António Correia

Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

In September 2000, an airborne synthetic aperture radar (SAR) mission acquired unprecedented full polarimetric P-band data over the Tapajos National Forest (Para State), which is an area in the Brazilian Amazon which has been continuously monitored in the last three decades. Eight land use/cover classes were identified, namely, primary forest, regeneration older than 25 years, regeneration between 12 and 25 years, regeneration between 6 and 12 years, regeneration younger than six years, crops/pasture, bare soil, and floodplain (FP). The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization. The iterated conditional mode (ICM) contextual classifier was applied to amplitude, intensity images, biomass index, and some polarimetric parameters (entropy, alpha angle, and anisotropy) extracted from the polarimetric P-band data. As the accuracy obtained for eight classes was not acceptable, another two sets, with five and four classes, were formed by the combination of the previous ones. They were defined by confusion matrix analysis and by the graphical analysis of average backscatter values, entropy, [alpha] angle, and anisotropy images and by the H/alpha plans of the land use samples. The classification accuracy with four classes (three levels of biomass plus FP) was then considered acceptable with a Kappa value of 76.81%, using the ICM classification with the adequate bivariate distribution for the HV and VV channels.

Towards an overarching classification model of CSCW and groupware: a socio-technical perspective

The development of groupware systems can be supported by the perspectives provided by taxonomies categorizing collaboration systems and theoretical approaches from the multidisciplinary field of Computer-Supported Cooperative Work (CSCW). In the last decades, multiple taxonomic schemes were developed with different classification dimensions, but only a few addressed the socio-technical perspective that encompasses the interaction between groups of people and technology in work contexts. Moreover, there is an ambiguity in the use of the categories presented in the literature. Aiming to tackle this vagueness and support the development of future groupware systems aware of social phenomena, we present a comprehensive classification model to interrelate technological requirements with CSCW dimensions of communication, coordination, cooperation, time and space, regulation, awareness, group dynamics, and complementary categories obtained from a taxonomic literature review.

Exploiting Classical Bibliometrics of CSCW: Classification, Evaluation, Limitations, and the Odds of Semantic Analytics

The purpose of this study is to conduct a bibliometric and taxonomy-based analysis of the field of CSCW to map its recent evolution at a quantitative and qualitative level. A model for semantic analytics and social evaluation is also discussed with emphasis on the hypothesis of putting crowds into the loop of bibliography classification process to improve the current labor-intensive, time-consuming, unrepeatable and sometimes subjective task of scientometricians. A total of 1,480 publications were carefully reviewed and subjected to scientometric data analysis methods and techniques. Analyzed parameters included document orientation, deviation from trend in the total number of citations, and publication activity by author’s affiliation country. A semantic classification of 541 publications allows identifying growing trends and lacking research indicators. At a human-centered perspective, limitations are unfilled in the limitative analytical spectrum, laborious and time-consuming processes of data seeking, gathering, cataloguing and analysis, subjective results at a taxonomic level, lack of more bibliographic data analytics perspectives, and absence of human-centered results concerning cognitive aspects in meta-knowledge research practices. Hypotheses are suggested towards a crowd-enabled model for bibliography evaluation in order to understand the ways as humans and machines can work cooperatively and massively on scientific data to solve complex problems.

Computer-Simulated 3D Virtual Environments in Collaborative Learning and Training: Meta-Review, Refinement, and Roadmap

3D3C worlds can constitute versatile and inexpensive solutions for collaborative learning and training. The embodied human interaction around virtual objects supports a set of activities, enriching the way people learn, work and socialize via graphical representations, and also using features such as Internet browsing, instant messaging, and online voice chat. Nevertheless, research on 3D virtual environments for online education and training identified a set of challenges which include but are not limited to a lack of institutional support, standardization, evaluation methods for measuring learning outcomes, integration between virtual learning environments and virtual worlds, and support for healthy aging and people with special needs. This chapter provides a comprehensive review of 3D3C worlds’ challenges and opportunities by updating the literature with a qualitative analysis covering 49 publications (2006–2014). The meta-review includes a research agenda based on 161 condensed meaning units.

Scientometric analysis of scientific publications in CSCW

Over the last decades, CSCW research has undergone significant structural changes and has grown steadily with manifested differences from other fields in terms of theory building, methodology, and socio-technicality. This paper provides a quantitative assessment of the scientific literature for mapping the intellectual structure of CSCW research and its scientific development over a 15-year period (2001–2015). A total of 1713 publications were subjected to examination in order to draw statistics and depict dynamic changes to shed new light upon the growth, spread, and collaboration of CSCW devoted outlets. Overall, our study characterizes top (cited and downloaded) papers, citation patterns, prominent authors and institutions, demographics, collaboration patterns, most frequent topic clusters and keywords, and social mentions by country, discipline, and professional status. The results highlight some areas of improvement for the field and a lot of well-established topics which are changing gradually with impact on citations and downloads. Statistical models reveal that the field is predominantly influenced by fundamental and highly recognized scientists and papers. A small number of papers without citations, the growth of the number of papers by year, and an average number of more than 39 citations per paper in all venues ensure the field a healthy and evolving nature. We discuss the implications of these findings in terms of the influence of CSCW on the larger field of HCI.

Crowdsourcing and Massively Collaborative Science: A Systematic Literature Review and Mapping Study

Current times are denoting unprecedented indicators of scientific data production, and the involvement of the wider public (the crowd) on research has attracted increasing attention. Drawing on review of extant literature, this paper outlines some ways in which crowdsourcing and mass collaboration can leverage the design of intelligent systems to keep pace with the rapid transformation of scientific work. A systematic literature review was performed following the guidelines of evidence-based software engineering and a total of 148 papers were identified as primary after querying digital libraries. From our review, a lack of methodological frameworks and algorithms for enhancing interactive intelligent systems by combining machine and crowd intelligence is clearly manifested and we will need more technical support in the future. We lay out a vision for a cyberinfrastructure that comprises crowd behavior, task features, platform facilities, and integration of human inputs into AI systems.

Reframing Taxonomy Development in Collaborative Computing Research: A Review and Synthesis of CSCW Literature 2003–2010

Technological evolution impacts the research and development of new solutions, as well as consumers’ expectations and behaviors. With the advent of the new millennium, collaboration systems and technologies were introduced to support ordinary cooperative work and inter-dependent, socially and culturally mediated practices as integral units of everyday life settings. Nevertheless, existing classification systems are limited in scope to analyze technological developments and capture the intellectual structure of a field, understood as an abstraction of the collective knowledge of its researchers and their socially mediated activities. Ten years after the introduction of Mittleman et al.’s taxonomy, we build upon earlier work and adopt this classification scheme to provide a descriptive, taxonomy-based analysis of four distinct venues focused on collaborative computing research: ACM CSCW, ACM GROUP, ECSCW, and CRIWG. The proposal consists of achieving evidence on technical attributes and impacts towards characterizing the evolution of socio-technical systems via (and for) taxonomic modeling. This study can also constitute an important step towards the emergence of new, potentially more valid and robust evaluation studies combining Grounded Theory with alternative methods and techniques.

SciCrowd: Towards a Hybrid, Crowd-Computing System for Supporting Research Groups in Academic Settings.

The increasing amount of scholarly literature and the diversity of dissemination channels are challenging several fields and research communities. A continuous interplay between researchers and citizen scientists creates a vast set of possibilities to integrate hybrid, crowd-machine interaction features into crowd science projects for improving knowledge acquisition from large volumes of scientific data. This paper presents SciCrowd, an experimental crowd-powered system under development “from the ground up” to support data-driven research. The system combines automatic data indexing and crowd-based processing of data for detecting topic evolution by fostering a knowledge base of concepts, methods, and results categorized according to the particular needs of each field. We describe the prototype and discuss its main implications as a mixed-initiative approach for leveraging the analysis of academic literature.