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1The workplace is a captive environment where the overall contribution of the meal served could be 2 an important element of the overall diet. Despite growing demand little information is available to 3 aid healthy dish selection. 4This study identifies information valued by consumers in the UK, Greece, Denmark and France using 5 best-worst scaling. Value for Money, Nutrition and Naturalness are key elements of information that 6 consumers require to be able to make a conscious decision about dish selection in all four countries. 7Latent class analysis shows that consumers align to one of five cluster groups, i.e., Value Driven, 8Conventionalists, Socially Responsible, Health Conscious and Locavores. 9Understanding key information needs can allow food operators to align their service with consumer 10 preferences across different market segments. 11

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Increase in usage of electronic communication tools (email, IM, Skype, etc.) in enterprise environments has created new attack vectors for social engineers. Billions of people are now using electronic equipment in their everyday workflow which means billions of potential victims of Social Engineering (SE) attacks. Human is considered the weakest link in cybersecurity chain and breaking this defense is nowadays the most accessible route for malicious internal and external users. While several methods of protection have already been proposed and applied, none of these focuses on chat-based SE attacks while at the same time automation in the field is still missing. Social engineering is a complex phenomenon that requires interdisciplinary research combining technology, psychology, and linguistics. Attackers treat human personality traits as vulnerabilities and use the language as their weapon to deceive, persuade and finally manipulate the victims as they wish. Hence, a holistic approach is required to build a reliable SE attack recognition system. In this paper we present the current state-of-the-art on SE attack recognition systems, we dissect a SE attack to recognize the different stages, forms, and attributes and isolate the critical enablers that can influence a SE attack to work. Finally, we present our approach for an automated recognition system for chatbased SE attacks that is based on Personality Recognition, Influence Recognition, Deception Recognition, Speech Act and Chat History. CCS CONCEPTS • Security and privacy → Phishing; • Computing methodologies → Supervised learning;

Completeness of metadata is one of the most essential characteristics of their quality. An incomplete metadata record is a record of degraded quality. Existing approaches to measure metadata completeness limit their scope in counting the existence of values in fields, regardless of the metadata hierarchy as defined in international standards. Such a traditional approach overlooks several issues that need to be taken into account. This paper presents a fine‐grained metrics system for measuring metadata completeness, based on field completeness. A metadata field is considered to be a container of multiple pieces of information. In this regard, the proposed system is capable of following the hierarchy of metadata as it is set by the metadata schema and admeasuring the effect of multiple values of multivalued fields. An application of the proposed metrics system, after being configured according to specific user requirements, to measure completeness of a real‐world set of metadata is demonstrated. The results prove its ability to assess the sufficiency of metadata to describe a resource and provide targeted measures of completeness throughout the metadata hierarchy.