Reduce Search Time Research Articles

Enhancing the identification of web genres by combining internal and external structures

Automating the identification of the genre of web pages becomes a promising research area in web pages classification, as it can be used to improve the quality of the web search result and to reduce search time. Many studies have been proposed to identify the genre of web pages. These studies differ with respect to three main factors which are the features used, the classification algorithm and the list of genres used for the evaluation. The main idea of this paper is to combine the predictions produced by different classifiers using the internal and external structures of a web page. To combine the predictions of the different classifiers we used different OWA operators and the Dempster-Shafer (DS) combination rule. Moreover, we proposed an improved DS combination method based on the ranks of the predicted genres. The experiments conducted using the two known datasets (KI-04 and SANTINIS), show that our study achieves better results in comparison with other ensemble classifiers and genre identification works as well.

Procedure for the Contextual, Textual and Ontological Construction of Specialized Knowledge Bases

Information Retrieval (IR) from data sources such as the web, databases, sensors, etc., and structuring it through ontologies is an ambitious research field that leads to the reduction of information search time, improvement of the quality of found information and the efficient decision making. In this work, we have proposed a system for extracting information from several data sources to enrich an ontology in the field of plant pathology. The objective of this work is to develop a knowledge base in the field of plant pathology, by structuring the data present in various data sources accessible mainly through the web. To do this, we first propose a modular development approach of a domain ontology. The construction of the ontology at first uses the text-based construction technique and then, we use the generic architecture of a web data extraction system technique from literature to propose a data extraction system for the enrichment of the different ontology modules of the domain. For each subsystem of our extraction system, we have developed the algorithms to be implemented.

Implementation of an Available Parking Space Detection System in Hectic Parking Lots

Parking automation has been developed for locating available parking spaces inside a parking lot with new technologies, such as automatic collection, capacity, or empty spaces’ detection; to both reduce search time and avoid crowds of vehicles waiting to park in it. A simple detection system of available spaces, within the Faculty and Staff parking lot of the Engineering and Chemistry Departments at the Autonomous University of Chihuahua, Mexico (UACH), was set up. First, an analysis of vehicle capacity was carried out to establish the peak hours of vehicles’ entrances and compare it to the parking lots’ capacity. For one working week (6 days), it was determined that Tuesdays have the highest vehicle occupancy, and Saturdays are the days with the least traffic. Second, the geometric design of the parking lot (divided into two areas because of the terrain topography: upper and bottom levels), its dimensions and location of parking spots and marks on the pavement for traffic directions, were obtained from its blueprint, to visualize how the surveillance cameras can be oriented to cover the area. A detection system prototype was created for a specific area of the parking lot, using an artificial vision technique to perform the video extraction, while the digital image processing was developed in MATLAB. To complement and achieve the objective, Object- Oriented Programming (OOP) was used to obtain the count analysis as well as the availability/occupancy of each of the parking areas. This information is shown on a web page, with a simple driver-friendly design, since it has each parking space number and its availability in blue with the word “Available” or, in the opposite case, the word “Taken” in pink.

Improvement of Information Retrieval Systems by Using Hidden Vertical Search

The exponential growth of the number of documents in digital libraries and on the Web calls for very intensive development of retrieval systems. One possible architectural approach to IRS, an architecture with hidden verticals, is proposed in this paper. In IRS with hidden verticals, documents from the searched corpus are stored into a predefined set of classes. The user's query is classified before the search, and searching is done only within the corresponding class. The performance of the proposed system is compared to the performance of standard IRS (that contains a unique inverted index) and IRS with cluster pruning (in which searching corpus is clustered and query is compared to the clusters' centroids first, then search is done only in the most similar cluster). Search time in the proposed system is 7.9 times shorter than in the standard IRS and 1.7 times shorter than in the system with cluster pruning. The precision of the proposed system is 2.59 times higher than the precision of the standard IRS, and 1.68 times better compared to the IRS with cluster pruning. The recall of the proposed system is 1.09 times smaller than the recall of the standard IRS, but it is 1.28 times better than the recall of the IRS with cluster pruning. Based on the above results, we can say that proposed approach reduces search time and increases search precision with a minimal reduction in recall.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization.

We report O-Pair Search, a new approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides using an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus.

Contextual cueing in preview search

Frequently finding a target in the same location within a familiar context reduces search time, relative to search for objects appearing in novel contexts. This learned association between a context and a target location requires several blocks of training and has long-term effects. Short-term selection history also influences search, where previewing a subset of a search context shortly before the appearance of the target and remaining distractors speeds search. Here we explored the interactions between contextual cueing and preview benefit using a modified version of a paradigm from Hodsoll and Humphreys (Journal of Experimental Psychology: Human Perception and Performance, 31(6), 1346–1358, 2005). Participants searched for a T target among L distractors. Half of the distractors appeared 800 ms before the addition of the other distractors and the target. We independently manipulated the repetition of the previewed distractors and the newly added distractors. Though the previewed set never contained the target, repetition of either the previewed or the newly added context yielded contextual cueing, and the effect was greater when the previewed context repeated. Another experiment trained participants to associate the previewed context with a target location, then disrupted the association in a testing phase. This disruption eliminated contextual cueing, suggesting that learning of the previewed context was associative. These findings demonstrate an important interaction between distinct kinds of selection history effects.

PARTITIONING KNOWN ENVIRONMENTS FOR MULTI-ROBOT TASK ALLOCATION USING GENETIC ALGORITHMS

A common challenge facing emergency services, particularly in response to fires and/or earthquakes, is the location and subsequent extraction of people from hazardous buildings in a timely manner. This usually requires emergency service workers to enter the building and put their own lives at risk, even when there may be no people to extract. However, given recent advances in autonomous robotics, drones are expected to help humans in tasks such as search and rescue, and similar tasks, where coverage and time are key parameters. The aim is to complete a comprehensive search of the environment as quickly as feasible. Using multiple drones rather than a single drone can reduce search time, although performance can be poor if the searching is non-coordinated. Therefore, partitioning a terrain is important in order to effectively distribute the drone search work so that good coverage can be achieved in a reasonable amount of time and redundant searching is eliminated. In this paper a novel Square Based Terrain Partitioning (SBTP) algorithm is presented using a genetic algorithm to partition a known environment into multiple domains in a multi-robot exploration system. In addition, a second genetic algorithm is presented to allocate the domain search workload such that, given a certain number of drones, the overall search time is minimized.

Loss allocation in distribution networks with distributed generators undergoing network reconfiguration

In this paper, a branch exchange based heuristic network reconfiguration method is proposed for obtaining an optimal network in a deregulated power system. A unique bus identification scheme is employed which makes the load flow and loss calculation faster due to its reduced search time under varying network topological environment. The proposed power loss allocation technique eliminates the effect of cross-term analytically from the loss formulation without any assumptions and approximations. The effectiveness of the proposed reconfiguration and loss allocation methods are investigated by comparing the results obtained by the present approach with that of the existing “Quadratic method” using a 33-bus radial distribution system with/without DGs.

Exploiting Obstacle Geometry to Reduce Search Time in Grid-Based Pathfinding

Pathfinding is the problem of finding the shortest path between a pair of nodes in a graph. In the context of uniform-cost undirected grid maps, heuristic search algorithms, such as A ★ and weighted A ★ ( W A ★ ), have been dominantly used for pathfinding. However, the lack of knowledge about obstacle shapes in a gird map often leads heuristic search algorithms to unnecessarily explore areas where a viable path is not available. We refer to such areas in a grid map as blocked areas (BAs). This paper introduces a preprocessing algorithm that analyzes the geometry of obstacles in a grid map and stores knowledge about blocked areas in a memory-efficient balanced binary search tree data structure. During actual pathfinding, a search algorithm accesses the binary search tree to identify blocked areas in a grid map and therefore avoid exploring them. As a result, the search time is significantly reduced. The scope of the paper covers maps in which obstacles are represented as horizontal and vertical line-segments. The impact of using the blocked area knowledge during pathfinding in A ★ and W A ★ is evaluated using publicly available benchmark set, consisting of sixty grid maps of mazes and rooms. In mazes, the search time for both A ★ and W A ★ is reduced by 28 % , on average. In rooms, the search time for both A ★ and W A ★ is reduced by 30 % , on average. This is achieved while preserving the search optimality of A ★ and the search sub-optimality of W A ★ .

Contextual-Cueing beyond the Initial Field of View-A Virtual Reality Experiment.

In visual search, participants can incidentally learn spatial target-distractor configurations, leading to shorter search times for repeated compared to novel configurations. Usually, this is tested within the limited visual field provided by a computer monitor. While contextual cueing is typically investigated on two-dimensional screens, we present for the first time an implementation of a classic contextual cueing task (search for a T-shape among L-shapes) in a three-dimensional virtual environment. This enabled us to test if the typical finding of incidental learning of repeated search configurations, manifested by shorter search times, would hold in a three-dimensional virtual reality (VR) environment. One specific aspect that was tested by combining virtual reality and contextual cueing was if contextual cueing would hold for targets outside the initial field of view (FOV), requiring head movements to be found. In keeping with two-dimensional search studies, reduced search times were observed after the first epoch and remained stable in the remaining experiment. Importantly, comparable search time reductions were observed for targets both within and outside of the initial FOV. The results show that a repeated distractors-only configuration in the initial FOV can guide search for target locations requiring a head movement to be seen.

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

Evaluation of eye movements and visual performance in patients with cataract

Eye movement is an essential component of visual perception. Eye movement disorders have been observed in many eye disease, and are thought to affect various visual performance in daily life. However, eye movement behaviors of the elderly with cataract are poorly understood, and the impact of cataract surgery on eye movements has not been investigated. In this study, we observed the eye movement behaviors in thirty patients with bilateral age-related cataract while performing three performance-based tasks (visual search, face recognition and reading). Eye movements were automatically recorded by an eye tracker during task performance. We found an overall improved visual performance postoperatively, presented as elevated percentage of correctly identified objects and faces, reduced search time and increased reading speed. Eye movement parameters were found significantly altered after cataract surgery. Fixation count, total fixation duration and total visit duration were markedly increased in the visual search task and face recognition task. The proportion of regressive saccades was obviously decreased in the reading task. These eye movement parameters were found to be correlated with the measures of visual performance. Our findings suggested a potential association between the eye movement disturbance and impaired visual performance, and provided a new insight on the potential usefulness of eye movement as an objective and valid tool to understand visual impairments caused by cataract, as well as evaluate practical outcomes of cataract surgery.

Fingerprint Classification through Standard and Weighted Extreme Learning Machines

Fingerprint classification is a stage of biometric identification systems that aims to group fingerprints and reduce search times and computational complexity in the databases of fingerprints. The most recent works on this problem propose methods based on deep convolutional neural networks (CNNs) by adopting fingerprint images as inputs. These networks have achieved high classification performances, but with a high computational cost in the network training process, even by using high-performance computing techniques. In this paper, we introduce a novel fingerprint classification approach based on feature extractor models, and basic and modified extreme learning machines (ELMs), being the first time that this approach is adopted. The weighted ELMs naturally address the problem of unbalanced data, such as fingerprint databases. Some of the best and most recent extractors (Capelli02, Hong08, and Liu10), which are based on the most relevant visual characteristics of the fingerprint image, are considered. Considering the unbalanced classes for fingerprint identification schemes, we optimize the ELMs (standard, original weighted, and decay weighted) in terms of the geometric mean by estimating their hyper-parameters (regularization parameter, number of hidden neurons, and decay parameter). At the same time, the classic accuracy and penetration-rate metrics are computed for comparison purposes with the superior CNN-based methods reported in the literature. The experimental results show that weighted ELM with the presence of the golden-ratio in the weighted matrix (W-ELM2) overall outperforms the rest of the ELMs. The combination of the Hong08 extractor and W-ELM2 competes with CNNs in terms of the fingerprint classification efficacy, but the ELMs-based methods have been demonstrated their extremely fast training speeds in any context.

Contextual cueing in preview search.

Frequently finding a target in the same location within a familiar context reduces search time, relative to search for objects appearing in novel contexts. This learned association between a context and a target location requires several blocks of training and has long-term effects. Short-term selection history also influences search, where previewing a subset of a search context shortly before the appearance of the target and remaining distractors speeds search. Here we explored the interactions between contextual cueing and preview benefit using a modified version of a paradigm from Hodsoll and Humphreys (Journal of Experimental Psychology: Human Perception and Performance, 31(6), 1346-1358, 2005). Participants searched for a T target among L distractors. Half of the distractors appeared 800 ms before the addition of the other distractors and the target. We independently manipulated the repetition of the previewed distractors and the newly added distractors. Though the previewed set never contained the target, repetition of either the previewed or the newly added context yielded contextual cueing, and the effect was greater when the previewed context repeated. Another experiment trained participants to associate the previewed context with a target location, then disrupted the association in a testing phase. This disruption eliminated contextual cueing, suggesting that learning of the previewed context was associative. These findings demonstrate an important interaction between distinct kinds of selection history effects.

Interpreting the Spatial-Temporal Structure of Turbulent Chemical Plumes Utilized in Odor Tracking by Lobsters

Olfactory systems in animals play a major role in finding food and mates, avoiding predators, and communication. Chemical tracking in odorant plumes has typically been considered a spatial information problem where individuals navigate towards higher concentration. Recent research involving chemosensory neurons in the spiny lobster, Panulirus argus, show they possess rhythmically active or ‘bursting’ olfactory receptor neurons that respond to the intermittency in the odor signal. This suggests a possible, previously unexplored olfactory search strategy that enables lobsters to utilize the temporal variability within a turbulent plume to track the source. This study utilized computational fluid dynamics to simulate the turbulent dispersal of odorants and assess a number of search strategies thought to aid lobsters. These strategies include quantification of concentration magnitude using chemosensory antennules and leg chemosensors, simultaneous sampling of water velocities using antennule mechanosensors, and utilization of antennules to quantify intermittency of the odorant plume. Results show that lobsters can utilize intermittency in the odorant signal to track an odorant plume faster and with greater success in finding the source than utilizing concentration alone. However, the additional use of lobster leg chemosensors reduced search time compared to both antennule intermittency and concentration strategies alone by providing spatially separated odorant sensors along the body.

State versus private provision: How does China’s market‐oriented reform affect healthcare delivery?

AbstractThe role of the government in health care provision remains a contested issue worldwide. Public hospitals dominate China’s health care industry. However, in the early 2000s, the eastern China city of Suqian privatized all its hospitals and relaxed entry barriers for private hospitals. We assess the impact of the pro‐market reform on hospital performance using a differences‐in‐differences approach. We find that the pro‐market reform decreased medical price and expenditure, improved self‐reported health outcomes, and reduced search time and cost for patients. We show that after the reform, Suqian residents had greater trust in doctors than did residents from other cities.

An experimentally generated peptide database increases the sensitivity of XL-MS with complex samples

Cross-linking mass spectrometry (XL-MS) is steadily expanding its range of applications from purified protein complexes to more complex samples like organelles and even entire cells. One main challenge using non-cleavable cross-linkers is the so-called n2 problem: With linearly increasing database size, the search space for the identification of two covalently linked peptides per spectrum increases quadratically. Here, we report an alternative search strategy that focuses on only those peptides, which were demonstrated to cross-link under the applied experimental conditions. The performance of a parallel XL-MS experiment using a thiol-cleavable cross-linker enabled the identification of peptides that carried a cleaved cross-link moiety after reduction and hence were involved in cross-linking reactions. Based on these identifications, a peptide database was generated and used for the database search of the actual cross-linking experiment with a non-cleavable cross-linker. This peptide-focused approach was tested on protein complexes with a reported structural model and obtained results corresponded well to a conventional database search. An application of the strategy on in vivo cross-linked Bacillus subtilis and Bacillus cereus cells revealed a five- to tenfold reduction in search time and led to significantly more identifications with the latter species than a search against the entire proteome. SignificanceInstead of considering all theoretically cross-linkable peptides in a proteome, identification and pre-filtering for a subset of cross-link peptide candidates allows for a dramatically decreased search space. Hence, there is less potential for the random accumulation of false positives ultimately leading to a higher sensitivity in the XL-MS experiment. Using the peptide-focused approach, a cross-linking database search can be conducted in a fraction of time while yielding a similar or higher number of identifications, thereby enabling the cross-linking analysis of samples of mammalian proteome complexity.

Network-aware Virtual Machine Migration Based on Gene Aggregation Genetic Algorithm

As a key technology of cloud computing, virtualization technology enables multiple virtual machines (VMs) to run on a host to meet the operational needs and environmental requirements of different applications, improving the efficiency of the host. However, the resource of the hosts is limited. When the VMs runs too many tasks, the host will be overloaded and exception occurs. Regarding the issue above, this paper considers the communication cost of virtual machine (VM) migration and proposes a VM Migration Algorithm based on Gene Aggregation Genetic Algorithm (VMM-GAGA). VMM-GAGA mainly solves the problem of allocation between VMs which to be migrated and underutilized hosts. In VMM-GAGA, a novel genetic coding method based on gene aggregation algorithm is proposed. The algorithm performs gene aggregation operations on VMs that have more communication and meet the conditions, which effectively reduces the number of genes in the chromosome., Experiments show that compared with the traditional genetic algorithm, VMM-GAGA reduces search time and communication costs.

Factors influencing prey capture success and profitability in Australasian gannets (Morus serrator).

ABSTRACTKnowledge of the factors influencing foraging efficiency in top predators can provide insights into the effects of environmental variability on their populations. Seabirds are important marine predators foraging in a highly temporally and spatially variable environment. While numerous studies have focussed on search time and its effects on foraging energetics in seabirds, relatively little is known about the factors influencing capture success and prey profitability in these predators. In the present study, animal-borne cameras were used to investigate the chase durations, capture success, handling durations and profitability of prey consumed by Australasian gannets (Morus serrator) (n=95) from two breeding colonies in south-eastern Australia exposed to different oceanographic conditions. Capture success was generally lower when individuals foraged alone. However, foraging in multi-species groups and in high prey densities increased chase time, while larger prey elicited longer handling times. While prey type influenced profitability, high prey density and foraging in multi-species groups was found to lower prey profitability due to increased time expenditure. While previous studies have found group foraging reduces search time, the increased profitability explains why some animals may favour solitary foraging. Therefore, future studies should combine search time and the currently found factors.

Service identification by enhanced K-mean algorithm in service-oriented architecture

Service identification with right level of granularity is the most critical aspect in service oriented architecture. Service identification is challenging due to several reasons such as lack of reuse of services, lack of right decision to choose the appropriate service. SOA-based project uses large repository, where in services are stored randomly in the repository. This causes considerable amount of search time when service is searched from the database. In case of trigger-based application, current service storage process will not be much effective due to inadequacy in service level agreement. In this regard, the author(s) explored the possibilities of service identification using k-means clustering which will reduce search time, service replication and increase the performance and reliability of the service. Proposed model has been experimentally validated and author(s) found significant decrease in service search time. This model will be helpful in building the applications with minimum service level agreement.