Enhancing Open and Distance Learning Materials: Validating the AFCI Model for Basic Chemistry

In this study, we present the validation of the AFCI (Analysis and Identification, Find Color, Color Composition, and Implementation) model as a problem-based approach to enhance the quality of multicolor output in open and distance learning materials. The primary objective was to evaluate the reliability and validity of the AFCI model, specifically designed to incorporate two-color separation techniques aimed at reducing costs while improving the quality of multicolor content. Our research encompassed the examination of printed modules and samples across diverse subject areas, including Basic Chemistry, Architecture, Enterprise System Design, and Teacher Profession. To establish the validity of the AFCI model, a comprehensive validation process was conducted, involving focused group discussions with experts specializing in graphic design, printing, and educational materials. The outcomes of this rigorous validation process revealed substantial validity coefficients (rα visibility = 0.98, rα attractiveness = 0.97, rα convenience = 0.91, rα emphasis = 1), affirming the model's effectiveness. Furthermore, the internal consistency of the AFCI model was confirmed with a Cronbach's Alpha coefficient of 0.86. This research significantly contributes to the realm of open and distance learning by introducing an innovative model for improving multicolor content quality. The AFCI model's successful validation underscores its potential to benefit practitioners and educators in developing high-quality teaching materials, thereby enhancing the overall learning experience in open and distance education

Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques

This thesis presents a research whose objective is to design an intrusion detection model for Mobile Ad hoc NETworks (MANET). MANET is an autonomous system consisting of a group of mobile nodes with no infrastructure support. The MANET environment is particularly vulnerable because of the characteristics of mobile ad hoc networks such as open medium, dynamic topology, distributed cooperation, and constrained capability. Unfortunately, the traditional mechanisms designed for protecting networks are not directly applicable to MANETs without modifications. In the past decades, machine learning methods have been successfully used in several intrusion detection methods because of their ability to discover and detect novel attacks. This research investigates the use of a promising technique from machine learning to designing the most suitable intrusion detection for this challenging network type. The proposed algorithm employs a combined model that uses two different measures (nonconformity metric measures and Local Distance-based Outlier Factor (LDOF)) to improve its detection ability. Moreover, the algorithm can provide a graded confidence that indicates the reliability of the classification. In machine learning algorithm, choosing the most relevant features for each attack is a very important requirement, especially in mobile ad hoc networks where the network topology dynamically changes. Feature selection is undertaken to select the relevant subsets of features to build an efficient prediction model and improve intrusion detection performance by removing irrelevant features. The transductive conformal prediction and outlier detection have been employed for feature selection algorithm. Traditional intrusion detection techniques have had trouble dealing with dynamic environments. In particular, issues such as collects real time attack related audit data and cooperative global detection. Therefore, the researcher is motivated to design a new intrusion detection architecture which involves new detection technique to efficiently detect the abnormalities in the ad hoc networks. The proposed model has distributed and cooperative hierarchical architecture, where nodes communicate with their region gateway node to make decisions. To validate the research, the researcher presents case study using GLOMOSIM simulation platform with AODV ad hoc routing protocols. Various active attacks are implemented. A series of experimental results demonstrate that the proposed intrusion detection model can effectively detect anomalies with low false positive rate, high detection rate and achieve high detection accuracy

On the Importance of Word Boundaries in Character-level Neural Machine Translation

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model

Pedagogical advances in business models at business schools: In the age of networks

Purpose – The purpose of this paper is to focus on potential advances in pedagogy and on the process of learning in business schools. It examines innovations in teaching and learning methods particularly in the context of networked organizations. Design/methodology/approach – It approaches, and examine the impact of, three key developments in business schools, namely, recent advances in IT, changes in the architecture of classrooms and learning spaces and advances in the way teaching is undertaken. Findings – The paper suggests that a blend between self-learning via distance approaches and face-to-face learning will increasingly become the norm. Face-to-face sessions might be in a “flat room” environment with a creative mix of short lectures, experiential, group learning and conceptual plenary lectures, software innovations, and digital textbooks “open plan” learning spaces would complement the instructional process. Research limitations/implications – There are clear implications for parallel IT (developments in course modules) and architectural innovations for the design of more effective and creative learning spaces. Practical implications – Improving pedagogy together with the physical design and layout of learning spaces is critical. The aim is, through enhanced participative pedagogy and “friendly” architecture, to improve learning by encouraging dialogue and closer interaction between students and professors from different disciplines and fields. Social implications – The authors argue that this model of collaborative learning and an interactive teaching framework should enable the same amount of learning material to be covered in a business school in approximately half-the-time required in conventional pedagogical approaches. Originality/value – It offers a prescription for a participative, technology enhanced and interactive teaching pedagogy that could produce more effective and efficient, teaching outcomes. This has strong implications for the sustainability, and funding capability, of many existing business schools and business school models. </jats:sec

Quality of experience aware adaptive hypermedia system

The research reported in this thesis proposes, designs and tests a novel Quality of Experience Layer (QoE-layer) for the classic Adaptive Hypermedia Systems (AHS) architecture. Its goal is to improve the end-user perceived Quality of Service in different operational environments suitable for residential users. While the AHS’ main role of delivering personalised content is not altered, its functionality and performance is improved and thus the user satisfaction with the service provided. The QoE Layer takes into account multiple factors that affect Quality of Experience (QoE), such as Web components and network connection. It uses a novel Perceived Performance Model that takes into consideration a variety of performance metrics, in order to learn about the Web user operational environment characteristics, about changes in network connection and the consequences of these changes on the user’s quality of experience. This model also considers the user’s subjective opinion about his/her QoE, increasing its effectiveness and suggests strategies for tailoring Web content in order to improve QoE. The user related information is modelled using a stereotype-based technique that makes use of probability and distribution theory. The QoE-Layer has been assessed through both simulations and qualitative evaluation in the educational area (mainly distance learning), when users interact with the system in a low bit rate operational environment. The simulations have assessed “learning” and “adaptability” behaviour of the proposed layer in different and variable home connections when a learning task is performed. The correctness of Perceived Performance Model (PPM) suggestions, access time of the learning process and quantity of transmitted data were analysed. The results show that the QoE layer significantly improves the performance in terms of the access time of the learning process with a reduction in the quantity of data sent by using image compression and/or elimination. A visual quality assessment confirmed that this image quality reduction does not significantly affect the viewers’ perceived quality that was close to “good” perceptual level. For qualitative evaluation the QoE layer has been deployed on the open-source AHA! system. The goal of this evaluation was to compare the learning outcome, system usability and user satisfaction when AHA! and QoE-ware AHA systems were used. The assessment was performed in terms of learner achievement, learning performance and usability assessment. The results indicate that QoE-aware AHA system did not affect the learning outcome (the students have similar-learning achievements) but the learning performance was improved in terms of study time. Most significantly, QoE-aware AHA provides an important improvement in system usability as indicated by users’ opinion about their satisfaction related to QoE

A GRID-BASED E-LEARNING MODEL FOR OPEN UNIVERSITIES

E-learning has grown to become a widely accepted method of learning all over the world. As a result, many e-learning platforms which have been developed based on varying technologies were faced with some limitations ranging from storage capability, computing power, to availability or access to the learning support infrastructures. This has brought about the need to develop ways to effectively manage and share the limited resources available in the e-learning platform. Grid computing technology has the capability to enhance the quality of pedagogy on the e-learning platform. In this paper we propose a Grid-based e-learning model for Open Universities. An attribute of such universities is the setting up of multiple remotely located campuses within a country. The grid-based e-learning model presented in this work possesses the attributes of an elegant architectural framework that will facilitate efficient use of available e-learning resources and cost reduction, leading to general improvement of the overall quality of the operations of open universities

Boosting Deep Open World Recognition by Clustering

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set. Since it is practically impossible to capture all possible semantic concepts present in the real world in a single training set, we need to break the closed world assumption, equipping our robot with the capability to act in an open world. To provide such ability, a robot vision system should be able to (i) identify whether an instance does not belong to the set of known categories (i.e. open set recognition), and (ii) extend its knowledge to learn new classes over time (i.e. incremental learning). In this work, we show how we can boost the performance of deep open world recognition algorithms by means of a new loss formulation enforcing a global to local clustering of class-specific features. In particular, a first loss term, i.e. global clustering, forces the network to map samples closer to the class centroid they belong to while the second one, local clustering, shapes the representation space in such a way that samples of the same class get closer in the representation space while pushing away neighbours belonging to other classes. Moreover, we propose a strategy to learn class-specific rejection thresholds, instead of heuristically estimating a single global threshold, as in previous works. Experiments on RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202