8 research outputs found
MyAds: A Social Adaptive System for Online Advertisement from Hypotheses to Implementation
Online advertisement is one of the major incomes for many companies; it has a role in the overall business flow and affects the consumer behavior directly. Unfortunately most users tend to block their ads or ignore them. MyAds is a social adaptive hypermedia system for online advertising and its main goal is to explore how to make online ads more acceptable. In order to achieve such a goal, various technologies and techniques are used. This paper presents a theoretical framework as well as the system architecture for MyAds that was designed based on a set of hypotheses and an exploratory study. The system then was implemented and a pilot experiment was conducted to validate it. The main outcomes suggest that the system has provided personalized ads for users. The main implications suggest that the system can be used for further testing and validating
Scaffolding for social personalised adaptive e-learning
This work aims to alleviate the weaknesses and pitfalls of the strong modern trend of e-learning by capitalising on and taking advantage of theoretical and implementation advances that have been made in the fields of adaptive hypermedia, social computing, games research and motivation theories. Whilst both demand for and supply of e-learning are growing, especially with the rise of MOOCs, the problems that it faces remain to be addressed, notably isolation, de-personalisation and lack of individual navigation. This often leads to poor learning experience. This work explores an innovative method of combining, threading and balancing the amount of adaptation, social interaction, gamification and open learner modelling for e-learning techniques and technologies. As a starting point, a novel combination of classical adaptation based on user modelling, fine-grained social interaction features and a Facebook-like appearance is explored. This has been shown to be able to ensure a high level of effectiveness, efficiency and satisfaction amongst learners when using the e-learning system. Contextual gamification strategies rooted in Self-Determination Theory (SDT) are then proposed, which have been shown to be able to ensure learners of the system adopt desirable learning behaviours and achieve pre-specified learning goals, thus providing a high level of motivation. Finally, a multifaceted open social learner modelling is proposed. This allows visualising both learners’ performance and their contributions to a learning community, provides various modes of comparison, and is integrated and adapted to learning content. Evidence has shown that this can provide a high level of effectiveness, efficiency and satisfaction amongst learners. Two innovative social personalised adaptive e-learning systems including Topolor and Topolor 2 are devised to enable the proposed approach to be tested in the real world. They have been used as online learning environments for undergraduate and postgraduate students in Western and Eastern Europe as well as Middle Eastern universities, including the University of Warwick, UK, Jordan University, Jordan, and Sarajevo School of Science and Technology, Bosnia and Herzegovina. Students’ feedback has shown this approach to be very promising, suggesting further implementation of the systems and follow-up research. The worldwide use of Topolor has also promoted international collaborations
Artificial intelligence driven anomaly detection for big data systems
The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources.
The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology.
The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naĂŻve anomaly detection training.
The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces
On the Existence of Characterization Logics and Fundamental Properties of Argumentation Semantics
Given the large variety of existing logical formalisms it is of utmost importance
to select the most adequate one for a specific purpose, e.g. for representing
the knowledge relevant for a particular application or for using the formalism
as a modeling tool for problem solving. Awareness of the nature of a logical
formalism, in other words, of its fundamental intrinsic properties, is indispensable
and provides the basis of an informed choice.
One such intrinsic property of logic-based knowledge representation languages
is the context-dependency of pieces of knowledge. In classical propositional
logic, for example, there is no such context-dependence: whenever two
sets of formulas are equivalent in the sense of having the same models (ordinary
equivalence), then they are mutually replaceable in arbitrary contexts (strong
equivalence). However, a large number of commonly used formalisms are not
like classical logic which leads to a series of interesting developments. It turned
out that sometimes, to characterize strong equivalence in formalism L, we can
use ordinary equivalence in formalism L0: for example, strong equivalence in
normal logic programs under stable models can be characterized by the standard
semantics of the logic of here-and-there. Such results about the existence of
characterizing logics has rightly been recognized as important for the study of
concrete knowledge representation formalisms and raise a fundamental question:
Does every formalism have one? In this thesis, we answer this question
with a qualified “yes”. More precisely, we show that the important case of
considering only finite knowledge bases guarantees the existence of a canonical
characterizing formalism. Furthermore, we argue that those characterizing
formalisms can be seen as classical, monotonic logics which are uniquely determined (up to isomorphism) regarding their model theory.
The other main part of this thesis is devoted to argumentation semantics
which play the flagship role in Dung’s abstract argumentation theory. Almost
all of them are motivated by an easily understandable intuition of what should
be acceptable in the light of conflicts. However, although these intuitions equip
us with short and comprehensible formal definitions it turned out that their
intrinsic properties such as existence and uniqueness, expressibility, replaceability
and verifiability are not that easily accessible. We review the mentioned
properties for almost all semantics available in the literature. In doing so we
include two main axes: namely first, the distinction between extension-based
and labelling-based versions and secondly, the distinction of different kind of
argumentation frameworks such as finite or unrestricted ones
Quality of distance e-learning at Saudi universities : students' perceptions
Ph. D. ThesisOne key tool for promoting social justice in the Kingdom of Saudi Arabia (SA) is to ensure
the growth and improvement of Distance e-Learning (DeL). This research study investigates
DeL from the perspective of one key group of stakeholders, the students who are currently
enrolled in DeL. Their views are presented on the importance and application of a set of
standards regarding quality, while exploration of the study setting and context highlights the
specificity of the education system in SA. A conception of quality in DeL is then explicated
through a reading of the history of Distance Education (DE), the usage of quality in education
today and the most significant current models of pedagogy and culture. This research hence
provides the basis for a pragmatic methodology to analyse the perceptions of students
regarding selected standards of quality.
A total of 591 students were surveyed in a mixed methods approach comprised of a
questionnaire and a focus group. The data gathered from surveying perceptions of students is
also used to construct a picture of the strengths and weaknesses of DeL in SA, as well as the
barriers and enhancements to learning resulting from its introduction. Here, culture is found to
be a major influence on the perceptions of the students, while DeL exists within a wider,
behaviourist educational tradition. If they are to be effective, the introduction of Western DeL
practices should therefore serve to negotiate the gap between the need for globalised skills
and the local culture and traditions.
This thesis identifies manifold issues arising from the student’s experiences that contribute to
the obstruction of their expectations about quality; notably, a lack of staff training, large class
sizes and a failure to employ technology (including Web2.0) adequately. Many of the
problems raised in this study reflect the rapid pace and unplanned nature of DeL’s
introduction in SA. The recommendations subsequently made about strategic and institutional
improvement suggest that quality is created through both progressive and planned chang