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5 results for “chattering”
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18
Chatter Prediction Data_1
<p>The files contain the machine tool configurations for two robot configurations which are used for milling process. They together can be used to train the ML algorithms to predict chatter. </p>
Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network
<p>This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu</p> <p>The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions.</p> <p>The information contained here includes:</p> <p>- Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth).</p> <p>- Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X.</p> <p>- There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information.</p> <p>- In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable.</p> <p>- Companion documents in PDF format in order to provide more detailed information on the datasets and results.</p> <p>Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence.</p> <p>www.ideko.es<br> www.dfki.de</p> <p>Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es</p> <p>October 2019</p>
A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109</p> <p><strong>Abstract</strong></p> <p>The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.</p> <p>The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development. </p> <ul> <li>Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)</li> <li>Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)</li> <li>Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)</li> <li>Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)</li> </ul> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p> <p><strong>Table 1</strong>. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development</p> <table> <tbody> <tr> <td> <p>Terminology</p> </td> <td> <p>List of synonyms and terms</p> </td> </tr> <tr> <td> <p>COVID-19</p> </td> <td> <p>Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus</p> </td> </tr> <tr> <td> <p>online learning</p> </td> <td> <p>online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures</p> </td> </tr> </tbody> </table> <p> </p>
Data from: Female in-nest chatter song increases predation
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