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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. 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Author co-citation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32, 163-172.。
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。
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？。
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)。
知识供应链(Knowledge Supply Chain Mode1,KSCM)什么是知识供应链知识供应链(Knowledge Supply Chain，KSC)是由美国的“下一代制造项目”(Next GenerationManufacturing Project，NGMP)提出的。
知识供应链是指通过需求与供应关系将知识的供应、创新、传播、使用等过程的相邻知识结点联系起来的，把概念转换为知识化产品，再到最终用户的一个功能网链J.Rechard Hall＆ PierpaoloAndfiam 从供应链的角度提出知识链的概念，认为知识链是一种管理供应链隐性知识的方法。
知识链的管理过程其实就是核心能力的识别、培育和转换的过程。
在这个意义上，美国学者C〃W 〃Holsapple和M．Singh 提出了一个系统的知识链模型(Knowledge Chain Mode1)，该知识链是从组织内的知识和组织的核心竞争能力的关系出发构建的，包括了知识链的主体部分和知识链的产出，知识链的主体部分包括了五种初级活动和四种高级活动。
五种初级的知识活动：知识获取、知识选择、知识生成、知识内化和知识外化。
四种高级的知识活动：领导、合作、控制和测量。
知识链的产出就是知识。
在两者的基础上通过提高竞争能力，组织敏捷性，商业名誉和创新来构建企业的竞争优势¨。
知识供应链是指以满足顾客需求为导向，通过知识创新，将知识的供应者、知识的创新者、知识的使用者连接起来，以实现知识的经济化、整体最优化以及利润最大化目标的网络结构模式。
这一概念强调以下几点：1．知识供应链的驱动力主要源自市场，以顾客需求为导向，是需求拉动式供应链模式。
2．企业的经营活动，特别是知识型企业的经营活动不再是以物流为中心，而是以知识流的活动为中心，围绕知识创新活动而展开，所以，在这个意义上讲，知识供应链是对传统的实物供应链即物流管理的扩展与深化。
3．在知识供应链上必存在一个核心主体来管理链上的创新活动，核心主体的创新能力对整个知识供应链起着决定性的作用。
2024年华为人工智能方向HCIA考试复习题库（含答案）一、单选题1.以下哪—项不属于MindSpore全场景部署和协同的关键特性?A、统一模型R带来一致性的部署体验。
B、端云协同FederalMetaLearning打破端云界限,多设备协同模型。
C、数据+计算整图到Ascend芯片。
D、软硬协同的图优化技术屏蔽场景差异。
参考答案：C2.在对抗生成网络当中,带有标签的数据应该被放在哪里?A、作为生成模型的输出值B、作为判别模型的输入值C、作为判别模型的输出值D、作为生成模型的输入值参考答案：B3.下列属性中TensorFlow2.0不支持创建tensor的方法是?A、zerosB、fillC、createD、constant参考答案：C4.以下哪一项是HiAI3.0相对于2.0提升的特点？A、单设备B、分布式C、多设备D、端云协同参考答案：B5.以下哪个不是MindSpore中Tensor常见的操作？A、asnumpy()B、dim()C、for()D、size()参考答案：C6.优化器是训练神经网络的重要组成部分,使用优化器的目的不包含以下哪项:A、加快算法收敛速度B、减少手工参数的设置难度C、避过过拟合问题D、避过局部极值参考答案：C7.K折交叉验证是指将测试数据集划分成K个子数据集。
A、TRUEB、FALSE参考答案：B8.机器学习是深度学习的一部分。
人工智能也是深度学习的一部分。
A、TrueB、False参考答案：B9.在神经网络中,我们是通过以下哪个方法在训练网络的时候更新参数,从而最小化损失函数的?A、正向传播算法B、池化计算C、卷积计算D、反向传播算法参考答案：D10.以下不属于TensorFlow2.0的特点是?A、多核CPU加速B、分布式C、多语言D、多平台参考答案：A11.以下关于机器学习中分类模型与回归模型的说法,哪一项说法是正确的?A、对回归问题和分类问题的评价,最常用的指标都是准确率和召回率B、输出变量为有限个离散变量的预测问题是回归问题,输出变量为连续变量的预测问题是分类问题C、回归问题知分类问题都有可能发生过拟合D、逻辑回归是一种典型的回归模型参考答案：C12.ModelArts平台中的数据管理中不支持视频数据格式。
计算机辅助教育试题（A）一、名词解释（12分，每题3分）1．CIIPS2．脚本模板3．网络化远程教育4．信度二、判断正误并改正（8分，每题2分）1．S-P表中，S线左方的面积与P线上方的面积相等。
2．最早开发出来的计算机辅助教育系统是美国IBM公司沃斯顿研究所于1985年研制出来的。
3．知识库是知识的集合，知识包括属性值和元组。
4．评价体系建立的基本原则是完备性、合理性和有效性。
三、简答题（48分，每题8分）1．请分别说出术语CBE和CAI的全称，以及两者之间的不同。
2．说出课件设计的各个阶段和每个阶段的作用。
3．列举常用的3种课件开发工具的名称，并比较分析它们的优缺点。
4．通常意义上的教学法与计算机辅助教学中的教学法有何不同？5．阐述多媒体资源库的基本组成。
6．简单介绍一下CMI系统的三个典型应用形式。
四、阐述题（32分，1、2题每题10分，3题12分）1．计算机辅助教学中是不是技术运用的越多越丰富越好？如果不是的话，我们又应该如何有效地运用技术来提高我们的教学效率？请举例说明。
2．计算机在CAI教学过程中起什么作用？如果要进行新知识的学习，你会选择哪些类型的CAI课件模式？为什么？如果是培养学生的综合能力，你又会选择哪些模式？为什么？3．回顾在此次课件开发过程中所进行的形成型评价和总结型评价，试分别说明它们的作用？一、名词解释（12分，每题3分）1．CIIPS：Classroom Instruction Information Process system，CIIPS，是一种能自动采集、处理和分析课堂教学中学生反应数据的实时处理系统。
2．脚本模板：一种脚本样式，包括了教学内容、结构体系、界面布局、交互设计等方面比较详细具体的参照方案，保证了课件脚本设计中共性操作的统一，帮助课件设计者利用模板将课件设计中的教学思想表现出来。
3．网络化远程教育：网络化远程教育是一种同时异地或异时异地进行的教学活动方式，是计算机网络和多媒体技术相结合的新一代教育形式。
KnowledgeBuilder、KnowledgeBase、StatefulKnowledgeSession、StatelessKnowledgeSession 先编写规则，再在代码中调用这些规则处理业务问题。
1.KnowledgeBuilder的作用就是用来在业务代码当中收集已经编写好的规则，然后对这些规则文件进行编译，最终产生一批编译好的规则包（KnowledgePackage）给其它的应用程序使用。
创建KnowledgeBuilder对象使用的是KnowledgeBuilderFactory 的newKnowledgeBuilder方法。
获取KnowledgePackage方法是builder. getKnowledgePackages().2.KnowledgeBase本身不包含任何业务数据对象（fact 对象），业务对象都是插入到由KnowledgeBase产生的两种类型的session 对象当中（StatefulKnowledgeSession和StatelessKnowledgeSession），通过session 对象可以触发规则执行或开始一个规则流执行。
创建一个KnowledgeBase 要通过KnowledgeBaseFactory对象提供的newKnowledgeBase方法来现。
这其中创建的时候还可以为其指定一个KnowledgeBaseConfiguration 对象，KnowledgeBaseConfiguration 对象是一个用来存放规则引擎运行时相关环境参数定义的配置对象。
KnowledgePackage 的集合添加到KnowledgeBase当中.kbase.addKnowledgePackages(kpackage).3.StatefulKnowledgeSession规则编译完成之后，接下来就需要使用一个API 使编译好的规则包文件在规则引擎当中运行起来。
distilling the knowledge in a neural networkKnowledge Distilling Is a method of model compression, which refers to the method of using a more complex Teacher model to guide a lighter Student model training, so as to maintain the accuracy of the original Teacher model as far as possible while reducing the model size and computing resources. This approach was noticed, mainly due to Hinton's paper Distilling the Knowledge in a Neural Network.Knowledge Distill Is a simple way to make up for the insufficient supervision signal of classification problems. In the traditional classification problem, the goal of the model is to map the input features to a point in the output space, for example, in the famous Imagenet competition, which is to map all possible input images to 1000 points in the output space. In doing so, each of the 1,000 points is a one hot-encoded category information. Such a label can provide only the supervision information of log (class) so many bits. In KD, however, we can use teacher model to output a continuous label distribution for each sample, so that the supervised information is much more available than one hot's. From another perspective, you can imagine that if there is only one goal like label, the goal of the model is to force the mapping of each class in the training sample to the same point, so that the intra-class variance and inter-class distance that are very helpful for training will be lost. However, using the output of teacher model can recover this information. The specific example is like the paper, where a cat and a dog are closer than a cat and a table, and if an animal does look like a cat or a dog, it can provide supervision for both categories. To sum up, the core idea of KD is that "dispersing" is compressed to the supervisory information of a point, so that the output of student model can be distributed as much as the output of match teacher model as possible. In fact, to achieve this goal, it is not necessarily teacher model to be used. The uncertain information retained in the data annotation or collection can also help the training of the model.。
PDM系统（Windchill）培训考试试卷姓名：_________________ 部门：____________________ 分数：__________第一部分（Windchill介绍课程）一、选择题：（不定项选择，每题4分，共60分）1、Windchill系统属于（ D ）A、企业资源管理系统B、工艺管理系统C、客户关系管理系统D、产品生命周期管理系统2、Windchill系统的主要对象有哪些（ABCD ）A、部件、成品B、文档、CAD文档C、变更项D、链接3、以下说法正确的是（ACD ）A、工作总揽是用户接收任务的地方B、产品库文件夹主要是用于组织产品数据C、PDMLink中提供详细的帮助和操作手册D、可以在产品上下文团队中查看该产品的参与人员4、Windchill PDMLink中可以进行哪种搜索（ABCD ）A、基本搜索B、高级搜索C、特定上下文搜索D、分类搜索5、某个零部件已经处于发布状态且其版本号为A.3，现在要对其进行修订变更，那么修订后的版本为下列哪个选项（ C ）；修订后，创建者需要对其进行相应修改，进行了二次检出检入，此时其版本号为下列哪个选项（ D ）A、A.4B、B.4C、B.1D、B.3E、A.5F、B.26、对于CAD文档的详细信息页面，下列说法正确的是（ABCD ）A、可以查看其基本信息（创建者、生命周期状态等）B、可以用Productview可视化直接在PDM内查看其内容C、可以查看CAD文档对象的关联关系（如：和零部件的关联关系）D、可以查看CAD文档对象的历史版本7、工程师为使用AutoCAD连接到Windchill PDMLink服务器，下列哪些说法是正确的（ABCD）A、工作区受到windchill系统的控制B、工作区分为服务器与本地机两端C、工作区可以创建多个D、需要输入用户名和密码进行验证8、在AutoCAD与Windchill PDMLink连线状态中，在AutoCAD菜单中对模型执行保存操作时（ B ）A、将模型从会话内存保存到服务器端的工作区B、将模型从会话内存保存到磁盘上的缓存位置（客户端工作区）C、将模型从客户端工作区移动到服务器端工作区D、将模型保存到AutoCAD会话内存中9、关于PartsLINK分类重用管理，下列说法正确的是（ABCD ）A可以通过分类搜索查询 B 可以针对不同类的零部件定义分类属性C 所有的零部件都可以指定分类D 目的在于建立零部件重用查询机制10、以下哪些为正确反映在PDM系统的产品结构（BD ）（Ａ）（Ｂ）（Ｃ）（Ｄ）11、以下哪些对象类型在提交到Windchill系统后可以进行升级流程签审（ABCD ）A、部件B、成品C、普通文档D、CAD文档12、以下哪些为变更的业务对象（ABC ）A、问题报告B、变更请求C、变更通告D、以上均不是13、下列关于创建普通文档的方法说法哪些是正确的（AB ）A、可以直接在PDM系统中进行创建B、可以通过桌面集成的方式进行创建C、只能在PDM系统中进行创建D、以上说法都不正确14、对于Windchill PDM系统中CAD对象的查看功能描述，下列说法正确的是（BC ）A、必须从PDM系统中下载到MCAD软件中进行图纸查看B、在PDM系统中，通过Productview工具可以直接进行图纸的查看C、在PDM系统中，进行图纸可视化查看的时候可以进行图纸圈阅D、以上说法均不正确15、对于Windchill PDM系统中变更，下列说法正确的是（ABCD ）A、一个变更通告可以关联多个变更任务B、没有问题报告，也可以创建变更请求C、变更任务中进行添加受影响数据和产生的数据D、变更流程结束后，产生的数据生效，生命周期状态会到发布的状态二、论述题（共40分）请描述对于Windchill变更管理体系的理解，至少包括以下方面：●问题报告、变更请求、变更通告、变更活动的业务意义（30分）●完全跟踪与快速跟踪的区别（5分）●监控任务的业务意义（5分）文案编辑词条B 添加义项?文案，原指放书的桌子，后来指在桌子上写字的人。
2021IBM_L2考试真题模拟及答案(2)1、对于生产过程中引入的有机溶剂，下面哪种说法正确？（）（多选题）A. 应在后续的生产环节给予有效去除B. 正文已明确列有残留溶剂检查的品种，必须对生产过程中引入的有机溶剂依法进行该项目检查C. 未在残留溶剂项下明确列出的有机溶剂，可以不检查D. 未在正文中列有此项检查的各品种，如生产过程中引入或产品中残留有机溶剂，均应按通则残留溶剂测定法检查并应符合相应溶剂的限度规定试题答案：A,B,D2、某客户每天要进行备份的应用多达25个，每个应用需要备份的数据量都不大。
之前采用物理磁带库进行数据备份，配置了2个LTO3的磁带驱动器。
据用户反映，目前存在备份窗口内无法完成备份任务的问题。
在这种情况下，推荐用户怎样解决当前问题？（）（单选题）A. 将LTO3磁带驱动器替换为LTO4B. 再增加2个LTO4磁带驱动器C. 增加磁带槽位D. 推荐用户使用虚拟带库提高并行备份任务数试题答案：D3、工艺规程如需更改，应当按照（）修订、审核、批准。
（单选题）A. 注册批件B. 相关的操作规程C. 质量要求D. 部门规定试题答案：B4、TS3100支持的最大槽位数为（）？（单选题）A. 1个B. 9个C. 24个D. 48个试题答案：C5、文件应当（）、条理分明，便于查阅。
（单选题）A. 分类存放B. 编号管理C. 分类发放D. 逐份存放试题答案：A6、在某些情况下，性能确认可与（）结合进行。
（多选题）A. 运行确认B. 安装确认C. 工艺验证D. 设备确认试题答案：A,C7、为了避免筛网、冲具污染到生产物料，下列做法正确的是（）。
（多选题）A. 不用筛网、冲具进行生产B. 挑选不易脱落的材质C. 应定期更换D. 有相应的保护措施试题答案：B,C,D8、一个客户有一个IO比较敏感，比较耗用缓存的应用，下面哪方面可能对性能影响比较明显？（）（单选题）A. 写缓存B. 服务器中的高速缓存C. 磁盘驱动器机械臂D. 磁盘阵列中的高速缓存试题答案：C9、中药饮片生产企业可从下列哪些途径购入中药材？（）（多选题）A. 供货商B. 农户C. 药材市场D. 农贸市场试题答案：A,B,C,D10、DS5300最多支持的主机登录数量是（）。
2022年招商证券DevOps教练理论考试注意事项1.本套考试题共有42道选择题（1-40单选、41-42多选），选对得分，选错不得分。
请选择最佳答案。
2.本套考试题覆盖日常培训内容，请参与考试人员集中精力1个小时内完成。
3.考试过程中，调整思维，揣摩考题的意图；考试结束后，认真总结。
您的姓名： [填空题] *_________________________________1-40题均为单项选择题1. 在工具中，小队可以在提交代码时可以设置哪些约束条件（） [单选题] *作者账号（Email）存在于系统中提交消息正则格式关联任务编号有效性以上均可(正确答案)2. 在组织级分支起名规范建议中，release开头的分支代表（） [单选题] *对应某个（测试）环境的代码对应某个规划中的版本的代码(正确答案)存放某个版本投产后的代码对应某个功能需求的代码3. 就平台的代码检查工具，目前说法有误的是（） [单选题] *在流水线中调用代码检查与门禁参数提交代码时主动执行代码检查设置团队自己用的检查规则与main分支比较，并设置增量代码的检查门禁(正确答案)4. 代码评审活动，对提交人，检查人，以下哪种行为是一个好的实践（） [单选题] *团队采用主干开发，开发人员本地开发，等一个故事完整开发完成后，提交一次代码提交代码时候，修改缺陷问题的提交注释写“改bug”评审的时候追求完美，所有内容都必须要遵守规范评审的时候不挑刺，谦虚礼貌的给出建议(正确答案)5. 以下哪项不是代码评审活动能够达成的效果（） [单选题] *尽早发现缺陷帮助开发人员成长，提升编程能力促进团队知识共享，形成一种健康的反馈文化，形成良好的团队规范促进团队对需求的一致理解(正确答案)6. 以下哪个效果是桌面检查活动无法达成的（） [单选题] *尽早的获取产品反馈，降低返工成本提高了移测质量，减少测试反复来回沟通的成本尽早发现缺陷，降低缺陷修复成本提高了开发的代码设计的质量(正确答案)7. 以下对桌面检查的描述，哪项是错误的（） [单选题] *所有的需求都应该做桌面检查(正确答案)桌面检查上遇到缺陷，修复缺陷后，如果缺陷阻碍测试，还需要再重新做一次桌面检查桌面检查不需要执行所有用例，主要关注主流程是否能走通，也可以演示一定的异常流程桌面检查通过的标准是产品、测试都认可了需求达到了可测试，符合产品需求预期的状态8. 在平台中管理流水线脚本，说法错误的是（） [单选题] *流水线可以使用静态编写的脚本，直接在平台上编辑流水线可以使用代码库中的Jenkinsfile平台提供配置库，用于管理公共Jenkinsfile流水线脚本只能每个代码库设置一个(正确答案)9. 平台的构建计划脚本是使用的哪一种编程语言（） [单选题] *Groovy(正确答案)RubyKotlinJavaScript10. 在构建计划的通知设置上，哪一项策略目前有误（） [单选题] *默认触发构建的人收到通知，渠道为站内信可以设置发邮件通知可以通知到聚力所有团队成员可以通知钉钉(正确答案)11. 在制品构建实践中，哪一项是推荐做法（） [单选题] *在自己电脑编译打包，通过图形界面上传制品库使用流水线编译打包，将制品保存在构建机使用流水线编译打包，通过API上传制品库，制品命名时带上版本号或提交编号或构建号(正确答案)使用流水线编译打包，通过API上传制品库，每个模块（制品包）固定上传路径与文件名12. 有关制品溯源，说法错误的是（） [单选题] *依赖管理是溯源的重要手段制品构建中加入构建元信息是溯源的重要手段使用流水线构建就能完整溯源(正确答案)保存制品时同时保存元信息是溯源的重要手段13. 有关环境管理（开发、测试、验收、生产等等），做法不推荐的是（） [单选题] *各环境的脚本、配置各自在服务器中保存，便于随时登录环境更新(正确答案)通过脚本调用的方式，自动化的执行环境的配置变更使用容器化的方式部署环境，减少环境底层差异环境配置即代码14. 如果你的程序有数据库来存储数据，推荐的做法是（） [单选题] *告诉团队成员测试环境，以测试环境的变更为主对比生产环境生成变更点，指定人手工执行在对数据库进行配置变更时，要编写为SQL脚本，通过流水线方式执行变更(正确答案)将要执行的变更，整理成word文档，由指定人员复制粘贴执行在编写SQL脚本时，只需要编写测试变更实施脚本，回滚脚本不需要15. 有关平台中的测试管理功能，做法不推荐的（） [单选题] *不使用目录进行分类，使用标签功能对测试案例进行标记，可以标记冒烟、回归、迭代X、功能模块等等，保持案例的唯一性，不重复将迭代设置为目录层级，每次迭代复制需要的老案例，并添加新案例(正确答案)目录设置上混合使用迭代与功能，并将逐步将迭代案例整理沉淀为功能案例使用案例库可以分割量大的案例，保持系统响应速度及时16. 有关测试自动化建议的的是（） [单选题] *可以不要自动化测试，人工测试即能满足团队交付自动化测试是测试员的专职工作，程序员只管写业务代码自动化测试需要录制操作脚本，没有界面的不能做可以在多个层次开展自动化测试设计，优先保证重要逻辑未出错，自动化测试需要时常维护保持新鲜(正确答案)17. 某小队2周迭代发布一个版本，产品经理经常在发版前提一些UI/交互相关的临时改动或缺陷，导致在发版前团队还在紧急修改代码、发布、测试，以下哪种活动能够改善此现象（） [单选题] *需求分析代码评审桌面检查(正确答案)投产评审18. 需求体系中，以下哪个描述是符合规范要求的（） [单选题] *先创建产品需求，再进行需求拆分成用户故事(正确答案)用户故事创建完，并进行研发，然后再创建一个产品需求进行关联开发成员接受到一个外部小队的求助，帮忙查询一个数据问题，开发人员创建了用户故事进行跟踪开发人员研发过程中发现一个技术可以改进点，创建了一个用户故事跟进19. 在回顾会议，小队认可做得好的，也会有可以做得更好的，最后小队形成改进事项，作为敏捷教练，在制定改进项时，应该采用的方式是什么（） [单选题] *告知：小队长告诉每个人应该负责哪个改进项咨询：小队长咨询成员的想法后，再决定如何安排改进项征询：小队成员做完决定后，问问小队长的想法委托：由小队成员自行决定改善项，小队长鼓励大家进行改善(正确答案)20. 以下哪个不是站会的目的（） [单选题] *监控风险跟进进展协同工作向领导汇报工作(正确答案)21. 回顾会议的目标是什么（） [单选题] *构建持续改进闭环(正确答案)无论我们发现了什么，考虑到当时的已知情况、个人的技术水平和能力、可用的资源，以及手上的状况，我们理解并坚信：每个人对自己的工作都已全力以赴检视问题，做复盘复盘并找到问题责任人22. 以下哪个不是看板的优点（ ) [单选题] *可视化，方便查看整体进展不同角色间的协同查看卡片流动及不同角色间的流转方便的数据统计，可用来作KPI考核(正确答案)23. 某个正在开展迭代工作的敏捷团队，识别到多个可能导致项目延期的问题，团队如何高效处理这种情况？ ( ) [单选题] *将问题拖延至之后的迭代，以保护本次迭代的速度。
