Knowledge diffusion, input supplier’s technological effort and technology transfer via vertical rel
knowledge diffusion
Visual Analysis of Scientific Discoveries and Knowledge DiffusionChaomei Chen1, Jian Zhang2, Michael S. Vogeley31 chaomei.chen@College of Information Science and Technology, Drexel University, Philadelphia, PA 19104-2875 (USA)2 jz85@College of Information Science and Technology, Drexel University, Philadelphia, PA 19104-2875 (USA)3 msv23@Department of Physics, College of Arts and Sciences, Drexel University, Philadelphia, PA 19104-2875 (USA) AbstractWe introduce a new visual analytic approach to the study of scientific discoveries and knowledge diffusion. Our approach enhances contemporary co-citation network analysis by enabling analysts to identify co-citation clusters of cited references intuitively, synthesize thematic contexts in which these clusters are cited, and trace how research focus evolves over time. The new approach integrates and streamlines a few previously isolated techniques such as spectral clustering and feature selection algorithms. The integrative procedure is expected to empower and strengthen analytical and sense making capabilities of scientists, learners, and researchers to understand the dynamics of the evolution of scientific domains in a wide range of scientific fields, science studies, and science policy evaluation and planning. We demonstrate the potential of our approach through a visual analysis of the evolution of astronomical research associated with the Sloan Digital Sky Survey (SDSS) using bibliographic data between 1994 and 2008. Implications on methodological issues are also addressed. IntroductionAnalyzing the evolution of a scientific field is a challenging task. Analysts often need to deal with the overwhelming complexity of a field of study and work back and forth between various levels of granularity. Although more and more tools become available, sense making remains to be one of the major bottleneck analytical tasks. In this article, we introduce a new visual analytic approach in order to strengthen and enhance the capabilities of analysts to achieve their analytical tasks. In particular, we will focus on the analysis of co-citation networks of a scientific field, although the procedure can be applied to a wider range of networks.Analyzing Dynamic NetworksMany phenomena can be represented in the form of networks, for example, friendship on FaceBook, trading between countries, and collaboration in scientific publications (Barabási, et al., 2002; Snijders, 2001; Wasserman & Faust, 1994). A typical path of analyzing a dynamic network may involve the following steps: formulate, visualize, clustering, interpret, and synthesize (See Figure 1). Many tools are available to support these individual steps. On the other hand, analysts often have to improvise different tools to accomplish their tasks. For example, analysts may divide the nodes of a network into clusters by applying a clustering algorithm to various node attributes. Clusters obtained in such ways may not match the topological structure of the original network, although one may turn such discrepancies into some good use. We are interested in processes that would produce an intuitive and cohesive clustering given the topology of the original network.The new procedure we are proposing is depicted in Figure 1b. It streamlines the key steps found in a typical path. The significance of the streamlined process is that it determines clusters based on the strengths of the links in the network. In Figure 1c, we show that the new procedure leads to several advantages such as increased clarity of network visualization,intuitive aggregation of cocited references, and automatically labeling clusters to characterize the nature of their impacts.Figure 1. A common path of network analysis (a) and a new procedure (b) and its effects (c). According to Gestalt principles, perceived proximity plays a fundamental role in how we aggregate objects into groups (Koffka, 1955). If some objects appear to be closer to each other than the rest of objects, we tend to be convinced that they belong together. Seeing objects in groups instead of individual objects is important in many cognitive and analytical activities. As a generic chunking method, we often use it to simplify a complex phenomenon so that we can begin to address generic properties.Figure 2 shows three illustrative examples of how clarity of displayed proximity can make the chunking task easy (Figure 2a) or hard (Figure 2c). Co-citation networks represent how often two bibliographic items are cited together, for example, authors in author co-citation networks (White & Griffith, 1981) and papers in document co-citation networks (Small, 1973). When analyzing co-citation networks, or more generic networks, we often find ourselves in the situation depicted in Figure 2c. Our goal is to find mechanisms that can improve the representation and approach the ideal case of Figure 2a. A hot topic in the graph drawing community, called constraint graph drawing, addresses this problem (Dwyer, et al., 2008). In this article, however, we propose an alternative solution that is in harmony with our overall goal for strengthening visual analytical capabilities of analysts.Figure 2. Clarity of displayed proximity plays an important role in chunking tasks.MethodsThe proposed procedure consists of the following key steps: constructing an integrated network of multiple networks, finding clusters of nodes in the network based on connectivity, selecting candidate labeling terms for each cluster, and visual exploration and analysis. In this article, we will focus on the new steps, namely clustering, labeling, and visual analysis.NetworksIt is possible to construct a wide range of networks from bibliographic data. For example, CiteSpace supports collaboration networks of coauthors, collaboration networks of institutions and countries, author co-citation networks, document co-citation networks, concept networks of noun phrases and keywords, and hybrid networks that consist of multiple types of nodes and links (Chen, 2006). For simplicity, we will primarily focus on document co-citation networks and relevant analysis.The study of an evolving scientific field often needs to focus on how the field evolves over several years. The notion of progressive knowledge domain visualization was introduced in order to accommodate such needs (Chen, 2004). Suppose we are interested in the evolution of a field in a time interval T, for example, between 1990 and 2008, time slicing is an operation that divides the interval T into multiple equal length sub-intervals T i. While CiteSpace implements non-overlapping sub-intervals, overlapping sub-intervals may be also considered. For each sub-interval, or time slice, a network can be constructed purely based on data falling into the time slice. For example, a co-citation network of 1990 can be constructed based on instances of co-citation found in scientific papers published in 1990. Similarly, a collaboration network of authors of 1990 would consist of researchers who have published together in 1990. In this article, the goal of our visual analysis is to assess the impact of the Sloan Digital Sky Survey (SDSS) (Chen, Zhang, Zhu, & Vogeley, 2007). The input data was retrieved from the Web of Science with a topic search of articles on SDSS between 1994 and 2008. We used one year as the length of our time slice. In each time slice, a co-citation network was constructed based on the co-citation instances made by the top 30 most cited records published in the corresponding time interval. The top 30 most cited records were determined by their original times cited in the Web of Science. These individual networks led to an integrated network of 259 nodes and 2,130 co-citation links. Our subsequent study will focus on this network. ClusteringWe utilize the spectral clustering technique to identify clusters in networks. Spectral clustering has many fundamental advantages over the traditional clustering algorithms such as k-means or single linkage. For example, results obtained by spectral clustering very often outperform the traditional approaches (Luxburg, 2006; Ng, Jordan, & Weiss, 2002; Shi & Malik, 2000).There are many reasons one might need to identify clusters in data given in the form of associative networks, for example, to find communities in a social network (Girvan & Newman, 2002). In such situations, the problem of clustering can be stated as the need to find a partition of the network such that nodes within a cluster would be tightly connected, whereas nodes between different clusters would be loosely connected or not connected at all. Consider our document co-citation network, this is equivalent to find a partition such that references within a cluster would be significantly more cocited than references from different clusters. Spectral clustering offers a solution to such graph partitioning problems. This view of clustering fits our needs perfectly and intuitively. In addition, since spectral clustering comes naturally for a network, it has the distinct advantage over alternative clustering algorithms that rely on node attributes rather than linkage. Compared to traditional linkage-based algorithms such as single linkage, spectral clustering has the advantage due to its linear algebra basis. Spectral clustering is implemented as an approximation to the graph partitioning problem with constraints stated above, i.e. members within clusters are tightly coupled, whereas members between clusters are loosely connected or disconnected.Enhancing the Clarify of LayoutAs a welcome by-product of spectral clustering, we enhance the clarity of network visualization by taking into account the graph partitioning information. Constrained graph drawing is currently a hot topic. The goal is to layout a graph with given constraints (Dwyer, et al., 2008). Given a graph partition, drawing the graph with minimal overlapping partition regions is one of the common special cases.One of the common analytical tasks in network analysis is to study the largest connected component of a network. The ability to find finer-grained clusters has significant theoretical and practical implications. Our previous studies show that co-citation networks may contain tightly knitted components. In other words, if the largest connected component is densely connected, it would be hard to identify meaningful sub-structures. Since spectral clustering works at the strength of links rather than the simple presence or absence of links, we expect that spectral clustering will find finer-grained clusters even within large connected components.We make simple modifications of force-directed graph layout algorithms to improve the clarity of such processes. As mentioned earlier, spectral clustering comes natural to any networks, such enhancements rely on information that is already available with given networks. Briefly speaking, once the clustering information is available, the layout algorithm would maintain the strength of a within-cluster link but downplay or simply ignore a between-cluster link during the layout process.Cluster LabelingOnce clusters are identified in a network, the next step is to help analysts make sense of the nature of these clusters, how they connect to one another, and how their relationships evolve over time. We introduce algorithmic cluster labeling to assist this step.Methodological IssuesTraditionally, clusters would be identified using an independent clustering process in contrast to the integrative and cohesive approach we described above. Traditionally, sense making identified clusters is essentially a manual process. Researchers often examine members of each cluster and sum up what they believe to be the most common characteristics of the cluster. There are two potential drawbacks with the traditional approach, especially in the study of co-citation networks. First, co-citation clusters could be too complex to lend themselves to simple eyeball examinations. The cognitive load required for aggregating and synthesizing the details is likely to be high. A computer-generated baseline list of candidate labeling terms would reduce the burden significantly. Second, and more importantly, studying co-citation clusters themselves does not necessarily reveal the actual impacts of these clusters. In fact, it is quite possible that co-citation clusters are referenced by subsequent publications not only in the same topical area, but also in topical areas that may be not obvious from the cited references alone. In other words, traditional studies often infer the nature of co-citation grouping, but they do not directly address the question of why a co-citation cluster is formed in the first place.Unlike traditional studies of co-citation networks, we focus on the citers to a co-citation cluster instead of the citees and label the cluster according to salient features selected from the titles and index terms of the citers. Our prototype implements a number of classic feature selection algorithms, namely term frequency by inverse document frequency (tf*idf) (Salton, Allan, & Buckley, 1994), log-likelihood ratio test (Dunning, 1993), mutual information (not discussed in this article), and latent semantic indexing (Deerwester, Dumais, Landauer, Furnas, & Harshman, 1990). Formal evaluations are beyond the scope of this article. As part of future work, we are planning cross-validations with labels generated by other means and astudy of topological distributions of labels selected by different algorithms in networks of terms.Selection by tf*idfGiven a cluster C, the citing set consists of articles that cite one or more members of the cluster. Candidate labeling terms for the clusters are selected from the titles, abstracts, or index terms of articles in the citing set. In this article, we focus on selecting labels from titles and index terms. First, we extract noun phrases from titles and compute weights of these phrases using tf*idf. A noun phrase may consist of a noun and possibly modified by one or more adjectives, for example, supermassive black hole. Plurals are stemmed using a few simple stemming rules. Using tf*idf has known drawbacks due to the term independency assumption. Nevertheless, its properties are widely known; this, it serves as a good reference point.Selection by log-likelihood ratio testThe log-likelihood ratio test we adapted in our approach measures how often a term is expected to be found within a cluster’s citer set to how often it is found within other clusters’ citer sets. It tends to identify the uniqueness of a term to a cluster.Selection by latent semantic indexingLatent semantic indexing, or latent semantic analysis (LSA), is another classic method for dimension reduction in text analysis. LSA utilizes the singular value decomposition (SVD) technique on a term by document matrix. In order to select candidate labeling terms of a cluster, we select the top 5 terms with the strongest coefficients on each of the first and second dimensions of the latent semantic space derived from the citer set of the cluster. ResultsFirst, we show how spectral clustering can enhance the clarity of network visualization. In Figure 3, the left image shows a visualization of the core of our SDSS co-citation network, the right image shows a cluster-enhanced visualization of the same network. Before the enhancement, York-2000 and Fukugita-1996 appear to be very close to each other. After the enhancement, it becomes clear that they belong to two distinct co-citation clusters. The improved clarity will be useful in the subsequent analysis of the domain.Figure 3. The effect of enhanced clarity (left: before; right: after).The four images in Figure 4 show various options of visual exploration. The two clusters in the middle now become separated from each other. Despite the numerous links between the two clusters, spectral clustering detected that they are two distinct clusters in terms of how they are cocited. The two images on the second row depict pivotal nodes (with purple rings) and nodes with citation burst (with red tree rings). The pivotal nodes play a brokerage role between different clusters. They are particularly useful in interpreting the macroscopic structure of a knowledge domain (Chen, 2006). The red lines in the lower right image depict co-citation instances made in a particular time slice, in this case, year 2001. These red linesshow that the middle cluster is essentially formed in 2001 with between-cluster co-citation links to two neighboring clusters. These features would allow analysts to pin point thespecific time when attention is paid to a cluster and how multiple clusters are connected.Figure 4. Various node and link attributes depicted for visual exploration.Figures 5 and 6 are screenshots of the region of some of the core clusters of the SDSS co-citation network. In Figure 5, clusters are labeled by title phrases selected by tf*idf. Four clusters (#9, #10, #11, and #12) have the identical label of “sloan digital sky survey.” The numbers in front of the labels are weights of the corresponding labels by tf*idf. The clarity of the layout is enhanced by spectral clustering. On the one hand, it appears that the four clusters are common enough to get the same label. On the other hand, we also know that each of them must play a unique role in the subsequent course of the field because they are separated by how they are cocited by researchers of the SDSS field.Figure 6 shows the same clusters but with labels selected by a different algorithm, i.e. the log-likelihood ratio test. The four clusters now have different labels. Note other clusters’ labels are changed too. Cluster 9 is labeled as field methane dwarf. Methane dwarfs are very cool brown dwarfs. They are smaller than a star, but larger than a planet, and they are very hard to detect because they are very faint in the sky 1The third labeling algorithm is based on latent semantic analysis (LSA). Unlike the first two labeling algorithms, the LSA-based labeling algorithm uses single words instead of multi-word noun phrases. The LSA-based labeling algorithm first identifies the primary and secondary dimensions of the latent semantic space derived from the citer set of each cluster. Next, it selects the top 5 terms with the strongest weights along each dimension. Table 1 lists the selected terms for the four clusters discussed above. The primary concept terms appear to correspond to the noun phrase labels identified by tf*idf. The secondary concept terms appear to be more specific. Taken these terms together for each cluster, we can tell that Cluster 9 is . Finding rare objects such as methane dwarfs is one of the first discoveries made possible by the SDSS survey. Cluster 10 is labeled as high-redshift quasar. The redshift measures how far the light of an astronomical object has been shifted to longer wavelengths due to the expansion of the Universe. The higher the redshift, the more distant the astronomical object. Finding high-redshift quasars is important for the study of the early evolution of the Universe. Cluster 11 is labeled as dust emission. Our subsequent analysis shows that the broader context of this cluster is dust emission from quasars. Cluster 12 is labeled as luminous red galaxy. This cluster is in fact the largest cluster in the SDSS co-citation network, concerning various properties of galaxies.In summary, labels selected log-likelihood ratio test appear to characterize the nature of clusters with finer-grained concepts than labels selected by tf*idf. Specific labels are useful for differentiating different clusters, whereas more generic labels tend to be easy to understand, especially for domain novices. The largest 11 clusters are summarized in Table 2.1/news/releases/19990531.dwarf.htmlabout methane dwarfs, Cluster 10 about quasars, Cluster 11 also about quasars, and Cluster 12 about galaxies.Figure 5. SDSS-core clusters (#9, #10, #11, #12) are separated but still labeled by tf*idf with thesame label sloan digital sky survey.Figure 6. The four SDSS-core clusters (#9, #10, #11, #12) now have finer-grained labels by log-likelihood ratio tests.In the rest of the article, we will triangulate labels selected by the three algorithms and examine the full titles of the most representative citing papers to determine the most appropriate labels of clusters.Table 1. Labels by LSA-based selection.A total of 22 co-citation clusters were found by spectral clustering. Table 2 shows the 11 largest clusters in terms of the number of references N. The first column (#) shows the cluster IDs. We applied the three labeling methods to the titles and index terms of citing papers of each cluster. The last column shows labels we chose subjectively based on the information shown in all other columns. The numbers in tf*idf columns are the term weights, e.g. (60.78) brown dwarf. The numbers in log-likelihood columns are the frequency of the corresponding terms. For example, (18) luminous red galaxy means that the term appears 18 times in the citer set. The numbers in the Most representative citers column are the number of cluster members the paper cites. For example, the (13) in front of the first title in Cluster 12 means that the paper cites 13 of the 45 members of the cluster. The table is sorted by cluster size.Table 2. The largest 11 clusters in the SDSS co-citation network.We can make the following observations. The tf*idf selection is often characterized by high-frequency and generic terms, but its power for differentiating clusters is relatively low. The log-likelihood selection is more useful for differentiating clusters, although some terms may be less representative than the tf*idf selection. The LSA-based selection appears to echo the tf*idf selection. Titles appear to be a better source than index terms for the purpose of labeling clusters because index terms tend to be overly broad.Highlighting co-citation links in consecutive time slices can help analysts to better understand the dynamics of the field of study. For example, as shown in Figure 7, Cluster 8 was highly cited in 2001 by high-redshift quasar papers with a few between-cluster co-citation links connecting the dust emission cluster (#9). In contrast, as shown in Figure 8, co-citation links made in 2005 suggest that the research in 2005 was essentially connecting three previously isolated clusters as opposed to adding within-cluster co-citation links. Cluster 5, background QSO, was cocited with Cluster 10 luminous red galaxy. Cluster 5 was also cocited with Cluster 8 high-redshift quasar.Discussions and ConclusionsWe have introduced a new procedure for analyzing the impact of a co-citation network. The new procedure shifts the focus from cited references to citers to these references and aims to characterize the nature of co-citation clusters in terms of how they are cited instead of inferring how they ought to be cited. Furthermore, the new procedure provides a number of mechanisms to aid the aggregation and interpretation of the nature of a cluster and its relationships with its neighboring clusters. The new methodology is supported by spectral clustering and enhanced network visualization capabilities to differentiate densely connected network components. In order to aid the sense making process further, we integrate multiple channels for the selection of candidate labels for clusters, ranging from saliency-focused term selection to uniqueness-focused selection.Figure 7. Co-citation links made in 2001 (in red), primarily in Cluster 8 and linking to Cluster 9.Figure 8. Co-citation links made in 2005 (in red) between Cluster 10 and Cluster 5.We are addressing some challenging methodological and practical issues. Further studies are needed to evaluate the new method at a deeper level. We have noticed that when our astronomy experts attempted to make sense of bibliographic clusters, they tend to use algorithmically selected terms as a starting point and find concepts at appropriate levels of abstraction. The final concepts they choose may not necessarily present in the original list of candidates. In such synthesizing processes, scientists appear to search for a match in the structure of their domain knowledge. If this is indeed the case, it implies that the primary challenge is to bridge the gap between piecemeal concepts suggested by automatically extracted terms and the more cohesive theoretical organization of the experts.Further studies are also necessary to compare with relevant methods such as clustering based on bibliographic coupling (Kessler, 1963; Morris, Yen, Wu, & Asnake, 2003). Comprehensive studies of the interrelationships between different labeling mechanisms are important too. For example, one may examine the positions of various labels of the same cluster in terms of their structural properties in a network of labels. Comparative studies with traditional co-citation network analysis will be valuable to provide the empirical evidence that may establish where the practical strengths and weaknesses of the new approach.In conclusion, the major contribution of our work is the introduction of a new and integrated procedure for analyzing and interpreting co-citation networks from the perspectives of citers. The new method has the potential to bridge the methodological gap between co-citation analysis and other citer-focused analytic methods. The method is readily applicable to a wider range of sense-making and analytical reasoning tasks with associative networks such as social networks and concept networks by cross-validating structural patterns with direct and focused content information.AcknowledgementsThis work is supported in part by the National Science Foundation (NSF) under grant number 0612129.NotesCiteSpace is freely available at /~cchen/citespace. Figures in thisarticle are available in color at /~cchen/papers/2009/issi2009/.ReferencesBarabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A, 311, 590-614.Chen, C. (2004). Searching for intellectual turning points: Progressive Knowledge Domain Visualization. Proc. Natl. Acad. Sci. USA, 101(suppl), 5303-5310.Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.Chen, C., Zhang, J., Zhu, W., & Vogeley, M. S. (2007). Delineating the citation impact of scientific discoveries. In Proceedings of JCDL 2007, (pp. 19-28).Deerwester, S., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41(6), 391-407.Dunning, T. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61-74.Dwyer, T., Marriott, K., Schreiber, F., Stuckey, P. J., Woodward, M., & Wybrow, M. (2008).Exploration of networks using overview+detail with constraint-based cooperative layout. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1293-1300.Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proc.Natl. Acad. Sci. USA, 99, 7821-7826.Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14, 10-25.Koffka, K. (1955). Principles of Gestalt Psychology (Reprint ed.): Routledge.Luxburg, U. v. (2006). A tutorial on spectral clustering: Max Planck Institute for Biological Cybernetics.Morris, S. A., Yen, G., Wu, Z., & Asnake, B. (2003). Timeline visualization of research fronts.Journal of the American Society for Information Science and Technology, 55(5), 413-422.Ng, A., Jordan, M., & Weiss, Y. 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ISTQB初级认证模拟题_中文
1.训练题之阳早格格创做•下列术语中哪一个是ISTQB术语表中缺陷(Defect)的共义词汇：Bba)Incidentb)Bugc)Mistaked)Error•硬件尝试脚段不妨是：BbA.创制缺陷B.确认硬件不妨仄常运止C.防止缺陷D.间接普及产品的卖价E.缩小所有产品启垦周期时间a)A, Bb)A, B, Cc)A, B, C 战 Dd)所有选项•根据ISTQB 定义的术语，“危害”是与下列哪一个选项闭联的？Cca)对付尝试者可定的反馈意睹b)将爆收反里效用及其连锁效力的果素c)大概爆收反里效用及其连锁效力的果素d)将对付被测对付象爆收反里效用及其连锁效力的果素•确认系统是可依照预期处事，进而正在系统是可谦足系统需要圆里获与自疑心.那样的尝试脚段最大概适用底下的哪个尝试阶段：C组件尝试b)集成尝试c)系统尝试d)返回尝试•辨别尝试的任务、定义尝试的目标以及为真止尝试目标战任务的尝试活动规格道明.上述止为主要爆收正在： Aaa)计划战统制b)分解战安排c)真止战真止d)尝试中断活动•ISTQB术语中的返回尝试的脚段是：Cca)考证建改的乐成b)防止功能编写的不完备或者疏漏c)保证建正历程中不引进新的缺陷d)帮闲步调员更佳天举止单元尝试•下列办法不妨普及战革新尝试人员战启垦人员闭系的是：B ba)明黑名目经理处事的要害性b)对付所创制的大概的缺陷以一种中坐的办法举止相通c)单元尝试、集成尝试战系统尝试皆由共一批尝试人员去完毕d)尝试人员介进代码调试•基础的尝试历程主要由底下哪些活动组成：DA.计划战统制(control)B.分解战安排C.真止战真止D.评估出心准则战尝试报告E.尝试中断活动a)A, B 战 Cb)A, B, C 战 Dc)除 E 以中所有选项d)所有选项•对付真止硬件尝试组的独力的办法，不妨采与的是：BbA.尝试的安排由启垦队伍的其余启垦人员完毕；B.尝试的安排由启垦人员自己完毕；C.尝试的安排独力于本名脚段启垦队伍；D.尝试的安排独力于本启垦企业，去自于独力的第三圆尝试机构.E.所有尝试活动由启垦人员去完毕a)A, B, Cb)A, B, C, Dc)A, C, Ed)所有选项•以下闭于尝试准则的形貌，精确的是： Bba)所有的硬件尝试不需要逃溯到用户需要；b)真足尝试是不可能的；c)尝试不妨隐现硬件潜正在的缺陷；d)步调员不需要防止查看自己的步调.•硬件尝试处事该当启初于：Bba)Coding之后；b)需要分解阶段；c)提要安排阶段；d)仔细安排阶段.•动做一个硬件尝试员，应具备哪些本领？DdA.具备佳偶心；B.工做灰心心态；C.批评的视线；D.闭注系统的细节的本领E.尝试技能；F.良佳的相通本领a)A+B+C ；b)D+E+F ；c)E+F；d)以上皆是.•以下大概引导缺陷的本果有：DA.环境果素；(大概引导做废)B.启垦技能；C.历程管制典型性；D.部分本领E.硬件的搀纯性；F.启垦的周期少短a)以上皆是；b)以上皆不是；c)A+B+C；d)D+E+F.•闭于硬件本量包管战硬件尝试的形貌，不精确的是 Dda)硬件本量包管战硬件尝试是硬件本量工程的二个分歧层里的处事；b)正在硬件本量包管的活动中也有一些尝试活动；c)硬件尝试是包管硬件本量的一个要害关节；d)硬件尝试人员便是硬件本量包管人员.•闭于尝试充分性的形貌，精确的是：Bba)惟有举止真足的尝试才充分；b)正在有限的时间战资材条件下，找出所有的硬件的过得，使硬件趋于完好，是不可能的；c)当继启尝试不创制新缺陷时；d)当局部尝试用例皆真止完后.•以下闭于尝试脚段的瞅面，不精确的是：Bba)硬件尝试的脚段是觅找过得，而且尽最大的大概找出最多的过得；b)找出硬件启垦人员的问题并评介启垦人员本领；c)一个乐成的尝试是创制了于今已创制的过得的尝试；d)尝试的脚段，是念以最少的人力、物力战时间找出硬件中潜正在的百般过得战缺陷，通过建正百般过得战缺陷普及硬件本量，防止硬件颁布后由于潜正在的硬件缺陷战过得制成的隐患所戴去的商业危害.•以下闭于尝试效用的形貌，不精确的是：Bba)尝试无法隐现硬件潜正在的缺陷；b)尝试能包管硬件的缺陷战过得局部找到；c)尝试只可道明硬件存留过得而不克不迭道明硬件不过得；d)所有的硬件尝试皆应逃溯到用户需要.第二章：硬件死命周期中的尝试（15%）2.训练题•可维护性尝试属于：Da)非功能尝试b)功能尝试c)结构尝试d)确认战返回尝试•有一个系统已经正在商场上运止了，那种情况对付系统举止建改，而后举止的尝试： Aa)维护尝试b)查支尝试c)组件尝试d)系统尝试•底下哪些是一个佳的尝试的特性：CA.每个启垦活动皆有相对付应的尝试止为B.每个尝试级别皆有其特有的尝试目标C.对付于每个尝试级别，需要正在相映的启垦活动历程中举止相映的尝试分解战安排D.硬件尝试的处事沉面该当集结正在系统尝试上a)C,Db)A,Bc)A,B,Cd)A,B,C,D•底下不妨动做组件尝试的尝试对付象的是：A aa)模块、对付象战类b)步调中的某身材系统c)所有硬件系统d)模块间的接心•组件尝试的用例安排主要参照的处事产品是：Aaa)组件规格道明b)系统需要规格道明c)用户脚册d)代码•底下闭于返回尝试道述精确的是： Dda)返回尝试只可正在系统尝试那个级别举止，不克不迭用于单元尝试战集成尝试b)返回尝试只适用于功能尝试，不适用于非功能尝试c)返回尝试皆是自动化真止的d)返回尝试是对付已被测过的步调真体正在建改缺陷后举止的沉复尝试，以此去确认正在那些变动后是可有新的缺陷引进系统•语句的覆盖率主要正在底下哪个尝试级别的尝试安排中思量：Cc系统尝试b)集成尝试c)组件尝试d)查支尝试•保守的或者里背对付象的单元尝试，需要的启垦处事：Dda)只消启垦尝试stub；b)只消启垦尝试driver；c)大提要共时启垦一个stub战多个driver；d)大提要共时启垦一个driver战多个stub.(一个出心，多个输出)•暂时大部分的硬件过得根源于_______________.Da)步调过得；b)分解战安排过得；c)尝试自己的过得；d)需要过得.第三章：固态技能（7%）3.训练题•多出心函数大概会爆收__B____问题a)爆收逻辑过得b)落矮稳当性c)爆收内存揭收d)落矮运止本能•使用固态尝试中的函数调用闭系图不克不迭够Ca)查看函数的调用闭系是可精确b)创制是可存留孤坐函数c)精确函数被调用频度，并对付那些函数举止沉面查看d)创制函数里里结构•底下对付固态尝试战动背尝试的辨别形貌精确的是：Aaa)固态尝试并不真真的运止硬件，而动背尝试需要运止硬件b)固态尝试需要借帮于博门的尝试工具，而动背尝试不需要c)固态尝试是由启垦人员真止的，而动背尝试是由博门的尝试人员完毕d)固态尝试是主假如为了减少尝试人员对付硬件的明黑，而动背尝试是为了创制缺陷•底下那个不属于固态分解：Dda)编码准则的查看b)步调结构分解c)步调搀纯度分解d)内存揭收•技能评审的脚段是:D da)包管硬件正在独力的模式下举止启垦b)创制硬件接易过得c)与名目管制无闭d)确认硬件切合预先定义的启垦典型战尺度第四章：尝试安排技能（30%）4.训练题•闭于鸿沟值的道法不精确的是： Dda)鸿沟值分解是一种补充等价区分的尝试用例技能b)它不是采用等价类的任性元素，而是采用等价类鸿沟的尝试用例c)步调正在处理洪量中间数值时皆是对付的，然而是正在鸿沟处极大概出现过得d)鸿沟值分解法思量了输进变量之间的依好闭系•对付于尝试过得的道法是:Ba)尝试的安排不妨用8020准则动做指挥.b)尝试后步调中残存的过得数目与该步调中已创制的过得数目成正比c)该当正在尝试处事真真启初前的较万古间内举止尝试计划d)尝试的效验由尝试用例的几及确定的覆盖指标决定•根据尝试条例中包罗的尝试目标，共时举止尝试安排、尝试真止的是： Aa a)探干脆尝试b)过得推测c)黑盒尝试d)乌盒尝试•底下哪个属于固态分解：DdA.编码准则的查看B.步调结构分解C.步调搀纯度分解D.内存揭收a)除C以中b)除A战C以中c)除C战D以中d)除D以中•如果步调的功能道明中含有输进条件的拉拢情况，则一启初便不妨采用__B__战判决表法. ba)等价类区分法b)果果图法c)正接考查法d)场景法•常常情况下基础功能尝试战本能尝试的真止程序是：C c基础功能的尝试战本能尝试共时举止b)先真止本能尝试，而后再举止基础功能的尝试c)先举止基础功能的尝试，而后再真止本能尝试d)基础功能尝试战本能尝试哪个先真止皆无所谓•如果一个4变量函数，使除一个以中的所有变量与仄常值，使结余变量与最小值、略下于最小值、仄常值、略矮于最大值战最大值，对付每个变量皆沉复举止.那样，对付于一个4变量函数，鸿沟值分解爆收的尝试用例数为：B b15b)17c)18d)20a)一个参数的与值范畴是正整数，那么那个参数的灵验鸿沟值的数目是： A一个b)二个c)三个d)四个D•某个步调有三个输进参数A,B战C，输进参数的灵验条件是A<B 战 C>B，如果应用等价类区分的技能，不妨死成的等价类有： dA B、C、 D、a)A,Cb)A,B,Cc)C,Dd)A,B,C,D•判决覆盖战语句覆盖之间的比较：A a100％的判决覆盖不妨包管100％的语句覆盖，反之则不可b)100％的语句覆盖不妨包管100％的判决覆盖，反之则不可c)100％的语句覆盖不妨包管100％的判决覆盖，反之亦然d)100％的语句覆盖战100％的判决覆盖之间不间接的通联•正在规格道明不真足的情况，最切合采与的尝试技能是：Bba)鉴于结构的尝试技能(黑盒尝试)b)鉴于体味的尝试技能c)鉴于规格道明的尝试技能d)以上皆切合•什么是等价类区分CcA.将尝试对付象的输进或者输出域区分成若搞部分B.从每一身材集结采用少量具备代表性的数据C.是一种黑盒尝试要领D.灵验值的等价类E.无效值的等价类a)A,B,C,Db)A,B,Cc)A,B,D,Ed)D,E•形貌乌盒尝试战黑盒尝试历程的分歧：AaA.乌盒尝试正在尝试对付象的表面举止B.黑盒尝试是正在源代码已知的情况下举止C.乌盒尝试用例是通过尝试对付象的使用道明或者需要安排D.乌盒尝试包罗语句覆盖战分支覆盖要领E.黑盒尝试是通过果果图的分解要领举止的a)A,B,Cb)A,Cc)A,B,C,D,Ed)D,E•状态变换尝试用例安排的真足定义真量：CcA.尝试对付象的初初化状态B.尝试对付象的输进C.预期截止或者预期的止为D.预期的最后状态a)A,B,Cb)A,Cc)A,B,C,Dd)C,D•根据乌盒尝试要领不妨安排变量0 <= X <= 100的尝试用例：Cca)0,20,100b)20,50,100c)1,0,1,50,99,100,101d)100,30,100,200•根据以下过程图安排语句覆盖的尝试用例Dda)尝试用例a=5，c=7；a=10，c=12b)尝试用例a=11，c=6；a=0，c=2c)尝试用例a=9，c=11；a=15, c=11d)尝试用例a=5，c=7；a=11，c=6•请根据条件（x>3，y<5）安排条件拉拢覆盖尝试用例：AaA.x=6，y=3B.x=6，y=8C.x=2，y=3D.x=2，y=8a)A,B,C,Db)A,B,Cc)A,B,Dd)C,D•乌盒尝试技能包罗Ca)鸿沟值分解、判决表、等价类区分、体味法b)判决覆盖、语句覆盖、用例分解c)鸿沟值分解、等价类区分、果果图分解、随机法d)判决表技能、路径覆盖、条件覆盖•语句覆盖战判决覆盖有什么分歧 DdA.语句覆盖步调中每一个推断起码要真止一次B.判决覆盖步调中每个推断的与真分支战与假分支起码经历一次.C.判决覆盖步调中百般拉拢起码真止一次D.语句覆盖是指步调中每一条语句起码被真止一次a)A,Cb)A,Bc)C,Dd)B,D第五章：尝试管制（20%）5.尝试计划主要由哪个角色控制制定：DAd尝试人员b)名目经理c)启垦人员d)尝试经理•尝试经理的任务常常不包罗： Cca)编写尝试计划b)采用符合的尝试战术战要领c)建坐战维护尝试环境d)采用战引进符合的尝试工具•对付于监控尝试周期时采与的度量要领，下列道述中不当的是： c da鉴于障碍战鉴于做废的度量：统计特定硬件版本中的障碍数.a)鉴于尝试用例的度量：统计各劣先级的尝试用例数量.b)鉴于尝试对付象的度量：统计代码战拆置仄台等覆盖情况.c)鉴于成本的度量：统计已经耗费的尝试成本，下一尝试周期的成本与预期支益的闭系.•常常情况下，背担尝试监控任务的人员是： Aa)尝试系统管制员b)尝试经理c)尝试真止人员d)尝试安排人员•下列哪个是尝试组独力的缺面？c c尝试人员需要特殊的训练b)尝试人员需要花时间相识所要尝试的产品的需要、架构、代码等c)启垦人员大概会得去对付产品本量的责任心d)创制独力尝试组会耗费更多成本•如果不搞佳摆设管制处事，那D启垦人员相互篡改各自编写的代码B.集成处事易以启展C.问题分解战障碍建正处事被搀纯化D.尝试评估处事受阻a)A、Cb)B、Dc)A、B、Cd)A、B、C、D•对付于尝试历程去道，哪些处事产品要纳进摆设管制？Aaa)尝试对付象（The test object）、尝试资料（the test material）战尝试环境b)问题报告战尝试资料c)尝试对付象d)尝试对付象战尝试资料•底下有闭鉴于危害的要领的形貌哪个是不精确的？Ca)识别的危害经时常使用于决断哪些需要更多尝试，哪些不妨缩小尝试b)识别的危害经时常使用于决断几尝试服务c)识别的危害经时常使用于决断使用何种尝试工具d)识别的危害经时常使用于决断使用何种尝试技能•下列活动中，不属于尝试计划活动的是：A 安排尝试用例b)决定尝试环境c)定义尝试级别d)估算尝试成本•事变报告中大概包罗的过得有：DA.步调过得B.规格道明中的过得C.用户脚册中的过得a) Ab)A、Cc)B、Cd)A、B、C•下列危害中，属于产品危害的是：B ba）硬件需要不精确b)由于使用硬件产品而引导人员伤亡c)硬件尝试人员战硬件启垦人员相通不畅d)硬件源代码本量矮下•硬件尝试团队的构制普遍可分为：_____A________战鉴于名脚段构制模式.a)鉴于尝试的构制模式；b)鉴于技能的构制模式；c)鉴于团队的构制模式；d)鉴于硬件的构制模式.•尝试报告不包罗的真量有：Dda)尝试时间、人员、产品、版本；b)尝试环境摆设；c)尝试截止统计；d)尝试通过/波折的尺度.•尝试人员（Tester）正在硬件摆设管制中处事主假如：Aaa)根据摆设管制计划战相闭确定，提接尝试摆设项战尝试基线；b)建坐摆设管制系统；c)提供尝试的摆设审计报告；d)建坐基线.第六章：硬件尝试工具（10%）•尝试管制工具大概包罗的功能：DdA.管制硬件需要B.管制尝试计划C.缺陷逃踪D.尝试历程中百般数据的统计战汇总a)除A以中b)除B以中c)除C战D以中d)以上局部•下列闭于尝试管制工具的道法中，最不妥当的是：d尝试管制工具与需要管制工具的集成有好处逃踪需要的真止情况b)尝试管制工具战事变管制工具的集成有好处举止再尝试c)尝试管制工具备帮于更佳天逃踪尝试用例的真止情况d)尝试管制工具不妨加快真止尝试用例的速度•引进自动化尝试工具时，属于次要思量果素的是： ba)与尝试对付象举止接互的本量b)使用的足本谈话典型c)工具支援的仄台d)厂商的支援战服务本量•下列闭于自动化尝试工具的道法中，过得的是 D 录制/回搁大概是缺累够的，还需要举止足本编程b)既可用于功能尝试，也可用于非功能尝试c)自动化尝试工具适用于返回尝试d)自动化尝试闭键的时间能代替脚工尝试•尝试东西（test harness）主要可用于D组件尝试、集成尝试b)集成尝试、系统尝试c)组件尝试、部分系统尝试d)组件尝试、集成尝试、部分系统尝试•下列闭于工具使用危害的道法中，不妥当的是：A a工具不妨或者多或者少普及尝试效用b)不佳的尝试历程或者老练的尝试要领，工具本去不克不迭像预期的那样落矮成本c)与脚工尝试相比较，使用自动化工具也大概会减少尝试成本d)训练战指挥有帮于落矮工具使用的危害•正在下列尝试典型中，不切合采与脚工尝试的是bba)仄安尝试b)背载尝试c)集成尝试d)再尝试。
人工智能领域中英文专有名词汇总
名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。
QMS注册审核员笔试题库(ISO9000审核员考试题库0
QMS复习题单选1．一个组织生产的产品设计是由国外总部提供的，组织向顾客提供成型产品，哪种说法正确( C )A．手册中可以不包括设计的相关内容B．它可以删减7.3，因为这没有设计能力C．不能删减7.3 D．因为总部己获得GB/T19001证书，不审7．3可以发带具有设计能力的证书2．质量管理评审的输出不包括（ C ）。
A．质量管理体系有效性改进B．体系过程有效性的改进C．生产实旅计划D．资源需求8．用于分析过程不合格品率波动状态的图形是( D )。
A．X-R 控制图B．C控制图C．U控制图D．P控制图16．生物上的完美令人不寒而栗，因为那等于同种生物没有变异，进化过程失去动力。
我们接受生物的多样化，可是却假定完美是物理和工程科学可欲而又可即的目标。
这种观念起源于我们对许多简单问题了解得比较透彻，而“简单”往往意味着对缺陷的忽略。
这段话主要支持了这样一种观点，即：( A )。
A．完美不可能在科学上实现B．生物科学比物理和工程科学更深奥C．缺陷对人类整个技术文明是绝对必须的D．在物理和工程科学中完美是可欲又可即的22．设计和开发活动中的“变换方法进行计算”的活动是( C )。
A．设计输出B．设计评审C．设计验证D．设计确25．检验是指通过观察和判断，适当时结合测量、试验或估量所进行的( D )。
A．管理性检查活动B．PDCA过程C．处理不良品的过程D．符合性评价29．产品实现的策划不要求包括( D )。
A．质量目标B．生产过程及其资源C．接收准则D．质量方针31．一个选择了外包过程的组织，如声称符合GB／T[9001—2008标准，则不可删减该标准( B )条款的要求。
A．7.3 B．7.4 C．7.5.2 D．7.5.434．这几项有关职工福利的方案，是全厂职工代表大会( D )的，任何人无权随意改动。
A．核定B．两定C．规定D．审定4、数据分析应提供过程和产品的特性和趋势，包括采取( B )的机会。
IPD试题(市场与销售)
IPD课程考试题（市场与销售）一、填空题1.PDT团队开发工作的的最主要输入是2.什么阶段结束时PDT要和IPMT签订合同：3.PDT的中英文全称分别是：，4.市场需求模板的输入是：和5.产品配置价格表应在提交。
6.市场策略模板的下游是。
7.在产品验证阶段市场和销售需要提供模板。
二、选择题（每题2分，共15题，共30分）1.某产品在产品发布以后，需要增加一个送料功能，应该走那个版本的开发流程：（）A、V版本B、R版本C、M版本D、C版本2.EC和PCR的分界点是哪个评审（）A、TR4B、TR5C、TR6D、PDCP3.概念阶段的业务计划重要的输入文件是？（）A、产品概念及初步技术方案B、产品包需求C、各业务领域的需求D、各业务领域的策略4.( )负责任命产品开发团队：A、IPMTB、IRBC、LPDTD、PQA5.在IPD中，职能部门的主要角色是：( )A、分配资源B、使用资源C、建设并提供资源D、管理项目6.计划阶段的WBS应分解到（）：A、1/2级B、3/4级C、5级D、6级7.LPDT 应该来自于：()A、研发部门B、市场部门C、制造部门D、可以来自任何一个功能部门8.在产品开发的决策评审（DCP）中，决策的主要依据是：( )A、项目任务书B、项目进度表C、业务计划书D、市场信息9.扩编PDT团队是在那个阶段（）A、概念阶段B、计划阶段C、开发阶段D、发布阶段10.PDT 编制WBS时，每项工作任务的完成周期应（）A、不超过10hB、不超过20hC、不超过30hD、不超过60h11.项目开发时，项目文档生效的标志是（）A、LPDT批准B、职能部门批准C、文档检入配置管理库D、通过评审12.下列选项中不是市场代表职责的是（）A、定义产品市场需求B、优化竞争对手分析C、策划市场开发D、确定产品开发进度13.下列选项中不是销售代表职责的是（）A、制定销售目标B、准备销售力量C、产品功能规划D、确保市场准入14.市场策略模板在哪个阶段提交? ( )A、概念阶段B、计划阶段C、开发阶段D、验证阶段15.市场宣传计划模板在哪个阶段提交？（）A、概念阶段B、计划阶段C、开发阶段D、验证阶段三、判断题（每题1分共10题）1.概念阶段开始做的阶段工作计划可以分解到WBS3/4级（）2.产品系统设计方案可以识别出关键物料（）3.市场需求不需要经过同行评审（）4.PDT团队负责产品的开发工作，不对产品销售情况负责（）5.概念阶段决策是基于有效的假设（）6.计划阶段决策是基于有效的假设（）7.市场代表代表营销部门做出承诺，进行市场竞争状况分析、定义市场需求()8.销售代表制定并执行产品销售策略,并保持和顾客(代理商)紧密联系,促进公司销售目标实现( )9.市场代表负责制定市场策略及计划、产品市场宣传计划，并监控和推动计划的实施( )10.市场需求模板在计划阶段完成（）四、简答题（每题5分，共5题，共25分）1.业务计划和项目计划的区别？2.对项目开发团队和职能部门之间的关系你是怎样理解的？3.简述决策评审（DCP）与技术评审（TR）的区别：4.简述在IPD实施过程中市场和销售需要提供哪些模板？5.简述销售代表的职责有哪些？五、论述题你认为IPD会对你的工作带来什么样的变化和影响，你需要IPD给你的工作带来什么样的帮助？。
「网络探究」以提升高阶思维及共通能力
• 數學家有什麼學歷 – 請到以下 的網站搜尋一下, 數學家都有什 麼學歷 • 數學家有什麼特質? 請到下列 幾位數學家的網頁找尋一下。 • 數學家少年時一般有什麼表現? 請到下列幾位數學家的網頁找 尋一下。 • 香港曾經出現過幾位世界著名 的數學家? • 請以300 字寫出你能否成為一 個數學家。如果能夠，要怎樣 才可以？ 解難示範 – 識知學徒 cognitive apprentice
資訊管理能力
• • • • • • • • 使用來自適當來源的資訊 使用適當科技以獲取資訊 使用適當媒介來表達資訊 可以透過完成探索任務來培養 處理大量資訊 使用適當的語言及形式來表達自己的意見 闡釋不同形式的資訊 流暢地表達資訊 為不同的目的/在不同的情境/對不同的對象都能作出適當 的回應 • 以批判的態度使用資訊 • 以創新或創意的方式使用資訊
謝謝
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.hk .hk/projects/webquest/ .hk/community/webquest/
任務二
• (A2)..設計一份小冊子 • (B2) 志云小學的小賣 給來參加講座的聽眾， 部一向售賣不健康的 為了讓他們更容易了 零食，如薯片、糖果、 解講座內容，小冊子 汽水等。健康大使需 需要包括一些簡明的 要知道同學的口味和 資料，如宋詞風格中 有關零食的營養資料， 的兩個派別，他們的 最後向小賣部提交一 份建議書，內容包括 分別與特色何在等等。 建議學校應售賣哪些 Comprehension 零食、原因及向同學 推廣的方法。
Reception Scaffold
解難示範 – 識知學徒 cognitive apprentice
A3
過程三
B3
• 每組同學須商討如何分工，並 根據不同任務瀏覽以下網頁， 尋找適合的資料作完成任務的 材料。如有任何疑問可找數學 學會老師或電郵大學教授詢問。 • 進入以下網頁，你可以找到丘 成桐教授的個人資料、他的個 人成尌、他的學習經驗專訪， 他對現今大學生的看法等資料。 • 進入以下網頁，你可以找到有 關菲爾茲獎 (或稱費爾茲獎)的 由來、得獎者資格、獎金等資 料。 • 進入以下網頁，你可以ension
电子行业英语大全
品质专业英语大全从事品质工作以来积累的常用英语，希望对有需要的朋友有所帮品质专业英语大全零件材料类的专有名词CPU: central processing unit(中央处理器）IC：Integrated circuit（集成电路)Memory IC：Memory Integrated circuit（记忆集成电路）RAM: Random Access Memory（随机存取存储器）DRAM: Dynamic Random Access Memory(动态随机存取存储器）SRAM: Staic Random Access Memory(静态随机存储器）ROM: Read—only Memory（只读存储器)EPROM：Electrical Programmable Read-only Memory（电可抹只读存诸器)EEPROM: Electrical Erasbale Programmable Read-only Memory（电可抹可编程只读存储器）CMOS：Complementary Metal-Oxide—Semiconductor（互补金属氧化物半导体)BIOS：Basic Input Output System（基本输入输出系统）Transistor：电晶体LED：发光二极体Resistor：电阻Variator：可变电阻Capacitor:电容Capacitor array：排容Diode：二极体Transistor：三极体Transformer：变压器（ADP)Oscillator：频率振荡器（0sc）Crystal：石英振荡器XTAL/OSC：振荡产生器（X）Relay：延时器Sensor：感应器Bead core：磁珠Filter：滤波器Flat Cable:排线Inductor:电感Buzzer：蜂鸣器Socket:插座Slot：插槽Fuse:熔断器Current:电流表Solder iron:电烙铁Magnifying glass：放大镜Caliper：游标卡尺Driver：螺丝起子Oven：烤箱TFT:液晶显示器Oscilloscope：示波器Connector：连接器PCB:printed circuit board（印刷电路板）PCBA: printed circuit board assembly（电路板成品）PP：并行接口HDD:硬盘FDD:软盘PSU:power supply unit（电源供应器）SPEC:规格Attach:附件Case：机箱,盖子Cover：上盖Base：下盖Bazel：面板（panel）Bracket：支架，铁片Lable：贴纸Guide：手册Manual：手册，指南Card:网卡Switch:交换机Hub:集线器Router：路由器Sample：样品Gap：间隙Sponge：海绵Pallet：栈板Foam：保利龙Fiber：光纤Disk:磁盘片PROG:程序Barcode：条码System：系统System Barcode：系统条码M/B:mother board：主板CD-ROM：光驱FAN：风扇Cable：线材Audio:音效K/B:Keyboard(键盘)Mouse：鼠标Riser card：转接卡Card reader：读卡器Screw:螺丝Thermal pad：散热垫Heat sink：散热片Rubber：橡胶垫Rubber foot：脚垫Bag:袋子Washer：垫圈Sleeve：袖套Config：机构Label hi—pot:高压标签Firmware label:烧录标签Metal cover:金属盖子Plastic cover：塑胶盖子Tape for packing:包装带Bar code：条码Tray：托盘Collecto：集线夹Holder：固定器，L铁Connecter：连接器IDE：集成电路设备，智能磁盘设备SCSI：小型计算机系统接口Gasket:导电泡棉AGP：加速图形接口PCI：周边组件扩展接口LAN:局域网USB:通用串形总线架构Slim：小型化COM:串型通讯端口LPT:打印口，并行口Power cord：电源线I/O:输入,输出Speaker：扬声器EPE：泡棉Carton：纸箱Button：按键，按钮Foot stand：脚架部门名称的专有名词QS:Quality system品质系统CS:Coutomer Sevice 客户服务QC：Quality control品质管理IQC:Incoming quality control 进料检验LQC:Line Quality Control 生产线品质控制IPQC：In process quality control 制程检验FQC:Final quality control 最终检验OQC:Outgoing quality control 出货检验QA：Quality assurance 品质保证SQA:Source（supplier) Quality Assurance 供应商品质保证（VQA) CQA：Customer Quality Assurance客户质量保证PQA rocess Quality Assurance 制程品质保证QE:Quality engineer 品质工程CE:component engineering零件工程EE：equipment engineering设备工程ME:manufacturing engineering制造工程TE：testing engineering测试工程PPE roduct Engineer 产品工程IE：Industrial engineer 工业工程ADM：Administration Department行政部RMA：客户退回维修CSDI：检修PC：producing control生管MC:mater control物管GAD: General Affairs Dept总务部A/D：Accountant /Finance Dept会计LAB: Laboratory实验室DOE:实验设计HR:人资PMC：企划RD：研发W/H：仓库SI：客验PD：Product Department生产部PA：采购(PUR: Purchaing Dept)SMT：Surface mount technology 表面粘着技术MFG：Manufacturing 制造MIS：Management information system 资迅管理系统DCC:document control center 文件管制中心厂内作业中的专有名词QT:Quality target品质目标QP：Quality policy目标方针QI:Quality improvement品质改善CRITICAL DEFECT:严重缺点（CR）MAJOR DEFECT：主要缺点（MA)MINOR DEFECT:次要缺点（MI)MAX:Maximum最大值MIN：Minimum最小值DIA iameter直径DIM imension尺寸LCL:Lower control limit管制下限UCL:Upper control limit管制上限EMI：电磁干扰ESD：静电防护EPA:静电保护区域ECN：工程变更ECO：Engineering change order工程改动要求（客户）ECR:工程变更需求单CPI:Continuous Process Improvement 连续工序改善Compatibility：兼容性Marking:标记DWG rawing图面Standardization：标准化Consensus：一致Code：代码ZD:Zero defect零缺点Tolerance：公差Subject matter:主要事项Auditor：审核员BOM：Bill of material物料清单Rework：重工ID：identification识别，鉴别,证明PILOT RUN：（试投产）FAI:首件检查FPIR：First Piece Inspection Report首件检查报告FAA：首件确认SPC：统计制程管制CP：capability index（准确度）CPK：capability index of process(制程能力)PMP：制程管理计划（生产管制计划)MPI：制程分析DAS efects Analysis System 缺陷分析系统PPB：十亿分之一Flux:助焊剂P/N：料号L/N：Lot Number批号Version：版本Quantity:数量Valid date:有效日期MIL-STD：Military-Standard军用标准ICT: In Circuit Test (线路测试)ATE：Automatic Test Equipment自动测试设备MO：Manafacture Order生产单T/U: Touch Up (锡面修补）I/N:手插件P/T：初测F/T: Function Test （功能测试-终测）AS 组立P/K：包装TQM：Total quality control全面品质管理MDA:manufacturing defect analysis制程不良分析(ICT) RUN—IN:老化实验HI-pot：高压测试FMI:Frequency Modulation Inspect高频测试DPPM: Defect Part Per Million(不良率的一种表达方式：百万分之一) 1000PPM即为0。
知识价值链模型
知识价值链模型概述知识价值链模型是一个整合模型，主要以彼得·杜拉克提出的知识工作者与下一个社会、迈克尔·波特的价值链、日本的野中郁次郎的知识螺旋、哈佛商学院的罗伯特·卡普兰及诺朗诺顿研究所所长戴维·诺顿的平衡计分卡与哈佛大学心理学家迦德纳(Howard Gardner)的多元智慧理论所推演而成。
知识价值链模型主要包含三部分：知识输入端(Input knowledge)、知识活动端(Knowledge activities)与价值输出端(Output values) (图1)。
在知识输入端的设计，是以知识经济的发展趋势与Drucker提出的知识工作者与下一个社会为基础；知识活动端主要是根据Porter的价值链与Nonaka的知识螺旋推演而得；价值(目标)输出端，则整合了罗伯特·卡普兰及戴维·诺顿的平衡计分卡与迦德纳的多元智慧理论。
以下将分别以知识输入端、知识活动面与知识输出端三个角度来说明知识价值链模型观念的推演过程。
[编辑]知识价值链模型的构成要素1.知识输入端(Input knowledge)根据彼得·杜拉克(2002)在《下一个社会》指出，知识工作者将支配未来公司的竞争力。
未来社会的信息与知识将很轻易可以透过信息基础建设(Information Infrastructure)的三个重要管道获得，即企业内部的局域网络(Intranet)、企业与企业间的合作网络(Extranet)和因特网所形成的企业对外网络 (Internet)。
由这些管道获得的知识将汇集至企业信息入口(Enterprise information portal, EIP)网站。
若再整合其它并非直接来自EIP管道的各种内隐知识(Tacit knowledge)与外显知识(Explicit knowledge)后，企业的知识将以广泛而多元的方式进入企业，并收敛至单一窗口而输入至企业组织的各式知识活动中(图2)。
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recipient country, both of which can be influenced to some extent by policies.4 Protection of intellectual property rights, labor market regulations, location of industries etc. will affect the ease of knowledge diffusion while absorptive capacity can be increased by education, subsidy for R&D, labor training etc. While there is unanimous agreement in the literature on appropriate policy as regard to absorptive capacity, policies with regard to knowledge diffusion are more controversial and deserve further study.
*
The author is affiliated to the HEC School of Management and the CEPR; e-mail: goh@hec.fr.
Introduction
It is widely believed that the potential for developing countries to grow by using technology already developed by the industrialized countries is considerable. Some of these some knowledge spillovers are passive and can occur at relatively low costs through trade in intermediate goods embodying the technology1 while the rest are active in the sense that agents from the developed countries need to incur resource costs to transfer the technology and agents from the developing countries need to engage in technological effort2 to adapt and gain mastery over the technology received (see e.g., Pack and Westphal 1986, Teece 1977, Mansfield and Romeo 1980). Thus different countries can grow at very different rates depending on the institutional barriers and the incentives these countries provide for the transfer and mastery of technology through various channels like trade, licensing, foreign direct investment and subcontracting.3
See Saggi (2002) for a recent survey of the literature. See also Van and Wan (1999) who argue that technology diffusion need not discourage technology transfer by multinationals because domestic agents acquire only partial knowledge and this knowledge is applied to products that do not compete with the multinationals.
In order to build the right incentive systems for encouraging greater transfer and mastery of foreign technology, policy makers need to have an understanding of the determinants of the incentives governing foreign firms’ willingness to transfer technology and domestic firms’ investment in technology mastery. Two factors that are widely cited as important in affecting the incentives for technology transfer are the ease of knowledge diffusion/imitation and the level of absorptive capacity in the
Keywords : Technology transfer, technological effort, developing countries, knowledge diffusion, buyer-supplier
JEL classification codes: F23; L13; O14; O19; O32; O33
Knowledge Diffusion, Supplier’s Technological Effort and Technology Transfer via Vertical Relationships
Ai-Ting Goh*
Abstract This paper studies the effect of knowledge diffusion on the incentives for developed countries’ (DC) firms to undertake costly technology transfer to their developing countries’ (LDC) suppliers whose cost of production varies inversely with their technological effort. When the incumbent supplier’s cost of improving efficiency is high, upstream (or, respectively, downstream) diffusion of knowledge to potential input (final output) producers encourages (discourages) technology transfer as it increases upstream (downstream) competition. However, and in sharp contrast to existing literature, when technological effort is less costly, upstream (downstream) knowledge diffusion discourages (encourages) technology transfer by reducing (increasing) the incumbent supplier’s technological effort.
In this paper we are interested in studying the impact of knowledge diffusion on technology transfer via buyer-supplier relationships. The reasons for doing so are twofold. First, several studies have documented that multinationals and developed
The term “passive” is used by Kelller (2002) to describe this type of knowledge spillovers whereby the knowledge so obtained does not add to the domestic stock of knowledge available for use by domestic inventors (as opposed to “active” knowledge spillovers). 2 The term technological effort is used by Pack and Westphal (1986) to represent explicit investment in technology mastery (e.g. effort used in acquiring technological information, in managing changes in products and processes, in creating new technology etc.) as opposed to passive learning by doing. 3 See Parente and Prescott (2000) for empirical evidence.