Hong Kong Applied Science and Technology Research Institute

Kangheng Wu

Yang Liu

Zhi Bin Lei

Chao Feng

Zhibin Lei

Hailiang Li

Man Yau Chiu

Cheuk Hang Chiu

Ji Hyoun Park

Vincent Wai-Leuk Tam

Wei Chen Huang

Lawrence Kwan Yeung

Ze Yang

Shiyu Liao

A method for a protocol in digital asset trading includes step as follow. Trading information is exchanged between the first and the second nodes, in which a first digital currency belonging to the first node and a second digital currency belonging to the second node are expected to be exchanged, and prices of the first and second digital currencies are time-dependent. An atomic swap script and a smart contract are generated. A compensation fee to be paid to the second node by the first node is computed. A redeeming or refunding event is performed in response to the atomic swap script and the smart contract with paying the compensation fee to the second node, in which the compensation fee is dependent on the prices of the first and second digital currencies such that is time-dependent.

Apparatus and method for providing protocol for digital asset trading

A method of high dimensional data analysis in real-time comprising executing dimension-reducing an input historical data set under a t-SNE model and determining from the resulting dimension-reduced data set a recent; further dimension-reducing the recent group data set under a PCA model; statistical analyzing the further dimension-reduced data set to determine a threshold group for distinguishing abnormal data from normal ones in a real-time data stream. The method may further include training a classifier using the abnormal or normal data set for predicting anomaly in the real-time data source system. Alternatively, a discrepancy training data set is computed from one of the normal and abnormal data sets and be used to train one of independent normal and abnormal data regression models; with the other one trained by transfer learning based on the trained one. The trained regression models are then used to predict discrepancy values.

Apparatus and method of high dimensional data analysis in real-time

A method for organizing and processing feature based data structures that can be used in linguistic spell checking and auto-correction, comprising: splitting an original dictionary into sub-dictionaries based on different values of a common feature such as high frequency words; receiving an input text that contains errors; determining a sub-dictionary selection feature from the input human-readable text; selecting the sub-dictionary based on the determined sub-dictionary selection feature; executing a first matching in the selected sub-dictionary, wherein a match is found if a similarity between the characters, words, or phrases in proximity of the errors in the input text and a character, word, or phrase in the sub-dictionary is above a threshold; if a unique match is found, the result is returned as an output to correct the errors; otherwise, executing a second matching with a raised threshold, and repeating the second matching until a unique match is found.

System and method for organizing and processing feature based data structures

A method for a vehicle management system with a perspective view camera generating perspective images of vehicles in a traffic direction, including performing an anti-perspective transform on the perspective images; separating each anti-perspective image into sub-images along one of a radial direction and a tangential direction relative to the traffic direction; determining a scale factor for each sub-image based upon measuring a scale of each vehicle at plural positions in one image using a frame difference method performing a scale transform for each sub-image using the corresponding scale factors for the sub-image; combining each of the scale transformed sub-images for each vehicle into corresponding enhanced anti-perspective images; performing vehicle detection for each vehicle based a combination of the enhanced anti-perspective images in which tracking is enhanced with an optimized detection box size range determined by the enhanced anti-perspective images; and performing vehicle tracking for each vehicle based on the combination of the enhanced anti-perspective images in which detecting is enhanced with the optimized detection box size range determined by the enhanced anti-perspective images.

Vehicle detection, tracking and localization based on enhanced anti-perspective transformation

A computer implemented method of load shedding used in stream computing system that considers the relative importance of each of the applications processing the incoming input data or events. The method of load shedding method also accounts for system physical constraints, such as memories and CPU utilization. The load shedding method first observes the workload of each application and arriving rate of the incoming input data or events. If the system is under an overloading condition, calculate a input data or event drop ratio for each application such that the projected sum of all applications' workload will be at or below the system capacity when the unprocessed input data or events are dropped according to the drop ratio for each application.

Configurable dynamic load shedding method in distributed stream computing system

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Hong Kong, China

Kangheng Wu