A Cloud-Based Architecture for an Affective Recommender System of Learning Resources

Proceedings of: 1st International Workshop on Cloud Education Environments (WCLOUD 2012), Antigua, Guatemala, November 15-16, 2012.One of the most common functionalities in cloudbased learning environments is the recommendation of learning resources. Many approaches have been proposed to deploy recommender systems into an educational environment. Currently, there is an increasing interest in including affective information into the process to generate the recommendations for the learner. In this paper, we propose a cloud-based architecture for a system that recommends learning resources according to the affective state of the learner. Furthermore, we provide the details of an implementation of the architecture along with a discussion on the advantages and disadvantages of the proposal.Work partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650), and “Consejo Social - Universidad Carlos III de Madrid”.Publicad

A Generic Architecture for Emotion-based Recommender Systems in Cloud Learning Environments

Cloud technology has provided a set of tools to learners and tutors to create a virtual personal learning environment. As these tools only support basic tasks, users of learning environments are looking for specialized tools to exploit the uncountable learning elements available on the internet. Thus, one of the most common functionalities in cloud-based learning environments is the recommendation of learning elements and several approaches have been proposed to deploy recommender systems into an educational environment. Currently, there is an increasing interest in including affective information into the process to generate the recommendations for the learner; and services offering this functionality on cloud environments are scarce. Hence in this paper, we propose a generic cloud-based architecture for a system that recommends learning elements according to the affective state of the learner. Furthermore, we provide the description of some use cases along with the details of the implementation of one of them. We also provide a discussion on the advantages and disadvantages of the proposal.Work partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project 2009/TIC-1650), and “Consejo Social - Universidad Carlos III de Madrid”.Publicad

Cloud-based Recommendation Systems: Applications and Solutions

Recommender systems have become extremely common in recent years, and are applied in a variety of applications. They help businesses increase their sales and customer satisfaction. More and more computing applications including recommender systems, are being deployed as cloud computing services. This papers presents some of the most common recommendation applications and solutions which follow SaaS, PaaS or other cloud computing service models. They are provided both from academia and business domain and use recent data mining, machine learning and artificial intelligence techniques. The tendency of these kind of applications is towards SaaS service model which seems the most appropriate especially for businesses

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

A Preliminary Study of Integrating Flipped Classroom strategy for Classical Chinese Learning

[[abstract]]This is a multiphase study which aims to investigate how to provide learners with an method to acquire classical Chinese through integrating mobile technology with the flipped classroom approach. Currently, in the first phase of study, the researcher adopts informant design through questionnaire survey to understand students' and instructors' perceptions of using mobile learning devices for classical Chinese learning, and afterwards the researcher constructs the system based on the pilot results. The pilot questionnaire results, structure of the developed mobile learning system and the practical application of the developed system for classical Chinese teaching and learning are described in the paper.[[notice]]補正完

Contributions to affective learning through the use of data analysis, visualizations and recommender sytems

Student modeling is an important issue in telematics learning environments, e.g. learning resources can be adapted based on the students' information. An emergent area of student modeling is the inclusion of affective information. The improvement of emotion detectors based on the students' events in different telematics learning environments is an open issue. Moreover, there is a need of proposing and evaluating new visualizations involving affective information, and proposing generic solutions for the recommendation of learning materials based on the affective information. This PhD proposes two different models for the detection of emotions in two different telematics learning environments. The first model uses a Hidden Markov Model to infer the emotions in a programming learning environment in which students should use different tools to learn how to program. The second model uses a set of rules to infer the emotions in a Massive Open Online Course platform in which students should solve exercises and watch videos. An evaluation of the first model for the detection of emotions was performed using a controlled experiment, comparing the results of the model with the students' answers regarding their emotions in different instants of times. The results showed that the model was not able to detect accurately the students' answers regarding their emotions. Other models of the literature applied in other learning environments were tested and they were not able to predict accurately the students' answers regarding their emotions. Therefore, the detection of emotions based on students' events in these types of environments might not be feasible, or the reference data of students' answers to a survey with different questions about emotions should be redefined. Moreover, this PhD proposes a set of affective-related visualizations for learning environments. Some of these visualizations only involve affective information, while others combine this affective information with other related to the students' activities with the learning platforms. Some of these visualizations were evaluated with real students and results showed a good usability, usefulness and effectiveness. Finally, this work proposes a generic framework for enabling the recommendation of learning resources based on affective information. The solution includes an Application Programming Interface for the definition of the different possible events. A specific implementation of this recommender has been developed as a plugin of the ROLE SDK platform.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Carlos Enrique Palau Salvador.- Secretario: Eva María Méndez Rodríguez, Eva Maria.- Vocal: Ruth Cobos Pére

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

[EN] This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.This work was supported in part by the EU's H 2020 research and innovation programme under the Marie SklodowskaCurie Grant Agreement 641794 (project DREAM-GO) and Grant Agreement 703689 (project ADAPT), in part by the FEDER Funds through COMPETE program, and in part by the National Funds through FCT under the Project UID/EEA/00760/2013. (Corresponding author: Tiago Pinto.)info:eu-repo/semantics/publishedVersio