Existing learning-based stereo compression methods usually adopt a unidirectional approach to encoding one image independently and the other image conditioned upon the first. This paper proposes a novel bidirectional coding-based end-to-end stereo image compression network (BCSIC-Net). BCSIC-Net consists of a novel bidirectional contextual transform module which performs nonlinear transform conditioned upon the inter-view context in a latent space to reduce inter-view redundancy, and a bidirectional conditional entropy model that employs interview correspondence as a conditional prior to improve coding efficiency. Experimental results on the InStereo2K and KITTI datasets demonstrate that the proposed BCSIC-Net can effectively reduce the inter-view redundancy and out-performs state-of-the-art methods.

Private and consortium blockchains have recently raised interest in the blockchain community around facilitating business collaboration. In a business network, multiple participants either (1) work with a centralized authority to maintain a complete and verifiable ledger, or (2) transact in a decentralized manner to maintain an independent shared ledger. A centralized, private blockchain system, such as Amazon QLDB, addresses scenario one with good price-to-performance ratio. The second scenario is commonly backed by a consortium blockchain network, such as Hyperledger Fabric, that requires greater and more complex hardware and software to handle complicated consensus and peer-to-peer communications. The disparate design objectives and resultant principles complicate efforts to handle both scenarios within a single blockchain system. Moreover, the two situations are often mixed together for a user in different business activities, and interchangeable with the evolution of the business collaboration. From the outset, users face a dilemma in choosing which blockchain system type will handle their long-term needs. In this paper, we present a novel Full-Spectrum Blockchain as a Service (FSBaaS) built on top of a high-performance centralized private blockchain runtime called Blockchain Lite and a Hyperledger Fabric blockchain runtime. It handles different scenarios flexibly by providing a unified interface for the two blockchain runtimes from programing model and RESTful APIs to the tenant model. A BaaS tenant can subscribe to the two runtimes simultaneously and migrate data from one to the other as needed. Experimental results contrasted the performance of two blockchain runtimes and the practicality of the proposed BaaS.

Some user needs can only be met by leveraging the capabilities of others to undertake particular tasks that require intelligence and labor. Crowdsourcing such capabilities is one way to achieve this. But providing a service that leverages crowd intelligence and labor is a challenge, since various factors need to be considered to enable reliable service provisioning. For example, the selection of an optimal set of workers from those who bid to perform a task needs to be made based on their reliability, expected reward, and distance to the target locations. Moreover, for an application involving multiple services, the overall cost and time constraints must be optimally allocated to each involved service. In this article, we develop a framework, named C rowd S ervice , that supplies crowd intelligence and labor as publicly accessible crowd services via mobile crowdsourcing. The article extends our earlier work by providing an approach for constraints synthesis and worker selection. It employs a genetic algorithm to dynamically synthesize and update near-optimal cost and time constraints for each crowd service involved in a composite service and selects a near-optimal set of workers for each crowd service to be executed. We implement the proposed framework on Android platforms and evaluate its effectiveness, scalability, and usability in both experimental and user studies.

Some user needs in real life can only be accomplished by leveraging the intelligence and labor of other people via crowdsourcing tasks. For example, one may want to confirm the validity of the description of a secondhand laptop by asking someone else to inspect the laptop on site. To integrate these crowdsourcing tasks into user applications, it is required that crowd intelligence and labor be provided as easily accessible services (e.g., Web services), which can be called crowd services. In this paper, we develop a framework named CROWDSERVICE which supplies crowd intelligence and labor as publicly accessible crowd services via mobile crowdsourcing. We implement the proposed framework on the Android platform and evaluate the usability of the framework with a user study.