Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253
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Article Citation - WoS: 1Using Text Mining for Research Trends in Empirical Software Engineering(Gazi Univ, 2021) Tokdemir, GulThis paper intends to examine the research trends in Empirical Software Engineering domain within the last two decades using text mining. It studies published articles in the relevant literature with an emphasis on abstracts of 10658 articles published in the literature on Experimental Software Engineering domain. Using a probabilistic topic modelling technique (Latent Dirichlet Allocation), it brings forward the main topics of research within this domain. By further analysis, the paper evaluates the changes of focus in published works in the last two decades and depicts the recent trends in research content wise. Through a timely comparison, it portrays the alteration of interest within empirical software engineering research and proposes a future research agenda to develop an advanced field, beneficial both for academics and practitioners.Article Citation - WoS: 23Citation - Scopus: 29Effective Social Productivity Measurements During Software Development: an Empirical Study(World Scientific Publ Co Pte Ltd, 2016) O'Connor, Rory V.; Clarke, Paul; Yilmaz, MuratMuch of contemporary scientific discussion regarding factors that influence software development productivity is undertaken in various domains where there is an insuflcient empirical basis for exploring socio-technical factors of productivity that are specific to a software development organization. The purpose of the study is to characterize the multidimensional nature of software development productivity and its social aspects as a set of latent constructs (i.e. variables that are not directly observed) for a medium-sized software company. To this end, we designed an exploratory in-depthfield study based on the hypothesized productivity constructs, which were modeled by a set of factors identified from literature reviews, and later refined by industrial focus groups. In order to demonstrate the applicability of our approach, we conducted confirmatory factor analysis with the data attained from a questionnaire with 216 participants. To investigate factors of influence further, we analyzed the impact of selected team-based variables over the latent constructs of productivity. Taken together, our findings confirm that such an approach can be used to explore the quantifiable influence of socio-technical factors that would affect productivity of a particular software development organization. Ultimately, the resulting model provides guidance to explore the comparative importance of a set of firm-specific factors that may help to improve the productivity of the organization.Article Citation - WoS: 2Citation - Scopus: 2Improvement of Dwt-Svd With Curve Fitting and Robust Regression: an Application To Astronomy Images(Kaunas Univ Technology, 2016) Elbasi, Ersin; Karadeniz, TalhaDWT-SVD is a frequency domain based eigenanalysis watermarking technique. In this work, we improve this method by exploring the relationship between the cover image's DWT singular values and those of the watermark. We show that, via the usage of curve fitting and robust regression, it is possible to achieve accurate results. We also demonstrate that the improved scheme is suitable for the watermarking of astronomy images. In addition to encoding and decoding examples, statistical results on stealth and robustness are deduced from the experiments so that the clear advance can be observed. Quality of the watermark is measured by testing against various attack types.Article Citation - WoS: 3Citation - Scopus: 4Automatic Coastline Detection Using Image Enhancement and Segmentation Algorithms(Hard, 2016) Caniberk, Mustafa; Maras, Hadi Hakan; Maras, Erdem EminCoastlines have hosted numerous civilizations since the earliest times of mankind due to the advantages they offer such as natural resources, transportation, arable areas, seafood, trade, and biodiversity. Coastal regions should be monitored vigilantly by planners and control mechanisms, and any changes in these regions should be detected with its human or natural origin, and future plans and possible interventions should be formed in these aspects to maintain ecological balance, sustainable development, and planned urbanization. Integrated coastal zone management (ICZM) provides an important tool to reach that goal. One of the important elements of ICZM is the detection of coastlines. While there are several methods to detect coastlines, remote sensing methods provide the fastest and the most efficient solutions. In this study, color infrared, grayscale, RGB, and fake infrared images were processed with the median filtering and segmentation software developed within the study, and coastal lines were detected by the edge detection method. The results show that segmentation with fake infrared images derived from RGB images give the best results.Conference Object Classification of Linked Data Sources Using Semantic Scoring(Ieice-inst Electronics information Communication Engineers, 2018) Dogdu, Erdogan; Kodaz, Halife; Yumusak, SemihLinked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs: comment and rdfs: label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results.Article Covariance Features for Trajectory Analysis(Kaunas Univ Technology, 2018) Karadeniz, Talha; Maraş, Hadi HakanIn this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series.