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400 results for “Trust”
York Archaeological Trust 1989.21.2205 3D Archaeological Use-wear
<p>Final 3D dataset for object 1989.21.2205 in the collections of York Archaeological Trust.</p> <p>1989.21.2205 is a small Samian ware cup fragment. Close analysis was not undertaken at time of data capture.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 1,765,786 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1989.21.2044.LL139 3D Archaeological Use-wear
<p>Final 3D dataset for object 1989.21.2044.LL139 in the collections of York Archaeological Trust.</p> <p>1989.21.2044.LL139 is a small campanulate bowl of Ebor Ware 1 (Monaghan 1997, 875)</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 5,722,904 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1989.24.71888-71893-71196 3D Archaeological Use-wear
<p>Final 3D dataset for object 1989.24.71888-71893-71196 in the collections of York Archaeological Trust.</p> <p>1989.24.71888-71893-71196 comprises three sherds of a North Gaulish White ware 4 mortarium (Tomber and Dore 1998, 75, NOG WH 4; Gillam Type 238 (1970, 25); Monaghan 1997, 939, no. 3407, fig. 374, p.940).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 14,916,975 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1975.6.7364.4244 Archaeological 3D Use-wear
<p>Final 3D dataset for object 1975.6.7364.4244 in the collections of York Archaeological Trust.</p> <p>1975.6.7364.4244 is a large Ebor Ware carinated bowl (Monaghan 1993, 783, 875).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 41,184,179 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1989.21.2393.LL42 Archaeological 3D Use-wear
<p>Final 3D dataset for object 1989.21.2393.LL42 in the collections of York Archaeological Trust.</p> <p>1989.21.2393.LL42 is a dish of Ebor Ware 2 (Monaghan 1997, 875) from 34-41 Blossom Street (Lion and Lamb).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 5,699,899 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1975.18.III.176 Archaeological 3D Use-wear
<p>Final 3D dataset for object 1975.18.III.176 in the collections of York Archaeological Trust.</p> <p>1975.18.III.176 is a mortarium of North Gaulish White ware 4 (Tomber and Dore 1998, 75, NOG WH 4) that has experienced modern reconstruction.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 52,103,085 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1981.7.33205.SY102 3D Archaeological Use-wear
<p>Final 3D dataset for object 1981.7.33205.SY102 in the collections of York Archaeological Trust.</p> <p>1981.7.33205.SY102 is a Mortarium in standard Ebor oxidised fabric (M3) (Monaghan 1997, 1028) from Coppergate.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 26,120,547 triangles - additional metadata included in associated spreadsheet.</p> <p> </p>
York Archaeological Trust 1989.21.2149.LL3 3D Archaeological Use-wear
<p>Final 3D dataset for object 1989.21.2149.LL3 in the collections of York Archaeological Trust.</p> <p>1989.21.2149.LL3 is a carinated bowl of Local Grey ware 1 (Monaghan 1997, 1027) from 34-41 Blossom Street (Lion and Lamb).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 8,869,379 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1988.27.19051 3D Archaeological Use-wear
<p>Final 3D dataset for object 1988.27.19051 in the collections of York Archaeological Trust.</p> <p>1988.27.19051 is a Mancetter-Hartshill White ware hammerhead mortarium (Tomber and Dore 1998, 189, MAH WH) from Barbican Car Park.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 22,305,551 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1988.24.71329.12375 Archaeological 3D Use-wear
<p>Final 3D dataset for object 1988.24.71329.12375 in the collections of York Archaeological Trust.</p> <p>1988.24.71329.12375 is a dish of Crambeck Reduced ware (Tomber and Dore 1998, 197, CRA RE).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 5,170,133 triangles - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1987.21.2309.AB4 3D Archaeological Use-wear
<p>Final 3D dataset for object 1987.21.2309.AB4 in the collections of York Archaeological Trust.</p> <p>1987.21.2309.AB4 is a dish of Crambeck Reduced ware (Tomber and Dore 1998, 197, CRA RE) from 22 Piccadilly (ABC Cinema).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>Final dataset consists of 6,359,694 triangles - additional metadata included in associated spreadsheet.</p>
Reference Architecture for Trust-based Digital Ecosystems
<p>Pre-recording of the presentation for the paper "Reference Architecture for Trust-based Digital Ecosystems"</p>
Report of demographic and employment description of each of the interviewees. Covidless Approach & Trust Project
<p>Report of demographic and employment description of each of the interviewees. Covidless Approach & Trust Project</p>
Supplementary Material for "Effective Notification Campaigns on the Web: A Matter of Trust, Framing, and Support"
<p>This record contains supplementary material for the paper <a href="https://www.usenix.org/conference/usenixsecurity21/presentation/maass">"Effective Notification Campaigns on the Web: A Matter of Trust, Framing, and Support", published at USENIX Security 2021</a>. It includes the following information:</p> <ul> <li>Data collection code</li> <li>Survey Questions</li> <li>Text of messages</li> <li>Anonymized dataset</li> <li>Parts of the evaluation code</li> </ul>
PRW Preprint Trust Survey Data
<p>Data to support findings published here: https://deltathink.com/news-views-preprints-and-covid-19-findings-from-our-prw-survey/</p>
Dataset for: Trust Trackers for Computation Offloading in Edge-Based IoT Networks
<p>The dataset used to generate graphs for: Matthew Bradbury, Arshad Jhumka and Tim Watson.<br> Trust Trackers for Computation Offloading in Edge-Based IoT Networks. In IEEE INFOCOM, 1–10. IEEE, 10–13 May 2021.</p> <p>Also includes instructions for experiment setup.</p> <p>Scripts from https://github.com/MBradbury/iot-trust-task-alloc are required to analyse and graph these results.</p>
Data from: Digit ratio (2D:4D) and prosocial behavior in economic games: no direct correlation with generosity, bargaining, or trust-related behaviors
Prenatal exposure to sex hormones exerts organizational effects on the brain which have observable behavioral correlates in adult life. There are reasons to expect that social behaviors—fundamental for the evolutionary success of humans—might be related to biological factors such as prenatal sex hormone exposure. Nevertheless, the existing literature is inconclusive as to whether and how prenatal exposure to testosterone and estrogen, proxied by the second-to-fourth digit ratio (2D:4D), may predict non-selfish behavior. Here, we investigate this question using economic experiments with real monetary stakes and analyze five different dimensions of social behavior in a comparatively large sample of Caucasian participants (n=560). For both males and females, our results show no robust association between right- or left-hand 2D:4D and generosity, bargaining, or trust-related behaviors. Moreover, no differences in behavior were found according to sex. We conclude that there is no direct correlation between 2D:4D and these social behaviors.
Data from: Chimpanzees trust conspecifics to engage in low-cost reciprocity
Many of humans' most important social interactions rely on trust, including most notably among strangers. But little is known about the evolutionary roots of human trust. We presented chimpanzees (Pan troglodytes) with a modified version of the human trust game—trust in reciprocity—in which subjects could opt either to obtain a small but safe reward on their own or else to send a larger reward to a partner and trust her to reciprocate a part of the reward that she could not access herself. In a series of three studies, we found strong evidence that in interacting with a conspecific, chimpanzees show spontaneous trust in a novel context; flexibly adjust their level of trust to the trustworthiness of their partner and develop patterns of trusting reciprocity over time. At least in some contexts then, trust in reciprocity is not unique to humans, but rather has its evolutionary roots in the social interactions of humans' closest primate relatives.
Community trust of government and non-governmental organizations during the 2014-16 Ebola epidemic in Liberia - survey responses
<p>The West African Ebola Virus Disease epidemic of 2014-16 cost more than 11,000 lives. Interventions targeting key behaviors to curb transmission, such as safe funeral practices and reporting and isolating the ill, were initially unsuccessful in a climate of fear, mistrust, and denial. Building trust was eventually recognized as essential to epidemic response and prioritized, and trust was seen to improve toward the end of the epidemic as incidence fell. However, little is understood about how and why trust changed during Ebola, what factors were most influential to community trust, and how different institutions might have been perceived under different levels of exposure to the outbreak. In this large-N household survey conducted in Liberia in 2018, we measured self-reported trust over time retrospectively in three different communities with different exposures to Ebola. We found trust was consistently higher for non-governmental organizations than for the government of Liberia across all time periods. Trust reportedly decreased significantly from the start to the peak of the epidemic in the study site of highest Ebola incidence. This finding, in combination with a negative association found between knowing someone infected and trust of both iNGOs and the government, indicates the experience of Ebola may have itself caused a decline of trust in the community. These results suggest that national governments should aim to establish trust when engaging communities to change behavior during epidemics. Further research on the relationship between trust and epidemics may serve to improve epidemic response efficacy and behavior uptake.</p>
Trust and Believe – Should We? Evaluating the Trustworthiness of Twitter Users
<p><strong>Trust and Believe – Should We? Evaluating the Trustworthiness of Twitter Users</strong></p> <p>This model is used to analyze the Twitter users and assigns a score calculated based on their social profiles, the credibility of his tweets, the h-indexing score of the tweets. Users with a higher score are not only considered as more influential but also their tweets are considered to have greater credibility. The model is based on both the user level and content level features of a Twitter user. The details for feature extraction and calculating the Influence score is given in the paper.</p> <p><strong>Description</strong><br> To extract the features from Twitter and generate the dataset we used Python. A modAL framework is used to randomly selects ambiguous data points from the unlabeled data pool using three different sampling techniques and the human manually annotates the selected data. We generate a dataset for 50000 Twitter users and then used different classifiers to classify the Twitter user either as Trusted or Untrusted.</p> <p><strong>Organization</strong><br> The project consists of the following files:</p> <p><strong>Dataset.csv</strong><br> The dataset consists of different features of 50000 Twitter users (Politicians) without labels.</p> <p><strong>Manually_labeled-Dataset.csv</strong><br> This CSV file contains all those Twitter users classified manually as Trusted or Untrusted</p> <p><strong>feature_extraction.py</strong><br> This python script is used to calculate the Influence score of a Twitter user and further used to generate a dataset. The Influence score is based on:</p> <p>- Social reputation of the user<br> - Content score of the tweets<br> - Tweets credibility <br> - Index score for the number of re-tweets and likes</p> <p><strong>Activelearner.ipynb</strong><br> To classify a large pool of unlabeled data, we used an active learning model (ModAL Framework). A semi-supervised learning algorithm ideal for a situation in which the unlabeled data is abundant but manual labeling is expensive. The active learner randomly selects ambiguous data points from the unlabeled data pool using three different sampling techniques and the human manually annotates the selected data. Further, we use four different classifiers (Support Vector Machine, Logistic Regression, Multilayer Perceptron and Random Forest) to classify the Twitter user as either Trusted Or Untrusted.</p> <p><strong>twitter_reputation.ipynb</strong><br> We used different regression models to test its performance on our generated dataset (It is only for testing, now no more part of our work). We train and evaluate our models using different regression models.<br> Training and testing three regression models:<br> 1. Multilayer perceptron<br> 2. Deep neural network<br> 3. Linear regression</p> <p><strong>twitter_credentials.py</strong><br> In order to extract the features of Twitter users first, one need to authenticate by providing the credentials given in this file.</p> <p><strong>Screen names (Screen_name_1.txt, Screen_name_2.txt, Screen_name_3.txt)</strong><br> These text files consist of all the Twitter user screen_names. All of them are politicians. We remove the names of all those politicians whose accounts are private. In addition, all those politicians who have no followers/followings are not on the list are also removed. The text of the tweets are not saved. Furthermore, we also remove duplicate names.</p> <p><strong>References</strong><br> [1] https://stackoverflow.com/questions/38881314/twitter-data-to-csv-getting-error-when-trying-to-add-to-csv-file</p> <p>[2] https://stackoverflow.com/questions/48157259/python-tweepy-api-user-timeline-for-list-of-multiple-users-error</p> <p>[3] https://gallery.azure.ai/Notebook/Computing-Influence-Score-for-Twitter-Users-1</p> <p>[4] https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html</p> <p>[5] https://towardsdatascience.com/deep-neural-networks-for-regression-problems-81321897ca33</p>
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