Initial Coin Offerings (ICOs) consist of an innovative form of capital raising. In the digital era, it has become technically possible to raise substantial funds within a very short period of time. This paper analyses the civil liability regime applying to misrepresentations com-municated to investors acquiring tokens in an ICO. Pursuant to the prospectus liability provisions of the Swiss Code of Obligations (CO), any actor participa-ting in the drafting or dissemination of prospectuses is liable for untrue, misleading representations or omis-sions of a material fact in prospectuses or similar state-ments. We argue that this regime applies by analogy to ICOs. This study portrays the various business models prevailing in the digital marketplace in order to discuss the legal qualification of tokens issued in an ICO. Pros-pectus requirements are detailed with a view to asses-sing how to translate them into the world of ICOs. We also address the elements of prospectus liability. In par-ticular, the advantages of the specific prospectus liabi-lity regime are compared with the general liability re-gime. Last but not least, this paper covers the transfer of the prospectus liability provisions into the new Fi-nancial Services Act that is currently under review by the Parliament.
Today's mobile data applications aspire to deliver services to a user anywhere – anytime while fulfilling his Quality of Service (QoS) requirements. However, the success of the service delivery heavily relies on the QoS offered by the underlying networks. As the services operate in a heterogeneous networking environment, we argue that the generic information about the networks' offered-QoS may enable an anyhow mobile service delivery based on an intelligent (proactive) selection of ‘any' network available in the user's context (location and time). Towards this direction, we develop a QoS-predictions service provider, which includes functionality for the acquisition of generic offered-QoS information and which, via a multidimensional processing and history-based reasoning, will provide predictions of the expected offered-QoS in a reliable and timely manner. We acquire the generic QoS-information from distributed mobile services' components quantitatively (actively and passively) measuring the applicationlevel QoS, while the reasoning is based on statistical data mining and pattern recognition techniques.
