LEE- Bilgisayar Mühendisliği Lisansüstü Programı

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/19206

Gözat

A social navigation approach for mobile assistant robots

(Lisansüstü Eğitim Enstitüsü, 2020) Kıvrak, Hasan ; Köse, Hatice ; 657666 ; Bilgisayar Mühendisliği Bilim Dalı

Robots are becoming a part of our lives and we expect robots to act in a similar way to avoid interference with our safety and social being. Robots which are employed in human-robot interactive social areas such as malls or hospitals should benefit from a compliant navigation approach that is built upon a level of human aware and socially intelligent behavior. This compliance is more than mere avoidance and requires legible robot motion so that rational agents as humans are should understand and predict the robot motion (eliminate uncertainty in robot behavior) to adapt their motions accordingly. Furthermore, the robot requires understanding social etiquettes and rules and anticipates social/ethical interactions as much as humans. Otherwise, no matter how efficiently the robot navigates from one point to another, it will be realized as an unsocial individual because of the possibility of violating people's social zones or blocking their way. Hence, the robot behavior will be realized as inhuman-like and affect the interaction quality with the humans negatively. Mobile robots with enhanced social skills by considering to interact with humans verbally or non-verbally (e.g. sign language) should have unified trajectory planning algorithms that not only calculate the shortest path while avoiding obstacles to the defined goal while navigating, but also have human awareness not to annoy any human. A large number of researchers are currently proposing socially aware navigation approaches. It is an active research field combining navigation, perception, and social intelligence. The primary motivation of all these approaches is increasing psychological safety and comfort in human-robot interactive social environments as much as possible. ROS is the de facto standard in research robotics and offers us the ability to use multiple platforms and languages and to incorporate standard solutions to robot problems. Therefore, we first integrated the Mantaro TeleMe2 telepresence robot into the ROS ecosystem to drive the robot autonomously through the newly proposed hardware architecture. Then, the robot is made ready to provide all the necessary nodes to perform social navigation by developing Teleme2 ROS packages from scratch. Robot navigation in an unknown dynamic environment prefers to solve localization and mapping problem concurrently. As a result, the robot uses simultaneous localization and mapping(SLAM) technique to localize itself (pose estimation) and map the environment as well as our socially-aware motion planning algorithm. For online motion planning, potential fields are a common approach for static environments. This approach is first adopted as a social force model (SFM) to describe the motion of pedestrians in very crowded escape scenarios. According to this model, human behavior is affected by some forces (think of a vector field over the space) for acceleration, deceleration and directional changes. The idea behind the model makes it a good candidate for local path planning and expected to generate more human-like trajectories for the robot. That enables a robot to imitate the comprehensibility of the inner dynamics of human motion efficiently dependent on its motion constraints. SFM-based motion planner is computationally light which is appropriate in an uncertain dynamic environment to re-plan frequently. The algorithm does not directly find a collision-free path for the robot. The technique outputs the desired acceleration vector through the dynamic interactions of the robot at each time step and integrates the acceleration into its motion in order to obtain the collision-free path. At every point in time, the robot looks at the resultant total force at the point and imposes/applies as a control law to determine the direction of travel and speed. SFM may be a good choice since we don't need to enforce that the robot exactly follows a reference path, but instead stays within limits guaranteeing people's safety and comfort. In the thesis, we propose a social navigation system under unknown environments by integrating SFM and SLAM. Except for SFM computational time efficiency, the application of conventional SFM to social robot navigation problems present shortcomings and limitations. One problem of the integration of two technologies is the noise of SLAM that causes undesired navigation of the social force model. We introduce the idea of multi-level mapping to filter the noise within reasonable computational cost. The other problem is that the robot may oscillate because it has no incentive not to do so due to sudden changes in force lengths, discontinues at some points and sensor noise. To this end, one solution is to ensure smoothing by constraining the change in forces. That way, you impose continuity in the steering. In addition, SFM-based local motion planner is used with A* global planner not to be stuck on local minima situations. The whole plan is not directly assigned to the robot since the global path has too many grid nodes and it is infeasible to follow the path in such a dynamic human uncertain environment. Therefore, the key path points of the global path are extracted by proposed subgoals selection algorithm. Extracted points are incrementally passed to the robot for smooth and legible robot navigation behavior. Finally, we conduct simulation and user experiments as well as evaluate the effect of the proposed idea. We verify the results in real environments as simulation environments have limitations with quantitative and qualitative evaluations. This study has been developed as a part of TUBITAK project 118E214. In the future, we will continue to develop the study further, for the social navigation of assistive robots in crowded environments such as hospitals and schools in accordance with the safety and social distance rules.
Face recognition and person re-identification for person recognition

( 2020) Başaran, Emrah ; Kamaşak, Mustafa Ersel ; Gökmen, MUhittin ; 629137 ; Bilgisayar Mühendisliği ; Computer Engineering

Yüz tanıma ve kişinin yeniden tanınması (KYT) uygulamalarına, bireysel ve toplumsal güvenlik, adli vakalar ve eğlence başta olmak üzere, birçok farklı alanda ihtiyaç duyulmaktadır. Yüz görüntüleri, kişi teşhisi için, zengin ve oldukça ayırt edici özellikler barındırmaktadır. Bunun yanında, yüz görüntülerinin temas ve iş birliği olmaksızın elde edilebilir olması, yüz tanıma uygulamalarının, iris ve parmak izi gibi diğer biyometrik tanımlayıcıları kullanan uygulamalara göre daha geniş bir uygulama sahasına sahip olmasına sebep olmaktadır. KYT probleminde ise, biyometrik tanımlayıcılardan ziyade, tüm vücut görüntüleri kullanılmaktadır. Bu problemde, temel olarak, farklı kameralar tarafından kaydedilen kişi görüntülerinin eşleştirilmesine çalışılmaktadır. Yüz görüntülerinin elde edilemediği veya görüntülerin yüz tanıma yapılabilecek seviyede kaliteye sahip olmaması gibi durumlarda, KYT, kişi teşhisi için önemli bir yöntemdir. Tez kapsamında, öncelikle, kişi teşhisi için son derece önemli olan yüz tanıma problemi ele alınmaktadır. Daha sonra, KYT problem için özgün yöntemler önerilmektedir. Bu çalışmada, KYT problemi iki farklı şekilde incelenmektedir. Bunun sebebi, KYT için en önemli ipuçlarını barındıran renk bilgisinin zayıf aydınlatılmış veya karanlık ortamlarda kaydedilen görüntülerden elde edilemediği zaman, KYT' nin farklılaşması ve daha da zorlu bir problem haline gelmesidir. Gerçekleştirilen çalışmaların ilkinde, görünür etki alanında elde edilen RGB görüntüler kullanılmaktadır. İkincisinde ise, RGB görüntüler ile birlikte kızılötesi görüntülerde kullanılarak karşıt etki alanında KYT problemi incelenmektedir. Bilimsel yazında gerçekleştirilen çalışmalarda, yüz tanıma problemi, genel olarak kimlik saptama ve kimlik doğrulama olmak üzere iki farklı şekilde ele alınmaktadır. Hem saptama hem de doğrulama için geliştirilen yüz tanıma sistemlerinin en önemli kısmı ise, yüz görüntüleri için betimleyicilerin nasıl oluşturulacağıdır. Yüz tanıma performansı, büyük oranda bu betimleyicilerin kalitesine bağlıdır. Bu tezin yüz tanıma problemi ile ilgili olan bölümünde, güçlü betimleyiciler elde edebilmek için, temel olarak yerel Zernike momentleri (YZM) kullanılarak geliştirilen gözetimsiz öznitelik çıkarma yöntemleri önerilmektedir. İlk olarak, bütünsel yüz görüntülerinden öznitelik çıkarımı üzerine odaklanılmıştır. Geliştirilen yöntemde, iki farklı şekilde yerel öznitelikler açığa çıkarılmaktadır. İlkinde, art arda iki kez uygulanan YZM dönüşümü sonucunda elde edilen karmaşık örüntü haritaları üzerinde faz-genlik histogramları (FGH) oluşturulmaktadır. İkincisinde ise, gri seviye histogramlar kullanılmaktadır. Bu histogramlar, yerel Xor operatörü ile YZM örüntü haritalarının kodlanması sonucunda üretilen gri seviye görüntüler üzerinde oluşturulmaktadır. Hem FGH' ler hem de gri seviye histogramlar, alt bölgelere ayrılmış bütünsel yüz görüntülerinin alt bölgelerinde ayrı ayrı hesaplanmaktadır. Ardından, her bir örüntü haritasından elde edilen tüm histogramlar art arda birleştirilerek öznitelik vektörleri oluşturulmaktadır. Son aşamada ise, bu vektörlerin boyutları indirgenmektedir. Önerilen yöntemde, boyut indirgeme işlemi için, Beyazlatılmış Temel Bileşenler Analizi (BTBA) kullanılmakta ve blok tabanlı bir yöntem izlenmektedir. Öncelikle, alt bölgeler bir araya getirilerek bloklar oluşturulmaktadır ve ardından bu bloklardan elde edilen öznitelik vektörlerinin boyutları ayrı ayrı indirgenmektedir. Kullanılan bu yöntemlerin yüz tanıma performansı üzerindeki etkileri ve elde edilen başarılar, Face Recognition Technology (FERET) veriseti kullanılarak ortaya konmuştur. Tez kapsamında gerçekleştirilen yüz tanıma ile ilgili çalışmaların ikinci bölümünde ise, öznitelik çıkarımının nirengi noktaları etrafında gerçekleştirildiği başka bir yöntem önerilmektedir. Bu yöntemde, nirengi noktaları etrafından yamalar çıkarılmaktadır ve öznitelik vektörlerinde kullanılan FGH' ler bu yamaların alt bölgelerinde hesaplanmaktadır. Yüz görüntülerinin hem yerel hem de bütünsel bilgilerini içeren öznitelikler elde etmek amacıyla, yöntem içerisinde bir görüntü piramidi kullanılmaktadır. Piramit içerisindeki görüntülerin YZM örüntü haritalarından ayrı ayrı öznitelikler çıkarılarak çok ölçekli betimleyiciler elde edilmektedir. Ardından, görüntü piramidinden elde edilen öznitelikler art arda birleştirilerek, her bir nirengi noktası için ayrı bir öznitelik vektörü oluşturulmaktadır. Son aşamada ise, vektörlerin boyutları, BTBA kullanılarak ayrı ayrı indirgenmektedir. Önerilen yöntemin performansını test etmek amacıyla, FERET, Labeled Faces in the Wild (LFW) ve Surveillance Cameras Face (SCface) verisetleri kullanılmıştır. Elde edilen sonuçlar önerilen yöntemin aydınlatma, yüz ifadesi ve poz gibi değişikliklere karşı dayanıklı olduğunu ortaya koymaktadır. Bunun yanında, yöntemin, kontrolsüz ortamlarda veya kızılötesi tayfta elde edilen düşük çözünürlüklü yüz görüntüleri üzerindeki başarısı da gösterilmektedir. Kişilerin yeniden tanınması (KYT) problemi, arka plan dağınıklığı, poz, aydınlatma ve kamera bakış açısı değişimleri gibi faktörlerden dolayı oldukça zorlu bir iştir. Bu unsurlar, güçlü ve aynı zamanda ayırt edici öznitelikler çıkarma sürecini ciddi oranda etkileyerek, farklı kişilerin başarılı bir şekilde ayırt edilmesini zorlaştırmaktadırlar. Son yıllarda, KYT üzerinde gerçekleştirilen çalışmaların büyük bir çoğunluğu, bahsedilen unsurlar ile başa çıkabilecek yöntemler geliştirmek için, derin öğrenme yöntemlerinden yararlanmaktadır. Genel olarak bu çalışmalarda, kişi görüntüleri için öğrenilen gösterimlerin kalitesi, vücut parçalarından yerel öznitelikler çıkarılarak artırılmaya çalışılmaktadır. Vücut parçaları ise, sınırlayıcı kutu tespit etme yöntemleri ile tespit edilmektedir. Bu tezde, KYT problemi için, derin öğrenme yöntemleri kullanılarak geliştirilen bir yöntem önerilmektedir. Bu yöntemde, diğer çalışmalarda olduğu gibi, vücut parçalarından yerel öznitelikler elde edilmektedir. Fakat, parçalar tespit edilirken, sınırlayıcı kutular yerine anlamsal ayrıştırma kullanılmaktadır. Vücut görüntülerinin anlamsal olarak ayrıştırılması, piksel seviyesindeki doğruluğu ve rastgele sınırları modelleyebilmesi nedeniyle, sınırlayıcı kutu tespit etme yöntemine göre doğal olarak daha iyi bir alternatif olmaktadır. Önerilen yöntemde, anlamsal ayrıştırma KYT problemi için etkin bir şekilde kullanılarak, deneylerin yapıldığı verisetleri üzerinde bilinen en yüksek performansa ulaşılmaktadır. Anlamsal bölütlemenin yanı sıra, Inception ve ResNet gibi yaygın olarak kullanılan derin öğrenme mimarilerinin KYT problemi için daha verimli bir şekilde eğitilmesini sağlayan bir eğitim yöntemi de önerilmektedir. Yöntemlerin başarısı, Market-1501, CUHK03 DukeMTMC-reID verisetleri üzerinde gerçekleştirilen deneyler ile gösterilmektedir. Bu tez kapsamında gerçekleştirilen diğer bir çalışma ise, görünür-kızılötesi karşıt etki alanında KYT (GK-KYT) problemidir. GK-KYT problemi, zayıf aydınlatılmış veya karanlık ortamlarda gözetim işleminin gerçekleştirilebilmesi için son derece önemlidir. Son yıllarda, görünür etki alanında gerçekleştirilen birçok KYT çalışması bulunmaktadır. Buna karşın, bilimsel yazında, GK-KYT ile ilgili çok az sayıda çalışma gerçekleştirilmiştir. KYT' de var olan poz/aydınlanma değişimleri, arkaplan karmaşası ve kapanma gibi zorluklara ek olarak kızılötesi görüntülerde renk bilgisinin olmaması, GK-KYT' yi daha zorlu bir problem haline getirmektedir. Sonuç olarak, GK-KYT sistemlerinin performansı tipik olarak KYT sistemlerinden daha düşüktür. Bu tezde, GK-KYT' nin performansını iyileştirmek için 4 akışlı bir yöntem önerilmektedir. KYT ile ilgili gerçekleştirilen çalışmalarda olduğu gibi, GK-KYT için de derin öğrenme tekniklerinden yararlanılmıştır. Önerilen yöntemin her bir akışında, giriş görüntülerinin farklı bir gösterimi kullanılarak ayrı bir derin evrişimli sinir ağ (DESA) eğitilmektedir. Bu şekilde, her bir akıştaki DESA modelinin farklı ve aynı zamanda tamamlayıcı öznitelikler öğrenmesi amaçlanmaktadır. Yöntemin ilk akışında, gri-seviye ve kızılötesi giriş görüntüleri kullanılarak bir DESA modeli eğitilmektedir. İkinci akıştaki giriş görüntüleri ise, RGB görüntüler ve kızılötesi kanalın tekrarlanmasıyla oluşturulan 3-kanallı kızılötesi görüntülerdir. Diğer iki akışta ise, giriş görüntüsü olarak, YZM dönüşümü ile elde edilen yerel örüntü haritaları kullanılmaktadır. Bu örüntü haritaları, üçüncü akışta, gri-seviye ve kızılötesi görüntülerden, son akışta ise, RGB ve 3-kanallı kızılötesi görüntülerden elde edilmektedir. Son adımda ise, bilimsel yazında önerilen bir yeniden sıralama algoritmalası kullanılarak görüntüler arasındaki uzaklık hesaplanmaktadır. SYSU-MM01 ve RegDB verisetleri üzerinde gerçekleştirilen deneyler ile, önerilen yöntemin başarısı ortaya konmuştur.
Resilient ultra dense networks under UAV coverage for disaster management

(Fen Bilimleri Enstitüsü, 2020) Bozkaya, Elif ; Canberk, Berk ; İletişim ağlarında dayanıklılık; hatalara, çevresel problemlere, teknolojiye dayalı kesintilere veya kötü niyetli saldırılara karşı, haberleşmenin işlevselliğini kabul edilebilir bir seviyede devam ettirebilmesidir. Ancak, günümüz iletişim ağları alt yapısının statik ve değişikliklere kolay adapte olamayan bir yapıda olduğu düşünüldüğünde, çok yönlü bir afet durumunda ağı yeniden düzenlemek, çok fazla zaman harcanmasına neden olmaktadır. Bir afet yönetiminde, durum değerlendirmesi, hasarlara karşı hızlı ve etkili önlem alınması, ve etkin iyileştirme mekanizmaları sunulması dayanıklı bir iletişim ağı ile mümkündür. Son yıllarda internete bağlı mobil cihaz sayısının artışıyla birlikte günümüzde aylık ortalama 17 exabytes olan global veri trafiğinin, 2021 yılında aylık ortalama 49 exabytes olması tahmin edilmektedir. Bu şartlar altında, baz istasyonları servis dışı kaldığında, günümüz mobil ağ sistemlerinde, mobil trafik akışını yönetebilecek başka yapılar bulunmamaktadır. Bu problemler, mobil haberleşme ağlarının tek bir noktadan esnek ve dinamik yönetilmemesinden kaynaklanır. Bu sorunu çözebilmek için, mobil veri trafiğinin adaptif bir şekilde yönetilmesi gerekmektedir. Son yıllarda iklimsel değişikliklerle meydana gelen afetler önlenememektedir. Bir doğal afet meydana geldiğinde, en önemli konu insan hayatlarının korunmasıdır. Böyle durumlarda, arama ve kurtarma çalışmalarının büyük oranda başarıya ulaştığı ilk 72 saat oldukça kritik bir zaman dilimidir. Diğer yandan, iletişimde ve durumsal farkındalıkta meydana gelebilecek eksiklikler, kurtarma görevlerinin etkinliğini azaltmaktadır. Örneğin, 2011 Japonya depreminden sonra, 4 gün boyunca baz istasyonlarının çoğu devre dışı kalmış ve ancak 7. günün sonunda İnternet servisi verilmeye başlanmıştır. Kurtarma ekipleri tarafından sadece ses için kullanılan uydu telefonlarının servis kalitesinde sıkıntılar yaşanmıştır. Benzer bir durum, 2010 yılında Haiti'deki deprem sonrasında da yaşanmıştır ve hasar gören servis sağlayıcı alt yapıları nedeniyle uzun süreli iletişim problemi ortaya çıkmıştır. Amerika kıtasının farklı yaşam alanlarında benzer doğa olayları her yıl tekrarlanmaktadır. Ayrıca, yaşadığımız coğrafyada Marmara depremi sonrası, iletişim ağları tamamen hasar görmüş ve kullanılamaz hale gelmiştir. Belirtilen felaket senaryolarında, mevcut kablosuz haberleşme altyapısında meydana gelen hasarlarla birlikte, altyapının kısıtlı fiziksel kaynaklarıyla veri taleplerinin karşılanması mümkün değildir. Bu maksatla, artan mobil trafik akışını ve baz istasyonlarının servis dışı kalması ile ortaya çıkan ağ yönetim sorunlarını çözmek için yeni uygulamalara ihtiyaç duyulmaktadır. Bu maksatla, bu çalışmada İnsansız Hava Araçlarının iletişim altyapısını desteklemek için kullanımı önerilmektedir. Servis dışı kalan baz istasyonlarının yerine havasal baz istasyonları son kullanıcılara hizmet verebilirler. Bu çalışmada amaç, felakete dayalı senaryolara karşı havasal baz istasyonları ile dayanıklı bir iletişim ağı tasarlamaktır. Dayanıklı ağ ise, afet yönetiminde havasal ağlarda meydana gelen ve iletişim ağlarını etkileyebilecek problemlere karşı iletişim ağının işlevselliğini kabul edilebilir bir seviyede devam ettirebilmesi olarak tanımlanmıştır. Bunun yanında, havasal baz istasyonların dayanıklı bir iletişim ağında kullanılması ile, havasal baz istasyonlarının iletişim ağı üzerinde olası etkileri artacaktır. En önemli problemlerden biri, havasal baz istasyonlarının uygun konumlara yerleştirilmesidir. Aksi takdirde, havasal baz istasyonlarının kapsama alanları, servis veren diğer karasal baz istasyonlarının kapsama alanları ile örtüşebilir. Böyle bir durumda, dayanıklı bir ağ elde edilmesi mümkün olmayacaktır. Özellikle, örtüşen bölgelerde bulunan son kullanıcıların hangi baz istasyonlarına tanımlanacağı ve örtüşmenin minimum düzeyde tutulacağı bir topoloji yönetimi tasarlamak oldukça önemlidir. Bunun yanında, havasal baz istasyonlarının yerleştirilmesi konusunda 3. boyutun da hesaplamalara dahil edilerek optimum yüksekliğin bulunması, dayanıklı bir ağ tasarımı için oldukça önemlidir. Dayanıklı bir ağ tasarımı için yukarıda bahsedilen problemlere ilave olarak havasal baz istasyonlarının enerji yönetiminin sağlanması, afet senaryolarının sürekliliği açısından önemlidir. Bir diğer önemli problem ise, aşırı yoğun ağlarda hizmet veren baz istasyonu sayısının fazlalığı ağın kapasitesini artırmaktadır. Bununla birlikte, baz istasyonu sayısının fazla olmasından dolayı hareketli kullanıcıların yol boyunca gerçekleştirecekleri geçiş sayısı da artmaktadır. Geçiş prosedürü kaynak ve hedef baz istasyonları arasında kontrol ve veri trafiğine ihtiyaç duymaktadır. Geçiş sıklığının artmasıyla kaynak ve hedef baz istasyonları arasındaki kontrol trafiği, kaynakların ve enerjinin daha fazla tüketilmesine neden olmaktadır. Aynı zamanda, hareketli kullanıcıların baz istasyonları arasındaki geçişlerde harcanan süreleri yani gecikmeleri artmaktadır ve bu durum kullanıcıların servis kalitesinde düşüşlere neden olmaktadır. Sonuç olarak, yukarıda belirtilen ve günümüz iletişim ağları alt yapısı için tasarlanan 2 boyutlu algoritmalar ve yöntemler, topolojiye havasal baz istasyonu dahil edildiğinde devre dışı kalacaktır. Bunun en başta gelen nedenlerinden biri, havasal baz istasyonlarının, 3 boyutlu hareketliliği ve herhangi bir yol topolojisine sınırlı kalmadan hareket edebilme kabiliyetidir. Bu tez çalışmasında, havasal baz istasyonlarının dayanıklı bir ağ tasarımında kullanılması durumunda, (i) havasal baz istasyonlarının konumlandırılması, (ii) enerji yönetimi, (iii) yol planlaması ve organizasyonu ile (iv) havasal baz istasyonları arasında gerçekleşebilecek handover (geçiş) prosedürünün yönetimi için, artan mobil veri trafiği ve dinamik topoloji değişikliklerine duyarlı, esnek ve merkezi bir yapı ile çözüm sunulmaktadır. Yazılım-tabanlı bir model ile kontrolör üzerinden havasal baz istasyonlarının yönetilmesi hedeflenmiştir. Bu kapsamda, karasal baz istasyonlarının servis dışı kaldığında bir kontrolör üzerinden gerçeklenecek fonksiyonlar ile havasal baz istasyonlarının güvenilir ve devamlı bir iletişim sağlaması için bir model önerilmiştir. Tez çalışmasının ilk kısmında, verilen bir coğrafi alanda, ihtiyaç duyulacak minimum sayıda havasal baz istasyonu sayısını tanımlayarak, 3 boyutlu konumlandırma problemi ele alınmıştır. Önerilen konumlandırma algoritması ile havasal baz istasyonlarının kapsama alanları tanımlanmış ve enerji yönetimi ile havada kalma süreleri kontrol edilmiştir. Özellikle, havasal baz istasyonlarının sınırlı kapasitede bataryaları olduğu ve havasal ağlara yeni katılan ya da ağdan ayrılan havasal baz istasyonları için ağın yeniden organize olması gerektiği düşünüldüğünde, mevcut havasal baz istasyonlarından en etkin şekilde yararlanmak oldukça önemlidir. Bu maksatla son kullanıcılara kabul edilebilir bir servis kalitesinde hizmet vermeleri için havasal baz istasyonlarının enerji yönetim mekanizmaları ile yeniden şarj edilebilmesi için ikmal istasyonlarının etkin yönetimi üzerinde bir model önerilmiştir. Havasal baz istasyonlarının değişken ve olası bağlantı kesintileri, kaynak tahsisi ve kanal planlamasında problemlere neden olmaktadır. Bu maksatla, havasal baz istasyonlarının yönetimi için ağ fonksiyonlarını yönetmek ve kritik zamanlarda, bu problemlere yönelik çözümler sunmak gerekmektedir. Özellikle, havasal baz istasyonlarında gerçek zamanlı izleme, uyarı tetikleme gibi kritik ortamlarda veri aktarımında meydana gelebilecek bir gecikme ağ fonksiyonlarının merkezi yönetimi ile gerçekleştirilebilir. Bu maksatla, tez çalışmasının son kısmında ise, havasal baz istasyonları arasında gerçekleşebilecek geçiş prosedürünün yönetimi için bir ağ mimarisinin gerçeklenmesi hedeflenmiştir. Karasal baz istasyonları için belirtilen geçişe bağlı problemler, havasal baz istasyonu kullanıldığında da geçerliliğini korumaktadır. Ek olarak, bu problem havasal baz istasyonlarının zaten kısıtlı olan kaynaklarını ve enerjilerini daha hızlı tüketmesine neden olmaktadır. Bu problemin çözümünde geçiş sıklığının ve dolayısıyla havasal baz istasyonlarının kullanıcı başına harcadığı enerjinin azaltılması için yazılım tabanlı ağ mimarisinde bir geçiş karar verme ve geçiş gerçekleştirme mekanizmaları önerilmektedir. Böylece dinamik ağ topolojilerine duyarlı, havasal baz istasyonları ile desteklenebilen ve havasal baz istasyonlarının kullanımından kaynaklanan problemleri minimize eden merkezi ve esnek bir yaklaşım önerilmiştir. ; 636993 ; Bilgisayar Mühendisliği Bilim Dalı

Resiliency in communication networks is the maintainability of the communication functionality at acceptable levels against possible errors, environmental problems, network outage due to technological causes or malicious attacks. However, it is tremendously time-consuming to redesign the network in a versatile disaster situation considering today's static and conservative communication network infrastructures. In disaster management; assessing the situation, taking immediate and effective precautions and proposing solutions for the optimization is only possible with a robust communication network infrastructure. Additionally, in the case of base station failures, there is no infrastructure to manage the mobile traffic in today's mobile network provider systems. In order to solve this problem, mobile data traffic should be managed adaptively. In recent years, the disasters which were caused by climatic changes cannot be prevented. In case of a natural disaster, the most important thing is to save people's lives. In a situation like this, the first 72 hours is crucially important to react immediately and this can only be possible with quick and effective search and rescue activities. On the other hand, the lack of awareness and communications will vitiate these activities. For example, after the 2011 Tohoku Earthquake in Japan, most of the base stations became out-of-service and the Internet became available barely after 7 days. Also, the service quality of the satellite phones, which are used only for voice communications by the save and rescue teams had decreased. A similar situation was experienced in 2010 after the earthquake in Haiti and long-term communication problems arose due to damaged service provider infrastructures. Similar natural events in the different habitats of the American continent are repeated every year. In addition, after the Marmara Earthquake in 1999, the communication networks have become completely damaged and unusable. In such cases, the continuity of communication is important. In the mentioned disaster scenarios, it is not possible to meet the data demands with the limited physical resources of the infrastructure along with the damages in the existing wireless communication infrastructure. To this end, novel applications are needed in order to solve the network management problems in case of an unanticipated failure. Today, with the increasing use of Unmanned Aerial Vehicles (UAV), many new applications are emerging in the communication sector. According to the Association for Unmanned Vehicle Systems International (AUVSI) Report, direct economic impact from the UAV industry in US is about 3.6 Billion Dollar in 2018 and is expected to exceed 5 Billion Dollar by 2025. In this thesis, UAVs are proposed to support the communication infrastructure as Aerial Base Stations (ABS) via a centralized controller to solve the problems for existing network infrastructures. ABSs have become a promising tool for post-disaster communications. ABS deployment assists terrestrial networks to minimize the disruptions caused by unexpected and temporary situations. Thus, it is aimed to design a resilient network management mechanism with ABSs. ABSs which will be located instead of the failed base stations are advantageous because they have low production and maintenance costs, they have error/damage tolerance and they can easily be controlled and located where humans have limited reach. However, because of the physical limitations with low-capacity power supplies, they have limited flying time, limited velocity and communication range. Moreover, the majority of energy consumption in aerial networks is not spent on computing or communication, but on the power required by engines and flying aerial vehicles. For all these reasons, there are various problems in the system design while trying to accomplish real-world problems and complicated duties. Therefore, in order to increase resiliency in aerial networks, a proper positioning management and a flight planning mechanism are both needed considering the relationship between ABS flight characteristics and energy consumption. Considering the stated reasons, we first focus on on-demand communication. Since on-demand communication can change over time and be hard to accurately predict, it needs to be handled in an online manner, accounting also for battery consumption constraints. This thesis presents an efficient software-based solution to operate ABSs by meeting these requirements which maximizes the number of covered users, and a scheduler which navigates and recharges ABSs in an energy-aware manner. To this end, we propose an energy-aware deployment algorithm and use an energy model to analyze the power consumption and thereby, improve the flight endurance. In addition, we evaluate a novel scheduling mechanism that efficiently manages the ABSs' operations. Our simulations indicate that our approach can significantly improve the flight endurance and user coverage. In the second part of the thesis, we consider that the continuity of the service has increased the challenge of providing satisfactory quality of service. The limited battery capacity and vertical movement with direction switching of ABSs result in frequent interruptions with additional problems related to increased interference, handover delay, and failure of the handover procedure. Therefore, the main goal is to model dynamic mobile network topology and create a scalable structure to manage possible handover procedure between ABSs. With this idea, a solution is presented in a flexible and centralized structure, which analyses the resiliency of the network and is sensitive to increased mobile data traffic and dynamic topology changes. We address the handover procedure in aerial networks by integrating a reinforcement based Q-learning framework. The proposed model enables to ABSs to learn the optimal deployment exploring a Temporal-Difference (TD) learning prediction method. Our study gives a centralized handover procedure avoiding additional overhead to the ABSs and the transition probabilities are estimated to decrease the risk of the handover failure ratio.
Compression of geometry videos by 3D-SPECK wavelet coder

(Lisansüstü Eğitim Enstitüsü, 2021) Bahçe Gülbak, Canan ; Bayazıt, Uluğ ; 723134 ; Bilgisayar Mühendisliği

A geometry image represents a manifold surface in 3D space as an 2D array of 3D points. This involves 3 steps : First, cutting the manifold which essential defines the boundary of the square, second, defining the parametrization which defines the interior of the square and lastly, rasterizing and scan converting the geometry and applying compression to it. By representing manifold 3D objects using a global 2D parametrization (mapping) it is possible to use existing video techniques to represent 3D animations. 2D-SPECK coder, discovered by Islam and Pearlman, codes sets of DWT coefficients grouped within subbands. SPECK coder is different from the other schemes in that it does not use trees which span and also exploits the similarity accross different subbands. It makes use of sets in the form of blocks. The main idea is to exploit the clustering of energy in frequency and space in the hierarchical structures of wavelet transformed images. 3D-SPECK coder, is an extension of the 2D-SPECK algorithm for compressing 3D data with high coding efficiency. A geometry video is formed as a sequence of geometry images where each frame is a remeshed form of a frame of an animated mesh sequence. For efficiently coding geometry videos by exploiting temporal as well spatial correlation at multiple scales, this thesis proposes the 3D-SPECK algorithm which has been successfully applied to the coding of volumetric medical image data and hyperspectral image data in the past. The thesis also puts forward several postprocessing operations on the reconstructed surfaces that compensate for the visual artifacts appearing in the form of undulations due to the loss of high frequency wavelet coefficients, cracks near geometry image boundaries due to vertex coordinate quantization errors and serrations due to regular or quad splitting triangulation of local regions of large anisotropic geometric stretch. Experimental results on several animated mesh sequences demonstrate the superiority of the subjective and objective coding performances of the newly proposed approach to those of the commonly recognized animated mesh sequence coding approaches at low and medium coding rates.
Etmen tabanlı bir anlamsal süreç çalışma ortamının geliştirilmesi

(Lisansüstü Eğitim Enstitüsü, 2021) Kır, Hüseyin ; Erdoğan, Takuhi Nadia ; 672532 ; Bilgisayar Mühendisliği

Kurumsal bilişim sistemleri alanı, uzun bir süre boyunca, kurumsal veriyi merkeze alan ve onun yönetimine odaklanan veri odaklı bilişim sistemleri tarafından hükmedilmiştir. Fakat zamanla bilginin de diğer üretim enstrümanları gibi kurumların hedeflerine ulaşmak için tükettikleri ve ürettikleri ara ürünler olduğu, asıl odaklanılması gereken bakış açısının üretimi sağlayan işlevler olduğu algısı yaygınlaşmaya başlamıştır. Bu yaklaşım ile, kurumsal veri/bilgi önemini korurken, merkeze iş süreçleri alınarak iş süreci yönetim sistemleri (İSYS) ortaya çıkmıştır. İSYS'ler kurumsal işleyişi temsil eden süreç modellerini girdi olarak alan ve katılımcıların eş güdümlü bir şekilde çalışmasını sağlayarak üretim süreçlerinin etkinliğini ve üretkenliğini arttırmayı hedefleyen genel yazılım sistemleridir. Bu sistemler zamanla gelişerek tüm süreç yaşam döngüsünü (tasarım, işletim, izleme, analiz ve iyileştirme) destekleyecek işlevselliklere erişmiştir. Geleneksel olarak İSYS'ler yönetmelikler ile detaylı bir şekilde tanımlanmış, öngörülebilen ve tekrarlanabilen süreçlerin modellenmesine ve işletilmesine odaklanmıştır. Bu süreçlerdeki olası tüm iş akışları tamamen bilinmektedir ve süreç katılımcılarının verebileceği kararlar önceden öngörülmektedir. Halihazırda, bu tür süreçler kurumsal süreçlerin büyük çoğunluğunu oluşturmaktadır. Ne var ki, kurumların %16'sı önceden öngörülemeyen olaylardan dolayı iş süreçlerini anlık olarak değiştirmek zorunda kaldıklarını, %10'u ise bazı süreçlerinin günlük olarak değiştiğini belirtmektedir. Aslen bu süreçler, mevcut İSYS'lerin yönetmekte yetersiz kaldığı, bilgi yoğun ve sanatsal süreçlerdir. Bilgi yoğun süreçler (BYS), yürütülmesi ve yönetilmesi çeşitli bilgi güdümlü karar verme görevlerini yerine getiren bilgi çalışanlarına bağlı olan süreçlerdir. Bu süreçler genelde üst seviyede bir iş akışına sahiptirler ama bu akışın detayları, dolaylı bir şekilde, sadece iş uzmanı tarafından bilinmektedir. Bu süreçler, formal bir süreç modeli ile ifade edilememekle beraber çoğu zaman yazılı bile değillerdir. Bilgi yoğun süreçlere örnek olarak enerji uzmanının bir hidroelektrik santrali projesini değerlendirme süreci örnek verilebilir. İş uzmanı, sunulan yapılabilirlik çalışmasının değerlendirilmesi, kamulaştırmaların gerekliliği ve uygunluğu, beklenen üretim projeksiyonlarının gerçekçiliği ve talep ile tutarlılığı gibi bir çok tecrübeye dayalı incelemeyi, duruma göre diğer iş uzmanlarına da (hukuk, planlama vb.) danışarak, süreci ilerletmektedir. Sürecin akışı tamamen anlık ihtiyaçlar doğrultusunda, iş uzmanının tecrübesi ile ortaya çıkmaktadır ve her değerlendirme süreci farklı bir akışa sahip olabilmektedir. Süreç yönetimi araştırma alanı, gelecekte sanal organizasyonların kurulacağı, dünyanın farklı yerlerindeki birbirini tanımayan insanların aynı sürece dahil olarak işbirliği içerisinde üretim yapabilecekleri bir geleceği hayal etmektedir. Yaşamakta olduğumuz Covid-19 pandemi süreci de bu eğilimi hızlandırarak, uzaktan birlikte çalışmayı bir zorunluluk haline getirmiştir. Bunun sonucunda, mevcut altyapıların desteklemediği, zorlu bilgi yoğun senaryolarda da süreç odaklı yaklaşımların uygulanması bir zorunluluk olmuştur. Günümüz İSY sistemlerinin bilgi odaklı süreç yönetimi hedefini hayata geçirebilmek için işbirliği, uyarlanabilirlik ve bağlam farkındalık gibi kavramların üzerine yoğunlaşması gerekmektedir. Bunun için, mevcut İSYS'lerin, bir dizi yeni gereksinimi desteklemeye başlaması gerekmektedir. Genel olarak bu gereksinimler: tüm kurumsal ortam, veri ve kuralların modellendiği bir kurumsal bilgi tabanının geliştirilmesi ve bu bilgi modeli üzerinde bilgi ile tetiklenen, kurallar ile şekillenen, organizasyon hedeflerine hizmet eden, dinamik işbirliklerinin yapılabildiği bir çalışma ortamının oluşturulması şeklinde özetlenebilir. Bu yöndeki araştırmalar ise hala, büyük oranda, akademik seviyededir ve sadece akıllı hata kotarma problemine odaklanmış durumdadır. Ayrıca, bu çalışmaların kurumsal standartlardan uzak oluşları ve uygulanabilirliklerindeki zorluklardan dolayı endüstriyel kullanımı yaygınlaşamamıştır. Tez çalışması kapsamında geliştirilen yöntem yüksek değişkenliğe sahip bilgi yoğun iş süreçlerinin yönetimi için üç hipotezi temel almaktadır. İlk olarak, süreç tasarımı sadece görev ve kontrol akışlarının modellenmesi ile sınırlı değildir, süreç uzayını oluşturan veri, kural, hedef, iş ortamı ve iş akış perspektiflerinin bütüncül bir şekilde ele alınması gerekmektedir. İkinci olarak, kapsüllemeyi ve bileşenleştirmeyi sağlamak için, süreç işletimleri, kurumsal bilgiyi güncelleyen görev akışları ile değil, her biri kendi hedefleri, inanışları, kararları ve yaşam döngüsü olan etkileşimli özerk varlıklar (akıllı yazılım etmenleri) üzerinden yönetilmelidir. İSY sistemlerinin nihai hedefi, iş akışlarının eş güdümünü sağlamaktan, iş uzmanlarının karar verme süreçlerine yardımcı olmaya doğru evrilmektedir. Bu doğrultuda, üçüncü hipotez olarak, bilgi çalışanlarının uzmanlıklarının en azından bir kısmı dijitalleştirilmeli ve özerk yazılım vekilleri tarafından yerine getirilmelidir. Bu amaçla, iki aşamalı bir yaklaşımla, tüm İSY yaşam döngüsünü destekleyen bir çözüm önerilmiştir. İlk olarak, iş süreçleri, kurumsal bilgi yönetimi ve çoklu etmen sistemleri modelleme paradigmalarını ve tasarım bileşenlerini kusursuz bir biçimde tümleştiren ve bir arada modellenmelerine olanak tanıyan, tümleşik bir modelleme metodolojisi geliştirilmiştir. Arttırımlı bir şekilde geliştirilen modeller organizasyon, iş ortamı, kurumsal stratejiler, işlevsellikler ve kısıtları anlamsal bir şekilde tanımlamakta ve kurumsal bilgi modelini oluşturmaktadır. Bu modellerin tasarımında, endüstride ve etmen tabanlı yazılım mühendisliğinde kullanılan standartlar ve en iyi uygulamalar, mümkün olduğunca yeniden kullanılarak, gerçek hayat problemlerinde kolay bir şekilde uygulanabilir olması hedeflenmiştir. Tez çalışmasının ikinci aşamasında, etmenlerin çalışma zamanında özerk bir şekilde hedefe yönelik ve bilgi odaklı davranış uyarlamaları yapmasına olanak tanıyan bir çoklu etmen tabanlı süreç işletim ortamı geliştirilmiştir. Geliştirilen bilgi modelini kullanan etmenler bilişsel yetenekler (hedef güdümlü planlama, kural uyumluluk, bilgi güdümlü davranışlar ve dinamik işbirlikleri gibi) sergileyerek, bilgi çalışanlarının karar verme süreçlerini desteklemeye çalışmaktadır. Bu amaçla, iş uzmanlarının karar verme yöntemlerinden esinlenerek geliştirilen buluşsal planlama yaklaşımı ile sergilenecek eylemlere çalışma zamanında, yeni bilgiler ortaya çıktıkça adım adım karar verilmekte ve hedefler ile gerçekler arasındaki boşluk kapatılmaya çalışılmaktadır. İş uzmanlarının hedefe yönelik davranış seçimi, süreç kalitesinin değerlendirilmesi, kurallara uygunluğun kontrolü, hata yönetimi ve dinamik müzakere ve işbirliği yetenekleri dijitalleştirilerek, etmenler tarafından yerine getirilebilir hale getirilmiştir. Bu sayede çalışma zamanında süreçlerin dinamik bir şekilde uyarlanabilmesi ve anlık etkileşimler ile yeniden şekillenerek organizasyon hedeflerine ulaşabilmesi sağlanmıştır. Gerçekleştirilen deneysel çalışmalar ile, süreç işletimi için yeterli kaynaklara sahip bir ortamda, tez kapsamında geliştirilen çerçevenin rastgele oluşturulan çalışma zamanı hatalarını başarılı bir şekilde kotarabildiğini ortaya koymuştur. Literatürdeki mevcut çalışmalar ile karşılaştırıldığında, geliştirilen sistemin, bilgi yoğun süreç yönetim sistemlerinin temel gereksinimlerinin büyük bir çoğunluğunu sağlayan, literatürdeki en kapsamlı çözüm olduğu ortaya konmuştur.
Heuristic algorithms for solving chemical shift assignment problem in protein structure determination

(Lisansüstü Eğitim Enstitüsü, 2021) Yılmaz Maden, Emel ; Uyar Etaner, Ayşe Şima ; Güntert, Peter, ; 709824 ; Bilgisayar Mühendisliği

Heuristic algorithms have been widely used in several different hard optimization problems not only in computer science but also in several other disciplines, including natural sciences, bioinformatics, electronics, and operational research, where computational methods are needed. Heuristic algorithms search for optimal solutions by maximizing or minimizing the given objectives depending on the need while satisfying the given conditions. Heuristic algorithms find solutions in a huge search space where many different possible solution candidates exist. Due to these conditions of the search space, systematic search techniques are not feasible for such kinds of problems. In this thesis, we applied several different heuristic approaches and their combinations on the chemical shift assignment problem of the Nuclear Magnetic Resonance (NMR) spectroscopy. NMR spectroscopy is one of the methods to determine the three-dimensional structure of proteins. The three-dimensional structure of proteins provides crucial information to detect the shape, structure and function of biological macromolecules. The protein structure also demonstrates the function of proteins by illustrating the interactions of the macromolecules with other proteins or small ligands. Therefore, the three-dimensional structure of a protein can form a basis for drug design against human diseases. NMR has many advantages compared to other techniques; however, NMR spectroscopy needs very advanced computational techniques for providing the protein structure. The chemical shift assignment of the atoms is one of the most challenging problems in NMR spectroscopy. It needs a considerable amount of time by an experienced spectroscopist if the determination is done manually or by a semi-automated method. Additionally, even if the remaining parts of the structure determination methods work perfectly, it is impossible to create the protein structure if the chemical shift assignments are not done correctly. Due to this complexity, the total number of protein structures obtained from NMR spectroscopy is very few compared to its alternative methods, such as X-ray crystallography. Due to its importance in NMR experiments, the chemical shift assignment problem has recently become one of the most critical research areas in the computational techniques of NMR spectroscopy. There have been many types of research on this problem; however, they are far from perfect. Some of these techniques can provide only partial solutions by assigning only the backbone atoms or only the sidechain atoms. Some of these methods require a very long computation time. Additionally, the results of many of the existing methods have a great area for improvement. In this thesis, we developed a novel method with the heuristic algorithms that provides a fully automatic assignment of the chemical shift values of NMR experiments. First, we studied the background of the problem along with the existing methods. Secondly, we proposed our methods that solve the problem with evolutionary algorithms. Thirdly, we performed experiments on several different datasets, compared the success of our methods against the state-of-the-art solutions of the problem, and continuously improved our methods. Finally, we performed further analysis on the results and proposed further work. First, the background of the chemical shift assignment problem is comprehensively studied from the computer science point of view. The optimization processes in heuristic algorithms, stochastic local search methods, iterative improvement, simple stochastic local search methods, hybrid, and population-based stochastic local search methods are discussed in detail. The ant colony optimization and the evolutionary algorithms are analyzed as the population-based stochastic local search methods. After these evaluations, the evolutionary algorithms appeared to be a suitable candidate for solving this problem since they already work with a population, which is a set of solution candidates. We also analyzed the NMR spectroscopy hardware, principles, and experiment steps in detail because the problem is a real application from NMR spectroscopy in natural sciences. Furthermore, we had a deep dive into the chemical shift assignment problem and into the protein structure and peptide formation areas, which are the basis for the NMR spectroscopy calculations. Afterwards, the existing methods for solving this problem are discussed with their drawbacks. Secondly, we proposed our methods for solving the problem with heuristic algorithms. Our method comprises several different evolutionary algorithms and their combinations with hill climbing, with each other, and constructive heuristic methods. More conventional approach genetic algorithm, GA, and multi-objective evolutionary algorithms, NSGA2 and NSGA3, are applied to the problem. The multi-objective evolutionary algorithms investigated each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. While defining the methods, we first defined the problem model, along with the existing conditions and the score function. We modeled the problem as a combinatorial optimization problem, where expected peaks are mapped onto the measured peaks. The chromosome of the algorithm is an array of the expected peaks and the values inside represent their mapped measured peaks. The objectives of the problem are defined in a score function. The constraints are not separately evaluated because they are already fulfilled by the problem model implicitly. Additional fine-tuning and changes are implemented on the algorithms to apply the NMR-specific behaviors to the problem model. Then, the following improvements are realized on the algorithms: We optimized the probability of applying crossover and mutation in the methods. The population initialization is optimized with a constructive initialization algorithm, which minimizes the search space to find better initial individuals. Furthermore, we optimized the population's diversity to find the optimum solutions by escaping from local optima. We also implemented hybrid algorithms by combining a hill-climbing algorithm with our proposed algorithms. Thirdly, we performed experiments on several datasets with a set of commonly used spectra. We also compared the results of our methods with the two state-of-the-art algorithms: FLYA and PINE. In almost all of these datasets, our algorithm GA yielded better results than PINE. Our algorithm NSGA2 produced better results than PINE in almost half of the datasets. Our NSGA3 algorithm yielded less than 10% correct assignments because only two objectives out of four objectives of our problem model create trade-off. NSGA3 algorithms are known to be successful on problems with more than three objectives. Additionally, our algorithms had better runtime performance than FLYA in more than half of the datasets. Our algorithms could assign all of the atoms in all datasets, which creates a huge completeness success of the problem, whereas FLYA and PINE algorithms could not provide a complete assignment. Furthermore, we observed in our results that splitting a large protein into smaller fragments improved our algorithms' results dramatically. Finally, we performed further analysis on our results. These analyses showed us that our algorithms often assigned different atoms than FLYA and PINE. Primarily the GA algorithm can provide good results on some parts of datasets where the state-of-the-art algorithms cannot make any assignment. In order to leverage this success of our algorithms, we proposed a hierarchical method. This method combines FLYA and our algorithm GA to benefit from the different success factors of each algorithm. The results showed that this approach improved the overall success of the algorithms. In future work, the three algorithms could be combined to achieve better results. Additionally, one can focus on distinguishing atoms that can be assigned consistently and more reliably than others. The assignment is only tentative so that fewer wrong assignments are done. Furthermore, the objective function of the problem can be remodeled to improve the performance of the algorithms. Additionally, our method can be extended in further work so that large proteins are split into smaller fragments before applying our algorithms, which will improve the overall results. In this thesis, we successfully implemented a fully automatic algorithm for solving the chemical shift assignment problem of NMR spectroscopy. Our method can automatically assign a significant part of the sidechain and backbone atoms without any parameter changes or manual interactions. We produced results that are comparable to the two very well know state-of-the-art algorithms. Our approaches could provide around a 70% success rate on these datasets and assign many atoms that other methods could not assign. Our algorithm outperformed at least one of these two state-of-the-art methods almost in all of our experiments. Additionally, the whole methods are implemented on the MOEA framework, enabling the further implementation of new algorithms easily.
Visual attention and visual distortion sensitivity based regional rate allocation in JPEG2000

(Lisansüstü Eğitim Enstitüsü, 2021) Pak, Mesut ; Bayazıt, Uluğ ; 662856 ; Bilgisayar Mühendisliği

This thesis study proposes a regional bit allocation method for improving the subjective quality for image encoding. This bit allocation method jointly uses the visual attention and visual distortion sensitivity levels of image regions for decreasing the perceptual distortions. Visual attention levels of image regions are estimated by using an exponential model of fixation durations. The human visual system is more sensitive to the distortions in structured regions than the distortions in complex textured regions. Therefore, a method for estimating distortion sensitivity, that distinguishes the structured regions from complex textures by using uniform distribution of gradient directions and connected sets of pixels having same gradient directions, is also proposed. The estimates for the visual attention level and the distortion sensitivity level are jointly used to modify the distortion contribution of each codeblock in bit allocation. The exponential model used to obtain the visual attention map of an image inputs the average of fixation duration maps of all viewer subjects. The fixation duration maps are based on eye-tracking experiments. For optimizing the perceptual quality by bit allocation, the encoder should decrease the perceptual distortions in visually conspicuous regions. The distortions in structured regions, such as object contours or letters, are very noticeable. Therefore, the distortion sensitivity estimation should determine the image regions containing true edges with significant lengths regardless of the edges' gradient magnitudes. The second visual distortion sensitivity issue is determining whether a region is complex textured or not. The complex textured regions can conceal distortions caused by lossy compression whereas in smooth regions such distortions are easily perceptible. The proposed method compares the entropy of the orientations of gradients within an image block against a threshold for classifying it as complex textured. The visual quality achieved by the proposed bit allocation method is compared with those achieved by well-known bit allocation methods (post-compression rate-distortion optimization, saliency map, foveation of fixations, and foveated just-noticeable-difference map) in order to validate the proposed method. Additionally, to assess the contribution of the use of visual distortion sensitivity to the perceived quality achieved by the proposed method, the reconstructed images resulting from bit allocation based on only the visual attention maps are also compared against those resulting from the proposed bit allocation method. The performance comparisons are primarily based on the paired comparison method developed by ITU-T that evaluates the subjective qualities of the images. In the pairwise comparative evaluation facilitated by a web page, the evaluator subjects are presented with the decoded images for different bit allocation methods pair by pair and vote for the differential quality of each pair. In addition to subjective evaluation, a more objective perceptual quality assessment method, known as Masked MS-SSIM, is also used. This assessment method calculates a similarity index between the reconstructed image and the original image for regions of interest. The plausibility of the subjective and objective comparison results are verified by using statistical hypothesis tests. In summary, the proposed bit allocation method has been experimentally shown to yield a substantially higher perceptual visual quality than the other well-known bit allocation methods. The method is conceivable for use in media server applications where the server processes the eye fixation data collected by the clients to obtain the visual attention map.
Software defect prediction with a personalization focus and challenges during deployment

(Lisansüstü Eğitim Enstitüsü, 2021) Eken, Beyza ; Kühn Tosun, Ayşe ; 723330 ; Bilgisayar Mühendisliği

Organizations apply software quality assurance techniques (SQA) to deliver high-quality products to their customers. Developing defect-free software holds a critical role in SQA activities. The increasing usage of software systems and also their rapidly evolving nature in terms of size and complexity raise the importance of effectiveness in defect detection activities. Software defect prediction (SDP) is a subfield of empirical software engineering that focuses on building automated and effective ways of detecting defects in software systems. Many SDP models have been proposed in two decades, and current state-of-the-art models mostly utilize artificial intelligence (AI) and machine learning (ML) techniques, and product, process, and people-related metrics which are collected from software repositories. So far now, the people aspect of the SDP has been studied less compared to the algorithm (i.e., ensembling or tuning machine learners) and data aspects (i.e., proposing new metrics). While the majority of people-focused studies incorporate developer or team related metrics into SDP models, recently personalized SDP models have been proposed. On the other hand, the majority of the SDP research so far now focuses on building SDP models that produce high rates of prediction performance values. Real case studies in industrial software projects and also the number of studies that research the applicability of SDP models in practice are relatively few. However, for an SPD solution to be successful and efficient, its applicability in real life is as important as its prediction accuracy. This thesis focus on two main goals: 1) assessing people factor in SDP to understand whether it helps to improve the prediction accuracy of SDP models, and 2) prototyping an SDP solution for an industrial setting and assessing its deployment performance. First, we made an empirical analysis to understand the effect of community smell patterns on the prediction of bug-prone software classes. The ''community smell'' term is recently coined to describe the collaboration and communication flaws in organizations. Our motivation in this part is based on the studies that show the success of incorporating community factors, i.e., sociotechnical network metrics, into prediction models to predict bug-prone software modules. Also, prior studies show the statistical association of community smells with code smells (which are code antipatterns) and report the predictive success of using code smell-related metrics in the SDP problem. We assess the contribution of community smells on the prediction of bug-prone classes against the contribution of other state-of-the-art metrics (e.g., static code metrics) and code smell metrics. Our analysis on ten open-source projects shows that community smells improve the prediction rates of baseline models by 3% in terms of area under the curve (AUC), while the code smell intensity metric improves the prediction rates by 17%. One reason for that is the existing ways of detecting community smell patterns may not be rich in terms of capturing communication patterns of the team since it only mines patterns through mailing archives of organizations. Another reason is that the technical code flaws (code smell intensity metric) are more successful in representing defect related information compared to community smells. Considering the challenging situation in extracting community patterns and the higher success of the code small intensity metric in SDP, we direct our research to focus on the code development skills of developers and the personalized SDP approach. Second, we investigate the personalized SDP models. The rationale behind the personalized SDP approach is that different developers tend to have different development patterns and consequently, their development may have different defect patterns. In the personalized approach, there is an SDP model for each developer in the team which is trained with the developer's own development history solely and its predictions target only the developer. Whereas in the traditional approach, there is a single SDP model that is trained with the whole team's development history, and its predictions target anyone in the team. Prior studies report promising results on the personalized SDP models. Still, their experimental setup is very limited in terms of data, context, model validation, and further explorations on the characteristics that affect the success of personalized models. We conduct a comprehensive investigation of personalized change-level SDP on 222 developers from six open-source projects utilizing two state-of-the-art ML algorithms and 13 process metrics collected from software code repositories that measure the development activity from size, history, diffusion, and experience aspects. We evaluate the model performance using rigorous validation setups, seven assessment criteria, and statistical tests. Our analysis shows that the personalized models (PM) predict defects better than general models (GM), i.e., increase recall by up to 24% for the 83% of developers. However, PM also increases the false alarms of GM by up to 12% for 77% of developers. Moreover, PM is superior to GM for those developers who contribute to the software modules that have been contributed by many prior developers. GM is superior to PM for the more experienced developers. Further, the information gained from various process metrics in prediction defects differs among individuals, but the size aspect is the most important one in the whole team. In the third part of the thesis, we build prototype personalized and general SDP models for our partner from the telecommunication industry. By using the same empirical setup that we use for the investigation of personalized models in open-source projects, we observe that GM detects more defects than PM (i.e., 29% higher recall) in our industrial case. However, PM gives 40% lower false alarms than GM, leading to a lower code inspection cost than GM. Moreover, we observe that utilizing multiple data sources such as semantic information extracted from commit descriptions and latent features of development activity and applying log filtering on metric values improve the recall of PM by up to 25% and lowers GM's false alarms by up to 32%. Considering the industrial team's perspective on prediction success criteria, we pick a model to deploy that produces balanced recall and false alarm rates: the GM model that utilizes the process and latent metrics and log filtering. Also, we observe that the semantic metrics extracted from the commit descriptions do not seem to contribute to the prediction of defects as much as process and latent metrics. In the fourth and last part of the thesis, we deploy the chosen SDP prototype into our industrial partner's real development environment and share our insights on the deployment. Integrating SDP models into real development environments has several challenges regarding performance validation, consistency, and data accuracy. The offline research setups may not be convenient to observe the performance of SDP models in real life since the online (real-life) data flow of software systems is different than offline setups. For example, in real life, discovering bug-inducing commits requires some time due to the bug life cycle, and this causes a data label noise in the training sets of an online setup. Whereas, an offline dataset does not have that problem since it utilizes a pre-collected batch dataset. Moreover, deployed SDP models need a re-training (update) with the recent commits to provide consistency in their prediction performance and to keep up with the non-stationary nature of the software. We propose an online prediction setup to investigate the deployed prototype's real-life performance under two parameters: 1) a train-test (TT) gap, which is a time gap between the train and test commits used to avoid learning from noisy data, and 2) model update period (UP) to include the recent data into the model learning process. Our empirical analysis shows that the offline performance of the SDP prototype reflects its online performance after the first year of the project. Also, the online prediction performance is significantly affected by the various TT gap and UP values, up to 37% and 18% in terms of recall, respectively. In deployment, we set the TT gap to 8-month and UP to 3-day, since those values are the most convenient ones according to the online evaluation results in terms of prediction capability and consistency over time. The thesis concludes that using the personalized SDP approach leads to promising results in predicting defects. However, whether PM should be chosen over GM depends on factors such as the ML algorithm used, the prediction performance assessment criteria of the organization, and developers' development characteristics. Future research in personalized SDP may focus on profiling developers in a transferable way instead of building a model for each software project. For example, collecting developer activity from public repositories to create a profile or using cross-project personalized models would be some options. Moreover, our industrial experience provides good insights regarding the challenges of applying SDP in an industrial context, from data collection to model deployment. Practitioners should consider using online prediction setups and conducting a domain analysis regarding the team's practices and prediction success criteria and project context (i.e., release cycle) before making deployment decisions to obtain good and consistent prediction performance. Interpretability and usability of models hold a crucial role in the future of SDP studies. More researchers are becoming interested in such aspects of SDP models, i.e., developer perceptions of SDP tools and actionability of prediction outputs.
Classification of melanoma malignancy in dermatology

(Lisansüstü Eğitim Enstitüsü, 2021) Gazioğlu, Bilge Süheyla ; Kamaşak, Mustafa Ersel ; 709938 ; Bilgisayar Mühendisliği

Cancer has become one of the most common diseases all over the world in recent years. Approximately 40% of all incidences is skin cancer. The frequency of sightings of skin cancer has increased by 10 times in the last 50 years, and the risk of developing skin cancer is about 20%. Skin cancer has symptoms such as abnormal tissue growth, redness, pigmentation abnormalities and nonhealing wounds. Melanoma is a rare type of skin cancer with higher mortality compared to other types of skin cancers. Melanoma can be defined as a result of uncontrolled division and proliferation of melanocytes. Worldwide, melanoma is the 20th most common cancer and there are an estimated 287,723 new cases (1.6% of all cancers). In USA, more than two hundred thousand new cases of melanoma were diagnosed in 2021 and it increases more rapidly than other forms of cancer. Melanoma incidence increased up to 237% in the last 30 years. In our country, Turkey, melanoma is relatively rare compared to the other countries. Cancer cells display a rapid grow and systematic spread. As in all types of cancer, early diagnosis is of great importance for the treatment of skin cancer. Early diagnosis improves treatment success and prognosis. To detect a melanoma, changes in color, shape and structure of the skin, swelling and stains on the skin are carefully examined by the physicians. Besides the physician investigation, computer aided diagnosis (CAD) mechanisms are recommended for early diagnosis. In this thesis, deep learning models have been used to determine whether skin lesions are benign or malignant melanoma. The classification of the lesions is considered from two different points of view. In the first study, effect of objects in the image and image quality on classification performance was examined by using four different deep learning models. In addition, sensitivity of these models was tested. In the second study, it was aimed to establish a pre-diagnosis system that could help dermatologists by proposing a binary classification (benign nevi or malignant melanoma) mechanism on the ISIC dataset. In clinical settings, it is not always possible to capture flawless skin images. Sometimes skin images can be blurry, noisy, or have low-contrast. In other cases, images can have external objects. The aim of the first study is to investigate the effects of external objects (ruler, hair) and image quality (blur, noise, contrast) using widely used Convolutional Neural Networks (CNN) models. Classification performance of frequently used ResNet50, DenseNet121, VGG16 and AlexNet models are compared. Resilience of the mentioned models against external objects and image quality was examined. Distortions in the images are discussed under three main headings: Blur, noise and contrast changes. For this purpose, different levels of image distortions were obtained by adjusting different parameters. Data sets were created for three different distortion types and distortion levels. Firstly, the most common external object in skin images is hair on skin. In addition, rulers are commonly used as a scale for suspicious lesions on skin. In order to determine the effect of external objects on lesion classification, three separate test sets were created. These sets consist of images containing a ruler, hair and no external object (none). The third dataset consists only of mole (lesion) images. With the three datasets, four models were trained and their classification performances were analyzed. In fact, the best result was expected to be classified with a higher accuracy of the dataset that did not contain any object except the lesion. However, when the results are analyzed, since the image set containing hair had the highest number of images in the total dataset, the best classification performance in our system was measured by using DenseNet model on this subset. As a result of these tests, ResNet model showed a better classification performance compared to other models. Melanoma images can be better recognized under contrast changes unlike the benign images, we recommend ResNet model whenever there is low contrast. Noise significantly degrades the performance on melanoma images and the recognition rates decrease faster compared to benign lesions in noisy set. Both classes are sensitive to blur changes. Best accuracy is obtained with DenseNet model in blurred and noisy datasets. The images contain ruler has decreased the accuracy and ResNet has better performance in this set. Hairy images have the best success rate in our system since it has the maximum number of images in total dataset. We evaluated the accuracy as 89.22% for hair set, 86% for ruler set and 88.81% for none set. We can infer that DenseNet can be used for melanoma classification with image distortions and degradations. As a general result of the first study, we can conclude that DenseNet can be used for melanoma classification since it is more resistant to image distortion. In recent years, deep learning models with high accuracy values in computer aided diagnosis systems have been used frequently in biomedical image processing research area. Convolutional neural networks are also widely used in skin lesion classification to increase classification accuracy. In another study discussed in this thesis, five deep learning models were discussed in order to classify the images in the specially created skin lesions dataset. The dataset used in this study consists of images from ISIC dataset. In the dataset which is available in 2020, there are two classes of benign and malignant and three diagnosis consist of nevus, melanoma and unknown. We only considered images with nevus and melanoma diagnosis. Dataset had 565 melanoma and 600 benign lesion images in total. We separated the 115 images for the class of malignant melanoma and 120 images for the benign nevi class as our test set. The rest of the data was used for model training. With pre-processing methods such as flipping and rotation, the training dataset has divided into 5 parts and the number of images in the train set was increased. DenseNet121, DenseNet161, DenseNet169, DenseNet201, ResNet18, ResNet50, VGGNet19, VGGNet16_bn, SqueezeNet1_1, SqueezeNet1_0 and AlexNet models were trained with each subset. Using these models an ensemble system was designed. In this system, results the models were combined with the majority voting method. The accuracy of the proposed model is 95.76 % over the data set.
Hybrid reciprocal recommendation with advanced feature representations

(Graduate Institute, 2021) Yıldırım, Ezgi ; Öğüdücü, Şule ; 674767 ; Department of Computer Engineering

Over the last few decades, with the rise of online web services such as Facebook, Amazon, and Netflix, Recommender Systems (RecSys) have taken an indisputable place in our daily lives. The application domain of RecSys has an extensive range from e-commerce to online advertisement that aims to suggest to users the right contents matching their preferences, and it is not limited to one-way interacting platforms. In some challenging application domains, RecSys are developed to serve multiple users at each decision, to mutually satisfy the accompanying parties. Where a matching problem occurs and the satisfaction of both parties is the key to success, those recommender systems are called Reciprocal Recommenders (Rec2) in literature. Differing from traditional one-directional recommendation problems, the reciprocal recommendation has more adversity to overcome, which outlines its characteristics. In this study, based on gradual research, we first seek the key points of a strong recommender system, and then, by the learned lessons from this part, focus on the reciprocal recommendation. For this purpose, we first seek answers to these questions in a general recommender system: • How can auxiliary data affect recommendation quality? • How can we easily integrate different data sources and different approaches to empower a recommender system? Then, in the second part, we shift our research focus towards reciprocal recommendation and try to answer the following research questions: • How can we effectively solve reciprocal recommendation problems without detriment to system performance? • How can we avoid vagueness of recommendations and explain conceptual associations of requested and offered characteristics? In recent years, deep learning has gained indisputable success in computer vision, speech recognition, and natural language processing. After its rising success in these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In the initial part of this thesis, we introduce a generalized neural network-based recommender framework that offers an easy-to-use platform to combine different data sources, approaches, and methods into a single recommender system. This framework, Neural Hybrid Recommender (NHR), also allows us to exploit the same data sources to find out more elaborate information by different learning functions. In our experiments, we have worked on item prediction problems, however, with a single change on the loss function, the framework can be used for rating prediction problems as well. To evaluate the effect of such a framework, we have tested our approach on benchmark and not yet experimented datasets; movie reviews and job applications of job-seekers from an online recruitment platform. The results in these real-world datasets show the superior performance of our approach in comparison with the state-of-the-art deep learning methods in Click-Through-Rate (CTR) prediction. With the use of auxiliary data in different forms, NHR models perform better than collaborative filtering methods that depend on interaction data only. On the movie recommendation task, based on the average of a group of experiments, NHR models achieve 2.03% relative improvements on HR@10 score and 2.51% on NDCG@10 over the most successful baseline used in the evaluation. With the same setup, the improvements on the job recommendation task become even higher; 2.60% and 2.91% on HR@10 and NDCG@10, relatively. Having more promising results on job recommendation with auxiliary data is since this task is far more complex than the movie recommendation task due to the multi-variate socio-economic dependencies in job applications. Our further experiment that investigates the effect of predictive factors, which define the predictive capability in neural networks, also verifies that. Increasing the model complexity without changing the other parameters did not deteriorate the success of models in job recommendation because complex problems are less prone to over-fit, which can usually result from high model complexity. In the latter part of this thesis, we propose a multi-objective learning approach for online recruiting. Online recruiting and online dating are the most known reciprocal recommendation problems. However, the reciprocal recommendation has gained little attention in the literature due to the lack of public datasets. We aim to resolve this shortage in our study. Since the satisfaction of both candidates and companies is indispensable for successful hiring as opposed to traditional recommenders, online recruiting should respect to expectations of all parties and meet their common interests as much as possible. For this purpose, we integrated our multi-objective learning approach into various state-of-the-art methods, whose success has been proven on similar prediction problems, and we achieved encouraging results. We propose one of the prominent architectures as a prototype of our multi-objective learning approach, however, our approach applies to any recommender system employing neural networks as its final decision-maker. Our multi-objective prototype has achieved 12.15% lower LogLoss and 6.37% higher AUC than its single-objective counterpart. Besides the predictive performance, our multi-objective approach has reduced the training and testing times by half. This speedup contributes to overcoming the time constraint that complex models suffer from, so critical in the era of deep learning. Furthermore, our prototype offers explainable recommendations thanks to its Factorization Machines (FM) component. Since explainability has recently gained importance with the global changes and for ethical reasons, we have paid special attention to the selection of our base model for prototyping. Consequently, our prototype offers the reasoning behind the recommendations, so that companies can use it when requested or needed. The explainable recommendation can create a transparent hiring process and so a fair and trustworthy environment for job-seekers. This can increase the turnover rate of users and thereby help to alleviate sparsity.
Identification of object manipulation anomalies for service robots

(Lisansüstü Eğitim Enstitüsü, 2021) Altan, Doğan ; Uzar Sarıel, Sanem ; 709912 ; Bilgisayar Mühendisliği

Recent advancements in artificial intelligence have resulted in an increase in the use of service robots in many domains. These domains include households, schools and factories to facilitate daily life in domestic tasks. Characteristics of such domains necessitate the intense interaction of robots with humans. These interactions necessitate extending the abilities of service robots to deal with safety and ethical issues. Since service robots are usually assigned to complex tasks, unexpected deviations of task state are highly probable. These deviations are called anomalies, and they need to be continually monitored and handled for robust execution. After an anomaly case is detected, it should be identified for effective recovery. For the identification task, a time series analysis of onboard sensor readings is needed since some anomaly indicators are observed long before the detection of the anomaly. These sensor readings need to be fused effectively for correct interpretations as they are generally taken asynchronously. In this thesis, the anomaly identification problem of everyday object manipulation scenarios is addressed. The problem is handled from two perspectives by considering the feature types that are processed. Two frameworks are investigated: the first one takes into account domain symbols as features while the second framework considers convolutional features. Chapter 5 presents the first framework to address this problem by analyzing symbols as features. It combines and fuses auditory, visual and proprioceptive sensory modalities with an early fusion method. Before they are fused, a visual modeling system generates visual predicates and provides them as inputs to the framework. Auditory data are fed into a support vector machine (SVM) based classifier to obtain distinct sound classes. Then, these data are fused and processed within a deep learning architecture. The architecture consists of an early fusion scheme, a long short-term memory (LSTM) block, a dense layer and a majority voting scheme. After the extracted features are fed into the designed architecture, the occurred anomaly is classified. Chapter 6 presents a convolutional three-stream anomaly identification (CLUE-AI) architecture that fuses visual, auditory and proprioceptive sensory modalities. Visual convolutional features are extracted with convolutional neural networks (CNNs) from raw 2D images gathered through an RGB-D camera. These visual features are then fed into an LSTM block with a self-attention mechanism. After attention values for each image in the gathered sequence are calculated, a dense layer outputs the attention-enabled results for the corresponding sequence. Mel frequency cepstral coefficients (MFCC) features are extracted from the auditory data gathered through a microphone in the auditory stage. This is followed by feeding these auditory features into a CNN block. The position of the gripper and the force applied by it are also fed into a designed CNN block. These resulting sensory modalities are then concatenated with a late fusion mechanism. Afterward, the resulting feature vector is fed into fully connected layers. Finally, the anomaly type is revealed. The experiments are conducted on real-world everyday object manipulation scenarios performed by a Baxter robot equipped with an RGB-D head camera on top and a microphone placed on the torso. Various investigations including comparative performance evaluations, parameter and multimodality analyses are studied to show the validity of the frameworks. The results indicate that the presented frameworks have the ability to identify anomalies with f-scores of 92% and 94%, respectively. As these results indicate, the CLUE-AI framework outperforms the other in classifying occurred anomaly types. Due to the requirements that the frameworks necessitate, the CLUE-AI framework does not require additional external modules such as a scene interpreter or a sound classifier as the other one does and provides better results compared to the symbol-based solution.
Hybridization of probabilistic graphical models and metaheuristics for handling dynamism and uncertainty

(Graduate School, 2021-06-30) Uludağ, Gönül ; Etaner Uyar, Ayşe Şima ; 504072510 ; Computer Engineering

Solving 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.
Türkçe zamansal ifadelerin etiketlenmesi ve normalleştirilmesi

(Lisansüstü Eğitim Enstitüsü, 2021-07-29) Uzun, Ayşenur ; Tantuğ, Ahmet Cüneyd ; 504161504 ; Bilgisayar Mühendisliği ; Computer Engineering

Yapısal olmayan metinden bilgi çıkarma alanında yapılan çalışmalar, doğal dil işleme alanında önemli bir yere sahiptir. Kelime kökü bulma, kelime sözcük türü etiketleme, kelime bağımlılık yapı ağacı çıkarım gibi yapısal çalışmaların yanı sıra, son senelerde bilgi çıkarım alanında yapılan çalışmalar önem kazanmıştır. Metin içerisinde tespit edilen semantik bilginin, yapısal bir forma normalleştirilmesi, bilginin çeşitli doğal dil işleme çalışmalarında etkili biçimde kullanılabilmesi için önem arz etmektedir. Zamansal ifade işaretleme ve normalizasyon çalışması, bilgi çıkarım sistemleri içerisinde önemli bir yere sahiptir. Metin içerisinde geçen olaylar hakkında zaman, süre, sıklık, aralık gibi bilgi taşıyan ifadelere (ör. bugün, iki ay sonra, 19 Temmuz'da, her hafta) zamansal ifadeler denilmektedir. Zamansal ifadelerin tespit edilmesi ve belirtilen standarda göre normalize edilmesi başta İngilizce, İspanyolca, Almanca, Çince, Arapça gibi dillerde yaygın bir araştırma alanıdır. Literatürde, bu diller için birçok zamansal ifade işaretleme ve normalizasyon sistemleri sunulmuş, manuel veya otomatik yöntemler ile zamansal ifadeleri işaretlenmiş veri setleri yayınlanmıştır. Sunulan bu sistemlerin, veri setleri üzerinde değerlendirilmesi için semantik değerlendirme seminerleri düzenlenmiştir. Bilgimiz dahilinde Türkçe literatüründe, bu zamana kadar herhangi bir zamansal ifadeleri işaretlenmiş, yapısal bir veri bankası yayınlanmamıştır. Ayrıca, baştan sona Türkçe zamansal ifade tespit ve normalizasyon görevlerini gerçekleştiren bir sisteme, literatür incelemelerimiz sırasında rastlanmamıştır. Bu tez çalışmasında, Türkçe zamansal ifade çıkarım ve normalizasyon alanında temel bir çalışma sayılabilecek, ilk uçtan uca ve Türkçe biçimbilimsel yapısının da dahil edildiği, kural tabanlı zamansal ifade etiketleme ve normalizasyon sistemi geliştirilmiştir. Sistemin geliştirilmesi ve test aşamasında kullanılmak üzere, 109 haber metninde yer alan zamansal ifadeler manuel yöntemle işaretlenmiştir. Tez kapsamında geliştirilen bu veri seti, gelecek araştırma çalışmalarında kullanılması amacı ile ortak kullanıma açılmıştır. Geliştirlen bu sistem, yayınlanan test veri seti üzerinde çalıştırılmıştır. Sistemin performansı, zamansal ifade etiketleme çalışmalarında kullanılan doğruluk (precision) ve tutarlılık (recall) formülleri kullanılarak ölçülmüştür. Metin içerisinde geçen zamansal ifadeler %89 F1 skoru başarısı ile tespit edilirken, doğru tespit edilen ifadelerin "type" ve "value" niteliklerinin normalizasyonunda sırasıyla %89 ve %88 F1 başarısı elde edilmiştir. Gelecek çalışmalarda, hata analizi ve sistem kısıtlamaları bölümlerinde bahsedilen eksiklikler ve tavsiyler göz önünde bulundurularak, daha yüksek başarımlı Türkçe zamansal ifade işaretleme ve normalizasyon çalışmaları gerçekleştirilebilir.

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