Miracle-dz/UniFAR-FTUs huggingface dataset:
Colour application on mammography image segmentation

The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).
Modeling as Teaching: Preparing Preservice Teachers to Implement Universal Design for Learning

Increasing diversity and growing achievement gaps among diverse groups in U.S. public schools has resulted in increased pressure on teacher education programs to prepare teachers effectively to meet the needs of contemporary students. Research is needed to establish best practices of teacher education that carry forward into future practice. Universal Design for Learning (UDL) has been proposed as a framework to help address the need for more flexible learning environments, but limited research has been conducted to determine best practices for supporting preservice teachers in learning this complex framework. In this dissertation study, I examine the notion that education research develops in ecological context before presenting a case for the current period of education research being one of “innovation. ” After recognizing the barriers to researching UDL and faculty modeling, solutions are presented and tested through the study. In the study, itself, I present the results of a group, time series design in which participants were exposed to an intervention in which UDL is explicitly modeled in contrast to a control condition which focused on traditional methods of lecture and textbook reading. Effect on efficacy in practicing UDL and attitudes toward inclusion are reported. A two-way repeated measures ANOVA suggested no statistically significant difference (p > .05) in preservice teacher attitudes toward inclusion as assessed by the Multidimensional Attitudes toward Inclusion Scale (Mahat, 2008). Likewise, a two-way repeated measures ANOVA suggested no statistically significant difference (p > .05) in preservice teacher sense efficacy in practicing Universal Design for Learning, as assessed by an adapted version of the Teacher Sense of Efficacy Scale (Tschannen-Moran & Hoy, 2001). Discussion, implications, and calls for further research follow.
Evovled 3G systems using channel dependent link adaptation for HSDPA

With the exponential increase in the use of mobile data traffic today, there is a constant need for higher user data speeds. As a result, an increased importance is given to the performance of present day wireless networks. One key problem that arises as a consequence, is to find the most effective way to optimize the radio resources such that the user gets the maximum possible throughput under a given channel condition. To put it in simple words: How well can you make the radio link adapt to a constantly varying wireless channel? The problem of link adaptation has been studied in detail and several solutions have been proposed. In evolved 3G networks with High Speed Packet Data Access (HSPA), the quality of the channel is measured by the user equipment (UE) on the downlink channel. This measure is then quantized into a value called the Channel Quality Indicator (CQI) which indicates the size of the transport block and the modulation scheme to be used. The CQI is then reported to the base station on the uplink. A single BLock Error Rate (BLER) target, typically 10%, is maintained throughout. Some of the solutions proposed for the link adaptation problem in HSPA involve adjusting the received CQI value based on different measures like for example: adding the effect of Doppler spread for different speeds. The objective of this Master Thesis is to conceive a novel algorithm for link adaptation by deciding whether the UE is in one of several known channel types. Each channel type is a combination of three parameters i.e. the channel model, geometry factor and UE speed. A statistical characterization of these channel types are made by the analysis of the statistical properties of a set of CQI reports from the UE. Once the channel types have been characterized, an accurate decision on a particular channel type can be made by analysing a given set of CQI values. Based on studies done at Ericsson prior to the thesis, it was decided that rather than use one BLER target as in the conventional implementation, three or four BLER targets would be used in this thesis implementation such that each channel type would have one optimal BLER target. Since there are thirty-one channel types and three or four BLER targets considered in this Thesis work (also a requirement from Ericsson), several channel types can have the same optimal BLER target. The incoming CQI value is adjusted based on the difference between the measured BLER and the BLER target that needs to be achieved. This adjusted CQI value indicates the transport block size, modulation and coding scheme that
Leveraging synthetic imagery for collision-at-sea avoidance

Maritime collisions involving multiple ships are considered rare, but in 2017 several United States Navy vessels were involved in fatal at-sea collisions that resulted in the death of seventeen American Servicemembers. The experimentation introduced in this paper is a direct response to these incidents. We propose a shipboard Collision-At-Sea avoidance system, based on video image processing, that will help ensure the safe stationing and navigation of maritime vessels. Our system leverages a convolutional neural network trained on synthetic maritime imagery in order to detect nearby vessels within a scene, perform heading analysis of detected vessels, and provide an alert in the presence of an inbound vessel. Additionally, we present the Navigational Hazards - Synthetic (NAVHAZ-Synthetic) dataset. This dataset, is comprised of one million annotated images of ten vessel classes observed from virtual vessel-mounted cameras, as well as a human “Topside Lookout” perspective. NAVHAZ-Synthetic includes imagery displaying varying sea-states, lighting conditions, and optical degradations such as fog, sea-spray, and salt-accumulation. We present our results on the use of synthetic imagery in a computer vision based collision-at-sea warning system with promising performance.
ATRİAL FİBRİLASYON NEDENİYLE KAROTİS ENDARTEREKTOMİSİ GEÇİREN HASTALARDA POSTOPERATİF ANTİKOAGÜLAN VE ANTİAGREGAN STRATEJİSİ - TEK MERKEZ TECRÜBESİ

Amac: Karotis arter stenozu ve atriyal fibrilasyonun birarada goruldugu hasta grubunda karotis endarterektomi sonrasi antikoagulan tedavi protokolu ile ilgili standart bir prosedur bulunmamaktadir. Bu hasta grubunda klinigimizde uyguladigimiz tedavi stratejimizi sunmaktayiz. Gerec ve Yontem: Calisma Haziran 2010 - Aralik 2017 arasinda karotis endarterektomisi operasyonunu gerceklestiren 2 cerrahin verilerinin toplandigi 424 hastayi ve 498 karotis endarterektomisi islemini icermektedir. Bu hastalarin 45’inde kronik veya paroksismal atriyal fibrilasyon mevcuttu. Hastalar warfarin ve aspirin tedavisi almaktaydi. Ortalama yas 63,4±7,9 idi. Hastalardan 27’si kadin 18’i erkekti. 37 hastada hipertansiyon, 17 hastada diyabetes mellitus mevcuttu. Hastalarin 9’una bilateral karotis arter stenozu nedeniyle cerrahi islem uygulandi. 33 hasta semptomatikti. 11 hastada koroner arter hastaligi, 5 hastada kardiyak valvuler patoloji mevcuttu. Bulgular: Hastalarin tamaminda lokorejyonel anestezi ile karotis endarterektomisi yapildi. Erken mortalite 1 hastada hipertansif intrakraniyal kanama sebebiyle gerceklesti. Hastalari ortalama takip suresi 68,4±19,2 ay idi. Takiplerde 1 hastada komorbid faktorlere bagli sebepler, 1 hastada malignite nedeniyle gec donemde mortalite gerceklesti. Hastalarin 3’unde insizyon bol gesinde hematom nedeniyle revizyon gerekti; fakat eksplorasyonda aktif kanama odagi tespit edilemedi. Hicbir hastada takipleri sirasinda restenoz saptanmadi. Tartisma: Atriyal fibrilasyonu olan hastalarda karotis endarterektomi sonrasi antikoagulan tedavi protokolu acisindan daha genis ve cok merkezli calismalara gereksinim vardir. Biz hastalarimizda INR degerini 2-3 arasinda tutacak sekilde warfarin tedavisi ile gunluk 100 mg aspirin kombinasyonunu tedavi stratejisi olarak uygulamayi tercih ettik.
Computational Modeling of Touchscreen Drag Gestures Using a Cognitive Architecture and Motion Tracking

ABSTRACT This article presents a computational model that predicts finger-drag gesture performance on touchscreen devices, by integrating the queueing network (QN) cognitive architecture and motion tracking. Specifically, the QN-based model was developed to predict two execution times: the finger movement time of drag-gesture (i.e., only the motion time of the finger touched and dragged on the surface of touchscreen) and the comprehensive process time of drag-gesture (i.e., the entire process time to complete the finger-drag task, including visual attention shift, memory storage and retrieval, and hand-finger movements). To develop predictive models for the finger movement time of drag-gesture, 11 participants’ motion data were collected and a regression analysis with parameters of hand-finger anthropometric data and eight angular directions was conducted. Human subject data from our previous study (Jeong & Liu, 2017a) were used to evaluate the QN-based model, generating similar outputs (R2 was more than 80% and root-mean square was less than 300 msec) for both execution times.
Canabinoides para Tratamento de Epilepsia em Crianças

Ana Luiza Rodrigues da Trindade (Pós-graduanda em Pediatria e Neonatologia Estácio/Fase), e-mail: ana.luiza. t@hotmail.com Martina Natalli Gois da Silva Anjos (Residente em Obstetrícia HU-UFS), e-mail: ntzinha@hotmail.com Aline de Carvalho Bastos (Mestranda em Saúde e Ambiente – UNIT) Sheila Jaqueline Gomes Santos Oliveira (Doutoranda em Ciências da Saúde – UFS, Orientador, Professor UNIT), e-mail: sheilagomes09@hotmail.com
SOBRE A VARIABILIDADE ESPACIAL E TEMPORAL DA ENERGIA CINÉTICA TURBULENTA (EKE) NA CONFLUÊNCIA BRASIL-MALVINAS

Este trabalho tem como objetivo estudar a variabilidade da energia cinética turbulenta (EKE) oceânica na região da Confluência Brasil-Malvinas (CBM). Dados altimétricos de anomalia de velocidade geostróficas foram utilizados para a estimativa da EKE e, ao mesmo tempo, o produto altimétrico de topografia oceânica também foi empregado para a investigação de feições oceanográficas na região de estudo, entre 1992 e 2011. A variabilidade espacial foi investigada a partir de análises de composições em diferentes estágios energéticos da EKE (baixa, média e alta). Foram utilizadas tendências lineares, análises espectrais e de ondeletas para avaliar a variabilidade temporal desta forma de energia. Ainda, foram feitas correlações cruzadas em relação a índices de modos de variabilidade climática. De maneira geral, foi observado que no regime de baixas energias a corrente atinge sua maior extensão ao sul e o regime mais energético ficou marcado pela presença de vórtices liberados pela corrente seguido de retração do primeiro meandro de retroflexão. A EKE teve tendência de aumento significativa ao longo dos 18 anos estudados, com uma taxa positiva de 0,23 cmÂ² s-2 por mês. Esse aumento pode ter ocorrido devido a duas causas: (i) o aumento da atividade de mesoescala na região e (ii) a uma variabilidade interanual da posição média da CBM, relacionada com a intensificação dos campos de ventos de larga escala do hemisfério Sul.
The clash between two worlds in human action recognition: Supervised feature training vs recurrent ConvNet

Raptis, Konstantinos. M.S., Purdue University, December 2016. The Clash between two worlds in Human Action Recognition: Supervised Feature Training vs Recurrent ConvNet. Major Professor: Gavriil Tsechpenakis. Action recognition has been an active research topic for over three decades. There are various applications of action recognition, such as surveillance, human-computer interaction, and content-based retrieval. Recently, research focuses on movies, web videos, and TV shows datasets. The nature of these datasets make action recognition very challenging due to scene variability and complexity, namely background clutter, occlusions, viewpoint changes, fast irregular motion, and large spatio-temporal search space (articulation con gurations and motions). The use of local space and time image features shows promising results, avoiding the cumbersome and often inaccurate frame-by-frame segmentation (boundary estimation). We focus on two state of the art methods for the action classi cation problem: dense trajectories and recurrent neural networks (RNN). Dense trajectories use typical supervised training (e.g., with Support Vector Machines) of features such as 3D-SIFT, extended SURF, HOG3D, and local trinary patterns; the main idea is to densely sample these features in each frame and track them in the sequence based on optical ow. On the other hand, the deep neural network uses the input frames to detect action and produce part proposals, i.e., estimate information on body parts (shapes and locations). We compare qualitatively and numerically these two approaches, indicative to what is used today, and describe our conclusions with respect to accuracy and e ciency.
Comparative Analysis of Face Recognition Techniques: An Overview

: Face recognition and face detection are important task for computer vision but it will show variation or pattern from human to human, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modeling, complex face surface reflection mechanism, etc. But this task more challenging when use recognition in group then it will increase the pattern overlapping, so use the optimization for identifying individual pattern. In this paper review on pattern recognition approaches of group images has been analyzed.
Multi-Dimensional Markov Model for Performance Evaluation of an Ethernet Switch

With continuously increasing use of switched Ethernet for supporting various applications, it is important to precisely characterize the performance of Ethernet switches to provide the required QoS (Quality of Service). In this paper we propose an exact mathematical model of an Ethernet switch based on a multi-dimensional Markov process. We discuss both continuous and discrete time variants of the proposed model. We put forward the problem of asymptotic analysis of steady-state distribution of the workload to evaluate the performance of the system. This approach is deeply related with the recent results on large deviations in Markovian systems. We provide a comprehensive study of a particular model called symmetrical geometric. For the special case of Binomial input flow, the asymptotic approach is compared with the exact recurrent formulae highlighting the numerical efficiency of the asymptotic approach.
Local stability performance analysis of islanded microgrid based on inner control loops approach

High dynamical behavior and performance are required to maintain stable islanded microgrid operation based on inverters. However, this is strongly conditioned by the implemented control strategy as well as the characteristic parameters of the islanded microgrids. This paper investigates the influence of the controller characteristics and the microgrid parameters, on the small-signal stability of islanded microgrid. It makes the coupling between both factors in order to carry out the local stability model depending on the microgrid parameters. The islanded microgrid fed by a single inverter-based source whose control is based on inner control loops approach, is considered. The influence of each parameter is examined by analyzing the dynamical behavior of the eigenvalues in the complex plane, and the dynamical performance of the controller, with Matlab/Simulink. Accordingly, the modelling and simulation results obtained, showed that the effects of these parameters help identify and define the limits of local stability of islanded microgrids.
Technologies for Information Access and Knowledge Management

Internet search engines established themselves as an everyday technology for many users. Search engines provide the main access to information on the Internet. According to estimates, some 500 million queries are sent to search engines every day (http://searchenginewatch.com/reports/article.php/2156461) in order to find the most relevant pages among the billions of pages on the Internet. The underlying technology for search engines is provided by information retrieval (IR), which is a key technology in the knowledge society. IR deals with the search for information and the representation, storage, and organisation of knowledge. Information retrieval is concerned with search processes in which a user needs to identify a subset of information that is relevant for his or her information need within a large amount of knowledge. For many years, information retrieval systems were mainly used by professional database hosts that provide online access to bibliographic references. Most knowledge objects were manually (or intellectually) indexed. This means that the representation in the form of index terms is selected by human information specialists usually out of a controlled vocabulary. The dominant retrieval model was the Boolean model that advocates an exact match between query and documents, which is inspired by set theory. In the 1960s, automatic indexing methods for texts were developed. They had already implemented the “bag-ofwords” approach, which still prevails. Although automatic indexing is widely used today, many information providers and even Internet services still rely on human information work. In the 1970s, research shifted its interest to partial-match retrieval models and proved their superiority over Boolean retrieval models. Vector-space and later probabilistic retrieval models were developed. However, it took until the 1990s for partial-match models to succeed in the market. The Internet played a great role in this success. All Web search engines were based on partial-match models and provided ranked lists as results rather than unordered sets of documents. Consumers got used to this kind of search systems, and all big search engines included partial-match functionality. However, there are many niches in which Boolean methods still dominate, for example, patent retrieval. The basis for information retrieval systems may be pictures, graphics, videos, music objects, structured documents, or combinations thereof. This article is mainly concerned with information retrieval for text documents.
Long Distance Vehicle License Plate Region Detection Using Low Resolution Feature of License Plate Region in Road View Images

For privacy protection, we propose a vehicle license plate region detection method in road view image served from portal site. Because vehicle license plate regions in road view images have different feature depending on distance, long distance vehicle license plate regions are not detected by feature of low resolution. Therefore, we suggest a method to detect short distance vehicle license plate regions by edge feature and long distance vehicle license plate regions using MSER feature. And then, we select candidate region of vehicle license plate region from detected region of each method, because the number of the vehicle license plate has a structural feature, we used it to detect the final vehicle license plate region. As the experiment result, we got a recall rate of 93%, precision rate of 75%, and F-Score rate of 80% in various road view images.
Toward Integrating Concept Hierarchies

Semantic processing normally requires constructing a concept hierarchy to, among others, provide the framework for assigning taxonomic relations in word definition or performing se-lectional restriction for subcategorization of predicates. Longman Dictionary of Contemporary English (LDOCE) [Pro78], for example, contains subject codes which form concept hierarchy of around 124 major categories. In order to obtain such concept hierarchy effectively, more and more researchers have tried to extract the hierarchy automatically fr m existing dictionaries, rather than to hand-craft a new hierarchy from scratch (e.g., [CBH ]). However, the arising issue yet to be tackled is how to integrate concept hierarchies from different dictionaries where the hierarchies may have complementary properties. In this paper, we discuss a mechanism to integrate three concept hierarchies, each of which has distinct property. The interpretation of such operations in terms of the manipulation of semantic feature sets is discussed to shed light of what effects they have on the semantic property of the hierarchy. A preliminary implementation of the integration process is also described. The integrated concept hierarchy is then applied to selectional restriction task in micro-electronics domain, which was impossible without integrating the original hierarchies.
Architectures and Security of Software Defined Networks for Internet of Things: State-of-the-Art and Challenges

Internet of Things (IoT) connects thousands of everyday use objects and devices under the same network. Hence, Software Defined Networking (SDN) is crucial in evolving IoT. SDN provides a programmable and adaptive networking through the use of dedicated central controller that can handle a multitude of different connected devices. Integrating SDN into IoT can enable intelligent routing, simplified data acquisition, transition, and analysis, centralized management of network resources and applications, and dynamic on-demand reconfiguration of the network. At the same time, they pose challenges in terms of performance, interoperability, scalability, reliability and security. In this paper, we present a comprehensive survey on SDN for IoT, i.e., the concepts of SDN-IoT and their impact on each other, several SDN-IoT architectures, security and privacy implications, and other challenges.