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- Kristan, M., et al.
(author)
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The Eighth Visual Object Tracking VOT2020 Challenge Results
- 2020
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In: Computer Vision. - Cham : Springer International Publishing. - 9783030682378 ; , s. 547-601
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ).
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- Chuzhoy, J., et al.
(author)
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A deterministic algorithm for balanced cut with applications to dynamic connectivity, flows, and beyond
- 2020
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In: Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS. - : IEEE Computer Society. ; , s. 1158-1167
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Conference paper (peer-reviewed)abstract
- We consider the classical Minimum Balanced Cut problem: Given a graph G, compute a partition of its vertices into two subsets of roughly equal volume, while minimizing the number of edges connecting the subsets. We present the first deterministic, almost-linear time approximation algorithm for this problem. Specifically, our algorithm, given an n-vertex m-edge graph G and any parameter 1 leq r leq O(log n), computes a (log m){r{2}}-approximation for Minimum Balanced Cut in G, in time O left(m{1+O(1/r)+o(1)} cdot (log m){O(r{2})} right). In particular, we obtain a (log m){1 epsilon}-approximation in time m{1+O(sqrt{epsilon})} for any constant epsilon > 0, and a (log m){f(m)}-approximation in time m{1+o(1)}, for any slowly growing function f(m). We obtain deterministic algorithms with similar guarantees for the Sparsest Cut and the Lowest-Conductance Cut problems. Our algorithm for the Minimum Balanced Cut problem in fact provides a stronger guarantee: It either returns a balanced cut whose value is close to a given target value, or it certifies that such a cut does not exist by exhibiting a large subgraph of G that has high conductance. We use this algorithm to obtain deterministic algorithms for dynamic connectivity and minimum spanning forest, whose worst-case update time on an n-vertex graph is n{o(1)}, thus resolving a major open problem in the area of dynamic graph algorithms. Our work also implies deterministic algorithms for a host of additional problems, whose time complexities match, up to subpolynomial in n factors, those of known randomized algorithms. The implications include almost-linear time deterministic algorithms for solving Laplacian systems and for approximating maximum flows in undirected graphs.
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- Guo, H., et al.
(author)
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Embracing modern technologies and urban development trends : Initial evaluation of a smart city enterprise architecture frameworks
- 2020
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In: European, Mediterranean, and Middle Eastern Conference on Information Systems. - Cham : Springer. - 9783030443214 - 9783030443221 ; , s. 247-257
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Conference paper (peer-reviewed)abstract
- The development of smart cities becomes increasingly reliant on leveraging modern technologies such as Internet of Things (IoT), big data, cloud computing, and Artificial Intelligence (AI). While the importance of applying such technologies has been widely recognized, they might not have been effectively discussed in the early design phase. As a widely applied planning and architecting tool, traditional Enterprise Architecture Frameworks (EAF) are not always able to meet requirements of urban development in an expected way. This might be alleviated by applying a smart city-oriented EAF which supports discussion of modern techniques in the early design phase. In the EU smart city project +CityxChange, an EAF was proposed to address such issues. In this article, we focus on an initial evaluation of the EAF proposed in the +CityxChange project according to the Design Science Research (DSR) method. We discuss how the EAF has enhanced the widely used EAF (i.e., The Open Group Architecture Framework, TOGAF for short) by extending its layer-based Enterprise Architecture (EA). We also present a sample scenario demonstrating how the EAF can be used.
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| 8. |
- Huang, R., et al.
(author)
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Prediction and Optimization of WAG Flooding by Using LSTM Neural Network Model in Middle East Carbonate Reservoir
- 2021
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In: Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021. - : Society of Petroleum Engineers.
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Conference paper (peer-reviewed)abstract
- Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.
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