Developing an introductory object-oriented programming course

The state of the object-oriented programming course in Lappeenranta University of Technology had reached the point, where it required changes to provide better learning opportunities and thus the learning outcomes. Based on the student feedback the course was partially dated and ineffective. The components of the course were analysed and the ineffective elements were removed and new methods were introduced to improve the course. The major changes included the change from traditional teaching methods to reverse classroom method and the use of Java as the programming language. The changes were measured by the student feedback, lecturer’s observations and comparison to previous years. The feedback suggested that the changes were successful; the course received higher overall grade than before.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.

Quantitative refinement of reaction-based biomodels

In the field of molecular biology, scientists adopted for decades a reductionist perspective in their inquiries, being predominantly concerned with the intricate mechanistic details of subcellular regulatory systems. However, integrative thinking was still applied at a smaller scale in molecular biology to understand the underlying processes of cellular behaviour for at least half a century. It was not until the genomic revolution at the end of the previous century that we required model building to account for systemic properties of cellular activity. Our system-level understanding of cellular function is to this day hindered by drastic limitations in our capability of predicting cellular behaviour to reflect system dynamics and system structures. To this end, systems biology aims for a system-level understanding of functional intraand inter-cellular activity. Modern biology brings about a high volume of data, whose comprehension we cannot even aim for in the absence of computational support. Computational modelling, hence, bridges modern biology to computer science, enabling a number of assets, which prove to be invaluable in the analysis of complex biological systems, such as: a rigorous characterization of the system structure, simulation techniques, perturbations analysis, etc. Computational biomodels augmented in size considerably in the past years, major contributions being made towards the simulation and analysis of large-scale models, starting with signalling pathways and culminating with whole-cell models, tissue-level models, organ models and full-scale patient models. The simulation and analysis of models of such complexity very often requires, in fact, the integration of various sub-models, entwined at different levels of resolution and whose organization spans over several levels of hierarchy. This thesis revolves around the concept of quantitative model refinement in relation to the process of model building in computational systems biology. The thesis proposes a sound computational framework for the stepwise augmentation of a biomodel. One starts with an abstract, high-level representation of a biological phenomenon, which is materialised into an initial model that is validated against a set of existing data. Consequently, the model is refined to include more details regarding its species and/or reactions. The framework is employed in the development of two models, one for the heat shock response in eukaryotes and the second for the ErbB signalling pathway. The thesis spans over several formalisms used in computational systems biology, inherently quantitative: reaction-network models, rule-based models and Petri net models, as well as a recent formalism intrinsically qualitative: reaction systems. The choice of modelling formalism is, however, determined by the nature of the question the modeler aims to answer. Quantitative model refinement turns out to be not only essential in the model development cycle, but also beneficial for the compilation of large-scale models, whose development requires the integration of several sub-models across various levels of resolution and underlying formal representations.

Open source solutions for multiplatform graphics programming

New emerging technologies in the recent decade have brought new options to cross platform computer graphics development. This master thesis took a look for cross platform 3D graphics development possibilities. All platform dependent and non real time solutions were excluded. WebGL and two different OpenGL based solutions were assessed via demo application by using most recent development tools. In the results pros and cons of the each solutions were noted.

Collectiveness within startup teams – Leading the way to initiating and managing collective pursuit of opportunities in organizational contexts

While traditional entrepreneurship literature addresses the pursuit of entrepreneurial opportunities to a solo entrepreneur, scholars increasingly agree that new ventures are often founded and operated by entrepreneurial teams as collective efforts especially in hightechnology industries. Researchers also suggest that team ventures are more likely to survive and succeed than ventures founded by the individual entrepreneur although specific challenges might relate to multiple individuals being involved in joint entrepreneurial action. In addition to new ventures, entrepreneurial teams are seen central for organizing work in established organizations since the teams are able to create major product and service innovations that drive organizational success. Acknowledgement of the entrepreneurial teams in various organizational contexts has challenged the notion on the individual entrepreneur. However, considering that entrepreneurial teams represent a collective-level phenomenon that bases on interactions between organizational members, entrepreneurial teams may not have been studied as indepth as could be expected from the point of view of the team-level, rather than the individual or the individuals in the team. Many entrepreneurial team studies adopt the individualized view of entrepreneurship and examine the team members’ aggregate characteristics or the role of a lead entrepreneur. The previous understandings might not offer a comprehensive and indepth enough understanding of collectiveness within entrepreneurial teams and team venture performance that often relates to the team-level issues in particular. In addition, as the collective-level of entrepreneurial teams has been approached in various ways in the existing literatures, the phenomenon has been difficult to understand in research and practice. Hence, there is a need to understand entrepreneurial teams at the collective-level through a systematic and comprehensive perspective. This study takes part in the discussions on entrepreneurial teams. The overall objective of this study is to offer a description and understanding of collectiveness within entrepreneurial teams beyond individual(s). The research questions of the study are: 1) what collectiveness within entrepreneurial teams stands for, what constitutes the basic elements of it, and who are included in it, 2) why, how, and when collectiveness emerges or reinforces within entrepreneurial teams, and 3) why collectiveness within entrepreneurial teams matters and how it could be developed or supported. In order to answer the above questions, this study bases on three approaches, two set of empirical data, two analysis techniques, and conceptual study. The first data set consists of 12 qualitative semi-structured interviews with business school students who are seen as prospective entrepreneurs. The data is approached through a social constructionist perspective and analyzed through discourse analysis. The second data set bases on a qualitative multiplecase study approach that aims at theory elaboration. The main data consists of 14 individual and four group semi-structured thematic interviews with members of core entrepreneurial teams of four team startups in high-technology industries. The secondary data includes publicly available documents. This data set is approached through a critical realist perspective and analyzed through systematic thematic analysis. The study is completed through a conceptual study that aims at building a theoretical model of collective-level entrepreneurship drawing from existing literatures on organizational theory and social-psychology. The theoretical work applies a positivist perspective. This study consists of two parts. The first part includes an overview that introduces the research background, knowledge gaps and objectives, research strategy, and key concepts. It also outlines the existing knowledge of entrepreneurial team literature, presents and justifies the choices of paradigms and methods, summarizes the publications, and synthesizes the findings through answering the above mentioned research questions. The second part consists of five publications that address independent research questions but all enable to answer the research questions set for this study as a whole. The findings of this study suggest a map of relevant concepts and their relationships that help grasp collectiveness within entrepreneurial teams. The analyses conducted in the publications suggest that collectiveness within entrepreneurial teams stands for cognitive and affective structures in-between team members including elements of collective entity, collective idea of business, collective effort, collective attitudes and motivations, and collective feelings. Collectiveness within entrepreneurial teams also stands for specific joint entrepreneurial action components in which the structures are constructed. The action components reflect equality and democracy, and open and direct communication in particular. Collectiveness emerges because it is a powerful tool for overcoming individualized barriers to entrepreneurship and due to collectively oriented desire for, collective value orientation to, demand for, and encouragement to team entrepreneurship. Collectiveness emerges and reinforces in processes of joint creation and realization of entrepreneurial opportunities including joint analysis and planning of the opportunities and strategies, decision-making and realization of the opportunities, and evaluation, feedback, and sanctions of entrepreneurial action. Collectiveness matters because it is relevant for potential future entrepreneurs and because it affects the ways collective ventures are initiated and managed. Collectiveness also matters because it is a versatile, dynamic, and malleable phenomenon and the ideas of it can be applied across organizational contexts that require team work in discovering or creating and realizing new opportunities. This study further discusses how the findings add to the existing knowledge of entrepreneurial team literature and how the ideas can be applied in educational, managerial, and policy contexts.

Did ISI fail and is neoliberalism the answer for Latin America? Re-assessing common wisdom regarding economic policies in the region

The second phase of Import Substituting Industrialization, commonly known as ISI2, involved the move in Latin America to "heavy" industrialization, from around 1950-80. This period of economic history has been reviled on both the Left and the Right as being one of either heightened dependency or one demonstrating the clear failure of state intervention in the economy. In this research note, a basic statistical analysis is used to back up other descriptive claims that the ISI2 period was rather one of mixed success, with macroeconomic volatility accompanying great progress in GDP and manufacturing growth. In a sense, the ISI2 period succeeded in industrializing the large economies of the period, and contrasts favorably with the record of the succeeding paradigm of neoliberalism. This research note seeks to raise questions about the way we look at the historical period of ISI2, and suggests that a more open-minded perspective could lead to a more effective and sustainable political economy paradigm for the region in the future.

Measuring software security from the design of software

The vast majority of our contemporary society owns a mobile phone, which has resulted in a dramatic rise in the amount of networked computers in recent years. Security issues in the computers have followed the same trend and nearly everyone is now affected by such issues. How could the situation be improved? For software engineers, an obvious answer is to build computer software with security in mind. A problem with building software with security is how to define secure software or how to measure security. This thesis divides the problem into three research questions. First, how can we measure the security of software? Second, what types of tools are available for measuring security? And finally, what do these tools reveal about the security of software? Measuring tools of these kind are commonly called metrics. This thesis is focused on the perspective of software engineers in the software design phase. Focus on the design phase means that code level semantics or programming language specifics are not discussed in this work. Organizational policy, management issues or software development process are also out of the scope. The first two research problems were studied using a literature review while the third was studied using a case study research. The target of the case study was a Java based email server called Apache James, which had details from its changelog and security issues available and the source code was accessible. The research revealed that there is a consensus in the terminology on software security. Security verification activities are commonly divided into evaluation and assurance. The focus of this work was in assurance, which means to verify one’s own work. There are 34 metrics available for security measurements, of which five are evaluation metrics and 29 are assurance metrics. We found, however, that the general quality of these metrics was not good. Only three metrics in the design category passed the inspection criteria and could be used in the case study. The metrics claim to give quantitative information on the security of the software, but in practice they were limited to evaluating different versions of the same software. Apart from being relative, the metrics were unable to detect security issues or point out problems in the design. Furthermore, interpreting the metrics’ results was difficult. In conclusion, the general state of the software security metrics leaves a lot to be desired. The metrics studied had both theoretical and practical issues, and are not suitable for daily engineering workflows. The metrics studied provided a basis for further research, since they pointed out areas where the security metrics were necessary to improve whether verification of security from the design was desired.

Of empty thoughts and blind intuitions Kant's answer to McDowell

This paper examines the relation between intuition and concept in Kant in light of John McDowell's neo-Kantian position that intuitions are concept-laden.2 The focus is on Kant's twofold pronouncement that thoughts without content are empty and that intuitions without concepts are blind. I show that intuitions as singular representations are not instances of passive data intake but the result of synthetic unification of the given manifold of the senses by the power of the imagination under the guidance of the understanding. Against McDowell I argue that the amenability of intuitions to conceptual determination is not due some pre-existing, absolute conceptuality of the real but to the "work of the subject."3 On a more programmatic level, this paper seeks to demonstrate the limitations of a selective appropriation of Kant and the philosophical potential of a more comprehensive and thorough consideration of his work. Section 1 addresses the unique balance in Kant's philosophy between the work on particular problems and the orientation toward a systematic whole. Section 2 outlines McDowell's take on the Kantian distinction between intuition and concept in the context of the Kant readings by Sellars and Strawson. Section 3 exposes McDowell's relapse into the Myth of the Given. Section 4 proposes a reading of Kant's theoretical philosophy as an epistemology of metaphysical cognition. Section 5 details Kant's original account of sensible intuition in the Inaugural-Dissertation of 1770. Section 6 presents the transition from the manifold of the senses to the synthesis in the imagination and the unification through the categories in the Critique of pure reason (1781 and 1787). Section 7 addresses Kant's formalism in epistemology and metaphysics.

Set and Spike

Brock Men's Volleyball makes a play in this 1978 match.

Perceived effectiveness of alternative programming : a case study /

The topic of this research was alternative programming in secondary public education. The purpose of this research was to explore the perceived effectiveness of two public secondary programs that are aJternative to mainstream or "regular" education. Two case study sites were used to research diverse ends of the aJtemative programming continuum. The first case study demonstrated a gifted program and the second demonstrated a behavioral program. Student needs were examined in terms of academic needs, emotional needs, career needs, and social needs. Research conducted in these sites examined how the students, teachers, onsite staff, and program administrators perceived that individual needs were met and unmet in these two programs. The study was qualitative and exploratory, using deductive and inductive research techniques. Similar themes of best practice that were identified in the case study sites aided in the development of a teaching and learning model. Four themes were identified as important within the case study sites. These themes included the commitment and motivation of teachers and the support of administration in the gifted program, and the importance of location and the flow of information and communication in the behavior program. Six themes emerged that were similar across the case study sites. These themes included the individual nature of programming, recognition of student achievement, the alternative program as a place of safety and community, importance of interpersonal capacity, priority of basic needs, and, finally, matching student capacity with program expectations. The model incorporates these themes and is designed as a resource for teachers, program administrators, parents, and policy makers of alternative educational programs.