Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Run-Time Selection of Coordination Mechanisms in Multi-Agent Systems

This paper presents a framework that enables autonomous agents to dynamically select the mechanism they employ in order to coordinate their inter-related activities. Adopting this framework means coordination mechanisms move from the realm of being imposed upon the system at design time, to something that the agents select at run-time in order to fit their prevailing circumstances and their current coordination needs. Empirical analysis is used to evaluate the effect of various design alternatives for the agent's decision making mechanisms and for the coordination mechanisms themselves

Finding a Mate With No Social Skills

Sexual reproductive behavior has a necessary social coordination component as willing and capable partners must both be in the right place at the right time. While there are many known social behavioral adaptations to support solutions to this problem, we explore the possibility and likelihood of solutions that rely only on non-social mechanisms. We find three kinds of social organization that help solve this social coordination problem (herding, assortative mating, and natal philopatry) emerge in populations of simulated agents with no social mechanisms available to support these organizations. We conclude that the non-social origins of these social organizations around sexual reproduction may provide the environment for the development of social solutions to the same and different problems.Comment: 8 pages, 5 figures, GECCO'1

Providing behaviour awareness in collaborative project courses

Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version

United Nations Development Assistance Framework for Kenya

The United Nations Development Assistance Framework (2014-2018) for Kenya is an expression of the UN's commitment to support the Kenyan people in their self-articulated development aspirations. This UNDAF has been developed according to the principles of UN Delivering as One (DaO), aimed at ensuring Government ownership, demonstrated through UNDAF's full alignment to Government priorities and planning cycles, as well as internal coherence among UN agencies and programmes operating in Kenya. The UNDAF narrative includes five recommended sections: Introduction and Country Context, UNDAF Results, Resource Estimates, Implementation Arrangements, and Monitoring and Evaluation as well as a Results and Resources Annex. Developed under the leadership of the Government, the UNDAF reflects the efforts of all UN agencies working in Kenya and is shaped by the five UNDG programming principles: Human Rights-based approach, gender equality, environmental sustainability, capacity development, and results based management. The UNDAF working groups have developed a truly broad-based Results Framework, in collaboration with Civil Society, donors and other partners. The UNDAF has four Strategic Results Areas: 1) Transformational Governance encompassing Policy and Institutional Frameworks; Democratic Participation and Human Rights; Devolution and Accountability; and Evidence-based Decision-making, 2) Human Capital Development comprised of Education and Learning; Health, including Water, Sanitation and Hygiene (WASH), Environmental Preservation, Food Availability and Nutrition; Multi-sectoral HIV and AIDS Response; and Social Protection, 3) Inclusive and Sustainable Economic Growth, with Improving the Business Environment; Strengthening Productive Sectors and Trade; and Promoting Job Creation, Skills Development and Improved Working Conditions, and 4) Environmental Sustainability, Land Management and Human Security including Policy and Legal Framework Development; and Peace, Community Security and Resilience. The UNDAF Results Areas are aligned with the three Pillars (Political, Social and Economic) of the Government's Vision 2030 transformational agenda