One of the advantages of CBM over other student modelling approaches (Mitrovic, Koedinger & Martin, 2003) is its independence from the problem-solving strategy employed by the student. CBM models students’ evaluative, rather than generative knowledge and therefore does not attempt to induce the student’s problem-solving strategy. CBM does not require an executable domain model, and is applicable in situations in which such a model would be difficult to construct (such as database design or SQL query generation). Furthermore, CBM eliminates the need for bug libraries, i.e. collections of typical errors made by students. On the contrary, CBM focuses on correct knowledge only. If a student performs an incorrect action, that action will violate some constraints. Therefore, a constraint-based tutor can react to misconceptions although it does not represent them explicitly. A violated constraint means that student’s knowledge is incomplete or incorrect, and the system can respond by generating an appropriate feedback message. Feedback messages are attached to the constraints, and they explain the general principle violated by the student’s actions. Feedback can be made very detailed, by instantiating parts of it according to the student’s action.
A good choice of safe signal is to have a real value which increases in proportion to the observation of normal behaviour in the monitored system. In the SCAN dataset, we use the burstiness or rate of change of tcp packet sending as a safe signal. This signal is also processed: min-max normalisation is used to transform the data into the range of 0 to 100. The normalised value is inverted so that the safe signal is zero when the burstiness level is high. Low burstiness is assumed to be an indicator of normal system behaviour, based on our expert knowledge of the problem domain. As a result we are left with two signal streams sampled at the same time and used as input to the dDCA and hDCA for this comparison.
Security experts use their knowledge to attempt attacks on an application in an exploratory and opportunistic way in a process known as penetration testing. However, building security into a product is the responsibility of the whole team, not just the security experts who are often only involved in the final phases of testing. Through the development of a black box security test plan, software testers who are not necessarily security experts can work proactively with the developers early in the software development lifecycle. The team can then establish how security will be evaluated such that the product can be designed and implemented with security in mind. The goal of this research is to improve the security of applications by introducing a methodology that uses the software system's requirements specification statements to systematically generate a set of black box security tests. We propose a methodology for the systematic development of a security test plan based upon the key phrases of functional requirement statements. We used our methodology on a public requirements specification to create 137 tests and executed these tests on five electronic health record systems. The tests revealed 253 successful attacks on these five systems, which are used to manage the clinical records for approximately 59 million patients, collectively. If non-expert testers can surface the more common vulnerabilities present in an application, security experts can attempt more devious, novel attacks.
The structure of the current document reflects the document aims detailed in Section 1.2. Section 2 provides an overview of the current status of the project and the significant milestones achieved thus far. Section 3 describes the capabilities of the prospective TDS especially with respect to its knowledge processing and information fusion activities. As the major technological artefact to be delivered by this project a detailed functional characterization of the prospective system is a central aspect of the requirements specification activity. Section 4 provides an overview of the range of technologies likely to be exploited in the context of the current development initiative. Since the aims of the current project are closely aligned with ongoing research initiatives in the IAM 7 group at the University of Southampton, e.g. AKT 8 , some of the technologies developed in the context of those projects will be exploited in the context of the current initiative, e.g. 3Store technology 9 . Furthermore, a range of extant technologies are likely to prove useful both at the modelling and implementation levels of the prospective system. These include, but are not necessarily limited to, the UML, the CommonKADS methodology, eKADS, PCPACK, OWL, RDF and JESS. Section 5 describes, in summary form, the knowledge infrastructure of the target domain. The knowledge infrastructure subsumes all those knowledge structures, e.g. domain conceptualizations, problem-solving methods, knowledge-rich contingencies, etc., that play a role in terms of ensuring problem-solving success in the target domain, i.e. in the area of humanitarian relief operations. The specification of the knowledge infrastructure should be described at the ‘knowledge level’ (Newell, 1980) and it is therefore necessarily independent of implementation detail, even to the extent of eschewing the representational biases of particular knowledge modelling/representation languages. Although it is perfectly acceptable to countenance one particular set of representational formalisms for the express purpose of implementing intelligent software, based on the software’s functional requirements and the representational leverage afforded by different implementation mechanisms, the same formalisms are seldom required for initial knowledge modelling activities in which the emphasis is on stakeholder communication and knowledge validation. The knowledge models developed for the current initiative therefore focus on representational techniques
Based on systems theory and system integration style, the NGTCS improves each train system potency and system safety and intelligence levels exploitation parallel watching, system-level “fail-safe”, knowledge sharing and fusion, common mode cause error shunning, and alarms for hot or incorrect operations by railway employees. It parallel monitors the signal system’s crucial subjects, like chase interval, train route setting, train speed protection, and TSR. The NGTCS is additionally able to create knowledge comparisons and do knowledge confirmation between systems. If incorrect outputs or system failures occur, the system can do system-level “fail-safe” so as to confirm the protection of train operations. CTC-and-ATP-based chase-interval parallel watching improves the responsibleness of train tracking intervals by 4.52 × 10 4 . Parallel watching of all crucial subjects is in a position to boost the responsibleness and therefore the safety of the complete train system. NGTCS can have an additional fusion with transportation organization, dispatching command, operation management, and employee’s operation superintendence.
The roles of paleoAP3 genes in non-core eudicot angiosperms are somewhat unclear. A variety of expression analyses have been carried out that, in general, support the idea that paleoAP3 genes have a conserved role in stamen identity specification, but their role in petal specification remains ambiguous. For instance, in many basal angiosperms, paleoAP3 genes show strong and ubiquitous expression in stamens, but often inconsistent, weak or patchy expression in petal primordia (Kim et al., 2005; Kramer and Irish, 1999; Zahn et al., 2005). In non-grass monocots, expression of paleoAP3 genes can be observed in developing petaloid organs in some taxa [e.g. in Lilium longiflorum (Tzeng and Yang, 2001)], but not in others [e.g. Aparagus officinalis (Park et al., 2003)]. Functional analyses in several monocot grasses have demonstrated that the paleoAP3 genes in these species are required for the development of stamens and lodicules (Ambrose et al., 2000; Nagasawa et al., 2003; Xiao et al., 2003). As these grass species lack petals, such studies cannot directly assess the roles of paleoAP3 genes in petal development. Furthermore, a chimeric AP3 gene containing a non-core eudicot paleoAP3 motif has been shown to be sufficient to rescue stamen, but not petal, identity in Arabidopsis (Lamb and Irish, 2003). By contrast, other studies have shown that ectopic overexpression of a monocot paleoAP3 gene can rescue both petal and stamen development, suggesting that levels of paleoAP3 gene expression might be important in determining developmental function (Whipple et al., 2004). Based on these observations, it has been suggested that the paleoAP3 genes lack the capacity to fully specify petal identity, although they may play subsidiary roles in petal cell-type differentiation (Kramer and Irish, 1999; Kramer and Irish, 2000).
OFE is not-for-profit, limited by guarantee, independent of any organisation, business focussed and nonevangelical. OFE operates across Europe both directly and via a network of partners – both national and community based. Based in London, but increasingly important it focusses its European parliamentary and governmental programmes from its Brussels office. OFE draws its membership from both the user and supply (software, hardware, services, integrator and consultancy) communities.
ISO 26262-2:2011, Road vehicles — Functional safety — Part 2: Management of functional safety ISO 26262-4:2011, Road vehicles — Functional safety — Part 4: Product development at the system level ISO 26262-5:2011, Road vehicles — Functional safety — Part 5: Product development at the hardware level ISO 26262-8:2011, Road vehicles — Functional safety — Part 8: Supporting processes
The traditional approach to service composition uses orchestration coordinators and arranges services according to a predetermined business logic and execution order  based on design choices about the type and granularity of available services. This execution order and logic is often expressed as a workflow using notations such as BPEL . Whilst service orchestration is a widely deployed form of system architec- ture, it can result in a failure to satisfy requirements in increasingly adaptive environ- ments as the predefined execution order and/or business logic can be rendered invalid by changes to a service consumer’s context. One advantage of service choreographies is that they impose fewer architecture-level constraints than orchestrations, and as a consequence have greater potential to deliver adaptive systems that continue to satisfy the evolving requirements on them. They make fewer design choices about the granu- larity of services to be invoked, an execution order for these services does not need to be specified, and business logic is specified independent of the services .
Communication allocation step depends on the chosen con- nection type employed for intercomponent data exchange, i.e. immediate or complex connection. Immediate connections rep- resent migration of data between ports, which are implemented as one place buffers with overwrite semantics. Such behavior can be accomplished using shared memory mechanism in ESE, i.e. intercomponent communication is mapped onto ESE read/write API calls. Complex connections that represent a reliable communication channel with functionality described through FIFO buffers can be implemented accordingly using FIFO channels in ESE, i.e. blocking send/ blocking receive API calls. At the time being, the transformation process is performed by hand. However, since it is based on the well defined semantics of both SaveCCM and target ESE behavioral model, it can easily be automated.
Figure 2, 4 and 5 illustrate examples of goals which are achieved from our patterns. The goal in figure 4 explains requirement specification c in section 3. According to figure 3, the arrowed line number (2), f is a global variable while expressions f = currentFl and reqFl(f) = True are additional conditions. An expression LiftState = ‘StopAtFloor’ is a cause of changing state which is also the cause of the arrowed line. Since this goal is declared as an Achieve, we use eventually temporal logic “ ” to describe time constraint. An expression [1,5] DoorState(f) = ‘Open’
As Figure 11-1 illustrates, the functional requirements that come from the various chunks of a use case can be sprinkled throughout a hierarchically organized SRS. Traceability analysis becomes important so that you can make sure every functional requirement associated with the use case traces back to a specific part of the use case. You also want to ensure that every piece of information in the use case leads to the necessary functionality in the SRS. In short, the way a use case is organized is different from the way many developers prefer to work. It gets even more confusing if you’re employing use cases to describe the bulk of the function- ality but have placed additional functional requirements that don’t relate to specific use cases into a supplemental specification (Leffingwell and Widrig 2003). This approach forces the developer to get some information from the use case documentation and then to scour the supplemental specification for other relevant inputs. Before your analysts impose a particular requirements-packaging strategy on the developers, have these two groups work together to determine the most effective ways to communicate requirements information. (See Chapter 12, “Bridging Documents.”)
Fundamentally, Menus focused around pecking orders of examination strategies can be utilized to give understudies in the mentoring methodology with conceivable decisions for data social affair activities. The creator has the capacity choose if the whole chain of importance or sub-orders is indicated to the understudy. It is additionally conceivable to center examination systems on anatomic structures by utilizing obtained connections between them. Overall, a menu essentially demonstrating an anatomic structure can be developed. Each sub-menu has an extra menu thing that pops up the agreeing examination techniques. Knowledge formalized within the architecture can be used for an automatic feedback generation in the tutoring process. The systems allow the quantitative or qualitative assessment of links between phenomena as done, for example, in the D3 expert systems.
The interface specification shall describe and document, in particular with the help of drawings, the product's physical and functional boundaries with other systems, sub- systems and equipment. It shall also describe and document the responsibility boundaries of all groups or individuals involved in the design.
based, and any anticipated changes due to hardware evolution, changing user needs, and so on. This section is useful for system designers as it may help them avoid design decisions that would constrain likely future changes to the system. Appendices These should provide detailed, specific information that is related to the
In our case study, we started by analyzing the archetypes in the openEHR Clinical Knowledge Manager to identify suitable archetypes. The most suitable one was openEHR- EHR-OBSERVATION.lab_test-histopathology, that models a generic anatomical pathology or histopathology test. In order to accommodate the additional concepts required by our phenotyping algorithm, the archetype was specialized using the LinkEHR archetype editor. The specialization, named openEHR-EHR-OBSERVATION.lab_test-histopathology- colorectal_screening, incorporates detailed information about adenoma findings, such as type, maximum size of the recorded dimensions (width, breadth and height), dysplasia grade, and whether they are sessile and/or advanced. Our case study requires concepts at different level of granularity: at finding and study levels. In order to represent study level concepts (maximum size of all adenomas and number of adenomas) we developed a second-level archetype (openEHR-EHR-EVALUATION.colorectal_screening.v1) from scratch.