In recent years, there has been a massive increase in multimedia data due to the increasing use of social media and communication technology. So, extracting useful information from a large set of data has become diffi...
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ISBN:
(纸本)9781665403603;9781665447751
In recent years, there has been a massive increase in multimedia data due to the increasing use of social media and communication technology. So, extracting useful information from a large set of data has become difficult and very time consuming. Multimedia Data (Text, Image, Audio, Video) summarization is a very useful technology that overcomes this challenge by eliminating information that is redundant or useless, and extracting only the relevant key details of the events in summaries. A lot of work has been done in this field to generate summaries in the form of text and images, but very limited research has been done to produce a multimodular summary especially on Asynchronous Data. This research work proposes an ILP based model, which takes a multimodal dataset (text, images, videos) as input and generates textual and image video summary as output. The results were obtained by comparing the two basic baseline's ROUGE values with the proposed model. Results for different modalities have confirmed that the proposed model performs better than the other baseline approaches.
Phase-only tapering for sidelobe suppression is a simple method of phased array design due to the simplification of antenna manufacturing. When using digital phase shifters, considering the quantization of the phase, ...
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ISBN:
(纸本)9781665468893
Phase-only tapering for sidelobe suppression is a simple method of phased array design due to the simplification of antenna manufacturing. When using digital phase shifters, considering the quantization of the phase, we employ integer linear programming to adjust the phased shifter and get a better pattern. The proposed method combines a fast sidelobe formula with a power reduction mathematical technique. Finally, we conduct numeric experiments to verify the proposed method.
When resolving logical contradictions in ontologies, Reiter's hitting set tree algorithm is often applied to satisfy the minimal change principle. To improve the efficiency, the researchers have proposed various a...
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When resolving logical contradictions in ontologies, Reiter's hitting set tree algorithm is often applied to satisfy the minimal change principle. To improve the efficiency, the researchers have proposed various algorithms by using a scoring function, defining new semantics or applying some heuristic strategies. However, these algorithms either sacrifice minimal change or are designed for less expressive ontologies like DL-Lite. In this paper, we propose a mathematic approach based on integer linear programming, which is an optimization problem of maximizing or minimizing a linear objective function, to deal with DL ontologies. Specifically, we define the integer linear programming-based model to resolve logical contradictions. To realize the model, we propose one algorithm to find a cardinality-minimal solution and two algorithms dealing with weighted ontologies. Our experiments are conducted over 70 real-life and artificial ontologies to compare our algorithms with those hitting set tree-based ones. Through the experiments, the prominent efficiency and effectiveness have been exhibited by our algorithms. They usually take about 0.4 s to find a solution while others spend more than 100 s in many cases. The experimental results also show that the first two algorithms could find the cardinality-minimal solutions and those with a minimal sum of weights, respectively.
This paper deals with a problem related to the observability of discrete event systems: the initial-state opacity. Given a set of system states (the secret), a system observation is called initial-state opaque if an a...
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This paper deals with a problem related to the observability of discrete event systems: the initial-state opacity. Given a set of system states (the secret), a system observation is called initial-state opaque if an agent (named intruder), who can partially observe the system, cannot determine whether the set of initial states consistent with an event sequence is included in the secret. Such a character can describe security problems in cyber-infrastructures, such as Internet and mobile communication networks or national defense service systems. This work presents a novel on-line methodology to verify the notion of initial-state opacity of discrete event systems that are modeled by labeled Petri nets. By working on-line, the intruder records an event and exploits integer linear programming problem for checking the initial-state opacity of the system's evolution under the given observation. A set of examples are shown to shed light on the efficiency of the presented methodology. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
In this study, we propose a method for acoustic event diarization based on a feature of deep embedding and a clustering algorithm of integer linear programming. The deep embedding learned by deep auto-encoder network ...
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In this study, we propose a method for acoustic event diarization based on a feature of deep embedding and a clustering algorithm of integer linear programming. The deep embedding learned by deep auto-encoder network is used to represent the properties of different classes of acoustic events, and then the integer linear programming is adopted for merging audio segments belonging to the same class of acoustic events. Four kinds of TV/movie audios (21.5 h in total) are used as experimental data, including Sport, Situation comedy, Award ceremony, and Action movie. We compare the deep embedding with state-of-the-art features. Further, the clustering algorithm of integer linear programming is compared with other clustering algorithms adopted in previous works. Finally, the proposed method is compared to both supervised and unsupervised methods on four kinds of TV/movie audios. The results show that the proposed method is superior to other unsupervised methods based on agglomerative information bottleneck, Bayesian information criterion and spectral clustering, and is little inferior to the supervised method based on deep neural network in terms of acoustic event error.
The explosive traffic growths are pushing the transport network close to its capacity limitation, raising critical issues about fiber capacity crunch. In this context, network coding has been emerging as the promising...
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The explosive traffic growths are pushing the transport network close to its capacity limitation, raising critical issues about fiber capacity crunch. In this context, network coding has been emerging as the promising technique to improve the network capacity efficiency thanks to the capability of better resources utilization. The application of network coding to the realms of failure recovery in optical networks has paved the new way for more efficient protection schemes and indeed, XOR network coding combined with dedicated protection has been proposed, investigated and developed to challenge the well-established understanding of trading capacity efficiency for recovery speed and vice versa. In order to maximize the benefits empowered by network coding in this case, the problem of 1 + 1 routing and network coding assignment (1 + 1 RNCA) has to be optimally solved. Apart from traditional 1 + 1 routing, the decision of network coding information has also to be taken into account including the selection of pair of demands for encoding and the respective coding node and coding links. In this paper, we propose a bi-objective integer linear programming model of the 1 + 1 RNCA problem aiming at minimizing the conventional routing cost as the primary objective and furthermore minimizing the number of nodes with coding capabilities as the secondary objective. Our formulation uses a weighting method to combine two objectives into an integrated one and we provide a rigorous analysis on configuring the weight coefficients to capture the desired priority of individual objectives. The efficiency of our integrated objective model in comparison with reference designs based on the single-objective model, 1 + 1 routing and 1 + 1 RNCA, is numerically evaluated on different realistic topologies and traffic sets. Extensive simulation demonstrates that our proposal outperforms traditional approaches when it could achieve the lowest routing cost while simultaneously employing minimal nu
This paper proposes a Petri net based mathematical programming approach to combinatorial optimization, in which we generate integer linear programming problems from Petri net models instead of the direct mathematical ...
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This paper proposes a Petri net based mathematical programming approach to combinatorial optimization, in which we generate integer linear programming problems from Petri net models instead of the direct mathematical formulation. We treat two types of combinatorial optimization problems, ordinary problems and time-dependent problems. Firstly, we present autonomous Petri net modeling for ordinary optimization problems, where we obtain fundamental constraints derived from Petri net properties and additional problem-specific ones. Secondly, we propose a colored timed Petri net modeling approach to time-dependent problems, wherewe generate variables and constraints for time management and for resolving conflicts. Our Petri net approach can drastically reduce the difficulty of the mathematical formulation in a sense that (1) the Petri net modeling does not require deep knowledge of mathematical programming and technique of integerlinear model formulations, (2) our automatic formulation allows us to generate large size of integer linear programming problems, and (3) the Petri net modeling approach is flexible for input parameter changes of the original problem.
Network coding techniques have been viewed as the promising venue to improve the network efficiency and indeed, have been widely explored in the realm of failure recovery in optical networks. The combination of near-i...
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Network coding techniques have been viewed as the promising venue to improve the network efficiency and indeed, have been widely explored in the realm of failure recovery in optical networks. The combination of near-instantaneous recovery achieved by dedicated protections and capacity efficiency enabled by network coding brings about new opportunities to challenge the well-established trade-off of trading speed recovery for capacity efficiency and vice versa. In this context, the use of all-optical XOR network coding has reshaped the traditional 1+1 optical path protection in transparent WDM optical networks and established a new problem, called, 1+1 routing, wavelength and network coding assignment (1+1 RWNCA) as the extension of the conventional 1+1 routing and wavelength assignment (1+1 RWA). In this paper, we propose a bi-objective integer linear programming model of the 1+1 RWNCA problem aiming at minimizing the wavelength resources as the primary objective and simultaneously minimizing the wavelength link usage as the secondary objective. Our formulation uses a weighting method to combine two objectives into an integrated one and we provide insights on setting up the weight vectors to capture the priority of individual objectives. The effectiveness of our integrated objective model in comparison with reference designs based on the single-objective model, 1+1 RWA and 1+1 RWNCA, is numerically evaluated on different realistic topologies and traffic sets. Extensive simulation highlights that our proposal uses as efficient as around 60% of the required wavelength link resources of reference designs and simultaneously achieve the highest performance on the primary objective of minimizing the wavelength resources while its computation time is a few time longer than its single objective counterpart model.
This paper establishes a new integer linear programming model for container loading problem. This model can be used to calculate the optimal loading plan for each container. To solve the model, in this paper, the mode...
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This paper establishes a new integer linear programming model for container loading problem. This model can be used to calculate the optimal loading plan for each container. To solve the model, in this paper, the model problem is decomposed into two easy to solve sub-problems: auxiliary problem (AP) and transportation problem, and prove by solving the two sub-problems can quickly and efficiently to find the optimal solution of the model. Finally, an example is given to illustrate the solution process, which shows that the algorithm can give the optimal stowage scheme quickly and effectively.
Cryptographic ICs have been widely applied to numerous security-critical environments nowadays. Fault injection has become a serious attack on cryptographic IC, especially soft-errors or single event upsets (SEUs) by ...
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Cryptographic ICs have been widely applied to numerous security-critical environments nowadays. Fault injection has become a serious attack on cryptographic IC, especially soft-errors or single event upsets (SEUs) by fine-resolution fault injection attacks. Detection and tamper evidence of these attacks become important. Traditional SEU diagnose methods usually require special sensors embedded into the circuits. However, these methods require non-trivial design and test effort, and usually just yield statistic results. In this paper, we formulate the detection fault injection attacks as a compressed sensing problem, due to sparsity of soft errors. Besides, due to the binary characteristic of the coefficient matrix and the variables, integer linear programming is adopted to reconstruct the soft error signals. Simulation results on a cryptographic IC demonstrate that the proposed method is capable to accurately detect the locations of soft-errors caused by fault injection attacks with negligible hardware overhead. The abnormal test output of scan-chains can be tamper evidence of the fault injection attacks.
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