Myanmar news summarization using different word representations

There is enormous amount information available in different forms of sources and genres. In order to extract useful information from a massive amount of data, automatic mechanism is required. The text summarization systems assist with content reduction keeping the important information and filtering the non-important parts of the text. Good document representation is really important in text summarization to get relevant information. Bag-of-words cannot give word similarity on syntactic and semantic relationship. Word embedding can give good document representation to capture and encode the semantic relation between words. Therefore, centroid based on word embedding representation is employed in this paper. Myanmar news summarization based on different word embedding is proposed. In this paper, Myanmar local and international news are summarized using centroid-based word embedding summarizer using the effectiveness of word representation approach, word embedding. Experiments were done on Myanmar local and international news dataset using different word embedding models and the results are compared with performance of bag-of-words summarization. Centroid summarization using word embedding performs comprehensively better than centroid summarization using bag-of-words

A Lightweight Size Estimation Approach for Embedded System using COSMIC Functional Size Measurement

Functional Size Measurement (FSM) is an important component of a software project that provides information for estimating the effort required to develop the measured software. Although the embedded software is time-consuming to develop, COSMIC FSM can be estimated to get more accurate function size. The traditional Function Point methods are designed to measure only business application domain and are problematic in the real-time domain. As a result, COSMIC Functional Size Measurement (FSM) method is designed to measure both application domains. The design diagrams such as UML, SysML and the well-defined FSM procedure must use to accurately measure the functional size of embedded system. We have already developed the generation model based on SysML metamodel with an example of elevator control system. In this paper, we applied the generation model that is the classification of the instance level of object based on UML metamodel. After that, this paper also showed the mapping rules which mapped between the generation model and COSMIC FSM to estimate the functional size of embedded software with the case study of cooker system. This paper also proposed the light weight generation method of COSMIC FSM by using the generation model

Whole-genome sequencing of multidrug-resistant Mycobacterium tuberculosis isolates from Myanmar.

Drug-resistant tuberculosis (TB) is a major health threat in Myanmar. An initial study was conducted to explore the potential utility of whole-genome sequencing (WGS) for the diagnosis and management of drug-resistant TB in Myanmar. Fourteen multidrug-resistant Mycobacterium tuberculosis isolates were sequenced. Known resistance genes for a total of nine antibiotics commonly used in the treatment of drug-susceptible and multidrug-resistant TB (MDR-TB) in Myanmar were interrogated through WGS. All 14 isolates were MDR-TB, consistent with the results of phenotypic drug susceptibility testing (DST), and the Beijing lineage predominated. Based on the results of WGS, 9 of the 14 isolates were potentially resistant to at least one of the drugs used in the standard MDR-TB regimen but for which phenotypic DST is not conducted in Myanmar. This study highlights a need for the introduction of second-line DST as part of routine TB diagnosis in Myanmar as well as new classes of TB drugs to construct effective regimens.Professor Sandy Smith Memorial ScholarshipThis is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.jgar.2016.04.00

Prevalence and seroprevalence of Plasmodium infection in Myanmar reveals highly heterogeneous transmission and a large hidden reservoir of infection.

Malaria incidence in Myanmar has significantly reduced over recent years, however, completeness and timeliness of incidence data remain a challenge. The first ever nationwide malaria infection and seroprevalence survey was conducted in Myanmar in 2015 to better understand malaria epidemiology and highlight gaps in Annual Parasite Index (API) data. The survey was a cross-sectional two-stage stratified cluster-randomised household survey conducted from July-October 2015. Blood samples were collected from household members for ultra-sensitive PCR and serology testing for P. falciparum and P. vivax. Data was gathered on demography and a priori risk factors of participants. Data was analysed nationally and within each of four domains defined by API data. Prevalence and seroprevalence of malaria were 0.74% and 16.01% nationwide, respectively. Prevalent infection was primarily asymptomatic P. vivax, while P. falciparum was predominant in serology. There was large heterogeneity between villages and by domain. At the township level, API showed moderate correlation with P. falciparum seroprevalence. Risk factors for infection included socioeconomic status, domain, and household ownership of nets. Three K13 P. falciparum mutants were found in highly prevalent villages. There results highlight high heterogeneity of both P. falciparum and P. vivax transmission between villages, accentuated by a large hidden reservoir of asymptomatic P. vivax infection not captured by incidence data, and representing challenges for malaria elimination. Village-level surveillance and stratification to guide interventions to suit local context and targeting of transmission foci with evidence of drug resistance would aid elimination efforts

Extraction of Reliable Information from the Web

Information extraction is one of the methods to retrieve information from complex web pages. With the use of multiple algorithms, intelligence, knowledge base, knowledge acquisition and filtering, people nowadays can benefited with the use of information extraction. Such application has been applied in several dimensions, such as new transcripts, insurance in formation, and weather reports. This proposed system extracts required laptop data from relevant web pages and convert them into a standard database. This paper uses STALKER algorithm to generate the rules for extracting the laptop information. The extract ed data are matched and recognized with built in keyword and entity tables using Named Entity Recognition (NER). And then, the system produces the required extracted information. By using this system, the user can get the meaningful laptop information and it also provides the user with easy access and time saving

CPU Usage Prediction Models for Virtualized Data Center

Resource allocation plays an important role inVirtualized Data Center (VDC). The applicationsrunning in VDC are mostly business criticalapplications with Quality-of-Service (QoS)requirements. Moreover, dynamic resource allocationand real time monitoring of the resource usage of VMsare also needed to reduce under resource utilization andover resource utilization. Therefore, resource usageprediction is required for dynamic resource allocationsystems. In efficient dynamic resource allocation, theresources are allocated to a VM while meeting theirService Level Agreement (SLA). The main contributionof this work is two-fold. The first is the generation ofCPU usage prediction models by applying differentpowerful machine learning techniques. The second isSLA evaluation on predicted value by using proposedSLA metric. To evaluate the efficiency of these models,experiments are carried out by using CPU profiles fromreal world data centre. According to the experiments,proposed resource prediction models have promisingaccuracy

Availability Analysis on Virtualized Two-Node Cluster System: Ratio of Restoration Rate and Failure Rate

Worldwide, businesses continually increasetheir dependence on IT systems for routine businessprocesses. The business processes which directlyrely on information systems and the supporting ITinfrastructure often require high levels ofavailability and recovery in the case of plannedand unplanned outage. High availability hasachieved by host per host redundancy, a highlyexpensive method with hardware and human costs.Virtualization technologies promise cost reductionthrough resource consolidation. By combiningvirtualization and HA clustering, it is possible tobenefit from increased manageability and savingfrom server consolidation through virtualizationwithout decreasing uptime of critical services.Using analytical modeling, we analyze multipledesign choices when dual physical servers are usedto host multiple virtual machines. We use Markovdecision process when we are concerned aboutoptimal decision at any arbitrary time. Numericalexamples are presented to illustrate theapplicability of the model

Single-Document Myanmar Text Summarization using Latent Semantic Analysis (LSA)

Due to an exponential growth in the generationof textual data, tools and mechanisms for automaticsummarization of documents is needed. Textsummarization is currently a major research topic inNatural Language Processing. There are variousapproaches to generate text summary. Among them, weproposed Myanmar text summarization using latentsemantic analysis (LSA). Latent semantic analysis(LSA) is a technique in natural language processing, ofanalyzing relationships between a set of documents andthe terms they contain by producing a set of conceptsrelated to the documents and terms. LSA is a retrievalmethod that uses a mathematical technique calledsingular value decomposition (SVD) to identifypatterns in the relationships between the terms andconcepts contained in an unstructured collection oftext. There is no LSA based sentence extraction inMyanmar language. This is the first LSA based TextSummarizer in Myanmar. We summarize Myanmarnews from Myanmar official websites such as 7daydaily, new-eleven, ThithtooLwin, etc

Adaptive Duplicate Detection in XML Document Based on Hash Function

The task of detecting duplicate records thatrepresents the same real world object in multipledata sources, commonly known as duplicatedetection and it is relevant in data cleaning anddata integration applications. Numerous approachesboth for duplicate detection in relational and XMLdata exist. As XML becomes increasingly popularfor data representation, algorithms to detectduplicates in XML documents are required.Previous domain independent solutions to thisproblem relied on standard textual similarityfunctions (e.g., edit distance, cosine metric) betweenobjects. However, such approaches result in largenumbers of false positives if we want to identifydomain-specific abbreviations and conventions.In this paper, we present a generalizedframework for duplicate detection, specialized toXML. The aim of this research is to develop anefficient algorithm for detecting duplicate incomplex XML documents and to reduce number offalse positive by using hash function algorithm

Isolation, characterization and antimicrobial activities of endophytic fungi from leaves, stems and inner barks of Azadirachta indica A. Juss.

The isolation, characterization and antimicrobial activities of endophytic fungi from leaves, stems and inner barks of Azadirachta indica A. Juss. (Neem) were studied. The plants were collected from University of Mandalay Campus. This study was carried out at Microbiology Laboratory of Botany Department, University of Mandalay from December 2017 to July 2018. Potato Dextrose Agar (PDA) medium was used for the isolation of endophytic fungi. Nine endophytic fungi were isolated and classified from leaves, stems and inner barks of Azadirachta indica A. Juss. Among them, ATL 1, ATL 2, ATL 3, ATL 4 were isolated from leaves. ATL 5, ATL 6 were isolated from stems. ATL 7, ATL 8, ATL 9 were isolated from inner barks. The isolated fungal strains were confirmed ATL 1 as Colletotrichum sp., ATL 2 as Curvularia sp., ATL 3 as Pestalotiopsis sp., ATL 4 as Nigrospora sp., ATL 5 as Aspergillus sp., ATL 6 as Trichoderma sp., ATL 7 as Fusarium sp., ATL 8 as Penicillium sp., ATL 9 as Aspergillus sp. Four isolated fungal strains: ATL 1 - ATL 4 from leaves of Azadirachta indica A. Juss. (Neem) were tested for antimicrobial activities at Biotechnology Research Department, Kyaukse. Those isolated fungal strains showed the antimicrobial activities against Staphylococcus aureus, Bacillus cereus, Escherichia coli and Candida albicans. The isolated fungal strains; ATL 2 (Curvularia sp.), ATL 3 (Pestalotiopsis sp.) and ATL 4 (Nigrospora sp.) showed the inhibition zones (25 mm) against Bacillus cereus and ATL 1 (Colletotrichum sp.), ATL 2 (Curvularia sp.), ATL 3 (Pestalotiopsis sp.) and ATL 4 (Nigrospora sp.) showed the inhibition zones (18 mm) against Escherichia coli. Those isolated endophytic fungi from leaves of Neem plants will be a good source of bioactive and antimicrobial activities against the pathogenic bacteria in agriculture and medicines