In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artif...In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.展开更多
A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the dis...A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the distance to the nearest obstacle from any given location. A Chamfer distance based sensor model to associate observations from a laser ranger finder to the map of the environment without the need for ray tracing, data association, or feature extraction is presented. It is shown that the robot can be localised by solving a non-linear optimisation problem formulated to minimise the Chamfer distance with respect to the robot location. The proposed algorithm is able to perform well even when robot odometry is unavailable and requires only a single tuning parameter to operate even in highly dynamic environments. As such, it is superior than the state-of-the-art particle filter based solutions for robot localisation in occupancy grids, provided that an approximate initial location of the robot is available. Experimental results based on simulated and public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.展开更多
Many image inverse problems are ill-posed for no unique solutions. Most of them have incommensurable or mixed-type objectives. In this study, a multi-objective optimization framework is introduced to model such ill-po...Many image inverse problems are ill-posed for no unique solutions. Most of them have incommensurable or mixed-type objectives. In this study, a multi-objective optimization framework is introduced to model such ill-posed inverse problems. The conflicting objectives are designed according to the properties of ill-posedness and certain techniques. Multi-objective evolutionary algorithms have capability to optimize multiple objectives simultaneously and obtain a set of trade-off solutions. For that reason, we use multi-objective evolutionary algorithms to keep the trade-off between these objectives for image ill-posed problems. Two case studies of sparse reconstruction and change detection are imple- mented. In the case study of sparse reconstruction, the measurement error term and the sparsity term are optimized by multi-objective evolutionary algorithms, which aims at balancing the trade-off between enforcing sparsity and reducing measurement error. In the case study of image change detection, two conflicting objectives are constructed to keep the trade-off between robustness to noise and preserving the image details. Experimental results of the two case studies confirm the multi-objective optimization framework for ill-posed inverse problems in image processing is effective.展开更多
In open normative multi-agent communities,an agent is not usually and explicitly given the norms of the host agents.Thus,when it is not able to adapt the communities's norms,it is totally deprived of accessing res...In open normative multi-agent communities,an agent is not usually and explicitly given the norms of the host agents.Thus,when it is not able to adapt the communities's norms,it is totally deprived of accessing resources and services from the host.Such circumstance severely affects its performance resulting in failure to achieve its goal.Consequently,this study attempts to overcome this deficiency by proposing a technique that enables an agent to detect the host's potential norms via self-enforcement and update its norms even in the absence of sanctions from a third-party.The authors called this technique as the potential norms detection technique(PNDT).The PNDT consists of five components: Agent's belief base; observation process; potential norms mining algorithm(PNMA);verification process; and updating process.The authors demonstrate the operation of the PNMA algorithm by testing it on a typical scenario and analyzing the results on several perspectives.The tests' results show that the PNDT performs satisfactorily albeit the success rate depends on the environment variables settings.展开更多
基金This work was supported in part by National Natural Science Foundation of China under Grants No.61101108，National S&T Major Program under Grants No.2011ZX03002-005-01
文摘In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.
文摘A novel algorithm for localising a robot in a known two-dimensional environment is presented in this paper. An occupancy grid representing the environment is first converted to a distance function that encodes the distance to the nearest obstacle from any given location. A Chamfer distance based sensor model to associate observations from a laser ranger finder to the map of the environment without the need for ray tracing, data association, or feature extraction is presented. It is shown that the robot can be localised by solving a non-linear optimisation problem formulated to minimise the Chamfer distance with respect to the robot location. The proposed algorithm is able to perform well even when robot odometry is unavailable and requires only a single tuning parameter to operate even in highly dynamic environments. As such, it is superior than the state-of-the-art particle filter based solutions for robot localisation in occupancy grids, provided that an approximate initial location of the robot is available. Experimental results based on simulated and public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.
基金This work was supported by the National Natural Science Foundation of China （Grant no. 61273317 and 61422209）, the National Top Youth Talents Program of China, the Specialized Research Fund for the Doctoral Program of Higher Education （Grant no. 20130203110011） and the Fundamental Research Fund for the Central Universities （Grant no. K5051202053）.
文摘Many image inverse problems are ill-posed for no unique solutions. Most of them have incommensurable or mixed-type objectives. In this study, a multi-objective optimization framework is introduced to model such ill-posed inverse problems. The conflicting objectives are designed according to the properties of ill-posedness and certain techniques. Multi-objective evolutionary algorithms have capability to optimize multiple objectives simultaneously and obtain a set of trade-off solutions. For that reason, we use multi-objective evolutionary algorithms to keep the trade-off between these objectives for image ill-posed problems. Two case studies of sparse reconstruction and change detection are imple- mented. In the case study of sparse reconstruction, the measurement error term and the sparsity term are optimized by multi-objective evolutionary algorithms, which aims at balancing the trade-off between enforcing sparsity and reducing measurement error. In the case study of image change detection, two conflicting objectives are constructed to keep the trade-off between robustness to noise and preserving the image details. Experimental results of the two case studies confirm the multi-objective optimization framework for ill-posed inverse problems in image processing is effective.
文摘In open normative multi-agent communities,an agent is not usually and explicitly given the norms of the host agents.Thus,when it is not able to adapt the communities's norms,it is totally deprived of accessing resources and services from the host.Such circumstance severely affects its performance resulting in failure to achieve its goal.Consequently,this study attempts to overcome this deficiency by proposing a technique that enables an agent to detect the host's potential norms via self-enforcement and update its norms even in the absence of sanctions from a third-party.The authors called this technique as the potential norms detection technique(PNDT).The PNDT consists of five components: Agent's belief base; observation process; potential norms mining algorithm(PNMA);verification process; and updating process.The authors demonstrate the operation of the PNMA algorithm by testing it on a typical scenario and analyzing the results on several perspectives.The tests' results show that the PNDT performs satisfactorily albeit the success rate depends on the environment variables settings.
