3D‐vision based detection,localization, and sizing of broccoli heads in the field

This paper describes a 3D vision system for robotic harvesting of broccoli using low‐cost RGB‐D sensors, which was developed and evaluated using sensory data collected under real‐world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning, and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier, and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally, we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground‐truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross‐validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalization capabilities of the system, confirming the strong potential of low‐cost 3D imaging for commercial broccoli harvesting.     

Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.     

Multi‐agent system for knowledge‐based event recognition and composition

This work presents a multi‐agent system for knowledge‐based high‐level event composition, which interprets activities, behaviour and situations semantically in a scenario with multi‐sensory monitoring. A perception agent (plurisensory agent and visual agent)‐based structure is presented. The agents process the sensor information and identify (agent decision system) significant changes in the monitored signals, which they send as simple events to the composition agent that searches for and identifies pre‐defined patterns as higher‐level semantic composed events. The structure has a methodology and a set of tools that facilitate its development and application to different fields without having to start from scratch. This creates an environment to develop knowledge‐based systems generally for event composition. The application task of our work is surveillance, and event composition/inference examples are shown which characterize an alarming situation in the scene and resolve identification and tracking problems of people in the scenario being monitored.     

Versatile Tuning of Humanoid Agent Activity

In this paper, we present an integration framework for heterogeneous motion generators. The objective is to outline issues that are currently easily solved in professional post-processing systems used in film and game production but which cannot be transposed as is to real-time systems with autonomous agents. We summarise our approach for articulated agent-modelling and their animation by combining heterogeneous motion generators, such as real-time motion capturing, key-framing, inverse kinematics, procedural walking. We propose an agent/action-oriented framework. Activity properties such as action simultaneity and motion blending, spatial coherence, motion-flow update schemes, agent attachments, and location corrections, are the main topics handled by our generic animation framework. Numerous examples throughout the paper illustrate our approach and outline encountered problems and solutions or open research directions.     

Verifying Temporal Properties of Reactive Systems: A STeP Tutorial

We review a number of formal verification techniques supported by STeP, the Stanford Temporal Prover, describing how the tool can be used to verify properties of several versions of the Bakery Mutual exclusion algorithm for mutual exclusion. We verify the classic two-process algorithm and simple variants, as well as an atomic parameterized version. The methods used include deductive verification rules, verification diagrams, automatic invariant generation, and finite-state model checking and abstraction.     

How Will We Interact with the Web of Data?

The Web is currently evolving from an information space of linked documents, to a Web of linked, machine-readable data. Perhaps counter-intuitively, this shift to publishing data for machine consumption raises many challenges for human-computer interaction. In this article I will discuss some of the implications of this trend for how we interact with the Web of data and consider how familiar tools such as the Web browser may need to develop.     

Collective knowledge systems: Where the Social Web meets the Semantic Web

What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the “collective intelligence” of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.     

Robust Classification for Imprecise Environments

In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.     

Unexpected products of the allyldiethylamine hydroformylation homogeneously catalyzed by rhodium complexes

Attempts to carry out the hydroformylation of allyldiethylamine homogeneously catalyzed by rhodium complexes led to unexpected formation of N,N,N,N-tetraethyl-1,4-diaminobutane and 4-(diethylamino)-1-butanole as final products. The role of the catalyst on the product formation and the reaction mechanism are briefly discussed.     

Modelling of the lignin peroxidase LIII of Phlebia radiata: use of a sequence template generated from a 3-D structure

A model of the lignin peroxidase LIII of Phlebia radiata wasconstructed on the basis of the structure of cytochrome c peroxidase(CCP). Because of the low percentage of amino acid identitybetween the CCP and the lignin peroxidase LIII of Phlebia radiata,alignment of the sequences was based on the generation of atemplate from a knowledge of the 3-D structure of CCP and consensussequences of lignin peroxidases. This approach gave an alignmentin which all the insertions in the lignin peroxidase were placedat loop regions of CCP, with a 21.1% identity for these twoproteins. The model was constructed using this alignment andthe computer program COMPOSER, which assembles the model asa series of rigid fragments derived from CCP and other proteins.Manual intervention was required for some of the longer loopregions. The -helices forming the structural framework, andespecially the haem environment of CCP, are conserved in theLIII model and the core is close packed without holes. A possiblesite of the substrate oxidation at the haem edge of LIII isdiscussed.