ABSTRACT Purpose: This short paper describes the dashboard design process for online hate speech monitoring for multiple languages and platforms. Methodology/approach: A case study approach was adopted in which the authors followed a research & development project for a multilingual and multiplatform online dashboard monitoring online hate speech. The case under study is the project for the European Observatory of Online Hate (EOOH). Results: We outline the process taken for design and prototype development for which a design thinking approach was followed, including multiple potential user groups of the dashboard. The paper presents this process's outcome and the dashboard's initial use. The identified issues, such as obfuscation of the context or identity of user accounts of social media posts limiting the dashboard's usability while providing a trade-off in privacy protection, may contribute to the discourse on privacy and data protection in (big data) social media analysis for practitioners. Research limitations/implications: The results are from a single case study. Still, they may be relevant for other online hate speech detection and monitoring projects involving big data analysis and human annotation. Practical implications: The study emphasises the need to involve diverse user groups and a multidisciplinary team in developing a dashboard for online hate speech. The context in which potential online hate is disseminated and the network of accounts distributing or interacting with that hate speech seems relevant for analysis by a part of the user groups of the dashboard. International Information Management Association
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As society has to adapt to changing energy sources and consumption, it is driving away from fossil energy. One particular area of interest is electrical driving and the increasing demand for (public) charging facilities. For municipalities, it is essential to adapt to this changing demand and provide more public charging facilities.In order to accommodate on roll-out strategies in metropolitan areas a data driven simulation model, SEVA1, has been developed The SEVA base model used in this paper is an Agent-Based model that incorporate past sessions to predict future charging behaviour. Most EV users are habitual users and tend to use a small subset of the available charge facilities, by that obtaining a pattern is within the range possibilities. Yet, for non-habitual users, for example, car sharing users, obtaining a pattern is much harder as the cars use a significantly higher amount of charge points.The focus of this research is to explore different model implementations to assess the potential of predicting free-floating cars from the non-habitual user population. Most important result is that we now can simulate effects of deployement of car sharing users in the system, and with that the effect on convenience for habitual users. Results show that the interaction between habitual and non habitual EV users affect the unsuccessful connection attempts based increased based on the size of the car-sharing fleet up to approximately 10 percent. From these results implications for policy makers could be drawn.
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Adopted on the fifteenth anniversary of resolution 1325, Security Council resolution 2242 has recognized for the first time the substantial link between climate change and the “Women, Peace and Security” (WPS) framework. Despite this landmark resolution, the intersections of environmental factors, conflict and violence against women remain largely absent from the Security Council's WPS agenda. Competition over natural resources is generally understood as a driver of conflict. The risk of insecurity and conflict are further increased by environmental degradation and climate change. It is therefore clear that the environment and natural resources must be integrated into the WPS agenda. This should necessarily include a discussion of indigenous rights to land and the gender-related dimensions of environmental factors. Indigenous women are disproportionately affected by environmental degradation, caused by resource extraction and increasingly compounded by climatic changes. This in turn exacerbates other vulnerabilities, including sexual and gender-based violence and other forms of marginalization. This article argues, by reference to the situation in West Papua, that unfettered resource extraction not only amplifies vulnerabilities and exacerbates preexisting inequalities stemming from colonial times, it also gives rise to gendered consequences flowing from the damage wreaked on the natural environment and thus poses a danger to international peace and security. As such, the Security Council's failure to recognize the continuous struggle of women in indigenous and rural communities against extractive economies and climate change impact as a security risk forms a serious lacuna within its WPS agenda. Originally published by Oxford University Press in Global Studies Quarterly, Volume 1, Issue 3, September 2021, ksab018, https://doi.org/10.1093/isagsq/ksab018
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In the road transportation sector, CO2 emission target is set to reduce by at least 45% by 2030 as per the European Green Deal. Heavy Duty Vehicles contribute almost quarter of greenhouse gas emissions from road transport in Europe and drive majorly on fossil fuels. New emission restrictions creates a need for transition towards reduced emission targets. Also, increasing number of emission free zones within Europe, give rise to the need of hybridization within the truck and trailer community. Currently, in majority of the cases the trailer units do not possess any kind of drivetrain to support the truck. Trailers carry high loads, such that while accelerating, high power is needed. On the other hand, while braking the kinetic energy is lost, which otherwise could be recaptured. Thus, having a trailer with electric powertrain can support the truck during traction and can charge the battery during braking, helping in reducing the emissions and fuel consumption. Using the King-pin, the amount of support required by trailer can be determined, making it an independent trailer, thus requiring no modification on the truck. Given the heavy-duty environment in which the King-pin operates, the measurement design around it should be robust, compact and measure forces within certain accuracy level. Moreover, modification done to the King-pin is not apricated. These are also the challenges faced by V-Tron, a leading company in the field of services in mobility domain. The goal of this project is to design a smart King-pin, which is robust, compact and provides force component measurement within certain accuracy, to the independent e-trailer, without taking input from truck, and investigate the energy management system of the independent e-trailer to explore the charging options. As a result, this can help reduce the emissions and fuel consumption.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low. As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers. To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is: How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems? HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases: I. Highway: non-professional drivers; II. Distribution Centre: professional drivers.
