《Flash动画制作》课程教学方法探讨与实践

根据《Flash动画制作》课程在教学实践中的特点,提出构建"理实同步,实例引导,任务驱动"的教学模式,在教学内容、教学方法等方面进行教学改革,并阐述了在Flash教学中的具体实施方法,提高了学生动手能力及综合素质。     

Correlation of high-pressure thermal conductivity,viscosity, and diffusion coefficients for n-alkanes

Thermal conductivity, viscosity, and self-diffusion coefficient data for liquid n-alkanes are satisfactorily correlated simultaneously by a method based on the hard-sphere theory of transport properties. Universal curves are developed for the reduced transport properties ^*, ^*, and D ^* as a function of the reduced volume. A consistent set of equations is derived for the characteristic volume and for the parameters R , R , and R _D, introduced to account for the nonsphericity and roughness of the molecules. The temperature range of the above scheme extends from 110 to 370 K, and the pressure range up to 650 MPa.     

Probabilistic heuristic estimates

Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality.To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.This research was made possible by support from Heuristicrats, the National Aeronautics and Space Administration, and the Rand Corporation.     

Interpreting the negative mood-helping literature via "mega"-analysis: A contrary view.

"Mega"-analysis was developed by M. Carlson and N. Miller (see record 1987-31249-001) as an extension of traditional meta-analytic procedures for conducting integrative reviews of existing research literatures. One such mega-analysis was conducted by Carlson and Miller to synthesize the literature on the relation between negative mood states and helping. That analysis found no support for a theoretical account (negative state relief) that had been confirmed previously by using various experimental approaches. In an attempt to reconcile the discrepancy, the logic and methods used in Carlson and Miller's mega-analysis of the negative mood-helping literature were examined, and several serious problems were found. These problems are discussed, and data are presented to show that the results of that mega-analysis, and perhaps all mega-analyses, should not be viewed with confidence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

LPNet: A DNN based latency prediction technique for application mapping in Network-on-Chip design

Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.     

Analysis of oceanaut operating performance using an integrated Bayesian network aided by the fuzzy logic theory

Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Artificial intelligence capability: Conceptualization,measurement calibration,and empirical study on its impact on organizational creativity and firm performance

Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.     

Creativity in non‐routine jobs: The role of transformational leadership and organizational identification

An increasing number of individuals work in jobs with little standardization and repetition, that is, with high levels of job non‐routinization. At the same time, demands for creativity are high, which raises the question of how employees can use job non‐routinization to develop creativity. Acknowledging the importance of social processes for creativity, we propose that transformational leaders raise feelings of organizational identification in followers and that this form of identification then helps individuals to develop creativity in jobs with little routinization. This is because organizational members evaluate and promote those ideas as more creative, which are in line with a shared understanding of creativity within the organization. To investigate these relationships, we calculated a mediated moderation model with 173 leader–follower dyads from China. Results confirm our hypotheses that transformational leadership moderates the relationship between job non‐routinization on employee creativity through organizational identification. We conclude that raising feelings of social identity is a key task for leaders today, especially when working in uncertain and fast developing environments with little repetition and the constant need to develop creative ideas.