ARTIST - a reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources

Urinetown: The Musical (April 8-10, 2021)

Program for Urinetown: The Musical (April 8-10, 2021). To view the photos from this production of Urinetown: The Musical, please click here

Machine Learning Meets the Semantic Web

Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation

ARTIST - a reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources

A reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources

ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.Comment: 17 pages, 12 figure

Group Decision Support for Resource Allocation Decisions in Three Person Groups

This research studied the effects of a Group Decision Support System (GDSS) for face-to-face negotiations in three-person groups. The GDSS equipped the groups with full information about each other’s individual preferences regarding a resource allocation problem. In a partial replication of a GDSS experiment, we examined the effect of this full information treatment on post-meeting consensus level, the level of integrative behaviour, and the level of distributive behaviour in the groups. 96 threeperson groups participated. Groups using the GDSS reached a higher level of post-meeting consensus compared to groups not using a GDSS. This finding supports the theory that negotiators lower their demands and increase their involvement if full information is available. No support could be found for the hypothesis that the treatment had an impact on integrative or distributive behaviour