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15 results for “search behavior”
Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"
<p>Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the<br> search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both<br> the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency.</p>
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques - Dataset
<p>This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "<strong>Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques</strong>"</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
As search engines have become the main information resources of our daily life, studies about search behavior on the internet have gained great popularity with the growing knowledge of how the search behavior itself can affect our daily decisions, e.g. what to purchase, where to travel and even how to define beauty. However, there is no consensus conclusion whether the search behavior itself or the linguistic meaning behind it that can affect their decision. After analyzing the linguistic meanings of 13,915 English words obtained from Google Trends and its profit gained from the US house market by automatic transactions. It is found that linguistic meanings can affect financial decision results as word clusters with supervised machine learning methods.
Head-motion and eye-gaze behavior reveal audio-visual target search strategies - dataset
<p>Participants were tasked with finding a target stimulus in the presence of a number of auditory distractors. </p> <p>The target stimulus (audio-only, audio-visual or visual-only) was presented at the central position for 3 seconds, after which the stimulus was moved to one of 24 positions around the participant. The other 23 positions all contained a visual distractor and 0, 1, 2, 3, 5, 7 or 11 auditory distractors (evenly spaced). </p> <p>Based on the eye and headtracking data, we calculated the FOV and target localization time and the maximum headrotation into the wrong direction. </p> <p>FOV localization time: time it took to bring target within FOV. <br>Target localization time: From FOV to response.</p> <p>These datasets contain both the tracking data and the summarized data. </p> <p>Columns "av_stim_x_y" list for each of the 24 AV stimuli (which both served as the targets and distractors), the id, the angle at which it was present during the trial and the visual and audio status.</p> <p>FUNDING: </p> <p>The research was supported by the Centre for Applied Hearing<br>research (CAHR) through a research consortium agreement with<br>GN Resound, Oticon, and Widex. The funders had no role in<br>study design, data collection and analysis, decision to publish, or<br>preparation of the article.</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
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Data from: Empirical evidence that large marine predator foraging behavior is consistent with area-restricted search theory
When prey is patchily distributed, predators are expected to spend more time searching for food in proximity of recent prey captures before searching in other areas. This behavior, known as area-restricted search, results in predators remaining localized in areas where prey had been detected previously because of the higher probability of encountering additional prey. However, few studies have tested these predictions on marine species because of the difficulties of observing feeding behavior. In this study, we utilized passive acoustic detections of echolocating dolphins to identify foraging behavior. C-PODs (click train detectors) were deployed for two years with an acoustic recorder attached to the same mooring during the second year. The time series of feeding buzzes, indicative of foraging behavior, revealed that both bottlenose (Tursiops truncatus) and common dolphins (Delphinus delphis) were more likely to stay in the area longer when foraging activity was high at the beginning of the encounter. The probability of foraging was also higher following previous foraging activity. This suggests that dolphins were feeding on spatially patchy prey and previous foraging experience influenced their movement behavior. This is consistent with the predictions of area-restricted search behavior, a nonrandom foraging strategy.
Characterizing long-range search behavior in Diptera using complex 3D virtual environments
<p>The exemplary search capabilities of flying insects have established them as one of the most diverse taxa on Earth. However, we still lack the fundamental ability to quantify, represent, and predict trajectories under natural contexts to understand search and its applications. For example, flying insects have evolved in complex multimodal 3D environments, but we do not yet understand which features of the natural world are used to locate distant objects. Here, we independently and dynamically manipulate 3D objects, airflow fields, and odor plumes in virtual reality over large spatial and temporal scales. We demonstrate that that flies make use of features such as foreground segmentation, perspective, motion parallax, and integration of multiple modalities to navigate to objects in a complex 3D landscape while in flight. We first show that tethered flying insects of multiple species navigate to virtual 3D objects. Using the apple fly, <i>Rhagoletis pomonella</i>, we then measure their reactive distance to objects and show that these flies<i> </i>use perspective and local parallax cues to distinguish and navigate to virtual objects of different sizes and distances. We also show that apple flies can orient in the absence of optic flow by using only directional airflow cues, and require simultaneous odor and directional airflow input for plume following to a host volatile blend. The elucidation of these features unlocks the opportunity to quantify parameters underlying insect behavior such as reactive space, optimal foraging, and dispersal, as well as develop strategies for pest management, pollination, robotics and search algorithms.</p>
Data from: Empirical evidence that large marine predator foraging behavior is consistent with area-restricted search theory
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Characterizing long-range search behavior in Diptera using complex 3D virtual environments
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Data from: Selective attention by priming in host search behavior of 2 generalist butterflies
In phytophagous insects such as butterflies, there is an evolutionary trend towards specialization in host plant use. One contributing mechanism for this pattern may be found in female host search behavior. Since search attention is limited, generalist females searching for hosts for oviposition may potentially increase their search efficacy by aiming their attention on a single host species at a time, a behavior consistent with search image formation. Using laboratory reared and mated females of two species of generalist butterflies, the comma, Polygonia c-album, and the painted lady, Vanessa cardui (Lepidoptera: Nymphalidae), we investigated the probability of finding a specific target host (among non-host distractors) immediately after being primed with an oviposition experience of the same host as compared to different host in indoor cages. We used species-specific host plants that varied with respect to growth form, historical age of the butterfly-host association, and relative preference ranking. We found improved search efficacy after previous encounters of the same host for some but not all host species. Positive priming effects were found only in hosts with which the butterfly has a historically old relationship and these hosts are sometimes also highly preferred. Our findings provides additional support for the importance of behavioral factors in shaping the host range of phytophagous insects, and show that butterflies can attune their search behavior to compensate for negative effects of divided attention between multiple hosts.
Task Switching Behavior Between Target Templates During Visual Search in Healthy Adults
ClinicalTrials.gov study NCT05786651. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans
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Data from: Selective attention by priming in host search behavior of 2 generalist butterflies
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Search for Biomarkers of Neurodegenerative Diseases in Idiopathic REM Sleep Behavior Disorder
ClinicalTrials.gov study NCT04048603. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The use of zebrafish reward mutants to search for novel transcripts mediating the behavioral effects of amphetamine
GEO Series GSE14399. Danio rerio. 3 samples. Type: Expression profiling by array.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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