Neuromodeling in horticulture and viticulture

The article considers the possibilities of using the artificial intelligence in horticulture and viticulture. At present, the artificial intelligence technologies are actively used in agriculture, which make it possible to effectively determine crop yields, automate the cropping and storage of agricultural produce, determine the condition of the soil, the composition and effective use of fertilizers, identify plant diseases and bring weeds under control using recognition methods. The use of the artificial intelligence methods in horticulture and viticulture has its own specific features: firstly, robotic complexes for harvesting cherries, apricots, apples, peaches and grapes; and secondly, the identification of fruit diseases by means photo recognition using neural networks’ machine learning

Soft computing-based methods for semantic service retrieval

University of Technology Sydney. Faculty of Engineering and Information Technology.Nowadays, a large number of business services have been advertised to customers via online channels. To access the published services, the customers typically search for the services by using search engines. Consequently, in order to meet the customers' desires, many researchers have focused on improving performance of the retrieval process. In the recent past, semantic technologies have played an important role in service retrieval and service querying. A service retrieval system consists of two main processes; service annotation and service querying. Annotating services semantically enables machines to understand the purpose of services, while semantic service querying helps machines to expand user queries by considering meanings of query terms, and retrieve services which are relevant to the queries. Because of dealing with semantics of services and queries, both processes can further assist in intelligent and precise service retrieval, selection and composition. In terms of semantic service annotation, a key issue is the manual nature of service annotation. Manual service annotation requires not just large amount of time, but updating the annotation is infrequent and, hence, annotation of the service description changes may be out-of-date. Although some researchers have studied semantic service annotation, they have focused only on Web services, not business service information. Moreover, their approaches are semi-automated, so service providers are still required to select appropriate service annotations. Similar to semantic service annotation, existing literature in semantic service querying has focused on processing Web pages or Web services, not business service information. In addition, because of issues of ubiquity, heterogeneity, and ambiguity of services, the use of soft computing methods offers an interesting solution for handling complex tasks in service retrieval. Unfortunately, based on the literature review, no soft-computing based methods have been used for semantic service annotation or semantic service querying. In this research, intelligent soft-computing driven methods are developed to improve the performance of a semantic retrieval system for business services. The research includes three main parts, namely, intelligent methods for semantically annotating services, querying service concepts, and retrieving services based on relevant concepts. Furthermore, a prototype of a service retrieval system is built to validate the developed intelligent methods. The research proposes three semantic-based methods; ECBR, Vector-based and Classification-based, for accomplishing each research part. The experimental results present that the Classification-based method, which is based on soft-computing techniques, performs well in the service annotation and outperforms both the ECBR and the Vector-based methods in the service querying and service retrieval

How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$ simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.Comment: Correction of typos and other minor correction