Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

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Now showing 1 - 7 of 7
  • Conference Object
    Citation - Scopus: 2
    Digital Storytelling on a Virtual Heritage Museum With Believable Agents
    (Ieee, 2021) Kalan, Kaan; Dikbayir, Hasan Saygin; Merdivanli, Ogulcan; Degirmenci, Utku Mert; Clarke, Paul M.; Gulec, Ulas; Yilmaz, Murat
    The preservation of cultural heritage plays a very important role in terms of the sustainability of societies since culture is one of the most important phenomena that hold individuals together. However, although the protection of cultural heritage is a very important issue for societies, there are serious problems in the accuracy of information or access to information due to the verbal or written communication of the subjects that need to be conveyed. In particular, there is a serious decrease in the sense of belonging in individuals due to the inability to transfer cultural heritage to the younger generations correctly. At this point, the aim of the study is to teach individuals the Turkish horror culture by experiencing them in a realistic environment with various different stories in a virtual reality-based gamified system instead of teaching them in writing or verbally. For this purpose, a 3D virtual museum was developed within the scope of the study, inspired by real characters and areas, and it was aimed for individuals to learn Turkish horror culture elements through tasks in different scenarios. The developed system was tested by 5 experts in the field as a preliminary study and the realism level of the created system was measured with the comments of the experts. According to the findings, it has been determined that the level of realism offered by the designed virtual environment is sufficient to explain the Turkish horror culture to individuals.
  • Conference Object
    Citation - WoS: 7
    Citation - Scopus: 10
    Ads-B Attack Classification Using Machine Learning Techniques
    (Ieee, 2021) Kacem, Thabet; Kaya, Aydin; Keceli, Ali Seydi; Catal, Cagatay; Wijsekera, Duminda; Costa, Paulo; Seydi Keceli, Ali
    Automatic Dependent Surveillance Broadcast (ADS-B) is one of the most prominent protocols in Air Traffic Control (ATC). Its key advantages derive from using GPS as a location provider, resulting in better location accuracy while offering substantially lower deployment and operational costs when compared to traditional radar technologies. ADS-B not only can enhance radar coverage but also is a standalone solution to areas without radar coverage. Despite these advantages, a wider adoption of the technology is limited due to security vulnerabilities, which are rooted in the protocol's open broadcast of clear-text messages. In spite of the seriousness of such concerns, very few researchers attempted to propose viable approaches to address such vulnerabilities. In addition to the importance of detecting ADS-B attacks, classifying these attacks is as important since it will enable the security experts and ATC controllers to better understand the attack vector thus enhancing the future protection mechanisms. Unfortunately, there have been very little research on automatically classifying ADS-B attacks. Even the few approaches that attempted to do so considered just two classification categories, i.e. malicious message vs not malicious message. In this paper, we propose a new module to our ADS-Bsec framework capable of classifying ADS-B attacks using advanced machine learning techniques including Support Vector Machines (SVM), Decision Tree, and Random Forest (RF). Our module has the advantage that it adopts a multi-class classification approach based on the nature of the ADS-B attacks not just the traditional 2-category classifiers. To illustrate and evaluate our ideas, we designed several experiments using a flight dataset from Lisbon to Paris that includes ADS-B attacks from three categories. Our experimental results demonstrated that machine learning-based models provide high performance in terms of accuracy, sensitivity, and specificity metrics.
  • Conference Object
    Citation - WoS: 1
    Topic Distribution Constant Diameter Overlay Design Algorithm (td-Cd
    (Ieee, 2017) Oztoprak, Kasim; Dogdu, Erdogan; Layazali, Sina
    Publish/subscribe communication systems, where nodes subscribe to many different topics of interest, are becoming increasingly more common in application domains such as social networks, Internet of Things, etc. Designing overlay networks that connect the nodes subscribed to each distinct topic is hence a fundamental problem in these systems. For scalability and efficiency, it is important to keep the maximum node degree of the overlay in the publish/subscribe system low. Ideally one would like to be able not only to keep the maximum node degree of the overlay low, but also to ensure that the network has low diameter. We address this problem by presenting Topic Distribution Constant Diameter Overlay Design Algorithm (TD-CD-ODA) that achieves a minimal maximum node degree in a low-diameter setting. We have shown experimentally that the algorithm performs well in both targets in comparison to the other overlay design algorithms.
  • Conference Object
    Citation - WoS: 9
    Citation - Scopus: 16
    Perlin Random Erasing for Data Augmentation
    (Ieee, 2021) Saran, Ayse Nurdan; Saran, Murat; Nar, Fatih
    In the last decade, Deep Learning is applied in a wide range of problems with tremendous success. Large data, increased computational resources, and theoretical improvements are main reasons for this success. As the dataset grows, the real-world is better represented, allows developing a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, or sometimes even challenging. Therefore, researchers proposed data augmentation methods to increase dataset size by creating variations of the existing data. This study proposes an extension to Random Erasing data augmentation method by introducing smoothness. The proposed method provides better performance compared to Random Erasing data augmentation method, which is shown using a transfer learning scenario on the UC Merced Land-use image dataset.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Intelligent Decision Support System for Energy Investments
    (Ieee, 2013) Milletsever, Ozlem; Inal, Ugur; Yavanoglu, Uraz; Kaplan, Orhan; Atli, Hacer; Tanis, Gizem
    In this study, Turkey's energy data were examined and decision support system was formed for Turkey. Energy is an important need for the economic development of the country. The energy requirements are increased due to population growth of Turkey. It was decided that this study is done after these requirements and Turkey energy policy were evaluated. 3 topics were discussed for the system. Natural gas, export, and transmission line length are estimated. Artificial neural network is used for energy estimation. In trainings, the energy data of Turkey Electricity Distribution Company are used. The results show that average success is 99%. This decision support system will contribute to Turkey Electricity Distribution Company.
  • Conference Object
    Citation - WoS: 5
    Citation - Scopus: 8
    Choosing Parameters To Achieve a Higher Success Rate for Hellman Time Memory Trade Off Attack
    (Ieee, 2009) Saran, Nurdan; Doganaksoy, Ali
    In 1980, Hellman proposed the Time Memory Trade Off (TWTO) attack and applied it on block cipher DES (Data Encryption Standard). Time Memory Trade Off attack is one of the methods that inverts a one way function. The resistance to TWO attacks is an important criterion in the design of a modern cipher Unlike the exhaustive search and table lookup methods, TWO is a probabilistic method, that is, the search operation may not find a preimage even if there exists one. Up to now, there are some approximate bounds for success rates of Hellman table by Hellman and Kusuda et al. In this study, we give a more precise approximation for the coverage of a single Hellman table. There is no precise guideline in the literature that points out how to choose parameters for Hellman TWO. We present a detailed analysis of the success rate of Hellman table via new parameters and also show how to choose parameters to achieve a higher success rate. The results are experimentally confirmed. We also discuss the Hellman's TMTO Curve.
  • Conference Object
    Citation - Scopus: 4
    A Drift-Reduced Scheme for Hierarchical Wavelet Coding Scalable Video Transmissions
    (Ieee, 2009) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.
    Scalable video coding allows for the capability of (partially) decoding a video bitstream when faced with communication deficiencies such as low handwidth or loss of data resulting in lower video quality. As the encoding is usually based on perfectly reconstructed frames, such deficiencies result in differently decoded frames at the decoder than the ones used in the encoder and, therefore, leading to errors being accumulated in the decoder. This is commonly referred to as the drift error. Drift-free scalable video coding methods also suffer from the low performance problem as they do not combine the residue encoding scheme of the current standards such as MPEG-4 and H.264 with scalability characteristics. We propose a scalable video coding method which is based on the motion compensation and residue encoding methods found in current video standards combined with the scalability property of discrete wavelet transform. Our proposed method aims to reduce the drift error while preserving the compression efficiency. Our results show that the drift error has been greatly reduced when a hierarchical structure for frame encoding is introduced.