380789_Artigo científico

8/6/2019 380789_Artigo cientfico

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Artigo cientficoIntroduo Pesquisa em Engenharia de

Computao


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Um artigo cientfico : Segundo Roth1:

Uma sntese de descobertas, acompanhadas de

avaliao e interpretao; Um trabalho original quanto s idias;

Um trabalho que reconhece as fontes utilizadas.1 ROTH, Audrey J. The research paper: process, form, and content. 7. ed. Belmont: Wadsworth,

1994.


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Um artigo cientfico no : Segundo Roth1:

Um resumo de outra fonte.

Repetio de idias de outros, sem crticas.

Uma srie de citaes.

Opinio pessoal no demonstrada.

Cpia do trabalho de outros.1 ROTH, Audrey J. The research paper: process, form, and content. 7. ed. Belmont: Wadsworth,

1994.


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Como escrever um resumo

(abstract)


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Orientaes importantes Respeitar o limite de palavras.

Incluirpalavras-chaves.

Incluirmais ou menos uma frase para cada

uma das partes de um resumo.


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Partes de um resumo Motivao por qu?

Importncia, dificuldade, impacto

Objetivo, soluo proposta

Mtodo

Resultados

Concluses mais importantes


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Exemplo de resumoWe consider the problem of nding duplicates in data streams.

Duplicate detection in data streams is utilized in variousapplications including fraud detection. We develop a solution

based on Bloom Filters [9], and discuss the space and timerequirements for running the proposed algorithm in both thecontexts of sliding, and landmark stream windows. We run a

comprehensive set of experiments, using both real andsynthetic click streams, to evaluate the performance of the

proposed solution. The results emonstrate that the proposedsolution yields extremely low error rates.(METWALLY, A.; AGRAWAL, D.; ABBADI, A. Duplicate Detection in Click

Streams. In: INTERNATIONAL WORLD WIDE WEB CONFERENCE, 2005,

Chiba. Proceedings ... New York: ACM, 2005.p. 12-21)


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Exemplo de resumoWepresent the bubble cursor a new target acquisition technique

based on area cursors. The bubble cursor improves upon areacursors by dynamically resizing its activation area depending on

theprox

imity of surrounding targets, such that only one target isselectable at any time. We also present two controlledexperiments that evaluate bubble cursorperformance in 1D and2D target acquisition tasks, in complex situations with multipletargets of varying layout densities. Results show that the bubblecursor significantly outperforms the point cursor and the object

pointing technique [8], and that bubble cursorperformance can beaccurately modeled and predicted using Fitts law.(GROSSMAN, T.; BALAKRISHNAN, R. The Bubble Cursor: Enhancing Target Acquisition by

Dynamic Resizing of the Cursors Activation Area. In: INTERNATIONAL CONFERENCE ON HUMAN FACTORS

IN COMPUTING SYSTEMS, 2005, Portland. Proceedings... New York: ACM, 2005. p. 281-290)


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Levantamento do Estado

da Arte


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Exemplo 1 Zhao et al. Face Recognition: A Literature

Survey. ACM Computing Surveys, Vol. 35, No.

4, December 2003, pp. 399458. 168 referncias

7 livros


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Exemplo 1 (Extratos)Caricatures [Brennan 1985; Bruce 1988; Perkins 1975]: A

caricature can be formally defined [Perkins 1975] as asymbol that exaggerates measurements relative to any

measure which varies from one person to another. Thus the

length of a nose is a measure that varies from person toperson, and could be useful as a symbol in caricaturing

someone, but not the number of ears. A standard caricaturealgorithm [Brennan 1985] can be applied to different qualitiesof image data (line drawings and photographs). Caricatures

of line drawings do not contain as much information asphotographs, but they manage to capture the importantcharacteristics of a face; experiments based on nonordinaryfaces comparing the usefulness of linedrawing caricatures

and unexaggerated line drawings decidedly favor the former[Bruce 1988].


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Exemplo 1 (Extratos)The last method that we review in this category is based

on recent advances in component-based

detection/recognition [Heisele et al. 2001] and

3D morphable models [Blanz and Vetter 1999]. The basicidea of component-based methods [Heisele et al. 2001]

is to decompose a face into a set of facial components

such as mouth and eyes that are interconntected by a

flexible geometrical model. (Notice how this method issimilar to the EBGM system [Okada et al. 1998; Wiskott

et al. 1997] except that gray-scale components are used

nstead of Gabor wavelets.)


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Exemplo 1 (Extratos)Until recently, there did not exist a common FRT

evaluation protocol that included large

databases and standard evaluation methods.This made it difficult to assess the status of FRT

for real applications, even though many existing

systems reported almost perfect performance

on small databases.


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Exemplo 1 (Extratos) Uma concluso:

Recognition of faces from a video sequence(specially a surveillance video) is still one of the

most challenging problems in face recognitionbecause video is of low quality and the imagesare small. Often, the subjects of interest are notcooperative, for example, not looking into the

camera. One particular difficulty in these

applications is how to obtain good-qualitygallery images. Nevertheless, video-based facerecognition systems using multiple cues have

demonstrated good results in relativelycontrolled environments. (grifo meu)


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Exemplo 2 S. Androutsellis-Theotokis and D. Spinellis. A

Survey ofPeer-to-Peer Content Distribution

Technologies. ACM Computing Surveys, Vol. 36,No. 4, December 2004, pp. 335371.


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Exemplo 2 (Extratos) Concluses:

Finally, as peer-to-peer technologies are stillevolving, there are a multitude of open research

problems, directions, and opportunities, including,but not limited to:

The design of new distributed object location,routing and distributed hash table data structures

and algorithms for maximizing performance,security and scalability, both in structured andunstructured network architectures.

(...)


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Exemplo 3 KEPHART, Jeffrey O. Research Challenges

of Autonomic Computing. ICSE05, May 15

21, 2005, St. Louis, Missouri, USA. 8 pginas

53 referncias


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Exemplo 3 (Extratos)One interesting line of research [11] uses information

theoretic techniques to identify optimally small and

efficient sets or sequences of probes that can inpoint

problems; efforts are being made now to extend thework to scale to larger systems and handle multiple

simultaneous failures. Problem localization techniques

such as these rely on a knowledge of patterns of

interconnection, or dependencies, among systemcomponents. This brings up yet another challenge that

is beginning to be addressed [22]: how to extract these

dependencies automatically as the systems

configuration continually shifts?


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Exemplo 3 (Extratos)Littman et al. [42] have taken a radically different approach

that completely bypasses diagnosis and localization.They formulate autonomic network repair as a

reinforcement learning problem in which numerousdifferent fault types are injected into the network duringtraining. The reinforcement learning algorithm explores

several alternative test and repair actions. Over time, thealgorithm learns which repair actions lead to the highest

reward without learning any intermediate problemdiagnosis representation. An important question iswhether this technique can scale to larger, more complexsystems in which the range of possible faults, elements,

and remediation actions is significantly larger.

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