End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach

Background The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes. Results We present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. Conclusion We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

Robot path planning based on genetic algorithm with hybrid initialization method

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

Autonomous path planning through application of rotated two-parameter overrelaxation 9-point Laplacian iteration technique

Autonomous path navigation is one of the important studies in robotics since a robot’s ability to navigate through an environment brings about many advancements with it. This paper suggests the iteration technique called half-sweep two parameter overrelaxation 9-point laplacian (HSTOR-9P) to be applied on autonomous path navigation and aims to investigate its effectiveness in performing computation for path planning in an indoor static environment. This iteration technique is a harmonic function that solves the Laplace’s equation where the modelling of the environment is based on. The harmonic functions are an appropriate method to be used on autonomous path planning because it satisfies the min-max principle, therefore avoiding the occurrence of local minima which traps robot’s movements, and that it offers complete path planning algorithm. Its performance is tested against its predecessor iteration technique. Results shown that HSTOR-9P iteration technique enables path construction in a lower number of iterations, thus, performs better than its predecessors.

Monitor Potential Attack Locations in a Specific Area within DTN Network

In this paper will discuss and examine message transmission from the attacker process within the scope of Delay Tolerance Networks (DTNs). DTNs are a new area of research that can be developed in networking. Delay-tolerant networks are those networks that may not have a complete path between networks end-to-end via direct links and may be under development for a long time. As part of the improvement, we will compare a survey of DTN routing protocols with a real region area, and then taking into account the possibilities of detecting the presence of areas of weakness that lead to penetration, which will occur in the nodes while on the move. In this study, we will use the ONE simulator to track messages within nodes.

Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation

The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction of vessel movements is essential for safety and tracking. However, the applications of prediction techniques have a high cost related to computational efficiency and low resource saving. This article presents a sample method to select historical data on vessel-specific routes to optimize the computational performance of the prediction of vessel positions and route estimation in real-time. These historical navigation data can help to estimate a complete path and perform vessel position predictions through time. This Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst) method works in a Vessel Traffic Service database to save computational resources when predictions or route estimations are executed. This article discusses AIS data and the artificial neural network. This work aims to present a prediction model that correctly predicts the physical movement in the route. It supports path planning for the Vessel Traffic Service. After testing the method, the results obtained for route estimation have a precision of 76.15%, and those for vessel position predictions through time have an accuracy of 81.043%.

Equality co-neighborhood domination in graphs

In this work, a new domination parameter is introduced in the graphs, which is called “equality co-neighborhood”. Various types of properties of this parameter have been studied. Additionally, this parameter has been determined for certain graphs, such as complete, path, cycle, wheel, star, and complete bipartite graphs. In addition, three types of graph operations have been discussed with some properties presented for the new type of domination. The first operation is a complement of the graph especially to the charts mentioned above. The second operation is the union of two graphs, and the third operation is the corona of two graphs.

Debugging Network Reachability with Blocked Paths

In this industrial case study we describe a new network troubleshooting analysis used by VPC Reachability Analyzer, an SMT-based network reachability analysis and debugging tool. Our troubleshooting analysis uses a formal model of AWS Virtual Private Cloud (VPC) semantics to identify whether a destination is reachable from a source in a given VPC configuration. In the case where there is no feasible path, our analysis derives a blocked path: an infeasible but otherwise complete path that would be feasible if a corresponding set of VPC configuration settings were adjusted.Our blocked path analysis differs from other academic and commercial offerings that either rely on packet probing (e.g., tcptrace) or provide only partial paths terminating at the first component that rejects the packet. By providing a complete (but infeasible) path from the source to destination, we identify for a user all the configuration settings they will need to alter to admit that path (instead of requiring them to repeatedly re-run the analysis after making partial changes). This allows users to refine their query so that the blocked path is aligned with their intended network behavior before making any changes to their VPC configuration.

Exploiting cooperative sensing for accurate target tracking in industrial Internet of things

Wireless sensor networks are a cornerstone of the Internet of things with many applications. An important aspect of such applications is target tracking using self-positioned known sensor nodes. Over the years, many schemes have been proposed to locate and track the target path. However, accuracy and reliable tracking remain an open area of research. In this article, we propose a dynamic cooperative multilateral sensing scheme for indoor industrial environments to improve target localization and tracking accuracy. The scheme is designed to select reliable nodes based on the distance between nodes within-cluster and to the target for reduced positioning error. Furthermore, a cluster node is dynamically selected based on distance from the base station. We simulate the proposed technique in scenarios with tracking at regular intervals and with the complete path. Furthermore, the performance of the scheme is also tested under different sensor coverage areas. The results show that the proposed scheme provides better target tracking with up to 19% higher accuracy in comparison to the traditional trilateration scheme.