Başlık için Bildiri Koleksiyonu listeleme
Toplam kayıt 113, listelenen: 78-97
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A Parallel Huffman Coder on the CUDA Architecture
(IEEE, 2014)We present a parallel implementation of the widely-used entropy encoding algorithm, the Huffman coder, on the NVIDIA CUDA architecture. After constructing the Huffman codeword tree serially, we proceed in parallel by ... -
Parallel Matrix Multiplication for Various Implementations
(IEEE, 2013)It has become increasingly common to see that supercomputing applications harness the massive parallelism of graphics cards to speed up computations. In this study, an analysis concerning to the time necessity for four ... -
Parallel matrix multiplication for various implementations
(IEEE Computer Society, 2013)It has become increasingly common to see that supercomputing applications harness the massive parallelism of graphics cards to speed up computations. In this study, an analysis concerning to the time necessity for four ... -
Parallelizing edge drawing algorithm on CUDA
(2012)Parallel computing methods are very useful in speeding up algorithms that can be divided into independent subtasks. Traditional multi-processor architectures have limited use due to their high cost and difficulties of their ... -
Pcf: Projection-Based Collaborative Filtering
(Scitepress, 2010)Collaborative filtering (CF) systems are effective solutions for information overload problem while contributing web personalization. Different memory-based algorithms operating over entire data set have been utilized for ... -
Privacy Risks for Multi-Criteria Collaborative Filtering Systems
(IEEE, 2017)In case that individuals feel their privacy is violated while using any recommender system, they might be willing to declare incorrect information or even completely refuse to use such services. To relieve customer concerns, ... -
Privacy-Preserving Collaborative Filtering on Overlapped Ratings
(IEEE Computer Soc, 2013)To promote recommendation services through prediction quality, there are some privacy-preserving collaborative filtering (PPCF) solutions enabling e-commerce parties to collaborate on partitioned data. It is almost probable ... -
Privacy-Preserving Concordance-based Recommendations on Vertically Distributed Data
(IEEE, 2012)Recommender systems are attractive components of e-commerce. Customers apply such systems to get help for choosing the appropriate product to purchase. To provide accurate and dependable referrals, recommender systems ... -
Privacy-preserving Eigentaste-based collaborative filtering
(Springer-Verlag Berlin, 2007)With the evolution of e-commerce, privacy is becoming a major concern. Many e-companies employ collaborative filtering (CF) techniques to increase their sales by providing truthful recommendations to customers. Many ... -
Privacy-Preserving Kriging Interpolation on Distributed Data
(Springer-Verlag Berlin, 2014)Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ... -
Privacy-preserving kriging interpolation on distributed data
(Springer Verlag, 2014)Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ... -
Privacy-Preserving Trust-based Recommendations on Vertically Distributed Data
(IEEE Computer Soc, 2011)Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed ... -
Providing naive Bayesian classifier-based private recommendations on partitioned data
(Springer-Verlag Berlin, 2007)Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ... -
Providing naïve Bayesian classifier-based private recommendations on partitioned data
(2007)Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ... -
Providing Private Recommendations on Personal Social Networks
(Springer-Verlag Berlin, 2010)Personal social networks are recently used to offer recommendations. Due to privacy concerns, privacy protection while generating accurate referrals is imperative. Since accuracy and privacy are conflicting goals, providing ... -
Providing private recommendations using naive Bayesian classifier
(Springer-Verlag Berlin, 2007)Today's CF systems fail to protect users' privacy. Without privacy protection, it becomes a challenge to collect sufficient and high quality data for CF. With privacy protection, users feel comfortable to provide more ... -
Quality benchmarking relational databases and Lucene in the TREC4 adhoc task environment
(2010)The present work covers a comparison of the text retrieval qualities of open source relational databases and Lucene, which is a full text search engine library, over English documents. TREC-4 adhoc task is completed to ... -
Quantification of Projective Distortion for Fiducial Markers
(IEEE, 2013)The aim of this study is to quantify the projective distortion of candidate quadrilaterals found in a square-framed fiducial marker detection algorithm. Based on the quantified value, candidates can be eliminated in such ... -
Quantification of projective distortion for fiducial markers [Duzlemsel dsaretciler dcin dzdusumsel carpikligin nicelendirilmesi]
(2013)The aim of this study is to quantify the projective distortion of candidate quadrilaterals found in a square-framed fiducial marker detection algorithm. Based on the quantified value, candidates can be eliminated in such ... -
Randomization-based Privacy-preserving Frameworks for Collaborative Filtering
(Elsevier Science BV, 2016)Randomization-based privacy protection methods are widely used in collaborative filtering systems to achieve individual privacy. The basic idea behind randomization utilized in collaborative filtering schemes is to add ...