Process mining aims at discovering processes by extracting knowledge about their different perspectives from event logs. The resource perspective (or organisational perspective) deals, among others, with the assignment of resources to process activities. Mining in relation to this perspective aims to extract rules on resource assignments for the process activities. Prior research in this area is limited by the assumption that only one resource is responsible for each process activity, and hence, collaborative activities are disregarded. In this paper, we leverage this assumption by developing a process mining approach that is able to discover team compositions for collaborative process activities from event logs. We evaluate our novel mining approach in terms of computational performance and practical applicability.

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This study investigated memory in children with temporal lobe epilepsy and the ability to discern hippocampal dysfunction with conventional memory tests that are typically used to detect more global memory impairment. All data was obtained retrospectively from the epilepsy surgery program at a local children’s hospital. The research population consisted of 54 children with intractable epilepsy of temporal onset, balanced across pathology types (with and without hippocampal disease) and other demographics. Each was given a clinical battery prior to surgical intervention, which included the WRAML/WRAML2 Verbal Learning subtest from which the dependent variables for this study were extracted. The research hypothesis had predicted that memory retention between verbal learning and recall would be worse for participants with pathology that included hippocampal sclerosis than for those with non-hippocampal temporal lobe pathology. A two-way mixed-design ANOVA was used to test the hypothesis, which allowed incorporation of variables of interest related to memory factors, pathology type, and hemispheric laterality, as well as their various interactions. There was a significant main effect for change in the number of words retained from the final learning trial to the delayed recall. Although the interaction between memory retention and pathology type was not statistically significant, the average of the memory scores as it related to pathology by side did show significance. Thus, results did not support the hypothetical relationship between retention and hippocampal function. However, additional exploratory analyses revealed that the final learning trial by itself was associated with hippocampal pathology, which applied only to those participants with left-hemisphere lesions. Logistic regression with the final learning trial correctly classified 74 percent of participants into the appropriate pathology category, with 81 percent sensitivity to hippocampal dysfunction. Mean participant memory scores were nearly one standard deviation below the normative mean for both delayed recall and total learning scaled scores, regardless of pathology type or lesion hemisphericity. Thus, while the conventionally used indices of the WRAML Verbal Learning test are useful for determining overall memory status, they are not specific to pathological substrate. The within-subject main effect showed an expected loss of information across the time of the delay, but overall the recall score showed no association with hippocampal functioning. This study revealed the possibility of measuring hippocampal function at statistically significant group levels using learning scores from a widely used measure of verbal memory, even in participants with intact contralateral mesial temporal structures. It also indicated that hippocampal structures do not play a role during recall measures given after a standard time delay. Data further demonstrated a role of the hippocampus for encoding and transferring information beyond short term/working memory into long term. During the learning process, the hippocampus appears to work in concert with short-term memory systems, but does not take over the encoding process until enough repetitions have occurred to saturate the working memory buffer. This research represents a small, yet important step forward in our understanding of the hippocampus, with potentially important implications for the future study of memory constructs and mensuration.

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Persons with Swedish as their first language often find it hard to learn subject-verb agreement when studying English. In Swedish this grammatical difficulty does not exist and it is therefore hard to introduce to learners that have Swedish as their native language.This investigation is based on the texts of 28 ninth graders of whom four were interviewed. My interest was in finding out how the students reflect on their own written work with a focus on subject-verb agreement with a particular focus on the third person singular s.My study shows that the four interviewed students claim that they never reflect on grammar in their spontaneous writing and that they never consciously try to apply rules that they have studied in school through being offered grammatical explanations. Instead they make their grammatical choices intuitively, using their procedural knowledge.

The United States of America has dropped behind many countries in terms of the Science and Engineering university degrees awarded since the beginning of the nineties. Multiple studies have been conducted to determine the cause of this decline in degrees awarded, and try to reverse the trend in US education. The goal of these studies was to determine the proper instructional methods that facilitate the knowledge acquisition process for the student. It has been determined that not one method works for all types of curriculum, for example methods that have been found to work effectively in curriculum that teaches procedures and physical systems often fail in curriculum that teaches abstract and conceptual content. The purpose of this study is to design an instructional method that facilitates teaching of abstract knowledge, and to demonstrate its effectiveness through empirical research. An experiment including 72 undergraduate students was conducted to determine the best method of acquiring abstract knowledge. All students were presented with the same abstract knowledge but presented in different types of organization. These organization types consisted of hierarchy referred as Bottom Up, Top Down, and Unorganized. Another factor that was also introduced is Graphing, which is a method that is believe to improve the learning process. The experiment was completed in 8 weeks and data was gathered and analyzed. The results strongly suggest that abstract knowledge acquisition is greatly improved when the knowledge is presented in a Bottom Up hierarchical fashion. On the other hand, neither Graphing nor the Top Down or Unorganized conditions affect learning in these novice students. iv This dissertation is dedicated to my parents who established my education career since the beginning of my first day of school. Their prayers and encouragements have an impact toward my life and education.

In recent years, GraphQL has become a popular Web API language. Due to GraphQL’spopularity, third-party libraries have also grown steadily alongside it. One such library isGraphQL Tools, which provides tools such as query delegation and schema wrapping. Onecore element of GraphQL is its schema. In part, the schema describes the relations betweendata in a data source. Every GraphQL server needs a schema, which in turn utilizes resolverfunctions that retrieve the data from the data source. Schema wrapping allows developers to create GraphQL schemas that delegate queriesto another remote server. This allows the user to modify the behavior of the remote schema,without modifying its contents. However, some JavaScript knowledge is required to im-plement the schema wrapping. This thesis proposes a declarative approach to wrappingschemas with the help of custom GraphQL directives. The main objective of the approach isto enable less knowledgeable developers to wrap schemas with less effort. The declarative approach was tested by implementing a prototype. The effects on per-formance was tested with a benchmarking tool called Linköping GraphQL Benchmark(LinGBM). The prototype delegated queries to a remote server also created by LinGBM.The benchmarking tool was run against both the wrapper server and the remote server.The results indicated that the overhead of the wrapper server was mostly due to internalfunctions of GraphQL Tools. Therefore, automatically generating a server with the pro-posed declarative approach did not seem to impose significant performance losses. Con-sidering the decreased programming effort by using the declarative approach, the slightperformance loss can be seen as manageable.

We investigated whether a child with Asperger?s Syndrome would demonstrate deficits in awareness of cognitive processing similar to those demonstrated for awareness of social interactions. The cognitive processes examined were memory and metamemory, or knowing about knowing. With regard to procedural metamemory, the child was unable to accurately predict his own memory, particularly which items he would not be able to recall. Declarative metamemory also was impaired. Tasks requiring imitation of the researcher or that were largely nonverbal resulted in particularly poor performance. The findings indicate that the child?s social deficits related to Asperger?s Syndrome extended to the cognitive domain. Overall, a deficit in cognitive awareness was observed.

Planning is a challenging research area since the early days of Artificial Intelligence. The planning problem is the task of finding a sequence of actions leading an agent from a given initial state to a desired goal state. Whereas classical planning adopts restricting assumptions such as complete knowledge about the initial state and deterministic action effects, in real world scenarios we often have to face incomplete knowledge and non-determinism. Classical planning languages and algorithms do not take these facts into account. So, there is a strong need for formal languages describing such non-classical planning problems on the one hand and for (declarative) methods for solving these problems on the other hand.In this thesis, we present the action language Kc, which is based on flexible action languages from the knowledge representation community and extends these by useful concepts from logic programming.We define two basic semantics for this language which reflect optimistic and secure (i.e. sceptical) plans in presence of incomplete information or nondeterminism. These basic semantics are furthermore extended to planning with action costs, where each action can have an assigned cost value. Here, we address optimal plans as well as plans which stay within a certain overall cost limit.Next, we develop efficient (i.e. polynomial) transformations from planning problems described in our language Kc to disjunctive logic programs which are then evaluated under the so-called Answer Set Semantics. In this context, we introduce a general new method for problem solving in Answer Set Programming (ASP) which takes the genuine "guess and check" paradigm in ASP into account and allows us to integrate separate "guess" and "check" programs into a single logic program. Based on these methods, we have implemented the planning system DLVK. We discuss problem solving and knowledge representation in Kc using DLVK by means of several examples. The proposed methods and the DLVK system are also evaluated experimentally and compared against related approaches. Finally, we present a practical application scenario from the area of design and monitoring of multi-agent systems. As we will see, this monitoring approach is not restricted to our particular formalism. / Austrian Science Funds (FWF)

Modern distributed object systems pass remote parameters based on their runtime type. This design choice limits the expressiveness, readability, and maintainability of distributed applications. While a rich body of research is concerned with middleware extensibility, modern distributed object systems do not offer programming facilities to extend their remote parameter passing semantics. Thus, extending these semantics requires understanding and modifying the underlying middleware implementation. This thesis addresses these design shortcomings by presenting (i) a declarative and extensible approach to remote parameter passing that decouples parameter passing from parameter types, and (ii) a plugin-based framework, DeXteR, that enables the programmer to extend the native set of remote parameter passing semantics, without having to understand or modify the underlying middleware implementation. DeXteR treats remote parameter passing as a distributed cross-cutting concern. It uses generative and aspect-oriented techniques, enabling the implementation of different parameter passing semantics as reusable application-level plugins that work with application, system, and third-party library classes. The flexibility and expressiveness of the framework is validated by implementing several non-trivial parameter passing semantics as DeXteR plugins. The material presented in this thesis has been accepted for publication at the ACM/USENIX Middleware 2008 conference. / Master of Science