Miracle-dz/UniFAR-FTUs huggingface dataset:
BLOG ORTAMINDA İŞBİRLİKLİ E-PORTFOLYO UYGULAMALARINA İLİŞKİN ÖĞRETMEN ADAYLARININ GÖRÜŞLERİ

Bu calismada, 2015-2016 egitim-ogretim yilinda Pedagojik Formasyon Egitimi Sertifika Programi'na devam eden ogretmen adaylarinin ogretim surecinde kucuk ve buyuk grupla blog kullanimina iliskin goruslerini ortaya koymak, ogretimin farkli alt sureclerinde cesitli etkinliklerle butunlestirmeye iliskin goruslerini belirlemek amaclanmaktadir. Bu amacla iki sinifla calisilmistir. Ilk sinif gruplara ayrilarak her bir grubun kendine ait blog sayfasinda calismasi, ikinci sinif da gruplara ayrilarak, tum gruplarin tek bir blog sayfasinda calismalari saglanmistir. Ogretim Teknolojileri ve Materyal Tasarimi dersinde her hafta yapilan etkinlikler blog ortaminda paylasilmis ve yorumlar yapilmistir. Ogrencilerin gorusleri yazili olarak alinmistir. Bu baglamda calisma, nitel bir durum calismasidir. Bulgular icerik analizi yontemiyle analiz edilerek temalar halinde kodlanmis ve gorsellerle desteklenerek sunulmustur.
Abandoned Land Classification Using Classical Theory Method

According to the official statistics the areas of abandoned agricultural land in Lithuania are gradually decreasing, but very slightly. The aim of this study is to research spatial determination and abandoned land classification in the territory of Vilnius District Municipality. Vilnius District Municipality was chosen for the research because it, although located near the capital of the country and has a high population density, it is still the district having the largest percent of abandoned land plots. A fast, cost-effective and sufficiently accurate method for determination of abandoned land plots would allow to constantly monitor, to fix changes and foresee the abandoned land plots reduction possibilities. In the study there was used the multispectral RGB and NIR color Sentinel-2 satellite images, the layer of the administrative boundary of Vilnius County and layer of abandoned agriculture land, which is available in Lithuanian Spatial Information Portal (www.geoportal.lt). The data was processed by Geographic Information System (GIS) techniques using classical classification Region Growing Algorithm. The research shows that NIR image classification result is more reliable than the result from RGB images.
Motion Planning of Autonomous Road Vehicles by Particle Filtering

This paper describes a probabilistic method for real-time decision making and motion planning for autonomous vehicles. Our approach relies on the fact that driving on road networks implies a priori defined requirements that the motion planner should satisfy. Starting from an initial state of the vehicle, a map, the obstacles in the region of interest, and a goal region, we formulate the motion-planning problem as a nonlinear non-Gaussian estimation problem, which we solve using particle filtering. We assign probabilities to the generated trajectories according to their likelihood of obeying the driving requirements. Decision making and collision avoidance is naturally integrated in the approach. We develop a receding-horizon implementation and verify the method in simulated real road scenarios and in an experimental validation using a scaled mobile robot setup with car-like dynamics. The results show that the method generates dynamically feasible trajectories for a number of scenarios, such as collision avoidance, overtaking, and traffic-jam scenarios. In addition, the computation times and memory requirements indicate that the method is suitable for real-time implementation.
Visual navigation method for indoor mobile robot based on extended BoW model

: This article proposes a new navigation method for mobile robots based on an extended bag of words (BoW) model for general object recognition in indoor environments. The scale-invariant feature transform (SIFT)-detection algorithm with the graphic processing unit (GPU) acceleration technology is used to describe feature vectors in this model. First, in order to add some redundant image information, statistical information of the spatial relationships of all the feature points in an image, i.e. relative distances and angles, is used to extend the feature vectors in the original BoW model. Then, the support vector machine (SVM) classifier is used to classify objects. Also, in order to navigate conveniently in unknown and dynamic indoor environments, a type of human – robot interaction method based on a hand-drawn semantic map is considered. The experimental results show that this new navigation technology for indoor mobile robots is very robust and highly effective.
Comparative Study Between PID and Fuzzy PID Controller for Speed Control of BLDC Motor

Because of advantages of BLDC motor, including high accuracy, low volume, and clear control characteristic is still using in a wide range of industrial application. This paper presents comparative study of the Classical Proportion Integral Derivative Controller (PID) and Fuzzy self-tuning PID for speed control of BLDC. With rising complexity and accuracy of control systems, only using FLC or PID cannot satisfy the required operating conditions for such speed control of such type of machines. Fuzzy self-tuning PID controller is presented as a solution of this problem. Fuzzy self tuning PID improves the overall system performance including time response. The modeling, control and simulation of BLDC motor have been done using MATLAB/SIMULINK package. Keywords— Brushless DC Motor (BLDC); Proportional Integration Derivative (PID Controller); Fuzzy Logic Controller (FLC); Fuzzy Self-Tuning PID;
Dislipemia diabética

La diabetes mellitus se asocia a un alto/muy alto/extremo riesgo cardiovascular. El tratamiento de todos sus factores de riesgo, que en la mayoría coeexisten, es fundamental para reducir la morbimortalidad de los pacientes con diabetes. Además, la característica de su displipemia es potencialmente aterogénica de forma cualitativa. El objetivo principal de los pacientes es el c-LDL y, en su defecto, el c-no-HDL. Las estatinas son el tratamiento de primera línea para reducir la concentración de c-LDL y el riesgo de enfermedad cardiovascular. Las nuevas guías de la ESC/EAS 2019 recomiendan no solo alcanzar objetivos c-LDL, sino reducir el c-LDL basal en un 50 %. El cambio en los hábitos de vida, incluyendo el ejercicio físico adecuado, un patrón saludable de alimentación, no consumir tabaco y una buena higiene del sueño, no debe ser excluido en las recomendaciones de todos los pacientes, sean o no diabéticos.
Joint Sparse Neighborhood Preserving Embedding

Feature extraction and feature selection are the two most commonly used methods for dimensionality reduction in the field of machine learning. A new method can do feature extraction and selection simultaneously was presented in this paper, we named the method as Joint Sparse Neighborhood Preserving Embedding (JSNPE). Our model can retain the local relationship when the data were projected in the new sunspace, because we construct an adjacency graph matrix based on the neighborhood relation of data points. What’s more, our model can obtain jointly sparse projection by introducing the L21 -norm regularization on the projection matrix. By using Lagrange multiplier method, we iteratively solve the objection function. We get the experimental results on two public face dataset to validate JSNPE is a better method than some well-known methods that used to feature extraction and selection.
Photo Voltaic Mppt and Inverter using Fuzzy Logic

In this paper the photo voltaic (i.e. PV) system is used with the SEPIC converter (i.e. single ended primary inductor converter) and MPPT (maximum power point tracker) by using fuzzy logic. The fuzzy logic controller is used for gaining high efficiency by outputting crisp values to the SEPIC for converting dc to dc that is a buck/boost converter for converting dc voltage level. The inverter is used for transforming dc to ac and reducing harmonics for better ac output.
Machine Learning Based Blind Decoding for Space–Time Line Code (STLC) Systems

In this correspondence paper, a novel machine learning (clustering) based blind decoding method is proposed for the space–time line code (STLC) systems without the information of modulation size and channels. The number of clusters, which is equivalent to the modulation size, is estimated from the received signals by using a $k$-mean cluster validation metrics, such as silhouette score and Davies–Bouldin index, and the cluster indices are directly mapped to the transmitted binary information bits. To improve the clustering performance, received signal normalization and the initial centroids of the clusters are designed by exploiting the combined STLC signal structure. From the numerical results, it is verified that the proposed blind decoding method can achieve near-coherent decoding performance with either small-size modulation, low noise at the receiver, or a large number of transmit antennas.
The Role of Electronic Word of Mouth In Improving Brand Image of Online Shop

Brand image is a strategy that is widely utilized by companies to win the competition. To identify the factors that influence the brand image of online store, empirical study was conducted to analyze the direct influence of electronic customer satisfaction, electronic word of mouth toward the brand image of online store, and to analyze the indirect effect of electronic customer satisfaction toward the brand image of online store through electronic word of mouth. Data were collected from 200 online store consumers and analyzed by Structural Equation Modeling method. From the results of processing data to test the proposed hypothesis, it is known that to improve the brand image of online stores, the online store manager should focus on how to boost the number of consumers who will provide reviews about online stores or push electronic word of mouth.
Asset Securitization Characteristics and Annual Report Narrative Disclosure Readability.

We examine whether asset securitizations affect a bank holding company’s (BHC’s) annual report narrative disclosure readability. We find that BHCs with securitizing more loans or holding more retained interests both have lower annual report readability. In addition, the effects of securitizing loans on annual report readability is mainly through the channel of earnings management, especially mortgage loans (namely low risk loans). Moreover, the effects of securitized mortgage loans and its retained interests on annual report readability become stronger after the regulatory changes of SFAS No.166 (2009) and SFAS No. 167 (2009). We also provide evidence that the effects of securitized consumer loans (namely high but highly verifiable risk) and all kinds of retained interests on annual report readability become stronger during the subprime crisis period.
Automatically Scalable Computation That Is More Scalable and Automatic

We present significant new development of the Automatically Scalable Computation (ASC) project, a new method of automatic parallelization that transforms parallelization into a machine learning problem. ASC speeds up computation by predicting future states of a program’s memory and registers and speculatively executing from those predicted states. Prior work on ASC has demonstrated the ability to speed up a limited class of programs using a single additional core. It has also suggested that ASC has the potential to scale that speedup to many cores for a larger class of programs. We realize that potential by developing a new version of ASC. Our version incorporates a new architecture that can scale to many cores as well as a notion of data dependency that expands the class of programs ASC can speed up by making the prediction problem much easier. Using this new version of ASC, we demonstrate automatic speedup on a large class of programs, including programs essential to scientific computing and programs resistant to other methods of automatic parallelization. This speedup increases near-linearly with the number of cores supplied to ASC.
Establishment of a Rapid, Lesion-Controllable Retinal Degeneration Model in Monkey for Preclinical Stem Cell Therapy

Background: Retinal degenerative disorders (RDs) are the main cause of blindness without curable treatment. Our previous studies have demonstrated that human induced pluripotent stem cells can differentiate into retinal organoids with all subtypes of retina, which provides huge promises for treating these diseases. Before it can be turned into reality, RD animal models are required to evaluate the safety and efficacy of stem cell therapy, and to develop the surgical tools and procedures for cell transplantation in patients. This study is to develop a monkey model of RD with controllable of lesion sites which can be rapidly prepared, for the studies of preclinical stem cell therapy among other applications.Methods: Sodium nitroprusside (SNP) in three doses was delivered into the monkey eye by subretinal injection (SI) and normal saline was applied as control. Structural and functional changes of the retinas were evaluated via multimodal imaging techniques and multifocal electroretinography (mfERG) before and after the treatment. Histological examination was performed to identify the target layer of the affected retina. The health status of monkeys was monitored during the experiment. Results: Well defined lesion with various degree of retinal degeneration was established at the posterior pole of retina as early as 7 days after SNP SI. The damage effect of SNP was dose-dependent. 0.05 mM SNP caused invisible structural changes in retina, similar to the control. 0.1 mM SNP led to the loss of outer retinal layer, including OPL, ONL and RPE, while 0.2 mM SNP impacted the entire layer of retina and choroid. MfERG showed reduced amplitude in the damaged region. The structural and functional damages were not recovered after 7-month follow-up. Conclusion: A simple, rapidly induced, lesion site-controllable, retinal degeneration model in monkey was established by the subretinal injection of 0.1 mM SNP. This monkey model closely mimics the histological changes of RDs, and provides a valuable platform for preclinical assessment of stem cell therapy.
Sensing, Measuring, and Modeling Social Signals in Nonverbal Communication

Humans convey their thoughts, emotions, and intentions through a concert of social displays: voice, facial expressions, hand gestures, and body posture, collectively referred to as social signals. Despite advances in machine perception, machines are unable to discern the subtleand momentary nuances that carry so much of the information and context of human communication.The encoding of conveyed information by social signals, particularly in nonverbal communication, is still poorly understood, and thus it is unclear how to teach machines touse such social signals to make them collaborative partners rather than tools that we use. A major obstacle to scientific progress in this direction is the inability to sense and measure the broad spectrum of behavioral cues in groups of interacting individuals, which hinders applying computational methods to model and understand social signals.In this thesis, we explore new approaches in sensing, measuring, and modeling social signals to ultimately endow machines with the ability to interpret nonverbal communication. This thesis starts by describing our exploration in building a massively multiviewsensor system, the Panoptic Studio, that can capture a broad spectrum of human social signaling—including voice, social formations, facial expressions, hand gestures, and body postures—among groups of multiple people. Second, leveraging this system equipped with more than 500 synchronized cameras, we then present a method to measure the subtle 3D movements of anatomical keypoints in face-to-face interaction, providing a new opportunityto computationally study social signals. In the last part of this thesis, we present a social signal prediction task to model nonverbal communication in a data-driven manner. We establish a new large-scale corpus from hundreds of participants containing various channels of social signal measurements. Leveraging this dataset, we verify that the social signals are predictive each other with strong correlations.
Wichtige Zoonosen: Q-Fieber

Das Q-Fieber (von query fever) ist eine durch den Erreger Coxiella (C.) burnetii ausgelöste und meist von Ziegen, Schafen oder Kühen auf den Menschen übertragene Zoonose. In den meisten Fällen verläuft die Infektion asymptomatisch bzw. unter dem Bild eines grippalen Infekts. Seltener treten granulomatöse Hepatitiden oder atypische Pneumonien auf. Bei ca. 1–5 % der Fälle resultiert ein chronischer Verlauf. Bei rekurrierender Symptomatik sollte insbesondere bei Patienten mit Valvulopathien oder bei Trägern von kardiologischen Devices, künstlichen Herzklappen oder Gefäßendoprothesen eine Endokarditis/-plastitis in Erwägung gezogen werden, die mit einer hohen Mortalitätsrate von bis zu 65% assoziiert ist. Da die Anzucht des Erregers über zelluläre Kulturverfahren Speziallaboren vorbehalten ist, basiert die Diagnose in der Regel auf serologischen Untersuchungen oder dem Erregerdirektnachweis mittels PCR. Zur Therapie stehen verschiedene antimikrobielle Substanzen – bei chronischen Verläufen ggf. in Kombination mit Hydroxychloroquin – zur Verfügung.
Hierarchical and Cooperative Download Method with File Partitioning for Mitigating Contents Server Load in Content Delivery Network

We propose a two step delivery method to reduce contents server load for large size content delivery. In our proposed method, a receiver receives a part of desired content from already content downloaded nodes (collaboration nodes). Furthermore, they transfer the received content to other receivers each other coordinately to gather the rest of the content. By applying our proposed method, contents server load can be reduced and download speed can be improved with using the upload bandwidth of some collaboration node and receivers effectively. Also we evaluate the performance by computer simulation. The results shows that the download time is reduced.
The effects of retinal image motion on the limits of spatial vision

Author(s): Ratnam, Kavitha | Advisor(s): Roorda, Austin | Abstract: Vision is not a static process. Our perception of the world is not merely a sequence of fixed snapshots but rather involves a dynamic process in which the visual input is synthesized over time to provide a more detailed and informative signal than would otherwise be possible using a fixed array of sensors. This dynamic signal is largely a result of fixational eye motion, or the constant ocular jitter that creates an ever-changing signal in each photoreceptor cell. It is not known how the visual system potentially exploits such transient signals to serve our finest spatial acuity, and how the relationship between visual acuity and the photoreceptor sampling limit can be muddled because of this fact. We used an adaptive optics scanning laser ophthalmoscope to precisely control the spatiotemporal input on a cellular scale in human observers to assess how acuity differed as a function of retinal image motion. Additionally, we investigated the purpose of fixational eye motion, and in particular microsaccades, in relocating stimuli to a preferred region within the central foveal region. Combined, these results show the utility of fixational eye movements in high spatial vision.
Recognition of electromagnetic sources with the use of deep neural networks

The article presents a method of recognizing sources of electromagnetic signal emission on the basis of signals generated by using deep neural networks. These signals are measured in electronic recognition receivers, processed into a digital signal and then undergo recognition. The main purpose of the article is to present software which, based on the detected signal, is to recognize it. The software can also be used as a subsystem in Electronic Intelligence (ELINT) devices, including detection of radiolocation systems, jammers, recognition of aircrafts, ships, vehicles based on the signal shape of radar cross section (RCS) and subsequently comparison them to the emitter database (EDB). The implementation of this system is presented in a simulation environment and with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized, calculated and written in database. The proposed software allows to examine a significant number of different signals. The article contains a description of components of software, such as signal base, learning subsystem and signal generator. The results of the system operation are presented in the form of screenshots from individual software components. The speed of software operation, the effectiveness of recognition systems using artificial neural networks is presented by means of tables and appropriate illustrations. Also presented is the problem of learning the neural networks at the GPUs (graphics processing units) and the way of choice the learning coefficients.
Optimising DVB-S2(X) Links in Nonlinear Environments

Satellite communication standards such as the second-generation digital video broadcasting for satellites, i.e., DVB-S2(X), exploit phase-shift keying (PSK) and amplitude and phase-shift keying (APSK) modulation schemes as they are more robust against nonlinearity than quadrature amplitude modulations (QAMs) [1] [2]. However, the system performance of satellite communication remains profoundly influenced by the presence of the nonlinear high power amplifiers (HPA). In this contribution, we analytically and numerically study the end-toend distortion of the APSK constellation from the digital IQ signal at the transmitter across to the nonlinear channel and back to the recovered digital IQ signal at the receiver. We demonstrate that the distortion depends not only on the input back-off (IBO) of the HPA but, critically, also on the roll-off of the matched digital root raised cosine (RRC) bandwidth-limiting filters used at transmitters and receivers. Consequently, we derive informed guidelines for the adaptive operation of satellite links. Focusing on 16 APSK (4+12 version) modulation, the main contributions of our work include; 1. We present an accurate estimation of the Gaussian spreading of the received symbols around reference constellation points. The digital pre-distortion at the transmitter is applied. It has been shown that the spreading depends on the choice of RRC roll-off factor and HPA IBO. This estimation enables us to obtain the equivalent additive Gaussian noise determined by the combined RRC and HPA characteristics. Significantly, we then proceed to demonstrate that the performance of the nonlinear channel can be approximated by a linear channel with an additional known noise source. 2. The informed estimation of the optimum IBO which can minimise the system bit error rate (BER) is obtained. Importantly we show that the optimum IBO depends on both the channel noise and the roll-off. 3. An adaptive operation of DVB-S2X link with dynamic power control is achieved, which maintains performance close to optimum by computationally efficient estimation of the combination of MODCOD, RRC roll-off and IBO operation on a given HPA. The paper is organized as follows. In Section II, we provide some backgrounds including the satellite link descriptions, the adverse impacts from nonlinearity and inter-symbol interference (ISI). In Section III, we introduce the digital pre-distortion algorithm and obtain the equivalent linear link by ‘translating’ the ISI into equivalent Gaussian noise. Then, the optimum HPA IBO under different RRC roll-off and channel noise is achieved. In Section IV, an adaptive operation specifying the combination of MODCOD, RRC roll-off and HPA IBO is proposed, which can maximise the system spectrum efficiency while still guaranteeing the BER requirement. Conclusions are drawn in Section V.