Exploit Sequencing Views in Semantic Cache to Accelerate XPath Query Evaluation

环境中微生物原位检测方法研究进展宋伟凤;李明聪;高峥【摘要】微生物是生态系统物质循环和能量流动的主要参与者,在生态系统中起着重要的作用.但在现有技术条件下可培养微生物所占比例极小,限制了微生物资源的开发利用.目前有许多方法,可以避开微生物不可培养的问题,直接对微生物进行原位检测.对此,将前人关于微生物生态的原位检测研究方法进行了综述,方便以后对这些方法的合理应用.分别从DNA水平、RNA水平和蛋白质水平,介绍了对应的原位微生物检测方法(如BrdU标记、DNA-SIP、FISH和环境转录物组等),比较了它们的优缺点,并介绍了如何将这些方法与目前流行的高通量测序、单细胞测序等技术相结合来捕获原位活性微生物组等.同时,还对这些方法的特点进行了比较,使得人们可以更清楚地了解在不同场景下对不同方法的选择.这些经过改进的新兴方法及其与其它方法的结合使用将有助于解决微生物生态学研究中出现过或即将出现的很多问题.地球上的各种生态系统复杂而庞大,包含的微生物种群也各有差异.各种原位检测方法对微生物生理生态做出了更加真实有效的描述,必将成为研究微生物生态的有力手段.%As major participants in ecosystem material cycle and energy flow,microorganisms play an important role in the ecosystem. However,the proportion of the cultivable microorganism under the existing technology is very small,which limits the exploit of microbial resources. At present,there are a number of approaches that can avoid the problem of uncultured microorganisms,which are designed to study in situ microbial activity. Regarding this,we summarized some research methods of studying in situ microbial ecology,allowing it convenient to reasonably use these methods in the future. This article introduces the correspondingmicrobial detection methods of BrdU-labeling,DNA-SIP, fluorescence in situ hybridization(FISH),and environmental transcriptome from DNA level,RNA level and protein level,respectively,and compares their advantages and disadvantages. It also introduces how to apply these methods combined with popular high-throughput sequencing and single cell sequencing technology to capture the in situ activity of microbial groups. At the same time,comparing the characteristics of these methods,so that we can more clearly understand the choice of different methods under different scenarios. These modified methods combined with other methods will be conducive to solve many have-been or will-happen problems in the study of microbial ecology. The ecosystems on the earth are complex and huge,in which the microbial populations vary. A variety of in situ detection methods have made a more realistic and effective description for the physiology and ecology of microorganisms,which will become a powerful tool for the study of microorganisms.【期刊名称】《生物技术通报》【年(卷),期】2017(033)010【总页数】7页(P26-32)【关键词】微生物原位检测;BrdU标记;DNA-SIP;荧光原位杂交;宏基因组;转录物组;生物正交反应【作者】宋伟凤;李明聪;高峥【作者单位】山东农业大学生命科学学院作物生物学国家重点实验室,泰安271000;山东农业大学生命科学学院作物生物学国家重点实验室,泰安 271000;山东农业大学生命科学学院作物生物学国家重点实验室,泰安 271000【正文语种】中文微生物是生物地球化学循环的驱动者，在生态系统中扮演着重要角色［1］。

2020 年软考高级信息系统项目管理师考试英语练习题及答案多套汇总1. Project Quality Management must address the management of the project and the () of the project. While Project Quality Management applies to all projects, regardless of the nature of their product, product quality measures and techniques are specific to the particular type of product produced by the project.A.performanceB.processC.productD.object【答案】C【解析】项目质量管理必须解决项目的管理和项目的管理问题。

尽管项目质量管理适用于所有项目，无论其（）的性质如何，但产品质量措施和技术是针对项目生产的特定类型产品的。

A、性能B、过程C、产品D、对象2.() is the budgeted amount for the work actually completedon the schedule activity or WBS component during a given time per i od.A.Planned valueB.Earned valueC.Actual costD.Cost variance【答案】B【解析】（）是给定时间段内计划活动或WBS 组件实际完成的工作的预算金额。

A、计划值B、挣值C、实际成本D、成本差异3.() involves comparing actual or planned project practices to those of other projects to generate ideas for improvement and to provide a basis by which to measure performance. These other projects can be within the performing or gani za t i on or outside of it, and can be within the same or in another application area.A.MetricsB.MeasurementC.BenchmarkingD.Baseline【答案】C【解析】（）将实际或计划的项目实践与其他项目的实践进行比较，以产生改进意见，并提供衡量绩效的依据。

© Proceedings of IADIS International Conference e-Society 2003edited by António Palma dos Reis and Pedro IsaíasJune 3-6, 2003, Lisbon, PortugalIADIS, pp. 365-372ADAPTIVE 3D INTERFACES FOR SEARCH RESULTVISUALIZATIONWojciech Wiza, Krzysztof Walczak, Wojciech CellaryDepartment of Information Technologies, The Poznan University of EconomicsMansfelda 4, 60-854 Poznan, Poland{wiza,walczak,cellary}@kti.ae.poznan.plA BSTRACTA new approach to three-dimensional interactive visualization of data retrieved from web search engines as a result of execution of a user query is presented. In the proposed method, the visualization interface applied to a particular result is selected from a number of available interfaces depending on the search result properties and information provided by a user. Two types of interfaces are described in the paper: holistic interfaces for presenting data classified according to different properties of the result and analytical interfaces for presentation of detailed result information. The use of a particular interface depends on its readability in the context of particular search result. The general readability rules for 3D interfaces are also presented. Possible mappings of search result properties to different visual properties of the interfaces are discussed and examples of different visualization interfaces are presented.1. I NTRODUCTIONThe World Wide Web consisting of billions of web pages may provide a user with almost every type of information needed. All kinds of content for education, research, entertainment and leisure can be found on the web. The main problem is to locate this bit of useful information in enormous volumes of not structured and not categorized data. To solve this problem search engines were created. However, as the amount of information gathered in the search engine databases grows, it becomes increasingly difficult to present them to the users in an understandable and manageable form.Most of search engines offer only simple textual interfaces that do not permit users to fully exploit the search results. A typical search engine displays search results by dozens of documents in a page-by-page principle. Retrieval of the next part of the result requires user interaction. Since the retrieved data is presented in small chunks, the user cannot see a global picture of the result. It is not possible to group or categorize the presented data. The only possible user interaction with the search result is selection of one of the presented links. Most of the existing search engines also limit the maximum number of records presented to a user. Typically, after presentation of several hundred records, a user has to re-specify the query. This approach is imposed by a requirement to keep the response times minimal, but it also often restrains the user from accessing important information.In the classical approach, documents constituting a search result are ordered arbitrarily based on a ranking algorithm specific to the particular search engine. Usually, the ranking algorithms used to calculate the relevancy factors of documents are sophisticated and take into account multiple different aspects of the user query and properties of the indexed documents. But even the most complicated algorithms can be decoyed by a specific design of the web page, e.g. by repeating keywords in pseudo-phrases not appearing in the page contents or by the use of popular keywords which are not semantically connected to the page. As a result addresses that do not really match the specified query may be presented as the most relevant. Furthermore, since users cannot change the ranking algorithm they do not have influence on the final order of the presented documents.At the current stage, progress in presenting the search results to users requires switching from classical textual to more advanced graphical user interfaces. There were several attempts to create graphical interfaces for search systems. In most of them 2D graphics has been used, but there were also attempts to apply 3D visualization. Several projects were carried out in HCIL [6] where the problem of visualization of big volumes of information has been addressed [5][17]. Other examples of projects applying 2D visualization are Antarctica [1] and InXight [9]. The projects exploiting 3D visualization of search results resulted indevelopment of several visualization methods like 3D cards augmented by visualization of semantic relationships between documents [10], city-like landscapes [1][14], or positioning of objects in the 3D space like in VR-VIBE project [2][18], or Cat-a-Cone project [3]. In the NIRVE project [4][11][15] the 3D visualization is enriched by a concept of data clustering. An example of a 3D visualization system accessible for web users is the ViOS system [16].Most of the search engines with 3D visualization interfaces developed up to now have not reached technological acceptance and commercial use, mainly because of the following drawbacks:• The applied 3D graphical interfaces visualized information in a single 3D environment; as a result, different volumes of information had to be presented in the same scene. In many cases this approach resulted in improper presentation of information and thus decreased user perception;• A user was presented with a 3D environment, where each document was represented by a 3D object. In the discussed solutions there were no attempts to present aggregated information first, and then – in response to user interaction – more specific information;• The proposed systems either allowed full user interaction with the interface but required installation of some dedicated software, or were based on open Internet standards (e.g., VRML) but lacked full interaction capabilities as a result of the shortcomings of the general purpose standards.In this paper a new method of interactive adaptive 3D visualization (AVE) of search results returned by indexing search engines is proposed. The visualization interface applied to a particular search result is automatically selected from a number of available interfaces depending on the search result properties and information provided by a user. Different types of interfaces are available, e.g. categorizing interfaces for presentation of aggregated data, detailed interfaces presenting details of the documents found, and comparative interfaces for comparison of different search results.The reminder of this paper is organized as follows. In Section 2 the concept of adaptive user interfaces is described. In Section 3 examples of different types of interfaces are presented. In Section 4 discussion over application of interface visual properties is presented. Section 5 summarizes the paper.2. T HE CONCEPT OF ADAPTIVE USER INTERFACEThe amount of information returned by an indexing search engine may vary significantly. As the response to a user query, the search engine may return several records or several hundreds of thousands of records. Consequently, it is not possible to create a single 3D environment capable to visualize the entire spectrum of possible search result volumes. In the AVE method, the visualization system selects from a number of available visualization interfaces the one that best describes the search result. Assignment of the appropriate interface is based on the search result quantitative and qualitative properties (see Figure 1) to maximize its readability. This process may be fully automatic, with the visualization engine using pre-programmed logic to select the best interface, or user-aided with a user selecting a set of preferred interfaces. The visualization engine may also present a user with a set of interfaces that fit the particular search result and a user may choose the best one in his/her opinion.The process of searching for the document of interest may be seen as a number of subsequent user queries where a query may narrow or broaden the previous search result. Using different levels of abstraction and applying the most appropriate 3D environment on each step, the AVE method permits to navigate from a high-level categorized, aggregated view of the entire search result, through categorized views of sub-results, up to precise visualization of information about particular documents of user interest. A user may formulate queries by adding and removing keywords, but also by interaction with 3D elements of the visualization environment (e.g., selecting objects by moving them to a predefined area). This multi-step process may be seen as a path through the visualized search results and is called exploration path (c.f. Figure 2). On this path the user is supported by an interface selection logic that helps to select appropriate interfaces. In sample exploration path presented, a user querying the search system with an ‘Initial’ interface, is presented with ‘Spheres’ interface, which groups results by domain. However, if all documents found are located on the same host, a ‘Spheres’ interface may be omitted and the result is visualized in ‘Hedgehog’ interface. Then a user may wish to visualize this same result using ‘City’ interface, and finally, browses documents of interest. Using specialized interfaces a user may also preview images and/or video files.Figure 2. Sample exploration pathOn each step of the AVE exploration path, the search result properties are mapped into one or more visual properties of a 3D scene. Each visual property, which may represent a single search result attribute, is called a visualization dimension. Visualization dimension may be represented as a property of an object (e.g., its color, size, shape, etc), or in another way, e.g., as a text associated with text node. Such assignment of document attributes to visualization dimensions may be dynamically changed by a user.Selection of the appropriate interface, either automatic or manual, should produce a visualization that is readable to a user. While many different aspects of the readability may be discussed, some of them seem to be the most important in context of 3D environments. We assume that a visualization interface is readable if, and only if the following postulates are satisfied: (1) there exists a viewpoint from which all presented objects can be observed; (2) size of glyphs (where glyph can be defined as a single graphical object representing multivariate data object [20]) should allow their easy manipulation and interaction; (3) occlusion of each two glyphs in the scene should permit a user to interact with every glyph in the scene; (4) each interface dimension should represent a unique search result attribute (e.g., color represents document content-type); (5) the domain of each search result property is properly transformed into domain of a visualization dimension; and (6) distance between subsequent dimension values must be distinguishable by a user.While both types of visualization – detailed and classifying – may be implemented by a single interface by changing only the meaning of visualization dimensions, in many cases a better visualization may be obtained by the use of specialized interfaces. Interfaces that are designed especially for presentation of detailed data are called analytical interfaces while interfaces designed to visualize aggregated data in a categorized way are called holistic interfaces (c.f. Figure 3).Modification dateHolisticInterfaceAnalytical InterfaceFigure 3. Selection of the visualization interfaceAn analytical interface is characterized by a high number of visualization dimensions (comparable with the number of search result attributes) and their visual separation. This permits detailed visualization of the search result allowing a user to observe and evaluate many different aspects of the displayed information.Unlike an analytical interface, a holistic interface is characterized by a small number of visualization dimensions. Such interface can be used to present a generalized view of the search result where a user can instantaneously recognize the nature of the data but not the particular details. In a holistic interface, the categorization criteria are selected automatically based on the search result properties such as number of domains and/or sub-domains, number of sites, number of languages, document content-types, semantic relationships between documents, etc.In both, analytical and holistic interfaces, the search result is always presented entirely. This permits a user not only to browse through the information but also to understand its nature. Appropriately constructed and applied interface permits a user to perceive trends in data faster and with bigger precision through clearly visible differences in colors, shapes, connectedness, continuity, symmetry, etc. A user may also apply different interfaces to the same search result in order to recognize different aspects of the same data set.3. E XAMPLES OF VISUALIZATION INTERFACES3.1 Analytical interfacesAnalytical interfaces are designed to present a user with detailed view of the search result. For this reason such interface should offer a high number of visualization dimensions. Typically, each object in analytical interface represents a single document, while the object properties reflect properties of the document. Therefore, with regards to readability prerequisites, such interface should be applied only to search results with relatively small number of records where visualization of document properties is also important.In Figure 4, two examples of analytical interfaces are presented. The interface presented in Figure 4a has 5 dimensions: shape representing language, color representing document type, position on Y-axis representing hostname, position on Z-axis representing document size, and position on X-axis representing document modification date. The interface presented in Figure 4b has 7 dimensions represented by X-, Y- (height of the floor) and Z-axis, color, texture, height and movement of the object.In Figure 5a, an example of an incremental analytical interface is presented. With the use of this interface, a user can successively specify keywords in subsequent queries. After each query, tiles representing documents containing higher number the specified keywords become more red, while tiles characterizing documents with lower number of keywords fade into light blue color.a. b.Figure 4. Analytical interfaces with (a) 5 and (b) 7 dimensionsa.b.Figure 5. Specialized analytical interfaces: (a) with incremental selection and (b) showing links on one web server The analytical interface presented in Figure 5b can be used to present documents of interest existing on the same host. In this interface, object shapes denote document type, their color represents language, while position on the sphere corresponds to the number of keywords found in a particular document.3.2 Holistic interfacesHolistic interfaces are used to present a classified view of the search result. A holistic interface may show a search result classified using either one or several criteria, thus it does not need many visualization dimensions. Such interface may also contain some analytical elements permitting better evaluation of the search result. In Figure 6a, an example of a holistic interface with one classification criterion is presented. In this example, a sphere is divided into multicolored slices representing different Internet domains. Size of a slice represents the number of hosts containing documents of interest, hence a user may instantly recognize domains, where the probability of finding useful information is the highest. To improve readability of the interface, a sphere is surrounded by small colored bullets with textual tags providing names of the domains represented by particular colors.a. b.Figure 6. Holistic interfaces: (a) single criterion and (b) multiple criteria An example of a holistic interface with two classification criteria is presented in Figure 6b. In this interface each sphere represents different languages of documents, while sphere segmentation represents Internet sub-domains.In the Figure 7, a holistic interface which permits to compare two queries is presented. The interface is equipped with two input fields that permit a user to enter two different queries and compare their results represented as two series of coaxial cylinders. Each of cylinders symbolize one sub-domain containing hosts with documents of interests. Tiles attached to each of the cylinders represent documents. All documents residing on this same web server share the same color.Figure 7. A holistic interface with analytical elements4. U SING INTERFACE VISUAL PROPERTIES FOR DATA VISUALIZATIONCreation of a readable interface that meets all prerequisites described in Section 2 requires understanding of the limitations of particular types of visualization dimensions. Each glyph can carry meaningful information through its position, size, shape, orientation, color and texture, and animation of each of the above.Abilities to recognize differences in size and location are highly correlated. For example, if objects have dimensions of several meters, the difference in size or location measured in millimeters is unnoticeable. Although a size of a glyph consists of 3 dimensions, it is perceived by a user holistically. Assignment of different search result attributes to single geometric dimensions (x, y, and z) is possible, but is incomprehensible for a user and most likely would not be noticed.Shape, with its potential variety, is a very capacious carrier of information. A high number of shapes can be recognized and distinguished by a user. The set of shapes that can be used for visualization consists ofseveral geometrical primitives like sphere, cone, or cylinder; some well-recognizable polyhedrons like cube, tetrahedron or octahedron; and a big number of real-life shapes such as cars, books, furniture, trademarks, symbols, etc. The use of polyhedrons with growing number of vertices is limited because differences become difficult to distinguish (e.g. difference between dodecahedron and icosahedron).Orientation of the glyph may be used only if the shape of the glyph permits to recognize the differences in orientation. Rotation of a cube may be in most cases easily noticed by a user, while the same transformation applied to a sphere cannot be identified. Small changes in orientation of a single glyph may be unnoticeable, but when this glyph is surrounded by a group of identical objects, even very small orientation differences may be immediately observed. Another problem is the change of orientation by certain angle along the glyph symmetry axis, which may lead to an impression, that no change was made at all (e.g. cube rotated by 90 degrees along an axis of symmetry).Color of a glyph is a very flexible dimension. It has been estimated in [8] that a color monitor has between 2 and 6 millions of different colors available, but it is evident, that only a small subset of them can be perceived by a non-trained user. Other works [13] show that only a small number of colors can be used effectively as labels for expressing data. It is estimated that only 5-10 colors may be instantly recognized [7]. Moreover, recognition of color differences is highly related with luminance, hue value, contrast, saturation, monitor properties, and even human eye properties. A surface of the glyph may bring a large amount of recognizable information. A user may easily differentiate between plain color surfaces and textured surfaces.A number of distinguishable textures is nearly unlimited, while they vary in color and pattern. Even if two objects share the same pattern and color, texture orientation may be different (e.g., rotated by 45 degrees) bringing information to a user. This, however, applies only to well recognizable geometric patterns like parallel lines, squares etc. Rotation of texture imitating, for instance, stone surface, is unrecognizable.Application of temporal dependencies to properties described above introduces additional informational dimension. Temporal coding is, however, limited in range and discreetness. The upper range limit depends on the property being animated: for some properties frequency of changes is limited by a visual inertia of a human eye and/or inertia of a display. For instance, a glyph changing its colors too fast is seen as having one. An object changing rapidly its position may not be visible on slow LCD screens. The lower limit of temporal coding is connected with: a) time when a user focuses on particular object (typically several seconds); and b) life time of the interface. An object property cannot be changed too slowly, because a user would not notice modification or the interface will be destroyed before.The number of distinguishable levels in temporal coding is very low. A user may perceive a small difference in temporal changes of a particular property only if it can be compared to the original speed/frequency or another object. For instance, speed change of an animated object either has to be changed significantly or another animated object must be visible to permit speed comparison. The overall number of objects having time-dependent property values should be also kept small. In fact, temporal changes as an information medium may be used only sporadically. The interface, where a number of objects change their position, color, and shape in the same time is very likely to be unreadable.The above issues should be carefully reconsidered while building interfaces, which may be used or designed especially for disabled people. In such case some properties cannot be used (e.g., color, when by people with inability to discriminate colors) or should be used with limited possible values (e.g. large differences in size of objects, in interfaces for people with partial blindness).5. C ONCLUSIONSFor testing and evaluation of the proposed AVE method, a prototype visualization system called Periscope has been developed [12]. The Periscope system is an intermediary system between users looking for documents on the Web and indexing search engines. The Periscope uses a set of interface models written in X-VRML language [19][21]. Interface models supplied with retrieved search result are presented to a user within standard VRML browser plug-in, like ParallelGraphics Cortona. Interface selection logic is also written in X-VRML.First trials of the Periscope system connected to a custom search engine database containing information about 70% of sites within the Polish domain (.pl) proved that the AVE method can be efficiently used for Web searching. Although, the system response time is usually higher than in case of popular search engines(like Altavista or Google) reaching up to 30 seconds for complex queries, the end-users felt that the accuracy of the information retrieved was higher, especially in the cases when the initial constraints were not quite precise. During the tests it turned out that users often use several different visualization interfaces with the same query for better localization of the desired data.Current tests focus on the ergonomics and perception of the 3D interfaces. Another group of tests is performed to determine what types of interfaces are the most preferred by end-users. Future works include optimization of the Periscope system architecture (faster response time, better load balance, higher security, etc.) and design of new interfaces, which consists of their graphical design, usability study, and proper inclusion in an exploration path.R EFERENCES[1] Antarctica homepage: http://www.antarcti.ca/[2] Benford, B., D. Snowdon, C. Greenhalgh, R. Ingram, I. Knox, C. Brown: VR-VIBE: A Virtual Environment forCo-operative Information Retrieval", Eurographics'95, 30th Au-gust - 1st September, Maastricht, The Netherlands, pp 349-360.[3] Cat-A-Cone project homepage: /~hearst/cac-overview.html[4] Cugini, J., S. Laskowski, M. Sebrechts: Design of 3D Visualization of Search Results: Evolution and Evaluation,Proceedings of IST/SPIE's 12th Annual Intl. Symposium: Electronic Imaging 2000, San Jose, CA, 23-28 January 2000.[5] Fekete, J. and Plaisant, C.: Interactive Information Visualization of a Million Items, Proc. of IEEE conference onInformation Visualization 2002, Boston, October 2002.[6] HCIL homepage: /hcil/[7] Healey, C.G.: Choosing effective colors for data visualization, Proc. of Conference IEEE Visualization ’96, pp.263-270[8] Hill, B., T. Roger, F.W. Vorhagen: Comparative analysis of the quantization of color spaces on the basis of theCIELAB color-difference formula, ACM Transactions on Graphics, 16(2) 1997, pp. 109-154[9] InXight homepage: /map/[10] Mukherjea, S., Y. Hara: Visualizing World-Wide Web Search Engine Results, NEC USA Inc. 1999 InternationalConference on Information Visualisation July 14 - 16, 1999 London, England[11] NIRVE project homepage: /iaui/vvrg/cugini/uicd/nirve-home.html[12] Periscope project homepage: http://periscope.kti.ae.poznan.pl/[13] Post, D.L., F.A. Greene: Color name boundaries for equally bright stimuli on a CRT: Phase I. Society forInformation Display, Digest of Technical Papers 86, pp. 70-73[14] Rossi, A.M., M. Varga: Visualization of Massive Retrieved Newsfeeds in Interactive 3D, Intl. Conference onInformation Visualisation July 14 - 16, 1999 London, England[15] Sebrechts, M., J. Vasilakis, M. Miller, J. Cugini, S. Laskowski: Visualization of Search Results: A ComparativeEvaluation of Text, 2D, and 3D Interfaces, Proceedings of SIGIR'99, pp. 3-10, eds. M. Hearst, F. Gey, and R. Tong, 22nd Intl. Conference on Research and Development in Information Retrieval, Berkeley, California, August 1999[16] ViOS – how does it works?: /vios.htm[17] Visualization of a Million Items project homepage: /hcil/millionvis/[18] VR-VIBE homepage: /vr/vrvibe.html[19] Walczak, K., W. Cellary: Building Database Applications of Virtual Reality with X-VRML, Proc. of the 7thInternational Conference on 3D Web Technology (Web3D-2002), Tempe, Arizona, USA, Feb. 24-28, 2002, pp.111-120[20] Ware, C.: Information Visualization, Academic Press 2000[21] X-VRML homepage: http://xvrml.kti.ae.poznan.pl/。

基于词向量模型的漏洞检测方法漏洞检测是信息安全领域中重要的任务之一，它的目标是发现和修复软件或系统中存在的潜在安全漏洞，以防止黑客利用这些漏洞对系统进行攻击。

随着互联网的迅猛发展和信息技术的广泛应用，漏洞检测方法的研究也越来越受到关注。

本文将介绍一种基于词向量模型的漏洞检测方法，并探讨其优势和应用前景。

一、词向量模型简介词向量模型是一种用于将自然语言处理成机器可理解的数学表示的方法。

它将每个单词映射到一个高维向量空间中，使得语义相近的单词在向量空间中的距离也比较近。

词向量模型可以通过深度学习算法进行训练，其中最著名的方法是Word2Vec。

通过使用词向量模型，我们可以计算两个单词之间的相似度，进行文本分类、信息检索等自然语言处理任务。

二、基于词向量的漏洞检测方法基于词向量的漏洞检测方法利用了文本中的语义信息来检测漏洞，相比传统的基于规则或关键字匹配的方法，具有更好的泛化性能和鲁棒性。

其主要步骤如下：1. 数据预处理：首先，我们需要从软件或系统的源代码中提取出文本数据，例如注释、变量名、函数名等。

然后，对这些文本数据进行分词和去除停用词等预处理操作，以便后续的词向量计算和漏洞检测。

2. 构建词向量模型：接下来，使用训练集的文本数据来训练一个词向量模型。

可以选择已经训练好的模型，如Word2Vec、GloVe等，也可以使用自己的数据集进行训练。

通过训练，每个单词都将被映射到向量空间中的一个向量。

3. 计算文本向量：对于每个源代码文本，我们将其表示为一个文本向量。

一种常见的方法是，将文本中的每个单词的词向量进行平均或加权平均，得到文本的表示。

这样，我们就可以用一个固定长度的向量表示任意长度的文本。

4. 漏洞检测：有了文本向量表示后，我们可以使用机器学习或深度学习算法来进行漏洞检测。

可以使用二分类模型，将漏洞和非漏洞的文本进行分类，也可以使用多分类模型，将不同类型的漏洞进行分类。

训练模型时，可以使用已标记的漏洞样本和非漏洞样本进行有监督学习。

第33卷第11期2016年11月计算机应用与软件Computer Applications and SoftwareVoL 33 No . 11Nov . 2016一种云环境下图像的安全检索方法韩威徐彦彦$冯春晖熊礼治徐正全(武汉大学测绘遥感信息工程国家重点实验室湖北武汉430079)摘要 为了保护数据机密性，具有敏感信息的图像在上传到云服务器之前需要进行加密，然而，这样做会给图像检索带来问题。

提出一种云环境下图像的安全检索方法，对图像颜色空间的R 和G 通道的D C T (D i SCrete C o s i n e T m n s f o m i )系数分别进行块内置乱 和块间置乱，对B 通道进行块内置乱加密；然后提取颜色矩、信息熵以及块间L B P (L 〇cal Binary Patterns)作为图像的特征向量，通过 比较图像的特征向量之间的曼哈顿距离来确定图像的相似度。

该方法保证了图像的机密性和检索精确度，能实现对图像的安全 检索。

关键词云环境图像安全检索颜色矩信息熵L B P中图分类号 T P 391.41文献标识码 AD 01：10. 3969/j. issn. 1000-386x. 2016. 11.029A SECURE IMAGE RETRIEVAL METHOD UNDER CLOUD ENVIRONMENTHan Wei Xu Yanyan * Feng Chunhui Xiong Lizhi Xu Zhengquan(StateKey Lab of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China )AbstractIn order to protect data privacy, the image with sensitive or private information needs to be encrypted before being uploaded tocloud server. H o w e v e r , this causes difficulties in im a g e retrieval. In this paper w e propose a secure ima g e retrieval m e t h o d un d e r the cloud environment. It carries out intra-block scrambling a n d inter-block scrambling o n D C T (discrete cosine transform) coefficients of R a n d G channels in image colour space separately, a n d m a k e s intra-block scrambling encryption o n B channel ； a n d then it extracts the colour m o m e n t , the entropy of information a n d the inter-block L B P (local binary patterns) as i m a g e ’ s feature vectors，a n d determines the similarity of images through comparing Manha t t a n distance b e tween these feature vectors. T h e proposed m e t h o d ensures both the ima g e confidentiality Cloud environment Secure ima g e retrieval Colour m o m e n t Information entropy L B Pa n d retrieval accuracy, a n d is able to achieve secure ima g e retrieval.Keywords〇引言随着云计算的快速发展，越来越多的用户选择在云服务器 上存储图像。

sequential 模型原理Sequential 模型是深度学习中常用的一种模型结构，它由一系列线性层按顺序堆叠而成。

这种模型结构非常直观和简单，适用于一些简单的任务和初学者入门。

下面我将从多个角度来解释Sequential 模型的原理。

首先，Sequential 模型是一种线性堆叠模型，它的每一层都恰好有一个输入张量和一个输出张量。

数据在模型中依次经过每一层，每一层的输出成为下一层的输入。

这种顺序结构使得 Sequential模型非常易于构建和理解。

其次，Sequential 模型的每一层可以是各种各样的神经网络层，比如全连接层、卷积层、池化层等。

这使得 Sequential 模型可以适用于不同类型的任务，包括图像识别、自然语言处理等。

另外，Sequential 模型的原理还涉及到前向传播和反向传播。

在前向传播过程中，数据从输入层经过每一层逐步传播，直至输出层得到模型的预测结果。

而在反向传播过程中，通过损失函数计算预测结果与真实标签之间的误差，并利用梯度下降等优化算法来调整模型参数，使得模型的预测结果更加接近真实标签。

此外，Sequential 模型还可以通过添加各种正则化项、激活函数等来增强模型的泛化能力和非线性拟合能力。

这些技术也是深度学习中非常重要的内容，对于理解 Sequential 模型的原理非常有帮助。

总的来说，Sequential 模型的原理包括线性堆叠结构、各种类型的神经网络层、前向传播和反向传播过程，以及正则化、激活函数等技术。

通过综合理解这些内容，可以更好地掌握 Sequential 模型的原理和应用。

标题：语义SLAM代码讲解一、背景介绍语义SLAM（Semantic SLAM）是指在SLAM（Simultaneous Localization and Mapping）的基础上，融合了语义信息的一种技术。

其核心思想是通过利用传感器等设备获取的数据，结合语义信息，实现对环境的理解和建模，从而实现地图构建和定位的目标。

在语义SLAM中，除了传统的几何信息外，还包括了环境中物体的语义信息，这样可以让机器人对环境有更深入的理解和认知，为其在未来的行为决策提供更多的信息支持。

二、语义SLAM的代码实现1. 传感器数据获取语义SLAM的代码实现需要获取传感器数据，以便对环境进行建模和定位。

常见的传感器包括激光雷达、相机等，它们可以用来获取环境的地图信息和语义信息。

在代码中，需要编写传感器数据获取的模块，将传感器采集的数据进行处理和解析，得到环境的特征信息和语义信息。

2. 地图构建在语义SLAM中，地图的构建不仅仅包括传统的几何地图，还需要将环境中的物体进行语义标记，以便机器人对环境有更深入的理解。

在代码实现中，需要设计地图构建的算法，将传感器获取的数据进行地图构建，同时将环境中的物体进行语义标记。

3. 语义信息融合语义SLAM的核心在于将语义信息融合到SLAM的框架中，以提高环境的理解和建模能力。

在代码实现中，需要设计语义信息融合的算法，将地图中的几何信息和语义信息进行融合，得到一个更加完整和丰富的环境模型。

4. 定位和路径规划语义SLAM的代码实现还需要实现定位和路径规划的功能。

通过对环境的建模和语义信息的理解，可以实现机器人在环境中的定位和路径规划，为其在复杂环境中的导航提供更强大的支持。

三、代码实现框架语义SLAM的代码实现可以采用传统的SLAM框架，将语义信息融合到其中。

常见的SLAM框架包括ORB-SLAM、LIO-SAM等，可以在这些框架的基础上进行扩展，实现语义SLAM的功能。

四、代码实现实例以下是一个简单的语义SLAM代码实现实例，基于ORB-SLAM框架：```c++// 传感器数据获取模块void SensorDataHandler(){// 传感器数据处理和解析// ...}// 地图构建算法void MapBuilding(){// 地图构建算法实现// ...}// 语义信息融合算法void SemanticFusion(){// 语义信息融合算法实现// ...}// 定位和路径规划模块void LocalizationAndPathPlanning() {// 定位和路径规划算法实现// ...}// 主函数int m本人n(){// 传感器数据获取SensorDataHandler();// 地图构建MapBuilding();// 语义信息融合SemanticFusion();// 定位和路径规划LocalizationAndPathPlanning();return 0;}```五、总结语义SLAM是一种结合语义信息的SLAM技术，可以让机器人对环境有更深入的理解和认知，为其在未来的行为决策提供更多的信息支持。

半监督的语义分割综述作者：吴坚来源：《电脑知识与技术》2021年第32期摘要：本文主要介绍图像处理中的半监督的语义分割的主要算法。

包括全卷积网络，分类激活匹配，多扩张卷积定位，对抗网络的半监督的语义分割，交叉一致性训练的半监督的语义分割等算法。

这些算法从不同的角度描述半监督语义分割的研究内容。

关键词：语义分割;半监督;损失函数中图分类号：TP18 文献标识码：A文章编号：1009-3044（2021）32-0131-031 语义分割概述语义分割是图像处理研究的一个分支，语义分割的目标主要是给图像指定语义标签，例如人，狗，路，鸟，飞机，等等，并且将其按语义标签进行图像的划分。

语义分割有着广泛的应用，如自主驱动和图像编辑等等。

目前语义分割是研究热点问题，语义分割有很多的研究方法，其中一种研究方法是根据监督类型对语义分割进行分类，如监督的语义分割、半监督的语义分割、弱监督的语义分割以及无监督的语义分割等[1，3，5，6]。

本文主要介绍半监督的语义分割。

2 全卷积网络FCN（Fully Convolutional Networks）2.1 FCN原理随着卷积神经网络CNN（Convolutional Neural Network）运用，使得语义分割的技术得以极大的发展。

在CNN的基础上Long提出了全卷积网络（Fully Convolutional Networks，简称FCN）。

FCN基于CNN的，而且依赖于空间坐标。

卷积网络的每一层数据是三维序列h×w×d，其中h和w是空间的维度，即h是高度，w是宽度。

d是特征或色彩通道的维数。

在较高层的位置相应于在图像中被路径连接的位置，称作接收域。

FCN具有卷积网络的特征如卷积，池化和激活函数等组成部分，并依赖于相对的空间坐标。

假定[xij]是某一个特定的层在位置为[（i，j）]的数据向量，[yij]为下一层的数据输出向量，该输出向量由下式计算：，其中k是核的大小，s是步长，[fks]定义为层的類型：如卷积或者池化的矩阵乘积、最大池化的空间最大值、或者激活函数的逐元的非线性的激活函数，以及其他的非线性层的函数。