Beaming into the rat world: enabling real-time intereaction between rat and human each at their own scale

Immersive virtual reality (IVR) typically generates the illusion in participants that they are in the displayed virtual scene where they can experience and interact in events as if they were really happening. Teleoperator (TO) systems place people at a remote physical destination embodied as a robotic device, and where typically participants have the sensation of being at the destination, with the ability to interact with entities there. In this paper, we show how to combine IVR and TO to allow a new class of application. The participant in the IVR is represented in the destination by a physical robot (TO) and simultaneously the remote place and entities within it are represented to the participant in the IVR. Hence, the IVR participant has a normal virtual reality experience, but where his or her actions and behaviour control the remote robot and can therefore have physical consequences. Here, we show how such a system can be deployed to allow a human and a rat to operate together, but the human interacting with the rat on a human scale, and the rat interacting with the human on the rat scale. The human is represented in a rat arena by a small robot that is slaved to the human"s movements, whereas the tracked rat is represented to the human in the virtual reality by a humanoid avatar. We describe the system and also a study that was designed to test whether humans can successfully play a game with the rat. The results show that the system functioned well and that the humans were able to interact with the rat to fulfil the tasks of the game. This system opens up the possibility of new applications in the life sciences involving participant observation of and interaction with animals but at human scale.

PROM-OPEN: PROMOTING OPEN SCIENCE IN AN ON-LINE E-LEARNING ENVIRONMENT

Peer-reviewed

Researching on and with Young People: Collaborating and Educating. Investigar con y sobre los jóvenes colaborando y educando

This paper reports on collaborative research on and with young people. In this study five groups of students in the final year of their Compulsory Secondary Education (CSE) from five different schools developed five ethnographic studies about how they communicate, express themselves and learn inside and outside school, with the support and collaboration of teachers and members of our research group. The paper begins by discussing the dimensions of collaboration in education, taking into account the contribution of collaborative and ooperative learning, and the potential of digital resources, situating earlier influences and characterizing the work realised. Then there is a description of the research carried out on and with the young people we invited to perform as investigators. The results focus on the description and conceptualization of the different types of collaboration that have emerged while carrying out the ethnographic studies in each of the schools using digital technologies. Finally, we discuss the implications and limitations of the work as a contribution to anyone interested in researching on and with young people, collaborating, educating and using digital resources.

La estadística y la probabilidad en Educación Infantil conocimientos disciplinares, didácticos y experienciales

This article presents an approach to disciplinary knowledge, and experiential learning necessary for the Early Childhood Education teachers can teach statistics and probability significantly, effective and systematic. First, are specified a set of basic knowledge about the discipline and exposed the contents sequenced by level; secondly, provides guidance on how they learn and how they should be taught the knowledge of statistics and probability in the first ages; and, finally, are some examples of activities implemented in kindergarten classrooms

Foodstuffs and medicines as legal categories in the EU and China. Functional foods as a borderline case

The thesis discusses the regulation of foodstuffs and medicines, and particularly the regulation of functional foods. Legal systems investigated are the EU and China. Both are members of the WTO and Codex Alimentarius, which binds European and Chinese rules together. The study uses three Chinese berries as case examples of how product development faces regulation in practice. The berries have traditional uses as herbal medicines. Europe and China have similar nutrition problems to be resolved, such as obesity, cardiovascular disease, and diabetes. The three berries might be suitable raw materials for functional foods. Consumer products with health-enhancing functions, such as lowering blood pressure, might legally be classifi ed either as foodstuffs or medicines. The classifi cation will depend on functions and presentation of the product. In our opinion, food and medicine regulation should come closer together so the classifi cation issue would no longer be an issue. Safety of both foodstuffs and medicines is strictly regulated. With medicines, safety is a more relative concept, where benefi ts of the product are compared to side-effects in thorough scientifi c tests and trials. Foods, on the other hand, are not allowed to have side-effects. Hygiene rules and rules on the use of chemicals apply. In China, food safety is currently at focus as China has had several severe food scandals. Newly developed foods are called novel foods, and are specifi cally regulated. The current European novel food regulation from 1997 treats traditional third country products as novel. The Chinese regulation of 2007 also defi nes novel foods as something unfamiliar to a Chinese consumer. The concepts of novel food thus serve a protectionist purpose. As regards marketing, foods are allowed to bear health claims, whereas medicines bear medicinal claims. The separation is legally strict: foods are not to be presented as having medicinal functions. European nutrition and health claim regulation exists since 2006. China also has its regulation on health foods, listing the permitted claims and how to substantiate them. Health claims are allowed only on health foods. The European rules on medicines include separate categories for herbal medicines, traditional herbal medicines, and homeopathic medicines, where there are differing requirements for scientifi c substantiation. The scientifi c and political grounds for the separate categories provoke criticism. At surface, the Chinese legal system seems similar to the European one. To facilitate trade, China has enacted modern laws. Laws are needed as the country moves from planned economy to market economy: ‘rule of law’ needs to replace ‘rule of man’. Instead of being citizens, Chinese people long were subordinates to the Emperor. Confucius himself advised to avoid confl ict. Still, Chinese people do not and cannot always trust the legal system, as laws are enforced in an inconsistent manner, and courts are weak. In China, there have been problems with confl icting national and local laws. In Europe, the competence of the EU vs. the competence of the Member States is still not resolved, even though the European Commission often states that free trade requires harmonisation. Food and medicine regulation is created by international organisations, food and medicine control agencies, standards agencies, companies and their organisations. Regulation can be divided in ‘hard law’ and ‘soft law’. One might claim that hard law is in crisis, as soft law is gaining importance. If law is out of fashion, regulation certainly isn’t. In the future, ‘law’ might mean a process where rules and incentives are created by states, NGOs, companies, consumers, and other stakeholders. ‘Law’ might thus refer to a constant negotiation between public and private actors. Legal principles such as transparency, equal treatment, and the right to be heard would still be important.

Coopetitive advantage – How firms create and appropriate value by collaborating with their competitors

Collaboration between competing firms (coopetition) has emerged as an important issue forn business practice in many industries. Extant literature has examined coopetition on many levels of analysis, but lacks clarity in distinguishing it explicitly from cooperation between noncompeting organizations. Because of this, the performance implications of coopetition from the perspective of an individual firm are still ambiguous – some research suggests positive results whereas other studies suggest detrimental outcomes. The aim in this dissertation is to narrow these gaps by exploring how firms create and appropriate value through collaboration with their competitors. The dissertation is divided into two parts. The first part comprises an overview of the relevant literature, as well as the conclusions of the whole study, and the second part includes six research publications. Both qualitative and quantitative methodologies are utilized. The results suggest that coopetition embodies the distinctive logic of value creation and appropriation from the perspective of an individual firm, and thus differs in terms of performance implications from cooperation between non-competitors. The distinction comes from the fact that competitors have somewhat similar understanding, capabilities and interest related to certain markets, which is potentially both challenging and beneficial in terms of the individual firm’s competitiveness. It appears from the findings that there are distinctive firm-external and firm-specific factors affecting the success of a coopetition strategy. This study makes three main contributions. First, on the conceptual level it shows the distinction between coopetition and cooperation between non-rivals as a collaborative inter-organizational relationship. Secondly, it sets out a framework and propositions that enhance understanding of how value is created and appropriated in coopetition from the perspective of an individual firm. Thirdly, it offers empirical evidence of how coopetition affects firms’ innovation and market performance, and identifies the focal internal and external factors involved. In general terms, the thesis adds to our knowledge of how a firm can successfully utilize a coopetition strategy in its pursuit of improved performance.

Enabling constructive alignment in programming instruction

In this thesis, simple methods have been sought to lower the teacher’s threshold to start to apply constructive alignment in instruction. From the phases of the instructional process, aspects that can be improved with little effort by the teacher have been identified. Teachers have been interviewed in order to find out what students actually learn in computer science courses. A quantitative analysis of the structured interviews showed that in addition to subject specific skills and knowledge, students learn many other skills that should be mentioned in the learning outcomes of the course. The students’ background, such as their prior knowledge, learning style and culture, affects how they learn in a course. A survey was conducted to map the learning styles of computer science students and to see if their cultural background affected their learning style. A statistical analysis of the data indicated that computer science students are different learners than engineering students in general and that there is a connection between the student’s culture and learning style. In this thesis, a simple self-assessment scale that is based on Bloom’s revised taxonomy has been developed. A statistical analysis of the test results indicates that in general the scale is quite reliable, but single students still slightly overestimate or under-estimate their knowledge levels. For students, being able to follow their own progress is motivating, and for a teacher, self-assessment results give information about how the class is proceeding and what the level of the students’ knowledge is.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

Communicative and Anticipatory Decision-Making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

An Open Source, DDI-based Curation System for Social Science Data

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The Purdue University Research Repository (PURR): An institutional data management service with a virtual research environment, data publication, and archiving

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

How to Hack the DSpace Community

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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.

Computational models for and from biology : simple gene assembly and reaction systems

The advancement of science and technology makes it clear that no single perspective is any longer sufficient to describe the true nature of any phenomenon. That is why the interdisciplinary research is gaining more attention overtime. An excellent example of this type of research is natural computing which stands on the borderline between biology and computer science. The contribution of research done in natural computing is twofold: on one hand, it sheds light into how nature works and how it processes information and, on the other hand, it provides some guidelines on how to design bio-inspired technologies. The first direction in this thesis focuses on a nature-inspired process called gene assembly in ciliates. The second one studies reaction systems, as a modeling framework with its rationale built upon the biochemical interactions happening within a cell. The process of gene assembly in ciliates has attracted a lot of attention as a research topic in the past 15 years. Two main modelling frameworks have been initially proposed in the end of 1990s to capture ciliates’ gene assembly process, namely the intermolecular model and the intramolecular model. They were followed by other model proposals such as templatebased assembly and DNA rearrangement pathways recombination models. In this thesis we are interested in a variation of the intramolecular model called simple gene assembly model, which focuses on the simplest possible folds in the assembly process. We propose a new framework called directed overlap-inclusion (DOI) graphs to overcome the limitations that previously introduced models faced in capturing all the combinatorial details of the simple gene assembly process. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs. We also introduce DOI graph-based rewriting rules that capture all the operations of the simple gene assembly model and prove that they are equivalent to the string-based formalization of the model. Reaction systems (RS) is another nature-inspired modeling framework that is studied in this thesis. Reaction systems’ rationale is based upon two main regulation mechanisms, facilitation and inhibition, which control the interactions between biochemical reactions. Reaction systems is a complementary modeling framework to traditional quantitative frameworks, focusing on explicit cause-effect relationships between reactions. The explicit formulation of facilitation and inhibition mechanisms behind reactions, as well as the focus on interactions between reactions (rather than dynamics of concentrations) makes their applicability potentially wide and useful beyond biological case studies. In this thesis, we construct a reaction system model corresponding to the heat shock response mechanism based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We also introduce for RS various concepts inspired by biology, e.g., mass conservation, steady state, periodicity, etc., to do model checking of the reaction systems based models. We prove that the complexity of the decision problems related to these properties varies from P to NP- and coNP-complete to PSPACE-complete. We further focus on the mass conservation relation in an RS and introduce the conservation dependency graph to capture the relation between the species and also propose an algorithm to list the conserved sets of a given reaction system.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.