LEE- Bilgisayar Mühendisliği Lisansüstü Programı
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Sustainable Development Goal "Goal 9: Industry, Innovation and Infrastructure" ile LEE- Bilgisayar Mühendisliği Lisansüstü Programı'a göz atma
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ÖgeA variational graph autoencoder for manipulation action recognition and prediction(Graduate School, 2022-06-23) Akyol, Gamze ; Sarıel, Sanem ; Aksoy, Eren Erdal ; 504181561 ; Computer EngineeringDespite decades of research, understanding human manipulation actions has always been one of the most appealing and demanding study problems in computer vision and robotics. Recognition and prediction of observed human manipulation activities have their roots in, for instance, human-robot interaction and robot learning from demonstration applications. The current research trend heavily relies on advanced convolutional neural networks to process the structured Euclidean data, such as RGB camera images. However, in order to process high-dimensional raw input, these networks must be immensely computationally complex. Thus, there is a need for huge amount of time and data for training these networks. Unlike previous research, in the context of this thesis, a deep graph autoencoder is used to simultaneously learn recognition and prediction of manipulation tasks from symbolic scene graphs, rather than using structured Euclidean data. The deep graph autoencoder model which is developed in this thesis needs less amount of time and data for training. The network features a two-branch variational autoencoder structure, one for recognizing the input graph type and the other for predicting future graphs. The proposed network takes as input a set of semantic graphs that represent the spatial relationships between subjects and objects in a scene. The reason of using scene graphs is their flexible structure and modeling capability of the environment. A label set reflecting the detected and predicted class types is produced by the network. Two seperate datasets are used for the experiments, which are MANIAC and MSRC-9. MANIAC dataset consists 8 different manipulation action classes (e.g. pushing, stirring etc.) from 15 different demonstrations. MSRC-9 consists 9 different hand-crafted classes (e.g. cow, bike etc.) for 240 real-world images. The reason for using such two distinct datasets is to measure the generalizability of the proposed network. On these datasets, the proposed new model is compared to various state-of-the-art methods and it is showed that the proposed model can achieve higher performance. The source code is also released https://github.com/gamzeakyol/GNet.
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ÖgeAğ iletişimlerinde temel yenilikçi çözümlerin standartlaştırılması(Lisansüstü Eğitim Enstitüsü, 2023-08-30) Kalkan, Muhammed Salih ; Seçinti, Gökhan ; 504191579 ; Bilgisayar MühendisliğiAğ iletişimlerindeki problemler oldukça eskiye dayanır. Bu problemleri çözmek için birçok çalışma yapılmıştır. Bu çalışmalar, günümüzde OSI model olarak adlandırdığımız, katmanlı bir iletişim yapısını ortaya çıkarmıştır. Bu katmanlardan birisi uygulama katmanıdır. Mesajlaşma ile ilgili problemler, bu katmana aittir. Dolayısıyla, mesajlaşma ile ilgili özellikler bu katmanda kullanılır. Bazı mesajlaşma özelliklerini standartlaştırmak için, bazı uygulama katmanı protokoller oluşturulmuştur. AMQP, MQTT vb. protokoller, uygulama katmanı protokollerine örnektir. Bu araştırmada da, temel yenilikçi çözümler uygulama katmanında değerlendirilir. Uygulamalar, mesajlaşma ile ilgili sorunları farklı şekillerde çözmektedir. Bazı özellikler uygulama koduyla, bazıları kütüphanelerle ve bazıları da protokollerle standardize edilerek sağlanır. Uygulama koduna eklenen mesajlaşma özelliklerinin her uygulama için tekrar tekrar yazılması gerekmektedir. Her uygulama için gerekli mesajlaşma özelliklerinin kodlarının tekrar tekrar yazılması, iş gücü kaybına, hata olasılığına, kodun her seferinde artan karmaşıklığına neden olur. Mesajlaşma sorunlarını kütüphane kodları ile çözmek, bu kütüphanenin diğer tüm uç noktalarla paylaşılmasını gerekli kılar. Bu nedenle mesajlaşma özelliklerinin bir protokol ile standardize edilmesi gerekmektedir. Bu çalışmada, yerel ağlarda ve IoT'de kullanılmak üzere temel yenilikçi özellikleri standartlaştırarak iş gücü kazancı sağlanması, uygulama kodunun karmaşıklığının azaltılması, çözümlerin her uç nokta için ortaklanması amaçlanmıştır. Bir protokol standardı oluşturmak için, protokollere ait özelliklerin arkaplan bilgisine ihtiyaç vardır. Bu yüzden öncelikle, ikili-metin protokoller, iletişim modelleri, merkezi-merkeziyetsiz yaklaşımlar gibi arkaplan bilgileri incelenmiştir. İkili protokoller, verileri ikili olarak ileten protokollerdir. Metin protokolleri, verileri Unicode veya ASCII olarak ileten protokollerdir. İkili protokoller, verilerin daha küçük boyutlarda iletilmesini sağladığı için performans açısından daha iyidir. Metin protokolleri, verileri daha büyük boyutlarda iletmesine karşın ikili protokollere kıyasla kolayca hata ayıklanabilir ve veriler insan tarafından okunabilirdir. Hem yüksek performans özelliği, hem verinin okunabilir olma özelliğine sahip olmak için, izleyici uç noktanın, ikili verilerin metin karşılıklarını bilmesi gerekir. Ayrıca ikili protokoller için bayt sırası (endianness) önemliyken, metin protokolleri için bayt sırası önemli değildir. Cihazın endianness tipi little-endian veya big-endian olabilir. İkili protokollerde, farklı endianness'e sahip iki cihaz iletişim kurduğunda, verilerin serileştirilmesinden önce ve verinin serisini çözümleme işleminden önce verilerin bayt adreslemesi tersine çevrilmelidir. Bu problemlerin çözümleri, uygulama katmanında standartlaştırılırsa, geliştiricilerin bu problemleri tekrar tekrar çözmeye çalışmasına gerek kalmaz. Sunucu-istemci modeli, birden fazla istemci uç noktasının tek bir sunucu uç noktasından hizmet talep ettiği bir modeldir. Yayınla-abone ol modeli, yayıncı ve abone uç noktalarının merkezi bir mesaj yönelimli ara yazılım aracılığıyla mesaj iletimlerini sağlayan bir modeldir. Uç noktalar, konulara abone olur veya mesajları yayınlar. Mesaj aracısı, yayınlanan mesajları, mesaja abone olan uç noktalara iletir. Mesaj aracısı, gevşek bağlantı ve esneklik sağlar. Uç noktalar, birbirlerinin varlığından bağımsız olarak mesajlaşmaya devam eder. Transformatörler ve filtreler, mesaj aracısı üzerinde çalışabilir. Gevşek bağlantı aynı zamanda bir dezavantajdır. Yayıncı uç noktaları, abone uç noktalarının iletişim kurup kurmadığından emin olamaz. Yayıncılar ve aboneler arttıkça, mesaj aracısını aşırı yükleyebilir. Mesaj aracısı, merkezi olduğundan darboğaza neden olabilir. Bu, yatay ölçeklenebilirliği sınırlar. İletileri doğrudan hedef uç noktalara iletmek yerine önce mesaj aracısına iletmek gecikmeyi artırır. Mesaj aracısı ile gelen bu problemlerden kurtulmak için, merkezi olmayan yayınla-abone ol modeline ihtiyaç vardır. Mesajlaşan uç noktalar için en büyük sorunlardan biri, uç noktalardan birinde mesaj yapılarının güncel olmaması veya yanlış implement edilmiş olmasıdır. Mevcut mesajlaşma protokolleri için, bir bağlantıdaki uç noktaların mesaj yapılarının uyumluluğunu kontrol etmeye yönelik standart bir yaklaşım yoktur. Bir iletişimde giden ve gelen mesajları izlemek kritik olabilir. Mesaj gönderme noktadan noktaya ise, üçüncü bir izleme uzak uç noktası iletişime dahil edilemez. IP paket başlığındaki hedef IP adresi, noktadan noktaya iletişim için tek bir cihaza ait olmalıdır. Bu problem, uygulama katmanında üçüncü uzak noktalara yönlendirme yapılarak çözülebilir. Birçok uygulama katmanı protokolü, taşıma katmanındaki bir protokole bağlıdır. Bu da gelecek kullanımları kısıtlayabilir. Örneğin, QUIC protokolü, TCP'nin yerini aldığını varsayalım. Artık TCP implementasyonlarının ortadan kalktığını varsayalım. Bu durumda, düzinelerce TCP tabanlı protokolün yeni bir sürümle QUIC tabanlı olması gerekecektir. Bu yüzden alt protokollerden soyutlanmak, gelecek kullanımlar için önemlidir. Birden çok protokol kullanmak için birden çok iletişim arabirimi oluşturulmalıdır. Ancak bir protokol, çoklu alt katman protokol ile kullanılabilir olma özelliğine sahip ise, tek bir iletişim arabirimi yeterli olacaktır. Bu çalışmada, mevcut protokollerin, bu sorunları ne kadar çözdüğüne dair veriler toplandı. Bu sorunları çözen özellikler ile mevcut protokolleri kullanarak bir tablo oluşturuldu. Diğer uygulama katmanı protokollerinin tüm bu özellikleri desteklemediği görülmektedir. Bu nedenle, bu özellikleri sağlayan yeni bir protokole ihtiyaç vardır. Bu protokolün adı mesajlaşma kontrol protokolüdür (MCP). MCP'nin hedeflediği kullanım alanı daha çok yerel ağ iletişimleridir. MCP, daha çok yerel ağ iletişimleri, asenkron iletişimler, non-stateless iletişimler ve gömülü sistemlerde kullanılabilecek özelliklere yoğunlaşmıştır. MCP'nin alt katman protokollerinden bağımsız olması için ve çoklu alt protokollerle kullanılabilmesi için MCP'nin iki bileşeni vardır: MCP Adaptörü ve iletişim arayüzü. MCP Adaptörü, MCP'nin ön koşullarını sağlamak için gereklidir. Alt protokollerin işlevlerini kullanmak için iletişim arayüzü gereklidir. Böylece MCP alt protokollerden bağımsız hale gelir ve birden fazla alt protokol ile kullanılabilir. MCP'de iki mesaj sınıfı vardır: MCP Standart Mesajı, MCP Uygulama Mesajı. MCP, MCP standart mesajları olarak adlandırılan, uygulama kodundan bağımsız yerleşik mesajlara sahiptir. 5 tür standart mesaj vardır: El Sıkışma Mesajı, Kalp Atışı Mesajı, Rol Başvuru Mesajı, Abone Olma Mesajı, Abonelikten Çıkma Mesajı. İstemciler, kullanıcı tanımlı mesajların yapılarını el sıkışma istek mesajı ile JSON formatında gönderir. Böylece uç noktaların mesaj uyumlulukları kontrol edilir. Sunucu, endianness tipini el sıkışma yanıt mesajı ile gönderir. İstemci, sunucunun endianness tipini öğrenir. İstemci ve sunucunun endianness türleri farklıysa, istemci verilerin bayt sıralamasını otomatik olarak değiştirir. Bağlantının canlı olup olmadığını tespit etmek için periyodik olarak kalp atışı mesajı gönderilir. Bir istemci, bir mesaja abone olmak için ya da bir mesajın aboneliğinden çıkmak için Abone Olma Mesajı ve Abonelikten Çıkma Mesajını kullanır. MCP uygulama mesajları, uygulama kodunda tanımlanan mesajlardır. Dört tür uygulama mesajı vardır: İstek-Yanıt Mesajı, Olay Mesajı, Başlangıç Mesajı, Rapor Mesajı. İstek-yanıt mesajları için, yalnızca ilgili istek mesajı alındığında ilgili yanıt mesajı oluşturularak iletişim sağlanır. Olay mesajları, bir olayın tetiklenmesi ile iletilir. Olay mesajları tüm bağlı abone istemcilerine gönderilir. Başlangıç mesajı, aslında bağlantı kurulduğunda tetiklenen bir olay mesajıdır. Rapor mesajı, aslında zamana göre tetiklenen bir olay mesajıdır. Yetkilendirme için rol tabanlı erişim kontrol yöntemi kullanılır. İstemcilerin MCP bağlantısında rolleri vardır. İstemcilerin rolleri, mesajlaşma arayüzündeki mesajların erişilebilirliğini belirler. Sunucu, her mesaj için hangi istemci rollerinin erişebileceğini belirler. Rollerin istemcilere atanmasını ise, admin rolündeki istemci gerçekleştirir. Noktadan noktaya iletişimde mesajları izlemek isteyen istemcilerin rolü, izleme rolüdür. İzleyici rolü, iletilerin erişilebilirliğinden bağımsızdır. Noktadan noktaya iletişimdeki tüm mesajlar monitör istemcisine iletilir. İzleme istemcisi, iletişime katılmak için bir bağlantı isteği gönderir. Monitör, bağlantı kurma aşamasında el sıkışma mesajı ile mesaj yapılarını alır ve iletişimdeki ikili verilerin metin karşılıklarını öğrenir. Böylece veriler ikili olarak iletilse de, metin olarak görüntülenebilir. Uygulama katmanında oluşturulan MCP protokolü, mesajlaşma problemlerini protokol kodunda çözerek problemlerin çözümünü standardize eder. Diğer uygulama katmanı protokolleri, MCP'nin çözdüğü tüm sorunları çözemez. Bu nedenle, MCP fark yaratır. MCP kullanılırsa, bu çalışmada belirtilen çözümlerin uygulama kodunda olmasına gerek kalmaz. Böylece uygulama kodunun karmaşıklığı azaltılmakta ve mesajlaşma özelliklerinde oluşabilecek hatalar ortadan kaldırılmaktadır. MCP sadece mesajlaşma için birçok özellik sunmakla kalmaz, aynı zamanda performansa da önem verir. Performans için, MCP dinamik başlık boyutunu kullanır ve MCP ikili protokoldür. MCP, temel mesajlaşma problemlerine odaklandığı ve performansı önemsediği için yerel ağların yanında IoT'ye de uygulanabilir. Gelecekte IoT alanında MCP'nin kullanılabilmesi için analizler yapılabilir. Sonuç olarak, MCP yenilikçi temel mesajlaşma özellikleri sağlar, bu özellikleri standardize ederek hata olasılığını azaltır ve uygulama kodunun karmaşıklığını azaltır.
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ÖgeDirectional regularization based variational models for image recovery(Graduate School, 2022-08-19) Türeyen Demircan, Ezgi ; Kamaşak, Mustafa E. ; 504152509 ; Computer EngineeringThis thesis explores how local directional cues can be utilized in image recovery. Our intent is to provide image regularization paradigms that encourage the underlying directionalities. To this end, in the first phase of the thesis work, we design direction-aware analysis-based regularization terms. We boost the structure tensor total variation (STV) functionals used in inverse imaging problems so that they encode directional priors. More specifically, we suggest redefining structure tensors to describe the distribution of the ``directional" first-order derivatives within a local neighborhood. With this decision, we bring direction-awareness to the STV penalty terms, which were originally imposing local structural regularity. We enrich the nonlocal counterpart of the STV in the same way, which were additionally imposing nonlocal image self-similarity beforehand. These two types of regularizers are used to model denoising and deblurring problems within a variational framework. Since they result in convex energy functionals, we also develop convex optimization algorithms by devising the proximal maps of our direction-aware penalty terms. With these contributions in place, the major barrier in making these regularizers applicable lies in the difficulty of estimating directional parameters (i.e., the directions/orientations, the dose of anisotropy). Although, it is possible to come across uni-directional images, the real-world images usually exhibit no directional dominance. It is easy to precisely estimate the underlying directions of uni-directional (or partially directional) images. However, arbitrary and unstable directions call for spatially varying directional parameters. In this regard, we propose two different parameter estimation procedures, each of which employs the eigendecompositions of the semi-local/nonlocal structure tensors. We also make use of total variation (TV) regularization in one of the proposed procedures and a filterbank of anisotropic Gaussian kernels (AGKs) in the other. As our image regularization frameworks require the guidance of the directional parameter maps, we use the term ``direction-guided" in naming our regularizers. Through the quantitative and the visual experiments, we demonstrate how beneficial the involvement of the directional information is by validating the superiority of our regularizers over the state-of-the-art analysis-based regularization schemes, including STV and nonlocal STV. In the second phase of the thesis, we shift our focus from model-driven to data-driven image restoration, more specifically we deal with transfer learning. As the target field, we choose fluorescence microscopy imaging, where noise is a very usual phenomenon but data-driven denoising is less applicable due to lack of the ground-truth images. In order to tackle this challenge, we suggest tailoring a dataset by handpicking images from unrelated source datasets. This selective procedure explores some low-level view-based features (i.e., color, isotropy/anisotropy, and directionality) of the candidate images, and their similarities to those of the fluorescence microscopy images. Based upon our experience on the model-driven restoration techniques, we speculate that these low-level characteristics (especially directions) play an important role on image restoration. In order to encourage a deep learning model to exploit these characteristics, one could embed them into the training data. In fact, we establish the possibility of offering a good balance between content-awareness and universality of the model by transferring only low-level knowledge and letting the unrelated images bring additional knowledge. In addition to training a feed-forward denoising convolutional neural network (DnCNN) on our tailored dataset, we also suggest integrating a small amount of fluorescence data through the use of fine-tuning for better-recovered micrographs. We conduct extensive experiments considering both Gaussian and mixed Poisson-Gaussian denoising problems. On the one hand, the experiments show that our approach is able to curate a dataset, which is significantly superior to the arbitrarily chosen unrelated source datasets, and competitive against the real fluorescence images. On the other hand, the involvement of fine-tuning further boosts the performance by stimulating the content-awareness, at the expense of a limited amount of target-specific data that we assume is available.
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ÖgeEffect of semi-supervised self-data annotation on video object detection performance(Graduate School, 2022-06-22) Akman, Vefak Murat ; Töreyin, Behçet Uğur ; 704191017 ; Computer SciencesAccess to annotated data is more crucial than ever when deep learning frameworks replace traditional machine learning methodologies. Even if the method is robust, training performance can be inadequate if the data has poor quality. Some methods were developed to address data-related issues. These methods, however, have a negative impact on algorithm complexity and processing cost. Errors related to human factors, such as misclassification or inaccurate labeling, should also be considered. Multiple steps in the data annotation process cost time and money. These steps can be listed as follows. Data gathering, annotation and formatting according to deep learning model architecture. Unfortunately, these steps are still not fully set to a standard and the whole process comes with a lot of difficulties. In this study, the effect of semi-supervised data annotation on video object detection is analysed by using the Soft Teacher algorithm. Soft Teacher is a Swin-Transformer backboned semi-supervised learning method which has a major advantage on overcoming limited data. Swin Transformer is a type of vision transformer. It creates hierarchical feature maps by merging image patches in deeper layers and has linear computation complexity to input image size. As a such, it can be used as a general-purpose backbone for tasks like classification and object detection. In Soft Teacher, there are two types of models; the Student model and the Teacher model. The Teacher model performs pseudo-labeling on weak augmented unlabeled images and the Student model is trained on both labelled and strong augmented unlabeled images while updating the Teacher model. Soft Teacher model was trained with open-source COCO data set that consists of 80 labels. The data set contains 118287 train, 123403 unlabeled and 5000 validation images, was created by the human. The Soft Teacher was trained with percent of 1, 5, 10 and 100 labelled data respectively. Then, using those trained Soft Teacher models, new data was created from the same raw data and some of the state-of-the-art object detection algorithms were trained with newly annotated data. To compare results, these object detection models were also trained with manual annotated data. The model trained with human data was shown to be less successful than the other in terms of mAPs. However, the model that was trained with self annotated data produced more false positives. Because, the trained model can perform mislabeling when generating new data. In conclusion, the results suggest that semi-supervised data annotation degrades the detection performance in expense of huge amounts of training time savings.
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ÖgeFight recognition from still images in the wild(Graduate School, 2022-06-22) Aktı, Şeymanur ; Ekenel, Hazım Kemal ; 504191539 ; Computer EngineeringViolence in general is a sensitive subject and can have a negative impact on both the involved people and witnesses. Fighting is one of the most common types of violence which can be defined as an act where individuals intend to harm each other physically. In daily life, these kinds of situations might not be faced too often, however, the violent content on social media is also a big concern for the users. Since violent acts or fights in particular are considered as an anomaly or intriguing for some, people tend to record these scenes and upload them on their social media accounts. Similarly, news agencies also regard them as newsworthy material in some cases. As a result, fighting scenes become available on social media platforms frequently. Some users may be sensitive to these kinds of media content and children who can be harmed due to the aggressive nature of the fight scenes also uses social media. These facts make it necessary to detect and put limitations on the distribution of violent content on social media. There are some systems focusing on violence and fight recognition on visual data. However, these works mostly propose methods on different domains for violence such as movies, surveillance cameras, etc., and the social media case remains unexplored. Furthermore, even if most of the fight scenes shared on social media are in video sequences, there is also a non-ignorable amount of image data depicting violent fighting. However, no work tackles the fight recognition from still images instead of videos. Thus, in this thesis, the problem of fight recognition from still images is investigated. In this scope, first, a novel dataset was collected from social media images which is named Social Media Fight Images (SMFI). The dataset was collected from Twitter and Google images and some frames were included from the video dataset of NTU CCTV-Fights. The fight samples were chosen among the samples which are recorded in uncontrolled environments. In order to crawl a large amount of data, different keywords were used in various languages. The non-fight samples were also chosen among the data crawled from social media in order to keep the domain consistent across the classes. The dataset is made publicly available by sharing the links to the images. For the classification of the Social Media Fight Images dataset, some image classification methods were applied to the dataset. First, Convolutional Neural Networks (CNN) were employed for the task and their performance was assessed. Then, a recent approach, Vision Transformer (ViT) was exploited for the classification of the fight and non-fight images. The comparison showed that the Vision Transformer gives better results on the dataset achieving a higher accuracy with less overfit. A further experiment was also held on investigating the effect of varying dataset sizes on the performance of the model. This was seen as necessary as the data shared on social media may be deleted in the future and it is not always possible to retrieve the whole dataset. So, the model was trained on different partitions of the dataset and the results showed that even if using more data is better, the model could still give satisfying performance even in absence of 60% of the dataset. Upon the successful results on fight recognition on still images problem, another experimental study was conducted on the classification of video-based datasets using a single frame from each sample. The experiment included four video-based fight datasets and results showed that three of them could be successfully classified without using any temporal information. This indicated that there might be a dataset bias for these three datasets where the inter-class visual difference is high across the classes. Cross-dataset experiments also supported this hypothesis where the trained models on these video datasets perform poorly on the other fight recognition datasets. Nonetheless, the network trained on the proposed SMFI dataset gave a promising accuracy on other datasets as well, showing that the dataset generalizes the fight recognition problem better than the others.
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ÖgeHybridization of probabilistic graphical models and metaheuristics for handling dynamism and uncertainty(Graduate School, 2021-06-30) Uludağ, Gönül ; Etaner Uyar, Ayşe Şima ; 504072510 ; Computer EngineeringSolving stochastic complex combinatorial optimisation problems remains one of the most significant research challenges that cannot be adequately addressed not only by deterministic methods but also by some metaheuristics. Today's real-life problems in a broad range of application domains from engineering to neuroimaging are highly complex, dynamic, uncertain, and noisy by nature. Such problems cannot be solved in a reasonable time because of some properties including noisy fitness landscape, high non-linearities, large scale, high multi-modality, computationally expensive objectives functions. The environmental variabilities and uncertainties may be occurred in the problem instance, the objective functions, the design variables, the environmental parameters, and the constraints. Thus, the variations and uncertainties may be due to a change in one or more of these components over time. It is commonly informed that the environmental dynamism is classified based upon the change frequency, predictability, and severity as well as whether it is periodic or not. Different types of variations and uncertainties may arise over time due to the dynamic nature of the combinatorial optimisation problem, and hence an approach chosen at the start of the optimisation may become inappropriate later on. It is expected that such search methodologies for the time-variant problems would be capable of adapting to the change not only efficiently but also quickly, as well as handling the uncertainty such as noise and volatility. On the other hand, it is crucial to identify and adjust the values of numerous parameters of the metaheuristic algorithm while balancing two contradictory criteria: exploitation (i.e., intensification) and exploration (i.e., diversification). Therefore, the self-adaptation is a critical parameter control strategy in metaheuristics for time-variant optimisation. There exists lots of study concerning time-variant problem to handle dynamism and uncertainty, yet a comprehensive approach to address different variations at once still seems to be a task to accomplish. The ideal strategies should take into consideration both environmental dynamism and uncertainties, whereas conventional approaches; however, problems are postulated as time-invariant and disregard this variability and uncertainties. Meanwhile, each real-world problem exhibits different types of changes and uncertainties. Thus, solving such complex problems remains extremely challenging due to the variations, dependencies, and uncertainties during the optimisation process. Probabilistic graphical models are the principal probabilistic model for which a graph expresses the conditional dependence structure to represent complex, real-world phenomena in a compact fashion. Hence, they provide an elegant language to handle complexity and uncertainty. Such properties of probabilistic graphical models have led to further developments in metaheuristics that can be termed probabilistic graphical models-based metaheuristic algorithms. Probabilistic graphical model-based metaheuristic algorithms are acknowledged as highly self-adaptive, and thus able to handle different types of variations. There is a range of probabilistic graphical model-based metaheuristic approaches, e.g., variants of estimation of distribution algorithms suggested in the literature to address dynamism and uncertainty. One of the remarkable state-of-the-art continuous stochastic probabilistic graphical model-based metaheuristic approaches is the covariance matrix adaptation evolution strategy. The covariance matrix adaptation evolution strategy approach and its variants (e.g. covariance matrix adaptation evolution strategy with the increasing population; Ipop-CMA-ES) have become a sophisticated adaptive uncertainty handling scheme. The characteristics of these approaches make them more plausible for handling uncertainty and rapidly changing variations. In recent years, the concept of semi-automatic search methodologies called hyper-heuristics has become increasingly important. Many metaheuristics operate directly on the solution space and utilize problem domain-specific information. However, hyper-heuristics are general methodologies that explore over the space formed by a set of low-level heuristics that perturb or construct a (set of) candidate solution(s) to make self-adaptive decisions for dynamic environments to deal with computationally difficult problems. Besides several impressive research studies that have been carried out on variants of probabilistic graphical model-based metaheuristic algorithms, there also exist many extensive research studies that have been working on machine learning-based optimisation approaches. One of the most popular such methods is the expectation-maximization algorithm, which is a widely used scheme for the optimisation of likelihood functions in the presence of latent (i.e., hidden) variables models. Expectation-maximization is a hill-climbing approach to finding a global maximum of a likelihood function that required achieving convergence to global optima in a reasonable time. One of the extremely challenging dynamic combinatorial optimisation problems is the unit commitment problem, which in the engineering application domain. The unit commitment problem is considered as an NP-hard, non-convex, continuous, constrained dynamic combinatorial optimisation problem in which turn-on/off scheduling of power generating resources is utilized over a given time horizon to minimize the joint cost of committing and de-committing. Another such problem is effective connectivity analysis, which is one of the neuroimaging application areas. The predominant scheme of inferring (i.e., estimating) effective connectivity is dynamic causal modelling, provides a framework for the analysis of effective connectivity (i.e., the directed causal influences between brain areas) and estimating their biophysical parameters from the measured blood oxygen level-dependent functional magnetic resonance responses. However, although, different kinds of metaheuristic- or machine learning-based algorithms have become more satisfying within different types of dynamic environments, neither metaheuristic- nor machine learning-based algorithms are capable of consistently handle the environmental dynamism and uncertainty. In this sense, it is indispensable to hybridize metaheuristics with probabilistic or statistical machine learning to utilize the advantages of both approaches for coping with such challenges. The main motivation of hybridization is to exploit the complementary aspect of different methods. In other words, hybrid frameworks are expected to benefit from the synergy effect. The design and development of hybrid approaches are considered to be promising due to their success in handling variations and uncertainties, and hence, increased attention in recent years has been focused on the fields of metaheuristics and their hybridization. Intuitively, the central idea behind such an approach is based on the two principal theories of the "no free lunch theorem" perspectives: one for supervised machine learning, and one for search/optimisation. Within the context of no free lunch theorem perspective, the following hybrid frameworks are addressed: (i) In the case of no free lunch theorem for search/optimisation, utilize machine learning approaches to enhance metaheuristics; (ii) In the case of no free lunch theorem for machine learning, utilize metaheuristics to improve the performance of machine learning algorithms. Within the scope of this dissertation, each proposed hybrid framework is built on the corresponding "no free lunch theorem" perspective. The first introduced hybrid framework is designed on the no free lunch theorem for search/optimisation concept, referred to as hyper-heuristic-based, dual population estimation of distribution algorithm (HH-EDA2). Within this notion, especially probabilistic model-based schemes are employed to enhance probabilistic graphical model-based metaheuristics that utilize the synergy of selection hyper-heuristic schemes and dual population estimation of distribution algorithm. HH-EDA2 is the form of a two-phase hybrid approach that performs offline and online learning schemes to handle uncertainties and unexpected variations of combinatorial optimisation problems regardless of their dynamic nature. The important characteristic feature of this framework is to integrate any multi-population estimation of distribution algorithms with any probabilistic model-based approach selection hyper-heuristic into the proposed approach. The performance of the hybrid HH-EDA2 along with the influence of different heuristic selection methods was investigated over a range of dynamic environments produced by a well-known benchmark generator as well as over unit commitment problem, which is known as NP-hard constrained combinatorial optimisation problem as a real-life case study. The empirical results show that the proposed approach outperforms some of the best-known approaches in the literature on the non-stationary environment problems dealt with. The second proposed hybrid framework is designed on the no free lunch theorem for machine learning, referred to as Bayesian-driven covariance matrix adaptation evolution strategy with an increasing population (B-Ipop-CMA-ES). Within this notion, especially probabilistic model-based metaheuristics are employed to enhance probabilistic graphical models that utilize the synergy of covariance matrix adaptation evolution strategy algorithm and expectation-maximization schemes. This hybrid framework performs the estimation of biophysical parameters of effective connectivity (i.e., dynamic causal modelling) that enable one to characterize and better understand the dynamic behaviour of the neuronal population. The main attestation of the B-Ipop-CMA-ES is to get rid of crucial issues of dynamic causal modelling, including prior knowledge dependence, computational complexity, and a tendency of getting stuck on local optima. B-Ipop-CMA-ES is capable of performing physiologically plausible models while converging to the global solution in computationally feasible time without relying on initial prior knowledge of biophysical parameters. The performance of the B-Ipop-CMA-ES framework was investigated on both synthetic and empirical functional magnetic resonance imaging datasets. Experimental results demonstrate that B-Ipop-CMA-ES framework outperformed the reference (expectation-maximization/Gauss-Newton) and other competing methods.
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ÖgeNovel data partitioning and scheduling schemes for dynamic federated vehicular cloud(Graduate School, 2022-08-23) Danquah, Wiseborn Manfe ; Altılar, Turgay ; 504122522 ; Computer EngineeringIn the last decade, many Intelligent Transport Systems (ITS) applications, that rely on the Internet cloud infrastructure have been deployed to provide services to clients in vehicular environments. However, the Internet cloud suffers from high latency in service access and intermittent Internet disconnection in vehicular environments. In response to these challenges, researchers have proposed the use of other distributed computing technologies such as mobile cloud computing, edge computing, and vehicular cloud computing which may rely on Wireless Local Area Networks (WLAN) or Ad-hoc networks for communication to provide computing resources and services in vehicular environments. Vehicular cloud computing, an emerging distributed computing paradigm that provides cloud services using the resources embedded in vehicles and Road Side Units (RSUs), is arguably the best alternative to other distributed computing systems because of its advantages. The provision of vehicular cloud services leads to efficient utilization of the abundant resources in embedded vehicles and therefore embraces green technology in vehicular environments. Considering that modern vehicles and RSUs have been designed with communication capabilities through the use of the Vehicular Ad-hoc Network (VANET), vehicular cloud computing may not require additional communication hardware infrastructure installation along roads and at parking lots before its deployment. Furthermore, access to vehicular cloud services may involve a relatively lower latency and fewer intermittent disconnections due to the close proximity of vehicular cloud resources to clients and the use of VANET which may provide better connections among vehicular resources and clients than the connection to Internet cloud in vehicular environments. As with all emerging technologies of which vehicular cloud is not an exception, some challenges need to be addressed before its full real-world deployment. The challenges of vehicular cloud computing are mainly caused by the unique characteristics of vehicular resources and the limited communication bandwidth of VANET. In vehicular environments, i.e., on roads and parking lots, vehicles are not permanently stationed but mobile, making the resources embedded in vehicles highly mobile. Also, the resources of vehicles that are organized to provide cloud services belong to different people (distributed ownership). The high mobility and distributed ownership of resources imply that vehicular resources may exit the provisioned vehicular cloud abruptly by either withdrawal of resources by the resource owner or intermittent network disconnection caused by the high mobility of vehicles. Therefore distributed ownership of resources and high mobility of vehicles cause vehicular resources to be volatile, making their availability and reliability unpredictable. The capacity constraint of the communication bandwidth of VANET, which serves as a communication backbone of vehicular cloud, implies that the transmission of data-intensive and bandwidth-intensive applications in vehicular environments is a challenge in vehicular cloud computing. Considering that the distributed ownership of resources, limited communication bandwidth, and high mobility of vehicles lead to low availability and low reliability of resources in vehicular cloud computing, they adversely affect almost all resource management operations, such as virtual machine migration. Therefore, the main focus of this dissertation is to propose novel solutions to address the challenges of vehicular cloud and ameliorate the adverse effects of the identified characteristics of vehicular cloud computing. As an introduction, the background of vehicular cloud computing and a detailed survey of resource management operations in vehicular cloud are presented using a three-phase taxonomy of resource management techniques proposed in this dissertation. Based on the review of vehicular cloud computing concepts, a novel distributed vehicular computing paradigm, Vehicular Volunteer Computing (VVC), is proposed in this dissertation. VVC is a volunteer computing platform where vehicle owners may donate their idle processing units towards the execution of scientific and other projects that are beneficial to a community, such as the "Compute The Cure" cancer project. The concept of a Dynamic Federated Vehicular Cloud (DFVC) is introduced in this dissertation to overcome the challenge of limited resource capacity and volatility of resources in vehicular cloud. The DFVC entails the organization of resources from different vehicles moving on the road to provide a specific vehicular cloud service such as Computation-as-a-Service (CaaS). Although the resources are collated from different vehicles in the formation of DFVC, they are organized as a single logical unit for the provision of services. The formation of a DFVC involves forming resource-based clusters, i.e., grouping vehicles with similar resource and mobility characteristics as a single unit and selecting a leader, known as a cluster head, to manage the resources in a cluster of vehicles. By considering the structure of resource-based clusters formed in a Region of Interest (RoI), two different DFVC schemes are proposed in this dissertation: the Cluster-Based Vehicular Cloud (CBVC) and the Platoon-Based Vehicular Cloud (PBVC). In the CBVC, vehicles (owners) with a high reputation and idle resources on an RoI on the road are organized into clusters without adhering to the condition that all cluster members use the same lane and maintain a fixed gap between vehicles. The PBVC, on the other hand, is a variant of the cluster-based vehicular cloud that requires strict adherence to a constant gap, i.e., the distance between all vehicles and the use of the same lane throughout the entire period of the provision of cloud services. In other words, the PBVC is a convoy with a constant gap between all vehicles whose resources are organized to offer specific cloud services. In order to address the challenge of limited computation capacity of resources and constraint communication bandwidth, a large divisible data load to be processed by the DFVC is partitioned and distributed to the individual vehicular nodes using efficient Data partitioning and Scheduling (DPS) schemes. One of the central themes of this dissertation is, therefore, to design and implement novel DPS schemes that consider the characteristics of vehicular resources of the DFVC and the communication channel of VANET: the communication backbone for DFVC. By considering the computation capacity of resources, data transmission bandwidth capacity, and communication delay experienced in data transmission in VANET, efficient DPS schemes proposed in this dissertation are designed through mathematical models developed using timing and data flow diagrams. The DPS schemes for the CBVC, and PBVC are modeled differently because of their unique characteristics and operations. The proposed DPS scheme for the CBVC was modeled with the consideration that the cluster head determines the data chunk for each vehicle using derived closed form mathematical equations and then distributes the data chunks directly as a single hop to the respective vehicles in the CVBC to process. After processing, the vehicles then transmit the processed data chunks directly to the cluster head. The DPS scheme for the CBVC is implemented as part of a unified data, resource, and channel management framework, which is referred to as the UniDRM in this dissertation. Considering different criteria or objectives for data partitioning and scheduling, three distinct DPS schemes, time-aware, cost-aware, and reliability-aware, are also proposed in this dissertation. For the PBVC, the data partitioning is carried out by the lead node, i.e., the first node of the platoon, using derived closed form equations. The determined data chunks of the platoon members are then distributed either directly (single hop) or through multi-hop transmission. The closed form equations were derived considering data flow and timing diagrams designed based on how vehicles in platoons exchange information with their neighbor nodes, which is referred to as platoon Information Flow Topologies (IFT). In this dissertation, existing platoon IFTs: the Bi-Directional (BD), Bi-Directional Lead (BDL), and the All- to- All IFT (A2A) are modified to derive mathematical models for six different DPS schemes, namely, the Bi-Directional-Recursive (BD-R), Bi-Directional Interlaced (BD-I), Bi-Directional Lead-Recursive (BDL-R), Bi-Directional Lead-Interlaced (BDL-I), Bi-Directional Lead Aggregate-Recursive (BDLA-R), and Bi-Directional Lead Aggregate-Interlaced (BDLA-I). Through realistic simulations developed via the use of the simulation platforms Omnet++, Sumo, Veins, and Plexe, a detailed performance analysis of the proposed DPS schemes were carried out. By developing the different DPS schemes for the PBVC, one of the long-standing challenges of divisible data partitioning and scheduling in the literature: the modeling and derivation of closed form equations for determining the percentage of data chunks and the processing time of the linearly arranged network of connected heterogeneous processors, is addressed. According to the literature, there are no closed form equations for the heterogeneous linear network of connected processors because of the complicated combinatorial terms that appear in expressions for individual data partitions while solving the recursive equations. However, using algebraic manipulations, closed form equations have been derived and modeled in this dissertation. In all, this dissertation presents novel solutions to key challenges of vehicular cloud computing, including the limited available capacity of resources in vehicles and the bandwidth constraint of the vehicular communications channel.
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ÖgeOcclusion robust and aware face recognition(Graduate School, 2023-05-25) Erakın, Mustafa Ekrem ; Ekenel, Hazım Kemal ; 504201532 ; Computer EngineeringOccluded faces, due to accessories such as sunglasses and face masks, present a challenge for current face recognition systems. This thesis provides a comprehensive exploration of the issues caused by occlusions, particularly upper-face and lower-face obstructions, in real-world scenarios. The increased prevalence of sunglasses and face masks, the latter due to the COVID-19 pandemic, has amplified the importance of addressing these problems. In this thesis, the Real World Occluded Faces (ROF) dataset is gathered, a collection of faces experiencing both upper and lower face occlusions, serving as a critical resource for this area of study. Contrary to synthetic occlusion data, the ROF dataset provides an authentic representation of the issue, which our benchmark experiments have shown to be a significant impediment for even the most sophisticated deep face representation models. These models, while highly effective on synthetically occluded faces, exhibit substantial performance degradation when tested against the ROF dataset. This research comprises two distinct, yet interconnected sections. The first stresses the vital role of real-world data for the design and refinement of occlusion-robust face recognition models. Our experiments demonstrate the increased challenges posed by real-world occlusions in comparison to their synthetic counterparts. This insight allows us to gauge the performance and limitations of various model architectures under different occlusion conditions. The second section presents a novel, occlusion-robust, and occlusion-aware face recognition system, designed to increase performance on occlusions caused by sunglasses and masks, with minimal impact on generic face recognition performance. The system incorporates an occlusion-robust face recognition model, an occlusion-aware model, and an innovative layer integrating the outputs of these models to minimize occlusion effects. This unique configuration ensures the system's resilience to occlusions, focusing less on occluded regions and more on overall facial recognition. This thesis provides a thorough investigation of the challenges presented by occluded face recognition and proposes an innovative solution for the same. It underscores the necessity of utilizing real-world data for developing robust face recognition models and introduces a novel occlusion-aware face recognition system. This work has the potential to significantly enhance the performance of occluded face recognition methods in various real-world scenarios.