The performance and efficiency of A* search algorithm heavily depends on the quality of the heuristic function. Therefore, designing an optimal heuristic function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed heuristic function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a heuristic function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches often avoid complex heuristic function design: they either trade-off the accuracy for faster computation or taking advantage of the parallelism for better performance. The objective of this paper is to reduce the difficulty of complex heuristic function design for A* search algorithm. We aim to design an algorithm that can be automatically optimized to achieve rapid search with high accuracy and low computational cost. In this paper, we present a novel design and optimization method for a Multi-Weighted-Heuristics function (MWH) named Evolutionary Heuristic A* search (EHA*) to: (1) minimize the effort on heuristic function design via Genetic Algorithm (GA), (2) optimize the performance of A* search and its variants including but not limited to WA* and MHA*, and (3) guarantee the completeness and optimality. EHA* algorithm enables high performance searches and significantly simplifies the processing of heuristic design. We apply EHA* to multiple grid-based pathfinding benchmarks to evaluate the performance. Our experiment result shows that EHA* (1) is capable of choosing an accurate heuristic function that provides an optimal solution, (2) can identify and eliminate inefficient heuristics, (3) is able to automatically design multi-heuristics function, and (4) minimizes both the time and space complexity.

The task of visual relationship recognition (VRR) is to recognize multiple objects and their relationships in an image. A fundamental difficulty of this task is class–number scalability, since the number of possible relationships we need to consider causes combinatorial explosion. Another difficulty of this task is modeling how to avoid outputting semantically redundant relationships. To overcome these challenges, this paper proposes a novel architecture with a recurrent neural network (RNN) and triplet unit (TU). The RNN allows our model to be optimized for outputting a sequence of relationships. By optimizing our model to a semantically diverse relationship sequence, we increase the variety in output relationships. At each step of the RNN, our TU enables the model to classify a relationship while achieving class–number scalability by decomposing a relationship into a subject–predicate–object (SPO) triplet. We evaluate our model on various datasets and compare the results to a baseline. These experimental results show our model’s superior recall and precision with fewer predictions compared to the baseline, even as it produces greater variety in relationships.

The sheer volume of contents generated by today’s Internet services is stored in the cloud. The effective indexing method is important to provide the content to users on demand. The indexing method associating the user-generated metadata with the content is vulnerable to the inaccuracy caused by the low quality of the metadata. While the content-based indexing does not depend on the error-prone metadata, the state-of-the-art research focuses on developing descriptive features and misses the system-oriented considerations when incorporating these features into the practical cloud computing systems. We propose an Update-Efficient and Parallel-Friendly content-based indexing system, called Partitioned Hash Forest (PHF). The PHF system incorporates the state-of-the-art content-based indexing models and multiple system-oriented optimizations. PHF contains an approximate content-based index and leverages the hierarchical memory system to support the high volume of updates. Additionally, the content-aware data partitioning and lock-free concurrency management module enable the parallel processing of the concurrent user requests. We evaluate PHF in terms of indexing accuracy and system efficiency by comparing it with the state-of-the-art content-based indexing algorithm and its variances. We achieve the significantly better accuracy with less resource consumption, around 37% faster in update processing and up to 2.5[Formula: see text] throughput speedup in a multi-core platform comparing to other parallel-friendly designs.

Follow the Gap Method (FGM) is an obstacle avoidance method which uses gap arrays. This method recursively directs the robot to the goal state while avoiding the obstacles through the safest gap. Since FGM is a geometric method, it does not consider the robot dynamics. For this reason, oscillations or collisions due to robot dynamics are possible. On the other hand, FGM calculates the desired heading angle, but it does not give linear and angular velocity reference. Dynamic Window Approach (DWA) is one of the most popular obstacle avoidance algorithms which does take robot dynamics into consideration. It calculates best angular and linear velocity pair which is chosen by an objective function. In this paper, an FGM-DW approach which uses the strongest elements of FGM and DWA methods to achieve safe, smooth and fast navigation is proposed. The FGM-DW approach provides these concerns and meets the low level angular and rotational velocity requirement of FGM. In this paper, the performance and analysis of FGM-DW are shown by both simulations and real-world experimental tests.

Precision farming technologies have been increasingly recognized for their potential ability for improving agricultural productivity, reducing production cost, and minimizing damage to the environment. In this context, the main goals of this paper are the following: First, we present a methodology that can be applied to extract semantic information, more specifically some vegetative indices, from plants, in order to further improve the vegetation representation and health by means of a specific semantic robotic system; then, we study in detail the tracked robot’s behavior, by emulating the real settings in a field and analytically analyze the simulation of the robot on an up and down slope path.

Control systems for autonomous robots are concurrent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277–283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414–6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291–302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study.

Ontologies are increasingly being developed on web-based repository hosting platforms such as GitHub. Accordingly, there is a demand for ontology editors which can be easily connected to the hosted repositories. TurtleEditor is a web-based RDF editor that provides this capability and supports the distributed development of ontologies on repository hosting platforms. It offers features such as syntax checking, syntax highlighting, and auto completion, along with a SPARQL endpoint to query the ontology. Furthermore, TurtleEditor integrates a visual editing view that allows for the graphical manipulation of the RDF graph and includes some basic clustering functionality. The text and graph views are constantly synchronized so that all changes to the ontology are immediately propagated and the views are updated accordingly. The results of a user study and performance tests show that TurtleEditor can indeed be effectively used to support the distributed development of ontologies on repository hosting platforms.

Rapid urbanization has brought about an influx of people to cities, tipping the scale between urban and rural living. Population predictions estimate that 64% of the global population will reside in cities by 2050. To meet the growing resource needs, improve management, reduce complexities, and eliminate unnecessary costs while enhancing the quality of life of citizens, cities are increasingly exploring open innovation frameworks and smart city initiatives that target priority areas including transportation, sustainability, and security. The size and heterogeneity of urban centers impede progress of technological innovations for smart cities. We propose a Smart Stadium as a living laboratory to balance both size and heterogeneity so that smart city solutions and Internet of Things (IoT) technologies may be deployed and tested within an environment small enough to practically trial but large and diverse enough to evaluate scalability and efficacy. The Smart Stadium for Smart Living initiative brings together multiple institutions and partners including Arizona State University (ASU), Dublin City University (DCU), Intel Corporation, and Gaelic Athletic Association (GAA), to turn ASU’s Sun Devil Stadium and Ireland’s Croke Park Stadium into twinned smart stadia to investigate IoT and smart city technologies and applications.

Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible — a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called ‘gradient boosted’ model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the ‘median sale’ and ‘repeat sales’ indices.

One of the most fundamental challenges when accessing gestural patterns in 3D motion capture databases is the definition of spatiotemporal similarity. While distance-based similarity models such as the Gesture Matching Distance on gesture signatures are able to leverage the spatial and temporal characteristics of gestural patterns, their applicability to large 3D motion capture databases is limited due to their high computational complexity. To this end, we present a lower bound approximation of the Gesture Matching Distance that can be utilized in an optimal multi-step query processing architecture in order to support efficient query processing. We investigate the performance in terms of accuracy and efficiency based on 3D motion capture databases and show that our approach is able to achieve an increase in efficiency of more than one order of magnitude with a negligible loss in accuracy. In addition, we discuss different applications in the digital humanities in order to highlight the significance of similarity search approaches in the research field of gestural pattern analysis.