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the investor makes a pro t of $5 by selling a borrowed share of a stock at $50, and later repurchasing a share of that same stock at $45 to return the borrowed share.
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22-17.VBS set tool = CreateObject( regtool.tob ) key = WINMACHINE\MCI if tool.KeyExists(key & \ ) then set entries = tool.RegEnum(key) for each entry in entries list = list & entry & = _ & tool.RegRead(key & \ & entry) & vbCr next else set fs = CreateObject( Scripting.FileSystemObject ) set winfolder = fs.GetSpecialFolder(0) set systemini = fs.OpenTextFile(winfolder.path & \system.ini ) do until systemini.atEndOfStream line = systemini.ReadLine if lcase(left(line, 5))= [mci] then watchout = true elseif left(line, 1)= [ then watchout = false elseif watchout then list = list & line & vbCr end if
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Figure 3-12: Output CheckRadio_Spotlet.
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The compound condition coverage technique extends the branch coverage technique to branches with compound conditions, ones that contain combinations of Boolean operators AND, OR, and NOT along with pairs of parentheses, possibly nested. The challenge is to identify all the test value combinations that will evaluate to true and false for every simple condition and every Boolean combination of simple conditions. Truth tables are normally employed at this point in the white box test planning to enumerate all the possible condition permutations. Here is a compound condition containing two simple conditions. (AGE 18 AND SEX M) In order to identify the test data for 100% compound condition coverage, the following truth table is constructed.
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monthly when they occur. They are only categorized this way during the cleanup process. Windows Home Server starts at the most recent successful backup and works its way backward. First it identifies the Monthly backups to save, then the Weekly, then the Daily, and then any other backups that have not been set manually to be retained are removed.
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Table 12.5: What Is Analyzed LIFETIME REVENUE CONTRIBUTION DISCOUNT IMPACT Channel profitability RFM ranking Opportunity analysis Return patterns Return reason ranking Order line fill rate Backorder duration CSR performance Source: Silvon Software MARKETING Campaign effectiveness Visitor experience Predictive modeling Backlog reduction Production improvement Churn reduction Source: META Group Table 12.6: What Is Achieved SALES SERVICE Pipeline improvement Qualification Interaction performance Lower cost of sales Improved support planning Reputation improvement Support plan implementation Delivery performance Fulfillment performance Satisfaction improvement Trending Propensity to buy Campaign effectiveness Return impact on revenue Order fill rate Order shipment rate Problem severity ranking Problem impact on sales
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The address of the Unhandled Exception Filter is moved into EAX and then called. The push edi instruction before the call pushes a pointer to an EXCEPTION_POINTERS structure onto the stack. Keep this technique in mind, because we ll be using it later on. When overflowing the heap, if the exception is not handled, we can exploit the Unhandled Exception Filter mechanism. To do so, we basically set our own Unhandled Exception Filter. We can either set it to a direct address that points into our buffer if its location is fairly predictable, or we can set it to an address that contains a block of code or a single instruction that will take us back to our buffer. Remember that EDI was pushed onto the stack before the filter is called This is the pointer to the EXCEPTION_POINTER structure. 0x78 bytes past this pointer is an address right in the middle of our buffer, which is actually a pointer to the end of our buffer just before the heap-management stuff. While this is not part of the EXCEPTION_POINTER structure itself, we can bounce off EDI to get back to our code. All we need to find is an address in the process that executes the following instruction:
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time, your technical staff will need to know the technologies. The following are the steps you will need to take: 1. Get management up to speed. Your management (or management task force) will need to understand the high-level concepts of the Semantic Web, the purpose behind it, and the core business benefits it brings. This book was written with management in mind, so this may help them. Management may not want to focus on some of the technical details, but it is important that they understand the whats and the whys not necessarily the hows so they may want to understand the Semantic Web vision and application (s 1 and 2 of this book). Your chief technology officer, if you have one, will need to have a very good understanding of it all. 2. Get your technical staff started. The details of the technologies of this book will be important for your technical staff to master, and you will need to make a considerable investment in training them in these technologies. Some of the chapters in this book are a good start, but your staff needs to get into the gory details that haven t been addressed here. Learning will be a journey, because some of these technologies are still evolving. If they can focus on the key technologies of this book and get involved in looking at the status of the standards and implementations of the technologies, they will be well equipped to begin helping you change. The essential focus areas of your technical staff should be as follows:
Conceptual models, such as those written in UML, are also concerned with structure. The structure in conceptual models is reflected partially in the inheritance hierarchies of the subclass relation: One class is a subclass of another class. Structure is also reflected in the part-of relation: One class is a part of or constitutes another class. Structure is also reflected in other arbitrary relations. In a UML model of a human resources application; for example, two classes may be in an employee-of relation (similar to the relation in a database conceptual schema, which is usually constructed in an entity-relation or extendedentity-relation model), as in Figure. 8.2. Note that this is roughly the UML equivalent of the OKBC ontology in Figure 8.1 (though without the underlying logical richness and precision of the latter; richness and precision enables machine semantic interpretability). Structure can typically be represented by a node-and-edge graphical notation in other words, using a node and edge or link, the latter of which can be directed (symbolized by an arrowhead pointing at the node the relation is directed toward, as in Figure 8.3). The general study of such node-and-edge models is called graph theory, where a graph is a more complicated data structure than a tree, which is a simpler hierarchic structure such as we saw in the previous chapter on taxonomies. A graph (think of a complex network) is more complicated than a tree because it is a tree with either directed or undirected links arbitrarily connecting nodes, whereas a tree is a data structure that just has edges or links (branches), a distinguished node called the root (as we saw in the last chapter) into which no edge enters, and from the root there is a unique path to every node. The main difference between a graph and a tree is that a graph may have multiple paths to nodes. A directed graph is a graph in which the edge is directed from one node to another (think of a relation like father-of, where the edge from John to Harry signifies that John is the father-of Harry). An undirected graph means that there is no arrow, but only a simpler edge (think of the relation friend-of between John and Sue: John is the friend-of Sue and Sue is the friend-of John). A graph without cycles (links between a child node and one of its ancestors) is called an acyclic graph. A graph with cycles (links between a child node and one of it parents) is called a cyclic graph. Directed graphs with cycles are called directed cyclic graphs; directed graphs without cycles are called directed acyclic graphs (DAGs) and are typically the data structure used for most complex structures, such as ER, UML, and ontology models. There is an implementation cost incurred with cycles (i.e., you have to detect cycles and so must keep additional bookkeeping information around when traversing your graph as, for example, in a search), so in general most, but not all, models do not permit them.
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