Passenger acceptance is a key factor for the successful integration, uptake and use of autonomous vehicles (AVs) in the domain of public transpor-tation. Especially knowing opinions and attitudes around safety, comfort and convenience. We discuss a pilot study conducted as part of a larger research project where AVs are being tested to transport members of the general public on a specified route with designated stops. We present preliminary findings of fieldwork conducted where people were asked their opinions and attitudes both before and after riding on an AV shuttle as a passenger for the first time. This allows us to compare user expectation beforehand with actual experience after-wards.
Processing data streams is increasingly gaining momentum, given the need to process these flows of information in real-time and at Web scale. In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. Research in these areas has proposed an ecosystem of solutions to query, reason and perform real-time processing over heterogeneous and distributed data streams on the Web. However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web. In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. The features of TripleWave were selected based on requirements of real use-cases, and support a diverse set of scenarios, independent of any specific RSP implementation. TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the Linked Sensor Data dataset). It can be invoked through both pull- and push-based mechanisms, thus enabling RSP engines to automatically register and receive data from TripleWave.
The increasing interdependence of firms and individuals throughout the world facilitates the development of the crowdlending market. Crowdlending is an emerging source of financing involving open calls to the public, generally via internet, to finance with loans individuals or companies (Meyer, 2007). The major role of crowdlending activities has been to bring new energy to a global economy that is unable to catch its breath following the recent financial crises (Berger, 2009). North America leads the world in crowdlending volumes, representing 58% of the world's market. But the global strong growth is due, in part, to the rise of Asia as a major crowdlending player with 21% of the world's market, putting the region slightly ahead of Europe (Pignon. 2015). As of today, Switzerland has not adopted specific regulation governing the practice of crowdlending, but the regulator has issued a fact sheet on this topic, informing the stakeholders of the crowdlending industry that some of their activities may be subject to banking regulation (Dietrich, 2015). In this context, this article get a general overview of the regulations adopted abroad, in particular in the USA and in the European Union, where countries such as the UK or France chose to adopt a more detailed regulation, some with financial limits applicable to crowdlending campaigns or with specific requirements regarding who would be authorized to invest in crowdlending campaigns or soft regulation in the form of Best Practices.
Research question: A social network programme called J’arrête de fumer was set up in 2016 in the six French-speaking cantons of Switzerland. It consists of Facebook groups where people agree on a date to quit smoking. A peak of relapse appears during the first three weeks of the programme. This research aims to explore the feasibility of building a Chatbot to help people to get over this peak in future iterations of the programme. Methods: It has been shown that the urge to smoke may be one of the reasons for relapses. Being able to distract users from the idea of smoking during these phases would help them to get through these three first weeks. Due to the large number of participants, a human intervention within the craving time frame is difficult to achieve, but such a constraint would be easier to overcome with ChatBots. Results: A ChatBot for the Telegram platform has been developed. It offers five different modules to overtake the time frame where the urge to smoke is greatest. Some of these modules, such as motivating comments and factual information, are already well used, but some others are less widely explored, like helping scientific research by classifying images or putting people in touch with each other as another form of distraction. Conclusion: ChatBots offer interesting opportunities for helping smoking cessation communities, as they would help participants during craving time frames and would be able to handle the large number of participants.
Content–based multimedia retrieval has been an active research domain since the mid 1990s. In the medical domain visual retrieval started later and has mostly remained a research instrument and less a clinical tool, even though a few tools for retrieval are employed in clinical work. The limited size of data sets due to privacy constraints is often mentioned as a reason for these limitations. Nevertheless, much work has been done in medical visual information retrieval, including the availability of increasingly large data sets and scientific challenges. Annotated data sets and clinical data for the images have now become available and can be combined for multi– modal retrieval. Much has been learned on user behavior and application scenarios. This text is motivated by the advances in medical image analysis and the availability of more public data large data sets that often include clinical data that can be combined for multimodal retrieval based on the experience available in the multimedia community. This text is a systematic review of recent work (concentrating on the period between 2011-2017) on content–based multi–modal retrieval and image understanding in the medical domain, where image understanding includes techniques such as detection, localization, and classification for leveraging visual content. The main conferences in the field are screened for relevant articles and these are presented in a structured way, identifying current limitations and areas where work is still much required. Objective of the work is to summarize the current state of research for multimedia researchers not working in the medical field. It provides ways to get data sets and identify promising research directions. The text highlights the areas of advances in the past six years and particularly a trend to use larger scale training data sets as well as deep learning approaches that can replace or complement hand–crafted feature extraction. Using images alone will likely only work in limited sub domains but combining multiple sources of data for multi–modal retrieval has the biggest chances of success, particularly for clinical impact. Future fields of research are identified in the text, as there is a high research potential in the medical multimedia domain.
This chapter clarifies the important aspects of biomedical texture analysis under the general framework introduced in Chapter 1. It was proposed that any approach can be character-ized as the combination of local texture operators and regional aggregation functions. The type of scale and directional information that can or cannot be modeled by categories of texture processing methods is revealed through theoretic analyses and experimental valida-tions. Several key aspects are found to be commonly overlooked in the literature and are highlighted. First, we demonstrate the risk of using regions of interest for aggregation that are regrouping tissue types of different natures. Second, a detailed study of the type of directional information important for biomedical texture characterization suggests that fun-damental properties lie in the local organization of image directions. In addition, it was found that most approaches cannot efficiently characterize the latter, and even fewer can do it with invariance to local rotations. We conclude by deriving novel comparison axes to evalu-ate the relevance of biomedical texture analysis methods in a specific medical or biological applicative context.
There is a growing interest in the treatment of animals used in the tourism industry, yet the academic literature on the animal welfare consideration exhibited by tourists is limited. This study seeks to identify if demographic differences amongst tourists engaging in animal-based tourism influence the importance they attribute to the ethical treatment of those animals. A statistical analysis of 136 completed questionnaires demonstrates statistically significant difference in animal welfare concerns between Asian and Western tourists. Suggestions for further research and managerial implications emanating from the findings conclude the paper.
The integration of autonomous vehicles (AVs) onto public roads presents both technical and social challenges. Public understanding and acceptance of AVs requires engagement with people who live in, work at or visit cities where they are deployed on public road networks. We investigate the impact of one of the first placements of AV passenger transport on public roadways: the Sion <<SmartShuttle>>. This late-breaking research presents preliminary results from interviews with local shopkeepers, residents, pedestrians and drivers to understand their attitudes and opinions of the shuttle. We also discuss video-based fieldwork that demonstrates how drivers negotiate next moves with one another through their windscreens using embodied signals such as gestures, lip-reading, and head nods to coordinate and manage a traffic situation. Finally, we consider the implications for how fully autonomous vehicles might be designed to take into account the subtle negotiations that road users engage in to coordinate with one another.
