"... In PAGE 4: ...The first one, based on absolute original research and modelling approach, the second, by basing the development on the OpenEHR framework and, the third, using the HL7 reference information model and refinement techniques. Table1 presents dimensioning that was considered for comparison. A short explanation of each area and criteria for evaluating follows: - Data Model: RIM from HL7, RM from OEHR and a possible original one.... ..."

"... In PAGE 4: ... All the early systems were essentially lab systems, built to explore some new methods that used adaptivity in an educational context. In contrast, several more recent systems pro- vide complete frameworks and even authoring tools for developing Web-based courses ( Table1 ). The appearance of a number of authoring tools indicates the maturity of adaptive educational hypermedia and a response to a Web-provoked demand for user-adaptive distance education courses.... ..."

"... In PAGE 2: ...ecome also applied in (user-adaptive) IR systems (e.g., [2]). 2 Learning Bayesian Networks for User Modeling Bayesian networks have become increasingly popular as one of the inference technique of choice for user-adaptive systems. Table1 lists some recent research of UM with BNs in a wide range of application scenarios that applies to some extent ML techniques. Note, that these systems (except our READY-system) use off-the-shelf learning methods that were not developed with the particular UM context in mind.... ..."

"... In PAGE 5: ...imilarly, MIPS 16.19.01 in D2 is lower than GO 0017076 in DU, and so on. Therefore the integrated user data DU could look like in Table4 , where the semantic correspondences have been applied. In general, the user may want to answer queries such as the number of human proteins that are involved in kinase activity from the integrated data or even to infer models based on the data available in order to use them to predict useful information about new unlabeled data (e.... ..."

"... In PAGE 5: ...imilarly, MIPS 16.19.01 in D2 is lower than GO 0017076 in DU, and so on. Therefore the integrated user data DU could look like in Table4 , where the semantic correspondences have been applied. In general, the user may want to answer queries such as the number of human proteins that are involved in kinase activity from the integrated data or even to infer models based on the data available in order to use them to predict useful information about new unlabeled data (e.... ..."

"... In PAGE 4: ... 2 and their functionality can be generally described with the integration steps mentioned above. Table1 provides an overview of these systems, presenting aspects of their federation, their common query language (CQL) and common data model (CDM) as well as whether they provide total transparency to the users or not. Some of the systems in Table 1 provide potential variations of the mediation approach to integration and these are briefly described below.... In PAGE 4: ... Table 1 provides an overview of these systems, presenting aspects of their federation, their common query language (CQL) and common data model (CDM) as well as whether they provide total transparency to the users or not. Some of the systems in Table1 provide potential variations of the mediation approach to integration and these are briefly described below. In BioKleisli [17], when users formulate queries in the Collection Programming Language (CPL) [18] they have to provide information of which data sources will be used and how.... ..."

"... In PAGE 16: ... good number of authoring tools, i.e., toolkits, systems, or shells that can be used by non-programming authors to develop an educational AHS. Two hands are still enough to count existing authoring systems ( Table2 ). The reason for that is reasonably clear.... In PAGE 16: ... Before producing a real authoring tool, a research group has to develop an explicit design approach that usually requires developing one or more educational AHS. As shown on Table2 , existing authoring systems were produced by research groups that had some previous experience in developing AHS. In addition to that, there are several systems that were created as design frameworks such as CAMELEON (Laroussi, et al.... ..."

"... In PAGE 6: ... Mode compress jess db javac mpeg mtrt jack jit 6% 30% 31% 19% 11% 7% 17% % Kernel intr 1% 28% 31% 23% 2% 4% 16% We begin by classifying the branch behavior of JVMs with different implementation styles, specifically trying to answer the following questions: What are the mixes of the branch instructions that are executed on a general purpose CPU when executing Java programs (using an interpreter or JIT compiler)? Are these different from those for traditional SPECInt95 programs? Do the different applications use branches in JVM and its native libraries in a similar way? If so, can we preoptimize Java run time system using a profile-based optimizer? How do Java executions in JIT and interpreter modes fare with a typical branch predictor? Do user and kernel codes exploit this microarchitectural feature in different ways? Based on this, can we suggest optimized and cost-effective speculative mechanisms for Java executions? 4.1 Branch Profiling A complete branch instruction profiling for a Java run time system and OS running the benchmarks is given in Table3 . Branch instructions are categorized as conditional branches, direct branches that unconditionally redirect instruction streams to a statically specified target encoded in the instruction itself, (non-return) indirect branches which transfer control to an address stored in a register, and return branches which always use jump and link instruction (e.... In PAGE 7: ...are conditional branches, etc. Table3 shows that kernel has more branches per instruction than that of user codes. This observation, together with the significance of the kernel activity shown in Table 2 suggest that kernel instructions and kernel branches play an important role in the execution of a Java program.... ..."

"... In PAGE 11: ... The development is so quick that we can ad a sixth model. The computer mediated communication, using automated response systems ( Table3 , fifth generation) means in two words adaptive interface, however in the project METOD we tackle with the problem of adaptive content and that could signify the sixth generation of distance learning. Table 3: Models of distance education by James C.... ..."