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2nd International Symposium on Search Based Software engineering, SSBSE 2010

作者： Magdaleno, Andréa Magalhães COPPE/PESC System Engineering and Computer Science Program UFRJ Rio de Janeiro Brazil NP2Tec Research and Practice Group in Information Technology UNIRIO Rio de Janeiro Brazil

ISBN: (纸本)9780769541952

A major activity performed by the manager before starting a software project is tailoring its development process. Such activity requires information about the context under which the project will be executed, including organizational, project, and team characteristics. In addition, it also requires pondering many factors and evaluating all existing constraints. In this scenario, we claim that a balance between collaboration and discipline can be the drivers to tailor software development processes in order to meet project and organization needs. With the purpose of facilitating this balancing, it is possible to automate some of the steps to solve the problem, reducing the effort required to execute this task and improving the obtained process. Therefore, this work presents an optimization-based approach where the balancing in process tailoring is defined, modeled and briefly analyzed. This approach uses collaboration and discipline as utility functions to select the most appropriate process for a software development project, considering its current context. © 2010 IEEE.

关键词： Constraint satisfaction problems

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Information Extraction to Improve Link Prediction in Scientific Social Networks

Information Extraction to Improve Link Prediction in Scienti...

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IEEE International Conference on computer Supported Cooperative Work in Design

作者： Victor Stroele Fernanda Campos Crystiam K. Pereira Geraldo Zimbrao Jano M. Souza PGCC - Postgraduate Program in Computer Science UFJF - Federal University of Juiz de Fora COPPE - Systems Engineering and Computer Program UFRJ - Federal University of Rio de Janeiro

ISBN: (纸本)9781509019168

Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.

关键词： Information Extraction Link Prediction Scientific Social Network

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Using regression error characteristic curves for model selection in ensembles of neural networks 14

Using regression error characteristic curves for model selec...

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14th European Symposium on Artificial Neural Networks, ESANN 2006

作者： De Pina, Aloísio Carlos Zaverucha, Gerson Universidade Federal do Rio de Janeiro COPPE/PESC Department of Systems Engineering and Computer Science C.P. 68511 Rio de Janeiro RJCEP. 21945-970 Brazil

ISBN: (纸本)2930307064

Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals greater than that value are considered as errors. The algorithm was empirically evaluated and its results were analyzed also by means of REC curves. © 2006 *** publication. All rights reserved.

关键词： Neural networks

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A massively parallel hardware architecture for ray-tracing

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International Journal of High Performance systems Architecture 2009年第1期2卷 26-34页

作者： Nery, A.S. Nedjah, N. França, F.M.G. Computer Architecture and Microelectronics Laboratory Systems Engineering and Computer Science Program COPPE P.O. Box 68511 21941-972 Rio de Janeiro Brazil Department of Electronics Engineering and Telecommunications Faculty of Engineering Universidade do Estado do Rio de Janeiro Rua Sao Francisco Xavier 524 Sala 5145-F Maracana 20.550-900 Rio de Janeiro RJ Brazil Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Department of Electronics Engineering and Telecommunications Faculty of Engineering State University of Rio de Janeiro Brazil Brazil

Real time performance of non-interactive rendering of three-dimensional scenes is usually unachievable. Ray tracing is one of the methods used for rendering such scenes. The performance achieved by a sequential software-based implementation of ray tracing is far from satisfactory. In contrast, many parallel implementations of ray tracing have been enabling real time performance, as the underlying algorithm can be massively parallelised. Thus, it is expected that a custom parallel design in hardware is likely to achieve the acceptable performance standards. In this paper, we propose a hardware architecture, which we call GridRT, capable of dealing with the main desirable features of ray tracing, such as shadows and reflections effects, imposing low requirements in terms of silicon area while achieving acceptable performance in terms of rendering time. This architecture achieves is efficient yet compact as it explores the massive parallelism offered by the intrinsic structure of the algorithm. The design exploits the usage the spatial data structure of regular grids. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Ray tracing

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An exploratory study on collaboration understanding in software development social networks

An exploratory study on collaboration understanding in softw...

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18th International Conference on Collaboration and Technology, CRIWG 2012

作者： Magdaleno, Andréa M. Araujo, Renata M. Werner, Cláudia M. L. UFRJ - Federal University of Rio de Janeiro COPPE - Systems Engineering and Computer Science Department P.O. Box 68511 21945-970 Rio de Janeiro RJ Brazil - UNIRIO Brazil

ISBN: (纸本)9783642332838

Collaboration is important for productivity, quality, and knowledge sharing in software development. In this context, the use of social networks analysis can help to track the level of collaboration in a development project. In this work, an exploratory study was conducted, in the context of free/open source software, using EvolTrack-SocialNetwork tool, to investigate collaboration in software teams. The preliminary results indicate a potential to increase one's ability to understand the course that the collaboration is taking. © 2012 Springer-Verlag.

关键词： Social sciences computing

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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

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Physical Review Applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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A Systematic Review of Cross- vs. Within-Company Cost Estimation Studies 10

A Systematic Review of Cross- vs. Within-Company Cost Estima...

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10th International Conference on Evaluation and Assessment in Software engineering, EASE 2006

作者： Kitchenham, Barbara Mendes, Emilia Travassos, Guilherme H. Department of Computer Science Keele University StaffordshireST5 5GB United Kingdom National ICT Australia Locked Bag 9013 AlexandriaNSW1435 Australia Computer Science Department The University of Auckland Private Bag 92019 Auckland New Zealand UFRJ/COPPE Systems Engineering and Computer Science Program Caixa Postal 68511 RJ Rio de Janeiro21941-972 Brazil

OBJECTIVE – The objective of this paper is to determine under what circumstances individual organisations would be able to rely on cross-company based estimation models. METHOD – We performed a systematic review of studies that compared predictions from cross-company models with predictions from within-company models based on analysis of project data. RESULTS – Ten papers compared cross-company and within-company estimation models, however, only seven of the papers presented independent results. Of those seven, three found that cross-company models were as good as within-company models, four found cross-company models were significantly worse than within-company models. Experimental procedures used by the studies differed making it impossible to undertake formal meta-analysis of the results. The main trend distinguishing study results was that studies with small single company data sets (i.e. © EASE *** rights reserved.

关键词： Cost estimating

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Development of hydroxyapatite-enhanced membrane for nanoplastics removal: multiple scenarios and mechanism exploration

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Journal of Hazardous Materials 2025年

作者： Mengna Li Guohe Huang Shuguang Wang Jing Huang Zeyuan Xu Environmental Systems Engineering Program University of Regina Regina Saskatchewan S4S 0A2 Canada China-Canada Center of Energy Environment and Sustainability Research UR-SDU School of Environmental Science and Engineering Shandong University Qingdao 266237 China Shandong Key Laboratory of Water Pollution Control and Resource Reuse School of Environmental Science and Engineering Shandong University Qingdao 266237 China Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

Nanoplastics (NPs) are widespread in wastewater systems and more toxic than microplastics (MPs), necessitating effective removal techniques. Although membranes can effectively remove MPs, their efficiency and mechanisms for NPs removal require further study. A novel hydroxyapatite functionalized PVDF (HAPF) membrane was developed for NPs removal. The optimized HAPF membrane was prepared via a one-step method with 800 mg/L HAP at pH 7.3 for 8 hours. It achieved a water flux of 4376.44 LMH, 3.4 times higher than that of the pristine PVDF membrane, while maintaining a polystyrene (PS) NPs rejection above 99.5%. The HAPF membrane maintained 95% of its pure water flux when treating wastewater with only PS NPs, but exhibited severe flux decline under varying conditions, such as surface-functionalized PS NPs, pH changes, or coexisting contaminants. Among these, the presence of MgSO 4 alongside PS NPs caused the most severe water flux reduction, with the HAPF membrane undergoing complete blocking, followed by the formation of a cake layer. XDLVO results revealed that there is an attractive interaction between the HAPF membrane and PS NPs, primarily driven by acid-base interaction. The HAPF membrane exhibited significantly lower R m and R f values than the PVDF membrane and achieved a flux recovery rate of 92.94% over multiple fouling-cleaning cycles. Its water flux was 18.9 times that of the PVDF membrane during long-term filtration. The developed HAPF membrane holds significant potential for advanced water treatment and fouling.

关键词： Hydroxyapatite functionalized PVDF (HAPF) membrane nanoplastic removal coexisting pollutants XDLVO theory Hermia theory

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