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In this paper, we present an Intelligent Tutoring System (ITS), for use in teaching the logic of computer programming and the programming language 'C'. The aim of the ITS is to adapt the delivered learning material and the lesson sequence to the knowledge level and learning needs of each individual student. The adaptation of the presented ITS is based on fuzzy logic and a machine learning technique. Particularly, the system uses the distance weighted k-nearest neighbor algorithm to detect the learner's knowledge level and abilities concerning computer programming during her/ his first interaction with the system. Next and during subsequent interactions of the learner with the system, fuzzy logic is used to identify the learner's current knowledge level and potential misconceptions. The system takes into consideration the knowledge dependencies that exist among the domain concepts of the learning material and, applying fuzzy rules, decides about the learning material that has to be delivered to the learner as well as the lesson sequence. The system has been fully implemented and evaluated through t-tests. The evaluation results show that the combination of machine learning (for initially identifying the student's learning abilities and needs) with fuzzy logic (for the continuous identification of the learner's current knowledge level and misconceptions) provides more personalized learning experience, promotes the active participation of students in the learning process and results in decrease in the number of dropouts. [ABSTRACT FROM AUTHOR]

This paper focuses on a special category of machine learning problems arising in cases where the set of available training instances is significantly biased towards a particular class of patterns. Our work addresses the so-called Class Imbalance Problem through the utilization of an Artificial Immune System-(AIS)based classification algorithm which encodes the inherent ability of the Adaptive Immune System to mediate the exceptionally imbalanced 'self' / 'non-self' discrimination process. From a computational point of view, this process constitutes an extremely imbalanced pattern classification task since the vast majority of molecular patterns pertain to the 'non-self' space. Our work focuses on investigating the effect of the class imbalance problem on the AIS-based classification algorithm by assessing its relative ability to deal with extremely skewed datasets when compared against two state-of-the-art machine learning paradigms such as Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs). To this end, we conducted a series of experiments on a music-related dataset where a small fraction of positive samples was to be recognized against the vast volume of negative samples. The results obtained indicate that the utilized bio-inspired classifier outperforms SVMs in detecting patterns from the minority class while its performance on the same task is competently close to the one exhibited by MLPs. Our findings suggest that the AIS-based classifier relies on its intrinsic resampling and class-balancing functionality in order to address the class imbalance problem. [ABSTRACT FROM AUTHOR]

Presents a study which developed a nonlinear tomographic reconstruction algorithms for inversion of data measured in scattering experiments. Developments in diffraction tomography (DT); Details on the Rytov series; Inversion of nonlinear data-generating models for DT.

Describes a nonlinear tomographic reconstruction algorithm developed for inversion of traveltime measurements that rely on perturbations of the straight line ray wave propagation model. Definition of the nonlinear data-generating models for traveltime tomography; Volterra expansions considered; Examination of the nonlinear tomographic inversion of traveltime data; Computer simulation of the nonlinear inversion algorithms.

Presents a time domain algorithm for computation of the maximum likelihood estimate of the location of a known scattering object from wide-band scattering data. Time-domain plane-wave spectra of the scattered wave; Estimation of object location; Conclusions.

We revisit geophysical diffraction tomography (GDT) and cast it into a stochastic framework, in which the geophysical formation to be imaged is modeled as a realization of a two-dimensional stationary random process. it is shown that the optimum reconstruction algorithm, in both the cases of offset vertical seismic profiling and well-to-well probing, attains a modified filtered backpropagation form. [ABSTRACT FROM AUTHOR]

We address the problem of reconstructing the directional derivative and/or the Laplacian of an object function f characterizing a weakly inhomogeneous scatterer directly from data collected in a set of scattering experiments. We employ the Rytov approximation to model the complex phase of the scattered wavefields and show that a minimum-norm least-squares solution can be obtained from the well known filtered backpropagation algorithm of diffraction tomography with appropriate modification of the tomographic filters employed on the filtering step of the algorithm. The procedure is illustrated by a computer simulation study. [ABSTRACT FROM AUTHOR]

The significant proliferation of AI-empowered systems and machine learning (ML) across various examined domains underscores the vital necessity for comprehensive and customised explainability frameworks to lead to usable and trustworthy systems. Especially in the medical domain, where validation of methodologies and outcomes is as important as the adoption rate of such systems, the requirements of the depth and the level of abstraction of the explainability are particularly important and necessitate a systemic approach to ensure a proper definition. Explainability and interpretability are important usability and trustworthiness properties of AI-empowered systems and, as such, constitute important factors for technology acceptance. In this paper, we propose a novel framework for explainability requirements in AI-empowered systems using the Technology Acceptance Model (TAM). This framework employs targeted ML (hierarchical clustering, k-means or other) to acquire a user model for personalised, multi-layered explainability. Our novel framework integrates a rule-based system, which guides the degree of trustworthiness to be achieved based on user perception and AI literacy level. We test this methodology in the case of AI-empowered medical systems to (1) assess and quantify the doctors' abilities and familiarisation with technology and AI, (2) generate layers of personalised explainability based on user ability and user needs in terms of trustworthiness and (3) provide the necessary environment for transparency and validation. To assess and quantify the doctors' abilities we have considered Rapid Estimate of Adult Literacy in Medicine (REALM) a tool commonly used in the medical domain to bridge the communication gap between patients and doctors. • Framework for personalised explainability in Artificial intelligence, based on Levels. • Levels refer to AI literacy level and level of abstraction. • Behavioural models were used that measure perception towards an innovation. • Framework is evaluated and implemented in an AI-empowered medical application. [ABSTRACT FROM AUTHOR]

Deals with a study that derived a translation property describing the field scattered from a known buried object placed at distinct locations. Configuration and scattering equations; Object location estimation; Discussion and future works.

No abstract received. [ABSTRACT FROM AUTHOR]

In this paper, we formulate the recommendation problem as a hybrid combination of one-class classification with collaborative filtering. Specifically, we decompose the recommendation problem into a two-level cascade scheme. In the first level, only desirable items are selected for each user from the large amount of all possible items, taking into account only a small portion of his/her available preferences. This is achieved via a one-class classification scheme trained only with positives examples, i.e. only with desirable items for which users have provided a rating value. In the second level, a collaborative filtering approach is applied to assign a rating degree to the items identified at the first level. The efficiency of our approach is analyzed theoretically in terms of best/worst case scenarios and respective lower/upper mean absolute error (MAE) bounds are computed. Moreover, our approach is experimentally tested against pure collaborative and cascade content-based approaches. The results show that our approach outperforms them in terms of MAE and, moreover, the experimental MAE is close to the theoretical lower bound corresponding to the best case scenario. The superiority of our approach is due to the existence of the one class classifier in the first level of the cascade. [ABSTRACT FROM AUTHOR]

This paper presents a user model server based on Web Services. User model servers are very important because they allow reusability of user modeling reasoning mechanisms which are typically very complex and difficult to construct from scratch. In this paper we show how the potential of interoperability, reusability and component sharing offered by the technology of Web Services have been exploited in the design of a user model server that performs decision making. The reasoning of the user modeling is based on a multi-criteria decision making theory and has been implemented as a Web Service to provide intelligent assistance to users over the Web. Reusability has been shown through the successful application of the user model server into two different applications: an e-mail and a file manager application. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the underlying data. Our work utilizes an original dataset which was derived during the conduction of an extended empirical study involving students of the Hellenic Open University. The educational profiles of the students were built by collecting their answers on a thoroughly designed questionnaire taking into account a wide range of personal characteristics and skills. The efficiency of the proposed approach was assessed in terms of cluster compactness. Specifically, we measured the average correlation deviation of the students' education profiles from the corresponding artificial memory antibodies that represent the acquired learning style stereotypes. Finally, the unsupervised learning procedure adopted in this paper was tested against a correlation-based version of the k-means algorithm indicating a significant improvement in performance for the AIS-based clustering approach. [ABSTRACT FROM AUTHOR]