61 research outputs found

    Turku Centre for Computer Science – Annual Report 2013

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    Due to a major reform of organization and responsibilities of TUCS, its role, activities, and even structures have been under reconsideration in 2013. The traditional pillar of collaboration at TUCS, doctoral training, was reorganized due to changes at both universities according to the renewed national system for doctoral education. Computer Science and Engineering and Information Systems Science are now accompanied by Mathematics and Statistics in newly established doctoral programs at both University of Turku and &Aring;bo Akademi University. Moreover, both universities granted sufficient resources to their respective programmes for doctoral training in these fields, so that joint activities at TUCS can continue. The outcome of this reorganization has the potential of proving out to be a success in terms of scientific profile as well as the quality and quantity of scientific and educational results.&nbsp; International activities that have been characteristic to TUCS since its inception continue strong. TUCS&rsquo; participation in European collaboration through EIT ICT Labs Master&rsquo;s and Doctoral School is now more active than ever. The new double degree programs at MSc and PhD level between University of Turku and Fudan University in Shaghai, P.R.China were succesfully set up and are&nbsp; now running for their first year. The joint students will add to the already international athmosphere of the ICT House.&nbsp; The four new thematic reseach programmes set up acccording to the decision by the TUCS Board have now established themselves, and a number of events and other activities saw the light in 2013. The TUCS Distinguished Lecture Series managed to gather a large audience with its several prominent speakers. The development of these and other research centre activities continue, and&nbsp; new practices and structures will be initiated to support the tradition of close academic collaboration.&nbsp; The TUCS&rsquo; slogan Where Academic Tradition Meets the Exciting Future has proven true throughout these changes. Despite of the dark clouds on the national and European economic sky, science and higher education in the field have managed to retain all the key ingredients for success. Indeed, the future of ICT and Mathematics in Turku seems exciting.</p

    The evaluation of elements regarding family training included in the works of some Turkish scholars between IX.-XII. centuries

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    It is seen that the main objective of the reforms realized in Kazakhstan and many Turkish societies especially about education is to ensure that the young generation adapts to the changing society in line with global demands. For the goals and objectives in this direction to be realized as a whole, it is clear that it would be appropriate to start the transformation with parenting education. When the history of pedagogy is examined, it is seen that the influence of Turkish culture is of great importance in the parenting education systems of Turkish societies. In this respect, today, the usage of extant parenting education elements in the education of future generation by examining the historical process in the formation of a spiritually developing consciousness and in reestablishing the tradition of humanitarian values should be accepted as a reality of the present. Accordingly,this study, which aims to evaluate elements regarding family training in the works of some Turkish scholars between IX.-XII. centuries was organized in the form of qualitative research. Document analysis was used as data collection method. In this study, the works of Al-Farabi, Yusuf Khass Hajib , Mahmud al-Kashgari and Hodja Ahmet Yesevi, who lived in the mentioned period, were considered as the main sources. In specified sources it was found that the personal aspect of  training, the generalization of training, the optimism of training, the connection of training with nature, the connection of training with  religion, the integrity and complexity of  training, the age and peculiarities of the child in training were emphasized.It is seen that the main objective of the reforms realized in Kazakhstan and many Turkish societies especially about education is to ensure that the young generation adapts to the changing society in line with global demands. For the goals and objectives in this direction to be realized as a whole, it is clear that it would be appropriate to start the transformation with parenting education. When the history of pedagogy is examined, it is seen that the influence of Turkish culture is of great importance in the parenting education systems of Turkish societies. In this respect, today, the usage of extant parenting education elements in the education of future generation by examining the historical process in the formation of a spiritually developing consciousness and in reestablishing the tradition of humanitarian values should be accepted as a reality of the present. Accordingly,this study, which aims to evaluate elements regarding family training in the works of some Turkish scholars between IX.-XII. centuries was organized in the form of qualitative research. Document analysis was used as data collection method. In this study, the works of Al-Farabi, Yusuf Khass Hajib , Mahmud al-Kashgari and Hodja Ahmet Yesevi, who lived in the mentioned period, were considered as the main sources. In specified sources it was found that the personal aspect of  training, the generalization of training, the optimism of training, the connection of training with nature, the connection of training with  religion, the integrity and complexity of  training, the age and peculiarities of the child in training were emphasized

    Margins and combined classifiers

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    An analysis of the efficiency of ontology and symbolic learning algorithms in indigenous knowledge representation

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    It is without a doubt that machine learning has been the area of focus in early days of artificial intelligence, but the early neural networks approach suffered some shortcomings and this led to a temporary decline in research capacity. New symbolic learning techniques have emerged since then which have yielded promising results and have led to a revival in research in machine learning. This has seen many researchers focusing on these techniques and experimenting with them by comparing their performances for different applications. With that in mind, the research thus decided to make an analysis of the symbolic approach against other approaches such as the neural network (connectionist) to evaluate the power of the former approach. This was done by first generating an ontology that acted as a representation of some collected indigenous knowledge. It is from this ontology that a dataset was generated. The dataset was made ambiguous to see the learning power of classifiers in such data. Two experiments were done, one using WEKA and the other using Orange as tools. The reason why the two experiments were used is because there was not a single tool which contained all the required learning algorithms. The research wanted to make use of ID3 and CN2 symbolic algorithm. However, WEKA had ID3 and not CN2 while Orange had CN2 and not ID3. The most important attributes from the ontology regarding the indigenous knowledge were the name of the plant, the province it is found and the disease the plant treats. Therefore the dataset had two features which were disease and province and one label which was the name of the plant. The learning algorithm was to use the two features to generate rules used to predict the label. However, there was ambiguity on the dataset. The challenge was that two different labels would contain the same features, thus leading to wrongful classification. This was the core of the research. Even though the learning model concluded this situation as wrongful classification, in real time, a system using the same learning model would provide desired and correct results. The only flow which was there is that the learning model simply used one label to predict under and ignore the other label with similar features. This was identified as a flow for both symbolic and non-symbolic algorithms. There is no way of giving suggestions in the case a user wants a different plant but with similar features. Therefore for classification using an ambiguous dataset, both these approaches proved to have the fore mentions flow. The research then decided to use recall to analyze the power of these approaches. It was discovered that ID3 has better recall than Multilayer perceptron and Naïve Bayes algorithms when using a training set. ID3 managed to recall clearly and effectively three of its classes by a probability of 1 while Bayes Net had only one class with recall probability of 1. To further investigate the issue of recall, cross validation was used to contrast the competence of recall of the three algorithms to strengthen the assertion that indeed ID3 has a better recall as compared to the other two algorithms. Three stages of cross-validation were done, one stage using 10 fold, the other 20 fold, and the last using 50 fold. For all the different stages of crossvalidation, Bayes Net proved to perform better in terms of recall than the other two algorithms. In cross-validation, MLP could recall approximately above 88% of the instances available in contrast to when using training set where the algorithm recall only two out of 18 instances. In overall the symbolic approach proved to be a commendable approach for use over the nonsymbolic approach. The study of machine learning involves the building of learning algorithms, improving upon learning algorithms or making comparisons of machine learning algorithms. The research raised awareness on some improvements that need to be done on not only symbolic algorithms but non-symbolic ones as well. Some improvements include improving on or coming up with algorithms that suggest alternative predictions in cases of ambiguity instead of doing wrongful classification and not reflect on other possibilities

    Autopoietic-extended architecture: can buildings think?

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    To incorporate bioremedial functions into the performance of buildings and to balance generative architecture's dominant focus on computational programming and digital fabrication, this thesis first hybridizes theories of autopoiesis into extended cognition in order to research biological domains that include synthetic biology and biocomputation. Under the rubric of living technology I survey multidisciplinary fields to gather perspective for student design of bioremedial and/or metabolic components in generative architecture where generative not only denotes the use of computation but also includes biochemical, biomechanical, and metabolic functions. I trace computation and digital simulations back to Alan Turing's early 1950s Morphogenetic drawings, reaction-diffusion algorithms, and pioneering artificial intelligence (AI) in order to establish generative architecture's point of origin. I ask provocatively: Can buildings think? as a question echoing Turing's own "Can machines think?" Thereafter, I anticipate not only future bioperformative materials but also theories capable of underpinning strains of metabolic intelligences made possible via AI, synthetic biology, and living technology. I do not imply that metabolic architectural intelligence will be like human cognition. I suggest, rather, that new research and pedagogies involving the intelligence of bacteria, plants, synthetic biology, and algorithms define approaches that generative architecture should take in order to source new forms of autonomous life that will be deployable as corrective environmental interfaces. I call the research protocol autopoietic-extended design, theorizing it as an operating system (OS), a research methodology, and an app schematic for design studios and distance learning that makes use of in-field, e-, and m-learning technologies. A quest of this complexity requires scaffolding for coordinating theory-driven teaching with practice-oriented learning. Accordingly, I fuse Maturana and Varela's biological autopoiesis and its definitions of minimal biological life with Andy Clark's hypothesis of extended cognition and its cognition-to-environment linkages. I articulate a generative design strategy and student research method explained via architectural history interpreted from Louis Sullivan's 1924 pedagogical drawing system, Le Corbusier's Modernist pronouncements, and Greg Lynn's Animate Form. Thus, autopoietic-extended design organizes thinking about the generation of ideas for design prior to computational production and fabrication, necessitating a fresh relationship between nature/science/technology and design cognition. To systematize such a program requires the avoidance of simple binaries (mind/body, mind/nature) as well as the stationing of tool making, technology, and architecture within the ream of nature. Hence, I argue, in relation to extended phenotypes, plant-neurobiology, and recent genetic research: Consequently, autopoietic-extended design advances design protocols grounded in morphology, anatomy, cognition, biology, and technology in order to appropriate metabolic and intelligent properties for sensory/response duty in buildings. At m-learning levels smartphones, social media, and design apps source data from nature for students to mediate on-site research by extending 3D pedagogical reach into new university design programs. I intend the creation of a dialectical investigation of animal/human architecture and computational history augmented by theory relevant to current algorithmic design and fablab production. The autopoietic-extended design dialectic sets out ways to articulate opposition/differences outside the Cartesian either/or philosophy in order to prototype metabolic architecture, while dialectically maintaining: Buildings can think

    Committing to the Waves: Emerson\u27s Moving Assignments

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    Committing to the Waves: Emerson\u27s Moving Assignments reads Ralph Waldo Emerson as a writer of assignments for living and working whose senses can be taken up across a wide array of creative and exploratory fields. Shifting between an interdisciplinary array of contexts ranging from philosophy and poetics to dance, performance, and somatic movement experiments, I join the practical sense of creative inquiry embodied in these fields to the abstract images of Emerson\u27s assignments. I argue that Emerson\u27s descriptions of intelligence and power, and so his approaches to navigating skepticism and loss, as well as the non-possessive sense of what self actually means to this thinker of self-reliance can be illuminated by reading from the non-dualist perspective that embodied inquiry offers. The dissertation also enacts the self-reliance that Emerson calls for by taking up my response to Emerson through my sense of his assignments. The first half of this study uses this embodied work as a resource for reading Emerson, situating his sense in relation to extra-literary and extra-philosophical research. The second half of the dissertation makes a pivot, taking Emerson as a resource for performance assignments, first in the form of a chapter written with poetic constraints, which approaches the question of how philosophical commitments might animate theater and actual performance, and finally by following Emerson\u27s instruction to the scholar to dive into her privatest presentiments to find where that privacy meets a public intelligence and intelligibility. The dissertation concludes with the documentation of Another Tree Dance, an original performance generated from that Emersonian private dive

    Neurons and Symbols: A Manifesto

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    We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

    Is the most likely model likely to be the correct model?

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-93).In this work, I test the hypothesis that the 2-dimensional dependencies of a deterministic model can be correctly recovered via hypothesis-enumeration and Bayesian selection for a linear sequence, and what the degree of 'ignorance' or 'uncertainty' is that Bayesian selection can tolerate concerning the properties of the model and data. The experiment tests the data created by a number of rules of size 3 and compares the implied dependency map to the (correct) dependencies of the various generating rules, then extends it to a composition of 2 rules of total size 5. I found that 'causal' belief networks do not map directly to the dependencies of actual causal structures. For deterministic rules satisfying the condition of multiple involvement (two tails), the correct model is not likely to be retrieved without augmenting the model selection with a prior high enough to suggest that the desired dependency model is already known - simply restricting the class of models to trees, and placing other restrictions (such as ordering) is not sufficient. Second, the identified-model to correct-model map is not 1 to 1 - in the rule cases where the correct model is identified, the identified model could just as easily have been produced by a different rule. Third, I discovered that uncertainty concerning identification of observations directly resulted in the loss of existing information and made model selection the product of pure chance (such as the last observation). How to read and identify observations had to be agreed upon a-priori by both the rule and the learner to have any consistency in model identification.(cont.) Finally, I discovered that it is not the rule-observations that discriminate between models, but rather the noise, or uncaptured observations that govern the identified model. In analysis, I found that in enumeration of hypotheses (as dependency graphs) the differentiating space is very small. With representations of conditional independence, the equivalent factorizations of the graphs make the differentiating space even smaller. As Bayesian model identification relies on convergence to the differentiating space, if those spaces are diminishing in size (if the model size is allowed to grow) relative to the observation sequence, then maximizing the likelihood of a particular hypothesis may fail to converge on the correct one. Overall I found that if a learning mechanism either does not know how to read observations or know the dependencies he is looking for a-priori, then it is not likely to identify them probabilistically. Finally, I also confirmed existing results - that model selection always prefers increasingly connected models over independent models was confirmed, as was the knowledge that several conditional-independence graphs have equivalent factorizations. Finally Shannon's Asymptotic Equipartition Property was confirmed to apply both for novel observations and for an increasing model/parameter space size. These results are applicable to a number of domains: natural language processing and language induction by statistical means, bioinformatics and statistical identification and merging of ontologies, and induction of real-world causal dependencies.by Beracah Yankama.S.M
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