Joining the Dots—Understanding the Value Generation of Creative Networks for Sustainability in Local Creative Ecosystems

In recent years, the ecological shift from an economically driven model of arts and culture to that of an ecosystem in the creative industries determined the emergence of a range of new bottom-up, place-based networks herewith referred to as “creative networks”. This article explores how these networks can generate sustainability for local creative ecosystems through a value network approach. Building on the quadruple helix model to identify the actors in these networks, this study explores the relationships and value flows between the actors of 22 identified creative networks across the UK. It then maps these relationships using data gathered through a mixed methodology that includes survey data and focus group research. Our findings show that creative networks operate as central nodes of the local creative ecosystem, functioning as a ‘glue’ inside the otherwise very heterogenous creative industries. From this position, creative networks can act as catalysts for sustainability. However, the economic, cultural, and social value created by creative networks is often overshadowed by other challenges including a lack of funding and a lack of understanding from policy makers or the public.

Cloud Zero-Day Attack Detection Using Hidden Markov Model with Transductive Learning

In cloud security, detecting attack software is considered an essential task. Among several attack types, a zero-day attack is considered as most problematic because the antivirus cannot able to remove it. The existing attack detection model uses stored data about attack characteristics, which fails to detect zero-attack where an altered attack is implemented for an antivirus system to detect the attack. To detect and prevent zero-day attacks, this paper proposed a model stated as Hidden Markov Model Transductive Deep Learning (HMM_TDL), which generates hyper alerts when an attack is implemented. Also, the HMM_TDL assigns labels to data in the network and periodically updates the database (DB). Initially, the HMM model detects the attacks with hyper alerts in the database. In the next stage, transductive deep learning incorporates k-medoids for clustering attacks and assign labels. Finally, the trust value of the original data is computed and computed in the database based on the value network able to classify attacks and data. The developed HMM_TDL is trained with consideration of two datasets such as NSL-KDD and CIDD. The comparative analysis of HMM_TDL exhibits a higher accuracy value of 95% than existing attack classification techniques.

CO-CREATING SYSTEMIC CHANGES FOR A CIRCULAR ECONOMY IN/UNDER CONSTRUCTION, PRELIMINARY LESSONS FROM THE FLEMISH LIVING LAB ON CIRCULAR CONSTRUCTION

Like any persistent challenge, the transition from a linear to a circular construction economy requires systemic changes. To divert from current practices that maintain the take-make-waste logic, we need new ways of doing and thinking within the value network of construction. In search for those enduring changes, a compass group advises the Flemish Living Lab on Circular Construction. This lab is a three-and-a-half-year project of study and experiment initiated by policy. During four participatory workshops, the compass group identified the most important system hurdles and necessary changes for achieving a circular construction economy, through subsequent steps of system analysis, translation and reflection. These outcomes have been synthesized and validated during the last workshop and are presented in this paper as a preliminary outcome of lab. Further, by bringing together forerunners of construction and co-creating together new knowledge, it is demonstrated how a well-guided compass group allows to bring into practice ‘participatory system modelling’. By consequently translating the resulting insights into the requirements for a targeted call for experiments, it is finally shown that this co-created knowledge has been ‘actionable’ for the policy makers of Circular Flanders, and triggered dozens of partnerships to submit an experiment.

FROM AN INSIDE-IN TOWARDS AN OUTSIDE-OUT URBAN DIGITAL TWIN: BUSINESS MODELS AND IMPLEMENTATION CHALLENGES

Urban Digital Twins are a virtual representation of a city environment with bi-directional communication links. They require collaborations between different actors in the urban ecosystems in order to provide a complete picture of the situation in the city. In order to define the complex relationships between the different actors in the Urban Digital Twin ecosystem, the business model literature helps to answer questions on how value can be created, and how the value network can be controlled. In this paper, we identified four different types of business models for Urban Digital Twins based on whether they are used by the government or the ecosystem, and whether the government or the ecosystem controls the value network of the Urban Digital Twin. Interviews were held in five different existing Urban Digital Twins to identify which challenges the different existing digital twins have when implementing the Urban Digital Twins. The outcomes of the business model scenarios support the design of Urban Digital Twins 1) by identifying which decisions need to be made by cities when developing Urban Digital Twins and 2) by proposing cloud requirements for technology providers supporting cities, in the development of Urban Digital Twins.

Combining Tree Search and Action Prediction for State-of-the-Art Performance in DouDiZhu

AlphaZero has achieved superhuman performance on various perfect-information games, such as chess, shogi and Go. However, directly applying AlphaZero to imperfect-information games (IIG) is infeasible, due to the fact that traditional MCTS methods cannot handle missing information of other players. Meanwhile, there have been several extensions of MCTS for IIGs, by implicitly or explicitly sampling a state of other players. But, due to the inability to handle private and public information well, the performance of these methods is not satisfactory. In this paper, we extend AlphaZero to multiplayer IIGs by developing a new MCTS method, Action-Prediction MCTS (AP-MCTS). In contrast to traditional MCTS extensions for IIGs, AP-MCTS first builds the search tree based on public information, adopts the policy-value network to generalize between hidden states, and finally predicts other players' actions directly. This design bypasses the inefficiency of sampling and the difficulty of predicting the state of other players. We conduct extensive experiments on the popular 3-player poker game DouDiZhu to evaluate the performance of AP-MCTS combined with the framework AlphaZero. When playing against experienced human players, AP-MCTS achieved a 65.65\% winning rate, which is almost twice the human's winning rate. When comparing with state-of-the-art DouDiZhu AIs, the Elo rating of AP-MCTS is 50 to 200 higher than them. The ablation study shows that accurate action prediction is the key to AP-MCTS winning.

An Ecosystem View of Peer-to-Peer Electricity Trading: Scenario Building by Business Model Matrix to Identify New Roles

This article introduces new roles in future peer-to-peer electricity trading markets. Following a qualitative approach, firstly, the value network of the current electricity market is presented. To do so, service streams, critical roles, activities, and their setting in the electricity market are identified. Secondly, in order to identify the main sources of uncertainty, the business model matrix framework is utilized to analyze peer-to-peer electricity trading. Thirdly, four future scenarios are built based on user involvement and customer ownership. The outcome of the scenario building is the emergence of new roles, brokers, and representatives in the future peer-to-peer electricity markets. Fourth, based on the four future scenarios, changes in the value network, new roles, and emerging/evolving activities are identified. Finally, the two new roles are discussed from grid structure, security and privacy, legal, and data protection perspectives. The data is gathered by conducting semi-structured interviews with stakeholders in the current electricity market as well as potential disruptors. This article elaborates on the configuration of the value network in the electricity market and highlights the changes that peer-to-peer trading imposes to the status quo. Through the outcomes of the value network analysis, it assists policy makers to consider the requirements and current market players to reconsider their business models.