Abstract We study the query version of constrained minimum link paths between two points inside a simple polygon $P$ with $n$ vertices such that there is at least one point on the path, visible from a query point. The method is based on partitioning $P$ into a number of faces of equal link distance from a point. This partitioning is essentially a link-based shortest path map. Initially, we solve this problem for two given points $s$, $t,$ and a query point $q$. Then, the proposed solution is extended to a general case for three arbitrary query points: $s$, $t$ and $q$. In the former case, we propose an algorithm with $O(n)$preprocessing time. For the latter case, we develop an algorithm with $O(n^3)$ preprocessing time. The link distance of a $q$-$visible$ path between $s$, $t$ and the path are provided in time $O(\log n)$ and $O(m+\log n)$, respectively, for the above two cases, where $m$ is the number of links.

The goal of influence maximization (IM) is to find a set of$k$nodes that maximize their influence spread over a social network. IM is a widely used model for information propagation in social networks. Although IM has been widely investigated in recent years, the fair propagation of information is still understudied. In this article, we consider the group-fairness-aware IM, where the objective is to ensure that information has been fairly distributed across different groups in the population. We extend the notion of group fairness in IM, introduced by Tsang et al., by considering the speed of information propagation in different groups of a social network and formulating it as a new group-fairness metric. We propose a multiobjective metaheuristic (SetMOGWO), based on the multiobjective gray wolf optimizer, to improve the fair propagation of information in IM concerning various fairness metrics. Experimental results show that the proposed algorithm outperforms the previous work with respect to all introduced fairness metrics. We also carry out detailed experimental analyses on real-world networks and report interesting relationships between fairness concepts and network characteristics, including the price of ensuring fairness in social networks, the effect of social groups’ structure on fairness, and the dependence among fairness metrics. Especially, we show that traditional IM techniques often neglect smaller groups. On the other hand, we show that, with a low cost, it can be ensured that these groups receive a fair portion of resources in fair order.

We study the query version of constrained minimum link paths between two points inside a simple polygon $P$ with $n$ vertices such that there is at least one point on the path, visible from a query point. The method is based on partitioning $P$ into a number of faces of equal link distance from a point, called a link-based shortest path map (SPM). Initially, we solve this problem for two given points $s$, $t$ and a query point $q$. Then, the proposed solution is extended to a general case for three arbitrary query points $s$, $t$ and $q$. In the former, we propose an algorithm with $O(n)$ preprocessing time. Extending this approach for the latter case, we develop an algorithm with $O(n^3)$ preprocessing time. The link distance of a $q$-$visible$ path between $s$, $t$ as well as the path are provided in time $O(\log n)$ and $O(m+\log n)$, respectively, for the above two cases, where $m$ is the number of links.

A study on one of the most important issues in a human action recognition task, i.e. how to create proper data representations with a high‐level abstraction from large dimensional noisy video data, is carried out. Most of the recent successful studies in this area are mainly focused on deep learning. Deep learning methods have gained superiority to other approaches in the field of image recognition. In this survey, the authors first investigate the role of deep learning in both image and video processing and recognition. Owing to the variety and plenty of deep learning methods, the authors discuss them in a comparative form. For this purpose, the authors present an analytical framework to classify and to evaluate these methods based on some important functional measures. Furthermore, a categorisation of the state‐of‐the‐art approaches in deep learning for human action recognition is presented. The authors summarise the significantly related works in each approach and discuss their performance.

Large scale distributed systems employ thousands of resources which inevitably suffer from the unavailability issue. Serious side effects like unexpected delay or failure in the application execution are probable in case of such an issue. The imposed outcome might then be catastrophic consequences for real time applications or penalties for the service providers. Better prediction of the resource unavailability helps diminishing the undesired outcomes. This paper proposes a resource availability prediction algorithm for the mentioned goal. The resource availability variation is modeled as a stochastic process. By analyzing the availability information of NDU resources and both physical and virtual machines of the PlantLab, we found that the transition probabilities among the availability levels are non-stationary. To cope with this characteristic, we introduce Availability Transition Patterns (ATPs); the ATPs are dynamically constructed and the transitions between them are modeled by a Markov chain. The future ATP is then predicted based on the constructed Markov chain, according to which the resource availability-level is predicted. Experimental results confirm the efficiency of the proposed prediction algorithm.

Illumination variation is a challenging issue in face recognition. In many conventional approaches the low‐frequency coefficients are usually discarded in order to compensate the illumination variations, and hence degrade the visual quality. To deal with these problems, an adaptive normalisation‐based method is proposed in this study. Each image is normalised according to its lighting attribute by mapping the low‐frequency components to the normal condition instead of discarding them by applying a novel statistical concept called light mapping matrix. The method preserves the low‐frequency facial features, maximising the intra‐individual correlation and improves the visual quality of face images in different lighting conditions.

The process of pairwise comparison (PC) in AHP requires the decision maker to fill a total number of kn elements in k matrices (n and k are the number of alternatives and criteria, respectively). The decision making process becomes a more time consuming process with a higher possibility of making human errors by the increase in the value of kn. In this paper, a self regulating approach to AHP's PC, inspired by the similar process in PROMETHEE II, is proposed. In this approach the decision maker does not have to identify all of the kn elements directly; therefore, the production of final results will be faster and even more accurate. The proposed approach is implemented and evaluated with real world data. The results show an outstanding consistency (Cons0.70) between the final output of this modified version of AHP and that of the other three multi-criteria decision making methods, which are used in our experiments.

This paper deals with dynamic vision, vehicle avoidance, navigation, and applications to guide dog robot and vehicle intelligent control system. Danger estimation of vehicles at the intersection is proposed in this paper. Traffic rules of vehicles and the locomotion strategy of robot is formulated, and finally a danger matrix is defined for each vehicle represented by its location and its moving direction. This method is implemented on an image processing system. The location, velocity, and the moving direction of a vehicle are measured by computer vision and its danger coefficient is evaluated. About 90% of vehicles at a T shape intersection have been successfully voted in danger judgment.

The general paradigm for vehicle detection and avoidance by proper selection of visual and sonar sensors are proposed. The most appropriate sensor for robot detection and navigation is properly selected depending on its functions. Four types of actions based on stereo-typed motion are used for the navigation of the robot: moving along wall, moving alone;, moving forward and tracking motion. The first approach is based on sonar, and the remaining are vision based actions. The visual detection of vehicle is based on the locomotion strategy: the sign pattern based stereo-typed motion. The sign pattern of vehicle is the shadow underneath the vehicle which is darker than any other parts of the asphalt paved road. The robot detects the sign pattern of vehicle from the distance of 30m and extracts its front shape in real time processing.>

This paper proposes a method for detecting and tracking a moving vehicle on the asphalt road based on vision. The distance between the robot and a moving vehicle, velocity, and its width are estimated during the tracking process. The authors show a real time moving vehicle detection method which is enough for detecting a moving vehicle for any weather conditions including fine, partly cloudy and cloudy. Furthermore, the method is applicable for the road covered partly or completely by shadows. Stationary vehicle is also distinguished from moving one by evaluating its velocity. The proposed method detects a vehicle from a distance of 25 m with a domestic video camera and extracts its front shape in real time.