Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing the information deficit in global health: lessons from a digital acute care platform in Sri Lanka

Lack of investment in low-income and middle-income countries (LMICs) in systems capturing continuous information regarding care of the acutely unwell patient is hindering global efforts to address inequalities, both at facility and national level. Furthermore, this of lack of data is disempowering frontline staff and those seeking to support them, from progressing setting-relevant research and quality improvement. In contrast to high-income country (HIC) settings, where electronic surveillance has boosted the capability of governments, clinicians and researchers to engage in service-wide healthcare evaluation, healthcare information in resource-limited settings remains almost exclusively paper based. In this practice paper, we describe the efforts of a collaboration of clinicians, administrators, researchers and healthcare informaticians working in South Asia, in addressing the inequality in access to patient information in acute care. Harnessing a clinician-led collaborative approach to design and evaluation, we have implemented a national acute care information platform in Sri Lanka that is tailored to priorities of frontline staff. Iterative adaptation has ensured the platform has the flexibility to integrate with legacy paper systems, support junior team members in advocating for acutely unwell patients and has made information captured accessible to diverse stakeholders to improve service delivery. The same platform is now empowering clinicians to participate in international research and drive forwards improvements in care. During this journey, we have also gained insights on how to overcome well-described barriers to implementation of digital information tools in LMIC. We anticipate that this north–south collaborative approach to addressing the challenges of health system implementation in acute care may provide learning and inspiration to other partnerships seeking to engage in similar work.
Research on optimization of parallel incremental updating algorithm for association rules

Pruning and Fast Updating algorithm(PFUP) always overloads the processor and decreases executing efficiency.An improved Parallel,Pruning and Fast Updating(PPFUP) algorithm is presented to solve these problems of PFUP.PPFUP algo- rithm based on shared-nothing workstations,partitions the locally-large itemsets and assigns them to different workstations, and synchronizes the tasks of computing on two databases to reduce the stress of memory.Simulation shows that the execu- tion time of PPFUP algorithm reduces to 11% compared with PFUP algorithm in the case of the same database and low support threshold.
Implementation of Clustering Based Feature Subset Selection Algorithm for High Dimensional Data

Feature Selection is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing the irrelevant and redundant features. Feature Selection is often an essential data prior to applying a learning algorithm. Machine learning algorithms are known to degrade in performance when faced with many features that are not necessary for predicting the desired output. The removal of irrelevant and redundant information often improves the performance of the machine learning algorithms. Feature selection techniques aim at reducing the number of unnecessary features in classification rules. The proposed Feature Subset Selection using clustering for high dimensional data works in two steps. First step, features are divided into clusters by using partitioning clustering methods. Second step, the representative feature that is strongly related to target class is selected from each cluster to form a subset of features. These features are then used for training using a machine learning algorithm and the results are compared with the original set of features. This is followed by comparison of the efficiency and accuracy of other feature selection algorithms using various combinations of machine learning algorithms.
Connectionism, Artificial Neural Networks and Reading

Baglanticilik (connectionism) insanin bilissel becerilerini anlamaya ve modellemeye yonelik alternatif bir yaklasimdir. Baglanticiligin uygulandigi alanlar arasinda insanoglunun gerceklestigi en onemli eylemlerden biri olan okumanin temelinde yatan bilissel surecleri anlama ve modelleme de bulunmaktadir. Baglanticilik, 1960 ve 1970'lerde yaygin bir sekilde kullanilan ve insanin bilissel isleme sureclerini ve okuma becerisini aciklamaya calisan yukaridan-asagiya (top-down) ve etkilesimli (interactive) yaklasimlarin temel varsayimlarini sorgulamistir. Baglanticilik yaklasimi, okuma sureclerinin nasil gerceklestigini aciklarken asagidan-yukariya (bottom-up) bir yaklasimi benimsemis, kurallar ve semboller yerine parametreler, agirliklar, baglantilar ve kisitlamalar tarafindan yonetilen bir oruntu tanima yontemini benimsemistir (artificial neural networks). Dilbilim, psikoloji, felsefe ve yapay zeka literaturunde baglanticilik yaklasimini temel alarak insanin okuma sureclerini irdelemeyi hedefleyen onemli sayida akademik calisma bulunmasina ve bu yaklasimin okuma becerisini anlamaya yonelik buyuk bir katki potansiyeli olmasina ragmen, baglanticilik yaklasiminin taninirligi henuz dusuk duzeydedir. Bu makalede, okuma becerisinin altinda yatan bilissel surecleri anlamaya ve aciklamaya calisan baglanticilik yaklasimlari ve yapay sinir ag modellemesi hakkinda temel bilgiler sunulmustur. Buna ek olarak, baglanticilik yaklasimi, ozellikle okuma alanyazinda yaygin bir bicimde kullanilan geleneksel yapidaki asagidan yukariya, yukaridan asagiya ve etkilesimli yaklasimlar gibi diger yaklasimlarla karsilastirilmistir. Son olarak, ilgili alanyazinda oldukca etkili oldugu kanitlanmis baglanticilik modellerine de yer verilmis ve bu yaklasimin okuma becerisini anlama ve modellemeye iliskin potansiyeline ve kisitliliklarina deginilmistir.
An Enhanced Approach of Building Extractionfrom Satellite Images Using SOFM & MRF Model

—Extracting Building features from satellite imagery is a vital research area in digital remote sensing field. Building detection & extraction is one of the complex and challenging task in GIS database. But this technique is useful for urban planning and obtaining more timely and accurate information during natural disasters like Earth quakes, Cyclone etc.At the first appearance, buildings are visible as easiest objects to detect and extract. But many difficulties meet on extracting buildings accurately, comprising various outlook angles, roof top complexity, environmental objects(Trees, Roads, vehicles etc) and additional objects which ambiguous the boundaries of the buildings that can be detected. Because of these reasons many algorithms deliver less quality of building extraction and also much time taken for detection. To solve this problem, integrating many efficient algorithms provides better results than individual algorithms. In this study, an enhanced approach for rising the quality and accuracy in detecting & extracting building textures with various and complex angles of roofs from urban area satellite images is proposed. First, an unsupervised image segmentation approach based on SOM(Self organizing maps) is applied to detect roof top regions. Then, the SOM combines with MRF(Markov Random field) spatial constraints for improved segmentation outcomes. This Hybrid approach of SOM and MRF used less data samples in training set. Experimental results obtained that the proposed method achieved excellent result in detecting and extracting rooftops in complex satellite images .
LEGENDRE EXPANSION METHODS FOR THE NUMERICAL SOLUTION OF NONLINEAR 2D FREDHOLM INTEGRAL EQUATIONS OF THE SECOND KIND

At present, research on providing new methods to solve non- linear integral equations for minimizing the error in the numerical calcula- tions is in progress. In this paper, necessary conditions for existence and uniqueness of solution for nonlinear 2D Fredholm integral equations are given. Then, two different numerical solutions are presented for this kind of equations using 2D shifted Legendre polynomials. Moreover, some re- sults concerning the error analysis of the best approximation are obtained. Finally, illustrative examples are included to demonstrate the validity and applicability of the new techniques.
Annotated Corpus for Citation Context Analysis

In this paper, we present a corpus composed of 85 scientific articles annotated with 2092 citations analyzed using context analysis. We obtained a high Inter-annotator agreement; therefore, we assure reliability and reproducibility of the annotation performed by three coders in an independent way. We applied this corpus to classify citations according to qualitative criteria using a medium granularity categorization scheme enriched by annotated keywords and labels to obtain high granularity. The annotation schema handle three dimensions: PURPOSE: POLARITY: ASPECTS. Citation purpose define functions classification: use, critique, comparison and background with more specific classes stablished using keywords: Based on, Supply; Useful; Contrast; Acknowledge, Corroboration, Debate; Weakness and Hedges. Citation aspects complement the citation characterization: concept, method, data, tool, task, among others. Polarity has three levels: Positive, Negative and Neutral. We developed the schema and annotated the corpus focusing in applications for citation influence assessment, but we suggest that applications as summary generation and information retrieval also could use this annotated corpus because of the organization of the scheme in clearly defined general dimensions.
An Adaptive Optimal Excitation Controller Based on Digital Signal Processor

An adaptive optimal excitation controller (AOEC) based on the digital signal processor (DSP) is proposed. By means of the powerful calculating capacity of DSP, AOEC can identify real time the generator operating procedures and provide identified parameters to a special discrete Riccati equation. Then optimal feedback gains are obtained by solving the Riccati equation. In theory, AOEC can ensure the controlled objects to maintain the optimal performance in any situation. Dynamic simulations on a single machine infinite bus system show that AOEC has good control performance and can provide the system with sufficient damp in the transient procedure. Measures to improve the identification algorithm in the implementation process of the adaptive algorithm are also discussed.
TBE EFFECTS OF THE TOXIC PHYTOPLANKTER ( HETEROSZGMA CARTERAE ) ON THE COON-STRIPE SHRTMP ( PANDALUS DANAE ) AND RAINBOW TROUT ( ONCORHI 7 VCHUS MYKISS )

The raphydophycean phytoplankter, Heterosigma carterae, produces oxyradicals that can kill M s h . However, effects of toxic H. carterae on shrimp are unknown and formed the objective of this study. In this investigation, PandaIus dame was studied as a representative shrimp, while rainbow trout was used as a toxicity bioindicator. Toxic H. carterae cells did not kill Pandzlus dm? ae within 24 h of exposure in spite of the injury to the shrimp gdls that occurred between 4 and 24 h, i.e. hypertrophy and fbsion of lamellae, rupture of the lamellar membranes, and atrophy of the filaments. These lesions impaired the aerobic respiratory system of this shrimp. The shrimp responded to the injury of the gills by hemocytic swarming at the lesion sites on the larnellae. Blood hypoxia occurred as the 0 2 concentration in the hemolymph decreased sigdicantly (Tukey's test; PK0.05) with time during 24-h exposure to a toxic culture of H. carterae, whilst lactate concentrations increased ca. 5 fold within a 12-h exposure before decreasing to the pre-exposure value. Lactate concentrations in the gills dropped signdicantly with time during a 24-h exposure to the toxic H. carterae culture. The reduction of lactate concentrations in the hemolymph and in the gdls upon exposure to the toxic H. carterae culture suggest that the animal might have utilized lactate for energy under toxic-H. carterae-induced hypoxia. The gdls of shrimp exposed to toxic H. carterae cells showed sigdicantly reduced catalase activities after 18 h of exposure, a significant increase in peroxidase activity at 6 h of exposure, followed by a steady decline to values not significantly diierent fiom the preexposure value. These data suggest that oxyradicals secreted by H. carterae might destroy catalase activity, whereas peroxidase remained active during the exposure to the toxic algal cells. After re-introduction to seawater for 6 weeks, the injured larnellae of the shrimp gds remained hyperthropied. However, healing processes had continued as indicated by hemocytic swarming at the site of the lesions of the lamellae. Molting process may have fostered the healing process by more evenly distributing the hemocytes along the lamellar membranes.
SIMULTANEOUS SPECTROPHOTOMETRIC ESTIMATION OF ATENOLOL AND LERCANIDIPINE HYDROCHLORIDE IN COMBINED DOSAGE FORM BY RATIO DERIVATIVE AND DUAL WAVELENGTH METHOD

A simple, economical, precise and accurate method for simultaneous determination of Atenolol (ATE) and Lercanidipine Hydrochloride (LER) in combined dosage form has been developed. The first method is based on Ratio Derivative (Method A) and second method is based on Dual Wavelength (Method B). The amplitudes 266.98 nm and 386.97 nm wavelength were selected to determine ATE and LER respectively, by method A. In method B, two wavelengths were selected for each drug in a way so that the difference in absorbance is zero for another drug. At wavelengths 234.01 nm and 238.66 nm Lercanidipine have equal absorbance therefore these two wavelengths were used to determine ATE and on similar basis 253.33 nm and 286.07 nm were selected to determine LER in combined formulation. The drugs obey Beer’s law in the concentration range of 25-125 µg mL -1 for ATE and 5-25 µg mL -1 for LER by the method A and 50-90 µg mL -1 for ATE and 10-18 µg mL -1 for LER by the method B. The % assay of LER and ATE was found to be in the range 99.98 – 101.12 % by the proposed methods. Recovery was found in the range of 98.4 - 101.0 % for both analytes by both methods. The results of analysis have been validated statistically and recovery studies confirmed the accuracy and reproducibility of the proposed methods which were carried out by following ICH guidelines.
Online Networks can Support the Rise of Virtual Leaders: An Actor-Network Theory Analysis

This research investigated how self-selected leaders spontaneously emerged in a virtual environment using the online medium to gain legitimacy and coverage. While conventional leadership has been studied extensively, ‘virtual leadership’ is a novel phenomenon, developing alongside technology. The questions of how virtual environments may be used to grow constructive participation, and the motives for so doing, are increasing in significance for a broad range of contexts. This research extends the Actor-Network Theory (ANT) proponed by Latour (1987) to account for virtual network development AbstrAct
Minimizing Energy Consumption of Computing-plus-Communication Tasks in Virtualized Networked Data Centers

In this paper, we propose a dynamic resource provisioning scheduler to maximize the application throughput and minimize the computing-plus-communication energy consumption in virtualized networked data centers. The goal is to maximize the energy-efficiency, while meeting hard QoS requirements on processing delay. The resulting optimal resource scheduler is adaptive, and jointly performs: i) admission control of the input traffic offered by the cloud provider; ii) adaptive balanced control and dispatching of the admitted traffic; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled virtual machines instantiated onto the virtualized data center. The proposed scheduler can manage changes of the workload without requiring server estimation and prediction of its future trend. Furthermore, it takes into account the most advanced mechanisms for power reduction in servers, such as DVFS and reduced power states. Performance of the proposed scheduler is numerically tested and compared against the corresponding ones of some state-of-theart schedulers, under both synthetically generated and measured real-world workload traces. The results confirm the delay-vs.energy good performance of the proposed scheduler.
Book Review–Reconstruction and Analysis of 3D Scenes: From Irregularly Distributed 3D Points to Object Classes

Follow up what we will offer in this article about reconstruction and analysis of 3d scenes from irregularly distributed 3d points to object classes. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.
A COMPARISON OF DIFFERENT CLASSIFICATION SYSTEMS FOR AUTOMATIC SINGER IDENTIFICATION

In this project, methods for automatic singer identification problem are investigated, and a singer identification system for 15 singers is implemented. The system consists of two parts. Firstly, the song as the input of the system is segmented into two parts: vocal part, which consists of the singers’ voice and instrument sounds, and non-vocal part which consists of only instruments’ sounds. Then, the identification step for modeling and classification of singer is applied. Both steps consist of the audio feature extraction and classification methods. In the beginning of feature extraction, preprocessing is applied to data such as down-sampling, normalization, pre-emphasizing, frame blocking and windowing. Energy, spectral flux, zero crossing rate, mel frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are used for feature extraction. Then, support vector machine (SVM), gaussian mixture model (GMM) and multilayer perceptron (MLP) classifiers are constructed for classification of the singer with using all these extracted features.
The plenoptic camera as a wavefront sensor for the European Solar Telescope (EST)

The plenoptic wavefront sensor combines measurements at pupil and image planes in order to obtain wavefront information from different points of view simultaneously, being capable to sample the volume above the telescope to extract the tomographic information of the atmospheric turbulence. After describing the working principle, a laboratory setup has been used for the verification of the capability of measuring the pupil plane wavefront. A comparative discussion with respect to other wavefront sensors is also included.
Multimodal Fusion for Combining Textual and Visual Information in a Semantic Model

In recent times, multimodal fusion that combines information from different data streams such as text and images, has attracted the attention of many researchers due to the benefit it provides for various analytical tasks. In the computational semantics community, recent work has extended distributional semantics to integrate features extracted from images as well as text. In this thesis, we propose a flexible and general framework for the integration of textand image-based features that includes both a latent mixing phase in which text and image features are mapped together onto a lower-dimensional space, and a multimodal similarity estimation phase, where the two channels are linearly combined either at the feature or at the scoring level. The framework is of sufficient generality that, with specific parameter choices, it encompasses combination techniques that have been previously proposed in the literature. In addition, it can automatically pick the best model for a task given development data. We evaluate our framework through extensive experiments on simulating similarity judgements, and concept categorization. A further experiment to examine the effect of different image-based features combined with a textual model into a multimodal architecture is also presented. It is shown that our system significantly outperforms a state-of-the-art text-based distributional semantic model on the MEN semantic relatedness evaluation dataset and, using the parameter values tuned on MEN, is also the top performer on the independent WordSim evaluation. In a similar manner, we obtain improvements on AP, a widely used concept classification benchmark, with the use of the parameters tuned on another dataset, BATTIG. The good performance of the multimodal models using a different test set from the one used for parameter tuning demonstrates the robustness of our approach. Moreover, we also show evidence that adding better visual features improves the performance of the system.
A comparison of the effects of soundfield amplification on acoustical characteristics and word recognition performance in relocatable and permanent classrooms

There are three acoustical characteristics that should be addressed when developing a good classroom listening environment: signal-to-noise ratio (SNR), reverberation time (RT), and levels o f background noise. Previous research has shown that soundfield (SF) amplification systems help to improve these three characteristics, thus improving the classroom listening environment. In the present study, two seventhgrade classrooms were used: one relocatable classroom and one permanent classroom. Acoustical characteristics and speech levels were measured in both classrooms under two amplification conditions (unamplified and amplified). Word recognition abilities o f 37 students were also measured under the two amplification conditions in both classrooms. Results showed neither classroom met the standards for optimal acoustics; however, SNRs were increased in both classrooms with the use o f soundfield amplification. Additionally, the word recognition scores (W RS) o f the children in the relocatable classroom were significantly worse under both amplification conditions than in the permanent classroom; WRS, however, increased when amplification was used compared to when it was not used. The results revealed the benefits o f utilizing soundfield amplification systems in both classrooms, especially relocatable classrooms. Clinical implications are also discussed. APPROVAL FOR SCHOLARLY DISSEMINATION T he author grants to the P rescott M em orial Library o f L ouisiana T ech U niversity the right to reproduce, by appropriate m ethods, upon request, any or all portions o f this D issertation. It is understood that '‘proper req uest” con sists o f the agreem ent, on the part o f the requesting party, that said reproduction is for his personal u se and that subsequent reproduction w ill not o ccu r w ithout w ritten approval o f the author o f th is D issertation . Further, any portions o f the D issertation used in b ook s, papers, and other w ork s must be appropriately referenced to this D issertation. F inally, the author o f this D isserta tion reserves the right to publish freely, in the literature, at any tim e, any or all portions o f this D issertation .