INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Content-based retrieval of technical drawings

Abstract: This paper presents a new approach to classify, index and retrieve technical drawings by content. Our work uses spatial relationships, shape geometry and high-dimensional indexing mechanisms to retrieve complex drawings from CAD databases. This contrasts with conventional approaches which use mostly textual metadata. Creative designers and draftspeople often re-use data from previous projects, publications and libraries of ready-to-use components. Usually, retrieving these drawings is a slow, complex and error-prone endeavour. Unfortunately, the widespread use of CAD systems, while making it easier to create drawings, exacerbates this problem, insofar as the number of projects grows enormously, without providing adequate searching mechanisms to support retrieving these documents. We describe an approach that supports automatic indexation of technical drawing databases through drawing simplification, feature extraction and efficient algorithms to index large amounts of data. We describe in detail our classification process and present results from usability tests on our prototype.

Asynchronous lease-based replication of software transactional memory

Abstract: Software Transactional Memory (STM) systems have emerged as a powerful middleware paradigm for parallel programming. At current date, however, the problem of how to leverage replication to enhance dependability and scalability of STMs is still largely unexplored. In this paper we present Asynchronous Lease Certification (ALC), an innovative STM replication scheme that exploits the notion of asynchronous lease to reduce the replica coordination overhead and shelter transactions from repeated abortions due to conflicts originated on remote nodes. These features allow ALC to achieve up to a tenfold reduction of the commit latency phase in scenarios of low contention when compared with state of the art fault-tolerant replication schemes, and to boost the throughput of longruning transactions by a 4x factor in high conflict scenarios.

XIS-UML Profile for eXtreme Modeling Interactive Systems

Abstract: The first version of the XIS profile addressed the development of interactive systems by defining models oriented only towards how the system should perform tasks. However, issues such as user-interface layouts, or the capture of interaction patterns, were not addressed by the profile, but only by the source-code generation process. This originated systems that, although functional, were considered by end-users as "difficult to use". In this paper we present the second version of the XIS UML profile, which is now a crucial component of the ProjectIT research project. This profile follows the "separation of concerns" principle by proposing an integrated set of views that address the various issues detected with the previous version of XIS. In addition, this profile also promotes the usage of extreme modeling, by relying on the extensive use of model-to-model transformation templates that are defined to accelerate the model development tasks

UAV-Based Situational Awareness System Using Deep Learning

Abstract: Situational awareness by Unmanned Aerial Vehicles (UAVs) is important for many applications such as surveillance, search and rescue, and disaster response. In those applications, detecting and locating people and recognizing their actions in near real-time can play a crucial role for preparing an effective response. However, there are currently three main limitations to perform this task efficiently. First, it is currently often not possible to access the live video feed from a UAV’s camera due to limited bandwidth. Second, even if the video feed is available, monitoring and analyzing video over prolonged time is a tedious task for humans. Third, it is typically not possible to locate random people via their cellphones. Therefore, we developed the Person-Action-Locator (PAL), a novel UAV-based situational awareness system. The PAL system addresses the first issue by analyzing the video feed onboard the UAV, powered by a supercomputer-on-a-module. Specifically, as a support for human operators, the PAL system relies on Deep Learning models to automatically detect people and recognize their actions in near real-time. To address the third issue, we developed a Pixel2GPS converter that estimates the location of people from the video feed. The result – icons representing detected people labeled by their actions – is visualized on the map interface of the PAL system. The Deep Learning models were first tested in the lab and demonstrated promising results. The fully integrated PAL system was successfully tested in the field. We also performed another collection of surveillance data to complement the lab results.

Semi-supervised single-label text categorization using centroid-based classifiers

Abstract: In this paper we study the effect of using unlabeled data in conjunction with a small portion of labeled data on the accuracy of a centroid-based classifier used to perform single-label text categorization. We chose to use centroid-based methods because they are very fast when compared with other classification methods, but still present an accuracy close to that of the state-of-the-art methods. Efficiency is particularly important for very large domains, like regular news feeds, or the web.We propose the combination of Expectation-Maximization with a centroid-based method to incorporate information about the unlabeled data during the training phase. We also propose an alternative to EM, based on the incremental update of a centroid-based method with the unlabeled documents during the training phase.We show that these approaches can greatly improve accuracy relatively to a simple centroid-based method, in particular when there are very small amounts of labeled data available (as few as one single document per class).Using one synthetic and three real-world datasets, we show that, if the initial model of the data is sufficiently precise, using unlabeled data improves performance. On the other hand, using unlabeled data degrades performance if the initial model is not precise enough.