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"abstract": "The ability to solve problems is a critical skill in all undergraduate engineering curricula. Students' capacity for problem solving is complicated by the fact that higher level reasoning skills, including the capacity for abstraction, are not innate in a person until the mid-twenties or later. The results of an exploratory study that looked at students' episodes of reasoning when solving problems in a sophomore level electrical circuits course are presented. Students' problem solving attempts are analyzed using the representation mapping framework developed by Hahn and Chater that is based on store representations of knowledge and how they are applied. This framework distinguishes between similarity and rules-based cognitive processes, and accounts for memory-bank, rules-based, similarity-based and prototype types of reasoning. Students were asked to think aloud when solving specific problems selected by the course instructor. The interviews were recorded, transcribed, and analyzed in detail to identify the types of reasoning and the degree of abstraction in the students' problem solving attempts. This study demonstrated that representation mapping is useful framework for studying students' problem solving skills in electrical engineering.",
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"content": "The ability to solve problems is a critical skill in all undergraduate engineering curricula. Students' capacity for problem solving is complicated by the fact that higher level reasoning skills, including the capacity for abstraction, are not innate in a person until the mid-twenties or later. The results of an exploratory study that looked at students' episodes of reasoning when solving problems in a sophomore level electrical circuits course are presented. Students' problem solving attempts are analyzed using the representation mapping framework developed by Hahn and Chater that is based on store representations of knowledge and how they are applied. This framework distinguishes between similarity and rules-based cognitive processes, and accounts for memory-bank, rules-based, similarity-based and prototype types of reasoning. Students were asked to think aloud when solving specific problems selected by the course instructor. The interviews were recorded, transcribed, and analyzed in detail to identify the types of reasoning and the degree of abstraction in the students' problem solving attempts. This study demonstrated that representation mapping is useful framework for studying students' problem solving skills in electrical engineering.",
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"title": "Ambiguity during engineering problem solving",
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"abstract": "Work place engineering problems are recognized as being complex and ambiguous. However, most of the problems students are asked to solve during their degree programs are classroom problems. Most studies of ambiguity in problem solving have defined ambiguity as a characteristic of the problems. We are interested in understanding how students interpret engineering problems, either creating or resolving ambiguity during their problem solving process. The research question that guides this study is: How is ambiguity manifested by engineering students as they solve problems? This study is part of a larger project examining engineering students' engineering problem solving across different types of engineering problems. Twenty materials engineering students were asked to solve four materials engineering problems during a think-aloud session. For the analysis, points of ambiguity during the problem-solving process were identified as periods in which the students specifically verbalized uncertainty. The points of ambiguity in the transcript were then analyzed through script analysis. More specifically textual instances of ambiguity were linked with three main periods and situational categories: a) the situation that caused the ambiguity, 2) what happened during the point of ambiguity, and 3) the reaction of the student after reaching the point of ambiguity. Results indicated that students experienced points of ambiguity in both closed- and open-ended problems. Several motifs were common to both types of problems, including confusion over how to use given values, not having necessary content knowledge, lack of self-confidence, and difficulty conceptualizing the problem. The lack of constraints inherent in the open-ended problems also caused additional ambiguity among some students. Our findings show that ambiguity can take many forms and is not just a function of the problem structure. Students reach points of ambiguity (e.g., points where they do not know what to do next), due to many factors. Some of these factors represent aspects of their academic and epistemological development. Our analysis to date suggests that students need more comprehensive training and practice to be prepared in facing problems that reflect the work in engineering practice. We are currently examining the data further to identify patterns in their response to ambiguity, which may help in developing pedagogical strategies and tools to support students' ability to successfully solve engineering problems.",
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"content": "Work place engineering problems are recognized as being complex and ambiguous. However, most of the problems students are asked to solve during their degree programs are classroom problems. Most studies of ambiguity in problem solving have defined ambiguity as a characteristic of the problems. We are interested in understanding how students interpret engineering problems, either creating or resolving ambiguity during their problem solving process. The research question that guides this study is: How is ambiguity manifested by engineering students as they solve problems? This study is part of a larger project examining engineering students' engineering problem solving across different types of engineering problems. Twenty materials engineering students were asked to solve four materials engineering problems during a think-aloud session. For the analysis, points of ambiguity during the problem-solving process were identified as periods in which the students specifically verbalized uncertainty. The points of ambiguity in the transcript were then analyzed through script analysis. More specifically textual instances of ambiguity were linked with three main periods and situational categories: a) the situation that caused the ambiguity, 2) what happened during the point of ambiguity, and 3) the reaction of the student after reaching the point of ambiguity. Results indicated that students experienced points of ambiguity in both closed- and open-ended problems. Several motifs were common to both types of problems, including confusion over how to use given values, not having necessary content knowledge, lack of self-confidence, and difficulty conceptualizing the problem. The lack of constraints inherent in the open-ended problems also caused additional ambiguity among some students. Our findings show that ambiguity can take many forms and is not just a function of the problem structure. Students reach points of ambiguity (e.g., points where they do not know what to do next), due to many factors. Some of these factors represent aspects of their academic and epistemological development. Our analysis to date suggests that students need more comprehensive training and practice to be prepared in facing problems that reflect the work in engineering practice. We are currently examining the data further to identify patterns in their response to ambiguity, which may help in developing pedagogical strategies and tools to support students' ability to successfully solve engineering problems.",
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"affiliation": "Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA",
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"affiliation": "Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA",
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"abstract": "Functional zero problem has always been the focus and difficulty in the National College Entrance Exam mathematics. Since functional zero problem involves a large amount of knowledge and operation, senior students often spend a lot of time and energy in solving the problem, but they have little or no effect. Therefore, it is significant to carry out research on solving functional zero problem. Polya, a famous American mathematics educator, put forward Polya’s problem-solving theory in the long-term practice of mathematics education. Its core content is a \"How to solve the problem table\". Polya’s problem-solving theory is conducive to the formation of effective problem-solving ideas for the solution of problems. In view of this, this research takes Polya’s problem-solving theory as the theoretical basis and tries to apply Polya’s problem-solving theory to solve functional zero problem: step one, understand the problem; step two, devise a plan; step three, carry out the plan; step four, look back.",
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"content": "Functional zero problem has always been the focus and difficulty in the National College Entrance Exam mathematics. Since functional zero problem involves a large amount of knowledge and operation, senior students often spend a lot of time and energy in solving the problem, but they have little or no effect. Therefore, it is significant to carry out research on solving functional zero problem. Polya, a famous American mathematics educator, put forward Polya’s problem-solving theory in the long-term practice of mathematics education. Its core content is a \"How to solve the problem table\". Polya’s problem-solving theory is conducive to the formation of effective problem-solving ideas for the solution of problems. In view of this, this research takes Polya’s problem-solving theory as the theoretical basis and tries to apply Polya’s problem-solving theory to solve functional zero problem: step one, understand the problem; step two, devise a plan; step three, carry out the plan; step four, look back.",
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"abstract": "Complex problem solving is one of the key skills for future jobs, but it has not been clearly operationalized. We hypothesized that it is a construct of inquiry, computational thinking, and mathematical problem solving. The hypothesis was empirically tested by administering tests to 261 high school students for assessing three inquiry skills, two dimensions of computational thinking and five mathematical problem solving skills. Confirmatory factor analysis showed a three factor model of inquiry skills and two factor model of computational thinking skills to have a good fit. Mathematical problem solving skills were characterized with a two factor model. These factors were combined in a higher-order factor model into one construct that could be defined as a complex problem solving skill.",
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"abstract": "Computer crime investigation and forensics is an important research work to combat criminal activity in cyberspace. But the complexity of computer criminal groups makes the computer crime forensics to be a challenge. Email is an important communication mean in computer crime communication. So the email forensics is needed to organized crime. This paper proposed an email forensics method based on graph clustering method and social network analysis (SNA). We analyze and mine emails data of the suspicious users' accounts using the new method, which can create email communication network graph for suspicious computer criminal organizations. The algorithm developed can analyze computer organization's structure and core members. The effectiveness of the method proposed has been proved by our experimental data and results. The research may help investigators to find more email evidence and case clues and improve investigators' email forensic capability, especially to the investigation for organized crime.",
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"content": "Computer crime investigation and forensics is an important research work to combat criminal activity in cyberspace. But the complexity of computer criminal groups makes the computer crime forensics to be a challenge. Email is an important communication mean in computer crime communication. So the email forensics is needed to organized crime. This paper proposed an email forensics method based on graph clustering method and social network analysis (SNA). We analyze and mine emails data of the suspicious users' accounts using the new method, which can create email communication network graph for suspicious computer criminal organizations. The algorithm developed can analyze computer organization's structure and core members. The effectiveness of the method proposed has been proved by our experimental data and results. The research may help investigators to find more email evidence and case clues and improve investigators' email forensic capability, especially to the investigation for organized crime.",
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"fullName": "Yan Hua Liu",
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"abstract": "Vehicles are fast becoming another important source of digital evidence in a criminal investigation. Traditionally, when a vehicle is involved in a crime scene (e.g. drink driving), the investigators focus on the acquisition of DNA, fingerprints and other identifying materials, usually non digital in nature. However, modern day cars, particularly smart or driverless cars, store a wealth of digital information, such as recent destinations, favorite locations, routes, personal data such as call logs, contact lists, SMS messages, pictures, and videos. In this paper, we describe some challenges associated with vehicle data forensics, an understudied area. Next, we present our case study on forensic acquisition and data analysis of an entertainment system on a Volkswagen car.",
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"content": "Vehicles are fast becoming another important source of digital evidence in a criminal investigation. Traditionally, when a vehicle is involved in a crime scene (e.g. drink driving), the investigators focus on the acquisition of DNA, fingerprints and other identifying materials, usually non digital in nature. However, modern day cars, particularly smart or driverless cars, store a wealth of digital information, such as recent destinations, favorite locations, routes, personal data such as call logs, contact lists, SMS messages, pictures, and videos. In this paper, we describe some challenges associated with vehicle data forensics, an understudied area. Next, we present our case study on forensic acquisition and data analysis of an entertainment system on a Volkswagen car.",
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"fullName": "Daniel Jacobs",
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"abstract": "Due to atmospheric turbulence, light randomly refracts in three dimensions (3D), eventually entering a camera at a perturbed angle. Each viewed object point thus has a distorted projection in a two-dimensional (2D) image. Simulating 3D random refraction for all viewed points via complex simulated 3D random turbulence is computationally expensive. We derive an efficient way to render 2D image distortions, consistent with turbulence. Our approach bypasses 3D numerical calculations altogether We directly create 2D random physics-based distortion vector fields, where correlations are derived in closed form from turbulence theory. The correlations are nontrivial: they depend on the perturbation directions relative to the orientation of all object-pairs, simultaneously. Hence, we develop a theory characterizing and rendering such a distortion field. The theory is turned to a few simple 2D operations, which render images based on camera and atmospheric properties.",
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"content": "Due to atmospheric turbulence, light randomly refracts in three dimensions (3D), eventually entering a camera at a perturbed angle. Each viewed object point thus has a distorted projection in a two-dimensional (2D) image. Simulating 3D random refraction for all viewed points via complex simulated 3D random turbulence is computationally expensive. We derive an efficient way to render 2D image distortions, consistent with turbulence. Our approach bypasses 3D numerical calculations altogether We directly create 2D random physics-based distortion vector fields, where correlations are derived in closed form from turbulence theory. The correlations are nontrivial: they depend on the perturbation directions relative to the orientation of all object-pairs, simultaneously. Hence, we develop a theory characterizing and rendering such a distortion field. The theory is turned to a few simple 2D operations, which render images based on camera and atmospheric properties.",
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"Atmospheric Turbulence",
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"Two Dimensional Image",
"3 D Random Refraction",
"Complex Simulated 3 D Random Turbulence",
"Distortion Vector Fields",
"Distortion",
"Two Dimensional Displays",
"Three Dimensional Displays",
"Correlation",
"Rendering Computer Graphics",
"Cameras",
"Refractive Index"
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"affiliation": "Div. Biostatistics, UC San Diego, La Jolla, CA, USA",
"fullName": "Armin Schwartzman",
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"affiliation": "Dept. Elect. Eng. & Comp. Sci., Northwestern University, Evanston, IL, USA",
"fullName": "Marina Alterman",
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"surname": "Alterman",
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{
"affiliation": "Viterbi Faculty of Electrical Eng., Technion - Israel Inst. Technology, Haifa, Israel",
"fullName": "Rotem Zamir",
"givenName": "Rotem",
"surname": "Zamir",
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{
"affiliation": "Viterbi Faculty of Electrical Eng., Technion - Israel Inst. Technology, Haifa, Israel",
"fullName": "Yoav Y. Schechner",
"givenName": "Yoav Y.",
"surname": "Schechner",
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"title": "Study of the Data Exchanging Safely and Quickly for Sudden Leakage of Dangerous Chemicals Emergency Decision System Based on VPN",
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"abstract": "The sudden leakage of dangerous chemicals emergency decision system can help the government, chemical accident emergency rescue center, fire fighting and the units of production, transportation, storage or use for dangerous chemicals generate maneuverable emergency rescue plan in the shortest time according to the geographical location and the specific situation of the accident. The system owns the national urban dangerous chemicals database and provides the target quantity and the distribution of the urban dangerous chemicals. Because the data of this system belong to the state public security secrets, the data transmission must be safe and fast absolutely. VPN is a safe, special and virtual network which is temporarily established in the Internet. The system realizes the transmission and maintenance of the dangerous sources data, ensures the data safety and enhances the efficiency of data transmission through VPN.",
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{
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"content": "The sudden leakage of dangerous chemicals emergency decision system can help the government, chemical accident emergency rescue center, fire fighting and the units of production, transportation, storage or use for dangerous chemicals generate maneuverable emergency rescue plan in the shortest time according to the geographical location and the specific situation of the accident. The system owns the national urban dangerous chemicals database and provides the target quantity and the distribution of the urban dangerous chemicals. Because the data of this system belong to the state public security secrets, the data transmission must be safe and fast absolutely. VPN is a safe, special and virtual network which is temporarily established in the Internet. The system realizes the transmission and maintenance of the dangerous sources data, ensures the data safety and enhances the efficiency of data transmission through VPN.",
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"normalizedAbstract": "The sudden leakage of dangerous chemicals emergency decision system can help the government, chemical accident emergency rescue center, fire fighting and the units of production, transportation, storage or use for dangerous chemicals generate maneuverable emergency rescue plan in the shortest time according to the geographical location and the specific situation of the accident. The system owns the national urban dangerous chemicals database and provides the target quantity and the distribution of the urban dangerous chemicals. Because the data of this system belong to the state public security secrets, the data transmission must be safe and fast absolutely. VPN is a safe, special and virtual network which is temporarily established in the Internet. The system realizes the transmission and maintenance of the dangerous sources data, ensures the data safety and enhances the efficiency of data transmission through VPN.",
"fno": "05634929",
"keywords": [
"Chemical Engineering Computing",
"Database Management Systems",
"Decision Support Systems",
"Electronic Data Interchange",
"Emergency Services",
"Internet",
"Virtual Private Networks",
"Data Exchange",
"Dangerous Chemicals Emergency Decision System",
"Virtual Private Network",
"Chemical Accident Emergency Rescue Center",
"Fire Fighting",
"National Urban Dangerous Chemicals Database",
"State Public Security Secrets",
"Data Transmission",
"Internet",
"Chemicals",
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"affiliation": null,
"fullName": "Hu Yanju",
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{
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"doi": "10.1109/ISME.2010.125",
"title": "Application of Data Mining in the Inquiry System of Dangerous Chemicals",
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"abstract": "The Inquiry System of Dangerous Chemicals was established to inquire the related information of dangerous chemicals fast and make right decisions promptly. It’s an important component of emergency decision-making system. Its primary mission is to implement the information inquiry of each kind of dangerous chemical promptly by use of the fuzzy inquiry technology. The paper gives a detailed introduction to the application of the fuzzy inquiry system.",
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"content": "The Inquiry System of Dangerous Chemicals was established to inquire the related information of dangerous chemicals fast and make right decisions promptly. It’s an important component of emergency decision-making system. Its primary mission is to implement the information inquiry of each kind of dangerous chemical promptly by use of the fuzzy inquiry technology. The paper gives a detailed introduction to the application of the fuzzy inquiry system.",
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"abstract": "We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared reflectance signatures. The Infrared Backscatter Imaging Spectroscopy (IBIS) technology is used to capture these reflectance signatures through a mobile cart-based system. An infrared laser is directed toward the target surface from a standoff distance, and an MCT focal plane array collects the backscatter images from different wavelengths of laser into what is known as a hyperspectral image cube (hypercube for short). The hypercubes from our measurements are represented in a matrix of numbers. These hypercubes have a 128x128 front face representing the spatial dimensions (the physical image), and a depth of 153 representing the spectral dimension, which contains the IBIS measurements from various wavelengths of laser. These data are fed into a deep neural network machine learning algorithm for classification into specific chemicals (analytes). We propose a data accentuation technique to improve classification performance on IBIS data. Furthermore, we demonstrate the capability of machine learning algorithms to perform pixel-by-pixel discrimination of chemicals within a hypercube image, as well as the simultaneous detection of multiple different chemicals. We also demonstrate the capacity of machine learning algorithms to perform with high precision and recall under noisy conditions. The algorithm goes beyond simply identifying the presence of a chemical, in that it is also able to pinpoint the spatial location of each chemical within the field of view.",
"abstracts": [
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"content": "We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared reflectance signatures. The Infrared Backscatter Imaging Spectroscopy (IBIS) technology is used to capture these reflectance signatures through a mobile cart-based system. An infrared laser is directed toward the target surface from a standoff distance, and an MCT focal plane array collects the backscatter images from different wavelengths of laser into what is known as a hyperspectral image cube (hypercube for short). The hypercubes from our measurements are represented in a matrix of numbers. These hypercubes have a 128x128 front face representing the spatial dimensions (the physical image), and a depth of 153 representing the spectral dimension, which contains the IBIS measurements from various wavelengths of laser. These data are fed into a deep neural network machine learning algorithm for classification into specific chemicals (analytes). We propose a data accentuation technique to improve classification performance on IBIS data. Furthermore, we demonstrate the capability of machine learning algorithms to perform pixel-by-pixel discrimination of chemicals within a hypercube image, as well as the simultaneous detection of multiple different chemicals. We also demonstrate the capacity of machine learning algorithms to perform with high precision and recall under noisy conditions. The algorithm goes beyond simply identifying the presence of a chemical, in that it is also able to pinpoint the spatial location of each chemical within the field of view.",
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"abstract": "A study was carried out which examined the extent to which young people aged 11-12, with no prior instruction in programming, are able to write computational rules which govern play in a 3D computer role-playing game. Expressing these rules required the use of common computational structures such as conditionals, sets and loops. We analysed the rules written for their structure and style, and recorded the types of errors made. It was found that although young people were able to abstract away from the game play experience, very few of the rules were error-free. The most common errors were errors of omission (leaving elements out that should have been included) rather than errors of commission (including elements which should not be part of the rule). These findings have implications for the design of the Flip language, which aims to support young people as they begin to develop computational skills through game design.",
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"abstract": "Currently, one of the greatest challenges facing higher education is to reduce the high rates of loss and dropout that occur in subjects in Mathematics, for which it is necessary to work on the creation of new learning environments that allow to awaken the interest of students towards these disciplines. The digital game NinjaMath is a platform game that aims to provide a novel resource for learning factorization in students of the undergraduate programs of the Metropolitan Technological Institute of the city of Medellín in Colombia. The game is built considering three essential elements that, according to various investigations, contribute to developing cognitive skills in students: specific rules and mechanics of the game, narrative, and aesthetics. The tool used to build the game is the Unity version 5.0.0f4 game development engine, using the C# programming language with the MonoDeveloper editor.",
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"abstract": "Perhaps the most common question that a microarray study can ask is, \"Between two given biological conditions, which genes exhibit changed expression levels?\" Existing methods for answering this question either generate a comparative measure based upon a static model, or take an indirect approach, first estimating absolute expression levels and then comparing the estimated levels to one another. We present a method for detecting changes in gene expression between two samples based on data from Affymetrix GeneChips. Using a library of over 200,000 known cases of differential expression, we create a learned comparative expression measure (LCEM) based on classification of probe-level data patterns as changed or unchanged. LCEM uses perfect match probe data only; mismatch probe values did not prove to be useful in this context. LCEM is particularly powerful in the case of small microarry studies, in which a regression-based method such as RMA cannot generalize, and in detecting small expression changes. At the levels of selectivity that are typical in microarray analysis, the LCEM shows a lower false discovery rate than either MAS5 or RMA trained from a single chip. When many chips are available to RMA, LCEM performs better on two out of the three data sets, and nearly as well on the third. Performance of the MAS5 log ratio statistic was notably bad on all datasets.",
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{
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"content": "Perhaps the most common question that a microarray study can ask is, \"Between two given biological conditions, which genes exhibit changed expression levels?\" Existing methods for answering this question either generate a comparative measure based upon a static model, or take an indirect approach, first estimating absolute expression levels and then comparing the estimated levels to one another. We present a method for detecting changes in gene expression between two samples based on data from Affymetrix GeneChips. Using a library of over 200,000 known cases of differential expression, we create a learned comparative expression measure (LCEM) based on classification of probe-level data patterns as changed or unchanged. LCEM uses perfect match probe data only; mismatch probe values did not prove to be useful in this context. LCEM is particularly powerful in the case of small microarry studies, in which a regression-based method such as RMA cannot generalize, and in detecting small expression changes. At the levels of selectivity that are typical in microarray analysis, the LCEM shows a lower false discovery rate than either MAS5 or RMA trained from a single chip. When many chips are available to RMA, LCEM performs better on two out of the three data sets, and nearly as well on the third. Performance of the MAS5 log ratio statistic was notably bad on all datasets.",
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"fullName": "Will Sheffler",
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"affiliation": "Brown University",
"fullName": "Eli Upfal",
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"affiliation": "Brown University",
"fullName": "John Sedivy",
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"affiliation": "University of Washington",
"fullName": "William Stafford Noble",
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"title": "A Three-Stage Method to Select Informative Genes from Gene Expression Data in Classifying Cancer Classes",
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"abstract": "The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: 1) pre-selecting genes using a filter method; 2) optimizing the gene subset using a multi-objective hybrid method; 3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.",
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{
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"content": "The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: 1) pre-selecting genes using a filter method; 2) optimizing the gene subset using a multi-objective hybrid method; 3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.",
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"fno": "3973a158",
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"fullName": "Mohd Saberi Mohamad",
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"title": "Analyzing genetic factors involved in recombinant protein expression enhancement",
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"abstract": "Understanding the genetic factors that promote recombinant protein accumulation in transgenic plants will provide insightful strategies for protein biofactory efficiency. Through combining biological and bioinformatics analysis, our work is to determine genetic and biological factors affecting increased protein accumulation of a bacterial cellulase enzyme in transgenic maize. Microarray experiments were performed on maize near-isogenic lines that exhibit high and low accumulation of the enzyme expressed from a transgene in seeds. Through microarray data analysis, two thousand three hundred thirteen genes were identified which exhibited at least a 1.5-fold change in expression level. One hundred sixty-one genes from the data set are shown to be statistically valid. Of these, eighty-two genes are up-regulated while seventy-nine genes are down-regulated. Preliminary functional analysis of the genes was conducted and several pathways of biological importance were tentatively identified. These genes code for four categories of proteins: known proteins, zein proteins, putative proteins and unknown proteins. Further functional clustering and annotation analysis for these genes will help construct and define networks of interaction as well as predict important metabolic pathways to understand the controlling mechanisms that lead to the hyper-accumulation phenomenon.",
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"content": "Understanding the genetic factors that promote recombinant protein accumulation in transgenic plants will provide insightful strategies for protein biofactory efficiency. Through combining biological and bioinformatics analysis, our work is to determine genetic and biological factors affecting increased protein accumulation of a bacterial cellulase enzyme in transgenic maize. Microarray experiments were performed on maize near-isogenic lines that exhibit high and low accumulation of the enzyme expressed from a transgene in seeds. Through microarray data analysis, two thousand three hundred thirteen genes were identified which exhibited at least a 1.5-fold change in expression level. One hundred sixty-one genes from the data set are shown to be statistically valid. Of these, eighty-two genes are up-regulated while seventy-nine genes are down-regulated. Preliminary functional analysis of the genes was conducted and several pathways of biological importance were tentatively identified. These genes code for four categories of proteins: known proteins, zein proteins, putative proteins and unknown proteins. Further functional clustering and annotation analysis for these genes will help construct and define networks of interaction as well as predict important metabolic pathways to understand the controlling mechanisms that lead to the hyper-accumulation phenomenon.",
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"affiliation": "Arkansas State University, Jonesboro, 72467 U.S.A.",
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"affiliation": "Arkansas State University, Jonesboro, 72467 U.S.A.",
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"title": "Concurrent analysis of copy number variations and expression profiles to identify genes associated with tumorigenesis and survival outcome in lung adenocarcinoma",
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"abstract": "Lung cancer has been one of the major causes of cancer-related death worldwide. To predict survival outcomes of lung cancer patients, many prognosis gene sets were identified by using gene expression microarrays. However, these gene sets were often inconsistent across independent cohorts. To identify genes with more consistency, we combined gene expression and copy number variations (CNVs). Affymetrix SNP 6.0 and u133plus2.0 microarrays were performed on 42 pairs of lung adenocarcinoma patients. The copy number varied regions (CNVR) existed in more than 30% samples were identified and 475 differentially expressed genes with concordant changes were selected for pathway analysis. Thirteen pathways were significantly enriched among the 475 CNV-associated genes, and survival analyses showed these pathways had generally consistent and significant prediction probabilities across three independent microarray studies. Therefore, integration between gene expression and copy number may help to lower false discovery rate and identify genes used to predict survival outcomes.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Lung cancer has been one of the major causes of cancer-related death worldwide. To predict survival outcomes of lung cancer patients, many prognosis gene sets were identified by using gene expression microarrays. However, these gene sets were often inconsistent across independent cohorts. To identify genes with more consistency, we combined gene expression and copy number variations (CNVs). Affymetrix SNP 6.0 and u133plus2.0 microarrays were performed on 42 pairs of lung adenocarcinoma patients. The copy number varied regions (CNVR) existed in more than 30% samples were identified and 475 differentially expressed genes with concordant changes were selected for pathway analysis. Thirteen pathways were significantly enriched among the 475 CNV-associated genes, and survival analyses showed these pathways had generally consistent and significant prediction probabilities across three independent microarray studies. Therefore, integration between gene expression and copy number may help to lower false discovery rate and identify genes used to predict survival outcomes.",
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"normalizedAbstract": "Lung cancer has been one of the major causes of cancer-related death worldwide. To predict survival outcomes of lung cancer patients, many prognosis gene sets were identified by using gene expression microarrays. However, these gene sets were often inconsistent across independent cohorts. To identify genes with more consistency, we combined gene expression and copy number variations (CNVs). Affymetrix SNP 6.0 and u133plus2.0 microarrays were performed on 42 pairs of lung adenocarcinoma patients. The copy number varied regions (CNVR) existed in more than 30% samples were identified and 475 differentially expressed genes with concordant changes were selected for pathway analysis. Thirteen pathways were significantly enriched among the 475 CNV-associated genes, and survival analyses showed these pathways had generally consistent and significant prediction probabilities across three independent microarray studies. Therefore, integration between gene expression and copy number may help to lower false discovery rate and identify genes used to predict survival outcomes.",
"fno": "05706578",
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"Lung",
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"authors": [
{
"affiliation": "Graduate Institute of Biomedical, Electronics and Bioinformatics, National Taiwan University",
"fullName": "Tzu-Pin Lu",
"givenName": "Tzu-Pin",
"surname": "Lu",
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{
"affiliation": "Graduate Institute of Physiology, National Taiwan University",
"fullName": "Liang-Chuan Lai",
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{
"affiliation": "Department of Public Health, Institute of Epidemiology, National Taiwan University",
"fullName": "K. Hsiao Chuhsing",
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{
"affiliation": "Department of Statistics and Informatics Science, Providence University",
"fullName": "Pei-Chun Chen",
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{
"affiliation": "Institute of Biotechnology, National Taiwan University",
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"affiliation": "Graduate Institute of Biomedical, Electronics and Bioinformatics, National Taiwan University",
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"abstract": "Malaria remains one of the most important public health concerns worldwide. It causes nearly half a million deaths every year, and about 40% of the world's population lives in the endemic regions of malaria. A major hurdle in antimalarial development is our limited understanding of the dynamic cellular networks in the malaria parasite. In this study, by coupling RNA-Seq analysis and network mining using a PageRank-based algorithm, we investigated the temporal-specific expression of parasite genes during the 48-hour red blood cycle, and identified genes that may play influential roles in parasite development and invasion. The just-in-time mechanism for gene expression may contribute to a dynamic yet effective adaptive strategy of the malaria parasite.",
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"content": "Malaria remains one of the most important public health concerns worldwide. It causes nearly half a million deaths every year, and about 40% of the world's population lives in the endemic regions of malaria. A major hurdle in antimalarial development is our limited understanding of the dynamic cellular networks in the malaria parasite. In this study, by coupling RNA-Seq analysis and network mining using a PageRank-based algorithm, we investigated the temporal-specific expression of parasite genes during the 48-hour red blood cycle, and identified genes that may play influential roles in parasite development and invasion. The just-in-time mechanism for gene expression may contribute to a dynamic yet effective adaptive strategy of the malaria parasite.",
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"abstract": "The ambiguous and highly subjective meanings assigned to the word \"interaction\" makes it difficult for researchers and practitioners, who work in the area of virtual environments and applications, to communicate and join their research efforts. The aim of this work is to provide more understanding on the interaction occurring inside networked virtual environments (Net-VE) by analysing the communicative and social aspects of computer-mediated interaction in multi-player games. The work is conducted using conceptual analysis by applying communicative action theory as a scientific framework. The main contribution of this work is the proposed interaction taxonomy which is analysed in the light of social theory. The successful application of social theory framework as a tool to analyse interaction indicates the importance of joining the research effort of various disciplines in order to achieve better results in the area of Net-VE interactions.",
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"content": "The ambiguous and highly subjective meanings assigned to the word \"interaction\" makes it difficult for researchers and practitioners, who work in the area of virtual environments and applications, to communicate and join their research efforts. The aim of this work is to provide more understanding on the interaction occurring inside networked virtual environments (Net-VE) by analysing the communicative and social aspects of computer-mediated interaction in multi-player games. The work is conducted using conceptual analysis by applying communicative action theory as a scientific framework. The main contribution of this work is the proposed interaction taxonomy which is analysed in the light of social theory. The successful application of social theory framework as a tool to analyse interaction indicates the importance of joining the research effort of various disciplines in order to achieve better results in the area of Net-VE interactions.",
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"abstract": "During code review, developers request clarifications, suggest improvements, or ask for explanations about the rationale behind the implementation choices. We envision the emergence of tools to support developers during code review based on the automatic analysis of the argumentation structure and communicative intentions conveyed by developers' comments. As a preliminary step towards this goal, we conducted an exploratory case study by manually classifying 499 questions extracted from 399 Android code reviews to understand the real communicative intentions they convey. We observed that the majority of questions actually serve information seeking goals. Still, they represent less than half of the annotated sample, with other questions being used to serve a wider variety of developers' communication goals, including suggestions, request for action, and criticism. Based on our findings we formulate hypotheses on communicative intentions in code reviews that should be confirmed or rejected by follow-up studies.",
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"content": "Scrollytelling - the long-form of articles used in journalism - uses long, narrative types of text (e.g., report, feature) to tell complex stories. Scrollytelling articles published online often use multimedia content, especially information graphics which is a powerful tool to communicate complex information. This study investigates which types of infographics are used for visualizing complex data in long-form journalism published online. The analysis focusses on the utilization of infographics in scrollytelling and how infographics are integrated in long-form articles.",
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"abstract": "Nowadays, data journalism was becoming more and more complex in the fields of visual presentation, and which design strategies should be adopted to improve users’ reading efficiency was of great importance for data journalism producer. The purpose of this research was to extract the features of information clues that have a direct impact on the users’ visual perception and information foraging behavior, and use these features to describe the impact of the information clues. First, respondents were interviewed on the basis of understanding of the research material, and then use thematic analysis method to encode, so as to extract the features of the information clues in the data journalism. The results showed that the information clues in the data journalism can be divided into “structural features” and “semantic features” and different types of information clues can affect users’ information foraging behavior at different levels as a whole. Our findings provide a new perspective for the pattern classification and recognition of the information clues in the data journalism and refine the explanation of users’ news reading behavior mechanism.",
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"content": "Nowadays, data journalism was becoming more and more complex in the fields of visual presentation, and which design strategies should be adopted to improve users’ reading efficiency was of great importance for data journalism producer. The purpose of this research was to extract the features of information clues that have a direct impact on the users’ visual perception and information foraging behavior, and use these features to describe the impact of the information clues. First, respondents were interviewed on the basis of understanding of the research material, and then use thematic analysis method to encode, so as to extract the features of the information clues in the data journalism. The results showed that the information clues in the data journalism can be divided into “structural features” and “semantic features” and different types of information clues can affect users’ information foraging behavior at different levels as a whole. Our findings provide a new perspective for the pattern classification and recognition of the information clues in the data journalism and refine the explanation of users’ news reading behavior mechanism.",
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"affiliation": "Wuhan University,School of Journalism and Communicaiton,Wuhan,China,430072",
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"doi": "10.1109/ASONAM55673.2022.10068697",
"title": "MOGPlay: A Decentralized Crowd Journalism Application for Democratic News Production",
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"abstract": "Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project [1] proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps). Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audio-visual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audio-visual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate three pilot crowd journalism live scenarios that validate the prototype.",
"abstracts": [
{
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"content": "Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project [1] proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps). Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audio-visual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audio-visual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate three pilot crowd journalism live scenarios that validate the prototype.",
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"affiliation": "Utrecht University,Netherlands",
"fullName": "Nishant Saurabh",
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"affiliation": "University of Stavanger,Norway",
"fullName": "Antorweep Chakravorty",
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"affiliation": "University of Amsterdam,Netherlands",
"fullName": "Zhiming Zhao",
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{
"affiliation": "University for Information Science and Technology,North Macedonia",
"fullName": "Atanas Hristov",
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"affiliation": "University of Klagenfurt,Austria",
"fullName": "Radu Prodan",
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"abstract": "Over the recent years, techniques from Document Images Analysis have brought new insights into the understanding of comics. This paper outlines the challenges and opportunities of conceiving accessible comics for visually impaired people. After an overview of the existing initiatives, we briefly highlight in what extent the Document community could help to achieve such a goal in a multidisciplinary way, by collaborating with fields such as Human-Computer Interactions, Cognitive Science and even Education Research.",
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"abstract": "During the era of technological advancement and usage of the e-learning environment, it is vital to examine the integration between the human-computer interaction (HCI) and the theory of cognitive load. A systematic review of the literature was conducted by searching studies associated with cognitive load theory and human-computer interaction concepts. Around 47 published papers were reviewed, and the analyses indicated that the cognitive load theory (CLT) is explicit in the context of HCI since the idea of relevant intellectual burden can be clarified and it minimizes the disadvantage of externalizing data on the interface and also supports to diminish memory load. Henceforth, it can be implied that cognitive load theory and human-computer integration approaches and ideas, enable users to learn the device based on the informative program and/or frameworks. Based on a review of the literature, a conceptual framework is proposed, which can be empirically tested in future studies. The researchers suggested that with the increase in an e-learning environment, there is a need for the study to understand and examine the integration between the HCI and CLT aspects, this will support the system developers to provide appropriate guidelines based on the user and task.",
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"abstract": "Mental disorder is a change of mental or behavioral pattern that causes sufferings and impairs the ability to function in ordinary life. In psychopathology, the assessment methods of mental disorder contain various types of uncertainties associated with signs and symptoms. This study identifies a method that addresses the issue of uncertainty in assessing mental disorder. The fuzzy logic knowledge representation schema can address uncertainty associated with linguistic terms including ambiguity, imprecision, and vagueness. However, fuzzy logic is incapable of addressing uncertainty due to ignorance, incompleteness, and randomness. So, a belief rule-based expert system (BRBES) has been designed and developed with the capability of handling the uncertainties mentioned. Evidential reasoning works as the inference engine and the belief rule base as the knowledge representation schema in this BRBES. The study shows that the results generated by BRBES are more reliable than that of Fuzzy Rule-based expert system and from a human expert.",
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"content": "In the calibration and detection of electric field strength, the importance of evaluating the uncertainty of the electric field probe to the reliability of the characterization results is self-evident. At present, the evaluation of the uncertainty of the electric field probe mainly adopts the method B of the Guide to the Expression of Uncertainty in Measurement (GUM), and no calibration or experimental data is used for this method, which results in waste of data and cannot satisfy different requirements. Uncertainty evaluation under conditions, based on Bayesian theory, proposes that the measurement results of the GTEM (Gigahertz Transverse Electromagnetic) cell have prior information and the evaluation method without uncertainty prior to the information to meet the different Needs under conditions. Comparing with GUM method, it is verified that Bayesian principle can be used to better evaluate the uncertainty of electric field probe under different conditions in GTEM cell, and has higher credibility.",
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"normalizedAbstract": "In the calibration and detection of electric field strength, the importance of evaluating the uncertainty of the electric field probe to the reliability of the characterization results is self-evident. At present, the evaluation of the uncertainty of the electric field probe mainly adopts the method B of the Guide to the Expression of Uncertainty in Measurement (GUM), and no calibration or experimental data is used for this method, which results in waste of data and cannot satisfy different requirements. Uncertainty evaluation under conditions, based on Bayesian theory, proposes that the measurement results of the GTEM (Gigahertz Transverse Electromagnetic) cell have prior information and the evaluation method without uncertainty prior to the information to meet the different Needs under conditions. Comparing with GUM method, it is verified that Bayesian principle can be used to better evaluate the uncertainty of electric field probe under different conditions in GTEM cell, and has higher credibility.",
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"abstract": "Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. We observe that with the addition of more cues and by minimizing uncertainty among the cues, the Bayesian framework becomes more confident. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here we provide project link for Deep Bayesian VQG https: //delta-lab-iitk.github.io/BVQG/.",
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"content": "Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. We observe that with the addition of more cues and by minimizing uncertainty among the cues, the Bayesian framework becomes more confident. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here we provide project link for Deep Bayesian VQG https: //delta-lab-iitk.github.io/BVQG/.",
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"normalizedAbstract": "Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. We observe that with the addition of more cues and by minimizing uncertainty among the cues, the Bayesian framework becomes more confident. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here we provide project link for Deep Bayesian VQG https: //delta-lab-iitk.github.io/BVQG/.",
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"affiliation": "Indian Institute of Technology,Kanpur",
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"abstract": "In several research fields, qualitative interviewing(i.e., face-to-face individual interviews and focus groups) is an essential approach to gather high-quality, and deep data about a wide range of subjective experiences. Although being a well-established research method, qualitative interviewing is fraught with various shortcomings such as difficulties in initiating the interview process, in eliciting tacit knowledge and in avoiding information redundancies. In this paper, we propose the use of visual tools (e.g., visualization software) as a support to facilitate the interview process and to gather sound qualitative data. Visual representations provide structure, context and explicitness, and thus foster knowledge elicitation and sharing in interviews. Our contribution is two-fold: First, we provide a methodological extension of qualitative interviewing. Second, we suggest a practical approach to use visuals in interviews, presenting concrete examples from the research field. We conclude our paper by discussing the limitations of our approach, and by suggesting directions for future research on visualization-based interviewing.",
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"title": "Scene-Based Qualitative Analysis and Modeling Tool for Situated Cognition",
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"abstract": "This paper describes a qualitative cognitive analysis and modeling tool (QCAM) for biological data collected using sensors, such as a simple brain-wave sensor, while executing an actual task. However, this type of sensor is generally less accurate than devices designed for medical use. It may be influenced by noise or personal differences amongst subjects. A qualitative approach is considered very effective for such data, because we can understand essential features by focusing on qualitative changes, such as increasing, decreasing, and steady changes in the data, and not just on the quantity of data. Therefore, in addition to statistical analysis, QCAM provides qualitative analysis and modeling, and allows us to verify the model by using qualitative reasoning. This paper explains QCAM and describes experimental results obtained by using real driving data, which combines movie data from a camera, acceleration data from a smart phone, and brain-wave data from a simple brain-wave sensor, obtained while a person is driving a vehicle.",
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{
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"content": "This paper describes a qualitative cognitive analysis and modeling tool (QCAM) for biological data collected using sensors, such as a simple brain-wave sensor, while executing an actual task. However, this type of sensor is generally less accurate than devices designed for medical use. It may be influenced by noise or personal differences amongst subjects. A qualitative approach is considered very effective for such data, because we can understand essential features by focusing on qualitative changes, such as increasing, decreasing, and steady changes in the data, and not just on the quantity of data. Therefore, in addition to statistical analysis, QCAM provides qualitative analysis and modeling, and allows us to verify the model by using qualitative reasoning. This paper explains QCAM and describes experimental results obtained by using real driving data, which combines movie data from a camera, acceleration data from a smart phone, and brain-wave data from a simple brain-wave sensor, obtained while a person is driving a vehicle.",
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"abstract": "Noise estimation is crucial in many image processing tasks such as denoising. Most of the existing noise estimation methods are specially developed for grayscale images. For color images, these methods simply handle each color channel independently, without considering the correlation across channels. In this work, we propose a multivariate Gaussian approach to model the noise in color images, in which we explicitly consider the inter-dependence among color channels. We design a practical method for estimating the noise covariance matrix within the proposed model. Specifically, a patch selection scheme is first introduced to select weakly textured patches through thresholding the texture strength indicators. Noticing that the patch selection actually depends on the unknown noise covariance, we present an iterative noise covariance estimation algorithm, where the patch selection and the covariance estimation are conducted alternately. Experimental results show that our method can effectively estimate the noise covariance. The practical usage is demonstrated with color image denoising.",
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"content": "Noise estimation is crucial in many image processing tasks such as denoising. Most of the existing noise estimation methods are specially developed for grayscale images. For color images, these methods simply handle each color channel independently, without considering the correlation across channels. In this work, we propose a multivariate Gaussian approach to model the noise in color images, in which we explicitly consider the inter-dependence among color channels. We design a practical method for estimating the noise covariance matrix within the proposed model. Specifically, a patch selection scheme is first introduced to select weakly textured patches through thresholding the texture strength indicators. Noticing that the patch selection actually depends on the unknown noise covariance, we present an iterative noise covariance estimation algorithm, where the patch selection and the covariance estimation are conducted alternately. Experimental results show that our method can effectively estimate the noise covariance. The practical usage is demonstrated with color image denoising.",
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"affiliation": "Swinburne University of Technology,Australia",
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"abstract": "Recent works have demonstrated that neural networks are vulnerable to adversarial examples. Although existing methods have achieved satisfactory attack success rates, most adversarial examples can be detected by statistical analysis and further removed. In previous methods, adversarial perturbations are added using adversarial loss and distance metrics, in which the positions of modified pixels are not considered. In this paper, we elaborate a microscopical regularization that introduces adversarial perturbations onto rich texture regions. The microscopical regularization is used to evaluate pixel-level differences between a normal image and its adversarial version. We further propose a novel optimization strategy of modification probability matrices to minimize the loss function that satisfies the restriction of L<inf>∞</inf>. Through extensive experiments, we show that our method can resist statistical analysis by a large margin and achieve better visual quality than others. The proposed microscopical regularization can also be combined with existing approaches to enhance the undetectability and robustness.",
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"content": "Recent works have demonstrated that neural networks are vulnerable to adversarial examples. Although existing methods have achieved satisfactory attack success rates, most adversarial examples can be detected by statistical analysis and further removed. In previous methods, adversarial perturbations are added using adversarial loss and distance metrics, in which the positions of modified pixels are not considered. In this paper, we elaborate a microscopical regularization that introduces adversarial perturbations onto rich texture regions. The microscopical regularization is used to evaluate pixel-level differences between a normal image and its adversarial version. We further propose a novel optimization strategy of modification probability matrices to minimize the loss function that satisfies the restriction of L<inf>∞</inf>. Through extensive experiments, we show that our method can resist statistical analysis by a large margin and achieve better visual quality than others. The proposed microscopical regularization can also be combined with existing approaches to enhance the undetectability and robustness.",
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"abstract": "Graphs effectively communicate data because they capitalize on the visual system’s ability to rapidly extract patterns. Yet, this pattern extraction does not occur in a single glance. Instead, research on visual attention suggests that the visual system iteratively applies a sequence of filtering operations on an image, extracting patterns from subsets of visual information over time, while selectively inhibiting other information at each of these moments. To demonstrate that this powerful series of filtering operations also occurs during the perception of visualized data, we designed a task where participants made judgments from one class of marks on a scatterplot, presumably incentivizing them to relatively ignore other classes of marks. Participants consistently missed a conspicuous dinosaur in the ignored collection of marks (93% for a 1s presentation, and 61% for 2.5s), but not in a control condition where the incentive to ignore that collection was removed (25% for a 1s presentation, and 11% for 2.5s), revealing that data visualizations are not “seen” in a single glance, and instead require an active process of exploration.",
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