Search Results (7)

AutoML is enabling many practitioners to use sophisticated Machine Learning pipelines even without being experienced in building application-specific solutions. Adapting AutoML to the field of unsupervised learning, particularly to the task of clustering, is challenging, as clustering is highly subjective and application-specific; the goal is not to find the best way to group data objects based on previously seen examples, but to find interesting new structures within potentially unknown data objects that provide actionable insights. The level of interestingness of a clustering is highly subjective and is subject to a variety of different characteristics making different clusterings of the same dataset (e.g., grouping people by age, gender, or special interests). In this paper, we propose an Automated Exploratory Clustering framework which determines multiple clusterings satisfying different notions of interestingness automatically. To this end, we generate multiple clusterings via AutoML processes and return a selection of clusterings, from which the user can explore the most preferred ones. We use different methods like the skyline operator to prune non-Pareto-optimal clusterings wrt. different dimensions of interestingsness and deliver a small set of valuable clusterings. In this way, our approach enables practitioners as well as domain experts to identify valuable clusterings without becoming experts in clustering as well, thus reducing human efforts and resources in finding application-specific solutions. Our empirical investigation with current state-of-the-art methods is carried out on a number of benchmark datasets, where a well-established ground truth can proxy for the wishes of a domain expert and multiple interestingness properties of the clusterings. Full article

In the contemporary era, where digital audio-visual media continues to evolve, the media landscape is increasingly converging with the urban landscape. This trend has made the importance of urban waterfront areas in city landscapes more pronounced. The evaluation method for the skyline of architectural groups has evolved from a subjective approach to a quantitative one. In recent years, the box-counting dimension method based on fractal theory has been widely used for this evaluation. According to this theory, the higher the fractal dimension value, the “more complex” the skyline, and the greater people’s preference for it. However, this evaluation method has certain limitations. In particular, “suddenly rising” tall buildings can raise the local fractal dimension value, yet they may disrupt the rhythm of the skyline. This paper attempts to introduce the Least-squares method to mark the vertical and horizontal axis values of the skyline of architectural groups, fit curves based on these values, and then compare the fitted curves with the actual skyline. This approach aims to improve the evaluation of “suddenly rising” buildings. By doing so, it supplements and optimizes traditional quantitative analysis solely based on fractal theory. Furthermore, the method is validated through a case study of the Qingdao (Shandong Province, China) Fushan Bay waterfront architectural group. Through this method, it is possible to more objectively identify buildings that “suddenly rise” in the skyline, improve the evaluation of the skyline based solely on complexity, and further extend the curve-fitting results into an evaluation of rhythm. Through multi-dimensional evaluation, this approach can effectively guide urban development. Full article

Cosmopolitan species are rare in red algae, which have a low-dispersal capacity unless they are dispersed by human-mediated introductions. Gelidium crinale, a turf-forming red alga, has a widespread distribution in tropical and temperate waters. To decipher the genetic diversity and phylogeography of G. crinale, we analyzed mitochondrial COI-5P and plastid rbcL sequences from collections in the Atlantic, Indian, and Pacific Oceans. Phylogenies of both markers statistically supported the monophyly of G. crinale, with a close relationship to G. americanum and G. calidum from the Western Atlantic. Based on the molecular analysis from these materials, Pterocladia heteroplatos from India is here merged with G. crinale. Phylogeny and TCS networks of COI-5P haplotypes revealed a geographic structure of five groups: (i) Atlantic-Mediterranean, (ii) Ionian, (iii) Asian, (iv) Adriatic-Ionian, and (v) Australasia-India-Tanzania-Easter Island. The most common ancestor of G. crinale likely diverged during the Pleistocene. The Bayesian Skyline Plots suggested the pre-LGM population expansion. Based on geographical structure, lineage-specific private haplotypes, the absence of shared haplotypes between lineages, and AMOVA, we propose that the cosmopolitan distribution of G. crinale has been shaped by Pleistocene relicts. The survival of the turf species under environmental stresses is briefly discussed. Full article

Skyline queries, which are based on the concept of Pareto dominance, filter the objects from a potentially large multi-dimensional collection of objects by keeping the best, most favoured objects in satisfying the user′s preferences. With today′s advancement of technology, ad hoc meetings or impromptu gatherings involving a group of people are becoming more and more common. Intuitively, deciding on an optimal meeting point is not a straightforward task especially when conflicting criteria are involved and the number of criteria to be considered is vast. Moreover, a point that is near to a user might not meet all the various users′ preferences, while a point that meets most of the users′ preferences might be located far away from these users. The task becomes more complicated when these users are on the move. In this paper, we present the Region-based Skyline for a Group of Mobile Users (RSGMU) method, which aims to resolve the problem of continuously finding the optimal meeting points, herein called skyline objects, for a group of users while they are on the move. RSGMU assumes a centroid-based movement where users are assumed to be moving towards a centroid that is identified based on the current locations of each user in the group. Meanwhile, to limit the searching space in identifying the objects of interest, a search region is constructed. However, the changes in the users′ locations caused the search region of the group to be reconstructed. Unlike the existing methods that require users to frequently report their latest locations, RSGMU utilises a dynamic motion formula, which abides to the laws of classical physics that are fundamentally symmetrical with respect to time, in order to predict the locations of the users at a specified time interval. As a result, the skyline objects are continuously updated, and the ideal meeting points can be decided upon ahead of time. Hence, the users′ locations as well as the spatial and non-spatial attributes of the objects are used as the skyline evaluation criteria. Meanwhile, to avoid re-computation of skylines at each time interval, the objects of interest within a Single Minimum Bounding Rectangle that is formed based on the current search region are organized in a Kd-tree data structure. Several experiments have been conducted and the results show that our proposed method outperforms the previous work with respect to CPU time. Full article

Drought is one of the main causes of mortality in holm oak (Quercus ilex) seedlings used in reforestation programs. Although this species shows high adaptability to the extreme climate conditions prevailing in Southern Spain, its intrinsic genetic variability may play a role in the differential response of some populations and individuals. The aim of this work was to identify proteins and derived proteotypic peptides potentially useful as putative markers for drought tolerance in holm oak by using a targeted post-acquisition proteomics approach. For this purpose, we used a set of proteins identified by shotgun (LC-MSMS) analysis in a drought experiment on Q. ilex seedlings from four different provenances (viz. the Andalusian provinces Granada, Huelva, Cadiz and Seville). A double strategy involving the quantification of proteins and target peptides by shotgun analysis and post-acquisition data analysis based on proteotypic peptides was used. To this end, an initial list of proteotypic peptides from proteins highly represented under drought conditions was compiled that was used in combination with the raw files from the shotgun experiment to quantify the relative abundance of the fragment’s ion peaks with the software Skyline. The most abundant peptides under drought conditions in at least two populations were selected as putative markers of drought tolerance. A total of 30 proteins and 46 derived peptides belonging to the redox, stress-related, synthesis,-folding and degradation, and primary and secondary metabolism functional groups were thus identified. Two proteins (viz., subtilisin and chaperone GrpE protein) were found at increased levels in three populations, which make them especially interesting for validation drought tolerance markers in subsequent experiments. Full article

Along with the application of the sensor network, there will be large amount of dynamic data coming from sensors. How to dig the useful information from such data is significant. Skyline query is aiming to identify the interesting points from a large dataset. The group-based skyline query is to find the outstanding Pareto Optimal groups which cannot be g-dominated by any other groups with the group same size. However, the existing algorithms of group-based skyline (G-Skyline) focus on the static data set, how to conduct advanced research on data stream remains an open problem at large. In this paper, we propose the group-based skyline query over the data stream. In order to compute G-Skyline efficiently, we present a sharing strategy, and based on which we propose two algorithms to efficiently compute the G-Skyline over the data stream: the point-arriving algorithm and the point-expiring algorithm. In our experiments, three synthetic data sets are used to test our algorithms; the experiments results show that our algorithms perform efficiently over a data stream. Full article

Algorithms for skyline querying based on wireless sensor networks (WSNs) have been widely used in the field of environmental monitoring. Because of the multi-dimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. To ensure the efficient use of sensor energy, a geometry-based distributed spatial query strategy (GDSSky) is proposed in this paper. Firstly, the paper presents a geometry-based region partition strategy. It uses the skyline area reduction method based on the convex hull vertices, to quickly query the spatial skyline data related to a specific query area, and proposes a regional partition strategy based on the triangulation method, to implement distributed queries in each sub-region and reduce the comparison times between nodes. Secondly, a sub-region clustering strategy is designed to group the data inside into clusters for parallel queries that can save time. Finally, the paper presents a distributed query strategy based on the data node tree to traverse all adjacent sensors’ monitoring locations. It conducts spatial skyline queries for spatial skyline data that have been obtained and not found respectively, so as to realize the parallel queries. A large number of simulation results shows that GDSSky can quickly return the places which are nearer to query locations and have larger pollution capacity, and significantly reduce the WSN energy consumption. Full article