With the popularization of wireless networks and mobile intelligent terminals, mobile crowd sensing is becoming a promising sensing paradigm. Tasks are assigned to users with mobile devices, which then collect and sub...
详细信息
With the popularization of wireless networks and mobile intelligent terminals, mobile crowd sensing is becoming a promising sensing paradigm. Tasks are assigned to users with mobile devices, which then collect and submit ambient information to the server. The composition of participants greatly determines the quality and cost of the collected information. This paper aims to select fewest participants to achieve the quality required by a sensing task. The requirement namely "t-sweep k-coverage" means for a target location, every t time interval should at least k participants sense. The participant selection problem for "t-sweep k-coverage" crowd sensing tasks is NP-hard. Through delicate matrix stacking, linear programming can be adopted to solve the problem when it is in small size. We further propose a participant selection method based on greedy strategy. The two methods are evaluated through simulated experiments using users' call detail records. The results show that for small problems, both the two methods can find a participant set meeting the requirement. The number of participants picked by the greedy based method is roughly twice of the linear programming based method. However, when problems become larger, the linear programming based method performs unstably, while the greedy based method can still output a reasonable solution.
In the period following the last financial crisis, equity markets have performed poorly. In consequence, equity long-only strategies have generally disappointed over this period. This has motivated the investigation o...
详细信息
ISBN:
(纸本)9781479923809
In the period following the last financial crisis, equity markets have performed poorly. In consequence, equity long-only strategies have generally disappointed over this period. This has motivated the investigation on whether better performance can be achieved by including equity options in the portfolios. We show that simple systematic option strategies improve portfolio performance. Results are supported by thorough backtesting and simulations.
The application of the Threshold Accepting (TA) algorithm in portfolio optimisation can reduce portfolio risk compared with a Trust-Region local search algorithm. In a benchmark comparison of several different objecti...
详细信息
ISBN:
(纸本)9781479923809
The application of the Threshold Accepting (TA) algorithm in portfolio optimisation can reduce portfolio risk compared with a Trust-Region local search algorithm. In a benchmark comparison of several different objective functions combined with different optimisation routines, we show that the TA search algorithm applied to a Conditional Value at Risk (CVaR) objective function yields the lowest Basel III market risk capital requirements. Not only does the TA algorithm outmatch the Trust-Region algorithm in all risk and performance measures, but when combined with a CVaR or 1% VaR objective function, it also achieves the best portfolio risk profile.
Agglutinative languages, such as Hungarian, use inflection to modify the meaning of words. Inflection is a string transformation which describe how can a word converted into its inflected form. The transformation can ...
详细信息
ISBN:
(纸本)9781479959969
Agglutinative languages, such as Hungarian, use inflection to modify the meaning of words. Inflection is a string transformation which describe how can a word converted into its inflected form. The transformation can be described by a transformational string. The words can be classified by their transformational string, so inflection is considered as a classification. Linear separability of clusters is important to create an efficient and accurate classification method. This paper review a linear programming based testing method of linear separability. This method was analyzed on generated data sets, these measurements showed the time cost of the algorithm grows polynomially with the number of the points. The accusative case of Hungarian was used to create a data set of 56.000 samples. The words were represented in vector space by alphabetical and phonetic encoding and left and right adjust, thus four different representation of words were used during the tests. Our test results showed there are non linear separable cluster pairs in both of the representations.
暂无评论