Anonymous ID: f5180d Aug. 2, 2021, 10:44 a.m. No.14251965   🗄️.is 🔗kun

History of Millstones

 

The grinding of grain by primitive hand stones can be traced back even further than recorded civilization, although the methods used in prehistoric milling are a matter of some conjecture and speculation. It is certain, however, that stones used for grinding grain have progressed through the centuries, from the small stones held in the hand which were used hammer-like fashion to pulverize grain against larger stones or a rock face, to the highly efficient power driven millstones that are still used to this day.

 

Geologists and archaeologists have come across primitive types of pounding stones of a similar shape in various parts of the world. It is interesting to note that the methods used to reduce grain or berries to a more edible form did not very much by the people in the early civilizations of the Old World, the original peoples of the New World, or by the aborigines of modern uncivilized countries. A similar kind of hammer stone appears to have been in universal use perhaps between 25 and 50 thousand years ago.

 

The improvements in the simple pounding methods came with the introduction of the mortar and pestle which gave more of a grinding action. The grain was placed in a bowel-like piece of rock, the mortar, and ground by the pestle, a club-shaped implement. This was held in the hand and worked up and down striking the grain. The same principle was also used with mortars fashioned from a section of a hardwood tree trunk, the grain being pounded with hardwood pestles. There were, of course, many variations in the kind of wood used and in the size of the mortars and pestles. The design largely depended on local timber availability and the needs of the people using those grinding implements.

 

https://www.angelfire.com/journal/pondlilymill/paper.html

 

Millstone making in England

 

Abstract

 

Since the author's paper 'Millstones, Quarries and Millstone Makers' published in 1977 a good deal of interest has been taken in the subject, and much further information has come to light. The present paper is therefore an updating of the previous one as far as England is concerned, and tries to avoid any extensive repetition of information and ideas given there. It also avoids discussion of millstone making in the Peak District of Derbyshire since that very special area was dealt with in some detail in a recent paper in this journal. A general account is first given of the development of the millstone industry, particularly during the last two or three centuries, and its division into two distinct parts: (1) the making of monolithic millstones from indigenous rock in rural quarries and quarry workshops, and (2) the fabrication of millstones from imported small pieces of French burr-stone in urban factories. The import and export trades associated with these activities, and the cost of millstones, are discussed. Augmented data on English quarries which made monolithic millstones (outside the Peak District of Derbyshire) is presented, and it is found that none of these quarries are in the south of England. Finally, data on the many firms of urban millstone makers is given and discussed where that previously published can be significantly augmented, and it is mainly for the north of England that this is the case. (There was millstone making of both kinds in Scotland too, but this has been separately discussed. Further work on millstone making in Wales is in hand under the auspices of Mr Owen Ward and the Welsh Mills Group.)

 

https://www.tandfonline.com/doi/abs/10.1179/iar.1987.9.2.167?src=recsys

 

Millstones, Quarries, and Millstone-Makers

 

The various types of millstone (monolithic and fabricated, face-grinders and edge-runners) are discussed, together with sizes and shapes, manufacture, dress, criteria of quality, etc. A list of known millstone quarry areas and individual quarries is given, covering Great Britain, with references to historical sources; and tables are presented giving the names, addresses, and dates of about 70 British firms which manufactured French-burr millstones in the 19th and early 20th centuries, together with a note of the location of some surviving identified examples of their work.

 

https://www.tandfonline.com/doi/abs/10.1179/pma.1977.001?journalCode=ypma20

 

https://quarnstensgrufvansvanner.se/onewebmedia/charles%20hockensmitht.pdf

Anonymous ID: f5180d Aug. 2, 2021, 11:23 a.m. No.14252179   🗄️.is 🔗kun

Amazing what we can tell a person, just using data from the device.

 

We can map the room, identify furniture, objects, animals and other people. We can tell if they're male or female. If female we can tell when they are cycling naturally or using birth control. And we can know what you are feeling, a based on your interest, marital and economic circumstance exactly what, placed in your newsfeed or in dynamic adverts directed at (you) will produce what behavior.

"We" in this case means the operators of (for example) an in game AI, or a reddit chatbot called H.A.N.K . This is the same data that is continuously harvested by social media, big tech, gaming companies, researchers in cognitive science, intelligence agencies.

 

This is not a future scenario, this technology has existed for sometime and it is in use now in some popular gaming environments.

 

Predicting Latent Narrative Mood using Audio and Physiologic Data

 

Human communication depends on a delicate interplay between the emotional intent of the speaker, and the linguistic content of their message. While linguistic content is delivered in words, emotional intent is often communicated

though additional modalities including facial expressions, spoken intonation, and body gestures. Importantly, the same message can take on a plurality of meanings, depending on

the emotional intent of the speaker. The phrase ”Thanks a lot” may communicate gratitude, or anger, depending on the tonality, pitch and intonation of the spoken delivery.

Given it’s importance for communication, the consequences of misreading emotional intent can be severe, particularly in high-stakes social situations such as salary negotiations or job interviews…¨

 

"… Machine-aided assessments of historic and real-time interactions may help facilitate more effective communication for such individuals by allowing for long-term social coaching and in-the-moment interventions. …

"In this paper, we present the first steps toward the realization of such a system. We present a novel multi-modal dataset containing audio, physiologic, and text transcriptions from 31 narrative conversations. As far as we know, this is the first experimental set-up to include individuals engaged in natural dialogue with the particular combination of signals we collected and processed: para-linguistic cues

from audio, linguistic features from text transcriptions (average postive/negative sentiment score), Electrocardiogram

(ECG), Photoplethysmogram (PPG), accelerometer, gyroscope, bio-impedance, electric tissue impedance, Galvanic

Skin Response (GSR), and skin temperature.

 

https://groups.csail.mit.edu/sls/publications/2017/TukaAlHanai_aaai-17.pdf

 

If there are 10 people talking or background, say–

 

Segregating Event Streams and Noise

with a Markov Renewal Process Model

 

We describe an inference task in which a set of timestamped event observations must be clustered into an unknown number of temporal sequences with independent and varying rates of observations. Various existing approaches to multi-object tracking assume a fixed number of sources and/or a fixed observation rate; we develop an approach to inferring structure in timestamped data produced by a mixture of an unknown and varying number of similar Markov renewal processes,

plus independent clutter noise. The inference simultaneously distinguishes signal from noise as

well as clustering signal observations into separate source streams. We illustrate the technique via

synthetic experiments as well as an experiment to track a mixture of singing birds. Source code is

available.

 

https://jmlr.csail.mit.edu/papers/volume14/stowell13a/stowell13a.pdf

 

Voice in different phases of menstrual cycle

among naturally cycling women and users of

hormonal contraceptives

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183462

Anonymous ID: f5180d Aug. 2, 2021, 11:39 a.m. No.14252273   🗄️.is 🔗kun

format fix

 

What we can tell about a person, just using data from a mobile device.

 

We can map the room, identify furniture, objects, animals and other people. We can tell if they're male or female. If female we can tell when they are cycling naturally, or using birth control. And we can know what you are feeling, a based on your interests, marital and economic circumstances, browser history and more exactly what, placed in your newsfeed or in dynamic adverts targeting (you) will motivate you to preform, voluntarily, the behavior we desire.

 

"We" in this case means the operators of (for example) an in-game AI, or a reddit chatbot called H.A.N.K . This device data is the same data continuously harvested by social media, big tech, gaming companies, researchers in cognitive science, and dozens of intelligence agencies.

 

This is not a future scenario, this technology has existed for some time, is in use now in popular gaming environments.

 

Here are three papers which when integrated with AI give the operator enormous control of target's decision making in any chosen area.

 

Predicting Latent Narrative Mood using Audio and Physiologic Data

 

Human communication depends on a delicate interplay between the emotional intent of the speaker, and the linguistic content of their message. While linguistic content is delivered in words, emotional intent is often communicated

 

though additional modalities including facial expressions, spoken intonation, and body gestures. Importantly, the same message can take on a plurality of meanings, depending on

 

the emotional intent of the speaker. The phrase ”Thanks a lot” may communicate gratitude, or anger, depending on the tonality, pitch and intonation of the spoken delivery.

 

Given it’s importance for communication, the consequences of misreading emotional intent can be severe, particularly in high-stakes social situations such as salary negotiations or job interviews…¨

 

"… Machine-aided assessments of historic and real-time interactions…"

 

"In this paper, we present the first steps toward the realization of such a system. We present a novel multi-modal dataset containing audio, physiologic, and text transcriptions from 31 narrative conversations. As far as we know, this is the first experimental set-up to include individuals engaged in natural dialogue with the particular combination of signals we collected and processed: para-linguistic cues

from audio, linguistic features from text transcriptions (average postive/negative sentiment score), Electrocardiogram

 

(ECG), Photoplethysmogram (PPG), accelerometer, gyroscope, bio-impedance, electric tissue impedance, Galvanic Skin Response (GSR), and skin temperature.

 

https://groups.csail.mit.edu/sls/publications/2017/TukaAlHanai_aaai-17.pdf

 

If there are 10 people talking or background, say–

 

Segregating Event Streams and Noise with a Markov Renewal Process Model

 

We describe an inference task in which a set of timestamped event observations must be clustered into an unknown number of temporal sequences with independent and varying rates of observations. Various existing approaches to multi-object tracking assume a fixed number of sources and/or a fixed observation rate; we develop an approach to inferring structure in timestamped data produced by a mixture of an unknown and varying number of similar Markov renewal processes, plus independent clutter noise. The inference simultaneously distinguishes signal from noise as well as clustering signal observations into separate source streams. We illustrate the technique via synthetic experiments as well as an experiment to track a mixture of singing birds. Source code is available.

 

https://jmlr.csail.mit.edu/papers/volume14/stowell13a/stowell13a.pdf

 

Voice in different phases of menstrual cycle among naturally cycling women and users of hormonal contraceptives

 

Riding theredcotton pony? The DARPA Ai knows.

 

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183462