高职计通专业学生创新能力的现状与培养研究

针对社会对应用创新型人才的迫切需求,结合杭州职业技术学院计通专业依托第二课堂活动即加强大学生应用创新能力培养的思路和做法,重点阐述了第二课堂活动的设计与组织,探索了如何实现第二课堂与学生专业学习的有机融合,以及第二课堂活动对大学生应用创新能力的影响。实践表明,第二课堂活动对大学生应用创新能力培养具有明显的"增强改性"作用。     

中职院校计算机维修技能培训模式探究

通过课题研究的形式,组织学生进行课外授课,以及一系列教学、实践,使学生掌握计算机维修前沿技能;通过比较这类培训模式与传统课堂教学对于学生知识技能掌握的有效程度,指出当前课堂教学模式与人才培养、就业市场存在较大偏差,提出了深化课程教学改革、强化维护维修技能培养的建议,具体的指导思想和实施方案,探索符合我校培养要求,迎合社会需求的计算机维修课程教学模式。     

Game‐Based Simulation for Cross‐Cultural Decision Making Training

Increasing globalization has had a major impact on manufacturing and service industries as well as on coalition operations conducted by the military. What is common to both the commercial and military sectors is the recent surge in interest in cross‐cultural decision making (CCDM) training. Existing CCDM training approaches tend to employ either some form of multi‐agent simulation or some variant of classical game theory. Despite their manifest benefits, these approaches have specific limitations that need to be overcome to create an effective cross‐cultural training system. Multi‐agent simulations typically lack theoretical underpinnings while classical game theory‐based approaches take a limited view of strategic decision making. Specifically, by adopting a Western view of rationality, game‐theoretic approaches fail to accommodate considerations such as fairness, altruism and reciprocity. Empirical research in strategic economic games has shown that humans respond to more than merely monetary incentives. In particular, research has shown that cultural norms play a central role in human decision making behavior, especially in non‐Western cultures. This paper presents an innovative approach to game‐based simulation that combines findings from behavioral game theory with classical game theory and multi‐agent simulation to exploit the strengths of each approach while making learning enjoyable, memorable, and fun. An illustrative game‐based simulation for CCDM training is also presented. The simulation framework is equally applicable to teaching other soft skills as well as skills that are too hazardous or too expensive to teach in the realworld through live exercises. © 2012 Wiley Periodicals, Inc.     

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Multi‐Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up‐to‐date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.     

Bladder Runner: Visual Analytics for the Exploration of RT‐Induced Bladder Toxicity in a Cohort Study

We present the Bladder Runner, a novel tool to enable detailed visual exploration and analysis of the impact of bladder shape variation on the accuracy of dose delivery, during the course of prostate cancer radiotherapy (RT). Our tool enables the investigation of individual patients and cohorts through the entire treatment process, and it can give indications of RT‐induced complications for the patient. In prostate cancer RT treatment, despite the design of an initial plan prior to dose administration, bladder toxicity remains very common. The main reason is that the dose is delivered in multiple fractions over a period of weeks, during which, the anatomical variation of the bladder – due to differences in urinary filling – causes deviations between planned and delivered doses. Clinical researchers want to correlate bladder shape variations to dose deviations and toxicity risk through cohort studies, to understand which specific bladder shape characteristics are more prone to side effects. This is currently done with Dose‐Volume Histograms (DVHs), which provide limited, qualitative insight. The effect of bladder variation on dose delivery and the resulting toxicity cannot be currently examined with the DVHs. To address this need, we designed and implemented the Bladder Runner, which incorporates visualization strategies in a highly interactive environment with multiple linked views. Individual patients can be explored and analyzed through the entire treatment period, while inter‐patient and temporal exploration, analysis and comparison are also supported. We demonstrate the applicability of our presented tool with a usage scenario, employing a dataset of 29 patients followed through the course of the treatment, across 13 time points. We conducted an evaluation with three clinical researchers working on the investigation of RT‐induced bladder toxicity. All participants agreed that Bladder Runner provides better understanding and new opportunities for the exploration and analysis of the involved cohort data.     

Reconsidering off‐task: a comparative study of PDA‐mediated activities in four classrooms

Mobile technology is ubiquitous and diverse and permeates many aspects of daily life at home, during leisure activities, and in public spaces. The study presented here is of two sixth grade classes in Michigan, USA and two seventh grade classes in Norway. The students and the teachers in these four classrooms were equipped with mobile technologies (PDAs). We found that the students' PDA‐mediated actions in the classroom were not exclusively used for the tasks and activities set by the teacher, but that the students also used the PDAs on their own initiative – so‐called ‘off‐task’ activities. We analyze the findings by reconsidering off‐task activities from a sociocultural perspective.     

Networks of Names: Visual Exploration and Semi‐Automatic Tagging of Social Networks from Newspaper Articles

Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract “interesting” neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same “globally interesting” neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results. We propose a user‐driven pattern‐based classifier for discovery and tagging of non‐taxonomic semantic relations. Our approach does not require any a‐priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico‐syntactic patterns as well as various domain‐specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback. We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text‐based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too abstract or overly specific relationship labels.     

A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes

We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out‐of‐core representation, based on per‐frame levels of hierarchically tiled non‐redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low‐bitrate codec able to store into fixed‐size pages a variable‐rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time‐critical operations, while a near‐lossless representation is employed to support high‐quality static frame rendering. A flexible high‐speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object‐space and image‐space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high‐quality snapshots generated from near‐lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi‐billion‐voxel frames.     

An analysis of collaborative problem‐solving activities mediated by individual‐based and collaborative computer simulations

Researchers have indicated that the collaborative problem‐solving space afforded by the collaborative systems significantly impact the problem‐solving process. However, recent investigations into collaborative simulations, which allow a group of students to jointly manipulate a problem in a shared problem space, have yielded divergent results regarding their effects on collaborative learning. Hence, this study analysed how students solved a physics problem using individual‐based and collaborative simulations to understand their effects on science learning. Multiple data sources including group discourse, problem‐solving activities, learning test scores, and questionnaire feedback were analysed. Lag sequential analysis on the data found that students using the two simulations collaborated with peers to solve the problem in significantly different patterns. The students using the collaborative simulations demonstrated active engagement in the collaborative activity; however, they did not transform discussions into workable problem‐solving activities. The students using the individual‐based simulation showed a lower level of collaboration engagement, starting with individual exploration of the problem with the simulation, followed by group reflection. The two groups also showed significant differences in their learning test scores. The findings and pedagogical suggestions are discussed in the hope of addressing critical activity design issues in using computer simulations for facilitating collaborative learning.     

Anatomy‐Guided Multi‐Level Exploration of Blood Flow in Cerebral Aneurysms

For cerebral aneurysms, the ostium, the area of inflow, is an important anatomic landmark, since it separates the pathological vessel deformation from the healthy parent vessel. A better understanding of the inflow characteristics, the flow inside the aneurysm and the overall change of pre‐ and post‐aneurysm flow in the parent vessel provide insights for medical research and the development of new risk‐reduced treatment options. We present an approach for a qualitative, visual flow exploration that incorporates the ostium and derived anatomical landmarks. It is divided into three scopes: a global scope for exploration of the in‐ and outflow, an ostium scope that provides characteristics of the flow profile close to the ostium and a local scope for a detailed exploration of the flow in the parent vessel and the aneurysm. The approach was applied to five representative datasets, including measured and simulated blood flow. Informal interviews with two board‐certified radiologists confirmed the usefulness of the provided exploration tools and delivered input for the integration of the ostium‐based flow analysis into the overall exploration workflow.     

Merging fuzzy logic,neural networks,and genetic computation in the design of a decision‐support system

The main goal of evolutionary computation is to provide a near optimal technique between exploration and exploitation of a search space. This approach is based on a genetic “engine” that operates the search of the optimal solution via biological‐based assumptions. Selection of the optimal maintenance interventions activity, that can be tackled with success thanks to an evolutionary approach able to correct the distresses on the road pavement, is a very complex task. This paper presents an experimental architecture that improves the evolutionary aspect with additional benefits deriving from a synergistic combination of other powerful techniques, in particular neural networks and fuzzy logic. The best rules for managing pavement maintenance activities, developed through a genetic selection, are judged by a neural network. By an appropriate introduction of simple and efficient fuzzy identifiers, the features of the distress to treat can be described in an efficient and natural way. We describe the main advantages arising from this hybrid approach discussing the applicability of the method with experimental results. © 2000 John Wiley & Sons, Inc.     

Information Problems and Company Behaviour vis‐à‐vis Continuous Management Training

The purpose of this study is to examine the degree and type of information the company holds on training, and to what extent the lack‐of‐information firms suffer in two senses, information on managerial resources and information on training supply, tends to condition their behavior vis‐à‐vis management training. Data from more than 300 Spanish companies with 50 or more workers are used to examine the connection between information asymmetries and company behavior in relation to continuous management training. The empirical study revealed that company readiness to engage in continuous management training appears to be significantly conditioned by all variables related to the existence of information on managers and training supply. Based on these results, underinvestment in managerial training would be associated with those cases in which information is unavailable or not distributed uniformly and transparently. This study contributes to the existing training literature by providing a novel link between information asymmetries (firm managers; firm training offerings) and their ability to influence training investment rates. © 2012 Wiley Periodicals, Inc.     

Model‐Based Visualization of Temporal Abstractions

We describe a new conceptual methodology and related computational architecture called Knowledge‐based Navigation of Abstractions for Visualization and Explanation (KNAVE). KNAVE is a domain‐independent framework specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration, in a context‐sensitive manner, of time‐oriented raw data and the multiple levels of higher level, interval‐based concepts that can be abstracted from these data. The KNAVE domain‐independent exploration operators are based on the relations defined in the knowledge‐based temporal‐abstraction problem‐solving method, which is used to abstract the data, and thus can directly use the domain‐specific knowledge base on which that method relies. Thus, the domain‐specific semantics are driving the domain‐independent visualization and exploration processes, and the data are viewed through a filter of domain‐specific knowledge. By accessing the domain‐specific temporal‐abstraction knowledge base and the domain‐specific time‐oriented database, the KNAVE modules enable users to query for domain‐specific temporal abstractions and to change the focus of the visualization, thus reusing for a different task (visualization and exploration) the same domain model acquired for abstraction purposes. We focus here on the methodology, but also describe a preliminary evaluation of the KNAVE prototype in a medical domain. Our experiment incorporated seven users, a large medical patient record, and three complex temporal queries, typical of guideline‐based care, that the users were required to answer and/or explore. The results of the preliminary experiment have been encouraging. The new methodology has potentially broad implications for planning, monitoring, explaining, and interactive data mining of time‐oriented data.     

Increasing the speed of fuzzy k‐nearest neighbours algorithm

Fuzzy k‐nearest neighbour (FKNN) is one of the most convenient classification approaches. The main challenge of this method is associated with finding the optimal values of its two hyperparameters. The present study attempts to decrease the running time of this approach by reducing the number of its hyperparameters through omitting the hyperparameter k. In the training phase of FKNN approach, the membership degree of each training data is refined by crisp KNN voting whereas a fuzzy voting is used in the training phase of our proposed approach. Training and test phases time complexities of our proposed approach are better than those of FKNN approach. The experiments on real data sets indicate that the accuracy of our proposed approach called ultra FKNN is higher than FKNN approach due to applying fuzzy voting instead of crisp voting in training phase. In addition, the training, test, and running time of our proposed approach are considerably less than those of FKNN.     

Kriging‐based robotic exploration for soil moisture mapping using a cosmic‐ray sensor

Soil moisture monitoring is a fundamental process to enhance agricultural outcomes and to protect the environment. The traditional methods for measuring moisture content in the soil are laborious and expensive, and therefore there is a growing interest in developing sensors and technologies which can reduce the effort and costs. In this work, we propose to use an autonomous mobile robot equipped with a state‐of‐the‐art noncontact soil moisture sensor building moisture maps on the fly and automatically selecting the most optimal sampling locations. We introduce an autonomous exploration strategy driven by the quality of the soil moisture model indicating areas of the field where the information is less precise. The sensor model follows the Poisson distribution and we demonstrate how to integrate such measurements into the kriging framework. We also investigate a range of different exploration strategies and assess their usefulness through a set of evaluation experiments based on real soil moisture data collected from two different fields. We demonstrate the benefits of using the adaptive measurement interval and adaptive sampling strategies for building better quality soil moisture models. The presented method is general and can be applied to other scenarios where the measured phenomena directly affect the acquisition time and need to be spatially mapped.     

Improved Quality Output through Computer‐Based Training: An Automotive Assembly Field Study

In this article, two field experiments, conducted in an automotive assembly plant, evaluate how computer‐based training of operational sequences and related quality information can support the assembly performance of the operators. The experiments were performed during the launch of a new vehicle. A comparison was made of learning progress and quality performance between a reference group of operators that only had regular training and a test group for which some of the regular training was replaced with individual computer‐based training. Both quantitative measures of the quality output and questionnaires and observations were used to evaluate the effects of computer‐based training. The results show a clear positive difference in learning progress and improvements in quality output for the test group compared with the reference group. This combined with positive attitudes expressed by the operators and their team leaders shows that this type of training is an effective way to train operators during launches of new vehicles in automotive production.     

Time‐Series Plots Integrated in Parallel‐Coordinates Displays

We present a natural extension of two‐dimensional parallel‐coordinates plots for revealing relationships in time‐dependent multi‐attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel‐coordinates display. A vertical slice through one of the axes of the parallel‐coordinates display would show a time‐series plot. For a focus‐and‐context Integration of both views, we embed time‐series plots between two adjacent axes of the parallel‐coordinates plot. Both time‐series plots are drawn using a pseudo three‐dimensional perspective with a single vanishing point. An independent parallel‐coordinates panel that connects the two perspectively displayed time‐series plots can move forward and backward in time to reveal changes in the relationship between the time‐dependent attributes. The visualization of time‐series plots in the context of the parallel‐coordinates plot facilitates the exploration of time‐related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.     

A Multi‐Level Model for Organizational Ambidexterity in the Search Phase of the Innovation Process

Innovation is one of the most critical means in supporting and improving the competitive position of the firm, in particular, and a firm's survival and growth depend greatly on its ability to balance the exploitation of existing knowledge with the exploration of new possibilities, by building ambidexterity capability. While different alternatives to realize the simultaneous reconciliation of exploration and exploitation at an operational level have been proposed, how organizations build ambidexterity capability is not fully understood. The aim of this paper is thus to explore how exploration and exploitation balancing can be achieved in practice. We decided to focus on the early phase of the process where firms search for new ideas with which to renew themselves. To this end, we analysed the search phase of a highly innovative technology‐based company by investigating structural design choices combined with the presence of specific roles and searching practices. The results show how the exploration and exploitation balancing act can actually be achieved and maintained through a multi‐level approach that integrates both the operational and the strategic levels. Our findings thus contribute to the organizational ambidexterity literature, by proposing a first interpretative model for dealing with ambidexterity in the search phase of the innovation process.     

Design and analysis of target‐sensitive real‐time systems

A significant number of real‐time control applications include computational activities where the results have to be delivered at precise instants, rather than within a deadline. The performance of such systems significantly degrades if outputs are generated before or after the desired target time. This work presents a general methodology that can be used to design and analyze target‐sensitive applications in which the timing parameters of the computational activities are tightly coupled with the physical characteristics of the system to be controlled. For the sake of clarity, the proposed methodology is illustrated through a sample case study used to show how to derive and verify real‐time constraints from the mission requirements. Software implementation issues necessary to map the computational activities into tasks running on a real‐time kernel are also discussed to identify the kernel mechanisms necessary to enforce timing constraints and analyze the feasibility of the application. A set of experiments are finally presented with the purpose of validating the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.     

Analysis of co‐articulation regions for performance‐driven facial animation

A facial gesture analysis procedure is presented for the control of animated faces. Facial images are partitioned into a set of local, independently actuated regions of appearance change termed co‐articulation regions (CRs). Each CR is parameterized by the activation level of a set of face gestures that affect the region. The activation of a CR is analyzed using independent component analysis (ICA) on a set of training images acquired from an actor. Gesture intensity classification is performed in ICA space by correlation to training samples. Correlation in ICA space proves to be an efficient and stable method for gesture intensity classification with limited training data. A discrete sample‐based synthesis method is also presented. An artist creates an actor‐independent reconstruction sample database that is indexed with CR state information analyzed in real time from video. Copyright © 2004 John Wiley & Sons, Ltd.