Gonçalo Marcelino

Bio: Gonçalo Marcelino is an academic researcher from Universidade Nova de Lisboa. The author has contributed to research in topics: News media & Social media. The author has an hindex of 3, co-authored 9 publications receiving 16 citations.

Bringing Hybrid Consistency Closer to Programmers

Abstract: Hybrid consistency is a new consistency model that tries to combine the benefits of weak and strong consistency. To implement hybrid consistency, programmers have to identify conflicting operations in applications and instrument them, which is a difficult and error prone task. More recent approaches automatize the process through the use of static analysis over a specification of the application.In this paper we present a new tool that is under development that tries to make the technology more accessible for programmers. Our tool is based on the same well-founded principles of existing work, but uses an intermediate verification language, Boogie, that improves the tool usability and scope in a number of ways. Using a general language for writing specifications makes specifications easier to write and improves expressiveness. Also, we leverage the language to add a library of CRDTs, which allows the programmer to solve conflicts without coordination. We discuss the features that we have already implemented and how they contribute to improve the technology.

A Benchmark of Visual Storytelling in Social Media

Abstract: Media editors in the newsroom are constantly pressed to provide a"like-being there" coverage of live events. Social media provides a disorganised collection of images and videos that media professionals need to grasp before publishing their latest news updated. Automated news visual storyline editing with social media content can be very challenging, as it not only entails the task of finding the right content but also making sure that news content evolves coherently over time. To tackle these issues, this paper proposes a benchmark for assessing social media visual storylines. The SocialStories benchmark, comprised by total of 40 curated stories covering sports and cultural events, provides the experimental setup and introduces novel quantitative metrics to perform a rigorous evaluation of visual storytelling with social media data.

Ranking News-Quality Multimedia

Abstract: News editors need to find the photos that best illustrate a news piece and fulfill news-media quality standards, while being pressed to also find the most recent photos of live events. Recently, it became common to use social-media content in the context of news media for its unique value in terms of immediacy and quality. Consequently, the amount of images to be considered and filtered through is now too much to be handled by a person. To aid the news editor in this process, we propose a framework designed to deliver high-quality, news-press type photos to the user. The framework, composed of two parts, is based on a ranking algorithm tuned to rank professional media highly and a visual SPAM detection module designed to filter-out low-quality media. The core ranking algorithm is leveraged by aesthetic, social and deep-learning semantic features. Evaluation showed that the proposed framework is effective at finding high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and a classification precision of 70%.

Ranking News-Quality Multimedia

Abstract: News editors need to find the photos that best illustrate a news piece and fulfill news-media quality standards, while being pressed to also find the most recent photos of live events. Recently, it became common to use social-media content in the context of news media for its unique value in terms of immediacy and quality. Consequently, the amount of images to be considered and filtered through is now too much to be handled by a person. To aid the news editor in this process, we propose a framework designed to deliver high-quality, news-press type photos to the user. The framework, composed of two parts, is based on a ranking algorithm tuned to rank professional media highly and a visual SPAM detection module designed to filter-out low-quality media. The core ranking algorithm is leveraged by aesthetic, social and deep-learning semantic features. Evaluation showed that the proposed framework is effective at finding high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and a classification precision of 70%.

A Benchmark of Visual Storytelling in Social Media

Abstract: Media editors in the newsroom are constantly pressed to provide a "like-being there" coverage of live events. Social media provides a disorganised collection of images and videos that media professionals need to grasp before publishing their latest news updated. Automated news visual storyline editing with social media content can be very challenging, as it not only entails the task of finding the right content but also making sure that news content evolves coherently over time. To tackle these issues, this paper proposes a benchmark for assessing social media visual storylines. The SocialStories benchmark, comprised by total of 40 curated stories covering sports and cultural events, provides the experimental setup and introduces novel quantitative metrics to perform a rigorous evaluation of visual storytelling with social media data.

Proving the Safety of Highly-Available Distributed Objects

Abstract: To provide high availability in distributed systems, object replicas allow concurrent updates. Although replicas eventually converge, they may diverge temporarily, for instance when the network fails. This makes it difficult for the developer to reason about the object's properties , and in particular, to prove invariants over its state. For the sub-class of state-based distributed systems, we propose a proof methodology for establishing that a given object maintains a given invariant, taking into account any concurrency control. Our approach allows reasoning about individual operations separately. We demonstrate that our rules are sound, and we illustrate their use with some representative examples. We automate the rule using Boogie, an SMT-based tool.

MMSUM Digital Twins: A Multi-view Multi-modality Summarization Framework for Sporting Events

Abstract: Sporting events generate a massive amount of traffic on social media with live moment-to-moment accounts as any given situation unfolds. The generated data are intensified by fans feelings, reactions, and subjective opinions towards what happens during the event, all of which are based on their individual points of view. Analyzing and summarizing this data will generate a comprehensive overview of the event in terms of how the event evolves and how fans react and view the event based on their perspectives. Previously, most of the summarization works ignore fan reactions and subjective opinions, and focus primarily on generating an objective-view summary. We believe that an effective and useful summary should consider human reactions, sentiment, and point of view, as opposed to simply describing what happens during the event. Accordingly, in this work, we propose MMSUM Digital Twins: a summarization framework that is capable of generating a multi-view multi-modal summary for sporting events in real-time. The proposed digital twins-based framework consists of four main components: sub-event recognition which detects the event’s key moments, tweet categorization, which determines which team the tweets’ writers support and assigns tweets to their teams, sentiment analysis to track fans’ state of mind, and image popularity prediction for selecting representative images. Furthermore, the MMSUM employs a visual-filtering model to address the issue of noisy images that inundate social media, compromising the summarization quality. We leverage the knowledge of sport fans to evaluate the generated multi-view summarization through an online user study. The experiment results confirm the effectiveness of our proposed approach for summarizing sporting events by considering multimedia data, sentiment, and subjective views of the event.

Invariant Safety for Distributed Applications.

Abstract: We study a proof methodology for verifying the safety of data invariants of highly-available distributed applications that replicate state. The proof is (1) modular: one can reason about each individual operation separately, and (2) sequential: one can reason about a distributed application as if it were sequential. We automate the methodology and illustrate the use of the tool with a representative example.

Improving the "Correct Eventual Consistency" Tool

Abstract: Preserving invariants while designing distributed applications under weak consistency models is difficult. The CEC (Correct Eventual Consistency Tool) is meant to aid the application designer in this task. It provides information about the errors during concurrent operations and suggestions on how and where to synchronize operations. This report presents two features of the tool: providing a counterexample for debugging and concurrency control suggestions.