The proposed work aims to discover the international technology competition and development of human-artificial intelligence (AI) collaboration on content creation in the film and animation industries to support the strategic planning, decision-making of R&D, and soft innovation. The study demonstrates a hybrid approach that combines technology life cycle (TLC) and latent Dirichlet allocation (LDA) topic modelling. We analyse 1,982 patents of AI collaborating on creating film and animation in the primary patent application countries (i.e., patents applied to the intellectual property offices of the USA, China, Korea, Japan, and European Patent Office, EPO) from 2010 to 2020. The TLC results show growing trends in the international technology competition. The major topic trends corresponding to TLC phases denote strong potential or future stagnation signals in different countries. The study provides the future R&D signals and suggests stimulating soft innovation with human-AI collaboration to face growing competition.

With the continuous growth and importance of data, the need for strong data protection becomes crucial. Encryption plays a vital role in preserving the confidentiality of data, and attribute-based encryption (ABE) offers a meticulous access control system based on attributes. This study investigates the integration of Fernet encryption with initialisation vector (IV) and ABE, resulting in a hybrid encryption approach that enhances both security and flexibility. By combining the advantages of Fernet encryption and IV-based encryption, the hybrid encryption scheme establishes an effective and robust mechanism for safeguarding data. Fernet encryption, renowned for its simplicity and efficiency, provides authenticated encryption, guaranteeing both the confidentiality and integrity of the data. The incorporation of an initialisation vector (IV) introduces an element of randomness into the encryption process, thereby strengthening the overall security measures. This research paper discusses the advantages and drawbacks of the hybrid encryption of Fernet and IV with ABE.

This paper aims to share the author's vision on possible research directions for big knowledge-based AI. A renewed definition of big knowledge (BK) and big knowledge systems (BKS) is first introduced. Then the first BKS model, called cloud knowledge social intelligence (CKEI) is provided with a hierarchy of knowledge as a service (KAAS). At last, a new semantics, the big-and-broad step axiomatic structural operational semantics (BBASOS) for applications on BKS is introduced and discussed with a practical distributed BKS model knowledge graph network KGN and a mini example.

Scientific experiments, simulations, and modern applications generate large amounts of data. Analysing resources required to process such big datasets is essential to identify application running costs for cloud or in-house deployments. Researchers have proposed keeping data in raw formats to avoid upfront utilisation of resources. However, it poses reparsing issues for frequently accessed data. The paper discusses detailed comparative analysis of resources required by in-situ engines and traditional database management systems to process a real-world scientific dataset. A resource monitoring framework has been developed and incorporated into the raw data query processing framework to achieve this goal. The work identified different query types best suited to a given data processing tool in terms of data to result time and resource requirements. The analysis of resource utilisation patterns has led to the development of query complexity aware (QCA) and resource utilisation aware (RUA) data partitioning techniques to process big raw data efficiently. Resource utilisation data have been analysed to estimate the data processing capacity of a given machine.

Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user's location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user's travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user's end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.

The emergence of service ecosystems can accelerate the industrial renewal required because of urgent global challenges. However, existing research has not sufficiently grasped the social dynamics of coevolution in ecosystems that enhance industrial renewal. This study aimed to advance ecosystem research through a practice lens and to present the key characteristics of industrial service ecosystem practice involved in industrial renewal. Consequently, its three characteristics - <i>accomplishment</i>, <i>attractiveness</i> and <i>actionability</i> - were configured based on an abductive study derived from the ecosystem literature, three practice-oriented approaches to learning, and two case ecosystem examinations. These features created the logic for resource integration and enhanced ecosystems to evolve as units, thus exceeding the actors' independent avenues of renewal. The findings of this study provided a deeper understanding of the coevolution in ecosystems needed to accelerate industrial renewal as well as a novel conceptualisation of an <i>ecosystem-as-practice</i> for further studies.

This study investigates cross-border commercial air passenger traffic in Africa, focusing on the development of the 15 busiest country pairs during the period 1989 to 2015. It explores dimensions not previously studied by using ICAO's 'Traffic by Flight Stage' (TFS) and data from the CEPII Gravity Dataset. The spatial results show on an uneven geographical distribution of country pairs with the centre of gravity to South, East and North-East Africa, with one long-distance corridor between Egypt and South Africa. Countries in North and West Africa have rather few linkages, except for Egypt. Central African countries are not represented among the 15 country pairs. Although the number of passengers and the rank among the countries have shifted, South Africa and Egypt stand out, as having most country pair connections. Factors such as changing economic, diplomatic and political relations have had an influence on changing country pair connections throughout the period. A number of variables were selected to investigate how they correlated with Africa's commercial passenger traffic. Of the seven variables selected, five did show on a correlation and two did partly so. In that view, Africa's air traffic follows rather typical patterns.

This study aims to identify and understand the perspectives of automotive aftersales stakeholders regarding current challenges posed by decarbonisation strategies. It examines potential responses that the automotive aftersales business could undertake to address these challenges. Semi-structured interviews were undertaken with automotive industry experts from Europe and Latin America. This paper focuses primarily on impacts of decarbonisation upon automotive aftersales and the potential role of data in that business. Results show that investment in technology will be a condition for businesses that want to remain active in the industry. Furthermore, experts agree that incumbent manufacturers are not filling the technology gap that the energy transition is creating in the automotive sector, a consequence of which will be the entrance of new players from other sectors. The current aftersales businesses will potentially lose bargaining control. Moreover, policy makers are seen as unreliable leaders of the transition agenda.

Unmanned stores are one of the latest conceptual developments in retail and have received much attention, especially in the context of COVID-19-related social restrictions and the associated changes in consumer behaviour. The concept considers the latest technological developments and promises to offer various benefits to consumers and retailers based on artificial intelligence and automation. Using a German sample, this paper aims to evaluate consumers' acceptance of and intention to use the most prominent innovative solutions in unmanned stores. A modified technology acceptance model (TAM) as a theoretical framework was applied to the study. The results of the structural equation modelling make two contributions to the existing literature: First, the acceptance criteria for unmanned stores have not been analysed previously. Second, the modified TAM could be confirmed in this study. We provide empirical evidence suggesting that significant numbers of consumers accept unmanned stores, especially if the stores are strategically located and when individuals have a high innovation affinity.

Significant work in service domain has focused on customer journey within e-service delivery system process (e-SDSP). Few studies have focused on process-centric approach to customer journey during delivery of e-services. This study aims to investigate the performance assessment of three online retailers (alternatives) using perceived service process during different stages of e-SDSP as a criterion for decision-making. TOPSIS is used in this paper to rate and evaluate multiple online retailers. Based on perceived service process as the criterion, results show that online retailer-2 outperforms other two online retailers. This study is one of the first to rate online retailers by utilising customer-perceived service process (latent variables) as a decision-making criterion throughout e-SDSP. The finding suggests that perceived searching process is the most essential criterion for decision-making, followed by the perceived after-sales service process, the perceived agreement process, and the perceived fulfilment process. Implications, limitations, and future scope are also discussed.