Planning Ability in Schizophrenia

This publication-based thesis investigates planning ability in psychiatric patients with schizophrenia, combining a problem solving perspective with a psychometric approach to assessing executive functions. The manuscripts presented address four research questions: (1) Is the newly developed Plan-a-Day test a reliable and valid measure of planning ability in schizophrenia? (2) Is planning ability – in particular as measured by the Plan-a-Day test – predictive of functional outcome? (3) Is a planning and problem solving training based on the Plan-a-Day concept effective in cognitive remediation? (4) How specific is a planning deficit in schizophrenia

Deep Learning for Period Classification of Historical Texts

In this study, we address the interesting task of classifying historical texts by their assumed period of writing. This task is useful in digital humanity studies where many texts have unidentified publication dates. For years, the typical approach for temporal text classification was supervised using machine-learning algorithms. These algorithms require careful feature engineering and considerable domain expertise to design a feature extractor to transform the raw text into a feature vector from which the classifier could learn to classify any unseen valid input. Recently, deep learning has produced extremely promising results for various tasks in natural language processing (NLP). The primary advantage of deep learning is that human engineers did not design the feature layers, but the features were extrapolated from data with a general-purpose learning procedure. We investigated deep learning models for period classification of historical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN), and recurrent neural networks (RNN). We demonstrate that the CNN and RNN models outperformed the paragraph vector model and supervised machine-learning algorithms. In addition, we constructed word embeddings for each time period and analyzed semantic changes of word meanings over time

EU accession and Poland's external trade policy

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Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Expertise in map comprehension: processing of geographic features according to spatial configuration and abstract roles

Expertise in topographic map reading is dependent on efficient processing of geographical information presented in a standardised map format. Studies have supported the proposition that expert map readers employ cognitive schemas in which prototypical configurations held in long term memory are employed during the surface search of map features to facilitate map comprehension. Within the experts’ cognitive schemas, it is assumed that features are grouped according to spatial configurations that have been frequently encountered and these patterns facilitate efficient chunking of features during information processing. This thesis investigates the nature of information held in experts’ cognitive schemas. It also proposes that features are grouped in the experts’ schemas not only by their spatial configurations but according to the abstract and functional roles they perform. Three experiments investigated the information processing strategies employed by firstly, skilled map readers engaged in a map reproduction task and secondly, expert map readers engaged in a location comparison exercise. In the first and second experiments, skilled and novice map readers studied and reproduced a town map and a topographic map. Drawing protocols and verbal protocols provided insights into their information processing strategies. The skilled map readers demonstrated superior performance for reproducing contour related data with evidence of the use of cognitive schemas. For the third experiment, expert and novice map readers compared locations within map excerpts for similarities of boundary extents. Eye-gaze data and verbal protocols provided information on the features attended to and the participants’ search patterns. The expert group integrated features into their cognitive schemas according to the abstract roles they performed significantly more frequently than the novices. Both groups employed pattern recognition to integrate features for some of the locations. Within a similar experimental design the second part of the third experiment examined whether experts also integrated the abstract roles of remote features and village grouping concepts within their cognitive schemas. The experts again integrated the abstract roles of physical features into their schemas more often than novices but this strategy was not employed for either the remote feature or grouping categories. Implications for map design and future Geographic Information Systems are discussed

Language and Speech Predictors of Reading Achievement in Preschool Children with Language Disorders

ABSTRACT LANGUAGE AND SPEECH PREDICTORS OF READING ACHIEVEMENT IN PRESCHOOL CHILDREN WITH LANGUAGE DISORDERS by Juliet K. Haarbauer-Krupa The purpose of this longitudinal study was to examine the relationship between language and reading in children diagnosed with developmental language disorder (DLD) during preschool. An archival data set was available for analysis. Preschool children with DLD who were assessed between 35 and 74 months for preschool language and speech abilities (Rapin, 1996) returned for language, speech and reading testing at age seven years. Children who enrolled in the study were a clinically referred sample, met criteria for average nonverbal intellectual functioning, and demonstrated below average performance on a composite language measure. To evaluate a hypothesis about the contribution of vocabulary, grammar, and speech articulation to reading outcome measures, a series of regression analyses tested models to identify predictors of reading achievement at age seven. Results indicated a strong, positive relationship between language skills assessed at both ages and reading comprehension. School-age language and speech skills explained 25% of the variance in reading comprehension after controlling for word identification skills. Grammar at school age was a significant unique predictor of reading comprehension. Preschool language and speech skills explained 22% of the variance after controlling for word identification skills. Speech articulation was not related to reading outcomes. In contrast, regression analyses suggested that language and speech skills did not predict word reading abilities. Children who had reading comprehension difficulties had weaker vocabulary, grammar and speech skills compared to children who had average and above comprehension skills. Findings support previous research describing a relationship between language skills and reading comprehension. Language skills measured at preschool can predict reading comprehension difficulties in elementary school for children with DLD. Results highlight the importance of early identification and intervention of language impairment in children to improve areas of vocabulary and grammar critical to reading success

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

Word association research and the L2 lexicon

Since its modern inception in the late nineteenth century, research on word associations has developed into a large and diverse area of study, including work with both applied linguistic and psycholinguistic orientations. However, despite significant recent interest in the use of word association to investigate second language (L2) vocabulary knowledge and testing, there has until now been no systematic attempt to review the wider word association research tradition for the benefit of second language-oriented researchers and practitioners. This paper seeks to address this, drawing together linguistic research from the past 150 years, with a focus on research published since 2000. We evaluate the current state of L2 word association research, before identifying methodological and theoretical themes from a broader range of disciplinary approaches. Emerging from this, new paradigms are identified which have potential to catalyse a new phase of work for second-language word association scholars, and which indicate priority foci for future work