Understanding and interpreting a script is essential for effective acting. Existing visualization methods, however, primarily focus on general narrative comprehension and often neglect actors' specific cognitive and expressive needs. To address this gap, we propose an actor-oriented visual analytics approach that automatically extracts key script features, including implicit emotions, causal relationships, and critical events, to support rapid comprehension of coherent storylines. These extracted features are integrated into a coordinated multi-view visualization, enabling actors to analyze and explore scripts comprehensively and gain deeper insights into the narrative. Furthermore, we introduce a method for structuring performance guidance, converting script events into interpretable, multi-stage recommendations that adapt to individual acting styles. Actor feedback is incorporated to iteratively refine these recommendations, ensuring alignment with personal performance traits. To demonstrate the practicality of our approach, we implement ActorVis, a system that operationalizes these methods and provides actors with interactive visualizations and adaptive performance guidance. Case studies and user evaluations confirm the approach's effectiveness in facilitating rapid script understanding and enhancing personalized performance support.

Abstract Computation should develop and become more powerful and flexible as a result of the expansion of apps and the incorporation of novel consumers into the realm of computing systems. The potential of live virtual machine (VM) migration among various clouds is one of the growing study fields in cloud computing. It can be more important when the cloud service quality that a user is presently using deteriorates or when a new cloud service is launched that is superior in performance, quality, and pricing to the existing services. Because this problem is NP‐Hard in nature, this article proposes a method to migrate the load from an over‐loaded VMs to the active machine that is least loaded using a fuzzy‐based shark smell optimization algorithm. This algorithm is based on a shark's ability to discover prey as a strong hunter in nature, based on the shark's smell sense and motion to the odor source. The suggested optimization technique mathematically models diverse shark behaviors in the search environment, which is seawater. The CloudSim is utilized to illustrate the efficiency of the approach compared to others. The outcomes reveal that energy consumption and execution time are better than Genetic Algorithm and Particle Swarm Optimization algorithms.

Abstract Abrupt development of resources and rising expenses of infrastructure are leading institutions to take on cloud computing. Albeit, the cloud environment is vulnerable to various sorts of attacks. So, recognizing malicious software is one of the principal challenges in cloud security governance. Intrusion detection system (IDS) has turned to the most generally utilized element of computer system security that asserts the cloud from diverse sorts of attacks and threats. As evident, no systematic literature review exists that focuses on cloud computing usage within IDS processes. The previous investigations had not considered the statistical analysis method. Hence, this paper examined the IDS mechanisms in cloud computing systematically. Twenty‐two articles have been obtained using defined filters divided into four sections: hypervisor‐based IDS, network‐based IDS, machine learning‐based IDS, and hybrid IDS. The comparison is performed depending on the outcomes illustrated in the investigations. It demonstrates that IDS precision, inclusiveness, overhead, and reaction time have been discussed in many studies. Simultaneously, less attention has been paid to cost‐sensitivity, functioning, attack tolerance, and intrusion facing. This paper has made an excellent effort to organize literature drawn from multiple sources into a manuscript.

Collaborative virtual environment (CVE) systems support many users to share a virtual world and to do collaborative work and entities exchange information on network by sending messages to maintain the consistency of the systems. To maintain the scalability, interest management is usually adopted in designing a large-scale CVE system. Entities in the CVE system are divided into groups and each entity only sends updates to the entities that are interested in it in order to reduce network traffic. This paper describes several grouping strategies: entity-based grouping and cell-based grouping and proposes an adaptive grouping scheme based on template matching for the tradeoff of reducing the superfluous messages and the cost of maintaining groups in order to minimize the overhead of grouping. The simulation results show that the scheme is fit for the implementation of a cost-effective and high performance CVE system

Collaborative virtual environment (CVE) systems support many users to share a virtual world and to do collaborative work and multiple-server architectures are widely used in which multiple servers are deployed and each server handles a partition of the virtual world. One of the key issues in the design of scalable CVE systems based on the multiple-server architecture is partitioning problem. In this paper, a task-based clustering method is proposed for solving the partitioning problem in CVE systems. Because of the uncertainty of the number of users in the CVE system, using the task-based clustering method, the virtual world is divided into the collaborative task areas according to the current number of users in the CVE system and each collaborative task area is assigned to a server. Though several experiments, the effectiveness of our proposed method is shown.