Digital Emotion Regulation on Social Media

Emotion regulation is the process of consciously altering one's affective state, that is the underlying emotional state such as happiness, confidence, guilt, anger etc. The ability to effectively regulate emotions is necessary for functioning efficiently in everyday life. Today, the pervasiveness of digital technology is being purposefully employed to modify our affective states, a process known as digital emotion regulation. Understanding digital emotion regulation can help support the rise of ethical technology design, development, and deployment. This article presents an overview of digital emotion regulation in social media applications, as well as a synthesis of recent research on emotion regulation interventions for social media. We share our findings from analysing state-of-the-art literature on how different social media applications are utilised at different stages in the process of emotion regulation

A graph automorphic approach for placement and sizing of charging stations in EV network considering traffic

This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach

Data-Driven Behavioural Biometrics for Continuous and Adaptive User Verification Using Smartphone and Smartwatch

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data

Dispatch management of portable charging stations in electric vehicle networks

The global market share of plug-in electric vehicles (PEVs) is on the rise, resulting in a rapid increase in charging demand in both spatial and temporal domains. The network and coverage of public fixed charging stations (FCSs) are currently constrained by infrastructure costs. As a result, FCSs are not as ubiquitous as traditional gas stations. In addition, as PEVs require a reasonably long time to recharge, waiting times at public charging stations can easily become excessive particularly during busy traffic hours. This paper introduces the new idea of allocating and dispatching portable charging stations (PCSs) in hotspot areas of EV network during busy traffic hours to: i) relieve the burden on FCSs, ii) minimize vehicle waiting times at charging stations, and iii) reduce the overlaps between total PEV demand and peak residential load. We formulate the research challenge for the smart management of PCSs as a constrained optimization problem and introduce a heuristic solution. Detailed simulation results for the Washington green highway EV network show that the proposed approach can significantly reduce the average PEV waiting times and decrease the average PEV loads at FCSs during peak hours by up to 64.7% and 67%, respectively

A Context-Aware Framework for Analysing Automotive Vehicle Security

Recent advancements in technology have transformed conventional mechanical vehicles into sophisticated computer systems on wheels. This transition has elevated their intelligence and facilitated seamless connectivity. However, such development has also escalated the possibility of compromising the vehicle’s cyber security expanding the overall cyber threat landscape. This necessitates an increased demand for security measures that manifest flexibility and adaptability instead of static threshold-based measures. Context-awareness techniques can provide a promising direction for such security solutions. Integration of context-awareness in security analysis helps in analysing the behaviour of the environment where IoT devices are deployed, enabling adaptive decision-making that aligns with the current situation. While the incorporation of context-awareness into adaptive systems has been explored extensively, its application to support the cyber security of vehicular ecosystem is relatively new. In this paper, we proposed a context-aware conceptual framework for automotive vehicle security that allows us to analyse real-time situations thereby identifying security threats. The usability of the framework is demonstrated considering an Electric Vehicle(EV) Charging case study

An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations

The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility’s stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility’s power saving and charging stations’ profit

A real-time energy management strategy for pumped hydro storage systems in farmhouses

© 2020 Elsevier Ltd This paper proposes a real-time energy management strategy for pumped hydro storage systems in farmhouses to manage surplus renewable energy. The proposed system meets both electricity and water demand in a farm. The novelty of this paper is its combination of a scheduling method and a real-time controller to take into account both present and future conditions of the microgrid. The scheduling part determines irrigation times, required stored water, and pumped hydro storage schedule. The real-time controller receives the schedule and current condition of the microgrid in order to adjust the pump power and turbine flow rate efficiently. Two methods of fuzzy logic and artificial neural network are tested to investigate which can address the forecast error problem more economically. An innovative approach is presented to produce target data for artificial neural network training. The designed system is simulated for 365 days to investigate the effect of real-time management on the performance of the microgrid on both sunny and cloudy days. The proposed energy management system is applied in an experimental setup, tested with a real pump and turbine. Results show that a real-time management system could keep the stored water level the same as the scheduling method; however, the pump and turbine can be controlled more cost-effectively. Finally, an economic study is conducted to determine the payback period of the system