What would Plato say? Concepts and notions from Greek philosophy applied to gamification mechanics for a meaningful and ethical gamification

Gamification, the integration of game mechanics in non-game settings, has become increasingly prevalent in various digital platforms; however, its ethical and societal impacts are often overlooked. This paper delves into how Platonic and Aristotelian philosophies can provide a critical framework for understanding and evaluating the ethical dimensions of gamification. Plato's allegory of the cave and theory of forms are used to analyse the perception of reality in gamified environments, questioning their authenticity and the value of virtual achievements, while Aristotle's virtue ethics, with its emphasis on moderation, virtue, and eudaimonia (true and full happiness), can help assess how gamification influences user behaviour and ethical decision-making. The paper critically examines various gamification elements, such as the hero's journey, altruistic actions, badge levels, and user autonomy, through these philosophical lenses, and addresses the ethical responsibilities of gamification designers, advocating for a balanced approach that prioritizes user well-being and ethical development over commercial interests. By bridging ancient philosophical insights with modern digital culture, this research contributes to a deeper understanding of the ethical implications of gamification, emphasizing the need for responsible and virtuous design in digital applications.Comment: Accepted for presentation at GamiFIN 202

Tracking the value of rail time over time

The value of rail time is an important economic parameter in the evaluation of many rail infrastructure projects, translating travel time savings into dollars to compare against project costs. For Sydney, the value of onboard train travel time has been estimated through Stated Preference market research. Three surveys have been undertaken over the last two decades. The first was undertaken in 1992; the second in 2003 and the third in 2010. Between 1992 and 2010, the value of time has been updated by reference to movements in fare and latterly by reference to wage rate indices. This paper describes the features of the three surveys and the values of time obtained and compares the values with other study estimates usually obtained as ‘by-products’ of patronage studies. The study looks at six alternative indices to update values of time and reviews the approaches used overseas. The alternative indices are then compared against the growth in the estimated values of time for Sydney. A composite index of the NSW wage index and the Consumer Price Index weighted in accordance with the share of employed and nonemployed passengers produced the closest fit

A review of policy and economic instruments for peak demand management in commuter rail

Urban rail systems in the larger Australian capital cities have considerable demand variability, with peak demand, mainly associated with daily commutes in many cases straining system capacity and adversely impacting on service levels and traveller satisfaction. Infrastructure responses and capacity improvements are expensive and require significant procurement lead times. System planners and managers dealing with all major transport modes have long recognised demand management as an effective tool to deliver greater efficiencies in the operation of transport infrastructure and reduce the need to invest in expensive infrastructure solutions.Literature dealing with policy and economic instruments to manage peak demand for rail and other relevant industries provides guidance for identifying best practices for possible consideration in commuter rail.In the area of transit, peak demand management constitutes the utilisation of a number of instruments with the objective of influencing travellers to vary their travel patterns in order to achieve more efficient utilisation of system resources and return a higher level of travel utility (satisfaction) to the transit system users. Instruments include social /institutional communication campaigns, quality of service, fares and/or system access fees, parking availability and pricing at transport terminals, as well as feeder services

Forecasting the Use of Passenger Lifts at Rail Stations

The first passenger lift in the NSW rail network was introduced in 1991 at Lithgow station. By May 2010, 268 passenger lifts have been installed at one hundred stations in the CityRail network. In most cases, lifts have been introduced to provide ‘easy access’ for disabled or passengers with mobility problems.This paper presents a four step methodology to forecast the usage of passenger lifts by passengers of different observed mobility such as passengers in wheelchairs or passengers with strollers or heavy luggage. The first step uses automatic lift ‘travel’ count data that records the number of times lifts move up or down. A forecasting model was developed that explained the number of times the lifts ‘travelled’ (moved) in terms of station entry and exit counts. The lift travel model also took into account the effect of the height of the lift and location of the lift defined as whether the lift connected the concourse and the platform or the station and the street.The second step involved fieldworker observation surveys to determine average lift occupancy - the number of passengers carried per lift travel. A model was developed that expressed lift occupancy in relation to the number of lift travels per hour. The third step combined the travel and occupancy forecasts to calculate the number of passenger using lifts.The fieldworkers also observed the profile of lift users which is used in the fourth step of the forecasting model. Lift users were categorised according to their observed mobility status such as: wheelchair passengers; passengers with strollers; passengers with heavy luggage; elderly/infirm; RailCorp staff; normal mobility etc. This data enabled lift patronage to be forecast by mobility category

Legal and ethical considerations regarding the use of ChatGPT in education

Artificial intelligence has evolved enormously over the last two decades, becoming mainstream in different scientific domains including education, where so far, it is mainly utilized to enhance administrative and intelligent tutoring systems services and academic support. ChatGPT, an artificial intelligence-based chatbot, developed by OpenAI and released in November 2022, has rapidly gained attention from the entire international community for its impressive performance in generating comprehensive, systematic, and informative human-like responses to user input through natural language processing. Inevitably, it has also rapidly posed several challenges, opportunities, and potential issues and concerns raised regarding its use across various scientific disciplines. This paper aims to discuss the legal and ethical implications arising from this new technology, identify potential use cases, and enrich our understanding of Generative AI, such as ChatGPT, and its capabilities in education.Comment: Accepted at the 1st International Conference of the Network of Learning and Teaching Centers in Greece: Transforming Higher Education Teaching Practic

3D Cylindrical Trace Transform based feature extraction for effective human action classification

Human action recognition is currently one of the hottest areas in pattern recognition and machine intelligence. Its applications vary from console and exertion gaming and human computer interaction to automated surveillance and assistive environments. In this paper, we present a novel feature extraction method for action recognition, extending the capabilities of the Trace transform to the 3D domain. We define the notion of a 3D form of the Trace transform on discrete volumes extracted from spatio-temporal image sequences. On a second level, we propose the combination of the novel transform, named 3D Cylindrical Trace Transform, with Selective Spatio-Temporal Interest Points, in a feature extraction scheme called Volumetric Triple Features, which manages to capture the valuable geometrical distribution of interest points in spatio-temporal sequences and to give prominence to their action-discriminant geometrical correlations. The technique provides noise robust, distortion invariant and temporally sensitive features for the classification of human actions. Experiments on different challenging action recognition datasets provided impressive results indicating the efficiency of the proposed transform and of the overall proposed scheme for the specific task