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300 results on '"Kaya, Kamer"'

54. On Partitioning Problems with Complex Objectives

Author

Kaya, Kamer, Rouet, François-Henry, Uçar, Bora, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Alexander, Michael, editor, D’Ambra, Pasqua, editor, Belloum, Adam, editor, Bosilca, George, editor, Cannataro, Mario, editor, Danelutto, Marco, editor, Di Martino, Beniamino, editor, Gerndt, Michael, editor, Jeannot, Emmanuel, editor, Namyst, Raymond, editor, Roman, Jean, editor, Scott, Stephen L., editor, Traff, Jesper Larsson, editor, Vallée, Geoffroy, editor, and Weidendorfer, Josef, editor

Published
2012
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56. Practical Threshold Signatures with Linear Secret Sharing Schemes

Author

Bozkurt, İlker Nadi, Kaya, Kamer, Selçuk, Ali Aydın, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Preneel, Bart, editor

Published
2009
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58. Robust Threshold Schemes Based on the Chinese Remainder Theorem

Author

Kaya, Kamer, Selçuk, Ali Aydın, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Vaudenay, Serge, editor

Published
2008
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59. A Verifiable Secret Sharing Scheme Based on the Chinese Remainder Theorem

Author

Kaya, Kamer, Selçuk, Ali Aydın, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Chowdhury, Dipanwita Roy, editor, Rijmen, Vincent, editor, and Das, Abhijit, editor

Published
2008
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61. Capture Resilient ElGamal Signature Protocols

Author

Acan, Hüseyin, Kaya, Kamer, Selçuk, Ali Aydın, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Levi, Albert, editor, Savaş, Erkay, editor, Yenigün, Hüsnü, editor, Balcısoy, Selim, editor, and Saygın, Yücel, editor

Published
2006
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62. Threshold Cryptography Based on Asmuth-Bloom Secret Sharing

Author

Kaya, Kamer, Selçuk, Ali Aydın, Tezcan, Zahir, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Levi, Albert, editor, Savaş, Erkay, editor, Yenigün, Hüsnü, editor, Balcısoy, Selim, editor, and Saygın, Yücel, editor

Published
2006
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66. Fast and Error-Adaptive Influence Maximization Based on Count-Distinct Sketches

Author

Gokturk, Gokhan and Kaya, Kamer

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Computer Science - Social and Information Networks, Data Structures and Algorithms (cs.DS)
Abstract

Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are frequently used for IM. In this work, we describe a fast, error-adaptive approach that leverages Count-Distinct sketches and hash-based fused sampling. To estimate the number of influenced vertices throughout a diffusion, we use per-vertex Flajolet-Martin sketches where each sketch corresponds to a sampled subgraph. To efficiently simulate the diffusions, the reach-set cardinalities of a single vertex are stored in memory in a consecutive fashion. This allows the proposed algorithm to estimate the number of influenced vertices in a single step for simulations at once. For a faster IM kernel, we rebuild the sketches in parallel only after observing estimation errors above a given threshold. Our experimental results show that the proposed algorithm yields high-quality seed sets while being up to 119x faster than a state-of-the-art approximation algorithm. In addition, it is up to 62x faster than a sketch-based approach while producing seed sets with 3%-12% better influence scores, Comment: 12 pages. Sent to IEEE Transactions on Knowledge and Data Engineering as a regular paper

Published
2022

67. Machine Learning-Based Load Distribution And Balancing In Heterogeneous Database Management Systems

Author

Abdennebi, Anes, Elakas, Anil, Tasyaran, Fatih, Ozturk, Erdinc, Kaya, Kamer, and Yildirim, Sinan

Abstract

For dynamic and continuous data analysis, conventional OLTP systems are slow in performance. Today's cutting-edge high-performance computing hardware, such as GPUs, has been used as accelerators for data analysis tasks, which traditionally leverage CPUs on classical database management systems (DBMS). When CPUs and GPUs are used together, the architectural heterogeneity, that is, leveraging hardware with different performance characteristics jointly, creates complex problems that need careful treatment for performance optimization. Load distribution and balancing are crucial problems for DBMSs working on heterogeneous architectures. In this work, focusing on a hybrid, CPU-GPU database management system to process users' queries, we propose heuristical and machine-learning-based (ML-based) load distribution and balancing models. In more detail, we employ multiple linear regression (MLR), random forest (RF), and Adaboost (Ada) models to dynamically decide the processing unit for each incoming query based on the response time predictions on both CPU and GPU. The ML-based models outperformed the other algorithms, as well as the CPU and GPU-only running modes with up to 27%, 29%, and 40%, respectively, in overall performance (response time) while answering intense real-life working scenarios. Finally, we propose to use a hybrid load-balancing model that would be more efficient than the models we tested in this work.

Published
2022

72. Boosting Graph Embedding on a Single GPU.

Author

Aljundi, Amro Alabsi, Akyildiz, Taha Atahan, and Kaya, Kamer

Subjects
GRAPH algorithms, MACHINE learning, GRAPHICS processing units, CLASSIFICATION algorithms, CHARTS, diagrams, etc.
Abstract

Graphs are ubiquitous, and they can model unique characteristics and complex relations of real-life systems. Although using machine learning (ML) on graphs is promising, their raw representation is not suitable for ML algorithms. Graph embedding represents each node of a graph as a $d$ d -dimensional vector which is more suitable for ML tasks. However, the embedding process is expensive, and CPU-based tools do not scale to real-world graphs. In this work, we present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints. GOSH employs a novel graph coarsening algorithm to enhance the impact of updates and minimize the work for embedding. It also incorporates a decomposition schema that enables any arbitrarily large graph to be embedded with a single GPU. As a result, GOSH sets a new state-of-the-art in link prediction both in accuracy and speed, and delivers high-quality embeddings for node classification at a fraction of the time compared to the state-of-the-art. For instance, it can embed a graph with over 65 million vertices and 1.8 billion edges in less than 30 minutes on a single GPU. [ABSTRACT FROM AUTHOR]

Published
2022
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84. Karp-Sipser based kernels for bipartite graph matching

Author

Kaya, Kamer, Langguth, Johannes, Panagiotas, Ioannis, Uçar, Bora, Faculty of Engineering and Natural Sciences (Sabanci University), Sabanci University [Istanbul], Simula Research Laboratory, Optimisation des ressources : modèles, algorithmes et ordonnancement (ROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Informatique du Parallélisme (LIP), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
QA075 Electronic computers. Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], ComputingMilieux_MISCELLANEOUS
Abstract

We consider Karp–Sipser, a well-known matching heuristic in the context of data reduction for the maximum cardinality matching problem. We describe an efficient implementation as well as modifications to reduce its time complexity in worst-case instances, both in theory and in practical cases. We compare experimentally against its widely used simpler variant and show cases for which the full algorithm yields better performance.

Published
2020

88. Iterative-improvement-based heuristics for adaptive scheduling of tasks sharing files on heterogeneous master-slave environments

Author

Kaya, Kamer and Aykanat, Cevdet

Subjects
Iterative methods (Mathematics) -- Analysis, Task analysis -- Methods, Heuristic programming -- Methods, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The problem of scheduling independent but file-sharing tasks on heterogeneous master-slave platforms is investigated and a three-phase scheduling approach involving initial task assignment, refinement and execution ordering phases is proposed. The results show that the proposed three-phase scheduling approach works much better than the existing one.

Published
2006

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