Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional GANs for semantic segmentation of multispectral satellite images

Algorithms for automatic semantic segmentation of the satellite images provide an effective approach for the generation of vector maps. Convolutional neural networks (CNN) have achieved the state-of-the-art quality of the output segmentation on the satellite images-to-semantic labels task. However, the generalization ability of such methods is not sufficient to process the satellite images that were captured in the different area or during the different season. Recently, the Generative Adversarial Networks (GAN) were introduced that can overcome the overfitting using the adversarial loss. This paper is focused on the development of the new GAN model for effective semantic segmentation of multispectral satellite images. The pix2pix1 model is used as the starting point of the research. It is trained in the semi-supervised setting on the aligned pairs of images. The perceptual validation has demonstrated the high quality of the output labels. The evaluation on the independent test dataset has proved the robustness of GANs on the task of semantic segmentation of multispectral satellite images.
Simulation of 7- Level AC–AC Sparse Modular Multilevel Converter

7-level AC-AC Sparse modular multilevel converter (SMMC) is proposed in this paper. It is a bi-directional multilevel converter and it is employed in high power and high voltage applications. It contains full bridge and half bridge SM’s on the middle arm, and low frequency converters on each side of the converter. In this converter more number of IGBT switches can work under soft switching mode. Control scheme is also proposed in this paper, it is utilized to obtain the capacitor voltage balancing. Capacitor voltages have some oscillations, harmonics will be decreased by applying modified PWM technique. Number of levels of the converter increases, voltage harmonics will be decreased and also switching frequency of the full bridge and half bridge arm IGBTs is decrease. The appropriateness of the 7-level AC/AC SMMC with Control scheme will be estimated by utilizing the MATLAB/SIMULINK software.
Writing across the Curriculum in ELT Training Courses: A Proposal Using Data-Driven Learning in Disciplinary Assignments.

The process toward academic literacy aims at developing academic reading and writing together with higher academic competences of increasing relevance for undergraduate students as future teachers and researchers. Such a process is even more complex in ELT vocational courses where non-English speaking trainees study English as a system while they are in the process of becoming proficient users of this foreign language. This paper shares our action research experience with undergraduate non-native students at an English teacher training program in Argentina. From the Writing Across the Curriculum (WAC) approach, the implementation of data-driven learning (DDL) assignments has proved to contribute to their process of enculturation and the promotion of subject learning while fostering the development of disciplinary thinking.
Synthesis of the switching control law for a quadrotor autopilot

The paper deals with the problem of synthesizing the time-optimal control law by the angular coordinates of an unmanned aerial vehicle with stabilization in the pitch and roll directions. The full mathematical model of the unmanned aerial vehicle is reduced to a system of the first-order differential equations, based on which the optimal control law is constructed. Control action in each plane depends only on the measured coordinates and is calculated in real time. It is believed that the dynamic model, described by a system of differential equations, contains complex roots, which indicate the oscillatory response of the controlled object to the control action. Some properties of the switching line and switching control are also considered in the paper. Some results of simulating the dynamics of the object under examination with a synthesized control law are presented.
Implementation of a Capacitive Proximity Sensor System for a Fully Maneuverable Modular Mobile Robot to Evade Humans

This paper describes an advanced approach for a dynamic collision prevention system for robots dedicated to collaborative applications in a shared human robot work environment. We developed a firmware that incorporates proximity sensor information along with a kinematic algorithm to achieve sensitive robotics for a modular mobile robot platform. The utilized sensor technology is based on capacitive sensing, capable to reliably detect humans in the vicinity of the robot platform. The kinematic algorithm is flexible in its design as it is scalable to an unlimited number of wheels and takes into account different geometric architectures such as standard and omni-directional wheels. The dynamic collision avoidance of approaching humans has been successfully demonstrated in a variety of experimental test scenarios demonstrating the capabilities of a sensitive mobile robot.
Improvement of Sonographic Appearance Using HAT-TOP Methods

Ultrasound consider of one of the most important tool in analysis of fetus development. In ultrasound images, the recognition closely adjacent tissues is very crucial process because of the noise that affected both image quality and sharpness. This study conducted to study the fetus images improvement using HAT-TOP transform as computing choice in order to increase the diagnostic accuracy in diagnosis of neonatal diseases. Many image-processing techniques were used to improve the images including Using HAT-TOP and Blind Deconvolution Algorithm. The results of the study showed HAT-TOP was best processing filter and define the fetus precisely.
Serotypes, Antibiogram and Genetic Relatedness of Pseudomonas aeruginosa Isolates from Urinary Tract Infections at Urology and Nephrology Center, Mansoura, Egypt

Background: Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen that represents a major problem in many hospitals because of its increased resistance to antibiotics and the ability to cause nosocomial infections. The present study aimed to phenotype and genotype isolates of P. aeruginosa from inpatients with UTIs at Urology and Nephrology center, Mansoura, Egypt to study their relatedness. Methods: Thirty nine isolates of P. aeruginosa were phenotypically typed by determination of O-serotypes by slide agglutination technique and antimicrobial resistance patterns by disk-diffusion method. The genetic diversity of isolates was illustrated by performing RAPD-PCR using M13 primer. Results: Serotypes O11, O6 and O10 were the most prevalent. Isolates showed high resistance rates to antipseudmonal antibiotics with high incidence (51.3%) of multidrug resistance (MDR). Amikacin was the most effective. A significant correlation was found between O6, O10 and MDR. A relatively high polymorphism was demonstrated among P. aeruginosa isolates by using RAPD-M13 fingerprinting. Cross transmission was suggested by phenotypically and clonally identical isolates. Conclusion: The study demonstrates the role of combining both classical and molecular typing as a valuable mean to study the origin and cross transmission of P. aeruginosa in UTIs for better assessment of treatment and infection control.
Bi-directional Afaan Oromo-English Statistical Machine Translation

Abstract English is a language that is widely spoken on different parts of the world. Most of the published literatures are available in English. Afaan Oromo language is one of the language spoken in Ethiopia, It is obvious that both Afaan Oromo and English speakers need the data or documents written in English or Afaan Oromo and they also need to communicate with each other. However, the communication gap needs to be bridged; therefore, handling this problem initiate this study to design a bi-directional Afaan Oromo-English statistical machine translation. In order to conduct the study the corpus was collected from different online sources which are parallel corpus suitable for machine translation. Experimental results shows that 18 % and 35 % BLEU score was registered from English-Afaan Oromo and from Afaan Oromo-English respectively. Word correspondence variation is the major challenge for this study. We are now working towards designing an optimal alignment for bi-directional Afaan Oromo-English statistical machine translation. Key word : Statistical machine translation; Sentence level alignment; Afaan Oromo.
Combining inter and intra-line cache compression

Caches are essential to today’s microprocessors. They close the huge speed gap between processors and memories. However, cache design presents an important tradeoff. A bigger cache size should increase performance and allow processors to perform faster, but it is also limited by its silicon, area, and power consumption costs. Today’s caches often use half of the silicon area in processor chips and consume a lot of power. Instead of physically increasing the cache size, effective cache capacity can be substantially increased if the data inside the cache is compressed. Current cache compression techniques focus only on one granularity, either compressing inside one cache line, or compressing similar cache lines together. In this work, we combine both compression techniques to leverage both inter-line and intra-line compression. We find that combining both techniques results in better compression than previously described methods, and also maintains the same performance as a normal uncompressed cache when running incompressible applications. We study and address the design considerations and tradeoffs that arise from such design. We address issues related to the design like cache structure and replacement policies. Then we present an implementation that achieves the best possible compression and performance while maintaining overheads as low as possible.
Big data cluster analysis and its applications

The increasing prevalence of Internet of things (IoT) technologies, smartphones, and social media services generates a huge amount of data, popularly known as ’big data’. Extracting useful information from big data is essential for many businesses and applications for providing better services and increasing their profits. For example, smart city solutions aim to use this wealth of data for formulating effective policies to solve the problems faced by citizens. These voluminous data are usually unlabeled, therefore, scalable and efficient unsupervised algorithms are required to manage and extract actionable information from big data. Cluster analysis is a useful unsupervised approach to discover the underlying groups and useful patterns in the data. Cluster Analysis for any data consists of three problems, (P1) cluster assessment, which asks “Do the data have clusters? If yes, how many? "; (P2) Clustering i.e., partitioning the data into clusters, and (P3) cluster validity, which asks “Are the clusters found useful? Is there a better one we did not find? " Traditional cluster analysis algorithms are not suitable for big data owing to its volume, variety, and velocity property. This thesis developed a suite of novel scalable algorithms to solve each of the three problems of cluster analysis, namely, cluster assessment, clustering, and cluster validity, for big data, that may be high-dimensional, anomalous and streaming. For demonstration, a novel scalable framework for predicting large-scale taxi trajectories is presented as a real application of big data clustering. Our first contribution addresses the high-dimensionality and scalability issues for soft clustering methods. Specifically, we developed a simple and computationally efficient framework for high-dimensional data clustering: CAFCM, which employs fuzzy c-means clustering on an ensemble of random projections to obtain multiple fuzzy clustering partitions, and then cumulatively aggregates them based on their quality to get a final output partition. The CAFCM framework scales linearly in the number of samples in the data and does not require any prior knowledge of
Performance Comparison of Intrusion Detection Machine Learning Classifiers on Benchmark and New Datasets

With the tremendous growth of the Internet and the continuous increase in malicious attacks on corporate networks, Intrusion Detection Systems (IDS) have been designed and adopted by organizations to accurately detect intrusion and other malicious activities. But these IDSs still suffer from setbacks such as False Positives (FP), low detection accuracy and False Negatives (FN). To enhance the performance of IDSs, machine learning classifiers are used to aid detection accuracy and greatly reduce the false positive and false negative rate. In this research we have evaluated six classifiers such as Decision Tree (J48), Random Forest (RF), K-Nearest Neighbor (K-NN), Nave Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) on three different types of datasets such as NSL-KDD, UNSW-NB15 and Phishing dataset. Our results show that K-NN and J48 are the best performing classifiers when it comes to detection accuracy, testing time and false positive rate.
Decentralized Scheduling for Many-Task Applications in the Hybrid Cloud

iii Chapter 1: Introduction 1 1.1 Problem Description . 1 1.2 Problem Statement . 1 1.3 Simulation Introduction . 2 1.4 Lessons Learned from Simulations . 2 1.5 Cloud Vector Scheduling Approach . 4 Chapter 2: Related Work 6 2.1 Improving Scheduling Algorithms . 6 2.2 Literature Review . 7 2.3 Scheduling Algorithms . 8 Chapter 3: Framework 12 3.1 Simulation Methodology . 12 3.2 Cloud ATC Experimental Methodology . 14 3.3 Vector-Based Scheduling . 16 Chapter 4: Software Architecture 19 4.1 Platforms and Resource Management . 19 4.2 Intra-Platform Communication . 25 4.3 Vector-Based Work Redistribution . 37 4.4 Experiment Parameterization . 39 4.5 Experiment Timeline . 42 Chapter 5: Experimental Results 47 5.1 Simulation Results . 47 5.2 Cloud ATC Results . 52 5.3 Vector Scheduling Experimental Definition . 54 5.4 Vector Scheduling Experimental Description . 56 5.5 Initial Cloud Vector Scheduling Results . 58 5.6 Cloud Vector Scheduling Experimental Results . 60 Chapter 6: Summary 67 Chapter 7: Future and In Progress Work 70
Laboratory Studies of Particle Cementation and PV module Soiling

Monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.
Beyond the Click: A First Look at the Role of a Microblogging Platform in the Web Ecosystem

As the largest microblogging service in China, Sina Weibo, is associated with a huge amount of traffic to and from other major websites. Thanks to a massive set of anonymized data from a major operator covering a large-scale metropolitan area, we extract the click-based transitions between Weibo and other websites. Such transitions have not been looked into in most studies that rely on the visible hyper-links among websites. Based on this unique dataset, we show that 38% of the outgoing transitions (leaving Weibo) go to video clip services. Comparatively, links related to news and long videos are clicked less but posted more. This indicates that photograph and text-sharing in microblog have been gradually replaced by video clip sharing. Also, ignoring video clip sharing, 53% of the transitions go to five of the largest Chinese websites, showing how closely connected Weibo has become in the Chinese Web ecosystem. We further look at users’ click behavior in transitions, and find that through co-clustering there do exist a number of prevalent and distinct behavior patterns, suggesting that only seven co-clusters can capture the click behavior of 192 000 studied users. Overall, our work is the first to unveil the significant role of microblogging platforms in the Web ecosystem. Considering the massive user base of Weibo, our findings can aid the design of more personalized link recommendation in Weibo, thus generating more referral traffic, i.e., user visits, from Weibo to other major websites.
Methods of collecting specimens of chronic prostatitis patients for bacterial culture

Purpose: To find a reliable and simple method to collect specimens of chronic prostatitis (CP) patitents for bacterial culture. Methods: Before using antibiotic specimens of 120 cases of CP for bacterial culture with three methods in an interval of 3 days were collected.A: Collecting prostatic fluid (EPS). B: Collecting the first voided 10~15 ml of urine (VB 1) and EPS. If VB 1 positive and infection in urinary tract, the VB 1 and EPS should collected after the infection was controlled. C:Mears Stamey method. Results: There was a significant difference between A and C (P 0.01 ),but no difference between B and C (P 0.05 ). Conclusions: It is a simple and reliable method of collecting VB 1 and EPS for bacterial cultrue.
Comparative study of maximum power point tracking algorithms for wind turbine using permanent magnet synchronous generator

Wind power is more and more developed as a renewable energy source. It is very essential to extract the maximum available power from the wind by operating the wind turbine at its optimal operating condition, called maximum power point tracking (MPPT). Perturb & Observe (P&O) is the simplest and mostly used algorithm for this purpose. However, this algorithm has its own disadvantages such as oscillation at maximum power point and wrong directionality under fast variation wind speed. Lots of publications are presented to solve these problems. In this paper, a conventional P&O algorithm, a modified MPPT algorithm and a fuzzy MPPT algorithm for variable speed wind turbine using permanent magnet synchronous generator (PMSG) are tested and compared in the terms of complexity, speed responses and the ability to acquire the maximal energy output.
Human Performance in Interpreting Robot-Generated 2d and 3d Maps

Robust mapping capabilities are a critical technology for intelligent robotic systems. They can (1) provide valuable information to human and robot teammates without requiring prior knowledge or experience and (2) enable other, higher-level behaviors, such as autonomous navigation and exploration. To maximize interpretability, a map must be coherent, accurate, and displayed in an intuitive fashion. However, maps inherently require a large amount of computational resources. Therefore, it is beneficial to determine the minimum amount of information that must be provided to a user to meet the specific mission requirements. The purpose of this study is to evaluate human performance on visual tasks using 2D and 3D maps generated from laser point cloud data. In a within-subjects study, 20 participants were tasked with locating and identifying objects, doorways, and windows in a two-story building. The characterizations made herein could ultimately influence how map data from robotic assets are shared and displayed.
Sıçanlarda deneysel duodenal perforasyon modelinde ePTFE greft ile primer dikişin karşılaştırılması

BACKGROUND: In this experimental study, we treated peptic ulcer perforation in the first portion of the duodenum using an ePTFE (expanded polytetrafluoroethylene) soft tissue graft and compared results with primary suturing. METHODS: Wistar Albino rats were randomized into two groups. A duodenal defect of 3 mm was created in the first portion of the duodenum in all 24 rats. Macroscopic evaluation was done to determine presence of intraabdominal leakage, peritonitis, intraabdominal adhesions, and the level of adhesion, if present. The adhesion severity scoring system was used to score severity of adhesions. Histology section score criteria were used for scoring in microscopic evaluation. RESULTS: There was no statistically significant difference between the two groups in the adhesion severity scoring according to grades. However, when overall scoring was considered, a statistically significant difference was determined between the two groups (p=0.045). According to microscopic examination, there was no statistically significant difference between the two groups when classified into none to minimal cell accumulation, thin immature granulations and moderately thick granulation (p=0.089, p=0.178, p=0.755); however, there was a statistically significant difference between the two groups in the thick vascular granulation class (p=0.005). CONCLUSION: Use of the ePTFE graft method is easy, and results are comparable to those achieved with primary suturing. However, our results need further confirmation with larger series in animals and also with human studies.
TOSCA-based SWITCH workbench for application composition and Infrastructure planning of time-critical applications

—Real-time applications, such as disaster early warning systems, live event broadcasting, video conferencing and online gaming, present particular challenges for successful development and deployment: they only achieve their expected business value when they meet critical requirements, such as high performance and availability for outstanding Quality of Service and Quality of Experience. The development of time-critical applications needs to be supported by a customized software engineering environment that offers support for the entire application life-cycle. However, there is a lack of (component-based) software workbenches/tools suitable for time-critical applications, supporting customized software engineering through the entire life-cycle. In this paper we present the SIDE Workbench developed during the course of the SWITCH project. It uses TOSCA extensively for exchange of information within the SWITCH platform, and offers component-based application composition, software component modelling, infrastructure planning and provisioning through the entire life-cycle of time-critical applications. To show the application composition process we describe the development of a containerized interactive multi-tier application on the SIDE Workbench that is mapped into TOSCA, from which the corresponding Docker Compose ﬁle is then created.
Semi-global Matching Based on a Modified Census Transform with Gradient Maps

Census transform has been identified to be very effective and robust for stereo matching. But it needs too much computation and the accuracy is limited. In this paper, a semi-global matching based on a modified census transform is proposed to decrease the computation. To take use of the parallel computation like CUDA, a new cost model is proposed by using the gradient maps. The proposed method performs better than the original method in the experiment. Compared to the original method, the proposed method reduces the time consumption while remaining the accuracy.