Search results for "020201 artificial intelligence & image processing"
showing 10 items of 1827 documents
Social Collaborative Viewpoint Regression with Explainable Recommendations
2017
A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…
Turing's error-revised
2016
Many important lines of argumentation have been presented during the last decades claiming that machines cannot think like people. Yet, it has been possible to construct devices and information systems, which replace people in tasks which have previously been occupied by people as the tasks require intelligence. The long and versatile discourse over, what machine intelligence is, suggests that there is something unclear in the foundations of the discourse itself. Therefore, we critically studied the foundations of used theory languages. By looking critically some of the main arguments of machine thinking, one can find unifying factors. Most of them are based on the fact that computers canno…
Biased graph walks for RDF graph embeddings
2017
Knowledge Graphs have been recognized as a valuable source for background information in many data mining, information retrieval, natural language processing, and knowledge extraction tasks. However, obtaining a suitable feature vector representation from RDF graphs is a challenging task. In this paper, we extend the RDF2Vec approach, which leverages language modeling techniques for unsupervised feature extraction from sequences of entities. We generate sequences by exploiting local information from graph substructures, harvested by graph walks, and learn latent numerical representations of entities in RDF graphs. We extend the way we compute feature vector representations by comparing twel…
An intelligent learning support system
2017
Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere e…
Supporting Institutional Awareness and Academic Advising using Clustered Study Profiles
2017
The purpose of academic advising is to help students with developing educational plans that support their academic career and personal goals, and to provide information and guidance on studies. Planning and management of the students’ study path is the main joint activity in advising. Based on a study log of passed courses, we propose to use robust, prototype-based clustering to identify a set of actual study path profiles. Such profiles identify groups of students with similar progress of studies, whose analysis and interpretation can be used for better institutional awareness and to support evidence-based academic advising. A model of automated academic advising system utilizing the possi…
Modelling Recurrent Events for Improving Online Change Detection
2016
The task of online change point detection in sensor data streams is often complicated due to presence of noise that can be mistaken for real changes and therefore affecting performance of change detectors. Most of the existing change detection methods assume that changes are independent from each other and occur at random in time. In this paper we study how performance of detectors can be improved in case of recurrent changes. We analytically demonstrate under which conditions and for how long recurrence information is useful for improving the detection accuracy. We propose a simple computationally efficient message passing procedure for calculating a predictive probability distribution of …
Recommending Serendipitous Items using Transfer Learning
2018
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommend…
Towards proactive context-aware self-healing for 5G networks
2017
In this paper, we suggest a new research direction and a future vision for Self-Healing (SH) in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH solutions may not be sufficient for the future needs of cellular network management because of their reactive nature, i.e., they start recovering after detecting already occurred faults instead of preparing for possible future faults in a pre-emptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. To address this problem, existing SONs need to be upgraded from reactive to proactive response. One of the dimensions in SH research is to employ more…
Analysis of the perceived self-‐efficacy in the use of ICT of pre-‐ service primary and preschool teachers
2017
La formación del profesorado en el uso de las TIC es un aspecto importante para conseguir una verdadera integración de las TIC en la educación. Según el modelo TPACK, esta formación no debe limitarse a contemplar de manera separada los aspectos relacionados con las TIC, el contenido curricular y la metodología. En el presente estudio se explora la percepción de autoeficacia en cuanto al uso de las TIC en el aula de una muestra de 107 estudiantes de los grados de Maestro/a en Educación Infantil y en Educación Primaria. Para ello se emplea una traducción del cuestionario para la autoevaluación de las competencias TIC elaborado por Tondeur et al. (2016). Los resultados del estudio muestran que…
On Temporal Aspects in Cross-Cultural e-Collaboration Between Finland and Japan Research Teams
2018
Time is an essential dimension in cross-cultural e-collaboration among research project teams. Understanding temporal aspects and project dynamics in cross-cultural research e-collaboration and related processes can improve team members' skills in cross-cultural communication and increase their cultural competence. The present case cultures are Finnish and Japanese, and the case universities are the University of Jyväskylä (Finland) and Keio University (Japan). Three issues are addressed in this article. First, cultural dimensions and time models in the cross-cultural e-collaboration context are discussed. Second, temporal aspects related to e-collaboration activities are introduced. Third,…