39,592 research outputs found
Leadership capability of team leaders in construction industry
This research was conducted to identify the important leadership capabilities for
Malaysia construction industry team leaders. This research used exploratory sequential
mix-method research design which is qualitative followed by quantitative research
method. In the qualitative phase, semi-structured in-depth interview was selected
and purposive sampling was employed in selecting 15 research participants involving
team leaders and Human Resource Managers. Qualitative data was analysed using
content and thematic analyses. Quantitative data was collected using survey
questionnaire involving 171 randomly selected team leaders as respondents. The data
was analyzed using descriptive and inferential statistics consisting of t-test, One-way
Analysis of Variance (ANOVA), Pearson Correlation, Multiple Regression and
Structured Equation Modeling (SEM). This study found that personal integrity, working
within industry, customer focus and quality, communication and interpersonal skill,
developing and empowering people and working as a team were needed leadership
capabilities among construction industry team leaders. The research was also able to
prove that leadership skill is a key element to develop leadership capability. A
framework was developed based on the results of this study, which can be used as a
guide by employers and relevant agencies in enhancing leadership capability of
Malaysia construction industry team leade
Web Content Extraction - a Meta-Analysis of its Past and Thoughts on its Future
In this paper, we present a meta-analysis of several Web content extraction
algorithms, and make recommendations for the future of content extraction on
the Web. First, we find that nearly all Web content extractors do not consider
a very large, and growing, portion of modern Web pages. Second, it is well
understood that wrapper induction extractors tend to break as the Web changes;
heuristic/feature engineering extractors were thought to be immune to a Web
site's evolution, but we find that this is not the case: heuristic content
extractor performance also tends to degrade over time due to the evolution of
Web site forms and practices. We conclude with recommendations for future work
that address these and other findings.Comment: Accepted for publication in SIGKDD Exploration
Learning to Generate Posters of Scientific Papers
Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthetic. In this paper, for the first time, we study the
challenging problem of learning to generate posters from scientific papers. To
this end, a data-driven framework, that utilizes graphical models, is proposed.
Specifically, given content to display, the key elements of a good poster,
including panel layout and attributes of each panel, are learned and inferred
from data. Then, given inferred layout and attributes, composition of graphical
elements within each panel is synthesized. To learn and validate our model, we
collect and make public a Poster-Paper dataset, which consists of scientific
papers and corresponding posters with exhaustively labelled panels and
attributes. Qualitative and quantitative results indicate the effectiveness of
our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI'16), Phoenix, AZ, 201
Efficient storage and decoding of SURF feature points
Practical use of SURF feature points in large-scale indexing and retrieval engines requires an efficient means for storing and decoding these features. This paper investigates several methods for compression and storage of SURF feature points, considering both storage consumption and disk-read efficiency. We compare each scheme with a baseline plain-text encoding scheme as used by many existing SURF implementations. Our final proposed scheme significantly reduces both the time required to load and decode feature points, and the space required to store them on disk
Design of Automatically Adaptable Web Wrappers
Nowadays, the huge amount of information distributed through the Web motivates studying techniques to\ud
be adopted in order to extract relevant data in an efficient and reliable way. Both academia and enterprises\ud
developed several approaches of Web data extraction, for example using techniques of artificial intelligence or\ud
machine learning. Some commonly adopted procedures, namely wrappers, ensure a high degree of precision\ud
of information extracted from Web pages, and, at the same time, have to prove robustness in order not to\ud
compromise quality and reliability of data themselves.\ud
In this paper we focus on some experimental aspects related to the robustness of the data extraction process\ud
and the possibility of automatically adapting wrappers. We discuss the implementation of algorithms for\ud
finding similarities between two different version of a Web page, in order to handle modifications, avoiding\ud
the failure of data extraction tasks and ensuring reliability of information extracted. Our purpose is to evaluate\ud
performances, advantages and draw-backs of our novel system of automatic wrapper adaptation
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