Browsing by Subject "Technology transfer"

Now showing 1 - 4 of 4

Open Access
First Turkish software product line engineering workshop summary
(Association for Computing Machinery, 2012-11) Tekinerdogan, B.
Open Access
Integration of structural and semantic models for multimedia metadata management
(IEEE, 2007-06) Little, S.; Martinelli, M.; Salvetti, O.; Güdükbay, Uğur; Ulusoy, Özgür; De Chalendar, G.; Grefenstette, G.
The management and exchange of multimedia data is challenging due to the variety of formats, standards and intended applications. In addition, production of multimedia data is rapidly increasing due to the availability of off-the-shelf, modern digital devices that can be used by even inexperienced users. It is likely that this volume of information will only increase in the future. A key goal of the MUSCLE (Multimedia Understanding through Semantics, Computation and Learning) network is to develop tools, technologies and standards to facilitate the interoperability of multimedia content and support the exchange of such data. One approach for achieving this was the creation of a specific "E-Team", composed of the authors, to discuss core questions and practical issues based on the participant's individual work. In this paper, we present the relevant points of view with regards to sharing experiences and to extracting and integrating multimedia data and metadata from different modes (text, images, video). © 2007 IEEE.
Open Access
Online Contextual Influence Maximization in social networks
(Institute of Electrical and Electronics Engineers Inc., 2017) Sarıtaç, Ömer; Karakurt, Altuğ; Tekin, Cem
In this paper, we propose the Online Contextual Influence Maximization Problem (OCIMP). In OCIMP, the learner faces a series of epochs in each of which a different influence campaign is run to promote a certain product in a given social network. In each epoch, the learner first distributes a limited number of free-samples of the product among a set of seed nodes in the social network. Then, the influence spread process takes place over the network, other users get influenced and purchase the product. The goal of the learner is to maximize the expected total number of influenced users over all epochs. We depart from the prior work in two aspects: (i) the learner does not know how the influence spreads over the network, i.e., it is unaware of the influence probabilities; (ii) influence probabilities depend on the context. We develop a learning algorithm for OCIMP, called Contextual Online INfluence maximization (COIN). COIN can use any approximation algorithm that solves the offline influence maximization problem as a subroutine to obtain the set of seed nodes in each epoch. When the influence probabilities are Hölder continuous functions of the context, we prove that COIN achieves sublinear regret with respect to an approximation oracle that knows the influence probabilities for all contexts. Moreover, our regret bound holds for any sequence of contexts. We also test the performance of COIN on several social networks, and show that it performs better than other methods. © 2016 IEEE.
Open Access
Wireless ATA: A new data
(2005) Özler, Serdar; Körpeoğlu, İbrahim
This paper introduces a new data transport architecture and protocol for storage that is implemented on wireless devices and that can be accessed through a short-range wireless access technology such as Bluetooth or 802.11. We call the protocol WATA (Wireless ATA), as its architecture is similar to current ATA and ATA-based technologies. In this paper, we give basic technical details of the protocol and discuss its main advantages and disadvantages over the current protocols, and talk about our decisions to implement a prototype system to see an actual implementation of the architecture.