Search results for: Nikolaj Troels Graf Von Malotky

Authors: Nikolaj Troels Graf Von Malotky, Robin Nicolay, Alke Martens

Abstract:

There exist a plethora of architectures for ITSs (Intelligent Tutoring Systems). A thorough analysis and comparison of the architectures revealed, that in most cases the architecture extensions are evolutionary grown, reflecting state of the art trends of each decade. However, from the perspective of software engineering, the main aspect of an ITS has not been reflected in any of these architectures, yet. From the perspective of cognitive research, the construction of the teaching process is what makes an ITS 'intelligent' regarding the spectrum of interaction with the students. Thus, in our approach, we focus on a behavior based architecture, which is based on the main teaching processes. To create a new general architecture for ITS, we have to define the prerequisites. This paper analyzes the current state of the existing architectures and derives rules for the behavior of ITS. It is presenting a teaching process for ITSs to be used together with the architecture.

Keywords: intelligent tutoring, ITS, tutoring process, system architecture, interaction process

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Authors: Troels Bo Jørgensen, Preben Hagh Strunge Holm, Henrik Gordon Petersen, Norbert Kruger

Abstract:

This paper presents a simulation framework for using machine learning techniques to determine robust robotic motions for handling deformable objects. The main focus is on applications in the meat sector, which mainly handle three-dimensional objects. In order to optimize the robotic handling, the robot motions have been parameterized in terms of grasp points, robot trajectory and robot speed. The motions are evaluated based on a dynamic simulation environment for robotic control of deformable objects. The evaluation indicates certain parameter setups, which produce robust motions in the simulated environment, and based on a visual analysis indicate satisfactory solutions for a real world system.

Keywords: deformable objects, robotic manipulation, simulation, real world system

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Authors: Jacob Livingston Slosser, Naja Holten Moller, Thomas Troels Hildebrandt, Henrik Palmer Olsen

Abstract:

In this paper, we propose a model for human-AI interaction in public administration that involves legal decision-making. Inspired by Alan Turing’s test for machine intelligence, we propose a way of institutionalizing a continuous working relationship between man and machine that aims at ensuring both good legal quality and higher efficiency in decision-making processes in public administration. We also suggest that our model enhances the legitimacy of using AI in public legal decision-making. We suggest that case loads in public administration could be divided between a manual and an automated decision track. The automated decision track will be an algorithmic recommender system trained on former cases. To avoid unwanted feedback loops and biases, part of the case load will be dealt with by both a human case worker and the automated recommender system. In those cases an experienced human case worker will have the role of an evaluator, choosing between the two decisions. This model will ensure that the algorithmic recommender system is not compromising the quality of the legal decision making in the institution. It also enhances the legitimacy of using algorithmic decision support because it provides justification for its use by being seen as superior to human decisions when the algorithmic recommendations are preferred by experienced case workers. The paper outlines in some detail the process through which such a model could be implemented. It also addresses the important issue that legal decision making is subject to legislative and judicial changes and that legal interpretation is context sensitive. Both of these issues requires continuous supervision and adjustments to algorithmic recommender systems when used for legal decision making purposes.

Keywords: administrative law, algorithmic decision-making, decision support, public law

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Authors: Da Jiang, Helene H. Fung, Jennifer C. Lay, Maureen C. Ashe, Peter Graf, Christiane A. Hoppmann

Abstract:

Being alone is often equated with loneliness. Yet, recent findings suggest that the objective state of being alone (i.e., solitude) can have both positive and negative connotations. The present research aimed to examine (1) affective experience in daily solitude; and (2) the association between everyday affect in solitude and well-being. We examined the distinct roles of culture and immigration in moderating these associations. Using up to 35 daily life assessments of momentary affect, solitude, and emotional well-being in two samples (Vancouver, Canada, and China), the study compared older adults who aged in place (local Caucasians in Vancouver Canada and local Hong Kong Chinese in Hong Kong, China) and older adults of different cultural heritages who immigrated to Canada (immigrated Caucasians and immigrated East Asians). We found that older adults of East Asian heritage experienced more positive and less negative affect when alone than did Caucasians. Reporting positive affect in solitude was more positively associated with well-being in older adults who had immigrated to Canada as compared to those who had aged in place. These findings speak to the unique effects of culture and immigration on the affective correlates of solitude and their associations with well-being in old age.

Keywords: solitude, emotion, age, immigration, culture

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Authors: Nayra Martin-Key, Erich Graf, Wendy Adams, Graeme Fairchild

Abstract:

Children and adolescents with Conduct Disorder (CD) have been shown to demonstrate impairments in emotion recognition, but it is currently unclear whether this deficit is related to specific emotions or whether it represents a global deficit in emotion recognition. An emotion recognition task with concurrent eye-tracking was employed to further explore this relationship in a sample of male and female adolescents with CD. Participants made emotion categorization judgements for presented dynamic and morphed static facial expressions. The results demonstrated that males with CD, and to a lesser extent, females with CD, displayed impaired facial expression recognition in general, whereas callous-unemotional (CU) traits were linked to specific problems in sadness recognition in females with CD. A region-of-interest analysis of the eye-tracking data indicated that males with CD exhibited reduced fixation times for the eye-region of the face compared to typically-developing (TD) females, but not TD males. Females with CD did not show reduced fixation to the eye-region of the face relative to TD females. In addition, CU traits did not influence CD subjects’ attention to the eye-region of the face. These findings suggest that the emotion recognition deficits found in CD males, the worst performing group in the behavioural tasks, are partly driven by reduced attention to the eyes.

Keywords: attention, callous-unemotional traits, conduct disorder, emotion recognition, eye-region, eye-tracking, sex differences

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Authors: Plessen Mogens Graf

Abstract:

Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys.

Keywords: time series segmentation, model-free, trading-inspired, multivariate data

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Authors: Roman Koky, Sylvie Graf

Abstract:

The relationship between the Roma and members of the majority is tense across Europe due to the fact that the Roma people are the most stigmatized minorities. Studies show that Roma is discriminated against on all levels of society. Improving intergroup relations between the Roma and members of the majority (i.e., non-Roma) is thus one of the most pressing issues of social psychological research. Intergroup contact theory is one of the most effective strategies for improving intergroup relations. However, current research has some limitations, such as the fact that most researchers focus primarily on the perspective of the majority, while the perspective of minorities (e.g., the Roma) is largely missing. Due to the persisting segregation of Roma, and thus the lack of opportunities for direct intergroup contact between the Roma and the majority, using direct intergroup contact as an intervention to reduce prejudice is difficult. In this research, we, therefore, focused on the effect of indirect forms of intergroup contact, particularly extended contact (i.e., experiences with outgroup members shared by fellow ingroup members such as friends or family). Extended contact functions as a descriptive social norm that informs about the actual amount of contact in one’s environment. In a group of Czech Roma (N = 226), the descriptive social norm was associated with ingroup injunctive social norm (e.g., the perceived support of intergroup contact with non-Roma by fellow ingroup members) and lower amount of prejudice toward the non-Roma. We discuss the findings with respect to possibilities to improve the relations between Roma and members of the majority across Europe.

Keywords: intergroup contact, prejudice, majority, minority, social norms

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Authors: Anett Nagy, Anna Maria Beke, Rozsa Graf, Magda Kalmar

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Premature birth involves developmental risks – the earlier the baby is born and the lower its birth weight, the higher the risks. The developmental outcomes for immature, low birth weight infants are hard to predict. Our aim is to identify the factors influencing infant and preschool-age development in very low birth weight (VLBW) and extremely low birth weight (ELBW) preterms. Sixty-one subjects participated in our longitudinal study, which consisted of thirty VLBW and thirty-one ELBW children. The psychomotor development of the infants was assessed using the Brunet-Lezine Developmental Scale at the corrected ages of one and two years; then at three years of age, they were tested with the WPPSI-IV IQ test. Birth weight, gestational age, perinatal complications, gender, and maternal education, were added to the data analysis as independent variables. According to our assessments, our subjects as a group scored in the average range in each subscale of the Brunet-Lezine Developmental Scale. The scores were the lowest in language at both measurement points. The children’s performances improved between one and two years of age, particularly in the domain of coordination. At three years of age the mean IQ test results, although still in the average range, were near the low end of it in each index. The ELBW preterms performed significantly poorer in Perceptual Reasoning Index. The developmental level at two years better predicted the IQ than that at one year. None of the measures distinguished the genders.

Keywords: preterm, extremely low birth-weight, perinatal complication, psychomotor development, intelligence, follow-up

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: data mining, digital libraries, digital preservation, file format

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

Abstract:

This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

Abstract:

This paper presents an approach for easy creation and classification of institutional risk profiles supporting endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support set up of the most important risk factors. Subsequently, risk profiles employ risk factors classifier and associated configurations to support digital preservation experts with a semi-automatic estimation of endangerment group for file format risk profiles. Our goal is to make use of an expert knowledge base, accuired through a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation of risk factors for a requried dimension for analysis. Using the naive Bayes method, the decision support system recommends to an expert the matching risk profile group for the previously selected institutional risk profile. The proposed methods improve the visibility of risk factor values and the quality of a digital preservation process. The presented approach is designed to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and values of file format risk profiles. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert and to define its profile group. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: linked open data, information integration, digital libraries, data mining

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