12,590 research outputs found
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readersâ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
J-MOD: Joint Monocular Obstacle Detection and Depth Estimation
In this work, we propose an end-to-end deep architecture that jointly learns
to detect obstacles and estimate their depth for MAV flight applications. Most
of the existing approaches either rely on Visual SLAM systems or on depth
estimation models to build 3D maps and detect obstacles. However, for the task
of avoiding obstacles this level of complexity is not required. Recent works
have proposed multi task architectures to both perform scene understanding and
depth estimation. We follow their track and propose a specific architecture to
jointly estimate depth and obstacles, without the need to compute a global map,
but maintaining compatibility with a global SLAM system if needed. The network
architecture is devised to exploit the joint information of the obstacle
detection task, that produces more reliable bounding boxes, with the depth
estimation one, increasing the robustness of both to scenario changes. We call
this architecture J-MOD. We test the effectiveness of our approach with
experiments on sequences with different appearance and focal lengths and
compare it to SotA multi task methods that jointly perform semantic
segmentation and depth estimation. In addition, we show the integration in a
full system using a set of simulated navigation experiments where a MAV
explores an unknown scenario and plans safe trajectories by using our detection
model
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.Comment: This work has been submitted to IROS/RAL 201
Initiating organizational memories using ontology network analysis
One of the important problems in organizational memories is their initial set-up. It is difficult to choose the right information to include in an organizational memory, and the right information is also a prerequisite for maximizing the uptake and relevance of the memory content. To tackle this problem, most developers adopt heavy-weight solutions and rely on a faithful continuous interaction with users to create and improve its content. In this paper, we explore the use of an automatic, light-weight solution, drawn from the underlying ingredients of an organizational memory: ontologies. We have developed an ontology-based network analysis method which we applied to tackle the problem of identifying communities of practice in an organization. We use ontology-based network analysis as a means to provide content automatically for the initial set up of an organizational memory
Identification of microservices from monolithic applications through topic modelling
Microservices emerged as one of the most popular architectural patterns in the recent years given the increased need to scale, grow and
flexibilize software projects accompanied by the growth in cloud
computing and DevOps. Many software applications are being submitted to a process of migration from its monolithic architecture to
a more modular, scalable and flexible architecture of microservices.
This process is slow and, depending on the projectâs complexity, it
may take months or even years to complete.
This paper proposes a new approach on microservice identification by resorting to topic modelling in order to identify services
according to domain terms. This approach in combination with
clustering techniques produces a set of services based on the original software. The proposed methodology is implemented as an
open-source tool for exploration of monolithic architectures and
identification of microservices. A quantitative analysis using the
state of the art metrics on independence of functionality and modularity of services was conducted on 200 open-source projects collected from GitHub. Cohesion at message and domain level metricsâ
showed medians of roughly 0.6. Interfaces per service exhibited a
median of 1.5 with a compact interquartile range. Structural and
conceptual modularity revealed medians of 0.2 and 0.4 respectively.
Our first results are positive demonstrating beneficial identification of services due to overall metricsâ resultsNational Funds through the Portuguese
funding agency, FCT - Fundação para a CiĂȘncia e a Tecnologia
within project UIDB/50014/202
- âŠ