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The design of recommendation method is the core of personalized recommendation, and the implementation of recommendation depends on the matching relation between user preference and resource object. This paper proposes a hybrid personalized recommendation method based on context-based collaborative filtering and knowledge recommendation, which is based on personalized recommendation knowledge model,...
Fog computing is mainly proposed for IoT applications that are geospatially distributed, large-scale, and latency sensitive. This poses new research challenges in real-time and scalable provisioning of IoT services distributed across Fog-Cloud computing platforms. Data-centric IoT services, as a dominant type of IoT services in large-scale deployments, require design solutions to speed up data processing...
The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed...
In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
Word embedding in the NLP area has attracted increasing attention in recent years. The continuous bag-of-words model (CBOW) and the continuous Skip-gram model (Skip-gram) have been developed to learn distributed representations of words from a large amount of unlabeled text data. In this paper, we explore the idea of integrating extra knowledge to the CBOW and Skip-gram models and applying the new...
With the rapid development of science, the academic community requires higher and higher quality of the published articles. This great responsibility is placed on editorial boards of journals, on program committees of conferences and their members. In addition, with a large number of scientific conferences held each year, searching for experts that would be invited to join the program committees is...
It is a great challenge to model and mine the e-commercial data, which is made up of multiple types of objects, such as products, users, comments and tags. To model the complicated interactive relationships in the the e-commercial data, we propose to transform the complex e-commercial data into a text-rich heterogeneous e-commercial network. Then three neural network based embedding algorithms named...
In an increasingly automated world, trust between humans and autonomous systems is critical for successful integration of these systems into our daily lives. In particular, for autonomous systems to work cooperatively with humans, they must be able to sense and respond to the trust of the human. This inherently requires a control-oriented model of dynamic human trust behavior. In this paper, we describe...
The prediction of the academic success of a student, the change of success according to causes and processes, and the examination of the consequences of this change are a general research topic that deals with many disciplines from different disciplines. Using the approach in this study, patterns of the subset of the data set were obtained by using methods of finding frequently repeated sub-graphs,...
Label noise-tolerant machine learning techniques address datasets which are affected by mislabelling of the instances. Since labelling quality is a severe issue in particular for large or streaming data sets, this setting becomes more and more relevant in the context of life-long learning, big data and crowd sourcing. In this contribution, we extend a powerful online learning method, soft robust learning...
We propose to study the impact of the representation of the data in defect prediction models. For this study, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose a model inspired in recent advances in Representation Learning which are able to automatically learn representations...
In this paper, we propose learning analytic tasks to understand the learning process in a smart classroom. Learning analytics can extract knowledge from a course to better understand students and their learning processes. The learning analytic tasks must evaluate different aspects in the course: the teaching and learning process, the student performance, and the pedagogical practices, among other...
In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising...
Social media are suffering the consequences of the overwhelming growth of the web and the polarization of information sources. Mechanisms for facilitating the categorization and the access to information are demanded by information brokers as well as by final users. This paper focuses on the analysis of measures of the quality of information based on social actions. Our aim is to determine how valuable...
Data management applications deployed on IaaS cloud environments must simultaneously strive to minimize cost and provide good performance. Balancing these two goals requires complex decision-making across a number of axes: resource provisioning, query placement, and query scheduling. While previous works have addressed each axis in isolation for specific types of performance goals, this demonstration...
We present a model-driven approach to the creation of online community web apps that leverages the capabilities of the DIME model-driven development environment for web applications. Thereby we specifically focus the community-specific needs and structural elements and functionality of the online community around the SEcube™ project. Starting with only vague requirements, we applied a double scaffolding...
We present a novel approach for detecting malicious user activity in databases. Specifically, we propose a new machine learning algorithm for detecting attacks such as a stolen user account or illegal use by a user. Our algorithm relies on two main components that examine the consistency of a user's activity and compare it with activity patterns learned from past access. The first component tests...
We consider a non-stationary data stream in which the data statistics may change abruptly from one sample to another, i.e. each sample might be generated from a different (unknown) source in a mixture of K sources. The problem of identifying the models and parameters of K sources, as well as the source switching model is investigated. We proposed an algorithm based on Bayesian Information Criterion...
In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat preceding utterances as a sequence of inputs, without considering dialog interactions. We design recurrent neural network (RNN) based contextual language models that specially track the interactions between speakers in a dialog...
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