Jar File Research Articles

Fast and reliable inference of semantic clusters

Document Indexing is but not limited to summarizing document contents with a small set of keywords or concepts of a knowledge base. Such a compact representation of document contents eases their use in numerous processes such as content-based information retrieval, corpus-mining and classification. An important effort has been devoted in recent years to (partly) automate semantic indexing, i.e. associating concepts to documents, leading to the availability of large corpora of semantically indexed documents. In this paper we introduce a method that hierarchically clusters documents based on their semantic indices while providing the proposed clusters with semantic labels. Our approach follows a neighbor joining strategy. Starting from a distance matrix reflecting the semantic similarity of documents, it iteratively selects the two closest clusters to merge them in a larger one. The similarity matrix is then updated. This is usually done by combining similarity of the two merged clusters, e.g. using the average similarity. We propose in this paper an alternative approach where the new cluster is first semantically annotated and the similarity matrix is then updated using the semantic similarity of this new annotation with those of the remaining clusters. The hierarchical clustering so obtained is a binary tree with branch lengths that convey semantic distances of clusters. It is then post-processed by using the branch lengths to keep only the most relevant clusters. Such a tool has numerous practical applications as it automates the organization of documents in meaningful clusters (e.g. papers indexed by MeSH terms, bookmarks or pictures indexed by WordNet) which is a tedious everyday task for many people. We assess the quality of the proposed methods using a specific benchmark of annotated clusters of bookmarks that were built manually. Each dataset of this benchmark has been clustered independently by several users. Remarkably, the clusters automatically built by our method are congruent with the clusters proposed by experts. All resources of this work, including source code, jar file, benchmark files and results are available at this address: http://sc.nicolasfiorini.info.

Semantic versioning and impact of breaking changes in the Maven repository

Backward-incompatible interface changes are widespread in software libraries.Breaking changes have impact on client systems using these software libraries.Software developers do not follow proposed versioning guidelines. Systems that depend on third-party libraries may have to be updated when updates to these libraries become available in order to benefit from new functionality, security patches, bug fixes, or API improvements. However, often such changes come with changes to the existing interfaces of these libraries, possibly causing rework on the client system. In this paper, we investigate versioning practices in a set of more than 100,000 jar files from Maven Central, spanning over 7 years of history of more than 22,000 different libraries. We investigate to what degree versioning conventions are followed in this repository. Semantic versioning provides strict rules regarding major (breaking changes allowed), minor (no breaking changes allowed), and patch releases (only backward-compatible bug fixes allowed). We find that around one third of all releases introduce at least one breaking change. We perform an empirical study on potential rework caused by breaking changes in library releases and find that breaking changes have a significant impact on client libraries using the changed functionality. We find out that minor releases generally have larger release intervals than major releases. We also investigate the use of deprecation tags and find out that these tags are applied improperly in our dataset.

De-Identified Personal Health Care System Using Hadoop

Hadoop technology plays a vital role in improving the quality of healthcare by delivering right information to right people at right time and reduces its cost and time. Most properly health care functions like admission, discharge, and transfer patient data maintained in Computer based Patient Records (CPR), Personal Health Information (PHI), and Electronic Health Records (EHR). The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this Big Data and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination of MR code and PIG queries. This application also uses advanced mechanism of using UDF (User Data File) which is used to protect the health care dataset. De-identified personal health care system is using Map Reduce, Pig Queries which are needed to be executed on the health care dataset. The application input dataset that contains the information of patients and de-identifies their personal health care. De-identification using Hadoop is also suitable for social and demographic data.

GESPA: classifying nsSNPs to predict disease association.

BackgroundNon-synonymous single nucleotide polymorphisms (nsSNPs) are the most common DNA sequence variation associated with disease in humans. Thus determining the clinical significance of each nsSNP is of great importance. Potential detrimental nsSNPs may be identified by genetic association studies or by functional analysis in the laboratory, both of which are expensive and time consuming. Existing computational methods lack accuracy and features to facilitate nsSNP classification for clinical use. We developed the GESPA (GEnomic Single nucleotide Polymorphism Analyzer) program to predict the pathogenicity and disease phenotype of nsSNPs.ResultsGESPA is a user-friendly software package for classifying disease association of nsSNPs. It allows flexibility in acceptable input formats and predicts the pathogenicity of a given nsSNP by assessing the conservation of amino acids in orthologs and paralogs and supplementing this information with data from medical literature. The development and testing of GESPA was performed using the humsavar, ClinVar and humvar datasets. Additionally, GESPA also predicts the disease phenotype associated with a nsSNP with high accuracy, a feature unavailable in existing software. GESPA’s overall accuracy exceeds existing computational methods for predicting nsSNP pathogenicity. The usability of GESPA is enhanced by fast SQL-based cloud storage and retrieval of data.ConclusionsGESPA is a novel bioinformatics tool to determine the pathogenicity and phenotypes of nsSNPs. We anticipate that GESPA will become a useful clinical framework for predicting the disease association of nsSNPs. The program, executable jar file, source code, GPL 3.0 license, user guide, and test data with instructions are available at http://sourceforge.net/projects/gespa. Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0673-2) contains supplementary material, which is available to authorized users.

JTSA: An open source framework for time series abstractions

Background and objectiveThe evaluation of the clinical status of a patient is frequently based on the temporal evolution of some parameters, making the detection of temporal patterns a priority in data analysis. Temporal abstraction (TA) is a methodology widely used in medical reasoning for summarizing and abstracting longitudinal data. MethodsThis paper describes JTSA (Java Time Series Abstractor), a framework including a library of algorithms for time series preprocessing and abstraction and an engine to execute a workflow for temporal data processing. The JTSA framework is grounded on a comprehensive ontology that models temporal data processing both from the data storage and the abstraction computation perspective. The JTSA framework is designed to allow users to build their own analysis workflows by combining different algorithms. Thanks to the modular structure of a workflow, simple to highly complex patterns can be detected. The JTSA framework has been developed in Java 1.7 and is distributed under GPL as a jar file. ResultsJTSA provides: a collection of algorithms to perform temporal abstraction and preprocessing of time series, a framework for defining and executing data analysis workflows based on these algorithms, and a GUI for workflow prototyping and testing.The whole JTSA project relies on a formal model of the data types and of the algorithms included in the library. This model is the basis for the design and implementation of the software application. Taking into account this formalized structure, the user can easily extend the JTSA framework by adding new algorithms.Results are shown in the context of the EU project MOSAIC to extract relevant patterns from data coming related to the long term monitoring of diabetic patients. ConclusionsThe proof that JTSA is a versatile tool to be adapted to different needs is given by its possible uses, both as a standalone tool for data summarization and as a module to be embedded into other architectures to select specific phenotypes based on TAs in a large dataset.

Intelligent Access to Sequence and Structure Databases (IASSD) - an interface for accessing information from major web databases.

With the advent of age of big data and advances in high throughput technology accessing data has become one of the most important step in the entire knowledge discovery process. Most users are not able to decipher the query result that is obtained when non specific keywords or a combination of keywords are used. Intelligent access to sequence and structure databases (IASSD) is a desktop application for windows operating system. It is written in Java and utilizes the web service description language (wsdl) files and Jar files of E-utilities of various databases such as National Centre for Biotechnology Information (NCBI) and Protein Data Bank (PDB). Apart from that IASSD allows the user to view protein structure using a JMOL application which supports conditional editing.AvailabilityThe Jar file is freely available through e-mail from the corresponding author.

Secure Distributed Accountability Framework for Data Sharing in Cloud Environment

Cloud computing is used to provide scalable services which are easily used over the internet as per the requirement. A major feature of the cloud services is that users data are remotely processed in unknown machines that users do not know or users are not operating these machines. While using these services provided by cloud computing, users fear of losing their own data. The content of data can be financial, health, personal. To resolve this problem, we use information accountability in decentralized format to keep track of the usage of the users data over the cloud. It is object oriented approach that enables enclosing our logging mechanism together with users data and apply access policies with respect to users data. We use JAR programming which provides the dynamic and traveling object functionality and to track any access to userâs data will call authentication and automated logging mechanism to the JAR files. Each access to users cloud data will be getting recorded in separate log file. To provide robust users control, distributed auditing functionality is also provided to track the usage of data. The proposed system also provides the authentication mechanism using external channels and also makes a log of user details from which the cloud data is accessed. Only data owner can retrieve the detail log of his data as per requirement. Data owner will provide the type of access to his data and based on that, authorized data user can access the data over the cloud environment.

Bioinformatics Study with an Implementation of Two Feature Extraction Algorithms for Protein Sequences

Predicting subcellular localizations of proteins is related to multi-label learning. A serial of computational approaches have been developed. This study focuses on the extracting protein features. The feature vector influences the performance of a predicting algorithm significantly. In this paper, two feature extraction algorithms named composition-transition-distribution and class pattern frequency were introduced and implemented in Java, respectively. This program provided a friendly graphical user interface where users can get these two kinds of features easily and quickly. Moreover, the results can be saved into a specified file for later use. Finally, this program can be compressed into a single jar file and runs on a computer which installed the proper JRE. We hope that this program would give researchers some help in the future.

ENCODING PERSONAL INFORMATION ON DATA SHARING IN CLOUD USING BASE64 ALGORITHM

A major feature of cloud services is that user data are processed remotely among machines. But user fears of losing control of their own data, particularly financial and health data can becomes a significant barrier to wide adoption of cloud services in order to avoid this problem we provide a novel approach, namely Cloud Information Accountability (CIA) for clients. So that the authorized client can only access the data in the cloud. Data owner store data in the format of JAR format thus client access data only by the permission of data owner. To strengthen user’s control also provide a distributed audit mechanism by push and pull mode. Base64 encoding algorithm is used for encoding the JAR file in order to secure JAR file from attackers. Log maintained and send periodically to the data owner.

An Efficient Approach using AES for Accountability in Cloud

computing promises to increase the velocity with which applications are deployed, increase innovation, and lower costs, all while increasing business agility. A major feature of the cloud services is that users' data are usually processed remotely in unknown machines that users do not own or operate. This lead to a situation that the owner may have a fear of loss of data. To avoid this problem the term accountability has been introduced here. Here the owner's data information are stored in the jar file. At the uploading time, one time password concept has been used to give more security to the data.log file access by the owner by either push or pull algorithm is also made much secure with an AES approach. Here we have proposed a novel algorithm for Log push and pull method which is comparatively simple than the existing approaches.In that AES has been is applied. We provide implementation details that describe the efficiency and effectiveness of the proposed approaches.

Implementation of e-patrolling using J2EE N-Tier Architecture

E-Patrolling – is an E Police Station which can maintain the records of crime like FIR, criminals’ details and police dept administration. The EPatrolling system uses a virtual reality interface to provide general information and online assistance. Citizens can download forms, make complaints, receive advice and/or report events of an illegal nature. This crime reporting portal provides facility for reporting online crimes, complaints, missing persons, show most wanted person details mailing as well as chatting. Any Number of clients can connect to the server. Every user first makes the fir login to sever to show the fir availability. The server can be any Web Server. An SMTP Server must be maintained for temporary storage of emails and chat jar files for enable the chatting facilities. The main modules in this project  Visitors  Registered Users  Administrator General Terms EPatrolling is an online crime reporting system which is based on J2EE n-Tier architecture provides facility to citizens to make online complaints, help in knowing online information about any crime, can do complaint about any missing person or any vehicle etc. [1]

The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions.

BackgroundThe Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background.ObjectiveThe objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web.MethodsWe utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system.ResultsA prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided.ConclusionsThe main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.

JContextExplorer: a tree-based approach to facilitate cross-species genomic context comparison

BackgroundCross-species comparisons of gene neighborhoods (also called genomic contexts) in microbes may provide insight into determining functionally related or co-regulated sets of genes, suggest annotations of previously un-annotated genes, and help to identify horizontal gene transfer events across microbial species. Existing tools to investigate genomic contexts, however, lack features for dynamically comparing and exploring genomic regions from multiple species. As DNA sequencing technologies improve and the number of whole sequenced microbial genomes increases, a user-friendly genome context comparison platform designed for use by a broad range of users promises to satisfy a growing need in the biological community.ResultsHere we present JContextExplorer: a tool that organizes genomic contexts into branching diagrams. We implement several alternative context-comparison and tree rendering algorithms, and allow for easy transitioning between different clustering algorithms. To facilitate genomic context analysis, our tool implements GUI features, such as text search filtering, point-and-click interrogation of individual contexts, and genomic visualization via a multi-genome browser. We demonstrate a use case of our tool by attempting to resolve annotation ambiguities between two highly homologous yet functionally distinct genes in a set of 22 alpha and gamma proteobacteria.ConclusionsJContextExplorer should enable a broad range of users to analyze and explore genomic contexts. The program has been tested on Windows, Mac, and Linux operating systems, and is implemented both as an executable JAR file and java WebStart. Program executables, source code, and documentation is available at http://www.bme.ucdavis.edu/facciotti/resources_data/software/.

DLRS: gene tree evolution in light of a species tree

PrIME-DLRS (or colloquially: 'Delirious') is a phylogenetic software tool to simultaneously infer and reconcile a gene tree given a species tree. It accounts for duplication and loss events, a relaxed molecular clock and is intended for the study of homologous gene families, for example in a comparative genomics setting involving multiple species. PrIME-DLRS uses a Bayesian MCMC framework, where the input is a known species tree with divergence times and a multiple sequence alignment, and the output is a posterior distribution over gene trees and model parameters. PrIME-DLRS is available for Java SE 6+ under the New BSD License, and JAR files and source code can be downloaded from http://code.google.com/p/jprime/. There is also a slightly older C++ version available as a binary package for Ubuntu, with download instructions at http://prime.sbc.su.se. The C++ source code is available upon request. joel.sjostrand@scilifelab.se or jens.lagergren@scilifelab.se. PrIME-DLRS is based on a sound probabilistic model (Åkerborg et al., 2009) and has been thoroughly validated on synthetic and biological datasets (Supplementary Material online).

A METHOD FOR DISCOVERING COMMON PATTERNS FROM TWO RNA SECONDARY STRUCTURES AND ITS APPLICATION TO STRUCTURAL REPEAT DETECTION

We propose an ab initio method, named DiscoverR, for finding common patterns from two RNA secondary structures. The method works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using an efficient dynamic programming algorithm. DiscoverR is able to identify and extract the largest common substructures from two RNA molecules having different sizes without prior knowledge of the locations and topologies of these substructures. We also extend DiscoverR to find repeated regions in an RNA secondary structure, and apply this extended method to detect structural repeats in the 3'-untranslated region of a protein kinase gene. We describe the biological significance of a repeated hairpin found by our method, demonstrating the usefulness of the method. DiscoverR is implemented in Java; a jar file including the source code of the program is available for download at http://bioinformatics.njit.edu/DiscoverR.

Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II

BackgroundNext generation sequencing provides detailed insight into the variation present within viral populations, introducing the possibility of treatment strategies that are both reactive and predictive. Current software tools, however, need to be scaled up to accommodate for high-depth viral data sets, which are often temporally or spatially linked. In addition, due to the development of novel sequencing platforms and chemistries, each with implicit strengths and weaknesses, it will be helpful for researchers to be able to routinely compare and combine data sets from different platforms/chemistries. In particular, error associated with a specific sequencing process must be quantified so that true biological variation may be identified.ResultsSegminator II was developed to allow for the efficient comparison of data sets derived from different sources. We demonstrate its usage by comparing large data sets from 12 influenza H1N1 samples sequenced on both the 454 Life Sciences and Illumina platforms, permitting quantification of platform error. For mismatches median error rates at 0.10 and 0.12%, respectively, suggested that both platforms performed similarly. For insertions and deletions median error rates within the 454 data (at 0.3 and 0.2%, respectively) were significantly higher than those within the Illumina data (0.004 and 0.006%, respectively). In agreement with previous observations these higher rates were strongly associated with homopolymeric stretches on the 454 platform. Outside of such regions both platforms had similar indel error profiles. Additionally, we apply our software to the identification of low frequency variants.ConclusionWe have demonstrated, using Segminator II, that it is possible to distinguish platform specific error from biological variation using data derived from two different platforms. We have used this approach to quantify the amount of error present within the 454 and Illumina platforms in relation to genomic location as well as location on the read. Given that next generation data is increasingly important in the analysis of drug-resistance and vaccine trials, this software will be useful to the pathogen research community. A zip file containing the source code and jar file is freely available for download from http://www.bioinf.manchester.ac.uk/segminator/.

Automated cleaning and pre-processing of immunoglobulin gene sequences from high-throughput sequencing

High-throughput sequencing (HTS) yields tens of thousands to millions of sequences that require a large amount of pre-processing work to clean various artifacts. Such cleaning cannot be performed manually. Existing programs are not suitable for immunoglobulin (Ig) genes, which are variable and often highly mutated. This paper describes Ig High-Throughput Sequencing Cleaner (Ig-HTS-Cleaner), a program containing a simple cleaning procedure that successfully deals with pre-processing of Ig sequences derived from HTS, and Ig Insertion—Deletion Identifier (Ig-Indel-Identifier), a program for identifying legitimate and artifact insertions and/or deletions (indels). Our programs were designed for analyzing Ig gene sequences obtained by 454 sequencing, but they are applicable to all types of sequences and sequencing platforms. Ig-HTS-Cleaner and Ig-Indel-Identifier have been implemented in Java and saved as executable JAR files, supported on Linux and MS Windows. No special requirements are needed in order to run the programs, except for correctly constructing the input files as explained in the text. The programs' performance has been tested and validated on real and simulated data sets.

Link-E-Param : 웹 애플리케이션 보안 강화를 위한 URL 파라미터 암호화 기법

URL 파라미터는 민감한 정보나 제공된 링크에서 임의로 변경하면 보안 위험이 발생하는 것을 포함할 수 있다. 본 논문에서는 전체 URL 파라미터들의 이름과 값을 동시에 암호화하는 Link-E-Param(Link with Encrypted Parameters) 기법을 제안한다. 이 기법은 기존의 일부 URL 파라미터를 감추는 방식과 달리, 공격자에 의한 악의적 URL 파라미터 분석을 근원적으로 불가능하게 함으로써 URL 분석에 기반 하여 웹 사이트로부터 정보를 빼내려는 시도를 막는 역할을 한다. 제안 기법은 서블릿 필터 형태로 구현되기 때문에 서버에 jar파일 설치 후 설정 파일을 작성하기만 하면 기존 프로그램을 수정할 필요 없이 적용이 가능하다. Link-E-Param에서는 다양한 암호화 알고리즘이 지원되도록 구현하였다. 구현된 필터를 적용하여 실험한 결과, 암호화 및 복호화로 인해 사용자가 느끼는 응답 시간의 증가가 수용 가능한 수준이라 볼 수 있는 2~3% 에 불과함을 보인다. An URL parameter can hold some information that is confidential or vulnerable to illegitimate tampering. We propose Link-E-Param(Link with Encrypted Parameters) to protect the whole URL parameter names as well as their values. Unlike other techniques concealing only some of the URL parameters, it will successfully discourage attacks based on URL analysis to steal secret information on the Web sites. We implement Link-E-Param in the form of a servlet filter to be deployed on any Java Web server by simply copying a jar file and setting a few configuration values. Thus it can be used for any existing Web application without modifying the application. It also supports numerous encryption algorithms to choose from. Experiments show that our implementation induces only 2~3% increase in user response time due to encryption and decryption, which is deemed acceptable.

Using MATLAB software with Tomcat server and Java platform for remote image analysis in pathology

BackgroundThe Matlab software is a one of the most advanced development tool for application in engineering practice. From our point of view the most important is the image processing toolbox, offering many built-in functions, including mathematical morphology, and implementation of a many artificial neural networks as AI. It is very popular platform for creation of the specialized program for image analysis, also in pathology. Based on the latest version of Matlab Builder Java toolbox, it is possible to create the software, serving as a remote system for image analysis in pathology via internet communication. The internet platform can be realized based on Java Servlet Pages with Tomcat server as servlet container.MethodsIn presented software implementation we propose remote image analysis realized by Matlab algorithms. These algorithms can be compiled to executable jar file with the help of Matlab Builder Java toolbox. The Matlab function must be declared with the set of input data, output structure with numerical results and Matlab web figure. Any function prepared in that manner can be used as a Java function in Java Servlet Pages (JSP). The graphical user interface providing the input data and displaying the results (also in graphical form) must be implemented in JSP. Additionally the data storage to database can be implemented within algorithm written in Matlab with the help of Matlab Database Toolbox directly with the image processing. The complete JSP page can be run by Tomcat server.ResultsThe proposed tool for remote image analysis was tested on the Computerized Analysis of Medical Images (CAMI) software developed by author. The user provides image and case information (diagnosis, staining, image parameter etc.). When analysis is initialized, input data with image are sent to servlet on Tomcat. When analysis is done, client obtains the graphical results as an image with marked recognized cells and also the quantitative output. Additionally, the results are stored in a server database. The internet platform was tested on PC Intel Core2 Duo T9600 2.8GHz 4GB RAM server with 768x576 pixel size, 1.28Mb tiff format images reffering to meningioma tumour (x400, Ki-67/MIB-1). The time consumption was as following: at analysis by CAMI, locally on a server – 3.5 seconds, at remote analysis – 26 seconds, from which 22 seconds were used for data transfer via internet connection. At jpg format image (102 Kb) the consumption time was reduced to 14 seconds.ConclusionsThe results have confirmed that designed remote platform can be useful for pathology image analysis. The time consumption is depended mainly on the image size and speed of the internet connections. The presented implementation can be used for many types of analysis at different staining, tissue, morphometry approaches, etc. The significant problem is the implementation of the JSP page in the multithread form, that can be used parallelly by many users. The presented platform for image analysis in pathology can be especially useful for small laboratory without its own image analysis system.

ON COMPARING TWO STRUCTURED RNA MULTIPLE ALIGNMENTS

We present a method, called BlockMatch, for aligning two blocks, where a block is an RNA multiple sequence alignment with the consensus secondary structure of the alignment in Stockholm format. The method employs a quadratic-time dynamic programming algorithm for aligning columns and column pairs of the multiple alignments in the blocks. Unlike many other tools that can perform pairwise alignment of either single sequences or structures only, BlockMatch takes into account the characteristics of all the sequences in the blocks along with their consensus structures during the alignment process, thus being able to achieve a high-quality alignment result. We apply BlockMatch to phylogeny reconstruction on a set of 5S rRNA sequences taken from fifteen bacteria species. Experimental results showed that the phylogenetic tree generated by our method is more accurate than the tree constructed based on the widely used ClustalW tool. The BlockMatch algorithm is implemented into a web server, accessible at http://bioinformatics.njit.edu/blockmatch. A jar file of the program is also available for download from the web server.