Validating the random search model for a double-target search task

2000 ◽  
Vol 1 (2) ◽  
pp. 157-167 ◽  
Author(s):  
Alan H. S. Chan ◽  
C. Y. Chan
Keyword(s):  
Search Task ◽  
Random Search ◽  
Search Model ◽  
Target Search

Multi-Target Search on Semantic Associations in Linked Data

2018 ◽  
Vol 14 (1) ◽  
pp. 71-97 ◽  
Author(s):  
Xiang Zhang ◽  
Erjing Lin ◽  
Yulian Lv
Keyword(s):  
Linked Data ◽  
Large Scale ◽  
Search Process ◽  
Search Model ◽  
Linguistic Features ◽  
Target Search ◽  
Search Results ◽  
Semantic Associations ◽  
Effectiveness And Efficiency ◽  
Virtual Document

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search contains multiple sub-queries, in which each sub-query represents a certain user need for a certain object in a group relationship. They first formularize the problem of association search, and then introduce their approach to discover Semantic Associations in large-scale Linked Data. Next, they elaborate their novel search model, the notion of Virtual Document they use to extract linguistic features, and the details of search process. The authors then discuss the way search results are organized and summarized. Quantitative experiments are conducted on DBpedia to validate the effectiveness and efficiency of their approach.

The Role of Firms in Gender Earnings Inequality: Evidence from the United States

2017 ◽  
Vol 107 (5) ◽  
pp. 384-387 ◽  
Author(s):  
Isaac Sorkin
Keyword(s):  
United States ◽  
Random Search ◽  
The United States ◽  
Search Model ◽  
High Wage ◽  
Men And Women ◽  
Gender Earnings Gap ◽  
Earnings Gap ◽  
The Us

This paper documents that in the US, men are more likely than women to work in both high-wage firms and high-wage industries. I then ask why this sorting occurs. I consider two main explanations: men and women have different preferences, and men and women have different opportunities. Through the lens of a simple random search model, I find that the dominant explanation for sorting is differences in opportunities. One implication of this result is that women are at firms that offer better nonpay characteristics, and this plays an important role in explaining the gender earnings gap.

Prediction of Information Transfer from Computer-Generated Dot Matrix Displays

1977 ◽  
Vol 21 (1) ◽  
pp. 38-42 ◽  
Author(s):  
Michael E. Maddox
Keyword(s):  
Fourier Analysis ◽  
Information Transfer ◽  
Random Search ◽  
External Validation ◽  
Predictor Variable ◽  
Predictor Variables ◽  
Search Model ◽  
Mathematical Treatment ◽  
Search Performance ◽  
Actual Performance

Two separate experiments derived and validated predictive metrics (or equations) of information transfer from dot matrix displays. The initial experiment involved three levels of dot size, three levels of dot shape, three levels of dot spacing, and two levels of ambient illuminance. The predictor variable pool was obtained by Fourier analysis of vertical and horizontal microphotometric scans of each experimental display combination. Multiple regression techniques were used to derive predictive equations for each task. Substantial proportions of experimental variance were accounted for by these equations. These proportions ranged from R2 = .47 for the menu search model to R2 = .57 for the reading task model. An external validation study was conducted using dot parameters equivalent to three commercially available displays. In addition, the matrix size was varied in this experiment. Except for the random search performance, the predictions derived in the first study correlated well with the meassured performance in this study (Spearman r = .73). The equations were found to be very sensitive to predictor variables which were outside the range of the original regression. The photometric scan and Fourier analysis methodology was found to be accurate and very repeatable in this research. This research has demonstrated that it is possible to account for large proportions of experimental variance on visual performance tasks with relatively simple display-related parameters. The proportion of variance accounted for by the derived models ranged from .47 for the menu search model to .57 for the Tinker SOR model. The terms used in the metrics inherently contain information about many display parameters usually treated as isolated from one another. The values predicted from the metrics have been shown to be well correlated with actual performance when the predictor variables are within the range of the original variables from which the metrics were derived. The methodology developed during this research has been shown to be valid and the photometric procedures and mathematical treatment have proved stable in a real experimental setting.

Distractor Ratio Influences Patterns of Eye Movements during Visual Search

Perception ◽  
10.1068/p2933 ◽  
2000 ◽  
Vol 29 (2) ◽  
pp. 241-250 ◽  
Author(s):  
Jiye Shen ◽  
Eyal M Reingold ◽  
Marc Pomplun
Keyword(s):  
Visual Search ◽  
Eye Movements ◽  
Search Task ◽  
Search Model ◽  
Search Performance ◽  
Manual Response ◽  
Guided Search ◽  
Different Types ◽  
Conjunctive Search

We examined the flexibility of guidance in a conjunctive search task by manipulating the ratios between different types of distractors. Participants were asked to decide whether a target was present or absent among distractors sharing either colour or shape. Results indicated a strong effect of distractor ratio on search performance. Shorter latency to move, faster manual response, and fewer fixations per trial were observed at extreme distractor ratios. The distribution of saccadic endpoints also varied flexibly as a function of distractor ratio. When there were very few same-colour distractors, the saccadic selectivity was biased towards the colour dimension. In contrast, when most of the distractors shared colour with the target, the saccadic selectivity was biased towards the shape dimension. Results are discussed within the framework of the guided search model.

scholarly journals Network Information Target Search Model and Strategy Based on Web Vertical Crawler System

2016 ◽  
Vol 9 (4) ◽  
pp. 171-178
Author(s):  
Bin Wang ◽  
Jian Zhang ◽  
Na Wang ◽  
Xiaohua Sun ◽  
Yanhui Wang
Keyword(s):  
Search Model ◽  
Target Search ◽  
Network Information

scholarly journals Activeness Improves Cognitive Performance in Human-Machine Interaction

2013 ◽  
Vol 17 (3) ◽  
pp. 425-432 ◽  
Author(s):  
Yusuke Tamura ◽  
◽  
Mami Egawa ◽  
Shiro Yano ◽  
Takaki Maeda ◽  
...  
Keyword(s):  
Reaction Time ◽  
Cognitive Performance ◽  
Search Task ◽  
Experimental Results ◽  
Human Machine Interaction ◽  
Target Search ◽  
Active Behavior ◽  
The Relationship ◽  
Machine Interaction

In human-machine interaction, automation brings both advantages and potentially unpredictable disadvantages to human cognitive performance. In this study, we hypothesized that active behavior improves cognitive performance in human-machine interaction, and verified this hypothesis through three experiments. Experiment 1 examined the relationship between activeness and reaction time in a target-search task. Experiments 2 and 3 analyzed the factors that improved cognitive performance. Experimental results demonstrated that activeness positively affects cognitive performance and suggested that predictability associated with activeness plays a key role in improving cognitive performance.

Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

2017 ◽  
Vol 70 (6) ◽  
pp. 1293-1311 ◽  
Author(s):  
Xiang Cao ◽  
A-long Yu
Keyword(s):  
Neural Network ◽  
Search Task ◽  
Search Algorithm ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Target Search ◽  
Team Members ◽  
Neural Network Algorithm ◽  
Som Neural Network ◽  
Self Organising Map

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

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