Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941

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Performance of Taiwanese Domestic Equity Funds During Quantitative Easing
(2015) Tan, Ömer Faruk
This study is the first to analyze performance of Taiwanese domestic equity funds between January 2009 and October 2014, the period during which quantitative redirected capital flows toward developing economies and the Taiwanese Stock Exchange Weighted Index compounded at approximately 12.9% annually. Adopting methods endorsed by earlier research, we evaluated 15 Taiwanese equity funds' performance relative to market averages using the Sharpe (1966) and Treynor (1965) ratios and Jensen's alpha method (1968). To test market timing proficiency, we applied the Treynor and Mazuy (1966) and Henriksson and Merton (1981) regression analysis methods. Jensen's alpha method (1968) was used to measure fund managers' stock selection skills. Results revealed that funds significantly under-performed Taiwan's average annual market return and demonstrated no exceptional stock-selection skills and market timing proficiency during the era of quantitative easing.
Bidirectional Recurrent Neural Network Language Models for Automatic Speech Recognition
(2015) Chen, Stanley; Sethy, Abhinav; Ramabhadran, Bhuvana; Arısoy, Ebru
Recurrent neural network language models have enjoyed great success in speech recognition, partially due to their ability to model longer-distance context than word n-gram models. In recurrent neural networks (RNNs), contextual information from past inputs is modeled with the help of recurrent connections at the hidden layer, while Long Short-Term Memory (LSTM) neural networks are RNNs that contain units that can store values for arbitrary amounts of time. While conventional unidirectional networks predict outputs from only past inputs, one can build bidirectional networks that also condition on future inputs. In this paper, we propose applying bidirectional RNNs and LSTM neural networks to language modeling for speech recognition. We discuss issues that arise when utilizing bidirectional models for speech, and compare unidirectional and bidirectional models on an English Broadcast News transcription task. We find that bidirectional RNNs significantly outperform unidirectional RNNs, but bidirectional LSTMs do not provide any further gain over their unidirectional counterparts.
Parameters Effects Study on Pulse Laser for the Generation of Surface Acoustic Waves in Human Skin Detection Applications
(2015) Chen, Kun; Wu, Sen; Li, Yanning; Li, Tingting; Fu, Xing; Dorantes-Gonzalez, Dante Jorge
Laser-induced Surface Acoustic Waves (LSAWs) has been promisingly and widely used in recent years due to its rapid, high accuracy and non-contact evaluation potential of layered and thin film materials. For now, researchers have applied this technology on the characterization of materials' physical parameters, like Young's Modulus, density, and Poisson's ratio; or mechanical changes such as surface cracks and skin feature like a melanoma. While so far, little research has been done on providing practical guidelines on pulse laser parameters to best generate SAWs. In this paper finite element simulations of the thermos-elastic process based on human skin model for the generation of LSAWs were conducted to give the effects of pulse laser parameters have on the generated SAWs. And recommendations on the parameters to generate strong SAWs for detection and surface characterization without cause any damage to skin are given.
Multi-Stream Long Short-Term Memory Neural Network Language Model
(2015) Saraçlar, Murat; Arısoy, Ebru
Long Short-Term Memory (LSTM) neural networks are recurrent neural networks that contain memory units that can store contextual information from past inputs for arbitrary amounts of time. A typical LSTM neural network language model is trained by feeding an input sequence. i.e., a stream of words, to the input layer of the network and the output layer predicts the probability of the next word given the past inputs in the sequence. In this paper we introduce a multi-stream LSTM neural network language model where multiple asynchronous input sequences are fed to the network as parallel streams while predicting the output word sequence. For our experiments, we use a sub-word sequence in addition to a word sequence as the input streams, which allows joint training of the LSTM neural network language model using both information sources.
A Decade of Discriminative Language Modeling for Automatic Speech Recognition
(2015) Arısoy, Ebru; Saraçlar, Murat; Dikici, Erinc
This paper summarizes the research on discriminative language modeling focusing on its application to automatic speech recognition (ASR). A discriminative language model (DLM) is typically a linear or log-linear model consisting of a weight vector associated with a feature vector representation of a sentence. This flexible representation can include linguistically and statistically motivated features that incorporate morphological and syntactic information. At test time, DLMs are used to rerank the output of an ASR system, represented as an N-best list or lattice. During training, both negative and positive examples are used with the aim of directly optimizing the error rate. Various machine learning methods, including the structured perceptron, large margin methods and maximum regularized conditional log-likelihood, have been used for estimating the parameters of DLMs. Typically positive examples for DLM training come from the manual transcriptions of acoustic data while the negative examples are obtained by processing the same acoustic data with an ASR system. Recent research generalizes DLM training by either using automatic transcriptions for the positive examples or simulating the negative examples.
Experimental Performance Analysis for Mobile Data Offloading in Heterogeneous Wireless Networks
(2016) Akpolat, Gamze; Zeydan, Engin; Tan, A. Serdar
...
Integration and Management of Wi-Fi Offloading in Service Provider Infrastructures
(2016) Zeydan, Engin; Tan, A. Serdar
Integration of offloading technologies into mobile network operator's infrastructures that provide heterogeneous access services is a challenging task for mobile operators. A connectivity management platform is a key element for heterogeneous mobile network operators in order to enable optimal offloading. In this study, development and integration of a connectivity management platform that uses a novel multiple attribute decision making algorithms for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. The proposed platform collects several terminal and network level attributes via infrastructure and client Application Programming Interfaces (APIs) and decides the best network access technology to connect for requested users. Through experimentation, we provide details on the platform integration with service provider's network and sensitivity analysis of the multiple attribute decision making algorithm.
Performance of Taiwanese Domestic Equity Funds During Quantitative Easing (conferenceobject)
(2016) Tan, Ömer Faruk
This study is the first analysis on the performance of Taiwanese domestic equity funds during the period of January, 2009 and October, 2014. For the period, quantitative redirected capital flowed toward developing economies and the Taiwanese Stock Exchange Weighted Index compounded at approximately12.9% annually. Adopting methods endorsed by earlier research, we evaluated 15 Taiwanese equity funds' performance relative to market averages using the Sharpe (1966) and Treynor (1965) ratios and Jensen's alpha method (1968). In testing market timing proficiency, we applied Treynor & Mazuy (1966) and Henriksson & Merton (1981) regression analysis methods. Jensen's alpha method (1968) was used to measure fund managers stock selection skills. The results of this study show that funds under-performed Taiwan's average annual market return significantly and demonstrates no exceptional stock-selection skills and market timing proficiency during the era of quantitative easing.
Compositional Neural Network Language Models for Agglutinative Languages
(2016) Saraçlar, Murat; Arısoy, Ebru
Continuous space language models (CSLMs) have been proven to be successful in speech recognition. With proper training of the word embeddings, words that are semantically or syntactically related are expected to be mapped to nearby locations in the continuous space. In agglutinative languages, words are made up of concatenation of stems and suffixes and, as a result, compositional modeling is important. However, when trained on word tokens, CSLMs do not explicitly consider this structure. In this paper, we explore compositional modeling of stems and suffixes in a long short-term memory neural network language model. Our proposed models jointly learn distributed representations for stems and endings (concatenation of suffixes) and predict the probability for stem and ending sequences. Experiments on the Turkish Broadcast news transcription task show that further gains on top of a state-of-theart stem-ending-based n-gram language model can be obtained with the proposed models.
Regression Analysis of Stock Exchanges During the Ramadan Period: Analysis of 16 Countries
(2016) Tan, A. Serdar; Özlem S.
...
Developing an Automatic Transcription and Retrieval System for Spoken Lectures in Turkish
(2017) Arısoy, Ebru
With the increase of online video lectures, using speech and language processing technologies for education has become quite important. This paper presents an automatic transcription and retrieval system developed for processing spoken lectures in Turkish. The main steps in the system are automatic transcription of Turkish video lectures using a large vocabulary continuous speech recognition (LVCSR) system and finding keywords on the lattices obtained from the LVCSR system using a speech retrieval system based on keyword search. While developing this system, first a state-of-the-art LVCSR system was developed for Turkish using advance acoustic modeling methods, then keywords were extracted automatically front word sequences in the reference transcriptions of video lectures, and a speech retrieval system was developed for searching these keywords in the lattice output of the LVCSR system. The spoken lecture processing system yields 14.2% word error rate and 0.86 maximum term weighted value on the test data.
A Ran/Sdn Controller Based Connectivity Management Platform for Mobile Service Providers
(Institute of Electrical and Electronics Engineers Inc., 2017) Ayhan, Gökhan; Koca, Melih; Zeydan, Engin; Tan, A. Serdar
In this demo, we demonstrate the integration of radio access network (RAN)/Software-Defined Networking (SDN) controller with a connectivity management platform designed for mobile wireless networks. This is an architecture designed throughout the EU Celtic-Plus project SIGMONA1. OpenDaylight based RAN/SDN controller and the application server are capable of collecting infrastructure and client related parameters from OpenFlow enabled switches and Android based phones respectively. The decision on the best access network selection is computed at the application server using a Multiple Attribute Decision Making (MADM) algorithm and instructed back to Android-based mobile client for execution of access network selection. © 2017 IFIP.
The Impact of D2d Connections on Network-Assisted Mobile Data Offloading
(IEEE, 2018) Tan, Ahmet Serdar; Zeydan, Engin
The exponential increase of mobile data traffic pushes mobile operators to seek more efficient heterogeneous communication techniques. In this study, multi-user extension methods for multiple attribute decision making algorithms for network-assisted data offloading in heterogeneous wireless networks are developed and performance evaluations are performed in the presence of Device-to-Device (D2D) connections. Evaluations are carried out using simulations to point out the metrics and factors influencing data offloading in heterogeneous networks. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions. Additionally, up to 67% increase in user satisfaction can be achieved when D2D density is kept 68% under a heavy load scenario. The simulation results also indicate the existence of optimal D2D densities in heterogeneous networks depending on the total number of users and available network capacity.
Parallelization and Performance Analysis of Reversible Circuit Synthesis
(IEEE, 2018) Susam, Ömercan; Arslan, Şuayb Şefik
Rising popularity of quantum computers in the last decade resulted in increased interest paid to reversible circuit synthesis process. In this work, a popular essential function-based synthesis algorithm known in the literature is parallelized using openMP library. Contrary to conventional way, essential functions are synthesized when needed without keeping a table-lookup library. When the reversible circuit is synthesized in parallel using a double core processor (4 active threads with hyperthearding technology), around 2.6 speed-up is demonstrated relative to the performance of serial synthesis work. Comparison between serial and parallel synthesis by using common benchmark circuits demonstrated that the performance of the proposed parallel synthesis is always better in the overall operation work load.
Turkish Broadcast News Transcription Revisited
(2018) Saraçlar, Murat; Arısoy, Ebru
Bu çalışmada yaklaşık on yıl önce gerçeklenen Türkçe haber programları için otomatik konuşma tanımayla yazılandırma sistemi güncel yöntemlerle yenilenerek aynı veri üzerindeki başarımı ölçülmüştür. Son yıllarda yapay sinir ağları temelli derin öğrenme yöntemleri konu¸sma tanıma hata oranlarında belirgin bir iyileşme sağlamıştır ve günümüzde yaygın olarak kullanılmaktadır. Bu bildiride geliştirilen konu¸sma tanıma sisteminin temel bileşenleri olan akustik ve dil modelleri için sinir ağları kullanılmıştır. Akustik modelleme için derin sinir a^gları hem çapraz entropi hem de ayırıcı dizi amaç işlevleriyle eniyilenmiştir. Ayrıca uzun süreli bağımlılıkları modellemek için yinelemeli sinir ağlarına benzer bir başarım gösteren ama daha çabuk eğitilebilen zaman gecikmeli sinir ağları kullanılmıştır. Daha sonra bunların ayırıcı eğitimle eniyilenmesi sonucunda en düşük hata oranlarına ulaşılmoştır. Dil modeli için ise yinelemeli sinir ağları kullanılmıştır. Bu yeni sinir ağları kullanan modeller ile kelime hata oranlarının yarılandığıve %10’un altına düştüğü gözlemlenmiştir.
Language Modeling for Turkish Text and Speech Processing
(Springer, 2018) Arısoy, Ebru; Saraçlar, Murat
This chapter presents an overview of language modeling followed by a discussion of the challenges in Turkish language modeling. Sub-lexical units are commonly used to reduce the high out-of-vocabulary (OOV) rates of morphologically rich languages. These units are either obtained by morphological analysis or by unsupervised statistical techniques. For Turkish, the morphological analysis yields word segmentations both at the lexical and surface forms which can be used as sub-lexical language modeling units. Discriminative language models, which outperform generative models for various tasks, allow for easy integration of morphological and syntactic features into language modeling. The chapter provides a review of both generative and discriminative approaches for Turkish language modeling.
Quality-Aware Wi-Fi Offload: Analysis, Design and Integration Perspectives
(2018) Mester, Yavuz; Buyruk, Hasan; Zeydan, Engin; Tan, A. Serdar
The rapid spread of smart wireless devices and expansion of mobile data traffic have increased the interest for efficient traffic offloading techniques in next-generation communication technologies. Wi-Fi offloading uses ubiquitous Wi-Fi technology in order to satisfy the ever increasing demand for mobile bandwidth and therefore is an appropriate methodology for mobile operators. As a matter of fact, design and integration of an offloading technology inside mobile network operators' infrastructures is a challenging task due to convergence issues between the The 3rd Generation Partnership Project (3GPP) and non-3GPP networks. Therefore, a connectivity management platform is a key element for integrated heterogeneous mobile network operators in order to enable smart and effective offloading. In this paper, analysis, design and integration of a connectivity management platform that uses a Multiple Attribute Decision Making (MADM) algorithm for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. In order to enhance the end-user's quality-of-experience (QoE), we have especially concentrated on the benefits that can be achieved by exploiting the presence of integrated service provider platform. Hence, the proposed platform can collect several User Equipment (UE) and network-based attributes via infrastructure and client Application Programming Interfaces (APIs) and decides on the best network access technology (i.e. 3GPP and non-3GPP) to connect to for requested users. We have also proposed multi-user extensions of the MADM algorithms for offloading. Through simulations and experiments, we provide details of the comparisons of the proposed algorithms as well as the sensitivity analysis of the MADM algorithm through an experimental set-up.
Evaluation of Diaphragm Conditions in Aac Floor Structureswith Rc Beams
(2018) İlki, Alper; Uğurlu, Koray; Demir, Cem; Comert, Mustafa; Halıcı, Ömer Faruk
Diaphragm action in floor structures is an important aspect that affects both local behaviors of individual members and consequently, the global response of a structure. The diaphragm action of a built structure, therefore needs to be compatible with the assumed diaphragm condition in the design phase to prevent unpredicted overloading of load bearing members in a seismic action. Autoclaved aerated concrete (AAC) is a cost-effective, lightweight and energy efficient material, and its usage as a construction material has rapidly increased in recent decades. However, there is a limited experience regarding the in-plane behavior of the floor structures made of AAC panels in terms of diaphragm action. In this paper, the in-plane response of AAC floors is experimentally investigated and the floor performance of a typical building is analytically investigated according to ASCE 7-16 (ASCE/SEI in Minimum design loads for buildings and other structures, The American Society of Civil Engineers, Reston, 2016). Full-scale experiments carried out through loading AAC floors in lateral directions to the panels, either parallel or perpendicular, provided important information about the damage progress and overall performance of such floors. A number of finite element modeling techniques that are generally used for modeling of AAC floors were examined and then validated through comparisons with test results. Finally, the diaphragm condition of a three-story building made of AAC walls and floor panels was assessed. The results indicated that the AAC floors in the examined building can be idealized as rigid diaphragms according to ASCE 7-16.
Performance Maximization of Network Assisted Mobile Data Offloading With Opportunistic Device-To Communications
(2018) Zeydan, Engin; Tan, A. Serdar
Mobile data traffic inside mobile operator's infrastructure is increasing exponentially every year. This increasing demand forces mobile network operators (MNOs) to seek for alternative communication methods in order to relieve the traffic load in base stations, especially in highly populated and crowded environments. Network assisted data offload and Device-to-Device(D2D) communications are two prominent methods to help MNOs solve this problem. In this study, a data offload framework is developed that incorporates network assisted multiple attribute decision making (MADM) for best network selection and D2D communications for exploiting user proximity in crowded environments. The performance of the framework is evaluated with simulation results as well as analytic solutions and performance bounds. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions and demonstrates the significant benefits of D2D communications when the density of D2D users is properly adjusted. The simulation results depict that up to 168% and 200% increase in user satisfaction and throughput can be achieved under high network load scenarios at optimal D2D density. (C) 2018 Elsevier B.V. All rights reserved.
Turkish Speech Recognition
(2018) Arısoy, Ebru; Saraçlar, Murat
Automatic speech recognition (ASR) is one of the most important applications of speech and language processing, as it forms the bridge between spoken and written language processing. This chapter presents an overview of the foundations of ASR, followed by a summary of Turkish language resources for ASR and a review of various Turkish ASR systems. Language resources include acoustic and text corpora as well as linguistic tools such as morphological parsers, morphological disambiguators, and dependency parsers, discussed in more detail in other chapters. Turkish ASR systems vary in the type and amount of data used for building the models. The focus of most of the research for Turkish ASR is the language modeling component covered in Chap. 4.

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