Actionable insights through association mining of exchange rates: a case study

Association mining is the methodology within data mining that researches associations among the elements of a given set, based on how they appear together in multiple subsets of that set. Extensive literature exists on the development of efficient algorithms for association mining computations, and the fundamental motivation for this literature is that association mining reveals actionable insights and enables better policies. This motivation is proven valid for domains such as retailing, healthcare and software engineering, where elements of the analyzed set are physical or virtual items that appear in transactions. However, the literature does not prove this motivation for databases where items are “derived items”, rather than actual items. This study investigates the association patterns in changes of exchange rates of US Dollar, Euro and Gold in the Turkish economy, by representing the percentage changes as “derived items” that appear in “derived market baskets”, the day on which the observations are made. The study is one of the few in literature that applies such a mapping and applies association mining in exchange rate analysis, and the first one that considers the Turkish case. Actionable insights, along with their policy implications, demonstrate the usability of the developed analysis approach

Multi-modal Egocentric Activity Recognition using Audio-Visual Features

Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance, cuboid are extracted from the video. The audio signal is characterized using a "supervector", obtained based on Gaussian mixture modelling of frame-level features, followed by a maximum a-posteriori adaptation. Then, the extracted multi-modal features are adaptively fused by MKL classifiers in which both the feature and kernel selection/weighing and recognition tasks are performed together. The proposed framework was evaluated on a number of egocentric datasets. The results showed that using multi-modal features with MKL outperforms the existing methods

Kalman süzgeci kullanarak imge onarımı

Görüntü işlemede karşılaşılan yaygın problemlerden biri verilen bozuk bir görüntüyü kullanarak onarım yapmaktır. Bu problem genelde görüntü onarımı olarak adlandırılır. Görüntü onarımı problemini çözmek için onarım modellerinin ya da doğrusal süzgeçlerin kullanılması gibi bir çok yaklaşım vardır. Bu tezde, görüntü onarımı için doğrusal süzgeç yöntemlerinden biri olan Kalman süzgeci yöntemi kullanılmıştır. Bu amaçla, iki farklı görüntü onarım problemi tanımlanmış ve her bir problem için farklı Kalman süzgeci yöntemi kullanılmıştır. Tezin birinci bölümünde, çoklu çerçeveden oluşan bir görüntü için basit bir skalar Kalman süzgeci yöntemi uygulanmıştır. Tezin ikinci bölümünde ise gürültülü görüntüler üzerinde iki boyutlu tam düzlemli blok Kalman süzgeci yöntemi kullanılmıştır. Benzetim sonuçları görüntü onarımında kullanılan diğer yöntemlerden bir kaçıyla karşılaştırılmıştır. A common problem in image processing is the restoration of an image from a given corrupted version. This problem is generally known as image restoration. There are various approaches to solve this problem like using restoration models or linear filtering. In this thesis, one of the linear filtering methods named Kalman filtering has been used for image restoration. For this purpose, two different image restoration scenarios have been defined and two different versions of Kalman filtering methods have been used for each of the scenarios. In the first part of the thesis, a simple scalar Kalman filter method is used for an image that contains multiple frames. In the second part of the thesis, a two-dimensional (2-D) full-plane block Kalman filter is used to restore noisy images. Simulation results are compared with some of the other restoration techniques

Determination of Effect of Nitrogen Fertilization on Some Quality Properties of Salep Orchid (Orchis sancta L.) Cultivated in Field Conditions in Turkey

Turkey has rich biodiversity due to located at the intersection of Europe-Siberia, Mediterranean and Iran-Turan flora regions. Orchidaceae family has a distinct place within this rich biodiversity. It has been reported total 204 orchid species those belongs to the 24 genera are grown in Turkey. The exports of the salep orchids were banned in 1974 by the Ministry of Agriculture due to high destruction of natural distribution areas of the plant. Despite the fact that nowadays the salep plants are protected by laws, the tubers of salep orchids still have been collected by people. Washing, boiling in water or milk, washing in cold water and drying stages are used to prevent growing activity of salep tubers. After this, tubers of salep are grind and prepared to use as salep powder. All of salep production is provided by collection of salep tubers from nature. For one kilogram of salep, 1000-4000 tubers is used. And it was assumed that our country produces 45 tons of tubers per year.This study was carried out to determine the effect of nitrogen fertilization on some quality characteristics of Orchis sancta L. grown in field conditions, in order to take part in agricultural cultivation of salep orchids. In the study, the effect of four fertilizer doses (0, 5, 10 and 15 kg/da) was investigated on starch ratio (%), mucilage ratio (%), protein ratio (%) and ash ratio (%). Mucilage ratio (salep mannia) was varied between 14% and 26% according to nitrogen fertilizer doses

More Efficient Secure Outsourcing Methods for Bilinear Maps

Bilinear maps are popular cryptographic primitives which have been commonly used in various modern cryptographic protocols. However, the cost of computation for bilinear maps is expensive because of their realization using variants of Weil and Tate pairings of elliptic curves. Due to increasing availability of cloud computing services, devices with limited computational resources can outsource this heavy computation to more powerful external servers. Currently, the checkability probability of the most efficient outsourcing algorithm is $1/2$ and the overall computation requires $4$ point addition in the preimage and $3$ multiplications in the image of the bilinear map under the one-malicious version of a two-untrusted-program model. In this paper, we propose two efficient new algorithms which decrease not only the memory requirement but also the overall communication overhead

Multi-modal Egocentric Activity Recognition using Audio-Visual Features

Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance, cuboid are extracted from the video. The audio signal is characterized using a "supervector", obtained based on Gaussian mixture modelling of frame-level features, followed by a maximum a-posteriori adaptation. Then, the extracted multi-modal features are adaptively fused by MKL classifiers in which both the feature and kernel selection/weighing and recognition tasks are performed together. The proposed framework was evaluated on a number of egocentric datasets. The results showed that using multi-modal features with MKL outperforms the existing methods

alpha- or beta-Substituted functional phthalocyanines bearing thiophen-3-ylmethanol substituents: synthesis, characterization, aggregation behavior and antioxidant activity

The tetra - or -thiophene substituted metal and metal-free phthalocyanines (Pcs) M[Pc(-OCH(2)Thiopen)(4)] and M[Pc(-OCH(2)Thiopen)(4)] {(-ThMet-MPc), (-ThMet-MPc) [ThMet: Thiophene methoxy], M=Zn(II), Co(II) and, 2H} were synthesized from the corresponding 3'-(thiophen-3-ylmethoxy)phthalonitrile or 4'-(thiophen-3-ylmethoxy)phthalonitrile (ThMePN). The structural characterization, spectral, and antioxidant properties of a series of new Pcs were also presented. Both - and -substituted Pc complexes increased solubility in polar solvents, such as THF, DMF, and DMSO. FT-IR, H-1-NMR, C-13-NMR, UV-vis, MALDI-TOF/MS spectral, and elemental analysis data were used to characterize the compounds. The aggregation behaviors of 3-8 were also investigated at different concentrations in THF. Antioxidant test methods, ,-diphenyl--picrylhydrazyl radical scavenging activity, metal chelating activity, and reducing power, were used to determine the antioxidant activities. 6 showed very good ferrous ion chelating activity of 81 +/- 1%. 6, 5, 4, and 3 showed better reducing power than trolox, ascorbic, acid and butylated hydroxytoluene, commercially used antioxidants

Formation of Umbilicaria decussata (Antarctic and Turkey) extracts based nanoflowers with their peroxidase mimic, dye degradation and antimicrobial properties

This work describes a unique and environmentally friendly approach for creating three-dimensional (3D) organic-inorganic flower shaped hybrid nanostructures called "nanoflower (NF)" by using Umbilicaria decussate (U. decussate) extract and copper ions (Cu2+). U. decussate species were collected from certain place in Antarctic and Turkey and extraction of each species were completed in methanol and water. The U. decussate extracts were used as organic components and Cu2+ acted as inorganic components for formation of U. decussate extracts based hybrid NFs. We rationally used these NFs as novel nanobiocatalyst and antimicrobial agents. These NFs exhibited peroxidase mimic, dye degradation and antimicrobial properties. The NFs were characterized with various techniques. For instance, the morphologies of the NFs were monitored by scanning electron microscope (SEM), presence of elements in the NFs were presented using Energy Dispersive X-Ray Analysis (EDX). Fourier-transform infrared spectroscopy (FT-IR) was used to elucidate corresponding bending and stretching of bonds in the NFs. The NFs acted as effective Fenton agents in the presence of hydrogen peroxide, and we demonstrated their peroxidase-like activity against guaiacol, dye degradation property towards malachite green and antimicrobial activity for Aeromonas hydrophila, Aeromonas sobria, Escherichia coli, Salmonella enterica and Staphylococcus aureus