Semantic Computing is an emerging research field that has drawn much attention from both academia and industry. It addresses the derivation and matching of semantics of computational "content" where "content" may be anything including text, multimedia, hardware, network, etc. which can be mapped to many areas in Computer Science that involve analyzing and processing the intentions of humans with computational content. This paper discusses some potential applications of Semantic Computing in Computer Science.

Abstract-Profile-based protocols such as Bluetooth 4.0 Low Energy (BLE) Technology have enabled very low-power devices to efficiently participate across multiple application domains in the Internet of Things (IoT). We propose a middleware layer called rimware to enable today's profile-based IoT nodes to realize the full potential of inter-application participation. First, the nodes need to be able to establish authenticated, secure connections to the cloud through trusted gateways using an adapter structure when the smartphone or tablet is not available. Second, a knowledge base in the cloud is needed to establish mapping between profiles on the device side and application semantics on the cloud side. Results show our rimware to provide a modular, extensible structure for integration across three applications while incurring minimal code size and communication overhead on BLE devices.

This paper describes a computing backend for a waterpipe monitoring system. Today, most such systems are divided into event-triggered and continuous monitoring, but they all lack systematic handling of data. Many systems simply store data in files with specific naming conventions and ad hoc formats, making them difficult to retrieve, maintain, disseminate, and analyze.To address these problems, our backend supports data management and dissemination. Unlike previous systems that store data in files or conventional databases before analysis, our modular architecture not only saves data in efficiently searchable ways by indexing as a baseline dataset but also detected events in discrete time manner and other processed data. To facilitate analysis, we design a plug-in structure to allow processing modules to perform inline processing and shorten detection time. For data dissemination, our architecture can compose multiple visualizations including geographical maps to create powerful tools to yield new insight into massive datasets. The backend system enables Internet web service for visualization, data mangement, and remote sensor control for better integration. Our system is applicable to not only water pipelines but also bridges and civil structures in general.Our proposed backend system has been implemented and validated through field deployment. One such system has been running for over 1.5 years and has collected millions of records to date. A Google Map intergrated visualization service has been developed to demostrate lively collected records in real-time. This is expected to be more helpful for better understanding of civil structures' behavior in the long term.