Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Article Artırılmış Gerçeklik için Brıef Betimleyicileri ve Yerelliğe Duyarlı Karma Yöntemi ile Nesne Arama(Pamukkale Üniversitesi, 2017) Özuysal, MustafaBu çalışmada mobil artırılmış gerçeklik için kullanılabilecek bir nesne arama yöntemi sunulmaktadır. Temel olarak yöntem anahtar nokta betimleyicilerinin eşleştirilmesine ve bu anahtar nokta eşlerinin geometrik kıstaslar ile süzülmesine dayanmaktadır. Eşlemenin hızlandırılması için gerekli iyileştirmeler detayları ile verilmektedir. Ayrıca, Yerelliğe Duyarlı Karma işleminin performansının bilgi erişim yaklaşımlarından faydalanılarak arttırılabileceği de gösterilmiştirArticle Citation - WoS: 3Citation - Scopus: 4Affordable person detection in omnidirectional cameras using radial integral channel features(Springer Verlag, 2019) Demiröz, Barış Evrim; Salah, Albert Ali; Baştanlar, Yalın; Akarun, LaleOmnidirectional cameras cover more ground than perspective cameras, at the expense of resolution. Their comprehensive field of view makes omnidirectional cameras appealing for security and ambient intelligence applications. Person detection is usually a core part of such applications. Conventional methods fail for omnidirectional images due to different image geometry and formation. In this study, we propose a method for person detection in omnidirectional images, which is based on the integral channel features approach. Features are extracted from various channels, such as LUV and gradient magnitude, and classified using boosted decision trees. Features are pixel sums inside annular sectors (doughnut slice shapes) contained by the detection window. We also propose a novel data structure called radial integral image that allows to calculate sums inside annular sectors efficiently. We have shown with experiments that our method outperforms the previous state of the art and uses significantly less computational resources.Article Citation - WoS: 1Citation - Scopus: 1A User-Assisted Thread-Level Vulnerability Assessment Tool(Wiley, 2019) Öz, Işıl; Topçuoğlu, Haluk Rahmi; Tosun, OğuzThe system reliability becomes a critical concern in modern architectures with the scale down of circuits. To deal with soft errors, the replication of system resources has been used at both hardware and software levels. Since the redundancy causes performance degradation, it is required to explore partial redundancy techniques that replicate the most vulnerable parts of the code. The redundancy level of user applications depends on user preferences and may be different for the users with different requirements. In this work, we propose a user-assisted reliability assessment tool based on critical thread analysis for redundancy in parallel architectures. Our analysis evaluates the application threads of a parallel program by considering their criticality in the execution and selects the most critical thread or threads to be replicated. Moreover, we extend our analysis by exploring critical regions of individual threads and execute redundantly only those regions to reduce redundancy overhead. Our experimental evaluation indicates that the replication of the most critical thread improves the system reliability more (up to 10% for blackscholes application) than the replication of any other thread. The partial thread replication based on critical region analysis also reduces the vulnerability of the system by considering a fine-grained approach.Article Citation - WoS: 9Citation - Scopus: 11A Qualitative Survey on Frequent Subgraph Mining(De Gruyter, 2018) Güvenoğlu, Büşra; Ergenç Bostanoğlu, BelginData mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.Editorial Computational Mirnomics(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Allmer, Jens; Yousef, MalikThe term MicroRNA or its contraction miRNA currently appears in 21,215 titles of abstracts, published between 1997 and now, available on Pubmed (2016-21-22:12:59 EET). 4,108 of these were published in 2016 alone which signifies the importance of miRNA-related research. MicroRNAs can be detected experimentally using various techniques like directional cloning of endogenous small RNAs but they are time consuming [1]. Additionally, it is necessary for the miRNA and its mRNA target(s) to be co-expressed to infer a functional relationship which is difficult, if not impossible, to achieve [2]. Since experimental approaches are facing such difficulties, they have been complemented by computational approaches [3] thereby defining the field of computational miRNomics.Correction Citation - WoS: 1Citation - Scopus: 1Correction To: Detection and Classifcation of Vehicles From Omnidirectional Videos Using Multiple Silhouettes(Springer, 2018) Karaimer, Hakkı Can; Barış, İpek; Baştanlar, YalınAn acknowledgements section was missing in this paper. It should read as follows:.Article Citation - WoS: 7Citation - Scopus: 5A Machine Learning Approach for Microrna Precursor Prediction in Retro-Transcribing Virus Genomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Saçar Demirci, Müşerref Duygu; Toprak, Mustafa; Allmer, JensIdentification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.Article Citation - WoS: 3Citation - Scopus: 3Elimination of Useless Images From Raw Camera-Trap Data(Türkiye Klinikleri Journal of Medical Sciences, 2019) Tekeli, Ulaş; Baştanlar, YalınCamera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast Fourier transform. To eliminate the images without animals, we propose a system combining convolutional neural networks and background subtraction. We experimentally show that the proposed approach keeps 99% of photos with animals while eliminating more than 50% of photos without animals. We also present a software prototype that employs developed algorithms to eliminate useless images.Article Citation - WoS: 2Privacy Issues in Post Dissemination on Facebook(Türkiye Klinikleri Journal of Medical Sciences, 2019) Sayın, Burcu; Şahin, Serap; Kogias, Dimitrios G.; Patrikakis, Charalampos Z.With social networks (SNs) being populated by a still increasing numbers of people who take advantage of the communication and collaboration capabilities that they offer, the probability of the exposure of people's personal moments to a wider than expected audience is also increasing. By studying the functionalities and characteristics that modern SNs offer, along with the people's habits and common behaviors in them, it is easy to understand that several privacy risks may exist, many of which people may be unaware of. In this paper, we focus on users' interactions with posts in a social network (SN), using Facebook as our research domain, and we emphasize some privacy leakages currently existing in Facebook's privacy policy. We also propose a solution to detected privacy issues, featuring a reference implementation of a tool based on a simulation, which visualizes the effect of potential privacy risks on Facebook and directs users to control their privacy. The proposed and simulated tool allows a post owner to observe the spreading area of his or her post depending on the selected privacy settings. Moreover, it provides preliminary feedback for all Facebook users that have interacted with this post, to make them aware of the possible privacy changes, aiming to give them a chance to protect the privacy of their interaction on this post by deleting it when an unwanted privacy change takes place. Finally, an online survey to increase privacy awareness in Facebook usage with over 500 volunteer participants has illuminated the need for such a tool or solution.Article Citation - WoS: 22Citation - Scopus: 40Correlation of Critical Success Factors With Success of Software Projects: an Empirical Investigation(Springer Verlag, 2019) Garousi, Vahid; Tarhan, Ayça; Pfahl, Dietmar; Coşkunçay, Ahmet; Demirörs, OnurSoftware engineering researchers have, over the years, proposed different critical success factors (CSFs) which are believed to be critically correlated with the success of software projects. To conduct an empirical investigation into the correlation of CSFs with success of software projects, we adapt and extend in this work an existing contingency fit model of CSFs. To archive the above objective, we designed an online survey and gathered CSF-related data for 101 software projects in the Turkish software industry. Among our findings is that the top three CSFs having the most significant associations with project success were: (1) team experience with the software development methodologies, (2) team's expertise with the task, and (3) project monitoring and controlling. A comprehensive correlation analysis between the CSFs and project success indicates positive associations between the majority of the factors and variables, however, in most of the cases at non-significant levels. By adding to the body of evidence in this field, the results of the study will be useful for a wide audience. Software managers can use the results to prioritize the improvement opportunities in their organizations w.r.t. the discussed CSFs. Software engineers might use the results to improve their skills in different dimensions, and researchers might use the results to prioritize and conduct follow-up in-depth studies on those factors.
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