Design and Evaluation of a Gamification-based Information System for Improving Student Attendance

Declining student attendance is a recurring concern in most educational environments, including tertiary education. Depending on the assumed cause of low attendance, different approaches have been proposed as a means of intervention for mitigating the problem. On the other hand, gamification is a relatively new approach that aims to increase engagement of participants in non-game contexts by utilizing techniques developed for and used in computer games. In this paper, we propose a novel approach that aims at intervening by applying gamification techniques for the purpose of increasing the extrinsic motivation for attendance in tertiary-level education settings. The approach is based on a cloud-based platform which features web and mobile clients. The main stakeholders are the lecturers, who can configure the environment, and the students who are the targeted participants. Unlike similar works, this approach aims at improving the student attendance at a wider scale, e.g. at the programme level rather than focusing on individual modules or classes. Additionally, it provides fine customization allowing the lecturers to opt in with custom settings. Finally, the paper describes some early results and paves the road for an extensive evaluation

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Towards a human-centered e-commerce personalization framework

This paper presents a personalization framework, namely PersonaWeb that adapts the visual and interaction design of E-Commerce Web environments based on human cognitive differences. In particular, it describes a user model formalization that incorporates a set of human cognitive factors (i.e., cognitive styles and working memory capacity) and an adaptation engine that personalizes the visual and interaction design attributes of E-Commerce product views. The proposed framework has been applied in a real-life E-Commerce Web-site and two subsequent user studies were conducted in which 135 users interacted with the personalized and the original (non-personalized) version of the same Web environment. Results indicate the added value of personalizing content and functionality of E-Commerce product views in terms of users' task completion performance

AN ATTEMPT TO DEFINE CONTEXT AWARENESS IN MOBILE E-HEALTH ENVIRONMENTS

Nurses, doctors, physiotherapists, psychologists and other professionals or specialists come together to provide care to home residing patients, making continuous assessment, diagnosis and treatment possible beyond the walls of hospitals. Such teams of professionals are focused on each individual patient, and are virtual, i.e. they make decisions without being together physically, dynamically, i.e. professionals come and go as needed, and collaborate, as they combine their knowledge to provide effective care. Our system, coined DITIS, is a web based system that enables the effective management and collaboration of virtual healthcare teams and accessing medical information in a secure manner from a variety of mobile devices from anytime and anyplace, adapting the information according to various parameters like, user role, access right, device capabilities and wireless medium. This paper introduces the DITIS system, and identifies the needs and challenges of co-ordinated teams of multidisciplinary healthcare professionals (HCPs) functioning in a context awareness environment under the wireless environment. Pilo

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Perimeter-Based Data Replication in Mobile Sensor Networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of events from the network in cases of failures. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures are less than 60% failure then we can recover over 80% of generated events exactly

A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking

This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques

Differences in options investors’ expectations and the cross-section of stock returns

We provide strong evidence that the dispersion of individual stock options trading volume across moneynesses (IDISP) contains valuable information about future stock returns. Stocks with high IDISP consistently underperform those with low IDISP by more than 1% per month. In line with the idea that IDISP reflects dispersion in investors’ beliefs, we find that the negative IDISP-return relationship is particularly pronounced around earnings announcements, in high sentiment periods and among stocks that exhibit relatively high short-selling impediments. Moreover, the IDISP effect is highly persistent and robustly distinct from the effects of a large array of previously documented cross-sectional return predictors.PostprintPeer reviewe

A critical review of the ship investment literature

This paper provides a critical review of the ship investment literature in the last 30 years following 43 journal articles and book chapters. The review is based on a framework that synthesizes and integrates the literature in terms of timing and viability of shipping investment, raising of funds and managing the investment. Based on this review, a number of promising directions for future research are laid out through the identification of the major contributions and the progress that has been made so far with regards to the broader thematic area of shipping investments, as well as through highlighting important knowledge gaps that could potentially serve as a research agenda for the future

Perimeter-Based Data Replication in Mobile Sensor Networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of events from the network in cases of failures. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures are less than 60% failure then we can recover over 80% of generated events exactly