101 research outputs found
Perancangan dan pembuatan perangkat lunak otomasi kantor di fakultas tehnik Universitas Muhammadiyah Malang dengan menerapkan metode BPM (Business Process Management) / Munarko
No full text31 hal.:bib., ill.,lamp.; 28 c
Pelatihan pembuatan dan perawatan website berbahasa Inggris untuk meningkatkan penjualan paket jasa tour dan travel di kecamatan Karangploso Malang / Munarko
No full text39 hal.:bib., ill.,lamp.; 28 c
Smart Searching for the Physiome Project
No full textThe Physiome Project has established standards to encode biosimulation models which can be archived and shared in the Physiome Model Repository (PMR). These standards aid the development of a virtual physiological human composed of smaller-scale physiological model constructs. More than 800 models at the start of this study
increased to more than 900 during the completion of this work. Searching for
physiologically related models requires some persistence from the user to discover
useful model entities for potential reuse. In this thesis, we present the stages,
methods, and implementation of the search for biosimulation models and detailed
information therein. We analyse user behaviour in searching for relevant models and
how successful their efforts are, when performed on existing search tool query logs
and questionnaires. Users mostly create short queries, although more descriptive
ones can be more successful and may facilitate the need for detailed information,
such as variables and parameters. We propose NLIMED and CASBERT utilising the
existing semantic annotations and Natural Language Processing (NLP). One of the
standard technologies for querying semantically annotated data is SPARQL; however,
it is difficult due to the syntax complexity. NLIMED provides an interface to convert a
free-text query to SPARQL. Then CASBERT proposes a different approach by encoding
each entity in the biosimulation model into an embedding using Transformers-based technology. While NLIMED helps locate specific information following the
nature of SPARQL, CASBERT offers a more exploratory search similar to popular
internet search engines and flexibility in searching for various information levels.
Finally, we bring the proposed methods to a ready-to-use web-based search tool.
We explore CASBERT to find unannotated entities utilising the model’s hierarchical
structure where the embedding pool of low-level entities represents higher-level
entities. This hierarchical embedding allows a broader search that includes detailed
information about models, images, experiment standards and results. Our thesis provides methods and software to maximise FAIRness of biosimulation models in
the Physiome Project. Scientists can find models and detailed information and
later verify, reproduce, and combine them. The generality of our methods allows
implementation in other standards, such as SBML, in the BioModels database. So
that in the future, we can extend to build multi-scale, multi-repository searches
Pelatihan teknologi game (unity dan blender) untuk meningkatkan kreativitas dan prestasi pada siswa SMA dan SMK di Malang/ Munarko
No full textiii, 27 hal.:ill.,lamp.; 28 c
Embeddings for Uberon and ILX lookup
No full textThis is an embedding set of terms in UBERON and ILX ontologies</p
BMSE Documentation and Tutorials
No full textBiosimulation Model Search Engine (BMSE) is a web-based search engine for
finding information in biosimulation models created using CellML and stored in
the Physiome Repository Model (PMR).
Types of information include variables, mathematical equations, constants,
components, models, images, and simulation results. This work uses Composite
Annotation Search Using BERT (CASBERT) to represent queries and entities in
biosimulation models (https://doi.org/10.1101/2022.11.22.517475).</p
A taxonomy of Malay social media text
No full textIn this paper, we proposed a preliminary taxonomy of Malay social media text. Performing text analytics on Malay social media text is a challenge. The formal Malay language follows specific spelling and sentence construction rules. However, the Malay language used in social media differs in both aspects. This impedes the accuracy of text analytics. Due to the complexity of Malay social media text, many researches has chosen to focus on classifying the formal Malay language. To the best of our knowledge, we are the first to propose a formal taxonomy for Malay text in social media. Narrow and informal categorisations of Malay social media text can be found amidst efforts to pre-process social media text, yet cherry-picked only some categories to be handled. We have differentiated Malay social media text from the formal Malay language by identifying them as Social Media Malay Language or SMML. They consists of spelling variations, Malay-English mix sentence, Malay-spelling English words, slang-based words, vowel-les words, number suffixes and manner of expression.This taxonomy is expected to serve as a guideline in research and commercial products
HII: Histogram Inverted Index For Fast Images Retrieval
No full textThis work aims to improve the speed of search by creating an indexing structure in CBIR system. We utilised an inverted index structure that usually used in text retrieval with a modification. The modified inverted index is built based on histogram data that generated using Multi Texton Histogram (MTH) and Multi Texton Co-Occurrence Descriptor (MTCD) from 10,000 images of Corel dataset. When building the inverted index, we normalised value of each feature into a real number and considered pairs of feature and value that owned by a particular number of images. Based on our investigation, on MTCD histogram of 5,000 data test, we found that by considering histogram variable values which owned by maximum 12% of images, the number of comparison for each query can be reduced by 67.47% in a rate, the precision is 82.2%, and the rate of access to disk is 32.83%. Furthermore, we named our approach as Histogram Inverted Index (HII).
Automatic Annotation of Query in Physiome Model Repository (PMR)
No full textThe Physiome Model Repository (PMR) is a collection of physiological and anatomical models written in XML based form. The PMR goal is to provide a robust platform for scientists in the biology-related domain such as bioengineering and biomedical so they can reuse, reproduce, collaborate, and exchange simulation experiments consistently and unambiguously. The stored models consist of elements of mathematical equations along with all variables, and description. Currently, a large number of elements has been annotated using ontology URIs and has been stored as RDF triples for easy management and retrieval. By using SPARQL, scientists are able to find relevant elements and use these for their works. However, the use of SPARQL in PMR needs sufficient knowledge about ontology URIs and elements needed which may cause difficulties. Therefore, we have developed an automatic annotation to map the user’s text query to ontology URIs. We have utilised textual information inside the PMR and ontology URIs' label, definition, and synonym from BioPortal to extract text-based features. We have also used the NLP parser to divide the query into candidate phrases. Utilising these features, we are able to annotate the candidate phrases and then select the final phrases with relatively high accuracy
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