Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normal Tablo"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Müzik eserlerinin sayısal ortama aktarılmasıyla birlikte, bilgisayar bilimleri müzikoloji çalışmalarının içerisinde kendine yer bulmaya başlamıştır. Müzik eserleri bilimsel araştırmalarda veri olarak kullanılmakta ve hesaplamalı müzik alanı bu alanda yapılan çalışmalar ile hızla gelişmektedir. Her ne kadar yapılan çalışmaların büyük bir bölümü sembolik olarak ifade edilmesi daha kolay olan Batı Müziği eserleri üzerine olsa da, Türk Müziği eserleri de artık çeşitli çalışmaların konusu olmaktadır. Türk Müziğinin temeli olan makam sistemi, bilgisayar bilimleri ile uğraşan araştırmacıların dikkatini çekmiş ve Türk Müziği eserleri veri madenciliği, makine öğrenmesi, sınıflandırma gibi çalışmaların konusu olmuştur. Bu çalışmada, bir ses dosyasından nota tanıma ile elde edildiği varsayılan ve 1261 Türk Müziği eserine ait sadece nota dizilerini içeren veri dosyası üzerinde makine öğrenmesi metodu ile makam tahmini yapmaya yönelik deneysel bir çalışma gerçekleştirilmiştir. MusicXML biçimindeki eserlerin makine öğrenmesi uygulamasında kullanılabilmesi amacıyla bir yazılım geliştirilmiş, bu yazılımla makine öğrenmesinde başarımı arttırmak için özgün veri kümesine dört farklı türetilmiş veri sahası eklenmiştir. Sonuç olarak, ‘Rastgele Orman’ algoritması ile makam tanımada %89,7 oranında başarım gözlemlenmiştir.

This article is a proof-of-concept case study to evaluate the functionality of a block metaphor–based linked data generator. In this work, we chose to produce linked data repository of recipes, which provide a medium for people to share their regional and healthy recipes with the masses. However, the same approach can also be adapted easily to other domains. Therefore, the applicability of our approach extends well beyond the food domain that we are considering in this article. As a medium for information sharing and understanding between heterogeneous systems, ontologies will play an important role in the realisation of the Internet of things (IoT) vision. Therefore, an ontology-based recipe repository would also be one of the basic blocks of a smart kitchen environment. However, building ontologies is a challenging task, especially for users who are not conversant in the ontology building languages. This article proposes an approach that can be used even by non-experts and facilitates the sharing and searching of recipe data. In our case, we exploit the features of the block paradigm to publish recipes in Linked Data format. In this way, users do not have to know the OWL (Web Ontology Language) syntax and the text input is kept minimal. As far as we know, this article is the first study that produces linked data using Blockly in the literature. We also conducted a user-based evaluation of the proposed approach using the System Usability Scale (SUS) questionnaire.

Pile foundations are critical structural elements, and their integrity is essential for ensuring the stability and safety of construction projects. Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. A dataset of 328 reflectograms collected from real construction sites, including 246 intact and 82 defective samples, was used to train and evaluate the model. To address class imbalance, oversampling techniques were applied. Several state-of-the-art pretrained CNN architectures were compared, with ConvNeXtLarge achieving the highest accuracy of 98.2%. The accuracy reported was achieved on a dedicated test set using real reflectogram data from actual construction sites, distinguishing this study from prior work relying primarily on synthetic data. The proposed novelty includes adapting pre-trained CNN architectures specifically for real-world pile integrity testing, addressing practical challenges such as data imbalance and limited dataset size through targeted oversampling techniques. The proposed approach demonstrates significant improvements in accuracy and efficiency compared to manual interpretation methods, making it a promising solution for practical applications in the construction industry. The proposed method demonstrates potential for generalization across varying pile lengths and geological conditions, though further validation with broader datasets is recommended.

Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)–based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset – namely, Hermos – for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data.

In order to present large amount of information on the Web to both users and machines, it is urgently needed to structure Web data. E-commerce is one of the areas where increasing data bottlenecks on the Web inhibit data access. Ontological display of the product information enables better product comparison and search applications using the semantics of the product specifications and their corresponding values. In this article, we present a framework called OPPCAT, which is used for semi-automatic ontology population from tabular data in e-commerce stores and product catalogues. As a result, OPPCAT allows tabular data to be used for mass production of ontology content. First, we present the common patterns in tabular data which obstruct semi-automatic production of ontologies. Then, we suggest solutions which automatically fix these errors. Finally, we define an algorithm to build ontology content semi-automatically.