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نتیجه جستجو - Java

تعداد مقالات یافته شده: 32
ردیف عنوان نوع
1 INACITY - INvestigate and Analyze a CITY
INACITY - بررسی و تجزیه و تحلیل یک شهر-2021
INACITY is a platform that integrates Geo-located Imagery Databases (GIDs), Geographical Information Systems (GIS), digital maps, and Computer Vision (CV) to collect and analyze urban street-level images. The platform’s software architecture is a client–server model, where the client-side is a simple Web page that allows the user to select regions of a map and select filters to analyze and visualize urban features. The server side is a Django-powered Web service with PostgreSQL and Neo4j databases. Users can select a region of a map, an image filter, and geographical features to analyze relevant urban characteristics as trees, for instance, using the platform. An open-source implementation of the platform is available. The architecture is extensible, and it is easy to add new modules or replace the existing ones with new digital maps, GIS databases, other CV filters, or other GIDs.© 2021 Published by Elsevier B.V. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).Code metadata Current code version v1.0Permanent link to code/repository used for this code version https://github.com/ElsevierSoftwareX/SOFTX-D-21-00075 Code Ocean compute capsule N/A Legal Code License Mozilla Public License 2.0Code versioning system used git Software code languages, tools, and services used Python, Django, Javascript, jquery, open layers, bootstrap, Docker, PostgreSQL, neo4j Compilation requirements, operating environments & dependencies Docker, docker-compose If available Link to developer documentation/manual http://inacity.org/docsSupport email for questions arturao@ime.usp.brSoftware metadata Current software version v1.0Permanent link to executables of this version https://github.com/arturandre/inacityLegal Software License Mozilla Public License 2.0Computing platforms/Operating Systems Linux, OS X, Microsoft Windows, Docker Installation requirements & dependencies Docker, docker-compose If available, link to user manual — if formally published include a reference to the publication in the reference listhttp://inacity.org/tutorialSupport email for questions arturao@ime.usp.br∗ Corresponding author.‌ E-mail address: arturao@ime.usp.br (Artur André Almeida de Macedo Oliveira).https://doi.org/10.1016/j.softx.2021.100777
Keywords: Geographical information system | Geoportal | Computer vision
مقاله انگلیسی
2 Ignis: An efficient and scalable multi-language Big Data framework
Ignis: یک چارچوب داده های بزرگ چند زبانه کارآمد و مقیاس پذیر-2020
Most of the relevant Big Data processing frameworks (e.g., Apache Hadoop, Apache Spark) only support JVM (Java Virtual Machine) languages by default. In order to support non-JVM languages, subprocesses are created and connected to the framework using system pipes. With this technique, the impossibility of managing the data at thread level arises together with an important loss in the performance. To address this problem we introduce Ignis, a new Big Data framework that benefits from an elegant way to create multi-language executors managed through an RPC system. As a consequence, the new system is able to execute natively applications implemented using non-JVM languages. In addition, Ignis allows users to combine in the same application the benefits of implementing each computational task in the best suited programming language without additional overhead. The system runs completely inside Docker containers, isolating the execution environment from the physical machine. A comparison with Apache Spark shows the advantages of our proposal in terms of performance and scalability.
Keywords: Big data | Multi-language | Performance | Scalability | Container
مقاله انگلیسی
3 Heterogeneous tree structure classification to label Java programmers according to their expertise level
طبقه بندی ساختار درخت ناهمگن به برچسب برنامه نویسان جاوا با توجه به سطح تخصص آنها-2020
Open-source code repositories are a valuable asset to creating different kinds of tools and services, utilizing machine learning and probabilistic reasoning. Syntactic models process Abstract Syntax Trees (AST) of source code to build systems capable of predicting different software properties. The main difficulty of building such models comes from the heterogeneous and compound structures of ASTs, and that traditional machine learning algorithms require instances to be represented as n-dimensional vectors rather than trees. In this article, we propose a new approach to classify ASTs using traditional supervised-learning algorithms, where a feature learning process selects the most representative syntax patterns for the child subtrees of different syntax constructs. Those syntax patterns are used to enrich the context information of each AST, allowing the classification of compound heterogeneous tree structures. The proposed approach is applied to the problem of labeling the expertise level of Java programmers. The system is able to label expert and novice programs with an average accuracy of 99.6%. Moreover, other code fragments such as types, fields, methods, statements and expressions could also be classified, with average accuracies of 99.5%, 91.4%, 95.2%, 88.3% and 78.1%, respectively.
Keywords: Big code | Machine learning | Syntax patterns | Abstract syntax trees | Programmer expertise | Decision trees | Big data
مقاله انگلیسی
4 The impact of entrepreneurship education and students entrepreneurial mindset: the mediating role of attitude and self-efficacy
The impact of entrepreneurship education and students entrepreneurial mindset: the mediating role of attitude and self-efficacy-2020
The main purpose of this study is to investigate the relationship between students entrepreneurship education and entrepreneurial mindset as well as understanding the mediating role of attitude and self-efficacy. The approach adopted in this study is a convenience random sampling method, which is widely used in entrepreneurship research. Participants were recruited from several universities in Malang of East Java in Indonesia undergoing an online survey and were calculated using structural equation modeling (SEM). The findings of this current study indicate that entrepreneurship education successfully influences entrepreneurial self-efficacy, entrepreneurial attitude, and the entrepreneurial mindset. On the other hand, entrepreneurial self-efficacy pro- motes entrepreneurial attitude instead of the entrepreneurial mindset. Furthermore, entrepreneurial attitude plays an essential role in mediating both entrepreneurship education and self-efficacy toward students entrepreneurial mindset.
Keywords: Education | Entrepreneurship education | Entrepreneurial self-efficacy | Attitudes towards entrepreneurship| Entrepreneurial mindset
مقاله انگلیسی
5 DNAxs/DNAStatistX: Development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles
DNAxs / DNAStatistX: توسعه و اعتبار یک مجموعه نرم افزاری برای مدیریت داده ها و تفسیر احتمالی پروفایل های DNA-2019
The data management, interpretation and comparison of sets of DNA profiles can be complex, time-consuming and error-prone when performed manually. This, combined with the growing numbers of genetic markers in forensic identification systems calls for expert systems that can automatically compare genotyping results within (large) sets of DNA profiles and assist in profile interpretation. To that aim, we developed a user-friendly software program or DNA eXpert System that is denoted DNAxs. This software includes features to view, infer and match autosomal short tandem repeat profiles with connectivity to up and downstream software programs. Furthermore, DNAxs has imbedded the ‘DNAStatistX’ module, a statistical library that contains a probabilistic algorithm to calculate likelihood ratios (LRs). This algorithm is largely based on the source code of the quantitative probabilistic genotyping system EuroForMix [1]. The statistical library, DNAStatistX, supports parallel computing which can be delegated to a computer cluster and enables automated queuing of requested LR calculations. DNAStatistX is written in Java and is accessible separately or via DNAxs. Using true and non-contributors to DNA profiles with up to four contributors, the DNAStatistX accuracy and precision were assessed by comparing the DNAStatistX results to those of EuroForMix. Results were the same up to rare differences that could be attributed to the different optimizers used in both software programs. Implementation of dye specific detection thresholds resulted in larger likelihood values and thus a better explanation of the data used in this study. Furthermore, processing time, robustness of DNAStatistX results and the circumstances under which model validations failed were examined. Finally, guidelines for application of the software are shared as an example. The DNAxs software is future-proof as it applies a modular approach by which novel functionalities can be incorporated
Keywords: DNA profile interpretation | DNA expert system | DNAxs | Likelihood ratio | Probabilistic genotyping | DNAStatistX | EuroForMix
مقاله انگلیسی
6 DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification
DeepClas4Bio: اتصال ابزارهای تصویربرداری با چارچوبهای یادگیری عمیق برای طبقه بندی تصویر-2019
Background and objective: Deep learning techniques have been successfully applied to tackle several image classification problems in bioimaging. However, the models created from deep learning frameworks cannot be easily accessed from bioimaging tools such as ImageJ or Icy; this means that life scientists are not able to take advantage of the results obtained with those models from their usual tools. In this paper, we aim to facilitate the interoperability of bioimaging tools with deep learning frameworks. Methods: In this project, called DeepClas4Bio, we have developed an extensible API that provides a common access point for classification models of several deep learning frameworks. In addition, this API might be employed to compare deep learning models, and to extend the functionality of bioimaging programs by creating plugins. Results: Using the DeepClas4Bio API, we have developed a metagenerator to easily create ImageJ plugins. In addition, we have implemented a Java application that allows users to compare several deep learning models in a simple way using the DeepClas4Bio API. Moreover, we present three examples where we show how to work with different models and frameworks included in the DeepClas4Bio API using several bioimaging tools — namely, ImageJ, Icy and ImagePy. Conclusions: This project brings to the table benefits from several perspectives. Developers of deep learning models can disseminate those models using well-known tools widely employed by life-scientists. Developers of bioimaging programs can easily create plugins that use models from deep learning frameworks. Finally, users of bioimaging tools have access to powerful tools in a known environment for them.
Keywords: Deep learning | Bioimaging | Image classification | Interoperability
مقاله انگلیسی
7 DeepLink: Recovering issue-commit links based on deep learning
DeepLink: بازیابی پیوندهای issue-commit براساس یادگیری عمیق-2019
The links between issues in an issue-tracking system and commits resolving the issues in a version con- trol system are important for a variety of software engineering tasks (e.g., bug prediction, bug localization and feature location). However, only a small portion of such links are established by manually including issue identifiers in commit logs, leaving a large portion of them lost in the evolution history. To recover issue-commit links, heuristic-based and learning-based techniques leverage the metadata and text/code similarity in issues and commits; however, they fail to capture the embedded semantics in issues and commits and the hidden semantic correlations between issues and commits. As a result, this semantic gap inhibits the accuracy of link recovery. To bridge this gap, we propose a semantically-enhanced link recovery approach, named DeepLink , which is built on top of deep learning techniques. Specifically, we develop a neural network architecture, using word embedding and recurrent neural network, to learn the semantic representation of natural language descriptions and code in issues and commits as well as the semantic correlation between issues and commits. In experiments, to quantify the prevalence of missing issue-commit links, we analyzed 1078 highly-starred GitHub Java projects (i.e., 583,795 closed issues) and found that only 42.2% of issues were linked to corresponding commits. To evaluate the effectiveness of DeepLink , we compared DeepLink with a state-of-the-art link recovery approach FRLink using ten GitHub Java projects and demonstrated that DeepLink can outperform FRLink in terms of F -measure.
Keywords: Issue-commit links | Deep learning | Semantic understanding
مقاله انگلیسی
8 A novel credential protocol for protecting personal attributes in blockchain
یک پروتکل معتبر جدید برای محافظت از ویژگی های شخصی در بلاکچین-2019
This paper proposes a novel user-centric and privacy-preserving credential scheme over the blockchain. The proposed protocol allows users to access services without revealing sensitive attributes. This new paradigm is based on an efficient short signature, which uses pairing and self-blindable credentials that are verifiable on the blockchain. Our scheme achieves the advanced features of anonymity, unlinkability, and untraceability of users. Moreover, confidentiality of users’ attributes and unforgeability of their credentials are met. We provide security proofs and a real-world use-case where the protocol can be ap- plied. To empirically assess the performance of our solution, the cryptographic components and communications between the various involved actors are implemented using GO and Java. In addition, an implementation for an online trading use case based on Hyperledger Fabric is provided. Finally, we prove the efficiency of our work by presenting some exper- imental results and exhibiting comparisons with known traditional credentials schemes.
Keywords: Blockchain | Online trading System | Hyperledger fabric | User-centric system | Privacy-preserving credential | Short signature | Pairing Self-blind | Security proofs
مقاله انگلیسی
9 چگونه توسعه کنندگان مسائل را حل می کنند و تکنیک های فنی برگشتی در اکوسیستم های اپاچی چیست؟
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 35
در طول تکامل نرم افزار، بدهی فنی (TD) به دنبال یک جریان ثابتی هستیم، که در آن روز و گاهی ده سال بعد بازپرداخت شده و پردازش می شود. مطالعات متعددی در مقالات انجام شده است که در مورد چگونگی جمع آوری بدهی فنی در کد منبع در طول زمان و عواقب این انباشت برای تعمیر و نگهداری نرم افزار مورد بررسی قرار گرفته است. با این حال، با وجود این می توان به تحقیقی که در مقیاس بزرگ وجود دارد و بر انواع مسائل ثابت شده و مقدار TD که در جریان تکامل نرم افزار پرداخت می شود، تمرکز داد. در این مقاله ما نتایج یک مطالعه موردی را ارائه می دهیم که در آن تحلیلی از پیشرفت پنجاه و هفت پروژه نرم افزاری منبع باز جاوا توسط بنیاد نرم افزار آپاچی؛ در سطح دانه بندی های موقتی لحظات هفتگی تحلیل کردیم. به طور خاص، ما بر میزان بدهی فنی که پرداخت می شود و انواع مسائل ثابت شده تمرکز می کنیم. یافته های این تحقیق نشان می دهد که یک زیر مجموعه کوچک از انواع موضوع ها مسئول بزرگترین درصد بازپرداخت TD است و بنابراین هدف قرار دادن نقض خاص تیم توسعه می تواند مزایای بیشتری به دست آورد.
کلمات کلیدی: تکامل نرم افزار | بدهی فنی | کاوش مخازن نرم افزار | مطالعه تجربی | بنیاد نرم افزار آپاچی
مقاله ترجمه شده
10 A requirement mining framework to support complex sub-systems suppliers
چارچوب استخراج نیازها برای پشتیبانی تامین کنندگان متشکل از زیر سیستم ها-2018
The design of engineered socio-technical systems relies on a value chain within which suppliers must cope with larger and larger sets of requirements. Although 70 % of the total life cycle cost is committed during the concept phase and most industrial projects originally fail due to poor requirements engineering [1], very few methods and tools exist to support suppliers. In this paper, we propose to methodologically integrate data science techniques into a collaborative requirement mining framework to enable suppliers to gain insight and discover opportunities in a massive set of requirements. The proposed workflow is a five-activity process including: (1) the extraction of requirements from documents and (2) the analysis of their quality by using natural language processing techniques; (3) the segmentation of requirements into communities using text mining and graph theory; (4) the collaborative and multidisciplinary estimation of decision making criteria; and (5) the reporting of estimations with an analytical dashboard of statistical indicators. We conclude that the methodological integration of data science techniques is an effective way to gain insight from hundreds or thousands of requirements before making informed decisions early on. The software prototype that supports our workflow is a JAVA web application developed on top of a graph-oriented data model implemented with the NoSQL NEO4J graph database. As a future work, the semi-structured as-required baseline could be a sound input to feed a formal approach, such as model- and simulation-based systems engineering.
keywords: Requirement ، Specification ، Data mining ، Decision-Making
مقاله انگلیسی
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