From global theories to local practice and original knowledge: learning the way through systems co-design

Information focused, learning centered, and systems enabled, Informed Systems (Somerville, United States) guides collaborative design (co-design) of the University for Business and Technology Knowledge Center in Pristina, Kosovo. Conceptual modeling activities since April 2017 engage students in integrating Informed Learning theory (Bruce, Australia) and Soft Systems Methodology (Checkland, England) to progress a shared vision to make local knowledge visible through co-created systems, services, and resources. Foundational Informed Learning categories, information and communication technologies, information sources, and information and knowledge generation - to progress information curation and knowledge management – illustrate the transformative potential of this theory-to-practice initiative, customized to local priorities and values

From Global Theories to Local Practice and Original Knowledge: Learning the Way through Systems Co-Design

Information focused, learning centered, and systems enabled, Informed Systems (Somerville, United States) guides collaborative design (co-design) of the University for Business and Technology Knowledge Center in Pristina, Kosovo. Conceptual modeling activities since April 2017 engage students in integrating Informed Learning theory (Bruce, Australia) and Soft Systems Methodology (Checkland, England) to progress a shared vision to make local knowledge visible through co-created systems, services, and resources. Foundational Informed Learning categories, information and communication technologies, information sources, and information and knowledge generation - to progress information curation and knowledge management – illustrate the transformative potential of this theory-to-practice initiative, customized to local priorities and values

Students’ and lecturers’ perceptions of computerised adaptive testing as the future of assessing students

The COVID-19 pandemic has been a catalyst for the increased adoption and acceptance of technology in teaching and learning, and assessment. Using a quantitative research design, the study surveyed 640 lecturers and students in higher education institutions in South Africa, using an online survey platform to attain lecturers’ and students’ views of adopting computerised adaptive testing (CAT) in their respective modules, and their perceptions of such a testing methodology. The study found that lecturers and students were comfortable engaging in online learning, with a large percentage being the most comfortable with assessing and completing exams, tests, and activities online. Positive perceptions of adopting CAT as an assessment tool for their qualifications were expressed, with the majority recommending their HEI to implement CAT. A statistical difference was found between race and personal perceptions of CAT. It was further established that the higher the level of knowledge and understanding of CAT exist, the higher the academic perceptions levels of CAT are

Improving cold-start recommendations using item-based stereotypes

Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model

Analysis of the efficiency of modern information technologies in e-learning and the use of interactive methods

Today, there is an urgent need to use distance technologies in education. The significant disadvantages of e-learning are students’ low self-motivation, and the insufficient level of formed social communication skills. The above disadvantages can be largely overcome with interactive teaching methods and appropriate information technologies in the educational process. A correct approach in including interactive technologies in e-learning provides significant positive effects. One of the main challenges in using interactive technologies in e-learning is how to organize efficient remote dialogue and joint work of the participants. The paper analyzes information technologies recommended at various stages of organizing and designing interactive learning technologies. When working in groups, students can use online services such as Google Sheets, Google Docs, and Google Slides. These services greatly facilitate the work on a joint project and help the project team prepare for its successful presentation. Since many e-learning platforms cannot provide high-quality communication for participants, video conferencing services and social networks should supplement them. E-learning platforms should ensure that learners work effectively on common tasks using interactive learning technologies. We believe that every modern e-learning platform should contain a software module similar in functionality to Google Docs, Google Sheets, and Google Slides. It will make it possible to successfully implement all stages of organizing and designing interactive learning technologies. We can evaluate the efficiency of the forms of conducting classes using mathematical modeling, in particular, game-theoretic modeling. To assess the efficiency of the traditional and interactive forms of conducting classes, it is advisable to use an antagonistic (matrix) game and a cooperative game, correspondingly

Cyberattacks detection in iot-based smart city applications using machine learning techniques

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Defacement Detection with Passive Adversaries

A novel approach to defacement detection is proposed in this paper, addressing explicitly the possible presence of a passive adversary. Defacement detection is an important security measure for Web Sites and Applications, aimed at avoiding unwanted modifications that would result in significant reputational damage. As in many other anomaly detection contexts, the algorithm used to identify possible defacements is obtained via an Adversarial Machine Learning process. We consider an exploratory setting, where the adversary can observe the detector’s alarm-generating behaviour, with the purpose of devising and injecting defacements that will pass undetected. It is then necessary to make to learning process unpredictable, so that the adversary will be unable to replicate it and predict the classifier’s behaviour. We achieve this goal by introducing a secret key—a key that our adversary does not know. The key will influence the learning process in a number of different ways, that are precisely defined in this paper. This includes the subset of examples and features that are actually used, the time of learning and testing, as well as the learning algorithm’s hyper-parameters. This learning methodology is successfully applied in this context, by using the system with both real and artificially modified Web sites. A year-long experimentation is also described, referred to the monitoring of the new Web Site of a major manufacturing company

Algoritmos de aprendizado de máquina para coordenação de interferência entre células

The current LTE and LTE-A deployments require larger efforts to achieve the radio resource management. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic optimization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machine-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the networks to be capable to automatically respond to the particular needs in the dynamic network traffic scenarios.Los despliegues actuales de LTE y LTE-A requieren mayor esfuerzo para la gestión de recursos radio debido al incremento de usuarios y a la gran demanda de servicios; en ese escenario, la optimización automática es un punto clave para evitar problemas como la interferencia inter-celda. El presente trabajo recopila propuestas de algoritmos de aprendizaje automático [machine learning] enfocados en resolver este problema. Las investigaciones buscan que los sistemas celulares consigan su auto-optimización, un concepto que se enmarca dentro del área de redes auto-organizadas [Self-Organized Networks, SON], cuyo objetivo es lograr que las redes respondan de forma automática a las necesidades de los escenarios dinámicos de tráfico de red.As implantações atuais de LTE e LTE-A exigem maior esforço para o gerenciamento de recursos rádio devido ao aumento de usuários e à alta demanda por serviços, neste cenário a otimização automática é um ponto-chave para evitar problemas como a interferência entre células. O presente trabalho coleta propostas de algoritmos de aprendizado automáticos focados na resolução deste problema. A pesquisa busca que os sistemas celulares alcancem a sua auto-otimização, um conceito que faz parte das redes auto-organizadas (Self-Organizing Networks, SON), cujo objetivo é garantir que as redes respondam automaticamente às necessidades dos cenários dinâmicos do tráfego de rede

Assessing stationarity in web analytics: A study of bounce rates

Evidence-based methods for evaluating marketing interventions such as A/B testing have become standard practice. However, the pitfalls associated with the misuse of this decision-making instrument are not well understood by managers and analytics professionals. In this study, we assess the impact of stationarity on the validity of samples from conditioned time series, which are abundant in web metrics. Such a prominent metric is the bounce rate, which is prevalent in assessing engagement with web content as well as the performance of marketing touchpoints. In this study, we show how to control for stationarity using an algorithmic transformation to calculate the optimum sampling period. This distance is based on a novel stationary ergodic process that considers that a stationary series presents reversible symmetric features and is calculated using a dynamic time warping algorithm in a self-correlation procedure. This study contributes to the expert and intelligent systems literature by demonstrating a robust method for sub-sampling time-series data, which are critical in decision making