12,836 research outputs found
Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods
Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case
Toward Visualization and Analysis of Traceability Relationships in Distributed and Offshore Software Development Projects
Offshore software development projects provoke new issues to the collaborative endeavor of software development due to their global distribution and involvement of various people, processes, and tools. These problems relate to the geographical distance and the associated time-zone differences; cultural, organizational, and process issues; as well as language problems. However, existing tool support is neither adequate nor grounded in empirical observations. This paper presents two empirical studies of global software development teams and their usage of tools. The results are then used to motivate and inform the construction of more useful software development tools. The focus is on issues that are tool-related but have not yet been solved by existing tools. The two software tools presented as solutions, Ariadne and TraVis, explicitly address yet unresolved issues in global software development and also integrate with prevalent other solutions
Use of IBM Collaborative Lifecycle Management Solution to Demonstrate Traceability for Small, Real-World Software Development Project
The Standish Group Study of 1994 showed that 53 percent of software projects failed outright and another 31 percent were challenged by extreme budget and/or time overrun. Since then different responses to the high rate of software project failures have been proposed. SEI’s CMMI, the ISO’s 9001:2000 for software development, and the IEEE’s JSTD-016 are some examples of such responses. Traceability is the one common feature that these software development standards impose.
Over the last decade, software and system engineering communities have been researching subjects such as developing more sophisticated tooling, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, applying traceability in specific domains such as Model Driven Development, product line systems and agile project environment. These efforts have not been in vain. The 2012 CHAOS results show an increase in project success rate of 39% (delivered on time, on budget, with required features and functions), and a decrease of 18% in the number of failures (cancelled prior to completion or delivered and never used). Since research has shown traceability can improve a project’s success rate, the main purpose of this thesis is to demonstrate traceability for a small, real-world software development project using IBM Collaborative Lifecycle Management.
The objective of this research was fulfilled since the case study of traceability was described in detail as applied to the design and development of the Value Adjustment Board Project (VAB) of City of Jacksonville using the scrum development approach within the IBM Rational Collaborative Lifecycle Management Solution. The results may benefit researchers and practitioners who are looking for evidence to use the IBM CLM solution to trace artifacts in a small project
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
Proximal business intelligence on the semantic web
This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to
improve specific information access and transcoding but not on how the information
can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology
language and then re-used to provide the invisibility of pervasive access; uncovering
more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO
The Pan-STARRS Moving Object Processing System
We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern
software package that produces automatic asteroid discoveries and
identifications from catalogs of transient detections from next-generation
astronomical survey telescopes. MOPS achieves > 99.5% efficiency in producing
orbits from a synthetic but realistic population of asteroids whose
measurements were simulated for a Pan-STARRS4-class telescope. Additionally,
using a non-physical grid population, we demonstrate that MOPS can detect
populations of currently unknown objects such as interstellar asteroids.
MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope
despite differences in expected false detection rates, fill-factor loss and
relatively sparse observing cadence compared to a hypothetical Pan-STARRS4
telescope and survey. MOPS remains >99.5% efficient at detecting objects on a
single night but drops to 80% efficiency at producing orbits for objects
detected on multiple nights. This loss is primarily due to configurable MOPS
processing limits that are not yet tuned for the Pan-STARRS1 mission.
The core MOPS software package is the product of more than 15 person-years of
software development and incorporates countless additional years of effort in
third-party software to perform lower-level functions such as spatial searching
or orbit determination. We describe the high-level design of MOPS and essential
subcomponents, the suitability of MOPS for other survey programs, and suggest a
road map for future MOPS development.Comment: 57 Pages, 26 Figures, 13 Table
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
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