Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of PMTCT and MCHS Integration in Indonesia, Analysis by Integration Analysis Framework and CFIR

Integration was strategy of PMTCT (Prevention of Mother to Child HIV Transmission) and MCHS (Mother and Child Health Services) since 2006. This systematic review explored integration level between PMTCT and MCHS. Literature searched from June 2015 to January 2016 through Google, Google Scholar, University Library Website, Portal Garuda, ProQuest, PubMed and 90 institutions in Jakarta. Keywords were PMTCT, HIV Mother, PPIA HIV Evaluation and HIV PMTCT Evaluation. We yielded 157 literatures from 1995-2015. Research should be done at Public Primary Health Care (PHC), discussed PMTCT implementation, and captured perspectives of staffs or decision makers. Thematic analysis was done using Atun’s Integration Analysis Framework and CFIR. We included 7 studies. No study explored overall dimensions of integration, especially planning function. Challenges were resources, execution, needs of patients, networks and communications, policies, leadership, and access to information. Only 4 studies showed PMTCT results. Coverage of first visit counseling was 9-100% and percentage of pregnant women tested was 3.9 -60%. PMTCT integration was partial. Integration was not a sole solution to results. Planning should be done together with regional and local level, involving stakeholders to disseminate PMTCT information, increasing ownership and leadership. We proposed Atun’s Framework and CFIR for further research.
Automatic Acquisition of Hyponyms

We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (I) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications To areas such as information retrieval are suggested.
Efficient circuit topologies for inductive power transfer

i Acknowledgements.iii Table of contents.iv Nomenclature.vii List of Figures.xi List of Tables.xv Chapter 1: Introduction . 1 1.1 Background . 1 1.1.
Isotropy, Reciprocity and the Generalized Bas-Relief Ambiguity

A set of images of a Lambertian surface under varying lighting directions defines its shape up to a three-parameter generalized bas-relief (GBR) ambiguity. In this paper, we examine this ambiguity in the context of surfaces having an additive non-Lambertian reflectance component, and we show that the GBR ambiguity is resolved by any non-Lambertian reflectance function that is isotropic and spatially invariant. The key observation is that each point on a curved surface under directional illumination is a member of a family of points that are in isotropic or reciprocal configurations. We show that the GBR can be resolved in closed form by identifying members of these families in two or more images. Based on this idea, we present an algorithm for recovering full Euclidean geometry from a set of uncalibrated photometric stereo images, and we evaluate it empirically on a number of examples.
Estimating Sample Size in Computing Simulation Experiments

A method is described for estimating and collecting the sample size needed to estimate the mean of a process (with a specified level of statistical precision) in a simulation experiment. Steps are also discussed for incorporating the determination and collection of the sample size into a computer library routine that can be called by the ongoing simulation program. We present the underlying probability model that enables us to denote the variance of the sample mean as a function of the autoregressive representation of the process under study and describe the estimation and testing of the parameters of the autoregressive representation in a way that can easily be “built into” a computer program. Several reliability criteria are discussed for use in determining sample size. Since these criteria assume that the variance of the sample mean is known, an adjustment is necessary to account for the substitution of an estimate for this variance. It is suggested that Student's distribution be used as the sampling d.
The Research on Knowledge Brokering Mechanism and Methods based on FCA

In this paper, the mechanism of knowledge brokering is analyzed and three models of knowledge brokering are proposed, of which knowledge broker network model is the most important one. For the knowledge transfer process, the concept itself is valuable tacit knowledge and the structures of the concept's relations are also valuable tacit knowledge. The key in the tacit knowledge transfer based on knowledge brokering is switching the explicitness of know-how knowledge to the explicitness of knows-who knowledge and switching the search of tacit knowledge to the search of experts who own the needed tacit knowledge. Formal concept analysis is a very good knowledge visual tool. Concept lattices can be used to describe the experts' knowledge, and the concept lattices will help the knowledge brokers find the right expert quickly.
A bound on the unequal error protection capabilities of rate k/n convolutional codes

In a previous paper by Mills and Costello (see ibid., p.114, 1993) the effective free distance vector of a convolutional encoder was defined. This paper presents a new bound relating two individual effective free distances. Evaluating the bound is a useful tool in determining the possible unequal error protection capabilities of encoders of specified rate and memory distribution. In addition, the bound allows a comparison between the effective free distance vector of a specific encoder and the theoretically optimal effective free distance vector. First, a bound on the Hamming weight of the sum of two vectors with known Hamming weights is presented. Then this bound is applied to effective free distances of encoders with both balanced and unbalanced memory distributions. <<ETX>>
Association of rs4784227-CASC16 (LOC643714 locus) and rs4782447-ACSF3 polymorphisms and their association with breast cancer risk among Iranian population

TOX3 and FOXA1 proteins are believed to be involved in the susceptibility of breast cancer. rs4784227-CASC16 and rs4782447-ACSF3, as single nucleotide polymorphisms (SNPs), located at the 16q may affect the FOXA1 DNA binding sequence change and therefore may enhance the FOXA1-binding affinity to the promoter of TOX3 gene. This study aimed to investigate the association of these SNPs/haplotypes with breast cancer susceptibility in an Iranian population. We conducted a case-control study of 1072 blood samples (505 breast cancer patients and 567 controls). Genotyping of rs4784227-CASC16 and rs4782447-ACSF3 SNPs was carried out by ARMS-PCR. Moreover, statistical analysis was done using SPSS version 20.0 (IBM Inc., Chicago, IL, USA), PHASE v 2.1 and SNP analyser 2.0. There was a strongly significant statistical association between alleles and genotypes of rs4784227-CASC16 with breast cancer risk in our study population (p<0.05). Moreover, a significant association was demonstrated between TA haplotype and breast cancer risk (OR=0.78; 95% CI (0.62-0.96); P-value=0.025). In this respect, although we did not observe a statistically significant association between rs4782447-ACSF3 with breast cancer susceptibility, the combination of the effects of rs4784227-CASC16 and rs4782447-ACSF3 SNPs may also affect the risk. This is in line with other studies suggesting these SNPs as risk-associated polymorphisms which may lead to a change in the affinity of FOXA1, as a distal enhancer, to TOX3 and thus change in TOX3 expression, which can eventually affect the risk of breast cancer.
Arm pose modeling for visual surveillance

Arm pose of individual person often indicates his/her behavior and interaction in a crowd. This paper focuses on how to model arm pose with respect to walking styles in visual surveillance. We propose to use a Bayesian network to represent the relationship between the upper arm, forearm and hand. Two sets of probabilistic templates are used to estimate the likelihoods of these three arm parts against their boundary, foreground and skin color features. The prior distributions, a critical part of the Bayesian formulation, are estimated using a kernel density estimator based on the CMU motion dataset, whereby MAP is applied to estimate the part’s orientation and size before fitting them to the arms. The proposed method is found to perform effectively on the CAVIAR sub-dataset and a HKU dataset, and compare well against another popular method. Potentially, it can be extended to track arm movements or to analyze the objects carried by individuals.
Teaching formal methods for computational linguistics at uppsala university

In the first sections of this paper, I discuss some of the motivation for teaching Formal Methods for interdisciplinary programs, such as Computational Linguistics, that use computational apparatus. In the second section, I share my experience by teaching such courses in the program for Computational Linguistics and Language Technology at Uppsala University. In the last section, I will present existing gaps in education on Formal Methods in interdisciplinary and science subjects.
Single Nucleotide Polymorphisms Associated with Alcohol-Induced Flushing Syndrome in Korean Population

Alcohol-induced flushing syndrome (AFS) is the condition commonly found among Asian population groups. It triggers flushing reaction, redness in face and body, even after small volume of alcohol intake. There are numerous studies currently available, exploring the possible cause and mechanism for Asians to develop flushing reaction [1-3]. The ethanol metabolism pathway is composed of two distinct parts-first, alcohol particles are broken down into acetaldehyde, catalyzed by alcohol dehydrogenase (ADH) family, then acetaldehyde is further oxidized into acetate by aldehyde dehydrogenase (ALDH) [4,5]. However, for any reason, when ADH and ALDH acquire variants in their genes, they fail to break down the byproducts and cause the acetaldehyde to build up in blood. This, as a result, can lead to flushing reaction, which is recognized as Korean J Leg Med 2019;43:71-80 https://doi.org/10.7580/kjlm.2019. 43.2. 71
Segmentation based image coding using fractals and the human visual system

A new segmentation-based image coding technique which performs segmentation based on roughness of textural regions and on properties of the human visual system (HVS) is presented. The image is segmented into texturally homogeneous region with respect to the degree of roughness as perceived by the HVS. The fractal dimension is used to measure the roughness of the textural regions. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into several classes. Three texture classes are chosen: perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each texture class. <<ETX>>
The Covert Set Cover Problem with Applications to Network Discovery

We address a version of the set-cover problem where we do not know the sets initially (and hence referred to as covert) but we can query an element to find out which sets contain this element as well as query a set to know the elements. We want to find a small set-cover using a minimal number of such queries. We present a Monte Carlo randomized algorithm that approximates an optimal set-cover of size OPT within O(logN) factor with high probability using O(OPT · log N) queries where N is the input size. We apply this technique to the network discovery problem that involves certifying all the edges and non-edges of an unknown n-vertices graph based on layered-graph queries from a minimal number of vertices. By reducing it to the covert set-cover problem we present an O(log n)-competitive Monte Carlo randomized algorithm for the covert version of network discovery problem. The previously best known algorithm (Beerliova et al., 2006) has a competitive ratio of Ω( √ n logn) and therefore our result achieves an exponential improvement.
New accelerated learning algorithm to reduce the oscillation of weights in multilayered neural networks

An accelerated learning algorithm is proposed. It is based on R.A. Jacob's heuristics with reduction of the oscillation by correcting the next point of weight to the bottom of a ravine. The performance is evaluated through several simulation examples and compared with several acceleration algorithms, such as the DBD (delta-bar-delta) algorithm and the EDBD (extended delta-bar-delta) algorithm. Simulation results showed that the proposed algorithm converges 3.7 times faster than the DBD algorithm, 1.8 times faster than the EDBD algorithm, and 19.4 times faster than the basic backpropagation. <<ETX>>
An online weight-based clustering algorithm for faces selection

With the scale of the application of face recognition getting bigger and bigger, A thinking of selecting faces as a preprocessing has conducted on how to improve the system's efficiency. After summarized and analyzed such a thinking, this paper has discussed the pros and cons of face data clustering by using the traditional K-means clustering algorithm. In addition, this paper has further proposed an online weight-based clustering algorithm (OWCA for short) that strengthens the advantages of face data clustering, restricts the computational complexity and develops an online processing scheme. Experimental results on the ORL face library demonstrated the correctness and effectiveness of OWCA.
Biometric Identification Based on Conjuctival Vasculature Pattern using Contourlet Transform

The performance of any biometric system depends on the reliable and robust feature extraction. Biometric recognition refers to the use of distinctive anatomical (e.g., fingerprints, face, iris, palm) and behavioral (e.g., speech) characteristics for automatically recognizing individuals. Iris recognition was found to be most accurate bio-metrics technology. Apart from the red, green, and blue (RGB) format, we analyze significance of using HSV, Otsu’s multi thresholding is applied on V channel, K-means clustering is applied to merge over segmented region to get sclera mask. Pyramidal directional filtering approach (Contourlets) for feature extraction for ocular biometrics are proposed. In this paper, we pursue a “true” two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. Our approach starts with a discrete domain construction. For Classification, linear discriminant analysis (LDA) is used.
Distributed Kernelized Locality-Sensitive Hashing for Faster Image Based Navigation

Abstract : Content based image retrieval (CBIR) remains one of the most heavily researched areas in computer vision. Different image retrieval techniques and algorithms have been implemented and used in localization research, object recognition applications, and commercially by companies such as Facebook, Google, and Yahoo! Current methods for image retrieval become problematic when implemented on image datasets that can easily reach billions of images. In order to process extremely large datasets, the computation must be distributed across a cluster of machines using software such as Apache Hadoop. There are many different algorithms for conducting content based image retrieval, but this research focuses on Kernelized Locality-Sensitive Hashing (KLSH). For the first time, a distributed implementation of the KLSH algorithm using the MapReduce programming paradigm performs CBIR and localization using an urban environment image dataset. This new distributed algorithm is shown to be 4.8 times faster than a brute force linear search while still maintaining localization accuracy within 8.5 meters.
Student Engagement and Writing Tasks in Science Classrooms

In science classrooms, students have difficulty understanding the terminology and new vocabularies used in science textbooks. In addition, science concepts and facts are challenging for most students. However, informational text or science concepts are not too difficult if students are able to use science vocabulary and language in writing. Incorporating writing tasks in science content and across content area will support full student participation in science learning activities. This paper summarizes the negative effects of lack of writing literacy in science classrooms, and points out the scarcity of writing tasks in core content areas such as science. In addition, it emphasizes the positive impacts of writing tasks as it promotes student engagement in a wide variety of scientific writing activities such as taking notes following hands-on activities, writing summaries following small-group reading from the texts, using concise data in graphs, and extending newly learned science concepts. Moreover, the paper provides an example of writing task and aligns with the Common Core State Standards (CCSS), which are currently used in Oklahoma, USA. The writing task is implemented in a middle school science classroom with the collaboration of science teacher and language arts teacher. Lastly, it enables science teachers to use a writing rubric in order to analyze how students use their imagination to articulate and organize scientific ideas.
Test generation for linear analog circuits

A test generation method for linear analog circuits is presented. The method is based on frequency domain analysis. The faults considered are abnormal value changes of elements, e.g., resistors, capacitors and inductors. The effect of design tolerance is considered in test generation. A procedure to determine the output ranges for acceptance or rejection is also proposed. The proposed method has been applied to several circuits at board level to generate test conditions for all elements to be tested. The method is also able to indicate which elements in the circuit are hard to test.
Comparative Study of Query Processing Architectures in Mobile Environment

Query processing deals with designing architecture that analyze queries and convert them into series of data manipulation operation. Query processing in a mobile environment is to form a high level query on different architectures, which is seen as a single query by to different environments into an efficient execution strategy and finding the execution of such architecture. In mobile environment, the various query processing architectures such as mobile environment architecture, distributed architecture, data broadcast architecture, mobile database environment architecture etc. The parameters used such as average power consumption, broadcast effectiveness, query efficiency, client utilization. This paper focuses on competent architecture for query processing in mobile environment. We propose comparative study of query processing based different architectures for exploring the mobile computing environment and to study various parameters related to different architectures of query processing in the mobile environment.
The 2018 Revised FDA Guidance for Early Alzheimer’s Disease: Establishing the Meaningfulness of Treatment Effects

The present report reviews the revised 2018 FDA guidance for early AD, with an emphasis on meaningfulness of clinical outcome assessments (COAs). A radical shift is evident in the importance given to establishing the meaningfulness of COAs in the 2018 draft versus the 2013 draft. The implications of this shift include the assertion that cognition is clinically meaningful, but that a persuasive effect on cognition, depending upon disease stage of the participants in the trial, is one that is of enough magnitude, established across multiple relevant domains, and can be supported by biomarkers reflecting underlying AD pathological changes. Meaningfulness is established through an understanding of the conceptual relevance of what is being measured and magnitude of any treatment effect. Precedent exists within other FDA guidance and independent good practices publications as to how meaningfulness may be assessed e.g. via evaluation of content validity and concepts such as minimally important difference. Additionally, FDA is developing a series of methodological Patient Focused Drug Development (PFDD) documents to provide further guidance on this topic, which are aimed at addressing gaps in methodology and recommended best practice. Importantly, application of PFDD approaches to AD is behind that in other areas and there is limited published content validity for COAs and a lack of supportive qualitative research. Initiatives to build robust conceptual models of AD and develop novel direct measures of meaningful health outcomes will have a significant impact on measurement of efficacy in clinical trials and on payer determinations of beneficiary value. Greater recognition of what is meaningful from the perspective of the patient and caregiver will inform regulatory reviews and determinations for payment and coverage of treatments.
The Exploitation of Parallel High Performance Systems in the FLAME Agent-Based Simulation Framework

Making use of high performance computers in agent-based simulation is a complex and dicult task. This paper looks at the approach being used within the EURACE project to exploit parallel computing technology in large-scale agent-based simulations involving millions of agents. The underlying software is the FLAME framework and the paper will give an overview of features and use of FLAME and discuss the implementation of techniques that attempt to exploit large parallel computing systems. Some early simulation results will be presented together with a discussion of the requirements for ecient parallel simulations and the performance of the current implementation
APPROACH WITHIN NATURAL LANGUAGE PROCESSING

The DAISY/ALIBABA-system developed within the WAl-project represents both a specific solution to the automatic indexing problem and a general framework for problems in the field of natural language processing, characterized by fuzziness and uncertaintyThe WAI approach to the indexing problem has already been published (3), (5). This paper however presents the under- lying paradigm of recognizing abstract objects. The basic concepts are described, including the decision theory approach used for recognition.
Factors Affecting Antiretroviral Therapy Adherence in Individuals with HIV in Kaimana District of Papua Barat

Objective: The present study aimed to identify factors affecting the adherence of individuals with human immunodeficiency virus-acquired immunodeficiency syndrome (HIV-AIDS) to antiretroviral therapy (ART) in Kaimana Hospital. Methods: The study design was descriptive correlational with a cross-sectional approach. Consecutive sampling was employed to obtain 81 respondents. Data were analyzed using chi-square and multivariate logistic regression analyses. Results: The results suggested that most respondents (74.1%) exhibited low medication adherence. The most significant factors that affect adherence were knowledge on HIV [odds ratio (OR) 10.748, p = 0.001], time since HIV diagnosis (OR 0.173, p = 0.018), alcohol consumption of 1–2 glasses/day (OR 0.184, p = 0.033), alcohol consumption of >2 glasses/day (OR 0.077, p = 0.027), and alcohol consumption of 0–1 glass/day (p = 0.040). Conclusion: Patients with HIV-AIDS having better knowledge on HIV-AIDS and their own health condition had higher ART adherence. Moreover, patients with higher alcohol consumption with longer time since HIV diagnosis had lower medication adherence. The study result suggested improving the administration of antiretroviral regimen and increasing the knowledge of individuals with HIV-AIDS regarding their own health status as measures to improve adherence.