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Articles 181 - 196 of 196
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Ceg 220-01: Introduction To C Programming For Engineers, Robert Helt
Ceg 220-01: Introduction To C Programming For Engineers, Robert Helt
Computer Science & Engineering Syllabi
This course provides a general introduction to computers as a problem-solving tool using the C programming language. Emphasis is on algorithms and techniques useful to engineers. Topics include data representation, debugging, and program verification. 4 credit hours. Prerequisite: MTH 229 (Calculus I) or EGR 101 (Engineering Mathematics).
Ceg 333-01: Introduction To Unix, Thomas Wischgoll
Ceg 333-01: Introduction To Unix, Thomas Wischgoll
Computer Science & Engineering Syllabi
No abstract provided.
Ceg 411/611-01: Microprocessor-Based System Design, Jack Jean
Ceg 411/611-01: Microprocessor-Based System Design, Jack Jean
Computer Science & Engineering Syllabi
No abstract provided.
Ceg 433/633-01: Operating Systems, Prabhaker Mateti
Ceg 433/633-01: Operating Systems, Prabhaker Mateti
Computer Science & Engineering Syllabi
The management of resources in multi-user computer systems. Emphasis is on problems of file-system design, process scheduling, memory allocation, protection, and tools needed for solutions. Course projects use the CIC++ language and include the design of portions of an operating system. 4 credit hours.
A Semantic Future For Ai, Rudi Studer, Anupriya Ankolekar, Pascal Hitzler
A Semantic Future For Ai, Rudi Studer, Anupriya Ankolekar, Pascal Hitzler
Computer Science and Engineering Faculty Publications
In our modern information society, people need to manage ever-increasing numbers of personal devices and conduct more of their work and activities online, often making use of heterogeneous services. The amount of information to be processed by each individual is constantly growing, making it increasingly difficult to control, channel, share and make constructive use of it. To mitigate this, computing needs to become much more human-centered, e.g. by presenting personalised information to users and by respecting personal preferences in controlling multiple devices or invoking various services. Appropriate representation of the semantics of the information and functionality of devices and services …
Show Me What You Mean! Exploiting Domain Semantics In Ontology Visualization, Ravi Pavagada, Christopher Thomas, Amit P. Sheth, William S. York
Show Me What You Mean! Exploiting Domain Semantics In Ontology Visualization, Ravi Pavagada, Christopher Thomas, Amit P. Sheth, William S. York
Kno.e.sis Publications
Ontologies build the backbone for many life-sciences applications. These ontologies, however, are represented in XML based languages that are meant for machine-consumption and hence are difficult for humans to comprehend. For a meaningful visualization of these ontologies, it is important that the display of entities and relationships captures the cognitive representation of the domain as perceived by the domain experts. In this paper we present OntoVista, an ontology visualization tool that is adaptable to the needs of different domains, especially in the life sciences. While keeping the graph structures as the predominant model, we provide a semantically enhanced graph display …
Taxaminer: Improving Taxonomy Label Quality Using Latent Semantic Indexing, Cartic Ramakrishnan, Christopher Thomas, Vipul Kashyap, Amit P. Sheth
Taxaminer: Improving Taxonomy Label Quality Using Latent Semantic Indexing, Cartic Ramakrishnan, Christopher Thomas, Vipul Kashyap, Amit P. Sheth
Kno.e.sis Publications
The development of taxonomies/ontologies is a human intensive process requiring prohibitively large resource commitments in terms of time and cost. In our previous work we have identified an experimentation framework for semi-automatic taxonomy/hierarchy generation from unstructured text. In the preliminary results presented, the taxonomy/hierarchy quality was lower than we had anticipated. In this paper, we present two variations of our experimentation framework, viz. Latent semantic Indexing (LSI) for document indexing and the use of term vectors to prune labels assigned to nodes in the final taxonomy/hierarchy. Using our previous results of taxonomy/hierarchy quality as the baseline we present results that …
Data Processing In Space, Time, And Semantics Dimensions, Farshad Hakimpour, Boanerges Aleman-Meza, Matthew Perry, Amit P. Sheth
Data Processing In Space, Time, And Semantics Dimensions, Farshad Hakimpour, Boanerges Aleman-Meza, Matthew Perry, Amit P. Sheth
Kno.e.sis Publications
This work presents an experimental system for data processing in space, time and semantics dimensions using current Semantic Web technologies. The paper describes how we obtain geographic and event data from Internet sources and also how we integrate them into an RDF store. We briefly introduce a set of functionalities in space, time and semantics dimensions. These functionalities are implemented based on our existing technology for main-memory based RDF data processing developed in the LSDIS Lab. A number of these functionalities are exposed as REST Web services. We present two sample client side applications that are developed using a combination …
Using Query-Specific Variance Estimates To Combine Bayesian Classifiers, Chi-Hoon Lee, Russell Greiner, Shaojun Wang
Using Query-Specific Variance Estimates To Combine Bayesian Classifiers, Chi-Hoon Lee, Russell Greiner, Shaojun Wang
Kno.e.sis Publications
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a novel "query specific" combination rule: After learning a set of simple belief network classifiers, we produce an answer to each query by combining their individual responses, using weights based inversely on their respective variances around their responses. These variances are based on the uncertainty of the network parameters, which in turn depend on the training datasample. In essence, this variance quantifies the base classifier's confidence of its response to this query. …
Semi-Supervised Conditional Random Fields For Improved Sequence Segmentation And Labeling, Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans
Semi-Supervised Conditional Random Fields For Improved Sequence Segmentation And Labeling, Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans
Kno.e.sis Publications
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. Our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood. Although the training objective is no longer concave, it can still be used to improve an initial model (e.g. obtained from supervised training) by iterative ascent. We apply our new training algorithm to the problem of identifying gene and protein …
An Investigation Of Codon Usage Bias Including Visualization And Quantification In Organisms Exhibiting Multiple Biases, Douglas W. Raiford, Travis E. Doom, Dan E. Krane, Michael L. Raymer
An Investigation Of Codon Usage Bias Including Visualization And Quantification In Organisms Exhibiting Multiple Biases, Douglas W. Raiford, Travis E. Doom, Dan E. Krane, Michael L. Raymer
Kno.e.sis Publications
Prokaryotic genomic sequence data provides a rich resource for bioinformatic analytic algorithms. Information can be extracted in many ways from the sequence data. One often overlooked process involves investigating an organism’s codon usage. Degeneracy in the genetic code leads to multiple codons coding for the same amino acids. Organism’s often preferentially utilize specific codons when coding for an amino acid. This biased codon usage can be a useful trait when predicting a gene’s expressivity or whether the gene originated from horizontal transfer. There can be multiple biases at play in a genome causing errors in the predictive process. For this …
Clustering Similarity Comparison Using Density Profiles, Eric Bae, James Bailey, Guozhu Dong
Clustering Similarity Comparison Using Density Profiles, Eric Bae, James Bailey, Guozhu Dong
Kno.e.sis Publications
The unsupervised nature of cluster analysis means that objects can be clustered in many ways, allowing different clustering algorithms to generate vastly different results. To address this, clustering comparison methods have traditionally been used to quantify the degree of similarity between alternative clusterings. However, existing techniques utilize only the point memberships to calculate the similarity, which can lead to unintuitive results. They also cannot be applied to analyze clusterings which only partially share points, which can be the case in stream clustering. In this paper we introduce a new measure named ADCO, which takes into account density profiles for each …
Semantics Enabled Dynamic Process Configuration, Kunal Verma, Karthik Gomadam, Jonathan Lathem, Amit P. Sheth, John A. Miller
Semantics Enabled Dynamic Process Configuration, Kunal Verma, Karthik Gomadam, Jonathan Lathem, Amit P. Sheth, John A. Miller
Kno.e.sis Publications
Web processes are the next generation workflows created using Web services. This paper addresses research issues in creating a framework for configuring and executing dynamic Web processes. Our approach is that of a multiparadigm constraint analysis for process configuration using quantitative and logical constraints. We also present a software architecture and an engineering approach for extending current Web service infrastructure to support dynamic Web processes. An execution environment, extending Apache Axis, one of the most popular SOAP implementations, to support dynamic process configuration is presented. Empirical evaluation of the system is performed to demonstrate the cost benefits of dynamic process …
Predicting Domain Specific Entities With Limited Background Knowledge, Christopher Thomas, Amit P. Sheth
Predicting Domain Specific Entities With Limited Background Knowledge, Christopher Thomas, Amit P. Sheth
Kno.e.sis Publications
This paper proposes a framework for automatic recognition of domain-specific entities from text, given limited background knowledge, e.g. in form of an ontology. The algorithm exploits several lightweight natural language processing techniques, such as tokenization and stemming, as well as statistical techniques, such as singular value decomposition (SVD) to suggest domain relatedness of unknown entities.
Driving Deep Semantics In Middleware And Networks: What, Why And How?, Amit P. Sheth
Driving Deep Semantics In Middleware And Networks: What, Why And How?, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Knowledge Modeling And Its Application In Life Sciences: A Tale Of Two Ontologies, Satya S. Sahoo, Christopher Thomas, Amit P. Sheth, William S. York, Samir Tartir
Knowledge Modeling And Its Application In Life Sciences: A Tale Of Two Ontologies, Satya S. Sahoo, Christopher Thomas, Amit P. Sheth, William S. York, Samir Tartir
Kno.e.sis Publications
High throughput glycoproteomics, similar to genomics and proteomics, involves extremely large volumes of distributed, heterogeneous data as a basis for identification and quantification of a structurally diverse collection of biomolecules. The ability to share, compare, query for and most critically correlate datasets using the native biological relationships are some of the challenges being faced by glycobiology researchers. As a solution for these challenges, we are building a semantic structure, using a suite of ontologies, which supports management of data and information at each step of the experimental lifecycle. This framework will enable researchers to leverage the large scale of glycoproteomics …