Browsing by Subject "Research problems"

Now showing 1 - 7 of 7

Open Access
Adaptive time-to-live strategies for query result caching in web search engines
(2012) Alıcı, Sadiye; Altıngövde, I. Ş.; Rıfat, Özcan; Cambazoğlu, B. Barla; Ulusoy, Özgür
An important research problem that has recently started to receive attention is the freshness issue in search engine result caches. In the current techniques in literature, the cached search result pages are associated with a fixed time-to-live (TTL) value in order to bound the staleness of search results presented to the users, potentially as part of a more complex cache refresh or invalidation mechanism. In this paper, we propose techniques where the TTL values are set in an adaptive manner, on a per-query basis. Our results show that the proposed techniques reduce the fraction of stale results served by the cache and also decrease the fraction of redundant query evaluations on the search engine backend compared to a strategy using a fixed TTL value for all queries. © 2012 Springer-Verlag Berlin Heidelberg.
Open Access
A game theoretical modeling and simulation framework for the integration of unmanned aircraft systems in to the national airspace
(AIAA, 2016) Musavi, Negin; Tekelioğlu, K. B.; Yıldız, Yıldıray; Güneş, Kerem; Onural, Deniz
The focus of this paper is to present a game theoretical modeling and simulation frame- work for the integration of Unmanned Aircraft Systems (UAS) into the National Airspace system (NAS). The problem of predicting the outcome of complex scenarios, where UAS and manned air vehicles co-exist, is the research problem of this work. The fundamental gap in the literature in terms of developing models for UAS integration into NAS is that the models of interaction between manned and unmanned vehicles are insufficient. These models are insufficient because a) they assume that human behavior is known a priori and b) they disregard human reaction and decision making process. The contribution of this paper is proposing a realistic modeling and simulation framework that will fill this gap in the literature. The foundations of the proposed modeling method is formed by game theory, which analyzes strategic decision making between intelligent agents, bounded rationality concept, which is based on the fact that humans cannot always make perfect decisions, and reinforcement learning, which is shown to be effective in human behavior in psychology literature. These concepts are used to develop a simulator which can be used to obtain the outcomes of scenarios consisting of UAS, manned vehicles, automation and their interactions. An analysis of the UAS integration is done with a specifically designed scenario for this paper. In the scenario, a UAS equipped with sense and avoid algorithm, moves along a predefined trajectory in a crowded airspace. Then the effect of various system parameters on the safety and performance of the overall system is investigated.
Open Access
İçerik tabanlı görüntü erişimi için sahne sınıflandırması
(IEEE, 2008-04) Çavuş, Özge; Aksoy, Selim
Son yıllarda çok geniş veri tabanlarının kullanımıyla birlikte içerik tabanlı görüntü indekslemesi ve erişimi önemli bir araştırma konusu halini almıştır. Bu çalışmada, görüntü indekslemesi için sahne sınıflandırmasını baz alan bir görüntü erişim sistemi tanımlanmıştır. Görüntülerden çıkarılan alt düzey öznitelikler görüntü indekslemesinde doğrudan kullanılmak yerine, bu öznitelikler sahne sınıflandırması için kullanılmış ve görüntüler sınıflandırma sonucunda elde edilen anlamsal sınıf bilgileriyle indekslenmiştir. Sahne sınıflandırması için “kelime kümesi” (bag of words) dokuman analizi yöntemi olarak bilinen tekniğin bir uyarlaması kullanılmıştır. Görüntü erişim sistemini insan algısıyla desteklemek ve anlambilimsel uçurumu en aza indirgemek için erişim senaryosuna tek sınıf sınıflandırıcı bazlı ilgililik geri beslemesi eklenmiştir. Bunun için, ilgili görüntüleri çok iyi modelleyen, ilgili olmayan görüntülerden de bir o kadar uzak duran bir hiperkure oluşturan destek vektör veri tanımlaması kullanılmıştır. Önerilen yöntemler Corel veri kümesinde denenmiş ve başarılı sonuçlar elde edilmiştir. Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. In this study, a content-based image retrieval system that is based on scene classification for image indexing is proposed. Instead of using low-level features directly, semantic class information that is obtained as a result of scene classification is used during indexing. The traditional "bag of words" approach is modified for classifying the scenes. In order to minimize the semantic gap, a relevance feedback approach that is based on one-class classification is also integrated. The support vector data description is used for learning during feedback iterations. The experiments using the Corel data set show good results for both classification and retrieval. ©2008 IEEE.
Open Access
A new approach to search result clustering and labeling
(Springer, Berlin, Heidelberg, 2011) Türel, Anıl; Can, Fazlı
Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.
Open Access
Research issues in peer-to-peer data management
(IEEE, 2007-11) Ulusoy, Özgür
Data management in Peer-to-Peer (P2P) systems is a complicated and challenging issue due to the scale of the network and highly transient population of peers. In this paper, we identify important research problems in P2P data management, and describe briefly some methods that have appeared in the literature addressing those problems. We also discuss some open research issues and directions regarding data management in P2P systems. ©2007 IEEE.
Open Access
Tarım alanlarında doğrusal odunsu bitki gruplarının otomatik sezimi
(IEEE, 2009-04) Akçay, H. Gökhan; Aksoy, Selim
Tarım alanlarının otomatik haritalanması ve izlenmesi önemli bir araştırma konusudur. Bu bildiride, çok yüksek çözünürlükteki uydu görüntülerinde doğrusal şeritler halindeki odunsu bitki gruplarının otomatik olarak sezilmesi için bir yöntem sunulmaktadır. Yötem, öznitelik çıkarma ve karar verme adımlarını sıradüzensel bir şekilde uygulayarak spektral, doku ve nesne şekil bilgisini bir arada kullanmaktadır. Farklı özellikte alanlardan elde edilen Quickbird görüntüleri üzerinde yapılan deneyler tatmin edici başarım göstermektedir. Automatic mapping and monitoring of agricultural landscapes is an important research problem. In this paper, we present a method for automatic mapping of linear strips of woody vegetation in very high-resolution imagery. The method combines spectral, textural and object shape information using hierarchical feature extraction and decision making steps. Experiments on Quickbird imagery from different sites show promising detection performance. ©2009 IEEE.
Open Access
Visual transformation aided contrastive learning for video-based kinship verification
(IEEE, 2017-10) Dibeklioğlu, Hamdi
Automatic kinship verification from facial information is a relatively new and open research problem in computer vision. This paper explores the possibility of learning an efficient facial representation for video-based kinship verification by exploiting the visual transformation between facial appearance of kin pairs. To this end, a Siamese-like coupled convolutional encoder-decoder network is proposed. To reveal resemblance patterns of kinship while discarding the similarity patterns that can also be observed between people who do not have a kin relationship, a novel contrastive loss function is defined in the visual appearance space. For further optimization, the learned representation is fine-tuned using a feature-based contrastive loss. An expression matching procedure is employed in the model to minimize the negative influence of expression differences between kin pairs. Each kin video is analyzed by a sliding temporal window to leverage short-term facial dynamics. The effectiveness of the proposed method is assessed on seven different kin relationships using smile videos of kin pairs. On the average, 93:65% verification accuracy is achieved, improving the state of the art. © 2017 IEEE.