Software systems are critical resources for our daily lives. Due to that, computer science students need to learn software engineering knowledge and skills to develop software systems with the expected quality, under budget, and on schedule. The size and complexity of software systems and the short time to market make it necessary to have various software developers working as a team. In this scenario, the success of a project depends on coordination, communication, and collaboration. The evolution of network, communication, and computer technologies made it possible for people from different countries to work on the same project. Therefore, software engineers must collaborate remotely with others in Global Software Engineering (GSE) projects. Hence, students must also learn how to collaborate with people from different countries, with specific expectations, and different cultures and languages. This study lists the main GSE challenges the students must be ready to deal with, how researchers have addressed GSE, and how GSE education has been approached in the last five years. Researchers focused on teaching approaches, what students experienced during GSE courses, and the importance of GSE in industry. Students had the opportunity to face unsolved GSE challenges, and GSE courses still need to be improved.

The learning game “Present of the Future” has shown great potential as an effective tool for promoting futures literacy and speculative design in various educational and professional contexts. Through interactive and collaborative scenarios, participants are encouraged to explore future possibilities and develop creative solutions to contemporary challenges. Despite its promising impact, the game still lacks a structured and systematic process for evaluating the user experience on three levels: the immediate reaction to the game experience, the retention of learning in the short and long term, and collaboration in creating future artifacts. We propose a detailed experiment and an evaluative questionnaire composed of open and closed questions to fill this gap and provide a foundation for critically analyzing these dimensions. Although the experiment has not yet been conducted, the research establishes a robust theoretical and methodological framework that will allow, in future stages, the collection and analysis of quantitative and qualitative data to complement and enrich this study. Thus, we expect that the future application of these practices will contribute to a deeper understanding of the pedagogical and collaborative effects of the game.

With the advancement of Artificial Intelligence (AI) technologies, the world has been increasingly reshaped across multiple domains - healthcare, finance, education, and creative fields. This expansion transforms individual tasks and facilitates complex collaborations where humans and machines work to- gether in unprecedented ways. Through machine learning and, more specifically, large language models (LLMs), AI now plays a crucial role in enhancing productivity. Recent technological advancements have significantly boosted the text generation capabilities of LLMs, enabling them to simulate personas that can represent groups or specific well-known individuals. By simulating expert perspectives, LLMs provide insights to guide human decision-making across various collaborative frameworks in Computer-Supported Cooperative Work (CSCW). Personas, as simulated identities, have proven helpful in group brainstorming sessions, consensus-building exercises, and virtual advisory boards, which help represent diverse viewpoints and facilitate group decisions. Among these CSCW methods, the Delphi method is a well-established approach for reaching consensus among experts through iterative feedback and structured discussions. This work introduces a framework to manage and evaluate LLM-generated personas simulating expert input in Delphi studies. Our contributions include a framework designed for evaluating LLM personas in Delphi studies using a text similarity validation technique comparing real and simulated expert opinions and an evaluation of a Delphi about Brazilian higher education, assessing the LLMs' reliability in real-world scenarios. Findings show LLMs can effectively replicate expert perspectives, enhancing AI integration in specialized decision-making.

Esports - games played competitively - comprise a major sector of the global games industry. Esports has been used as a testbed for game AI and game analytics for two decades. This article presents a multivocal literature mapping of available research that focuses strictly on the use of artificial intelligence approaches in Multiplayer Online Battle Arena (MOBA) games, one of the most popular esports genres and the one most widely used for game AI and game analytics research. A mapping is performed on relevant publications published between 2011 and 2022 and systematically examines them to extract similarities, gaps, and main findings. We analyzed 124 publications to identify the most studied topics, the most commonly used techniques, and the most commonly applied evaluation methods. The results show that League of Legends and DOTA are the most studied games, with outcome prediction being the most popular research topic. Finally, we provide an analysis of the potential future flagship areas for research in the domain, considering the gaps found in the white and grey literature.

The military has developed wargames to explore their doctrines and train operational concepts. Wargames are often designed to teach educational objectives and share and produce knowledge. The success of a wargame design depends on how it achieves these objectives. Players are the main actors in wargames and often group into teams. The final result of the wargame depends on the collaboration and interaction between the members of each team. Therefore, this work aims to understand how collaboration occurs in wargames. Moreover, we observed the activities and processes during a large-scale wargame to gather how the players collaborate. A better understanding of collaboration in wargames may provide insights for game designers to improve the wargames design process to consider how the players collaborate.

Victory prediction in online video games has become an important application for machine learning due to the large amount of data generated by these games and their growing popularity. The creation of professional leagues also drives these applications, as teams want to know their chances of victory and know what are the determining factors to achieve it. Thus, in this research, we analyze whether pre-game information can be explored for victory prediction of professional matches of League of Legends (LoL), one of the main MOBA games. In experiments, we benchmarked different feature sets and algorithms to assess the victory predictions in LoL. The results show that historical performance information is the most accurate features for performing this task. The induced models, especially Random Forest and Logistic Regression, achieved AUC values of 0.97.

In the last decade, games with a purpose (GWAP) have emerged as an effective way to recruit and organize a large number of volunteers to help address difficult challenges. In previous work, a framework for crowdsourced collection, cleaning, and measurement of digital content was presented and used in the design of Cassino Musical, a game for procuring and measuring the musical talent of unknown artists. Preliminary results from an online experiment with the game provide basic proof that GWAP can be successfully applied to the task of measuring musical talent, and are described in this paper.

The Joint Planning Process of the Brazilian Ministry of Defense comprises the elaboration of possible courses of action (COA) to be adopted in a joint operation. The Commander, advised by his General Staff, assesses each COA and selects subjectively the one that best meets the mission statement. Such decision-making process has no simulation tool to assist the Commander and his General Staff in confronting their own COA with possible enemy COAs. We analyzed how the Brazilian Ministry of Defense (MD) codifies knowledge in such simulated confrontation, their structures and the knowledge flow. Starting with the information from MD doctrines, we structured the explicit knowledge in models and diagrams, which describe business processes, taxonomies, conceptual maps, and mental maps. Understanding the knowledge creation process increases the efficiency of an operation, while reduces confrontation errors, minimizing revision efforts and optimizing the use of the operational capabilities of the Armed Forces.

Identifying and measuring a musical talent is not a simple task that could be performed by computers. In addition to the technical challenges, it also involves social, emotional and cultural issues. This problem is aggravated when the task needs to be performed taking as input the huge amount of music content available on the Internet today. In this paper we report work in progress on a social interactive Game with a Purpose using fun as a key motivational factor to assist in recruitment and measurement of musical talent.

Search engines are traditionally designed to support a single user working alone; however, the literature describes various problems faced by children in the use of this search model. The construction of knowledge is enriched when collaboration is aggregated to search tasks. We propose a collaborative information retrieval architecture for children. The design was guided by the dimensions of 5W+1H (who, what, where, when, why, how) and identified seven remote opportunities for collaboration in the search process. The social search model of the architecture integrates parents and children and aims at improving the search process for children. The architecture uses explicit and implicit social collaboration. Implicit collaboration includes collaborative filtering and content-based filtering using the Protocol for Web Description Resource (POWDER). Intelligent agents assist the coordination of communication, negotiation and collaboration processes.

The aim of this paper is to present a framework for crowd sourced collection, cleaning and measurement of digital content that follows the dynamics of a game with a purpose (GWAP) for capturing the collective knowledge and also for computing tasks through players' interactions. Using this framework we developed a game called Cassino Musical as a solution to the recruitment and measurement of musical talent from unknown artists who post videos of their musical performances on the Internet.

Over the past 5 decades society has been increasingly requiring immediate results, and this is reflected in business. Due to this, the attention given to strategy, strategy management and strategy planning has increased. The use of the Balanced Scorecard (BSC) is a way of achieving this strategic capability. Moreover, a proper BSC has effective metrics to track its goals. This work proposes a system that recommends metrics in accordance with the goals of a BSC. The recommendation is made using data mining techniques, in particular association rules.

Design is a knowledge intensive activity, which depends on information to be undertaken. Previous designs, recent innovations, new techniques and materials must be taken into account when working on a new project, whatever its nature. Therefore, retrieving appropriate information is of paramount importance to the design process. Information retrieval is an established field, which has seen great progress in recent years. However, as the amount of unstructured information available increases, it becomes harder to retrieve the most relevant information using standard techniques and, frequently, users end up with results that don't fulfill their information needs. In this paper, we present an approach to improve retrieval using query expansion through ontologies allied to faceted navigation. We constructed the Ontoogle tool, which uses an ontology describing a domain to expand the query and define result ranking and presentation using Faceted Navigation. This proposal was evaluated in a case study and the results indicate that this approach contributes to increased satisfaction and confidence of users during their search process.

Recommendation systems are largely used nowadays to support collaborative tasks. However it is important to consider the knowledge about each user of the system. In this paper we describe the use of knowledge to improve the recommendation system of the Business Process Cooperative Editor (BPCE), a collaborative business process modeling tool. We introduce the concept of Knowledge Vectors, and how they are applied to BPCE, balancing recommendations with the knowledge the recommender has on the subject of the evaluated model.

This paper describes a collaborative tool, ARARA (Artifacts and Requirements Awareness Reinforcement Agents), that provides awareness in Software Development Process (SDP). Ontologies support this application for they are used by project team members when preparing the artifacts, propagating the concepts through them, and used by agents as knowledge base to retrieve information, tag the artifacts, detect changes, and search for linked artifacts that may be affected by a change on a previous artifact. This collaborative tool can improve team members awareness automatically no matter where they are geographically working, once agents are responsible for tracking and notifying requirements, or any other changes, to team members, including consequent candidates for modifications.

Business process management systems are largely used nowadays. Process Modeling, whether for depicting new processes or for optimizing existing ones, requires knowledge and proper tools. However, most process models are created from scratch, not having reuse promoted, i.e., not considering existing

In order to evaluate information retrieval algorithms it is imperative to use a dataset as a test database. However, access to such datasets is often difficult and expensive, since building them is a time-consuming and costly task. This paper presents a collaborative approach to dataset creation that uses a data quality evaluation technique based on fuzzy theory, to assist users in selecting suitable Web documents for their datasets. These documents are automatically captured by a crawler and assessed on information derived from their metadata.

Conferences are an important environment for academic knowledge exchange, and are valued by researchers as a good way to increase ones social network and meet potential collaborators. The question we investigate in this work is whether conferences actually influence collaboration between researchers, providing a starting point for joint work. To conduct this investigation, we used the DBLP database containing 640.977 distinct authors, 1.057.207 papers published in 21.354 journals and conferences spanning over more than 70 years. Using the concept of coauthorship as evidence of collaboration, we found that at best only 123.886 collaborations (from a total of 2.685.224, or 4,61%) could have emerged from the authors having met at a conference. This leads us to the conclusion that conference environments do not significantly promote collaboration among participants.

Corporate and personal computers are flooded by a huge amount of data. Among them, there are irrelevant, similar, false, wrong and obsolete data. Besides, the treatment of this data is relatively complex. Systems need to check, transform, adapt, and summarize data in order to use it. These activities spend time and money of many companies that should be concerned for business rules. In RSS feeds, we have these problems: the users receive a big number of news, sometimes irrelevant or duplicated. The tools to cope with these data do not provide efficient mechanisms to manager information overload. To reach this goal, it is necessary to introduce a new complexity to feed readers, what is sometimes undesirable. Therefore, we propose the autonomic collaborative RSS that transfers the system complexity to data, facilitating the system development. At the same time, it allows to incorporate data treatment rules, as well, data filtering through data killing patterns.

Currently, Fourier and cosine discrete transformations are used to classify documents. This article proposes a new strategy that uses wavelets in the representation and reduction of data text. Wavelets have been extensively used for dimensionality reduction in the field of signal processing. In this work, we show that a text document, after being subjected to a simple process of reorganization of its terms, can be treated like a signal and analyzed by signal processing tools. We demonstrate that this new representation is able to describe the most relevant features of documents in a synthetic representation and this new perspective improves the performance of the classification algorithm.

It may be very difficult to design in advance a global system model which accomplishes all organizational unit idiosyncrasies in complex organizations dispersed across large geographical regions. In this work, we propose the use of our Coppeer framework for agent based peer-to-peer application development to implement an environment in which distributed systems can be collaboratively and incrementally built. The key points of the approach are the use of semi-structured documents as data model, the partitioning of system code in several loosely coupled agents and the use of ontologies to conciliate differences in organizational unit vocabularies.

In this work, we first present a model of emergent design which allows a community of designers to collectively translate a set of requirement specifications into a completed design. Then, we describe the COPPEER framework, an environment for developing and running collaborative peer-to-peer applications which directly supports the main requirements elicited in our proposal

In this paper we consider a process which complements the learning process for building personal knowledge through the exchange of knowledge chains. This approach consists in the partial automatization of the process of creating knowledge chains, through the use of the technology from agents, ontologies and data mining. The agents will monitor all media used by the learner, and will classify its content using an ontology. From there, we want to create and recommend a chain to the learner. This point became important when we observed that the learners weren't motivated to create their chains, which, normally, takes a lot of effort

This paper presents SRST, a tool designed to support knowledge management and collaboration activities of the student-supervisor relationship that arise during the development of a graduate thesis. In universities, graduate students account for the greatest part of the research work force, and are usually in close collaboration with their supervisors. Most student-supervisor interaction can be characterized as a form of knowledge management. SRST unites these concepts in a single and original tool and deals with four of the elements related to a thesis: "ideas", "tasks", "discussions", and "documents"