Abstract. The safety of Cyber-physical system(CPS) is up to its behavior, and it is a key property for CPS to be applied in critical application fields. A method for CPS behavior modeling and safety verification is put forward in this paper. The behavior model of CPS is described by extended hybrid system description language(E-HYSDEL).The formal definition of hybrid program(HP) is given, and the behavior model is transformed to HP based on the definition. The safety of CPS is verified by inputting the HP to KeYmarea. The advantage of the approach is that it models CPS intuitively and verify it's safety strictly avoiding the state space explosion
Reverse-geocoding performs an important function for many mobile applications, converting geographic latitude & longitude coordinates into real-world physical locations. While the resulting reverse-geocoded locations can be invaluable for many mobile apps, the process comes at a high cost: either battery power must be expended to invoke a cloud server, or local storage must be used to keep detailed cartographic data to run the process on the phone. In our work we reduce these costs by exploiting the user's geolocality and perform on-smartphone caching of reverse-geocoded locations obtained from calls to the cloud. To that end, we configured three different geospatial region-definition schemes (convex hulls, radial boundaries, and our own cartographic sparse hashes), implemented Android software to perform this caching, and explored cache propagation via preemptive pushing. We evaluated our system using a data set of 1.1 million geotagged photos taken with smartphones and show that our caching: (1) reduces the number of cloud server calls by over 70% for neighborhood granularity and by over 85% for city granularity; and (2) consumes less than 1MB of hash-encoded data even for a complete precomputation of the San Francisco Bay Area.
Abstract:The main problems of collaborative filtering are initial rating, data sparsity and recommendation in time. A recommendation approach based on HMM model, which creates nearest neighbour set by simulating the user behaviours of web browsing, is a good way to solve the above problems. However, the HMM or model parameters constantly vary with customer's changing preference. When there is a new type of data to join, the HMM can only be discovered by relearn, which will affect real time of recommendation. Therefore a recommendation approach based on DBN and HMM is proposed. The approach will improve real time recommendation, and experiments shows that it has high recommendation quality.
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