群智感知中的参与者信誉评估方案

针对群智感知网络中参与者群体大,且获取和提交任务几乎不受限制,使得群智感知网络存在数据冗余度高和数据质量不能得到保证的问题,提出了针对参与者提交数据质量和可信度的信誉评估方案——参与者信誉评估方案（PRES）。从参与者提交数据的响应时间、距离、历史信誉度、数据相关性和数据质量五个方面对参与者信誉进行评估,将这五个参数数值化,并利用逻辑回归模型建立回归方程,得出参与者本次提交数据后的信誉度。PRES得出的参与者信誉度在[0.0,1.0]范围内,且集中分布于[0.0,0.2]和[0.8,1.0]区间,使得群智感知网络容易选择合适的参与者,且评估结果表明PRES评估的准确率均在90%以上。     

An integration of enhanced social force and crowd control models for high-density crowd simulation

Neural Computing and Applications - Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to...     

Spice: a cognitive agent framework for computational crowd simulations in complex environments

Pedestrian behavior is an omnipresent topic, but the underlying cognitive processes and the various influences on movement behavior are still not fully understood. Nonetheless, computational simulations that predict crowd behavior are essential for safety, economics, and transport. Contemporary approaches of pedestrian behavior modeling focus strongly on the movement aspects and seldom address the rich body of research from cognitive science. Similarly, general purpose cognitive architectures are not suitable for agents that can move in spatial domains because they do not consider the profound findings of pedestrian dynamics research. Thus, multi-agent simulations of crowd behavior that strongly incorporate both research domains have not yet been fully realized. Here, we propose the cognitive agent framework Spice. The framework provides an approach to structure pedestrian agent models by integrating concepts of pedestrian dynamics and cognition. Further, we provide a model that implements the framework. The model solves spatial sequential choice problems in sufficient detail, including movement and cognition aspects. We apply the model in a computer simulation and validate the Spice approach by means of data from an uncontrolled field study. The Spice framework is an important starting point for further research, as we believe that fostering interdisciplinary modeling approaches will be highly beneficial to the field of pedestrian dynamics.     

Parameter estimation and comparative evaluation of crowd simulations

We present a novel framework to evaluate multi‐agent crowd simulation algorithms based on real‐world observations of crowd movements. A key aspect of our approach is to enable fair comparisons by automatically estimating the parameters that enable the simulation algorithms to best fit the given data. We formulate parameter estimation as an optimization problem, and propose a general framework to solve the combinatorial optimization problem for all parameterized crowd simulation algorithms. Our framework supports a variety of metrics to compare reference data and simulation outputs. The reference data may correspond to recorded trajectories, macroscopic parameters, or artist‐driven sketches. We demonstrate the benefits of our framework for example‐based simulation, modeling of cultural variations, artist‐driven crowd animation, and relative comparison of some widely‐used multi‐agent simulation algorithms.     

Online evaluation of bid prediction models in a large-scale computational advertising platform: decision making and insights

Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences at scale. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing message for a user and to purchase impressions at the right price, we rely heavily on bid prediction and optimization models. Even though the bid prediction models are well studied in the literature, the equally important subject of model evaluation is usually overlooked or not discussed in detail. Effective and reliable evaluation of an online bidding model is crucial for making faster model improvements as well as for utilizing the marketing budgets more efficiently. In this paper, we present an experimentation framework for bid prediction models where our focus is on the practical aspects of model evaluation. Specifically, we outline the unique challenges we encounter in our platform due to a variety of factors such as heterogeneous goal definitions, varying budget requirements across different campaigns, high seasonality and the auction-based environment for inventory purchasing. Then, we introduce return on investment as a unified model performance (i.e., success) metric and explain its merits over more traditional metrics such as click-through rate or conversion rate. Most importantly, we discuss commonly used evaluation and metric summarization approaches in detail and propose a more accurate method for online evaluation of new experimental models against the baseline. Our meta-analysis-based approach addresses various shortcomings of other methods and yields statistically robust conclusions that allow us to conclude experiments more quickly in a reliable manner. We demonstrate the effectiveness of our evaluation strategy on real campaign data through some experiments.     

Empirical evaluation of performance models of pointing accuracy and speed with a PC mouse

This study looks at the performance model of mouse movement from the following three viewpoints: (a) effects of the direction of movement on the performance model, (b) optimal formula to define the size of targets in the performance model, and (c) comparison of fit to the pointing time among five performance models. As a result, it was shown that the fit to the experimental data did not differ among four conditions of direction of movement The contribution of the performance model was found to be the highest when the square of the area of a target is used as the size of a target. Moreover, the performance model based on the multiple‐regression analysis was better than that based on Fitts's law.     

Algebraic computational models of OR-parallel execution of Prolog

A specification of the OR-parallel execution of Prolog programs, using CHOCS (calculus of higher order communicating systems) [24], is presented in the paper. A translation is defined from Prolog programs and goals to CHOCS processes: the execution of the CHOCS process corresponding to a goal mimics the OR-parallel execution of the original Prolog goal. In the translation, clauses and predicate definitions of a Prolog program correspond to processes. To model OR-parallelism, the processes , corresponding to clauses (having the same head predicate ) start their execution concurrently, but, in order to respect the depth-first search rule, each is guarded by the termination of the executions of processes 's, . The computational model is proved correct with respect to the semantics of Prolog, as given in [4, 5]. Our model, because of its algebraic specification, can be easily used to prove properties of the parallel execution of Prolog programs. Moreover, the model exploits the maximum degree of parallelism, by giving the Prolog solutions in parallel, without any order among them. However, this model, being close to the Prolog semantics definition, contains sources of inefficiency which make it unpractical as a guide for the implementation. To overcome these problems, a new computational model is defined. This model is obtained by modifications of the basic one and thus its correctness can be easily proved. Finally, we show how to obtain models of different real implementations of OR-parallel Prolog by slight modification of the new model. The relations among all these models, in terms of parallelism degree, are studied by using the concepts of bisimulation and simulation, developed for concurrent calculi. Received: 5 May 1995 / 28 May 1996     

Brokering strategies in computational grids using stochastic prediction models

In this paper we propose a new routing policy to route jobs to clusters in computational grids. This routing policy is based on index tables computed at each cluster. These tables can be computed off-line or on-line. Their computations use predictions about the average future behavior of the grid. We show how can be used in practice for task allocations in computational grids. We also report numerous simulations providing numerical evidence of the efficiency of our index routing policy compared with the classical brokers used in most production grids today.     

Market linkages, variance spillovers, and correlation stability: Empirical evidence of financial contagion

To model the contemporaneous relationships among Asian and American stock markets, a simultaneous equation system with GARCH errors is introduced. In the estimated residuals, the correlation matrix is analyzed over rolling windows and using a correlation matrix distance, which allows a graphical analysis and the development of a statistical test of correlation movements. Furthermore, a methodology that can be used to identify turmoil periods on a data-driven basis is presented. The previous results are applied in the analysis of the contagion issue between Asian and American stock markets. The results show some evidence of contagion, and the proposed statistics identify, on a data-driven basis, turmoil periods consistent with the ones currently assumed in the literature.     

Categorizing facial expressions: a comparison of computational models

Recognizing expressions is a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high-dimensional data, so here we use two dimensionality reduction techniques: principal component analysis and curvilinear component analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images may be identified. Subsequently, the faces are classified using a support vector machine. We show that it is possible to differentiate faces with a prototypical expression from the neutral expression. Moreover, we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 5 components. We also investigate the effect size on face images, a concept which has not been reported previously on faces. This enables us to identify those areas of the face that are involved in the production of a facial expression.     

How computational models help explain the origins of reasoning

Developmental psychology is ready to blossom into a modern science that focuses on causal mechanistic explanations of development rather than just describing and classifying the skills that children show at different ages. Computational models of cognitive development are formal systems that track the changes in information processing taking place as a behavior is acquired. Models are generally implemented as psychologically constrained computer simulations that learn tasks such as reasoning, categorization, and language. Their principal use is as tools for exploring mechanisms of transition (development) from one level of competence to the next during the course of cognitive development. They have been used to probe questions such as the extent of 'pre-programmed' or innate knowledge that exists in the infant mind, and how the sophistication of reasoning can increase with age and experience.     

Post-buckling behavior of thin steel plates using computational models

Thin plates loaded in plane will buckle at very small loads, and due to unavoidable out-of-plane imperfections, the theoretical buckling load cannot be observed experimentally. If the plate is adequately supported along its boundaries, it will be able to carry a much higher load than the theoretical buckling load.Computational models can be used to study the post buckling behaviour of thin plate structures up to failure. Failure of such structures is usually due to large out-of-plane deflections, yielding, and rupture. Therefore, the computational model should include the effects of geometric and material nonlinearities. In this paper, the nonlinear finite element analysis program NONSAP and ANSR-III were modified and used in the analysis. Since these programs did not include the suitable elements and material properties to conduct the subject study, new elements and new material properties were added to the programs. In particular, a thin shell element was added and the solution routines were modified to improve its accuracy and efficiency.The modified programs were used on a Super Computer to calculate the post buckling strength of stiffened and unstiffened plates subjected to uniaxial compression, and plates subjected to in plane bending or shear. Crippling of plates subjected to in-plane or eccentric edge compressive loads was also examined. The results from the computational models were compared with test results and reasonable agreements were obtained. A computational model was developed for a multi-story thin steel plate shear wall subjected to cyclic loading and the results from the model were compared with experimental results, and again agreement was achieved.     

Application of hybrid computational intelligence models in short-term bus load forecasting

Artificial neural networks (ANNs) are a favorable scheme in load forecasting applications mainly due to their endogenous capacity of robust modeling of data sets with highly non-linear relationship between inputs and outputs. Usually, the inputs correspond to historical load values, exogenous variables like temperature, day type identification codes and others. The outputs refer to the load values under examination. The majority of the load forecasting related literature focuses in aggregated load system level. While contemporary research efforts focus in smart grid technologies, there is need to study the characteristics of small scaled loads. Bus load forecasting refers to prediction of the demand patterns in buses of the transmission and distribution systems. Bus load exhibits low correlation with the aggregated system load, since it is characterized by a high level of stochasticity. Hence, a proper selection and formulation of the forecasting model is essential in order to keep the prediction accuracy within acceptable ranges. The treatment of bus load characteristics is held with computational intelligence techniques such as clustering and ANN. Neural network based systems are a favorable scheme in recent years in price and load predictions over traditional time series models. ANN can fully adapt expert knowledge and modify their parameters accordingly to simulate the problem`s attributions through training paradigms. Thus, ANN based systems are an essential choice, justified by the paper`s findings, for highly volatile time series. This work focuses on the short-term load forecasting (STLF) of a number of buses within the Greek interconnected system. Firstly, a modified version of the ANN already proposed for the aggregated load of the interconnected system is employed. To enhance the forecasting accuracy of the ANN, the load profiling methodology is used resulting to the formulation of two novel hybrid forecasting models. These models refer to the combination of the ANN with a clustering algorithm, resulting to superior performance. Simulation results indicate that the combination captures and successfully treats the special characteristics of the bus load patterns. The scope of the present paper is to develop efficient forecasting systems for short-term bus load predictions. This is a current research challenge due to the high interest for smart grids and demand side management applications by utilities, regulators, retailer and energy service companies. Bus load forecasting appears to be a more difficult engineering problem compared to forecasting of the total load of a country. No hybrid models for bus load predictions have been presented so far in the literature. Two novel clustering based tools are developed and successfully tested in a number of loads covering different types of electricity consumers and demand levels.     

Empirical evaluation of distributed pair programming

Pair programming, in which two individuals share a single computer to collaboratively develop software, has been shown to have many benefits in industry and in education. One drawback of pair programming is its collocation requirement, which limits its use to situations where the partners can physically meet. A tool that supported distributed pair programming, in which the partners could pair from separate locations, would remove this impediment.This paper discusses the development and empirical evaluation of such a tool. A significant feature of this tool is the presence of a second cursor that supports gesturing. Students who used the tool in their introductory programming course performed as well as collocated students on their programming assignments and final exam. These students also spent less time working by themselves. They also felt that the gesturing feature was useful and used it regularly.     

The realist approach for evaluation of computational intelligence in software engineering

Innovations in Systems and Software Engineering - Secured software development must employ a security mindset across software engineering practices. Software security must be considered during the...     

Experimental evaluation of pheromone models in ACOPlan

In this paper the system ACOPlan for planning with non uniform action cost is introduced and analyzed. ACOPlan is a planner based on the ant colony optimization framework, in which a colony of planning ants searches for near optimal solution plans with respect to an overall plan cost metric. This approach is motivated by the strong similarity between the process used by artificial ants to build solutions and the methods used by state?Cbased planners to search solution plans. Planning ants perform a stochastic and heuristic based search by interacting through a pheromone model. The proposed heuristic and pheromone models are presented and compared through systematic experiments on benchmark planning domains. Experiments are also provided to compare the quality of ACOPlan solution plans with respect to state of the art satisficing planners. The analysis of the results confirm the good performance of the Action?CAction pheromone model and points out the promising performance of the novel Fuzzy?CLevel?CAction pheromone model. The analysis also suggests general principles for designing performant pheromone models for planning and further extensions of ACOPlan to other optimization models.