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Account sharing is a significant problem for online recommender systems to generate accurate personalized recommendations. To solve this problem, one not only has to identify whether an account is shared, but also needs to recognize the different users sharing that account. However, to generate relevant, personalized recommendations, the particular user under a shared account has to be correctly identified...
This paper proposes the design of an adaptive e- learning system with gamification elements. In the context of the increasing need to keep learners motivated among so many distractions, our project aims to help a user acquire knowledge at his own pace, in a captivating environment and as flexible as possible. To achieve that the solution focuses on the course model, adaptive questions and a reward...
Advances in modeling and knowledge representation, data mining, semantic Internet, analytical methods and open data are the basis for new models of knowledge analysis. The growth of information and data exceeds the ability of organizations to analyze them. This problem is particularly expressed in terms of knowledge and learning processes. Analytical methods can be successfully applied in studying...
New and unseen network attacks pose a great threat to the signature-based detection systems. Consequently, machine learning-based approaches are designed to detect attacks, which rely on features extracted from network data. The problem is caused by different distribution of features in the training and testing datasets, which affects the performance of the learned models. Moreover, generating labeled...
When looking at an image, humans shift their attention towards interesting regions, making sequences of eye fixations. When describing an image, they also come up with simple sentences that highlight the key elements in the scene. What is the correlation between where people look and what they describe in an image? To investigate this problem, we look into eye fixations and image captions, two types...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
Training and retraining of experts oriented to the work in the Analytical Centers requires the use of modern training technologies. It is promising to create and integrate into the Analytical Center structure an Intelligent Training System that provides dynamic experts training in a mixed (offline / online) information space, taking into account their motivation, level of education, information and...
An improvement of the support of people with special needs' education will promote a social integration of such people. Developing mathematical and software methods and tools means to create a scientific base of information technologies, which task is to support an inclusive education. Leaned on the analysis of the nowadays support of inclusive education with information technologies, the model of...
In this paper the problem of improving the reliability of nonlinear dynamic objects fault diagnosing is presented. Model-based diagnostics nonparametric identification method is used. Diagnostic models are constructed on the base of Volterra kernels wavelet transforms. The effectiveness of the suggested diagnostic models based on Volterra kernels wavelet transforms is analyzed on the basis of simulation...
A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally...
Background Modelling is a crucial step in background/foreground detection which could be used in video analysis, such as surveillance, people counting, face detection and pose estimation. Most methods need to choose the hyper parameters manually or use ground truth background masks (GT). In this work, we present an unsupervised deep background (BG) modelling method called BM-Unet which is based on...
The Belief Rule Base (BRB) has been used in modeling the complex nonlinear systems. Traditionally, the construction of BRB is under the conjunctive assumption which requires covering each and every possible combination of all the referenced values of all the attributes. Later, the disjunctive assumption of BRB is proposed which does not require simultaneously taking the status of all the attributes...
This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based...
This paper presents an analysis on intersection conditions that allow a flight vehicle to achieve the interception of a high-speed and maneuvering target. The intersection conditions contain four parameters, including the intersection angle and initial distance and velocities of a fight vehicle and the target. Firstly, the relative motion model between a flight vehicle and the target with intersection...
In this paper, a novel modeling method of soft sensing is presented to measure the spent catalyst's carbon content in fluidized-bed catalytic cracking. This model focuses on improving the existing soft sensing models' performances, such as higher measurement accuracy, more robust working performance, or better generalization ability. To build this model, firstly, we choose the primary variables and...
This paper studies prediction based run-time system reconfiguration strategy to tolerate environment change and hardware malfunction on many-core embedded systems. System reconfiguration will invoke application migration, which may significantly impact system's timing behaviors, therefore, it is vital important to select an appropriate migration strategy after which the system's performance is still...
An increasing number of digital images are being shared and accessed through websites, media, and social applications. Many of these images have been modified and are not authentic. Recent advances in the use of deep convolutional neural networks (CNNs) have facilitated the task of analyzing the veracity and authenticity of largely distributed image datasets. We examine in this paper the problem of...
Churn prediction is very important to the insurance industry. Therefore, there is a big value to investigate how to improve its performance. More importantly, a good model can be used by a common service provider and benefit many companies. State-of-the-art methods either use 1) shallow models such as logistic regression, with sophisticated feature engineering, or 2) deep models that learn features...
With the explosive growth of information on the Internet, it becomes more and more important to improve the efficiency of information acquisition. Automatic text summarization provides a good means for quick acquisition of information through compression and refinement. While existing methods for automatic text summarization achieve elegant performance on short sequences, however, they are facing...
Transforming the Thai society into a knowledge-based one requires enabling all Thais to have equal access to education and the development of human resources through continuous lifelong learning, as outlined by the objective of Thailand's National Education Standards. Thus, this paper aimed to study and identify the relationship between general knowledge and depth knowledge for lifelong learning in...
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