Submission Deadline: 30 May 2022 (closed) View: 135
In computer science, especially in computability theory and computational complexity theory, a model of computation is a model which describes how an output of a mathematical function is computed given an input. It describes how units of computations, memories, and communications are organized.
Nowadays, devices such as mobile devices, information-sensing Internet of things devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks are collecting data with sizes that exceed the capacity of traditional software to process. Using computation model encapsulated with probabilities, functions and theories, allows people to process huge amount of data with high accuracy and efficiency. A computational model can cope with complexity in ways that verbal arguments cannot, resulting in satisfactory answers for what would otherwise be ambiguous arguments. Furthermore, computational models can manage complexity at several levels of analysis, allowing data from various levels to be integrated and connected.
Given the above, the aim of this special issue is to introduce novel algorithms or implementations that exploit the models of computation to tackle multiple optimization problems. We invite high quality scientific contributions that explore the specification and practice of computation models. Real-world applications of the proposed algorithms are of significant interest.
Potential topics include but are not limited to the following:
1 Sequential models (finite state machines; Post–Turing machines and tag machines; Pushdown automata; Register machines; Random-access machines; Turing machines)
2 Functional models (Abstract rewriting systems; Combinatory logic; General recursive functions; Lambda calculus)
3 Concurrent models (Actor model; Cellular automaton; Interaction nets; Kahn process networks; Logic gates and digital circuits; Petri nets; Synchronous Data Flow)
4. Deep learning for data managements
5. Model-based knowledge transfer methods
6. Machine learning, deep learning, and optimization techniques for advanced information management
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