An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201

Real-Time Siamese Multiple Object Tracker with Enhanced Proposals

Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://github.com/lorenzovaquero/SiamMOTIONComment: Accepted at Pattern Recognition. Code available at https://github.com/lorenzovaquero/SiamMOTIO

STDnet: Exploiting high resolution feature maps for small object detection

The accuracy of small object detection with convolutional neural networks (ConvNets) lags behind that of larger objects. This can be observed in popular contests like MS COCO. This is in part caused by the lack of specific architectures and datasets with a sufficiently large number of small objects. Our work aims at these two issues. First, this paper introduces STDnet, a convolutional neural network focused on the detection of small objects that we defined as those under 16 × 16 pixels. The high performance of STDnet is built on a novel early visual attention mechanism, called Region Context Network (RCN), to choose the most promising regions, while discarding the rest of the input image. Processing only specific areas allows STDnet to keep high resolution feature maps in deeper layers providing low memory overhead and higher frame rates. High resolution feature maps were proved to be key to increasing localization accuracy in such small objects. Second, we also present USC-GRAD-STDdb, a video dataset with more than 56,000 annotated small objects in challenging scenarios. Experimental results over USC-GRAD-STDdb show that STDnet improves the [email protected] of the best state-of-the-art object detectors for small target detection from 50.8% to 57.4%. Performance has also been tested in MS COCO for objects under 16 × 16 pixels. In addition, a spatio-temporal baseline network, STDnet-bST, has been proposed to make use of the information of successive frames, increasing the [email protected] of STDnet in 2.3%. Finally, optimizations have been carried out to be fit on embedded devices such as Jetson TX2This research was funded by Gradiant, Spain, and also partially funded by the Spanish Ministry of Economy and Competitiveness under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32 (MICINN), and the Galician Ministry of Education, Culture and Universities, Spain under grant ED431G/08. Brais Bosquet is supported by the Galician Ministry of Education, Culture and Universities, Spain . These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Real-time siamese multiple object tracker with enhanced proposals

Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://www.github.com/lorenzovaquero/SiamMOTIONThis research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2021/048, ED431G 2019/04]. These grants are co-funded by the European Regional Development Fund (ERDF). Lorenzo Vaquero is supported by the Spanish Ministerio de Universidades under the FPU national plan (FPU18/03174). We also gratefully acknowledge the support of NVIDIA Corporation for hardware donations used for this researchS

Tracking more than 100 arbitrary objects at 25 FPS through deep learning

Most video analytics applications rely on object detectors to localize objects in frames. However, when real-time is a requirement, running the detector at all the frames is usually not possible. This is somewhat circumvented by instantiating visual object trackers between detector calls, but this does not scale with the number of objects. To tackle this problem, we present SiamMT, a new deep learning multiple visual object tracking solution that applies single-object tracking principles to multiple arbitrary objects in real-time. To achieve this, SiamMT reuses feature computations, implements a novel crop-and-resize operator, and defines a new and efficient pairwise similarity operator. SiamMT naturally scales up to several dozens of targets, reaching 25 fps with 122 simultaneous objects for VGA videos, or up to 100 simultaneous objects in HD720 video. SiamMT has been validated on five large real-time benchmarks, achieving leading performance against current state-of-the-art trackersThis research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2017/69, accreditation 2016–2019, ED431G/08]. These grants are co-funded by the European Regional Development Fund (ERDF). Lorenzo Vaquero is supported by the Spanish Ministerio de Universidades under the FPU national plan (FPU18/03174)S

Short-term anchor linking and long-term self-guided attention for video object detection

We present a new network architecture able to take advantage of spatio-temporal information available in videos to boost object detection precision. First, box features are associated and aggregated by linking proposals that come from the same anchor box in the nearby frames. Then, we design a new attention module that aggregates short-term enhanced box features to exploit long-term spatio-temporal information. This module takes advantage of geometrical features in the long-term for the first time in the video object detection domain. Finally, a spatio-temporal double head is fed with both spatial information from the reference frame and the aggregated information that takes into account the short- and long-term temporal context. We have tested our proposal in five video object detection datasets with very different characteristics, in order to prove its robustness in a wide number of scenarios. Non-parametric statistical tests show that our approach outperforms the state-of-the-art. Our code is available at https://github.com/daniel-cores/SLTnetThis research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Repairing Alignments of Process Models

Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds