varunr14/newsummary huggingface dataset:
This paper is devoted to two issues involved in the one class support vector machine i.e . the optimization algorithm and the kernel parameter selection . For appropriate choices of parameters the primal maximum margin problem of OCSVM is equivalent to a nearest point problem . A generalized Gilbert algorithm is proposed to solve the nearest point problem . Compared with the algebraic algorithms developed for OCSVM such as the well known sequential minimal optimization algorithm the GG algorithm is a novel geometric algorithm that has an intuitive and explicit optimization target at each iteration . Moreover an improved MIES is developed for the Gaussian kernel parameter selection . IMIES is implemented by constraining the geometric locations of edge and interior sample mappings relative to OCSVM separating hyper planes . The experimental results on 2 D artificial datasets and benchmark datasets show that IMIES is able to select suitable kernel parameters and the GG algorithm is computationally more efficient while achieving comparable accuracies to the SMO algorithm . 


Summary: The primal maximum margin problem of OCSVM is equivalent to a nearest point problem. A generalized Gilbert GG algorithm is proposed to solve the nearest point problem. An improved MIES is developed for the Gaussian kernel parameter selection. The GG algorithm is computationally more efficient than the SMO algorithm.
Knowledge reduction is a basic issue in knowledge representation and data mining . Although various methods have been developed to reduce the size of classical formal contexts the reduction of formal fuzzy contexts based on fuzzy lattices remains a difficult problem owing to its complicated derivation operators . To address this problem we propose a general method of knowledge reduction by reducing attributes and objects in formal fuzzy contexts based on the variable threshold concept lattices . Employing the proposed approaches we remove attributes and objects which are non essential to the structure of a variable threshold concept lattice i.e . with a given threshold level the concept lattice constructed from a reduced formal context is made identical to that constructed from the original formal context . Discernibility matrices and Boolean functions are respectively employed to compute the attribute reducts and object reducts of the formal fuzzy contexts by which all the attribute reducts and object reducts of the formal fuzzy contexts are determined without changing the structure of the lattice . 


Summary: We proposed a general method of knowledge reduction in formal fuzzy contexts. We gave some judging methods of attribute characteristics in fomal fuzzy contexts. We constucted the discernibility functions to calculating the attribute reducts.
This study analyzed the Cost efficiency and Revenue efficiency of 207 certified public accountant firms in Taiwan by using the additive efficiency decomposition DEA approach . Furthermore this study applied the Tobit regression to explore the relationship between CPA firms and intellectual capital . The study found that the Big 4 and CPA firms that practiced auditing in China were relatively efficient in both cost and revenue . In addition this research discovered that CPA firms relying mainly on auditing are more efficient in creating revenue and utilizing costs . Furthermore the Tobit regression was employed to evaluate whether IC affected CPA firms cost efficiency and revenue efficiency . This study found that IC played an important role in performance representation both in cost efficiency and revenue efficiency . Therefore this study suggests that CPA firms should manage IC efficiently to enhance CPA firms competitive abilities . 


Summary: We apply a two stage DEA model to evaluate cost efficiency and revenue efficiency. The additive efficiency decomposition DEA approach is utilized. We examine whether intellectual capital affects performance through Tobit models. We find that intellectual capital has a positive impact on CPA firms performance.
From traditional clusters to cloud systems job scheduling is one of the most critical factors for achieving high performance in any distributed environment . In this paper we propose an adaptive algorithm for scheduling modular non linear parallel jobs in meteorological Cloud which has a unique parallelism that can only be configured at the very beginning of the execution . Different from existing work our algorithm takes into account four characteristics of the jobs at the same time including the average execution time the deadlines of jobs the number of assigned resources and the overall system loads . We demonstrate the effectiveness and efficiency of our scheduling algorithm through simulations using WRF that which is widely used in scientific computing . Our evaluation results show that the proposed algorithm has multiple advantages compared with previous methods including more than 10 reduction in terms of execution time a higher completion ratio in terms of meeting soft deadlines and a much smaller standard deviation of the average weighted execution time . Moreover we show that the proposed algorithm can tolerate inaccuracy in system load estimation . 


Summary: WRF is used to discuss characteristic of non linear speedup parallel jobs. We give an analysis under different loads for non linear speedup parallel jobs. An adaptive scheduling method is used to schedule resources for parallel jobs. Simulations based on WRF are used to test our method.
In multiple attribute decision making different attribute weights may generate different solutions which means that attribute weights significantly influence solutions . When there is a lack of sufficient data knowledge and experience for a decision maker to generate attribute weights the decision maker may expect to find the most satisfactory solution based on unknown attribute weights called a robust solution in this study . To generate such a solution this paper proposes a robust evidential reasoning approach to compare alternatives by measuring their robustness with respect to attribute weights in the ER context . Alternatives that can become the best with the support of one or more sets of attribute weights are firstly identified . The measurement of robustness of each identified alternative from two perspectives i.e . the optimal situation of the alternative and the insensitivity of the alternative to a variation in attribute weights is then presented . The procedure of the proposed approach is described based on the combination of such identification of alternatives and the measurement of their robustness . A problem of car performance assessment is investigated to show that the proposed approach can effectively produce a robust solution to a MADM problem with unknown attribute weights . 


Summary: We propose a robust evidential reasoning approach for multiple attribute decision making. The possible set of best alternatives is identified using unknown attribute weights. The robustness of the alternatives in the set is measured from two perspectives. A robust rank order of the alternatives in the set is generated by their robustness. Intervals of utilities and relevant constraints are handled in the proposed approach.
In order to follow modern trends in contemporary building architecture which is moving off the limits of current fire design models assumption of homogeneous temperature conditions used for structural fire analysis needs to be validated . In this paper it is described how temperature distribution in a medium size fire compartment has been investigated experimentally by conducting fire test in two storey experimental building in September 2011 in the Czech Republic . In the upper floor a scenario of travelling fire was prepared . It has been observed that as flames were spreading across the compartment considerable temperature gradients appeared . Numerical simulation of the travelling fire test conducted using FDS has been compared with simulation of compartment fire under uniform temperature conditions to highlight the potential impact of the gas temperature heterogeneity on structural behaviour . The temperature measurements from the fire test have been used for validation of the numerical simulation of travelling fire . The fire test has provided important data for design model of travelling fire and shown that its impact on structural behaviour is not in agreement with the assumption of homogenous temperature conditions . 


Summary: Temperature distribution in medium fire compartment is investigated experimentally. Numerical simulation of the travelling fire test is completed by using FDS. Measurements from the travelling fire test are used to validate the simulation. Travelling fire is compared to compartment fire with uniform temperature conditions. A potential impact of temperature heterogeneity on a structure is highlighted.
Smoke is a leading cause of death in fire . To minimize the potential harm from the smoke hazards in the course of a fire a rational virtual reality based fire training simulator taking full account of the various aspects of smoke hazards has been developed and is described herein . In this simulator a visualization technique based on volume rendering and fire dynamics data has been especially designed to create a realistic and accurate smoke environment for the purposes of effective virtual training which allows the trainees to experience a realistic and yet non threatening fire scenario . In addition an integrated assessment model of smoke hazards is also established in order to assess the safety of different paths for evacuation or rescue in virtual training which allows the trainees to learn to identify the safest path . Two case studies of a subway station and a primary school demonstrated a high level of accuracy and smooth interactive performance of the proposed simulator which is thus shown to be valuable for the training of both people who might become trapped in fire and firefighters engaged in learning the proper rescue procedures . 


Summary: A VR fire training simulator with smoke hazard assessment capacity is proposed. Realistic and accurate smoke environment is created for virtual training. Integrated smoke hazard in the path for evacuation or rescue is accurately assessed. A subway station and a primary school are investigated for virtual fire training. The simulator helps trainee identify the safest path and minimize smoke hazards.
This paper presents an open and integrated framework that performs the structural design optimization by associating the improved sequential approximation optimization algorithm with the CAD CAE integration technique . In the improved SAO algorithm a new estimate of the width of Gaussian kernel functions is proposed to enhance the surrogate models for SAO . Based on the improved surrogate models an adaptive sampling strategy is developed to balance the exploration exploitation in the sampling process which better balances between the competence to locate the global optimum and the computation efficiency in the optimization process . Fewer function evaluations are required to seek the optimum which is of great significance for computation intensive structural optimization problems . Moreover based on scripting program languages and Application Programming Interfaces integration between commercial CAD and CAE software packages is implemented to expand the applications of the SAO algorithm in mechanical practices . Two benchmark tests from simple to complex from low dimension to moderate dimension were performed to validate the efficacy of the proposed framework . Results show that the proposed approach facilitates the structural optimization process and reduces the computing cost immensely compared to other approaches . 


Summary: An improved sequential approximation optimization algorithm is developed. An estimate of width of Gaussian kernel functions for surrogate models is proposed. An adaptive sampling strategy is developed based on the improved surrogate model. A framework that integrates CAD CAE and SAO tools is developed.
The main goal of this paper was to develop an integrated simulation design of experiments model to optimize a petrol station queuing system and sales rate . Initially the petrol station operating system was simulated using Witness 2014 simulation software . Then the responses of simulation were deployed as the input of DOE . Two level full factorial experiments with center points were performed where the simulated model parameter studied were number of pump number of cashier and inter arrival times . The response variables analyzed were queue length and sales rate . The obtained model from experimental design revealed that number of cashier and inter arrival time were significant in determining the queue length while all the factors and their interaction were significantly affecting the sales rate . 


Summary: A case study was simulated to provide a realistic depiction of an operating system. Investigated factors were number of pump number of cashier and IATs. 2 level full factorial experiments were used for further analysis of simulation. The performance of a service industry queuing system has been analyzed. The influential parameters on queue length and sales rate have been obtained.
The present article proposes an advanced methodology for numerically simulating complex noise problems . More precisely we consider the so called multi stage acoustic hybrid approach which principle is to couple sound generation and acoustic propagation stages . Under that approach we propose an advanced hybrid method which acoustic propagation stage relies on Computational AeroAcoustics techniques . To this end first an innovative weak coupling technique is developed which allows an implicit forcing of the CAA stage with a given source signal coming from an a priori evaluation whether the latter evaluation is of analytical or computational nature . Then thanks to additional innovative solutions the resulting CAA based hybrid approach is optimized so that it can be applied to realistic and complex acoustic problems in an easier and safer way . All these innovative features are then validated on the basis of an academic test case before the resulting advanced CAA based hybrid methodology is applied to two problems of flow induced noise radiation . This demonstrates the ability of the here proposed method to address realistic problems by offering to handle at the same time both acoustic generation and propagation phenomena despite their intrinsic multiscale character . 


Summary: An advanced hybrid method for computational acoustics is proposed. Acoustic propagation is simulated via the Perturbed Euler Equations. Acoustic generation and propagation stages are weakly coupled through an interface. The method is optimized via innovative features forcing derivation interpolation . The method is validated and illustrated via noise problems of increasing complexity.
In reinforced concrete structural experiments the development of concrete surface cracks is an important factor of concern to experts . One conventional crack observation method is to suspend a test at a few selected testing steps and send inspectors to mark pen strokes on visible cracks but this method is dangerous and labor intensive . Many image analysis methods have been proposed to detect and measure the dark shadow lines of cracks reducing the need for manual pen marking . However these methods are not applicable for thin cracks which do not present clear dark lines in images . This paper presents an image analysis method to capture thin cracks and minimize the requirement for pen marking in reinforced concrete structural tests . The paper presents the mathematical models procedures and limitations of our image analysis method as well as the analysis flowchart the adopted image processing and analysis methods and the software implementation . Finally the results of applying the proposed method in full scale reinforced concrete bridge experiments are presented to demonstrate its performance . Results demonstrate that this method can capture concrete surface cracks even before dark crack lines visible to the naked eye appear . 


Summary: An image analysis method for crack observation in a concrete pier is proposed. This method manifests concrete cracks before there became visible to the naked eyes. We present the procedures flowchart and software implementation of this method.
One of the most important activities in software project planning involves scheduling tasks and assigning them to developers . Project managers must decide who will do what and when in a software project with the aim of minimizing both its duration and cost . However project managers often struggle to efficiently allocate developers and schedule tasks in a way that balances these conflicting goals . Furthermore the different criteria used to select developers could lead to inaccurate estimation of the duration and cost of tasks resulting in budget overruns delays or reduced software quality . This paper proposes an approach that makes use of multi objective optimization to handle the simultaneous minimization of project cost and duration taking into account several productivity related attributes for better estimation of task duration and cost . In particular we focus on dealing with the non interchangeable nature of human resources and the different ways in which teams carry out work by considering the relationship between the type of task interdependence and the productivity rate of developers as well as the communication overhead incurred among developers . The approach is applied to four well known optimization algorithms whose performance and scalability are compared using generated software project instances . Additionally several real world case studies are explored to help discuss the implications of such approach in the software development industry . The results and observations show positive indications that using a productivity based multi objective optimization approach has the potential to provide software project managers with more accurate developer allocation and task scheduling solutions in a more efficient manner . 


Summary: Proposed approach adopts MOGAs to minimize software project cost and duration. Solutions representing resource allocations and task schedules are evolved. Objective functions consider productivity of developers and task interdependence. The performance and scalability of four MOGAs were compared using several datasets. MOCell NSGA II and SPEA2 outperform PAES in the majority of project instances.
Automatic segmentation of non stationary signals such as electroencephalogram electrocardiogram and brightness of galactic objects has many applications . In this paper an improved segmentation method based on fractal dimension and evolutionary algorithms for non stationary signals is proposed . After using Kalman filter to reduce existing noises FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal . In order to select two acceptable parameters of FD in this paper two authoritative EAs namely genetic algorithm and imperialist competitive algorithm are used . The proposed approach is applied to synthetic multi component signals real EEG data and brightness changes of galactic objects . The proposed methods are compared with some well known existing algorithms such as improved nonlinear energy operator Varri s and wavelet generalized likelihood ratio methods . The simulation results demonstrate that segmentation by using KF FD and EAs have greater accuracy which proves the significance of this algorithm . 


Summary: Recognition of physiological signal patters involving non stationarity. The non stationary signal patterns are partitioned into variable size stationary segments. Deterministic features are recognized using Kalman filter. Indeterministic features are estimated using fractal dimensions. A revolutionary approach is proposed to determine the best feature sets.
The Colliding Bodies Optimization algorithm is a metaheuristic algorithm inspired by the physics laws of collision in which each candidate solution is modeled as an agent with mass body in proportion to the fitness of the solution . In this paper a modified version of CBO denoted by MCBO is utilized to optimize the cost of bridge superstructures . The problem consists of 17 variables and 101 implicit constraints based on AASHTO standard specifications and construction limitations . The optimization is performed for bridges with different span lengths and deck widths and with various unit costs of concrete . A comparison among the PSO CBO and MCBO algorithms is conducted which shows the efficiency and robustness of the MCBO algorithm . 


Summary: A modified version of CBO denoted by MCBO is utilized to optimize the cost of bridge superstructures. The problem consists of 17 variables and 101 implicit constraints based on AASHTO standard. Optimization is performed for bridges with different span lengths deck widths and with various unit costs of concrete. A comparison among the PSO CBO and MCBO algorithms is conducted.
Colliding Bodies Optimization is a new multi agent algorithm inspired by a collision between two objects in one dimension . Each agent is modeled as a body with a specified mass and velocity . A collision occurs between pairs of objects and the new positions of the colliding bodies are updated based on the collision laws . In this paper Enhanced Colliding Bodies Optimization which uses memory to save some best solutions is developed . In addition a mechanism is utilized to escape from local optima . The performance of the proposed algorithm is compared to those of standard CBO and some optimization techniques on some benchmark mathematical functions and three standard discrete and continuous structural design problems . Optimization results confirm the validity of the proposed approach . 


Summary: A new multi agent algorithm inspired by a collision between two objects in one dimension is presented. An enhanced colliding bodies optimization which uses memory to save some best solutions is developed. A mechanism is utilized to escape from local optima. Performance of the proposed algorithm is compared to those of standard CBO and some optimization techniques.
Computational efficiency is still a great challenge for the generation of the Medial Axis for complicated CAD models . Current research mainly focuses on CPU based MA generation methods . However most of the methods emphasize using a single CPU . The highly efficient methods based on parallel computing are still missing . In this study a parallel method based on multi CPU is proposed for the efficient MA generation of CAD models using distance dilation . By dividing the whole model into several parts for which MAs are calculated in parallel and then combined computational efficiency can be greatly improved in theory and the computation time can be reduced nearly K times if K CPUs are used . Firstly an adaptive division method is proposed to divide the voxelized model into blocks which have nearly the same number of voxels to balance the computational burden . Secondly the local Euclidean Distance Transform is calculated for each block based on the existing distance dilation method . Thirdly the complete inter dilation method is proposed to compute the influence between different blocks to get a global EDT for each block . Finally each block generates a sub MA separately and then all the generated MAs are combined to obtain the final MA . The last three processes can be efficiently conducted in parallel by using multiple CPUs . Several groups of experiments are conducted which demonstrate the good performance of the proposed methods in terms of efficiency . 


Summary: An efficient approach to generating MA in parallel by multiple CPUs is proposed. An adaptive division method is proposed to maximize the parallelism degree. Inter dilation is proposed to ensure the correctness of final MA.