Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin

Dialectal elements in the vocabulary of the Uyghur Khanate inscriptions

One of the significant problems with Old Turkic inscriptions is that it is not known by which peoples’ or tribe’s Turkic language the inscriptions were written in. Although among the clans and persons who wrote and erected the large inscriptions of the Turkic and Uyghur Khanates, those of Köl Tegin, Bilge Kaghan, Şine Usu, Tariat, Tes and Karabalghasun I were identified, the peoples or clans having erected the other inscriptions are mostly unknown. The most serious problem encountered by researchers in consideration of the tribal seals present in the inscriptions is the uncertainty whether the seal belonged to the tribe that wrote or erected the inscription, or the tribe that was in power at that time. This paper investigates the inscriptions of the Uyghur Khanate. Our scrutiny is based on the examination of the peculiarities of the Uyghur Khanate inscriptions which cannot be observed in any other inscriptions of Mongolia, Yenisei, Altai and Kyrgyzstan. By substituting these peculiar words with other words to be found in other inscriptions, an attempt has been made to prove that these words are Uyghur dialectal words. After an inquiry whether the words were used subsequent to the runic period, etymological suggestions concerning the words have also been put forward

Quadruplet Selection Methods for Deep Embedding Learning

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by using a multi-task learning framework, in which the hierarchical labels (coarse and fine labels) of the samples are utilized both for classification and a quadruplet-based loss function. In order to improve the recognition strength of the learned features, we present a novel feature selection method specifically designed for four training samples of a quadruplet. By experiments, it is observed that the selection of very hard negative samples with relatively easy positive ones from the same coarse and fine classes significantly increases some performance metrics in a fine-grained dataset when compared to selecting the quadruplet samples randomly. The feature embedding learned by the proposed method achieves favorable performance against its state-of-the-art counterparts.Comment: 6 pages, 2 figures, accepted by IEEE ICIP 201

THE FAULT DIAGNOSIS MODEL BASED ON ARTIFICIAL IMMUNE SYSTEM USING GENETIC ALGORITHM

Bu çalışmada asenkron motor arızalarını tespit etmek için yapay bağışık sistem tabanlı arıza teşhis metodu önerilmiştir. Önerilen metot kırık rotor çubuğu arızlarını tespit etmek için negatif seçim algoritmasını kullanır. Arıza ile ilgili özellikler motor akımın bir fazının ilk fark filtrelemesi ve hilbert dönüşümü kullanılarak elde edilir. Bu özellik sinyallerinin faz uzayı nonlineer zaman serileri analizi yöntemi ile elde edilerek negatif seçimin giriş verisi oluşturulur. Hilbert tabanlı dönüşüm olarak adlandırılan yeni özellik sinyali faz uzayında kırık rotor çubuğu arızalarını ayırt etmek için kullanışlıdır. Orjinal negatif seçim algoritmasında detektörler rastgele üretilir. Fakat rasgele üretilen detektörler iki probleme sahiptir. Birincisi öz olmayan uzay kapsanmayabilir. İkincisi benzer detektörlerin üretimini engellemek için herhangi bir sınırlama yoktur. Genetik algoritma negatif seçimin detektörlerini optimize etmek ve üretmek için kullanılmıştır. Minimum detektör sayısı ile öz olmayan uzayın maksimum kapsanması sağlanmıştır. Önerilen yöntemin doğruluğu zaman adımlı birleştirilmiş sonlu elaman durum uzayı ile elde edilen simülasyon verileri kullanılarak doğrulanmıştır. In this study, artificial immune system based fault diagnosis method has been proposed to detect the induction motor faults. The proposed method uses negative selection algorithm to detect broken rotor bar faults. Fault related features are obtained using Hilbert transform and first difference filtering of one phase motor current.The phase space of these feature signals is obtained using a nonlinear time series analysis and they constitute the input data of the negative selection. The new feature signal called Hilbert based transform is quite useful to separate broken rotor bar faults in the phase space. In the original negative selection algorithm detectors are randomly generated. But randomly generated detectors have two problems. The first is that the non-self space may not be covered, completely. The second problem is that there is not any restriction to deny generation of similar detectors. The genetic algorithm is used to generate and optimize the detectors of the negative selection. The maximum coverage of non-self space with minimum detector numbers is ensured. The accuracy of method has been verified using simulation data that obtained by time-stepping coupled finite element state space method

A Deep Learning-Based Hybrid Approach to Detect Fastener Defects in Real-Time

A fastener is an important component used to fix the rail in railways. Defects in this component cause the rail and ballast to remain unstable. If the defective fasteners are not replaced in time, it is inevitable that the train will derail, and serious accidents will occur. Therefore, this component should be inspected periodically. Conventional image processing-based control systems are affected by noise and different lighting conditions in the real environment. In this study, it is aimed to determine the defects of fasteners with a deep learning-based hybrid approach. The YOLOv4-Tiny method is used for fastener detection and localization. This method gives accurate results, especially for the detection of small objects. After the fastener position is determined, a new lightweight convolutional neural network model is used for defect classification. The proposed convolutional neural network for classification has a small network structure because it uses depth-wise and pointwise convolution layers. When the experimental results are compared with other known transfer learning methods, better results were obtained in terms of training/test time and accuracy

Öğretmen adaylarının matematik kaygısı ile bilgibilimsel inançları arasındaki ilişki üzerine bir çalışma.

There is dearth of literature on the relationship between epistemological beliefs and mathematics anxiety levels at the national level. It is important from to analyze the relationship between the two variables which are effective on student teachers’ performance of mathematics as other disciplines. The aim of the study is to explore the relationship between mathematics student teachers’ epistemological beliefs and anxiety levels considering all the sub-dimensions of the variables. The study uses the quantitative paradigm and the correlational model. The sample of the study comprised of the 547 student teachers trained in Selcuk and Marmara Universities. The data collection tools are ‘epistemological beliefs scale’, ‘mathematics anxiety scale’ and personal information form. Statistical analyses were made using descriptive statistics, Pearson Product correlation and chi-square formulas. Most of the correlations between all dimensions of the two variables showed statistically meaningful relationships. The statistically meaningful and slightly below average correlations are among the noteworthy findings of the study whose reasons and implications are discussed in the paper.Ülkemizde bilgibilimsel inançlar ve kaygı arasındaki ilişkiyi araştıran hiçbir çalışmaya rastlanmamıştır. İki değişken arasındaki ilişkiyi araştırmak öğretmen adaylarının matematikte ve diğer disiplinlerde ki performansları üzerine etkisi açısından önemlidir. Araştırmanın amacı, ilköğretim ve ortaöğretim matematik bölümü öğrencilerinin bilgibilimsel inançları ile matematik kaygıları arasındaki ilişkiyi her iki değişkenin bütün alt boyutları hesaba katılarak araştırmak olarak belirlenmiştir. Araştırmada nicel paradigma ve ilişkisel tarama modeli kullanılmıştır. Araştırmanın örneklemini, 2007-2008 öğretim yılında Selçuk ve Marmara Üniversitesi'nde öğrenim görmekte olan 547 öğretmen adayı oluşmaktadır. Veri toplama aracı olarak 'Bilgibilimsel İnançlar Ölçeği', 'Matematik Kaygısı Ölçeği" ve "Kişisel Bilgi Formu"; veri analizi için ise betimleyici istatistiklerin yanı sıra Pearson ve Ki-kare korelasyon formülleri kullanılmıştır. Bilgibilim ölçeğinin bütün alt boyutları ile matematik kaygı ölçeğinin kendisi ve alt boyutları arasında hesaplanan katsayıların çoğu istatistiksel olarak anlamlı ilişki göstermektedir. Öğrenmenin yeteneğe bağlı olduğuna olan inanç ile matematik kaygısı alt boyutları arasında bulunan orta seviyeye yakın ilişkiler manidar bulunmuş ve bunun sebepleri tartışılmıştır

Barriers to Cement Industry Towards Circular Economy

Cement, as the main component of concrete, is a crucial industrial product for economic development and civilization. Nevertheless, its production is highly energy-intensive, environmentally polluting, and a source of extreme CO2 emissions. For success in the transition to the circular economy and accelerating sustainable manufacturing in the cement industry, understanding and addressing the main barriers are essential. Using the above point of view, this study intends to address the challenges and barriers of the cement industry in the transition to a circular economy, define the causal relationships between these barriers, and determine the necessary practical implications to overcome the barriers. Systematic literature review and focus group study results enable a holistic model that integrates research results and business practical criteria. The DEMATEL method is used for the clarification of causal relations between factors. A total of 18 barriers in 6 clusters have been revealed to be used for managerial implications to speed up the transition to CE applications in the cement business. Out of 18 barriers, 6 were effect groups, which were the outcomes due to the remaining 12 causing barriers. The top three cause factors are an unstable waste market, lack of management competencies, and unstable macroeconomic conditions, while the leading three effect factors are revealed as giving priority to other issues, insufficient organisational structures, and deviations in product quality. Although there are many studies on CE in cement, they are concentrated on technical and laboratory studies enabling the use of different alternative materials as inputs to the cement process. Studying and revealing the barriers holding back the cement sector in the transition to CE is this study’s core contribution, making it novel and unique

Antinuclear antibody testing in a Turkish pediatrics clinic: is it always necessary?

Introduction: the term anti-nuclear antibody (ANA) is used to define a large group of autoantibodies which specifically bind to nuclear elements. Although healthy individuals may also have ANA positivity, the measurement of ANA is generally used in the diagnosis of autoimmune disorders. However, various studies have shown that ANA testing may be overused, especially in pediatrics clinics. Our aim was to investigate the reasons for antinuclear antibody (ANA) testing in the general pediatrics and pediatric rheumatology clinics of our hospital and to determine whether ANA testing was ordered appropriately by evaluating chief complaints and the ultimate diagnoses of these cases. Methods: the medical records of pediatric patients in whom ANA testing was performed between January 2014 and June 2016 were retrospectively evaluated. Subjects were grouped according to the indication for ANA testing and ANA titers. Results: ANA tests were ordered in a total of 409 patients during the study period, with 113 positive ANA results. The ANA test was ordered mostly due to joint pain (50% of the study population). There was an increased likelihood of autoimmune rheumatic diseases (ARDs) with higher ANA titer. The positive predictive value of an ANA test was 16% for any connective tissue disease and 13% for lupus in the pediatric setting. Conclusion: in the current study, more than one-fourth of the subjects were found to have ANA positivity, while only 15% were ultimately diagnosed with ARDs. Our findings underline the importance of an increased awareness of correct indications for ANA testing