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"abstract": "In the last years, there was a growing interest in the use of Big Data models to support advanced data analysis functionalities. Many companies and organizations lack IT expertise and adequate budget to have benefits from them. In order to fill this gap, a model-based approach for Big Data Analytics-as-a-service (MBDAaaS) can be used. The proposed model, composed by declarative, procedural and deployment (sub) models, can be used to select a deployable set of services based on a set of user preferences shaping a Big Data Campaign (BDC). The deployment of a BDC requires that the selection of services has to be carried out on the basis of coherent and non conflictual user preferences. In this paper we propose an OWL ontology in order to solve this issue.",
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"title": "2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)",
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"title": "Learning and Applying Ontology for Machine Learning in Cyber Attack Detection",
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"abstract": "In cyber security, the ontology is invented to provide vocabulary in a generalized machine-processable language for downstream works such as attack detection. Meanwhile, machine learning (ML) as a promising intelligent field, is widely investigated to achieve the automation of these tasks. Existing ML-based methods suffer from confines of specific data and preprocessing, while applying ontology with machine learning methods is still rarely discussed. In this paper, 1) we propose a novel approach for automatic attack detection by generating ontology with deep learning through neural network embeddings; 2) we validate the learned ontology by comparing it with a manual ontology built by security expert, the results demonstrates that the latent representation learned with neural networks could serve as a novel ontology format so as to provide a generalized machine-processable language for downstream works, which is the intention of the ontology; 3) finally, we develop a platform to achieve the entire intelligent ontology learning and utilization for cyber attack detection. Our experimental results shows that our proposed ontology is promising to collaborate with machine learning based methods in order to improve the intelligent intrusion detection for cyber security.",
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"content": "In cyber security, the ontology is invented to provide vocabulary in a generalized machine-processable language for downstream works such as attack detection. Meanwhile, machine learning (ML) as a promising intelligent field, is widely investigated to achieve the automation of these tasks. Existing ML-based methods suffer from confines of specific data and preprocessing, while applying ontology with machine learning methods is still rarely discussed. In this paper, 1) we propose a novel approach for automatic attack detection by generating ontology with deep learning through neural network embeddings; 2) we validate the learned ontology by comparing it with a manual ontology built by security expert, the results demonstrates that the latent representation learned with neural networks could serve as a novel ontology format so as to provide a generalized machine-processable language for downstream works, which is the intention of the ontology; 3) finally, we develop a platform to achieve the entire intelligent ontology learning and utilization for cyber attack detection. Our experimental results shows that our proposed ontology is promising to collaborate with machine learning based methods in order to improve the intelligent intrusion detection for cyber security.",
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"normalizedAbstract": "In cyber security, the ontology is invented to provide vocabulary in a generalized machine-processable language for downstream works such as attack detection. Meanwhile, machine learning (ML) as a promising intelligent field, is widely investigated to achieve the automation of these tasks. Existing ML-based methods suffer from confines of specific data and preprocessing, while applying ontology with machine learning methods is still rarely discussed. In this paper, 1) we propose a novel approach for automatic attack detection by generating ontology with deep learning through neural network embeddings; 2) we validate the learned ontology by comparing it with a manual ontology built by security expert, the results demonstrates that the latent representation learned with neural networks could serve as a novel ontology format so as to provide a generalized machine-processable language for downstream works, which is the intention of the ontology; 3) finally, we develop a platform to achieve the entire intelligent ontology learning and utilization for cyber attack detection. Our experimental results shows that our proposed ontology is promising to collaborate with machine learning based methods in order to improve the intelligent intrusion detection for cyber security.",
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"doi": "10.1109/CISCE50729.2020.00065",
"title": "Ontology Construction for Security Analysis of Network Nodes",
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"abstract": "Security related data in cyberspace is multi-source and heterogeneous, so it cannot be effectively used in network security analysis. To solve this problem, a network security ontology model for network node security analysis is proposed. The model reflects the security status of network nodes through three key sub domains: network assets, security information, defense strategy, and modeling concepts and relationships in a hierarchical and structured form. Ontology related language is used for query, and user-defined rules are used to enhance the reasoning ability of ontology, so as to assist network security analysis. Experiments and quality evaluation results show that the ontology model can integrate multi-source data, query and reason the network security status, and has higher quality evaluation than the same type of ontology.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Security related data in cyberspace is multi-source and heterogeneous, so it cannot be effectively used in network security analysis. To solve this problem, a network security ontology model for network node security analysis is proposed. The model reflects the security status of network nodes through three key sub domains: network assets, security information, defense strategy, and modeling concepts and relationships in a hierarchical and structured form. Ontology related language is used for query, and user-defined rules are used to enhance the reasoning ability of ontology, so as to assist network security analysis. Experiments and quality evaluation results show that the ontology model can integrate multi-source data, query and reason the network security status, and has higher quality evaluation than the same type of ontology.",
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"affiliation": "National University of Defense Technology,College of Electromagnetic Countermeasure,Hefei,China",
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"affiliation": "National University of Defense Technology,College of Electromagnetic Countermeasure,Hefei,China",
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"abstract": "Accurate detection and classification of primary cilia in microscopy images is an essential and fundamental task for many biological studies including diagnosis of primary ciliary dyskinesia. Manual detection and classification of individual primary cilia by visual inspection is time consuming, and prone to induce subjective bias. However, automation of this process is challenging as well, due to clutter, bleed-through, imaging noise, and the similar characteristics of the non-cilia candidates present within the image. We propose a convolutional neural random forest classifier that combines a convolutional neural network with random decision forests to classify the primary cilia in fluorescence microscopy images. We compare the performance of the proposed classifier with that of an unsupervised k-means classifier and a supervised multi-layer perceptron classifier on real data consisting of 8 representative cilia images, containing more than 2300 primary cilia using precision/recall rates, ROC curves, AUC, and Fβ-score for classification accuracy. Results show that our proposed classifier achieves better classification accuracy.",
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"abstract": "Choice is a pervasive feature of social life that profoundly affects us. Ranking results can be used as a reference to help people make a correct choice. But there are two problems. One problem is that fixed ranking results instead of the ranking methods are provided to people by service providers as a reference when making choice at most time. For example, TIMES World University Rankings can be used as a reference when choosing a college. However, in the numerous factors that affect objects ranking, people have their own understanding on the effect of each factor on objects ranking. Using mobile phone-selection as a practical case, some people think performance of a mobile phone is more important, while others hold the view that appearance of a mobile phone is more attractive. What's more, there are many ranking methods proposed, such as The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and expert marking. Using only one kind of ranking methods for object ranking may lead to over objective or subjective ranking results. Although various ranking algorithm are studied, very little is known about the detailed development and deployment of the ranking services. This paper proposes a comprehensive solution of Ranking as a Service (RaaS), with the manifold contributions: Firstly, we use combination weighting method in RaaS and it can overcome the defects of subjective and objective weighting methods. Secondly, we develop ranking service APIs that bring convenience to people when making choices. Thirdly, ranking service provides ranking results for people according to their own understanding on the effect of each factor on objects ranking. Fourthly, this paper is arguably the first one that proposes using Ranking as a Service. Finally, we evaluate and analyze the proposed strategies and technologies in accordance to the experimental results.",
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"Geographical Maps",
"Human Development Index",
"Europe",
"Geographic Information Systems",
"Information Analysis",
"Data Visualization",
"Information Systems",
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"abstract": "Feature extraction is of great importance in condition monitoring and fault diagnosis of rolling machinery. Nonlinear Dimensionality Reduction (NDR) theories brought a new idea for recognizing and predicting the underlying nonlinear behavior. In this paper, we propose a NDR based feature extraction method for fault classification of rolling element bearing. Original feature spaces are constructed by time- and frequency-domain feature selection method, NDR based feature exaction scheme is proposed to acquire the lowdimensional embeddings from feature space, which provide a more truthful low-dimensional representation compared to the linear DR methods. In order to systematically and quantitatively investigate the performance of NDR method, we compare the three nonlinear DR methods: Isometric Mapping(Isomap), Locally Linear Embedding (LLE), and Local Tangent Space Alignment(LTSA) with the intent of determining a reduced subspace representation in which the fault classes of rolling element bearing are more easily discriminable. Evaluation of the classification performance is done by Support Vector Machine (SVM), a supervised classifiers. Additionally, with optimal neighborhood size, binary code combinations based on NDR embedded results are given for fault recognition. Experiments on 6 fault data sets are used for fault and severity classification. Quantitative evaluation results suggest that NDR methods are superior in identifying potential novel classes within the data.",
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"abstract": "Random projection (RP) is a simple and efficient tool for dimensionality reduction and has been applied in many machine learning algorithms. Theoretical guarantees ensure that the method approximately preserves the pairwise Euclidean distances with high probability. However, worst-case theoretical bounds on the required target dimension of the projection are not sufficiently tight for direct practical use, so for a large data set most often much lower values are being used in practice. While theory also suggests that this may be due to the low complexity structure of the underlying data support, this structure is typically unknown in practice. Here we investigate the use of a previously proposed battery of data complexity indicators (DCIs) to gain insight into how random projection changes the data complexity for classification problems. Our experimental results show that complexity increases with compression, but in most cases we can project data onto fairly low dimensions while maintaining nearly the same level of data complexity as that of the original data. This may offer some guidance for the choice of the target dimension in practice, although it should be interpreted with caution.",
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"content": "Random projection (RP) is a simple and efficient tool for dimensionality reduction and has been applied in many machine learning algorithms. Theoretical guarantees ensure that the method approximately preserves the pairwise Euclidean distances with high probability. However, worst-case theoretical bounds on the required target dimension of the projection are not sufficiently tight for direct practical use, so for a large data set most often much lower values are being used in practice. While theory also suggests that this may be due to the low complexity structure of the underlying data support, this structure is typically unknown in practice. Here we investigate the use of a previously proposed battery of data complexity indicators (DCIs) to gain insight into how random projection changes the data complexity for classification problems. Our experimental results show that complexity increases with compression, but in most cases we can project data onto fairly low dimensions while maintaining nearly the same level of data complexity as that of the original data. This may offer some guidance for the choice of the target dimension in practice, although it should be interpreted with caution.",
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"normalizedAbstract": "Random projection (RP) is a simple and efficient tool for dimensionality reduction and has been applied in many machine learning algorithms. Theoretical guarantees ensure that the method approximately preserves the pairwise Euclidean distances with high probability. However, worst-case theoretical bounds on the required target dimension of the projection are not sufficiently tight for direct practical use, so for a large data set most often much lower values are being used in practice. While theory also suggests that this may be due to the low complexity structure of the underlying data support, this structure is typically unknown in practice. Here we investigate the use of a previously proposed battery of data complexity indicators (DCIs) to gain insight into how random projection changes the data complexity for classification problems. Our experimental results show that complexity increases with compression, but in most cases we can project data onto fairly low dimensions while maintaining nearly the same level of data complexity as that of the original data. This may offer some guidance for the choice of the target dimension in practice, although it should be interpreted with caution.",
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"abstract": "Diabetes is one of the important issues in the medical field in which blood glucose level control and regulation needs to permanent care. Determination of appropriate rate of insulin injection in order to blood glucose level stabilization to a normal level is vital for diabetics. In this paper, optimal plasma blood glucose tracking using subcutaneous insulin administration is investigated. Here one of the recent mathematical models is used for glucose-insulin regulation system which consists of delayed nonlinear differential equations and is one of the most comprehensive models in this field, known as Palumbo. In this paper for the first time, employing combination of the fuzzy logic controller optimized by genetic algorithm with Palumbo control method is used to regulate the blood glucose level in diabetic patients. One of the most important features of the proposed controller is the ability to set the parameters for patients using genetic algorithm. Finally, the simulation results of the fuzzy control method and fuzzy-genetic control method are compared with the control method based on Palumbo feedback linearization, which indicates the proper functioning of the proposed controller for tracking of desired blood glucose level at the lowest possible error.",
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"abstract": "Type II Diabetes (T2D) is one of the most common lifestyle diseases which is characterized by insulin resistance. Lack of insulin's proper working causes uncontrollable blood glucose rise in the body which leads to life taking situations. Therefore, early detection of T2D is imperative to save many lives. Towards this goal, this work presents a machine learning-based prediction model to detect T2D. The Q-learning algorithm belonging to the Reinforcement Learning (RL) paradigm has been applied to the PIMA Indian Women diabetes dataset in developing the detection model. The model identifies patients with T2D using three factors (such as Body Mass Index, glucose level and age of subject) by generating an off-policy based RL and making the learning agent to find an optimal policy for the factors. The information of a subject can be in any of 330 possible states. The proposed RL model's accuracy, Precision, Recall, F-measure and AUC values have been compared with the state-of-the-art techniques such as K Nearest Neighbors and Decision Tree. The performance of the proposed RL-based T2D prediction outperforms the K Nearest Neighbors and Decision Tree.",
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"abstract": "1,1-diamino-2,2-dinitroethylene (Fox-7) has proven to be a promising energetic material due to its low shock sensitivity and high thermal stability, and has been the subject of several Department of Defense (DoD) theoretical investigations employing Hartree-Fock and density functional theory. However, it is well-known that many energetic crystals have a significant binding contribution resulting from dispersion, a phenomena that is not accurately described by computational methods that are not explicitly correlated. This paper details the use of a state-of-the art quantum chemistry method, coupled-cluster theory, to compute a six-dimensional, dimer potential energy surface of the Fox-7 energetic molecule, which will be used to develop a pair potential for use in molecular dynamics simulations. In addition, a dimer potential energy function determined using symmetry-adapted perturbation theory is described and molecular dynamics simulations using the fitted potential are presented.",
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"normalizedAbstract": "1,1-diamino-2,2-dinitroethylene (Fox-7) has proven to be a promising energetic material due to its low shock sensitivity and high thermal stability, and has been the subject of several Department of Defense (DoD) theoretical investigations employing Hartree-Fock and density functional theory. However, it is well-known that many energetic crystals have a significant binding contribution resulting from dispersion, a phenomena that is not accurately described by computational methods that are not explicitly correlated. This paper details the use of a state-of-the art quantum chemistry method, coupled-cluster theory, to compute a six-dimensional, dimer potential energy surface of the Fox-7 energetic molecule, which will be used to develop a pair potential for use in molecular dynamics simulations. In addition, a dimer potential energy function determined using symmetry-adapted perturbation theory is described and molecular dynamics simulations using the fitted potential are presented.",
"fno": "06018002",
"keywords": [
"Coupled Cluster Calculations",
"Explosives",
"Molecular Dynamics Method",
"Organic Compounds",
"Perturbation Theory",
"Potential Energy Functions",
"Potential Energy Surfaces",
"Energetic Materials",
"Coupled Cluster Theory",
"1 1 Diamino 2 2 Dinitroethylene",
"Shock Sensitivity",
"Thermal Stability",
"Hartree Fock Theory",
"Density Functional Theory",
"Quantum Chemistry Method",
"Six Dimensional Dimer Potential Energy Surface",
"Energetic Molecule",
"Molecular Dynamics Simulations",
"Dimer Potential Energy Function",
"Symmetry Adapted Perturbation Theory",
"Computational Modeling",
"Chemistry",
"Materials",
"US Department Of Defense",
"Potential Energy",
"Force",
"Weapons"
],
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"affiliation": null,
"fullName": "DeCarlos E. Taylor",
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"title": "A Common Computational Science Environment for High Performance Computing Centers",
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"abstract": "The Computational Sciences Environment (CSE) was developed to provide a standard development platform for data analysis, visualization, and software testing and evaluation. The Classified Data Analysis and Assessment Center (CDAAC), in conjunction with the Computational Sciences and Engineering Branch (CSEB) of the Army Research Laboratory, assembled a set of open source data analysis tools and applications, software management and testing tools, and libraries necessary to run the tools; and made them available as a package called CSE. CSE also provides experimental software builds for users who might need newer features than what is currently available in the release package. The CSE team provides support for developers, the end-user, and distributed development teams. Tests are regularly run on the software, both current and release, and the results are submitted to quality dashboards for review using CTest. CTest is part of an open-source software building tool called CMake. Developers can use CSE as a template and can customize it to meet their specific project goals. CSE provides developers with a common tool set to assist in developing portable high performance computing (HPC) Applications. Development of CSE has been managed through an open-source project management application that provides Software Configuration Management (SCM) integration of the CSE repository, informational wikis, bug tracking, and feature requests. To support distributed development teams, CSE provides project management tools, software repositories, SCM, and online software quality dashboards.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The Computational Sciences Environment (CSE) was developed to provide a standard development platform for data analysis, visualization, and software testing and evaluation. The Classified Data Analysis and Assessment Center (CDAAC), in conjunction with the Computational Sciences and Engineering Branch (CSEB) of the Army Research Laboratory, assembled a set of open source data analysis tools and applications, software management and testing tools, and libraries necessary to run the tools; and made them available as a package called CSE. CSE also provides experimental software builds for users who might need newer features than what is currently available in the release package. The CSE team provides support for developers, the end-user, and distributed development teams. Tests are regularly run on the software, both current and release, and the results are submitted to quality dashboards for review using CTest. CTest is part of an open-source software building tool called CMake. Developers can use CSE as a template and can customize it to meet their specific project goals. CSE provides developers with a common tool set to assist in developing portable high performance computing (HPC) Applications. Development of CSE has been managed through an open-source project management application that provides Software Configuration Management (SCM) integration of the CSE repository, informational wikis, bug tracking, and feature requests. To support distributed development teams, CSE provides project management tools, software repositories, SCM, and online software quality dashboards.",
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"normalizedAbstract": "The Computational Sciences Environment (CSE) was developed to provide a standard development platform for data analysis, visualization, and software testing and evaluation. The Classified Data Analysis and Assessment Center (CDAAC), in conjunction with the Computational Sciences and Engineering Branch (CSEB) of the Army Research Laboratory, assembled a set of open source data analysis tools and applications, software management and testing tools, and libraries necessary to run the tools; and made them available as a package called CSE. CSE also provides experimental software builds for users who might need newer features than what is currently available in the release package. The CSE team provides support for developers, the end-user, and distributed development teams. Tests are regularly run on the software, both current and release, and the results are submitted to quality dashboards for review using CTest. CTest is part of an open-source software building tool called CMake. Developers can use CSE as a template and can customize it to meet their specific project goals. CSE provides developers with a common tool set to assist in developing portable high performance computing (HPC) Applications. Development of CSE has been managed through an open-source project management application that provides Software Configuration Management (SCM) integration of the CSE repository, informational wikis, bug tracking, and feature requests. To support distributed development teams, CSE provides project management tools, software repositories, SCM, and online software quality dashboards.",
"fno": "06018025",
"keywords": [
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"Data Analysis",
"Data Visualisation",
"Program Testing",
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"Public Domain Software",
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"Software Quality",
"Software Tools",
"Computational Science Environment",
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"Software Testing",
"Classified Data Analysis And Assessment Center",
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"Libraries",
"Software Package",
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"C Test",
"Open Source Software",
"High Performance Computing Centre",
"Software Configuration Management",
"Informational Wikis",
"Project Management Tools",
"Software Repositories",
"Software Quality",
"SCM",
"Software",
"Libraries",
"Data Visualization",
"Testing",
"Data Analysis",
"US Department Of Defense",
"Buildings"
],
"authors": [
{
"affiliation": "US Army Res. Lab. (ARL), Aberdeen Proving Ground, MD, USA",
"fullName": "Jerry Clarke",
"givenName": "Jerry",
"surname": "Clarke",
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{
"affiliation": "US Army Res. Lab. (ARL), Aberdeen Proving Ground, MD, USA",
"fullName": "John Vines",
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"surname": "Vines",
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{
"affiliation": "US Army Res. Lab. (ARL), Aberdeen Proving Ground, MD, USA",
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{
"affiliation": "US Army Res. Lab. (ARL), Aberdeen Proving Ground, MD, USA",
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{
"affiliation": "US Army Res. Lab. (ARL), Aberdeen Proving Ground, MD, USA",
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{
"affiliation": "ARL, Lockheed Martin, Aberdeen Proving Ground, MD, USA",
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{
"affiliation": "ARL, Lockheed Martin, Aberdeen Proving Ground, MD, USA",
"fullName": "Nathaniel Waisbrot",
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{
"affiliation": "ARL, Lockheed Martin, Aberdeen Proving Ground, MD, USA",
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"surname": "Martin",
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{
"affiliation": "ARL, Lockheed Martin, Aberdeen Proving Ground, MD, USA",
"fullName": "Ken Leiter",
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{
"affiliation": "Kitware Inc., Clifton Park, NY, USA",
"fullName": "Bill Hoffman",
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"title": "Preemptive resource management for dynamically arriving tasks in an oversubscribed heterogeneous computing system",
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"abstract": "We design resource management heuristics that assign serial tasks to the nodes of a heterogeneous high performance computing (HPC) system. The value of completing these tasks is modeled using monotonically decreasing utility functions that represent the time-varying importance of the task. The value of completing a task is equal to its utility function at the time of its completion. The overall performance of this system is measured using the total utility earned by all tasks during some interval of time. To maximize the performance of such a system where the preemption of tasks is possible, we have designed, analyzed, and compared a set of resource allocation heuristic techniques. We combine two utility-aware heuristics with three different preemption techniques to create six preemption-capable heuristics. We also consider the two utility-aware heuristics without preemption. We use simulation studies to evaluate this set of eight heuristics and compare them with an FCFS heuristic, which is often used in real systems, and random assignments. In general, our set of eight heuristics is able to significantly outperform the comparison heuristics, and the preemption-capable heuristics are able to significantly increase the utility earned compared to the heuristics that do not use preemption. We analyze the performance tradeoffs among the different preemption-capable heuristics under a variety of oversubscribed workload environments.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We design resource management heuristics that assign serial tasks to the nodes of a heterogeneous high performance computing (HPC) system. The value of completing these tasks is modeled using monotonically decreasing utility functions that represent the time-varying importance of the task. The value of completing a task is equal to its utility function at the time of its completion. The overall performance of this system is measured using the total utility earned by all tasks during some interval of time. To maximize the performance of such a system where the preemption of tasks is possible, we have designed, analyzed, and compared a set of resource allocation heuristic techniques. We combine two utility-aware heuristics with three different preemption techniques to create six preemption-capable heuristics. We also consider the two utility-aware heuristics without preemption. We use simulation studies to evaluate this set of eight heuristics and compare them with an FCFS heuristic, which is often used in real systems, and random assignments. In general, our set of eight heuristics is able to significantly outperform the comparison heuristics, and the preemption-capable heuristics are able to significantly increase the utility earned compared to the heuristics that do not use preemption. We analyze the performance tradeoffs among the different preemption-capable heuristics under a variety of oversubscribed workload environments.",
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"normalizedAbstract": "We design resource management heuristics that assign serial tasks to the nodes of a heterogeneous high performance computing (HPC) system. The value of completing these tasks is modeled using monotonically decreasing utility functions that represent the time-varying importance of the task. The value of completing a task is equal to its utility function at the time of its completion. The overall performance of this system is measured using the total utility earned by all tasks during some interval of time. To maximize the performance of such a system where the preemption of tasks is possible, we have designed, analyzed, and compared a set of resource allocation heuristic techniques. We combine two utility-aware heuristics with three different preemption techniques to create six preemption-capable heuristics. We also consider the two utility-aware heuristics without preemption. We use simulation studies to evaluate this set of eight heuristics and compare them with an FCFS heuristic, which is often used in real systems, and random assignments. In general, our set of eight heuristics is able to significantly outperform the comparison heuristics, and the preemption-capable heuristics are able to significantly increase the utility earned compared to the heuristics that do not use preemption. We analyze the performance tradeoffs among the different preemption-capable heuristics under a variety of oversubscribed workload environments.",
"fno": "3408a054",
"keywords": [
"Resource Management",
"US Department Of Defense",
"Computational Modeling",
"High Performance Computing",
"Time Measurement",
"Heterogeneous Computing",
"Utility Functions",
"Resource Management Heuristics",
"Scheduling",
"Preemption"
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"authors": [
{
"affiliation": "Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA",
"fullName": "Dylan Machovec",
"givenName": "Dylan",
"surname": "Machovec",
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},
{
"affiliation": "Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA",
"fullName": "Sudeep Pasricha",
"givenName": "Sudeep",
"surname": "Pasricha",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA",
"fullName": "Anthony A. Maciejewski",
"givenName": "Anthony A.",
"surname": "Maciejewski",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA",
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"content": "Ample evidence shows that the Qwerty layout is inefficient for one-finger typing on virtual keyboards: letters in common digrams are placed on opposite sides of the keyboard, resulting in an unnecessarily long finger-travel distance. In this paper, we report on using simulated annealing for modifying keyboard layout to reduce finger-travel distance for entering letters of the English alphabet. Our investigation led to improved 3 X 10, 4 X 7, and 5 X 6 layouts, all of which reduced the weighted sum of finger-travel distances for all digrams (denoted by d) by about 40% over Qwerty. The most improved layout is the 5 X 6 layout, which reduces d from 3.31 key widths (the value of d for Qwerty) to 1.78 key widths. These layouts also outperform the best layout reported in the literature.",
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"abstract": "This work presents the layout area of encoded and decoded multiplexers, two essential building blocks of modern FPGAs, in FinFET. Layouts with both 2 and 3 metal layers based on ASAP7 Predictive Design Kit are presented. The layout area is then compared with the prediction of two equation-based models: the VPR area model and the COFFE area model. We found that, with the original model parameters which are adjusted for planar technologies, these two equation-based models are not accurate in predicting FinFET layout area with error ranges of -2.3% to +86.5% and -32.7% to +19.3% for the VPR and COFFE models, respectively. Furthermore, when the model parameters are specifically adjusted for FinFET, the error ranges remain to be large with -19% to +31% and -26.6% to +25% for the VPR and COFFE models, respectively. These data underline the importance of verifying equation-based models against actual layout areas in FPGA architectural studies, especially when there are significant changes in the underlining process technologies.",
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"content": "This work presents the layout area of encoded and decoded multiplexers, two essential building blocks of modern FPGAs, in FinFET. Layouts with both 2 and 3 metal layers based on ASAP7 Predictive Design Kit are presented. The layout area is then compared with the prediction of two equation-based models: the VPR area model and the COFFE area model. We found that, with the original model parameters which are adjusted for planar technologies, these two equation-based models are not accurate in predicting FinFET layout area with error ranges of -2.3% to +86.5% and -32.7% to +19.3% for the VPR and COFFE models, respectively. Furthermore, when the model parameters are specifically adjusted for FinFET, the error ranges remain to be large with -19% to +31% and -26.6% to +25% for the VPR and COFFE models, respectively. These data underline the importance of verifying equation-based models against actual layout areas in FPGA architectural studies, especially when there are significant changes in the underlining process technologies.",
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"abstract": "Portable Document Format (PDF), as one of the most popular file format, is especially useful for educational documents such as text books, articles, or papers in which we can preserve the original graphic appearance and conveniently share online. Detecting and extracting information from tables in PDF files can provide a plethora of structural data to construct educational knowledge graphs. However, most of the existing methods rely on PDF parsing tools and natural language processing techniques, which generally require training samples and are frail in handling cross-page tables. In light of this, in this paper, we propose a novel OpenCV-based framework to extract the metadata and specific values from PDF tables. Specifically, we first highlight the visual outline of the tables. Then, we locate tables using horizontal and vertical lines and get the coordinates of tabular frames in each PDF page. Once the tables are successfully detected, for each table, we detect the cross-page scenarios and use the Optical Character Recognition (OCR) engine to extract the specific values in each table cell. Differing from other machine learning based methods, the proposed method can achieve table information extraction accurately without labeled data. We conduct extensive experiments on real-world PDF files. The results demonstrate that our approach can effectively deal with cross-page tables and only need 6.12 seconds on average to process a table.",
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"abstract": "Program visualizations help students understand the runtime behavior of other programs. They are educational tools to complement lectures or replace inefficient static drawings. A recent survey found 46 program visualizations developed from 1979 to 2012 reported that their effectiveness is unclear. They also evaluated learner engagement strategies implemented by visualization systems, but other learning principles were not considered. Learning principles are potential key factors in the success of program visualization as learning tools. In this paper, we identified 16 principles that may contribute to the effectiveness of a learning tool based on Vygotsky's learning theory. We hypothesize that some of these principles could be supported by incorporating visual concrete allegories and gamification. We conducted a literature review to know if these principles are supported by existing solutions. We found six new systems between 2012 and 2015. Very few systems consider a learning theory as theoretical framework. Only two out the 16 learning principles are supported by existing visualizations. All systems use unconnected visual metaphors, two use concrete visual metaphors, and one implemented a gamification principle. We expect that using concrete visual allegories and gamification in future program visualizations will significantly improve their effectiveness.",
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"content": "Program visualizations help students understand the runtime behavior of other programs. They are educational tools to complement lectures or replace inefficient static drawings. A recent survey found 46 program visualizations developed from 1979 to 2012 reported that their effectiveness is unclear. They also evaluated learner engagement strategies implemented by visualization systems, but other learning principles were not considered. Learning principles are potential key factors in the success of program visualization as learning tools. In this paper, we identified 16 principles that may contribute to the effectiveness of a learning tool based on Vygotsky's learning theory. We hypothesize that some of these principles could be supported by incorporating visual concrete allegories and gamification. We conducted a literature review to know if these principles are supported by existing solutions. We found six new systems between 2012 and 2015. Very few systems consider a learning theory as theoretical framework. Only two out the 16 learning principles are supported by existing visualizations. All systems use unconnected visual metaphors, two use concrete visual metaphors, and one implemented a gamification principle. We expect that using concrete visual allegories and gamification in future program visualizations will significantly improve their effectiveness.",
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"normalizedAbstract": "Program visualizations help students understand the runtime behavior of other programs. They are educational tools to complement lectures or replace inefficient static drawings. A recent survey found 46 program visualizations developed from 1979 to 2012 reported that their effectiveness is unclear. They also evaluated learner engagement strategies implemented by visualization systems, but other learning principles were not considered. Learning principles are potential key factors in the success of program visualization as learning tools. In this paper, we identified 16 principles that may contribute to the effectiveness of a learning tool based on Vygotsky's learning theory. We hypothesize that some of these principles could be supported by incorporating visual concrete allegories and gamification. We conducted a literature review to know if these principles are supported by existing solutions. We found six new systems between 2012 and 2015. Very few systems consider a learning theory as theoretical framework. Only two out the 16 learning principles are supported by existing visualizations. All systems use unconnected visual metaphors, two use concrete visual metaphors, and one implemented a gamification principle. We expect that using concrete visual allegories and gamification in future program visualizations will significantly improve their effectiveness.",
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"abstract": "This paper proposes a novel approach to build and deploy learning analytics dashboards in multiple learning environments. Existing learning dashboards are barely portable: once deployed on a learning platform, it requires considerable effort to deploy the dashboard elsewhere. We suggest constructing dashboards from lightweight web applications, namely widgets. Our approach allows to port dashboards with no additional cost between learning environments that implement open specifications (Open Social and Activity Streams) for data access and use widget APIs. We propose to facilitate reuse by sharing the dashboards and widgets via a centralized analytics repository.",
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