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This paper concerns with the event-based dynamic output feedback control for the synchronization of fuzzy neural networks under mixed delay and deception attacks. A weighted sum-based dynamic event-triggered mechanism (WSDETM) is developed to save the communication resources while preserving a satisfactory system performance. A dynamic output feedback controller (DOFC) is designed to achieve exponential synchronization of fuzzy neural networks. To reduce the data traffic, both communication channels from the sensor to DOFC and DOFC to Zero-Order Holder are subject to WSDETM. Different from the traditional deception attacks modeled by Bernoulli process, we adopt the more general Markov process modeling deception attacks. By using the cone-complimentarity linearization algorithm, the DOFC and WSDETM parameters are carried out. The effectiveness of the proposed method is demonstrated with two numerical cases.

Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra’s algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hop-based solutions. However, the hierarchy-based solutions require large search space for long-distance queries, while the hop-based solutions result in high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an efficient query processing algorithm with performance guarantees by visiting part of the labels for the source and the destination based on the hierarchy. We propose a novel algorithm to construct the H2H-Indexbased on distance preserved graphs. We also extend the H2H-Indexand propose a set of algorithms to identify the shortest path between vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.

The paper considers the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time. Based on a delayed state feedback controller, the stability of coupled impulsive neural networks with time-varying delays and saturating actuators can be achieved in finite time. Combined with Lyapunov-based finite-time stability theory, some sufficient conditions are obtained to ensure the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time by using polytopic representation approach and sector nonlinearity model approach, respectively. Moreover, the setting time of coupled impulsive neural networks with saturating actuators is given, and it is found to be related to both the initial state and impulse effect. Furthermore, as special cases, some finite-time stability results of coupled impulsive neural networks with saturating actuators are given under a memoryless controller. Finally, two simulation examples are used to test the effectiveness of the obtained results.

The paper considers the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time. Based on a delayed state feedback controller, the stability of coupled impulsive neural networks with time-varying delays and saturating actuators can be achieved in finite time. Combined with Lyapunov-based finite-time stability theory, some sufficient conditions are obtained to ensure the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time by using polytopic representation approach and sector nonlinearity model approach, respectively. Moreover, the setting time of coupled impulsive neural networks with saturating actuators is given, and it is found to be related to both the initial state and impulse effect. Furthermore, as special cases, some finite-time stability results of coupled impulsive neural networks with saturating actuators are given under a memoryless controller. Finally, two simulation examples are used to test the effectiveness of the obtained results.

Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a query graph is within a threshold. As GED computation is NP-hard, the existing works adopt the filtering-and-verification paradigm to reduce the number of GED verifications, and they mainly focus on designing filtering techniques while using the now out-dated algorithm A*GED for verification. In this paper, we aim to speed up GED verification, which is orthogonal to the index structures used in the filtering phase. We propose a best-first search algorithm AStar+-LSa which improves A*GED by (1) reducing memory consumption, (2) tightening lower bound estimation, and (3) improving the time complexity for lower bound computation. We formally show that AStar+-LSa has a lower space and time complexity than A*GED. We further modify AStar+-LSa into a depth-first search algorithm to contrast these two search paradigms, and we extend our algorithms for exact GED computation. We conduct extensive empirical studies on real graph datasets, and show that our algorithm AStar+-LSa outperforms the state-of-the-art algorithms by several orders of magnitude for both GED verification and GED computation.

© 2020 Association for Computing Machinery. Computing top-k nearest neighbors (kNN) is a fundamental problem in road networks. Existing solutions either need a complicated parameter configuration in index construction or incur high costs when scanning an unbounded number of vertices in query processing. In this paper, we propose a novel parameter-free index-based solution for the kNN query based on the concept of tree decomposition in large road networks. Based on our index structure, we propose an efficient and progressive algorithm that returns each result in a bounded delay. We also optimize the index structure, which improves the efficiency of both index construction and index maintenance in large road networks. We conduct extensive experiments to show the efficiency of our proposed algorithms and the effectiveness of our optimization techniques in real-world road networks from ten regions.

Computing the shortest path between two vertices is a fundamental problem in road networks that is applied in a wide variety of applications. To support efficient shortest path query processing, a plethora of index-based methods have been proposed in the literature, but few of them can support dynamic road networks commonly encountered in practice, as their corresponding index structures cannot be efficiently maintained when the input road network is dynamically updated. Motivated by this, we study the shortest path index maintenance problem on dynamic road networks in this paper. We adopt Contraction Hierarchies (CH) as our underlying shortest path computation method because of its outstanding overall performance in pre-processing time, space cost, and query processing time and aim to design efficient algorithms to maintain the index structure, shortcut index, of CH when the input road network is dynamically updated. To achieve this goal, we propose a shortcut-centric paradigm focusing on exploring a small number of shortcuts to maintain the shortcut index. Following this paradigm, we design an auxiliary data structure named SS-Graph and propose a shortcut weight propagation mechanism based on the SS-Graph. With them, we devise efficient algorithms to maintain the shortcut index in the streaming update and batch update scenarios with non-trivial theoretical guarantees. We experimentally evaluate our algorithms on real road networks and the results demonstrate that our approach achieves 2-3 orders of magnitude speedup compared to the state-of-the-art algorithm for the streaming update.

Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a query graph is within a threshold. As GED computation is NP-hard, the existing works adopt the filtering-and-verification paradigm to reduce the number of GED verifications, and they mainly focus on designing filtering techniques while using the now out-dated algorithm A*GED for verification. In this paper, we aim to speed up GED verification, which is orthogonal to the index structures used in the filtering phase. We propose a best-first search algorithm AStar+-LSa which improves A*GED by (1) reducing memory consumption, (2) tightening lower bound estimation, and (3) improving the time complexity for lower bound computation. We formally show that AStar+-LSa has a lower space and time complexity than A*GED. We further modify AStar+-LSa into a depth-first search algorithm to contrast these two search paradigms, and we extend our algorithms for exact GED computation. We conduct extensive empirical studies on real graph datasets, and show that our algorithm AStar+-LSa outperforms the state-of-the-art algorithms by several orders of magnitude for both GED verification and GED computation.

© 2018 Association for Computing Machinery. Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hopbased solutions. However, the hierarchy-based solutions require a large search space for long-distance queries while the hop-based solutions result in a high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an e?cient query processing algorithm with performance guarantees by visiting part of the labels for the source and destination based on the vertex hierarchy. We also propose an algorithm to construct the H2H-Index based on distance preserved graphs. The algorithm is further optimized by computing the labels based on the partially computed labels of other vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.

© 2018 Association for Computing Machinery. Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hopbased solutions. However, the hierarchy-based solutions require a large search space for long-distance queries while the hop-based solutions result in a high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an e?cient query processing algorithm with performance guarantees by visiting part of the labels for the source and destination based on the vertex hierarchy. We also propose an algorithm to construct the H2H-Index based on distance preserved graphs. The algorithm is further optimized by computing the labels based on the partially computed labels of other vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.

© 2018 Association for Computing Machinery. Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.

© 2018 Association for Computing Machinery. Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.

© Springer International Publishing AG, part of Springer Nature 2018. Path skyline query is a fundamental problem in bicriteria network analysis and is widely applied in a variety of applications. Given a source s and a destination t in a bicriteria network G, path skyline query aims to identify all the skyline paths from s to t in G. In the literature, PSQ is a fundamental algorithm for path skyline query and is also used as a building block for the afterwards proposed algorithms. In PSQ, a key operation is to record the skyline paths from s to v for each node v that is possible on the skyline paths from s to t. However, to obtain the skyline paths for v, PSQ has to maintain other paths that are not skyline paths for v, which makes PSQ inefficient. Motivated by this, in this paper, we propose a new algorithm PSQ+ for the path skyline query. By adopting an ordered path exploring strategy, our algorithm can totally avoid the fruitless path maintenance problem in PSQ. We evaluate our proposed algorithm on real networks and the experimental results demonstrate the efficiency of our proposed algorithm. Besides, the experimental results also demonstrate the algorithm that uses PSQ as a building block for the path skyline query can achieve a significant performance improvement after we substitute PSQ+ for PSQ.

© Springer International Publishing AG, part of Springer Nature 2018. Path skyline query is a fundamental problem in bicriteria network analysis and is widely applied in a variety of applications. Given a source s and a destination t in a bicriteria network G, path skyline query aims to identify all the skyline paths from s to t in G. In the literature, PSQ is a fundamental algorithm for path skyline query and is also used as a building block for the afterwards proposed algorithms. In PSQ, a key operation is to record the skyline paths from s to v for each node v that is possible on the skyline paths from s to t. However, to obtain the skyline paths for v, PSQ has to maintain other paths that are not skyline paths for v, which makes PSQ inefficient. Motivated by this, in this paper, we propose a new algorithm PSQ+ for the path skyline query. By adopting an ordered path exploring strategy, our algorithm can totally avoid the fruitless path maintenance problem in PSQ. We evaluate our proposed algorithm on real networks and the experimental results demonstrate the efficiency of our proposed algorithm. Besides, the experimental results also demonstrate the algorithm that uses PSQ as a building block for the path skyline query can achieve a significant performance improvement after we substitute PSQ+ for PSQ.

This paper presents an improved model-based reasoning method to test circuit faults. The testing procedure is applicable even when the target system contains multiple faulty modes. Using our method, the observation could be planned appropriately to guarantee correct solutions to be in the restricted candidate space. The existent consistency-checking method and abductive reasoning method are special cases of our method. The relationship between the testing procedure and the corresponding prime implication is analyzed for algorithmic implementation. © 2005 Elsevier B.V. All rights reserved.

This paper presents an improved model-based reasoning method to test circuit faults. The testing procedure is applicable even when the target system contains multiple faulty modes. Using our method, the observation could be planned appropriately to guarantee correct solutions to be in the restricted candidate space. The existent consistency-checking method and abductive reasoning method are special cases of our method. The relationship between the testing procedure and the corresponding prime implication is analyzed for algorithmic implementation. © 2005 Elsevier B.V. All rights reserved.

We propose a many-sorted general framework to incorporate algebraic computation with logical reasoning, which equally encompasses following systems as special cases: lattice-valued fuzzy logic, operator fuzzy logic, operator fuzzy logic for belief, operator fuzzy logic for argumentation, fuzzy logic, probabilistic logic, annotated logic, language of signed formulas, autoepistemic logic. © 2003 SRCE University Computing Centre.

A general method for model-based diagnosis is developed, which can handle multiple faulty modes, and will enable users to analyze the completeness of the system model, and to choose the observation subset appropriately, in order to have small diagnostic space with the right solutions in. The existent consistency-based diagnosis and abductive diagnosis are special cases of this method. The relationship between the diagnostic procedure and corresponding prime implication is analyzed for implementation. © 2003 SRCE University Computing Centre.

We propose a many-sorted general framework to incorporate algebraic computation with logical reasoning, which equally encompasses following systems as special cases: lattice-valued fuzzy logic, operator fuzzy logic, operator fuzzy logic for belief, operator fuzzy logic for argumentation, fuzzy logic, probabilistic logic, annotated logic, language of signed formulas, autoepistemic logic. © 2003 SRCE University Computing Centre.

A general method for model-based diagnosis is developed, which can handle multiple faulty modes, and will enable users to analyze the completeness of the system model, and to choose the observation subset appropriately, in order to have small diagnostic space with the right solutions in. The existent consistency-based diagnosis and abductive diagnosis are special cases of this method. The relationship between the diagnostic procedure and corresponding prime implication is analyzed for implementation. © 2003 SRCE University Computing Centre.

The gain dynamics in electrically-pumped InGaAs quantum dot amplifiers at 300 K is measured to be in the subpicosecond range for both ground and excited state transitions, promising for all-optical signal processing at >40 GHz repetition rates.