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1 |
Publish–Subscribe approaches for the IoT and the cloud: Functional and performance evaluation of open-source systems
رویکردهای انتشار – اشتراک برای اینترنت اشیا و ابر: ارزیابی عملکرد و کارایی سیستمهای منبع باز-2022 Publish–Subscribe systems facilitate the communication between services or applications. A
typical system comprises the publisher, the subscriber, and the broker but, may also feature
message queues, databases, clusters, or federations of brokers, apply message delivery policies,
communication protocols, security services, and a streaming API. Not all these features are
supported by all systems or, others may be optional. As a result, there is no common ground
for the comparison of Publish–Subscribe systems. This paper presents a critical survey and
taxonomy of Publish–Subscribe systems, of their design features and technologies. The concepts
of message queuing, publish–subscribe systems, and publish–subscribe protocols for the cloud
and the IoT are discussed and clarified. The respective evaluation is about seven state-of-the-art
open-source systems namely, Apache Kafka, RabbitMQ, Orion-LD, Scorpio, Stellio, Pushpin, and
Faye. For the sake of fair comparison, a minimum set of common features is identified in all
systems. All systems are evaluated and compared in terms of functionality and performance
under real-case scenarios.
keywords: صف پیام | انتشار – اشتراک | معیارها | ارزیابی | Message queue | Publish–subscribe | Benchmarks | Evaluation |
مقاله انگلیسی |
2 |
Leading successful government-academia collaborations using FLOSS and agile values
پیشرو همکاریهای موفق دولت و آکادمی با استفاده از FLOSS و مقادیر چابک-2020 Government and academia share concerns for efficiently and effectively servicing societal demands, which includes the development of e-government software. Government-academia partnerships can be a valu- able approach for improving productivity in achieving these goals. However, governmental and academic institutions tend to have very different agendas and organizational and managerial structures, which can hinder the success of such collaborative projects. In order to identify effective approaches to overcome collaboration barriers, we systematically studied the case of the Brazilian Public Software portal project, a 30-month government-academia collaboration that, using Free/Libre/Open Source Software practices and agile methods for project management, developed an unprecedented platform in the context of the Brazil- ian government. We gathered information from experience reports and data collection from repositories and interviews to derive a collection of practices that contributed to the success of the collaboration. In this paper, we describe how the data analysis led to the identification of a set of three high-level decisions supported by the adoption of nine best practices that improved the project performance and enabled professional training of the whole team. Keywords: Project management | Government-Academia collaboration | Free software | Open source software | Agile methodologies | e-Government |
مقاله انگلیسی |
3 |
FLEXI: A high order discontinuous Galerkin framework for hyperbolic–parabolic conservation laws
FLEXI: یک چارچوب بالا و ناپیوسته گالرکین برای قوانین مربوط به حفاظت بیش از حد-پارابولیک-2020 High order (HO) schemes are attractive candidates for the numerical solution of multiscale
problems occurring in fluid dynamics and related disciplines. Among the HO
discretization variants, discontinuous Galerkin schemes offer a collection of advantageous
features which have lead to a strong increase in interest in them and related
formulations in the last decade. The methods have matured sufficiently to be of practical
use for a range of problems, for example in direct numerical and large eddy simulation
of turbulence. However, in order to take full advantage of the potential benefits of these
methods, all steps in the simulation chain must be designed and executed with HO in
mind. Especially in this area, many commercially available closed-source solutions fall
short. In this work, we therefore present the FLEXI framework, a HO consistent, opensource
simulation tool chain for solving the compressible Navier–Stokes equations on
CPU clusters. We describe the numerical algorithms and implementation details and
give an overview of the features and capabilities of all parts of the framework. Beyond
these technical details, we also discuss the important but often overlooked issues of
code stability, reproducibility and user-friendliness. The benefits gained by developing
an open-source framework are discussed, with a particular focus on usability for the
open-source community. We close with sample applications that demonstrate the wide
range of use cases and the expandability of FLEXI and an overview of current and future
developments. Keywords: Discontinuous Galerkin | High order | Large eddy simulation | Computational fluid dynamics | Open-source software | Shock capturing |
مقاله انگلیسی |
4 |
CaseSolver: An investigative open source expert system based on EuroForMix
CaseSolver: یک سیستم خبره تحقیقاتی منبع باز مبتنی بر EuroForMix-2019 For very serious crimes, reporting scientists often have to contend with complex cases where literally hundreds
of items are submitted by investigators for analysis. In order to efficiently expedite the challenge of comparing
reference profiles to evidence profiles, many of which are mixtures, we have developed an investigative open
source expert system CaseSolver. We have analysed a real case based on GlobalFiler involving 119 evidence
profiles and 3 reference profiles. To provide a demonstration of the power of the system we also added the three
references to a fictive large database of 1 million individuals in order to test subsequent recovery of the presumed
true contributors. CaseSolver was used on a Fusion 6C validation study involving 25 two- to four-person
mixture profiles based on 14 reference profiles. The sequential use of simple allele comparison, the qualitative
model (forensim) and the quantitative model (EuroForMix) makes the analysis very fast and accurate – and
finally, the software generates a list of potential match candidates which can be exported as a report. From these
two studies we found that the resolution of match candidates from CaseSolver was the same as that reported by a
scientist who worked manually through the samples, except that CaseSolver highlighted two manual errors. For
the validation study we found low template DNA samples giving negative results, which demonstrate the limitations
of the tool; but overall our assessment shows that CaseSolver will benefit all analyses involving mixture
interpretation and screening. Importantly, CaseSolver removes the very time-consuming aspect of manual
comparison and gives improved quality by preventing manual errors. Keywords: DNA comparison | Likelihood ratio | Database searching | Expert system |
مقاله انگلیسی |
5 |
Multi-pattern algorithm for first-break picking employing open-source machine learning libraries
الگوریتم چند الگویی برای انتخاب دوره اول با استفاده از کتابخانه های یادگیری ماشین منبع باز-2019 Accurate first-break (FB) onsets are crucial in processing seismic data, e.g. for deriving statics solution or building
velocity models via tomography. Ever-increasing volumes of seismic data require automatic algorithms for FB
picking. Most of the existing techniques are based either on simple triggering algorithms that rely on basic properties
of seismic traces or on neural network architectures that are too complex to be easily trained and re-used in
another survey. Herewe propose a solution that addresses the issue ofmulti-level analysis using a time-sequence
pattern-recognition process implemented in the newest open-source machine learning library like Keras or
Scikit-Learn.We use well-established methods such as STA/LTA, entropy, fractal dimension or higher-order statistics
to provide patterns required for generative model training with artificial neural networks (ANN), Support
Vector Regression (SVR) and an implementation of gradient boosted decision trees – XGBoost (Extreme Gradient
Boosting). FB picking is cast as the binary classification that requires a model to differentiate FB sample from all
other samples in a seismic trace. Ourworkflow (provided freely as a JupyterNotebook) is robust, easily adaptable
and flexible in a way of adding new pattern generators that might contribute to even better performance, while
already-trainedmodels can be saved and re-used for another dataset collected with similar acquisition parameters
(e.g., in multi-line surveys). Application to real seismic data showed that the models trained on 1000 and
340,100 manually-picked FB onsets are able to predict FB on the rest of 470,000 of traces with the success rate
of nearly 90% and 95%, respectively Keywords: Deep learning | Neural network | Pattern recognition | First-break picking |
مقاله انگلیسی |
6 |
یک پروژه گردآوری شده و کد منبع باز برای تولید شبیه سازهای مدلسازی مبتنی بر وب جنگل
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 18 مدیریت پایدار جنگل نیازمند سیستمهای پشتیبانی از تصمیم جهت بررسی طرح های امکانپذیر و پیش بینی پیامدهای این تصمیمات می باشد. مدلسازهای جنگل نوعا" سیستمهای پیچیده ای از معادلات را برای پیش بینی رفتار جنگل ها تولید می کنند که استفاده از مدلهای جنگل را برای کاربران نهایی به صورت کلی دشوار می کنند و بر انتقال دانش و فناوری اثر می گذارند. برای غلبه بر این دشواری ها و تسهیل استفاده عملی از آنها، مدلها می توانند به صورت نرم افزار جهت تولید شبیه سازهای جنگلی سازگار با کاربران یکپارچه سازی شوند. در این مقاله ما ForestMTIS را معرفی و توصیف می کنیم، یک پروژه محاسبه ابری تالیف شده و منبع – باز قابل ویرایش برای تولید شبیه سازهای جنگل که برای مدلهای آماری، غیرفضایی، جبری، غیرمتراکم، تک گونه و برجسته برای رشد و بهره تولید شده است. ما استفاده از ForestMTIS را برمبنای توسعه FlorNExT®، اولین برنامه کاربردی آن، برمبنای یک دیدگاه مشارکتی برای ساخت مدل رشد و بهره و مدیریت پایدار جنگل و قابل دسترسی برای تعداد زیادی از کاربران در شمال پرتغال نشان می دهیم.
کلیدواژه ها: شبیه ساز جنگل | ASP.Net | محاسبات ابری | نرم افزار به عنوان یک خدمات | انتقال دانش |
مقاله ترجمه شده |
7 |
Knowledge management in OSS communities: Relationship between dense and sparse network structures
مدیریت دانش در جوامع OSS: : رابطه ساختار شبکه متراکم و پراکنده-2018 Some authors in the literature have addressed knowledge transfer via weak ties between organization’s units
which are themselves strongly tied inside (e.g. Hansen, 1999). Some others have investigated knowledge
management among open-source-software (OSS) developers and discussed factors influencing knowledge
transfer within development teams (e.g. Joshi and Sarker, 2006). In the domain of open source software (OSS)
communities, more companies are now attempting to establish relationships to benefit from these potential
value-creating communities; and project managers could in fact target different goals within software devel
opment teams including knowledge transfer within and between teams. We step forward to distinguish
knowledge transfer within groups as opposed to knowledge transfer between groups; where relevant projects are
bundled into separate strongly intra-connected groups. In knowledge management literature there is a trade-off
between sparse network structures (Burt, 2000, 2002) versus dense network structures (Walker et al., 1997;
Coleman, 1988). It is argued that the former facilitates the diffusion and generation of ideas among groups, while
the latter affects the implementation of idea within each dense group. To our best knowledge, there has been no
study to investigate the relationship between dense and sparse network structures. We propose that knowledge
transfer within dense groups has a positive influence on knowledge transfer between sparse groups, in that intragroup
density, group size, developers centrality and betweenness could impact intergroup coupling. To prove our
hypothesis, we use a complex network of open source software (OSS) as the domain of interest, where developers
represent nodes and two developers contributing to a project task represent a network tie. Developers con
tributing to tasks in groups other than their own can explore novel ideas via sharing knowledge, whereas de
velopers contributing to tasks inside groups exploit ideas to improve those projects. We investigate the idea both
analytically and empirically within 4 months, 8 months and 1 year lagged time, and finally show that intragroup
density has a positive whereas developers’ centrality has a negative influence on intergroup coupling.
Keywords: Knowledge transfer ، Open source software network ، Intergroup diffusion of innovation ، Intragroup density ، Intergroup coupling |
مقاله انگلیسی |
8 |
How founders’ social capital affects the success of open-source projects: A resource-based view of project teams
چگونه سرمایه اجتماعی بنیانگذاران بر موفقیت پروژه های منبع باز اثر می گذارد؟ یک دیدگاه مبتنی بر منبع برای تیم های پروژه-2018 Volunteers under the open-source paradigm organize themselves and coordinate their efforts through the Internet to develop new products and services. Researchers have recognized open-source project founders’ social capital as an important factor to determine the performance of innovations in the open-source software (OSS) context. This study extends previous research by considering the founders’ social capital as a means to create strategic resources of project teams. We use data collected from an OSS development community to identify the role of founders’ social capital in team resource acquisition and utilization. We also clarify its inconsistent effects on innovation performance. From a resource-based view, we find that team size, as a manifestation of human resources, and team brokerage, as a manifestation of organizational resources, are determined by the social capital of project founders, and, in turn, have effects on innovation performance. However, team size and team brokerage contribute differently to innovation performance. The findings enrich our understanding of the impact of founders’ social capital in OSS communities and provide OSS project leaders and firm managers with guidelines on boosting their chances for successful projects.
keywords: Innovation |Open source |Product development |Resource-based view |Social capital |
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 |
Matminer: An open source toolkit for materials data mining
Matminer: مجموعه ابزار منبع باز برای مواد داده کاوی-2018 As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze
these materials data sets and build predictive models is becoming more important. This manuscript introduces
matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and
predicting materials properties. Matminer provides modules for retrieving large data sets from external data
bases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science.
It also provides implementations for an extensive library of feature extraction routines developed by the ma
terials community, with 47 featurization classes that can generate thousands of individual descriptors and
combine them into mathematical functions. Finally, matminer provides a visualization module for producing
interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning
and data analysis packages already developed and in use by the Python data science community. We explain the
structure and logic of matminer, provide a description of its various modules, and showcase several examples of
how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new
methodologies.
Keywords: Data mining ، Open source software ، Machine learning ، Materials informatics |
مقاله انگلیسی |