Family planning success in two cities in Zaire

Both projects described here, Matadi and Kananga, helped health providers in those two cities offer clinical family planning services. But their approaches differed markedly. The family education program in Matadi concentrated on pioneering community-based distribution of contraceptives, with carefully supervised distributors. The Kanaga Project emphasized clinical supervision and pleasing the clients; introduced social marketing with loose supervision of retailers; and provided an information team skilled in face-to-face group meetings, plus a weekly radio program. Four factors common to both projects seemed to contribute to their success: The single-minded dedication of staff members to making family planning work. An uninterrupted supply of affordable contraceptive methods available through outlets at many locations. Enough organizational autonomy to be able to respond to problems as they arose. Such autonomy made project personnel identify more with project goals and feel responsible for achieving project objectives. Regular and supportive supervision of those responsible for service delivery. Both projects emphasized regular contact with clinic personnel - Matadi also included distributors. These contacts bolstered morale by showing that the project administration was closely following service providers'activities and by transmitting to providers the staff's enthusiam for project activities. Supervisory visits included administrative functions such as collecting service statistics and controlling inventory, but these activities were handled in a friendly, nonthreatening manner that encouraged service providers to perform their tasks well. The fourth factor is adequate funding. Both projects had special funding that allowed them to experiment with approaches for increasing contraceptive prevalence. That funding may partly explain their organizational autonomy and may have contributed to the sense of purpose and esprit de corps that developed among project staff. Larger-scale programs in Zaire have operated with significant financial constraints, so it would be unfair to compare them with these more successful projects. Special funding does not guarantee project success but may make it far more likely, conclude the authors.Health Monitoring&Evaluation,Adolescent Health,ICT Policy and Strategies,Early Child and Children's Health,Reproductive Health

double committee adaboost

Abstract In this paper we make an extensive study of different combinations of ensemble techniques for improving the performance of adaboost considering the following strategies: reducing the correlation problem among the features, reducing the effect of the outliers in adaboost training, and proposing an efficient way for selecting/weighing the weak learners. First, we show that random subspace works well coupled with several adaboost techniques. Second, we show that an ensemble based on training perturbation using editing methods (to reduce the importance of the outliers) further improves performance. We examine the robustness of the new approach by applying it to a number of benchmark datasets representing a range of different problems. We find that compared with other state-of-the-art classifiers our proposed method performs consistently well across all the tested datasets. One useful finding is that this approach obtains a performance similar to support vector machine (SVM), using the well-known LibSVM implementation, even when both kernel selection and various parameters of SVM are carefully tuned for each dataset. The main drawback of the proposed approach is the computation time, which is high as a result of combining the different ensemble techniques. We have also tested the fusion between our selected committee of adaboost with SVM (again using the widely tested LibSVM tool) where the parameters of SVM are tuned for each dataset. We find that the fusion between SVM and a committee of adaboost (i.e., a heterogeneous ensemble) statistically outperforms the most used SVM tool with parameters tuned for each dataset. The MATLAB code of our best approach is available at bias.csr.unibo.it/nanni/ADA.rar

A Social Rehabilitation Program Implementation and Analysis: A Demonstration-Experimental Proposal

The intent of this proposal is to institute a social rehabilitation program on a ward of approximately 40-50 patients and then to assess and analyze the results of the program. Evaluation is to be undertaken through the utilization of a control group of approximately the same size, composition, and general philosophy located within the same hospital. In designing a research proposal for such a hospital, three important issues must be addressed as the effects of the program have ramifications that radiate throughout the hospital structure. The three issues, in the order of the manner they will be outlined, are operational problems, administrative problems, and finally methodological issues

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning

Background: Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results: In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifcally designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion: HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa.Comment: 16 pages, 14 figure

Agregação de ranks baseada em grafos

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Neste trabalho, apresentamos uma abordagem robusta de agregação de listas baseada em grafos, capaz de combinar resultados de modelos de recuperação isolados. O método segue um esquema não supervisionado, que é independente de como as listas isoladas são geradas. Nossa abordagem é capaz de incorporar modelos heterogêneos, de diferentes critérios de recuperação, tal como baseados em conteúdo textual, de imagem ou híbridos. Reformulamos o problema de recuperação ad-hoc como uma recuperação baseada em fusion graphs, que propomos como um novo modelo de representação unificada capaz de mesclar várias listas e expressar automaticamente inter-relações de resultados de recuperação. Assim, mostramos que o sistema de recuperação se beneficia do aprendizado da estrutura intrínseca das coleções, levando a melhores resultados de busca. Nossa formulação de agregação baseada em grafos, diferentemente das abordagens existentes, permite encapsular informação contextual oriunda de múltiplas listas, que podem ser usadas diretamente para ranqueamento. Experimentos realizados demonstram que o método apresenta alto desempenho, produzindo melhores eficácias que métodos recentes da literatura e promovendo ganhos expressivos sobre os métodos de recuperação fundidos. Outra contribuição é a extensão da proposta de grafo de fusão visando consulta eficiente. Trabalhos anteriores são promissores quanto à eficácia, mas geralmente ignoram questões de eficiência. Propomos uma função inovadora de agregação de consulta, não supervisionada, intrinsecamente multimodal almejando recuperação eficiente e eficaz. Introduzimos os conceitos de projeção e indexação de modelos de representação de agregação de consulta com base em grafos, e a sua aplicação em tarefas de busca. Formulações de projeção são propostas para representações de consulta baseadas em grafos. Introduzimos os fusion vectors, uma representação de fusão tardia de objetos com base em listas, a partir da qual é definido um modelo de recuperação baseado intrinsecamente em agregação. A seguir, apresentamos uma abordagem para consulta rápida baseada nos vetores de fusão, promovendo agregação de consultas eficiente. O método apresentou alta eficácia quanto ao estado da arte, além de trazer uma perspectiva de eficiência pouco abordada. Ganhos consistentes de eficiência são alcançadas em relação aos trabalhos recentes. Também propomos modelos de representação baseados em consulta para problemas gerais de predição. Os conceitos de grafos de fusão e vetores de fusão são estendidos para cenários de predição, nos quais podem ser usados para construir um modelo de estimador para determinar se um objeto de avaliação (ainda que multimodal) se refere a uma classe ou não. Experimentos em tarefas de classificação multimodal, tal como detecção de inundação, mostraram que a solução é altamente eficaz para diferentes cenários de predição que envolvam dados textuais, visuais e multimodais, produzindo resultados melhores que vários métodos recentes. Por fim, investigamos a adoção de abordagens de aprendizagem para ajudar a otimizar a criação de modelos de representação baseados em consultas, a fim de maximizar seus aspectos de capacidade discriminativa e eficiência em tarefas de predição e de buscaAbstract: In this work, we introduce a robust graph-based rank aggregation approach, capable of combining results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to incorporate heterogeneous models, defined in terms of different ranking criteria, such as those based on textual, image, or hybrid content representations. We reformulate the ad-hoc retrieval problem as a graph-based retrieval based on {\em fusion graphs}, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we show that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused. Another contribution refers to the extension of the fusion graph solution for efficient rank aggregation. Although previous works are promising with respect to effectiveness, they usually overlook efficiency aspects. We propose an innovative rank aggregation function that it is unsupervised, intrinsically multimodal, and targeted for fast retrieval and top effectiveness performance. We introduce the concepts of embedding and indexing graph-based rank-aggregation representation models, and their application for search tasks. Embedding formulations are also proposed for graph-based rank representations. We introduce the concept of {\em fusion vectors}, a late-fusion representation of objects based on ranks, from which an intrinsically rank-aggregation retrieval model is defined. Next, we present an approach for fast retrieval based on fusion vectors, thus promoting an efficient rank aggregation system. Our method presents top effectiveness performance among state-of-the-art related work, while promoting an efficiency perspective not yet covered. Consistent speedups are achieved against the recent baselines in all datasets considered. Derived from the fusion graphs and fusion vectors, we propose rank-based representation models for general prediction problems. The concepts of fusion graphs and fusion vectors are extended to prediction scenarios, where they can be used to build an estimator model to determine whether an input (even multimodal) object refers to a class or not. Performed experiments in the context of multimodal classification tasks, such as flood detection, show that the proposed solution is highly effective for different detection scenarios involving textual, visual, and multimodal features, yielding better detection results than several state-of-the-art methods. Finally, we investigate the adoption of learning approaches to help optimize the creation of rank-based representation models, in order to maximize their discriminative power and efficiency aspects in prediction and search tasksDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Fake Review Detection using Data Mining

Online spam reviews are deceptive evaluations of products and services. They are often carried out as a deliberate manipulation strategy to deceive the readers. Recognizing such reviews is an important but challenging problem. In this work, I try to solve this problem by using different data mining techniques. I explore the strength and weakness of those data mining techniques in detecting fake review. I start with different supervised techniques such as Support Vector Ma- chine (SVM), Multinomial Naive Bayes (MNB), and Multilayer Perceptron. The results attest that all the above mentioned supervised techniques can successfully detect fake review with more than 86% accuracy. Then, I work on a semi-supervised technique which reduces the dimension- ality of the input features vector but offers similar performance to existing approaches. I use a combination of topic modeling and SVM for the implementation of the semi-supervised tech- nique. I also compare the results with other approaches that consider all the words of a dataset as input features. I found that topic words are enough as input features to get similar accuracy compared to other approaches where researchers consider all the words as input features. At the end, I propose an unsupervised learning approach named as Words Basket Analysis for fake re- view detection. I utilize five Amazon products review dataset for an experiment and report the performance of the proposed on these datasets