported outcomes overtime has become a common strategy to help patients track their health. However, traditional line graphs and bar charts might be less accessible to people with limited numeracy and graph literacy. Although patient-reported outcomes (PRO) among prostate cancer survivors may be collected with extant validated instruments, such as the Expanded Prostate Cancer Index (EPIC-26), survivors with limited numeracy and graph literacy may not find traditional charts and graphs accessible. The Graphical Representation of Symptoms of Prostate Cancer (GRASP) research team embarked on a 4-stage design process with prostate cancer survivors with limited graph literacy to investigate design considerations for timeline visualizations, including preferences, acceptability, and understandability. Through focus groups, surveys, and the recurring engagement of a community-based Patient Advisory Board, we identified 3 design approaches that reflect the needs of low graph literacy patients as well as show promise to increase acceptance and comprehension. We are developing these designs into interactive prototypes for evaluation through user testing. Findings from our design process provide insight into effective strategies for engaging vulnerable patients for visualization co-design using in-person and remote methods. Finally, the design considerations we identified for prostate cancer survivors with limited graph literacy that may provide insight for visualizations of PROs for other vulnerable groups.",
"abstracts": [
{
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"content": "Visualizing patient-reported outcomes overtime has become a common strategy to help patients track their health. However, traditional line graphs and bar charts might be less accessible to people with limited numeracy and graph literacy. Although patient-reported outcomes (PRO) among prostate cancer survivors may be collected with extant validated instruments, such as the Expanded Prostate Cancer Index (EPIC-26), survivors with limited numeracy and graph literacy may not find traditional charts and graphs accessible. The Graphical Representation of Symptoms of Prostate Cancer (GRASP) research team embarked on a 4-stage design process with prostate cancer survivors with limited graph literacy to investigate design considerations for timeline visualizations, including preferences, acceptability, and understandability. Through focus groups, surveys, and the recurring engagement of a community-based Patient Advisory Board, we identified 3 design approaches that reflect the needs of low graph literacy patients as well as show promise to increase acceptance and comprehension. We are developing these designs into interactive prototypes for evaluation through user testing. Findings from our design process provide insight into effective strategies for engaging vulnerable patients for visualization co-design using in-person and remote methods. Finally, the design considerations we identified for prostate cancer survivors with limited graph literacy that may provide insight for visualizations of PROs for other vulnerable groups.",
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"normalizedAbstract": "Visualizing patient-reported outcomes overtime has become a common strategy to help patients track their health. However, traditional line graphs and bar charts might be less accessible to people with limited numeracy and graph literacy. Although patient-reported outcomes (PRO) among prostate cancer survivors may be collected with extant validated instruments, such as the Expanded Prostate Cancer Index (EPIC-26), survivors with limited numeracy and graph literacy may not find traditional charts and graphs accessible. The Graphical Representation of Symptoms of Prostate Cancer (GRASP) research team embarked on a 4-stage design process with prostate cancer survivors with limited graph literacy to investigate design considerations for timeline visualizations, including preferences, acceptability, and understandability. Through focus groups, surveys, and the recurring engagement of a community-based Patient Advisory Board, we identified 3 design approaches that reflect the needs of low graph literacy patients as well as show promise to increase acceptance and comprehension. We are developing these designs into interactive prototypes for evaluation through user testing. Findings from our design process provide insight into effective strategies for engaging vulnerable patients for visualization co-design using in-person and remote methods. Finally, the design considerations we identified for prostate cancer survivors with limited graph literacy that may provide insight for visualizations of PROs for other vulnerable groups.",
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"keywords": [
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"Traditional Line Graphs",
"Prostate Cancer Survivors",
"Visualization Co Design",
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"affiliation": "University of Washington,Department of Biomedical Informatics and Medical Education,Seattle,WA",
"fullName": "Lauren Snyder",
"givenName": "Lauren",
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"affiliation": "University of Washington,Department of Biomedical Informatics and Medical Education,Seattle,WA",
"fullName": "Ayan Anandkumar Saraf",
"givenName": "Ayan Anandkumar",
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"affiliation": "University of Washington,Department of Biomedical Informatics and Medical Education,Seattle,WA",
"fullName": "Reggie Casanova-Perez",
"givenName": "Reggie",
"surname": "Casanova-Perez",
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"affiliation": "University of California,Department of Urology,Los Angeles,CA",
"fullName": "Sarah E. Connor",
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"title": "Perspective Matters: Sharing of Crisis Information in Social Media",
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"abstract": "In this paper, we examined information sharing behavior in social media when one was taking the perspective of self versus other. We found that imagining self in a disaster center, Fukushima, Japan, increased the likelihood of sharing crisis information relative to imagining another person, John, in the same place. People's intention to share crisis information by default, without being asked to take any perspective, paralleled the intention to share when taking another person's perspective. Moreover, when the information was associated with negative feelings, such as worry or fear, it was more likely to be shared, when the information was perceived confusing or uninteresting, it was less likely to be shared.",
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"abstract": "The integration of social media technologies with second screen devices during the broadcasts of in-real-life events facilitates a mode of online conversation we refer to as the social soundtrack. In this research, we compute the correlations between the comments people post in the social soundtrack on various platforms (i.e., Twitter, Instagram and Tumblr) and the terms people search for on a major web search engine (i.e., Google). The broadcast media event for this research is Super Bowl 2015 commercials. Using statistical t-tests, we compare the correlations between the relative volume of searching, obtained via Google Trends, and the relative volume of social soundtrack postings on each of three social media platforms for two temporal phases (Pre and Post) for Super Bowl 2015. We exclude the game day from our research due to insufficiency of granularity for search data on the game day. Research results show that there is no overall significant difference in phase correlation between social media and search data. However, at the individual level, there are brands that do show significant correlation between phases. The number of significant positive correlations between the social soundtrack postings and web search concerning brands are considerably high compared to the number of significant negative correlations in both phases. The research results are important in identifying the temporal trends and interplay between type of social media platforms and searching concerning the sharing of brand mentions in word-of-mouth marketing. The result will eventually help retailers focusing on the brands with higher correlations to lever the opportunity of electronic word of mouth advertising.",
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"content": "The integration of social media technologies with second screen devices during the broadcasts of in-real-life events facilitates a mode of online conversation we refer to as the social soundtrack. In this research, we compute the correlations between the comments people post in the social soundtrack on various platforms (i.e., Twitter, Instagram and Tumblr) and the terms people search for on a major web search engine (i.e., Google). The broadcast media event for this research is Super Bowl 2015 commercials. Using statistical t-tests, we compare the correlations between the relative volume of searching, obtained via Google Trends, and the relative volume of social soundtrack postings on each of three social media platforms for two temporal phases (Pre and Post) for Super Bowl 2015. We exclude the game day from our research due to insufficiency of granularity for search data on the game day. Research results show that there is no overall significant difference in phase correlation between social media and search data. However, at the individual level, there are brands that do show significant correlation between phases. The number of significant positive correlations between the social soundtrack postings and web search concerning brands are considerably high compared to the number of significant negative correlations in both phases. The research results are important in identifying the temporal trends and interplay between type of social media platforms and searching concerning the sharing of brand mentions in word-of-mouth marketing. The result will eventually help retailers focusing on the brands with higher correlations to lever the opportunity of electronic word of mouth advertising.",
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"content": "Fuzzy ARTMAP is one of the families of the neural network architectures based on ART(Adaptive Resonance Theory) in which supervised learning can be carried out. However, it usually tends to create more categories than are actually needed. This often causes the so called overfitting problem, namely the performance of the networks in test set is not monotonically increasing with the additional training epochs and category creation, for fuzzy ARTMAP. In order to avoid the overfitting problem, Carpenter and Tan [Carpenter and Tan, 1993] proposed a confidence-based pruning method by eliminating those categories that were either less useful or less accurate. This paper proposes yet another alternative pruning method that is based on the Minimal Description Length (MDL) principle. The MDL principle can be viewed as a tradeoff between theory complexity and data prediction accuracy given the theory. We adopted Cameron-Jones' error encoding scheme and Quinlan's modifier for theory encoding to estimate the fuzzy ARTMAP theory description length. A greedy search algorithm of the minimum description length to prune the fuzzy ARTMAP categories one by one is proposed. The experiments showed that fuzzy ARTMAP pruned with the MDL principle gave better performance with far fewer categories created than the original fuzzy ARTMAP and other machine learning systems on a number of benchmark clinical databases such as heart disease, breath cancer and diabetes databases.",
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"abstract": "Food safety is an aspect that must be monitored properly as it can contribute to food-borne diseases if not properly maintained. There are various food safety assurance systems that have been introduced which act as a tool to reduce the risk of contamination. However, such a system can be difficult to maintain because it requires a comprehensive evaluation of all components involved in food production. In addition, from the Islamic perspective, food safety should be compromised with the Shariah law as it emphasizes the Halalan-Thoyyiba concept. This is the key reason for the introduction of Halal Certification (HC) system. This paper attempts to highlight the common but critically important requirement of Halal Inspection (HI) which is the key process of HC system. Qualitative research methods have been implemented through the interview with five Halal Auditors and series of observations on the existing HI process which had been conducted at 10 food premises throughout Malaysia. This research is performed based on the Computer Supported Cooperative Work (CSCW) framework and focused on the HI process for small and medium-sized food premises such as restaurants, cafés, food kiosks and hotel kitchens. The results revealed the requirement of HI which is the critical control points during the inspection process. The requirement is depicted as the element of the newly developed Halal Virtual Inspection Model. This is a noble work as it exposed the important criteria of HI which requires special attention in strengthening Malaysia's HC system through the application of information technology.",
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"title": "2021 IEEE International Conference on Big Data (Big Data)",
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"title": "GraSSNet: Graph Soft Sensing Neural Networks",
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"abstract": "In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.",
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"content": "In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.",
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