Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker’s utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network’s random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks’ random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model.     

Steel ratios for cost optimum reinforced concrete beams

The amount of main-reinforcement steel in reinforced concrete beams is limited by maximum and minimum steel ratios. No guidance is given by design codes as how to choose the ratios within the permissible range. Using systematic direct search, the influence of various factors on the optimal steel ratio for single-reinforced concrete beams is studied. Using the design code of the American Concrete Institute, it is found that the most influential factors in selection of steel ratios are support conditions and material costs. Applied loads and yield strength of steel are shown to have some influence.     

Effect of the solvent type on the formation and physical properties of polyacrylonitrile fibers via a solvent‐free coagulation bath

Polyacrylonitrile (PAN) fibers were fabricated via a dry‐jet wet‐spinning technique, and a solvent‐free coagulation bath system was adopted. The effects of different types of dope solvent on the formation and physical properties of the PAN fibers were investigated. Dimethylformamide and dimethyl sulfoxide (DMSO) were selected as the solvents and were added to a spinning solution consisting of 18 wt % PAN. The PAN fibers were examined with field emission scanning electron microscopy, differential scanning calorimetry, and thermogravimetric analysis. The field emission scanning electron micrographs revealed that the PAN fibers with the DMSO solvent exhibited a more circular shape and a smoother skin. The PAN fibers with the DMSO solvent had their glass‐transition temperature (T_g) at 121°C. This study indicated that the different types of dope solvent used in the dope preparation did not affect T_g of the PAN fibers because of the solvent‐free coagulation bath system; however, they significantly affected the physical formation of the PAN fibers. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Analysing the Combined Effect of Barrier Wall and Freshwater Injection Countermeasures on Controlling Saltwater Intrusion in Unconfined Coastal Aquifer Systems

Water Resources Management - Freshwater recharge wells and underground flow barriers are among several methods proposed for controlling saltwater intrusion (SWI) into coastal aquifers. In this...     

Accuracy improvement of high resolution satellite image georeferencing using an optimized line-based rational function model

The georeferencing procedure of high resolution satellite images (HRSIs) using proper mathematical models is an important step in 3D spatial information extraction. Since the 2000s, line-based mathematical models are considered more in photogrammetric communities. This could be due to the unique characteristics of linear features such as more reliable procedure of automatic matching as well as abundance of linear features in satellite images. In addition, the irreplaceable characteristics of Rational Function Models (RFM) such as generality and its independence to the sensor model makes it a proper mathematical model for this purpose. Although, traditional selection of the best order and terms’ combination of RFM due to the deficiency of physical interpretation of terms is not easily feasible. Hence, in this article, an optimization algorithm based on a binary particle swarm optimization (PSO) is developed to determine the optimum uncorrelated terms’ combination of a line-based RFM. For this purpose, a population of particles (representative of different RFM structures) is initialized randomly with a string of binary values, indicating the presence or omission of the corresponding terms. This is followed by directly using some conjugate lines in the image and object spaces as ground control lines (GCLs) to solve the unknown parameters of the RFM for each particle. The root mean square error (RMSE) of some check points (CPs) for each particle is considered to be its cost function and used to update the velocity of particles. The procedure is repeated to reach a stopping criteria. A comprehensive evaluation on the proposed model in comparison to the traditional line-based RFM is examined. Two different HRSIs (a GeoEye and an Ikonos Images) over different areas of Iran are used for this purpose. Based on the results, a traditional line-based RFM could not reach to sub-pixel accuracy. In contrast, the results show the potential of the proposed optimized line-based RFM to increase the accuracy to better than 0.8 pixel as well as reduce the systematic errors and the number of required control information, significantly.     

Characterization and mechanical properties of polyacrylonitrile/silica composite fibers prepared via dry-jet wet spinning process

Polyacrylonitrile (PAN)/silica composite fibers were fabricated by dry-jet wet spinning process. PAN/silica composite fibers were characterized with SEM and FTIR. The former revealed that beads were formed and aggregated when silica content was more than 1 wt.%, while the latter confirmed the presence and increment of silica content. The tensile test was performed to obtain the mechanical properties of PAN/silica composite fibers, which showed an optimum Young's modulus at 5.94 GPa and tensile strength at 1.07 MPa at 1 wt.% silica. Therefore, the addition of silica particle at 1 wt.% has enhanced the mechanical properties of PAN/silica composite fibers.     

A blockchain-enabled security management framework for mobile edge computing

Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.