Heterogeneous Information Networks (HINs) play a crucial role in modeling and analyzing multimedia systems and heterogeneous data. They provide a comprehensive understanding of entities and relationships within complex data structures. However, integrating HINs with machine learning tasks poses challenges that require specific models or vector space representation. This paper proposes an innovative embedding propagation graph method for HINs with textual data. By leveraging language models like BERT, our method propagates contextual text embeddings, combining the network’s topological information and the semantic information of textual objects, which are then propagated to non-textual objects within the network. The method facilitates the integration of machine learning techniques with various modeling approaches, enhancing analysis capabilities in multimedia and heterogeneous data domains. Through robust experimental evaluations on different datasets and in three application domains, our method demonstrates competitive performance, enabling direct comparison of entities and relationships within a unified latent space. This research highlights the potential of HINs for intelligent analysis and information retrieval in multimedia systems and heterogeneous data contexts.
Studies based on traditional data sources like surveys, for instance, offer poor scalability. The experiments are limited, and the results are restricted to small regions (such as a city or a state). The use of location-based social network (LBSN) data can mitigate the scalability problem by enabling the study of social behavior in large populations. When explored with Data Mining and Machine Learning techniques, LBSN data can be used to provide predictions of relevant cultural and behavioral data from cities or countries around the world. The main goal of this work is to predict and explore user behavior from LBSNs in the context of tourists’ mobility patterns. To achieve this goal, we propose PredicTour, which is an approach used to process LBSN users’ check-ins and to predict mobility patterns of tourists with or without previous visiting records when visiting new countries. PredicTour is composed of three key blocks: mobility modeling, profile extraction, and tourist’ mobility prediction. In the first block, sequences of check-ins in a time interval are associated with other user information to produce a new structure called "mobility descriptor”. In the profile extraction, self-organizing maps and fuzzy C-means work together to group users according to their mobility descriptors. PredicTour then identifies tourist’ profiles and estimates their mobility patterns in new countries. When comparing the performance of PredicTour with three well-known machine learning-based models, the results indicate that PredicTour outperforms the baseline approaches. Therefore, it is a good alternative for predicting and understanding international tourists’ mobility, which has an economic impact on the tourism industry, particularly when services and logistics across international borders should be provided. The proposed approach can be used in different applications such as recommender systems for tourists, and decision-making support for urban planners interested in improving both the tourists’ experience and attractiveness of venues through personalized services.
Context: Software Ecosystems (SECO) portals are web interfaces that allow a developer to access an ecosystem. They allow the consumption of information and communication by actors. Motivation: Improving the Developer Experience (DX) is an important concern for developers to be engaged with a SECO portal. Transparency helps in the understanding of the information made available and in the communication between the actors. Problem: If the DX is unsatisfactory, developers can abandon the portal and consequently the SECO. Objective: The objective of this work is to use methods and tools to capture multimodal and interaction information in SECO portals, aiming to promote the improvement of transparency aspects that can lead to the improvement of DX in such portals. Research Method: Studies were carried out with developers and methods were used for quantitative and qualitative data analysis. Results: Factors that influence DX in the software development process and factors related to transparency that influence DX were identified based on reports from the study participants. Contributions: The main contribution of this work is to support the engagement of developers in SECO portals and encourage the improvement of transparency about the information and processes made available.
Skin cancer is a global public health challenge, accounting for approximately one-third of cancer diagnoses worldwide. The state of Espírito Santo has tens of thousands of inhabitants of European descent. Most of them have fair skin and are engaged in family farming, often exposed to the sun. The combination of this vulnerable phenotype with such sun exposure results in a high incidence of skin cancer in the state. Since 1987, the Federal University of Espírito Santo has maintained a dermatological and surgical assistance program, providing free care to the most vulnerable population. Starting from a partnership that began in 2018, the Dermatological Analysis Software (SADE) was developed, a system used to collect, manage, and screen skin lesions during the program’s care. Since its implementation, the software has had a significant impact on assisting the population, reducing both waiting and service times. Additionally, SADE has enabled a range of technical and scientific achievements, such as publications, awards, and participation in events.
Este estudo analisa o comportamento dos usuários do Telegram durante as eleições presidenciais de 2022 em grupos políticos, visando compreender as interações e trocas de informações entre eleitores com diferentes posições políticas. Para preencher as lacunas de conhecimento nesse campo específico de pesquisa, uma análise abrangente dos dados foi realizada, identificando padrões de comportamento adotados pelos usuários. O estudo contribui para o entendimento do papel das redes sociais, especificamente o Telegram, nas dinâmicas eleitorais contemporâneas. Além disso, o dataset construído também é outra contribuição do estudo e ele será disponibilizado para a comunidade científica.
