For the following ratings given to items by the users, perform the tasks below:
 
           Item 1 Item 2 Item 3 Item 4 Item 5 
User 1   ?           3         ?         3          4 
User 2   4           ?         ?         2          ? 
User 3   ?           ?         3         ?          ? 
User 4   3          ?         4        ?           3 
User 5   4          3         ?        4          4

Note: '?' means user has not rated that item.

For all of these tasks, use Pearson Correlation Coefficient as similarity measure and mean-centered prediction function.
Show your working clearly for :
a) Find nearest neighbors of user 1 for k=2.
b) Apply user-based Collaborative Filtering to predict R(U1, I3) (read: rating for user 1 and item 3).
c) Apply item-based Collaborative Filtering to predict R(U3, I2) and R(U4, I4). (HINT: USE CASCADING)
d) Say we had an option of using mode-centered prediction function instead of using mean. What do you think would be the effect of using mode as a baseline instead of average? Can using mode ever improve the performance of recommender system?

Analyze and write about the results of a visual and an auditory lexical decision task. Analyze a large data set that includes students’ responses for both the visual and auditory modality. Lab is a write-up of these findings.

1. One large data file that includes all experimental trial responses for both the Visual and Auditory modality tasks. Note that the sample size for the visual experiment is 16 and for the auditory experiment is 15.

Presentedz with visual words and pseudo-words in one experiment and auditory words and pseudo-words in a second experiment. Respond whether you saw/heard a real word of English or not. In the visual task, remained fixated on a cross (i.e., ‘+’) presented in the centre of the screen, while the stimulus was visually presented either to the left or right of the fixation point. In the auditory experiment, you heard words and pseudo-words presented either to your left or right ear.

  Words Pseudo-words Number 60 60 Pronounceable N/A Yes Hemi-field/Ear Presentation 30 left/30 right 30 left/30 right String Length

Range: 5-7

Mean: 6.01 (SD = 0.71)

Range: 5-7

Mean: 5.85 (SD = 0.78)

Phoneme Length

Range: 5-7

Mean: 5.45 (SD = 0.59)

Range: 3-6

Mean: 4.58 (SD = 0.87)

Number of Morphemes 1 N/A

In each experiment, there were 60 words and 60 pseudo-words. It was the same 120 items presented in each experiment. Only their modality of presentation changed. Thirty words and 30 pseudo-words were presented to the left (side of the screen in the visual experiment, ear in the auditory experiment), while the other 30 words and other 30 pseudo-words are presented to the right. Side of presentation was balanced across participants using two lists. Each list contained the same set of words and pseudo-words; only presentation side was counter-balanced across lists.

Independent samples t-tests revealed no difference in orthographic length between the words and pseudo-words (t(117) = -1.59, p = 0.11); there is, however, a reliable phoneme length difference between the words and pseudo-words (t(104.28) = -6.37, p < 0.001). We also recorded whether the participant was a native speaker of English or not and whether they are right- or left-hand dominant. Feel free to use these factors in your analysis.

1. Given what you know about the organization of the visual and auditory pathways and lateralization of language processing, state, in very clear terms, what your predictions are. Provide specific predictions for your observation (dependent) variable(s), and select and name your predictor (independent) variable(s). 
2.  
3. Provide a detailed list of the processing steps in R that is performed to arrive at your results. For example, do you want to include incorrect trials trials in your analysis of both dependent variables? Do not provide the R code, but instead write out these steps in plain English.  
4. Provide a series of plots that provide an illustration of your results. You should minimally include a plot of the accuracy and reaction time results and this should be done for each modality. You may also included histograms of your reaction time distributions, but this is not necessary. Be sure your axes are appropriately labeled. 
5. Provide a table that presents your numerical findings in a clear manner. Remember to include both the sample-level averages and standard deviations per condition. 
6. Discuss your results in light of the predictions in Q1 and your findings in Q3 and Q4. Make specific reference to your predictions and the extent to which they are borne out. 
7. Get creative: Incorporate one other factor (e.g., native language, handedness). State your predictions and provide a breakdown of the data in terms of these factors. 
8. Design your own research question: Consider how you might use the same experimental design to understand another linguistic phenomenon and population (e.g. stop VOT, vowel length, lexical tone, Mandarin speakers, French speakers etc.). State your research question, and provide and motivate specific predictions for your dependent variable(s) as a function of your predictor variable(s). Note: Ideally, the new research question will still involve language lateralization. Consult the Gazzaniga readings online for ideas based on the particular specializations of each hemisphere. 

All staff at a company have started working remotely due to a global pandemic. To transition to remote work, the company has migrated to SaaS collaboration tools. The human resources department wants to use these tools to process sensitive information but is concerned the data could be:

Leaked to the media via printing of the documents

Sent to a personal email address

Accessed and viewed by systems administrators

Uploaded to a file storage site

Which of the following would mitigate the department's concerns?

Options:
A. Data loss detection, reverse proxy, EDR, and PGP
B. VDI, proxy, CASB, and DRM
C. Watermarking, forward proxy, DLP, and MFA
D. Proxy, secure VPN, endpoint encryption, and AV

Dear Hardikmaths3,

the last few questions I post,  you keep saying 1.  Can you please confirm the following questions?

You are building a chatbot for a restaurant by using the Bot Framework Composer.

Users must be able to browse available meal combinations and select the one they want.

You need to ensure that the users can use the chatbot to browse images, prices, and descriptions of the food and select a meal combination.

What feature should you include?

Select only one answer.

1. TextCard

2. carousel

3. SuggestedAction

4. cardReceipt card

Answer:

You plan to use Text Analytics to detect the language of reviews.

You need to route the reviews that are multi-lingual to a human translator.

Which field in the JSON response should you use to filter records?

Select only one answer.

1. score

2. errors

3. statistics

4. name

Answer:

You have an application that performs Speech to Text services for users by using the Speech SDK.

You need to add the ability to perform Text to Speech in the application.

Besides the text, which other values should you include in your queries?

Select only one answer.

1.  the pronunciation

2.  the voice

3.  a keyword

$.  the phrase list

the answer: 

You build an application that uses Language Understanding.

You need to ensure that the application support speech inputs by using the Speech service.

What should you configure for Language Understanding?

Select only one answer.

1.  Enable the Speech priming setting during model publishing.

2.  Add additional utterances to the intents in the model before publishing.

3.  Set the endpoint URL parameter verbose to true.

4.  Enable the Normalize punctuation setting on the application.