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I have to do this assignment until Thursday morning. I have to use Prolog.

Note: "Word-tuple" is a 3-tuple of the form "(word,POS,sense_number)"

Note: "Synset" is the list of word-tuples of a synset.

 

  1. Define a predicate offset_to_synset(+Offset,?Synset) that succeeds if Synset is the list of all the word-tuples of Offset. (Tip: use findall/3.)
  2. Define a predicate wordTuple_to_offset(?WordTuple,?Offset) that succeeds if WordTuple belongs to the synset with offset Offset.
  3. Define a predicate wordTuple_to_synset(+WordTuple,?Synset) that succeeds if WordTuple belongs to Synset. Look up a few words using your new predicate.
  4. Define a predicate wordTuple_to_gloss(+WordTuple,-Gloss) that succeeds is Gloss is the gloss that corresponds to WordTuple. Look up a few words using your new predicate.
  5. Define a predicate synonymous(?WordTuple1,?WordTuple2) that succeeds if WordTuple1 and WordTuple2 are distinct and synonymous word-tuples.
  6. Define a predicate polysemous(?Word) that succeeds if Word is a polysemous word.
  7. Compute the number of polysemous words in WordNet. (Tip: use a combination of findall/3, list_to_set/2 and length/2.)
  8. Define a predicate meronym(?Offset1,?Offset2) that succeeds if Offset2 is a meronym of Offset1 (note that the predicates for the three different kinds of meronyms are spread out over three files).
  9. Define a predicate hyponym(?Offset1,?Offset2) that succeeds if Offset1 is a direct hyponym of Offset2.
  10. Define a predicate coordinate(?Offset1,?Offset2) that succeeds if Offset1 and Offset2 have the same hypernym.
  11. Hypernymy is a transitive relation. Define a predicate trans_hypernym(?Offset1,?Offset2) which computes this relation.
  12. Define a predicate trans_hypernym_path(?Offset1,?Offset2,?Path) that succeeds if Path is is a list of offsets leading from Offset1 to Offset2.
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in Chemistry·
24 Jun

Step 1. Go to the link below to view demonstration videos.

Step 2. Choose three videos from the web site. You should view each videos several times.

Step 3. After a preliminary view of the videos, look at the document below. This document includes the information that you should note when you either view demonstrations or conduct experiments. It also includes the rubric so you will understand how to earn points.

Step 4. Now that you have chosen your video demonstrations, focus on one demonstration at a time. View the video again, taking notes carefully. After you are certain that you understand the concepts of the demonstration, you are ready to write your report. Use the guide below to write your reports.

Step 5. Follow the same procedure for each of the three videos. You will have three lab reports to submit. Save your documents to your computer in one file. Use the Browse or Find function to attach, then click Submit to send your document for grading.

TITLE PAGE

The title is the name of the demonstration video. The title should conciselydescribe the basic concept that is demonstrated in the lab activity.

 

 

 

 

Purpose

 

a) Purpose - State the purpose of the lab beginning with the following words, To determine:

 

Hypothesis

 

b) Hypothesis -State the hypotheses of the lab.

(The hypotheses should be an 'if/then' statement.)

 

 

c. Equipment -List the equipment used in the lab.

d. Procedure – List the procedure used in the lab.

e. Data Table – List the data that is generated from the demonstration. You may want to create a data table. You may also create a bullet list.

 

 

Conclusion

 

 

 

Conclusion – Write 3 paragraphs that include the following information:

Paragraph 1:

a. Introduction: State the experiment

b. State the purpose of the lab

c. State the hypotheses.

d. State the outcome

Paragraph 2:

e. Discuss at least 2 sources of error that could happen: give a detailed explanation of each and how this may affect the collected data.

Paragraph 3:

f. Conclusion: provide a final summary of the demonstration.

 

 

PLEASE SOMEONE HELP PLEASEEEEEEEEEEEEEEEEE

 

IT WOULD MEAN A LOT OF ANYONE CAN PLEASEEEEEEEEEEEEEEEEEEEE

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The Importance of Data in AI and Machine Learning
  • Data is crucial for AI and Machine Learning: Data plays a vital role in the development and improvement of AI and Machine Learning models. The performance of these models greatly depends on the quality and quantity of data used for training.

  • Data preparation is essential: Before using data for training AI and Machine Learning models, it needs to be prepared and cleaned. This includes removing any irrelevant data, handling missing values, and dealing with outliers. Proper data preparation helps to ensure that the models are trained on accurate and reliable data.

  • Data diversity is important: To build robust AI and Machine Learning models, it is important to use diverse and representative data. Using data from a single source or a single type of data can lead to bias and poor performance. Diverse data helps to ensure that the models can handle a wide range of inputs and scenarios.

  • Data privacy is a concern: With the increasing use of AI and Machine Learning, there is a growing concern about data privacy. It is important to ensure that data is collected, stored, and used in a way that protects the privacy and security of individuals. This includes obtaining informed consent for data collection and anonymizing data to prevent identification of individuals.

  • Data ethics is crucial: In addition to data privacy, there are also ethical considerations when it comes to using data for AI and Machine Learning. It is important to ensure that the data is used in a way that is fair, transparent, and unbiased. This includes avoiding discrimination and ensuring that the benefits of AI and Machine Learning are shared equitably.

  • Continuous learning: AI and Machine Learning models are not static – they require continuous learning and improvement. This means that they need to be trained on new data on a regular basis to ensure that they remain accurate and up-to-date.

  • Data is a valuable asset: In today's digital age, data is a valuable asset for businesses and organizations. By leveraging AI and Machine Learning, they can extract valuable insights from their data and make informed decisions.

  1. In conclusion, data is a critical component of AI and Machine Learning. Proper data preparation, diversity, privacy, ethics, continuous learning, and valuing data as an asset are all essential considerations when it comes to using data for AI and Machine Learning.

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