XML Format Research Articles

Development of Automatic Verification Environment for In-Vehicle Controller Area Network

Due to the development of modern techniques, in the recent years, electronic vehicles and autopilot systems have beensignificant emerged in automobile and IT industrial. This leads the electronics automotive systems and auto-control systems consistedof a lot of high performance Electronic Control Units(ECUs) connected by controller area network (CAN). For realizing morecomplicated design in ECUs, this work integrates real-time OS and network management function. The results improve the CANbusnodes' designing level to as a gateway to interconnect CANbus nodes. As the number of CANbus nodes increase, the verification processis more and more complicated and takes much time. For speeding up the verification process, this work uses CANoe package toprogram the testing script for automotive verification environment. Then the engineer can connect the testing device by CAN to theenvironment for automatic verification. The engineer can define the network messages of the CANbus nodes and tune the design asthe validating progress. The testing results present as XML format and can be transferred to HTML pages for readability. Hence, thiswork realizes an automatic verification environment for CANbus in-vehicle networks.

Processing the Textual Information Using Open Natural Language Processing (NLP)

NLP is the application of computational technique and component of AI helps to analysis and synthesis of Standard English Language. It draws from many disciplines like computer science and computational linguistics to process and analyze large amount of natural language data. To identify the phrases in language that refers to specific types of entities and relations in text. The problem identified in N-gram approach is, balance weight is placed between in-frequent and frequent grams. It is efficient only for small amount of textual data. Thus, it is difficult to discover the named entities available in the corpus. The main aim of this paper is to overcome the limitations available in n-gram approach and facilitates to find desired pieces of entities by using Open NLP tool kit. It helps to store the information in a XML format that provides an easy way of querying and processing. Hence the information extracted from the web is an unstructured form. This approach is a promising way for processing the text in an efficient manner. To enhance the effectiveness of text mining, the researcher focused on the task of the NLP to discover knowledge from many unstructured text documents that leads to the largest available source of knowledge. The experiments and results shown in this paper with accuracy as 0.95 prove the confidence level is better than the N-grams approach.

A Bibliometric Analysis of the Scientific Production and Collaboration between Graduate Programs in Manufacturing Engineering in Brazil

In Brazil, most of the scientific research is developed within the scope of the Brazilian university Graduate Programs, which host the Master’s and Doctorate courses. These Graduate Programs are evaluated by the Coordination for the Improvement of Higher Education Personnel, an organ of the Brazilian Ministry of Education, which classifies the Graduate Programs in a ranking that ranges from 1 to 7, with 7 being the level of excellence. Among the inputs used by this evaluation process, there are the Brazilian researcher Lattes CVs, which contain all the details of the researchers’ academic life, such as their academic background, areas of professional activity, intellectual production, student supervisions, collaboration networks, etc. The CVs of Brazilian researchers are publicly available for download in XML format. In this paper, we undertake a bibliometric analysis of the CVs taken from researchers who are part of a sample of Brazilian Graduate Programs in Manufacturing Engineering that have national ranking greater or equal to 4. For a period of 10 years (2008-2017), we sought to identify the most published topics, vehicles most used for publication, the basic training of the researchers, and the potential existence of collaboration networks between the Graduate Programs. Among other results, we could identify that 29 institutions are Alma Mater of 68.36% of the researchers analyzed. In addition, it was possible to verify that only two Brazilian scientific events account for 7.5% of the total of publications for the analyzed sample and period.

Psims - A Declarative Writer for mzML and mzIdentML for Python

mzML and mzIdentML are commonly used, powerful tools for representing mass spectrometry data and derived identification information. These formats are complex, requiring non-trivial logic to translate data into the appropriate representation. Most published implementations are tightly coupled to data structures. The most complete implementations are written in compiled languages that cannot expose the complete flexibility of the implementation to external programs or bindings. To our knowledge, there are no complete implementations for mzML or mzIdentML available to scripting languages like Python or R. We present psims, a library written in Python for writing mzML and mzIdentML. The library allows writing either XML format using built-in Python data structures. It includes a controlled vocabulary resolution system to simplify the encoding process and an identity tracking system to manage entity relationships. The source code is available at https://github.com/mobiusklein/psims, and through the Python Package Index as psims, licensed under the Apache 2 common license.

Towards an ontology-based iconography

This article describes work undertaken at the Warburg Institute in London into the definition of machine-readable ontologies for the identification of iconographic subjects. Iconography, a descriptive discipline concerned with the identification of the content or subject of an image, is a core component of the wider discipline of iconology, the study of the meanings of images in their cultural or historical contexts. The research detailed here attempts to define the core of an ontology for the indicators of an iconographic subject that would be employed by an art historian in making an identification: these are encoded in OWL, the Web Ontology Language. The article demonstrates how such an ontology may be queried in XML format using simple XQUERY queries. Future directions for this research are discussed, including its possible integration with image recognition technologies to facilitate more automated approaches to iconographic identification.

Toward Kurdish language processing: Experiments in collecting and processing the AsoSoft text corpus

In this article, we introduce the first Kurdish text corpus for Central Kurdish (Sorani) branch, called AsoSoft text corpus. Kurdish language, which is spoken by more than 30 million people, has various dialects. As one of the two main branches of Kurdish, Central Kurdish is the formal dialect of Kurdish literature. AsoSoft text corpus is of size 188 million tokens and has been collected mostly from Web sites, published books, and magazines. The corpus has been normalized and converted into Text Encoding Initiative XML format. In both collecting and processing the text, we have faced several challenges and have proposed solutions to them. About 22% of the corpus is topic annotated with six topic tags, and a topic identification task has been done to evaluate the correctness of annotation. The computational experiments of the Central Kurdish text processing are also presented with the support of related supplementary statistics. For the first time, the validity of Zipf’s law for Central Kurdish is presented and also perplexity of this language is calculated using standard N-gram language models. The perplexity of Central Kurdish is 276 for a tri-gram language model.

Measurements and Modelization of the Rosario City Heat Island, Argentina - Preliminary Results

In relation to climate, cities introduce a perturbation with respect to the adjacent (non-urban) lands, since houses, buildings, industries, streets, among others, are made of materials that usually have different optical and thermal properties. In particular, they storage heat in a larger fraction than bare land. Consequently, it is of interest to analyse the main variable that can characterize this difference, the ambient temperature. We present results of the measurements done with a thermometer (with an estimated maximum uncertainty of 0.2°C) during the period June 2013 – June 2014, in different months of the year with clear sky and non-intense windy climatic conditions. The data were obtained at noon (around 13:00 local hour = Universal Time – 3 hours), traversing the city in the East-West and North-South directions. Particularly, in this work, we analyse the ambient temperature variation between the central dry Montenegro plaza and the Green Urquiza park. The first one is located among medium-height buildings (3 stories as a mean), while the second one is placed about 1770 meters away, in the border of the city, very near the coast of the large Paraná river (having it 60 km wide, including the delta islands). The mean temperature difference in the yearly investigated period was +0.7°C, being higher in the central dry plaza than in the green park. Results for different climatic periods are: for autumn-winter, +0.3°C and for spring-summer, +1.1°C. A possible explanation of this behaviour is the different angular incidence of solar radiation at the altitudinal level that air temperature is measured, being lower in autumn-winter (with the Sun at approximately 40° mean elevation angle) than in spring-summer (~70° mean elevation angle). We have also made model calculations, employing the Urban Weather Generator (UWG) model, developed by Bueno et al. at Massachusetts Institute of Technology, USA. As for the introduction of the climatological and building parameters, we have used climatological data from a rural meteorological station in the Energy Plus weather file format (.epw) and a parametric description of urban area in .xml format. We have also obtained a representation of the temperature behaviour in the central plaza, considering three scenarios. Scenario A represents the rural climate; scenario B portrays the current situation and scenario C verifies the Energy efficiency and solar protection in building N° 8757 Ordinance introduced for the first time in Argentina, by the Municipality of Rosario city. The highest temperature was found in scenario B, being 2.5 °C higher than in scenario A. Scenario C presents a temperature 1.5 °C higher than scenario A. In conclusion, the urban heat island data obtained is of interest in the design of acclimatisation systems (since normally the non-urban airport temperature data is employed). Also, they can be used as a simulation at present of what will happen in the future, due to the temperature increase produced by the emission of greenhouse gases, responsible of the global warming.

PMC text mining subset in BioC: about three million full-text articles and growing.

Interest in text mining full-text biomedical research articles is growing. To facilitate automated processing of nearly 3 million full-text articles (in PubMed Central® Open Access and Author Manuscript subsets) and to improve interoperability, we convert these articles to BioC, a community-driven simple data structure in either XML or JavaScript Object Notation format for conveniently sharing text and annotations. The resultant articles can be downloaded via both File Transfer Protocol for bulk access and a Web API for updates or a more focused collection. Since the availability of the Web API in 2017, our BioC collection has been widely used by the research community. https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/.

A Mapping-Based Sharing Approach for Heterogeneous XML Data Supporting User Personalization

With the development of computer science and technology, there is an urgent demand on integration of heterogeneous data focus on personalized information service. In this paper, we explore two aspects on the usage of the heterogeneous data stored in the form of XML, and propose a mapping-based sharing approach for heterogeneous data supporting user personalization. First, a method is proposed to automatically standardize the heterogeneous XML data, which are provided by different users. The standardization process includes two steps: 1) An XML document is first parsed to a DOM tree, and then the DOM tree is transformed to a reduction tree. By introducing synonym table, the mapping relation can be obtained after the process that the reduction tree is transformed to a standard tree. 2) With that mapping relation, the XSLT file can be generated, with which the standard XML document containing standard data can be easily generated. Second, a method is proposed to support user personalization on data display. The mapping relation mentioned above is first stored in a well-defined form, which can be used to transform the standard XML document into a personalized XML document so as to display users' personalized data. The methodology proposed in this paper has been applied to an application of a sharing platform for the multi-source agricultural spatial data. The user-friendly interface and experience of the platform show that the methodology proposed is feasible.

The recommendation system for cyclists LvivBicycleMap

This paper presents the structure of the advisory system for cyclists LvivBicycleMap. The developed system uses the modular principle that allows quick upgrading. The constructed algorithm and model are based on Petri networks theory, which allows to investigate the dynamics of the designed system. The Java language is used to implement the software, that provides platform independence of the system. The information provision is implemented using the MySQL database and the developed information model. Information model uses the XML format, which provides an efficient data exchange between the subsystems of the "smart" city system. The system tools are implemented as an Android application. The aim of the developed application is to help cyclists spend their time in the best way on the streets of the Lviv of city. It contains a map the following information: bike rental locations, shops, workshops, parking, etc. In addition to this, stakeholder can use developed application to lay the best route along bicycle tracks of the Lviv city to the destination and to get the information about problem areas on a special web-site in order to avoid them.

Representation of Knowledge through Ontology for Swine Flu Disease in Semiarid Tropical Regions

The practice of using ontologies in medicine is mainly focussed on the representation and re-organization of medical terminologies. Physicians developed their own specialized languages and lexicons to help them store and communicate general medical knowledge and patient-related information efficiently. Such terminologies, optimized for human processing, are characterized by a substantial amount of implicit knowledge. Medical information systems, on the other hand, need to be able to communicate complex and detailed medical concepts unambiguously. This is noticeably a complex task and requires a intense analysis of the composition and concepts of medical terminologies. But it can be accomplished by fabricating medical domain ontologies for demonstrating medical terminology systems. Moreover, there are limited ontologies available in the domain of pulmonary diseases and as such tropical diseases like swine-flu(H1N1) are yet unexplored. Swine-flu is rapidly spreading disease and the information for it available from books, newspaper articles and internet which is unmanageable and unorganized. Therefore, in this paper, we propose to develop a medical ontology based information system for Swine-flu. We have implemented this system using Protege 4.1 Beta Software and then hosted it onto Apache Tomcat Server. The deployed version can be viewed in XML and RDF formats and DL query can be used where user can get answers to their queries when questioned. Finally, to evaluate the performance functional testing with Fact++ Reasoner is used with ontology browser.

Advanced prediction of learner's profile based on Felder-Silverman learning styles using web usage mining approach and fuzzy c-means algorithm

Automatic prediction of learner's profile is an important requirement for personalised e-learning. This can be provided based on the learning behaviours of the learners. In this work, the learning behaviour is captured using the web usage mining technique, preprocessed and converted into the XML format based on sequences of accessing contents. These sequences are mapped to the eight categories of Felder-Silverman learning style model (FSLSM) using fuzzy c-means (FCM) algorithm. A gravitational search based back propagation neural network (GSBPNN) algorithm is used for the prediction of learning styles of a new learner. In this algorithm, the neural network approach is modified by calculating the weights using gravitational search algorithm. The accuracy of the prediction model is compared with the basic back propagation neural network (BPNN) algorithm. The result shows that the captured data is labelled as per FSLSM and the accuracy is more in GSBPNN as compare to BPNN.

Automatic generation of agent-based models of migratory waterfowl for epidemiological analyses

Seasonal migration of waterfowl, in which avian influenza viruses are enzootic, plays a strong role in the ecology of this disease. Consequently, detailed analysis of migratory patterns and its influence on disease ecology is needed to aid design and assessment of prophylaxis and containment strategies. Accordingly, this paper proposes a novel methodology for generating a global agent-based model involving detailed migratory patterns of waterfowl. The methodology transforms geographic information systems (GIS) data to generate metapopulation for agents that model collocated flocks of birds. Generic migratory corridors are suitably adapted to model migratory flyways for each waterfowl metapopulation. The resulting data is generated in XML format compatible with our simulation-based epidemiological analysis environment called SEARUMS. Case studies conducted using SEARUMS and the generated models for high-risk waterfowl species indicate good correlation between simulated and observed viral dispersion patterns, demonstrating the effectiveness of the proposed methodology.

The extraction of security situation in heterogeneous log based on Str-FSFDP density peak cluster

In order to reduce the false alarm rate in the process of security events extraction and discover a wide range of anomalies by scrutinising various logs, an improvement of Str-FSFDP (a fast search and find of peak density based data stream) clustering algorithm in heterogeneous log analysis is presented. Because of the advantages in data attribute relationship analysis for mixed attributes data, this algorithm can classify log data into two types whose corresponding distance measure metrics are designed. Twelve attributes are defined in the unified XML format for clustering in this paper. These attributes are divided by the characteristics of each type of log and the importance of expressing a security event. To match the new micro cluster characteristic vector mentioned in the Str-FSFDP algorithm, this paper uses time gap to improve the UHAD (unsupervised anomaly detection model) framework. The time gap is designed as a threshold value based on micro cluster strategy. Experimental results reveal that the framework using Str-FSFDP clustering algorithm with time threshold can improve the aggregation rate of the log events and reduce the false alarm rate.

Automatic generation of agent-based models of migratory waterfowl for epidemiological analyses

Seasonal migration of waterfowl, in which avian influenza viruses are enzootic, plays a strong role in the ecology of this disease. Consequently, detailed analysis of migratory patterns and its influence on disease ecology is needed to aid design and assessment of prophylaxis and containment strategies. Accordingly, this paper proposes a novel methodology for generating a global agent-based model involving detailed migratory patterns of waterfowl. The methodology transforms geographic information systems (GIS) data to generate metapopulation for agents that model collocated flocks of birds. Generic migratory corridors are suitably adapted to model migratory flyways for each waterfowl metapopulation. The resulting data is generated in XML format compatible with our simulation-based epidemiological analysis environment called SEARUMS. Case studies conducted using SEARUMS and the generated models for high-risk waterfowl species indicate good correlation between simulated and observed viral dispersion patterns, demonstrating the effectiveness of the proposed methodology.

The extraction of security situation in heterogeneous log based on Str-FSFDP density peak cluster

In order to reduce the false alarm rate in the process of security events extraction and discover a wide range of anomalies by scrutinising various logs, an improvement of Str-FSFDP (a fast search and find of peak density based data stream) clustering algorithm in heterogeneous log analysis is presented. Because of the advantages in data attribute relationship analysis for mixed attributes data, this algorithm can classify log data into two types whose corresponding distance measure metrics are designed. Twelve attributes are defined in the unified XML format for clustering in this paper. These attributes are divided by the characteristics of each type of log and the importance of expressing a security event. To match the new micro cluster characteristic vector mentioned in the Str-FSFDP algorithm, this paper uses time gap to improve the UHAD (unsupervised anomaly detection model) framework. The time gap is designed as a threshold value based on micro cluster strategy. Experimental results reveal that the framework using Str-FSFDP clustering algorithm with time threshold can improve the aggregation rate of the log events and reduce the false alarm rate.

Spatial Rule-Based Simulations: The SRSim Software.

SRSim combines rule-based reaction network models with spatial particle simulations allowing to simulate the dynamics of large molecular complexes changing according to a set of chemical reaction rules. As the rule can contain patterns of molecular complexes and specific states of certain binding sites, a combinatorially complex or even infinitely sized reaction network can be defined. Particles move in a three-dimensional space according to molecular dynamics implemented by LAMMPS, while the BioNetGen language is used to formulate reaction rules. Geometric information is added in a specific XML format. The simulation protocol is exemplified by two different variants of polymerization as well as a toy model of DNA helix formation. SRSim is open source and available for download.

Adapted Learning Path Using Genetic Algorithm: Introducing Fuzzy Logic

In this work, searching for the path adapted to the learner profile and to the pedagogic objective of the formation has being automated. We have studied the problem as an “Optimization Problem”. Using Genetic Algorithms, the system seeks an optimal path starting from the learner profile to the pedagogic objective passing by intermediate courses. This application allows designing a descriptive sheet for each resource existing in the database, in XML format, to facilitate their reuse in the adaptive e-learning. This sheet is conceived basing on the pre-requisite and post-acquired concepts of the course in question, and is used as an input for our system in order to provide each learner with the resources that are most adapted to his profile. This paper discusses the approach of using fuzzy logic to present the input data used in our application and then the optimization using this data. We propose a fuzzy module to integrate in our adaptive e-learning system for selecting a new adapted path. Due to the use of genetic algorithms, we need to separate fuzzy module from other modules in order to keep the same degree of complexity. Thus the system will be more efficient.

A Novel Interface for the Graphical Analysis of Music Practice Behaviors.

Practice is an essential part of music training, but critical content-based analyses of practice behaviors still lack tools for conveying informative representation of practice sessions. To bridge this gap, we present a novel visualization system, the Music Practice Browser, for representing, identifying, and analysing music practice behaviors. The Music Practice Browser provides a graphical interface for reviewing recorded practice sessions, which allows musicians, teachers, and researchers to examine aspects and features of music practice behaviors. The system takes beat and practice segment information together with a musical score in XML format as input, and produces a number of different visualizations: Practice Session Work Maps give an overview of contiguous practice segments; Practice Segment Arcs make evident transitions and repeated segments; Practice Session Precision Maps facilitate the identifying of errors; Tempo-Loudness Evolution Graphs track expressive variations over the course of a practice session. We then test the new system on practice sessions of pianists of varying levels of expertise ranging from novice to expert. The practice patterns found include Drill-Correct, Drill-Smooth, Memorization Strategy, Review and Explore, and Expressive Evolution. The analysis reveals practice patterns and behavior differences between beginners and experts, such as a higher proportion of Drill-Smooth patterns in expert practice.

Game Popularity Tracking System

The purpose of this research is to develop a game popularity tracking system that can generate popularity values once every 24 hours using daily data from Google Analytics. Within the scope of this project, game popularity is defined as the fact that the quality of a certain game is liked by many people. There are 5 metrics that can be used to calculate game popularity: users count, unique page views (UPV), average time on page (ATP), the difference of unique page views, and average time on page from the day before. To generate the popularity values daily, the combination of weighted average, exponential moving average, and exponential smoothing algorithm are used. The final result of this project is a service that can run automatically and also provides an HTTP API that could fetch popularity values of games from gaming sites, sending the data in XML format for other services to use when needed. The service is built using Node.JS and other in-trend technologies such as Google Analytics API v4, OAuth 2.0, Express.JS, etc.