Fisnik Dalipi

Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges

The emergence and evolution of Internet of Things (IoT) offers great advantages to improve substantially the management over electricity consumption and distribution to the benefit of consumers, suppliers and grid operators. However, introducing IoT related devices and technologies in smart grids might lead to new security and privacy challenges. Though necessary technological innovations to ensure secure communication are being developed, more work is still required towards more secure standards for communication between devices and Smart Grids. This paper provides an overview about the security and privacy challenges of IoT applications in smart grids. Furthermore, we highlight and analyze some solutions and practices being used to cope with security and privacy requirements for IoT on deployment and management of smart grid. We address three types of challenge domains: customer domain, information and communication domain, and the grid domain.

Towards Understanding the MOOC Trend: Pedagogical Challenges and Business Opportunities

Undoubtedly, MOOCs have the potential to introduce a new wave of technological innovation in learning. In spite of the great interest among the educators and the general public MOOCs have generated, there are some challenges that MOOCs might face when it comes to examining and determining the best pedagogical approaches that MOOCs should be based on. Moreover, MOOCs are facing also challenges towards building a consistent business model. The main objective of this paper is to shed more light on the MOOCs phenomenon, by analyzing and discussing some benefits and drawbacks of MOOCs from the pedagogical and business perspectives. Therefore, in this paper we provide an in-depth analysis of MOOCs challenges and opportunities towards determining pedagogical innovations. We also analyze current trends of MOOCs expansion to create new educational markets by overpassing the bricks-and-mortar educational institutions. To do so, we conduct a SWOT analysis on MOOCs. Finally, we provide possible directions and insights for future research to better understand how MOOCs can be improved to lead to greater innovations in the higher education landscape to answer the needs of a knowledge-based economy.

An Analysis of Learner Experience with MOOCs in Mobile and Desktop Learning Environment

Massive Open Online Courses (MOOCs) are now the most recent topic within the field of e-learning. They have the potential to influence the higher education environments significantly worldwide by creating a completely new and large market of educational resources by overpassing the traditional universities market share due to their physical limitations. However, due to the many differences between mobile devices and desktop environments, the introduction of mobile technology in MOOC environment is challenging. Hence, the main objective of this paper is to study and compare the learner’s experience in different learning environments by using mobile devices and PCs while performing given tasks related to MOOCs. To achieve this goal, we conduct a subjective experiment with various MOOCs related tasks to be performed in mobile and desktop learning environment. The results of the findings show that the difficulties learners have experienced in the mobile environment are more expressed. Moreover, their satisfactory level is much higher in the desktop environment.

Analysis of Online Social Networks Posts to Investigate Suspects Using SEMCON

Analysing users’ behaviour and social activity for investigating suspects is an area of great interest nowadays, particularly investigating the activities of users on Online Social Networks (OSNs) for crimes. The criminal activity analysis provides a useful source of information for law enforcement and intelligence agencies across the globe. Current approaches dealing with the social criminal activity analysis mainly rely on the contextual analysis of data using only co-occurrence of terms appearing in a document to find the relationship between criminal activities in a network. In this paper, we propose a model for automated social network analysis in order to assist law enforcement and intelligence agencies to predict whether a user is a possible suspect or not. The model uses web crawlers suited to retrieve users’ data such as posts, feeds, comments, etc., and exploits them semantically and contextually using an ontology enhancement objective metric SEMCON. The output of the model is a probability value of a user being a suspect which is computed by finding the similarity between the terms obtained from the SEMCON and the concepts of criminal ontology. An experiment on analysing the public information of 20 Facebook users is conducted to evaluate the proposed model.

An intelligent model for predicting the occurrence of skiing injuries

Artificial neural networks offer a unique way to model very complex and innovative systems that can be very effective in anticipating various accident severities. In this article, we propose a neural-network-based model, able to predict the number of severe injuries caused while skiing. The proposed system is intended for use by ski patrol and medical personnel to better prepare themselves in advance for treating ski-injured persons. The ski patrol and any other medical personnel will be able to know the statistics, type and severity of the injuries occurred, and most importantly, will be benefiting from having predictions for each day. Considering that, the number of injured people in a particular place each day was estimated, the results are very promising suggesting that such a system would prove beneficial in accurately predicting skiing injuries.

Rethinking the conventional learning paradigm towards MOOC based flipped classroom learning

The recent proliferation of Massive Open Online Courses (MOOCs) has initiated a plethora of research endeavors revolving around new pedagogical methods in higher education. Integrating MOOCs in blended learning can be beneficial in different ways for both learners and instructors. In this position paper, we aim to provide a brief and comprehensive review about the challenges that higher education institutions in Macedonia and Kosovo face while coping with the new trends of flexible or blended learning. Moreover, after describing some real cases of MOOC based flipped classroom learning, we also provide some recommendations in order to enhance and enrich learning experience by employing innovative pedagogies.

Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

An Analysis of Social Collaboration and Networking Tools in eLearning

Many online learning websites and learning management systems (LMS) provide social collaboration and networking tools to aid learning and to interact with peers for knowledge sharing. The benefit of collaborating with each other is certainly undeniable, such tools, however, can be a distraction from the actual tasks for learners. The paper presents a study on social media tools supported by various eLearning systems to understand the impact on students learning activities. A survey questionnaire is designed for this purpose. The data is collected from students who have had experience using different massive open online course (MOOC) eLearning platforms and LMS from various universities. The results of the survey indicate that more than 95 % of the participants use at least one of the social tools in their daily life activities, and almost 84 % of them have used these tools in connection with the eLearning systems. It is also interesting to note that 92 % of the participants intend to use social tools for study purposes. The results indicate that there is a need to integrate more of these social media tools into eLearning systems.

A novel system architecture for efficient management of skiing injuries

Mobile applications and the emergence of cloud computing are considered as main drivers of extending the scope of health services and empowering the eHealth, resulting in a new branch developed very rapidly in recent years, named mHealth, the use of mobile applications for healthcare. In this paper, we propose a system architecture for ski injury registration. Our research work is inspired by the need of integrating mHealth apps in managing skiing injuries to provide higher healthcare service quality and faster availability of data. Our approach focuses both on the design of interfaces for mobile devices and presenting an architecture for the digital ski injury registration system. The design of the registration system is intended to greatly simplify the workflow between ski patrollers and the medical centers and help improve healthcare services. By the use of this system, the ski patroller will be able to provide some useful information to the doctors at the hospital in advance and in a timely manner. We employ user-centered design while developing the mobile interfaces for the ski patrollers, the nurses and the doctors. We conducted a test of a pilot of the ski patroller system in collaboration with the ski patroller in Trysil, Norway. The test had two-evaluation points and based on the results of the tests, we obtained implications for improving the design of mobile interfaces for the proposed architecture.

The impact of environmental factors to skiing injuries: Bayesian regularization neural network model for predicting skiing injuries

Skiing is a winter sport that is found very attractive to many people. Nevertheless, this sport is considered among high-risk sports due to the potential danger of severe injury or death. This is because of variable weather and terrain conditions, obstacles including other skiers, high speeds, trees, etc. Artificial Neural Networks have many applications in predicting the occurrence of various accident severities. In this article, we study the impact of the environmental factors to potential risk factor assessment in skiing. Hence, we apply the Bayesian Regularization Back Propagation neural network (BRBP) to predict the number of severe injuries in skiing, based on the data obtained from our prototype ski-injury registration system, the estimated bindings of environmental conditions, and the potential risk for resulting number of personal injuries. Through comparing with Levenberg Marquardt Back Propagation (LMBP), in terms of prediction accuracy, our experimental results show that BRBP has better performance by achieving higher predictive accuracy.