Too often 2 peoples look at the same data, image or video but never have the same opinion or judgment on it. A way to correct that and align the criteria of everybody at the same level is to have a golden sample with specific criteria of what the optimal answer would be and then train on it.
This project was to build a training app for new personnel that get hire to let them align their criteria with our specification. It was also built as a white label so we can add as many images or criteria as needed. Allowing not all criteria to be display or filled depending on what is displayed. Some criteria can be black & white (buttons) and other are more opinion based (slider).
When someone start the training, it will select a picture from the Database at “random” according to an algorithm to avoid displaying the same picture many time. The user will fill some or all the criteria depending on the setup.
After adding all needed criteria, the user can add details as comment on the decision if needed. There is also external resources that can be added as link to help the user make the right decision. When all is complete the user must accept or reject the image quality in general.
Once the user click Accept/Reject, the system will validate and tell the user visually if he was right or not and give some example on what was wrong. Error is human so if the user want to contest the golden sample he can and add a description on why he think it should be accepted or rejected.
The list of criteria and structure (button or slider) can be easily be changed or added in the database as part of the setup of the system. New golden picture can be added via the UI if you have the manager permission or directly via the node API.
Many items are tracked like Success/Failure but also how many time the image was rejected and the user click accepted. What kind of criteria fail the most, what image was the most contested, what image fail the most, histogram of the number of minutes pass per image per user vs all others, number of image trained on per day/weeks/months.
Having complex analytics can help the user better understand his weakness and also help the manager understand what is the most difficult to understand for people in general.
Emmy and Hisako are proud to present the release of Spy or Not, a gamified psycholinguistics experiment made in collaboration with the Accents Research Lab at Concordia University headed by Dr. Spinu.
It is commonly observed that some people are “Good with Accents.” Some people can easily imitate various accents of their native language, while others appear struggle with imitation. This research is dedicated to building free OpenSource phonetics scripts to extract the acoustic components of native speakers and “Good with Accents” speakers to transfer the technical details in a visualizable format to applied linguists on the ground who are working with accented (clinical and non-native) speakers.
In order to collect non-biased judgements from native speakers, a pilot study was designed and run by Dr. Spinu and her students. Images and supporting sound effects were created and the perceptual side of the pilot was disguised as the game “Spy or Not?” The game has since gathered over 8,000 data points by crowdsourcing the judgements to determine the degree (on an 11 point scale) of which participants were “Good with Accents.” This a novel approach to the coding problems that experimenters frequently encounter.
Participation in this project furthers research in phonetics and phonology in addition to experimental methodology in the age of the social web. Our hope is that our readers will Tweet their “Good with Accents” scores and help us get more participants, especially native speakers of Russian English accents, Sussex English accents and South African accents, accents we could never access at the scale we need in a lab setting. Visit the free online game, or play offline by downloading the game at the Chrome Store or on Google Play as a Android App.
Languages differ in how they overtly mark functions and their arguments, if they overtly mark at all… This month’s iLanguage game shows an example of the “Ergative-Absolutive” system, present in Hindi-Urdu, Walpiri, Inuktitut among others. In reality, its not as complicated as its name might indicate, in fact, we argue its quite logical.
In 9.10.a and 9.10.c we see that the Experiencer of travel is rather consistently is marked with -aq, as is Experiencer of greet in 9.10.b and 9.10.d. What might surprise you if you speak English, French or any other “Nominative-Accusative” language is that the -aq is consistently on the Experiencer, regardless of whether its the subject or the object.
In 9.10.e and 9.10.g we see that 1st person Experiencers appear on the verb, not as pronouns. This might sound familiar if you studied/speak Spanish.
9.10.f is particularly exciting since we don’t have enough data to say what is going on. We recommend stopping the next Yup’ik Eskimo speaker you run into and asking them to give you a verb that ends in a consonant, they might put -aq on the end when you give them a context to bring them to say he xe-ed yesterday…
Want to see more language data? Examples are taken from I-Language: An Introduction to Linguistics as Cognitive Science by Daniela Isac and Charles Reiss. http://linguistics.concordia.ca/I-language/
This month’s Guess the code shows an example of Functional Application which is an operation in compositional semantics. As you all know, in mathematics and in programming a function takes an input argument from some specified domain and yields an output value. Applying a function f to an argument x yields the value for that argument, which can be written as f(x). In beginner semantics, this same procedure is what happens when verb takes its object.
verb(object) ~ function(argument)
The only mystery in this example is the denotation double brackets which indicate that it is the denotation, not the orthographic word, which is being operated upon.
The code also gives us two trees to show that in English functional application applies to the right, and in Turkish it applies to the left.
iLanguage is proud to present its first installment of its monthly Language games! Your first task is to identify the language in the first image. Then in the next image you have several more tasks; guess the spectrogram, decipher the programming language and analyze data from a natural language. Post your answers in the comment section. Enjoy!