Christos Christodoulopoulos

A Group Formation Tool in an E-Learning Context

In this paper we present a Web-based group formation tool that supports the instructor to automatically create both homogeneous and heterogeneous groups based on up to three criteria and the learner to negotiate the grouping. Moreover, the instructor is allowed to manually group learners based on specific criteria. A discriminative feature of this tool is the use of the fuzzy c-means algorithm for homogeneous grouping, which provides for each learner the probability of belonging to different groups. This information is also provided to the instructor to support him/her in manually exchanging learners or intervening in the initial grouping. Moreover, the learners are informed for the groups formed and they are allowed to negotiate their group assignment. Preliminary evaluation results provide indications for the efficiency of the proposed approach informing homogeneous and heterogeneous groups in a real context.In e-learning initiatives, sequencing problem concerns arranging a particular set of learning units in a suitable succession for a particular learner. Sequencing is usually performed by instructors, who create general and ordered series rather than learner personalized sequences. This paper proposes an innovative intelligent technique for learning object automated sequencing using particle swarms. E-learning standards are promoted in order to ensure interoperability. Competencies are used to define relations between learning objects within a sequence, so that the sequencing problem turns into a permutation problem and AI techniques can be used to solve it. Particle Swarm Optimization (PSO) is one of such techniques and it has proven with good performance solving a wide variety of problems. An implementation of the PSO, for learning object sequencing, is presented and its performance in a real scenario is discussed.

A realistic approach to source localization using a wireless robotic network

This paper investigates the problem of target localizing by a communicating robotic swarm in an unknown environment. Robots have to collaboratively search for the target while avoiding obstacles in their way. Emphasis is given on how physical constraints such as obstacles and communication links affect the swarms operation. Finally, simulation results of the proposed system in a small scale area are presented and evaluated and possible uses of the system are discussed.

FEVER: A LARGE-SCALE DATASET FOR FACT EXTRACTION AND VERIFICATION

In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss

Two Decades of Unsupervised POS Induction: How Far Have We Come?

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A Bayesian mixture model for part-of-speech induction using multiple features

. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.