Search

This panel aims to invite perspectives from “global” and “local” actors within the Computer-Supported Cooperative Work (CSCW) community towards fostering a greater understanding of the support that SIGCHI infrastructures can provide for strengthening ties across various borders. Members of the SIGCHI Executive Committee will host a panel to identify challenges and opportunities in this regard, and invite all attendees of CSCW 2021 to participate, providing inputs and feedback.

Previous initiatives have identified the potential of the Latin American region for CSCW research. However, CSCW work in the region is still disconnected, produced by small, isolated groups that often have a hard time learning about each other and finding opportunities to do research together. This workshop is an initiative of the SIGCHI Latin America Committee to bring these groups together to discuss their research, the challenges they perceive, and the possible initiatives that could be conducted to strengthen the region.

This design fiction presents a Hormonal Tracking System (HORTS) that helps to gather, in groups of explorers, highly effective individuals, and control the mental health of those involved in colonizing Mars. We build our future world based on the mass adoption of wearable sensors, in algorithms to create matches and diffusion of surveillance methods. We establish a parallel with the surveillance methods current, increasingly widespread among the population, and with the existing intrusive techniques various environments. This raises questions about the panoptic surveillance to which participants are submitted and the meaning of individuality and privacy in these settings.

Traditional and Empirical knowledge databases are becoming essential for the preservation of a region's culture. Without proper curation this knowledge might be lost over time. The goal of this work is to present a web system to support the co-curation of data derived from empirical knowledge. Our proposal also provides the interconnection between visitors' contributions with data already available in Linked Open Data repositories. The web system was evaluated by specialists from the Rio de Janeiro Botanical Garden, who verified the impact of the system implantation in cultural environments related to medicinal plants.

Empirical and Traditional knowledge is part of the concept of intangible cultural heritage, which represents immaterial cultural heritage items such as facts, stories, and traditions that are part of local culture. However, this knowledge might be lost over time. Encouraging public participation to record empirical knowledge is a way to preserve the culture of a region and make it known to others. This paper presents a web system that aims to record and to preserve empirical knowledge data. To achieve this goal, the tool uses three combined approaches: (i) crowdsourcing calls oriented to content creation by visitors; (ii) collaborative curation among experts for selection, evaluation, and publication of content; and (iii) interconnection between user contributions and data already available in open repositories on the web (in LOD format). The tool was applied in the Botany domain to curate empirical and cultural data about medicinal plants, where experts from the Rio de Janeiro Botanical Garden evaluated it.

A growing amount of data is currently widely available for consumption. This has created a new trend towards data-based narratives. News outlets and storytellers in general use data and graphics to convey their messages, argue for or against a point of view and convince their audience. This style of writing requires certain skills related to data retrieval and manipulation to create and interpret. This so-called "Data Literacy" is not always developed in schools, which leads to citizens who not able to read a data-based piece critically. In this project, we plan to investigate the current status of data literacy in schools, and propose strategies to improve it.

The rapid growth of available data and computational power have brought about the wide adoption of algorithmic solutions, especially using Artificial Intelligence and Machine Learning techniques. Thus, users routinely interact with AI-based systems, even though they might not be aware of it: tasks such as searching, diagnostics, credit analysis, and movie recommendations now employ AI techniques. These techniques analyze large volumes of data, iteratively processing and combining them in multiple ways. This often makes both the techniques and the results hard to understand, despite the (frequently) good results.

As individuals age, issues such as forgetfulness, falling or exposure to elements can become serious problems that end up reducing their independence. Caretakers or family often accompany seniors when they wish to go out, even for something as simple as a walk around the neighborhood. The SafeNeighborhood project attempted to create a safety network to allow seniors more independence and provide support should it be necessary. The project was dependent on community ties and the social capital contained in them. However, in large modern cities, neighborhoods are no longer home to densely connected people, and most neighborhoods have a large floating population (eg., people who are there only for work.) While working on this project, we observed that seniors tend to create their own routines and their own personal networks, which could be leveraged for support. These personal neighborhoods are discussed in this paper, with pointers to further investigations.

Human mobility patterns can be identified through the analysis of GPS and smartphone data. This identification has been the theme of several studies, particularly in the smart cities domain. Considering that patterns identify human routines, changes in these patterns may also provide useful information. This paper reports the analysis of GPS data from a group of Chinese in Beijing from April 2009 till October 2012, identifying routine routing patterns and sudden collective breaks on those patterns. Artificial Intelligence techniques were used to identify mobility patterns. Olympic games dates and the 2009 pollution peak were identified though our method. We believe it can be a powerful tool to infer mass events occurring in any part of the world.

Startups ecosystem is a creative environment to aim promoting entrepreneurship and the birth of innovative business. In this complex social network, the activities derive from the relationship of interdependence and co-evolution between actors with different purposes, who share resources to achieve common goals. The dynamic of this environment requires understanding the interests and capacities of each participant as a way to identify convergences and possible partnerships that can help in the development of new businesses. This paper presents a method to assist the identification interests and vocations from relationships extracted from interactions in social network tools in startups ecosystems. To verify applicability, an empirical study was conducted from a conversation group by Whatsapp held by managers of technology business incubators from Brazilian federal institutes of education and research.

When a novice needs help, often the best solution is to find a human expert who is capable of answering the novice's questions. But often, novices have difficulty characterizing their own questions and expertise and finding appropriate experts. Previous attempts to assist expertise location have provided matchmaking services, but leave the task of classifying knowledge and queries to be performed manually by the participants. We introduce Expert Finder, an agent that automatically classifies both novice and expert knowledge by autonomously analyzing documents created in the course of routine work. Expert Finder works in the domain of Java programming, where it relates a user's Java class usage to an independent domain model. User models are automatically generated that allow accurate matching of query to expert without either the novice or expert filling out skill questionnaires. Testing showed that automatically generated profiles matched well with experts' own evaluation of their skills, and we achieved a high rate of matching novice questions with appropriate experts.

Web browsing, like most of today’s desktop applications, is usually a solitary activity. Other forms of media, such as watching television, are often done by groups of people, such as families or friends. What would it be like to do collaborative Web browsing? Could the computer provide assistance to group browsing by trying to help find mutual interests among the participants? Let’s Browse is an experiment in building an agent to assist a group of people in browsing, by suggesting new material likely to be of common interest. It is built as an extension to the single user Web browsing agent Letizia. Let’s Browse features automatic detection of the presence of users, automated “channel surfing” browsing, and dynamic display of the user profiles and explanation of recommendations.q 1999 Elsevier Science B.V. All rights reserved.