Trending December 2023 # Parc And Sri: Two Keys To Apple’s History Join Forces # Suggested January 2024 # Top 12 Popular

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Two organizations key to Apple’s history are to be merged into one, as Xerox’s Palo Alto Research Center (PARC) has been donated to SRI International. This will see PARC and SRI research teams working together on future projects.

PARC was where Steve Jobs famously saw the graphical user interface and mouse that were used in the LISA and then Macintosh, while SRI was where Siri was developed …

Background

There’s a little more to the connection between Apple and both PARC and SRI International than the simplified stories usually recounted.

The Steve Jobs visit to PARC

Stanford University points out that the story usually told about Steve’s visit to PARC isn’t accurate. The myth is that Steve saw the pioneering work the organization was doing with a graphical user interface and mouse, and immediately rushed back to Apple to set up the work, which would lead to the creation of the LISA and Macintosh.

In fact, PARC had been publicly sharing the project for some time, and Apple engineers were already using those ideas to work on both the LISA and the Macintosh prior to Steve’s visit. The reason Jef Raskin and others wanted Steve to visit PARC was to see for himself the potential of the technology they were working on – as both GUI and mouse could be seen on existing hardware.

There were actually two visits by groups from Apple to Xerox PARC in 1979. Steve Jobs was on the second of the two.

Jef Raskin, who helped arranged both visits, explained that he wanted Jobs to visit PARC to understand work that was already going on at Apple. The Macintosh project had escaped the chopping block several times, and Raskin had tried to explain to Jobs the significance of the technologies it was incorporating. By showing that other companies considered this kind of work exciting, Raskin hoped to boost the value of the Macintosh’s work in Jobs’ eyes.

There’s even more to it, in that the Macintosh team were actually ahead of the LISA team in developing this revolutionary new approach to computing – a story Raskin tells here.

PAL became CALO became Siri

The distant origins of Siri could be said to be even older. Way back in 1946, Stanford University formed the Stanford Research Institute (SRI) as an innovation center, which became a standalone organization and was later spun off into the company SRI International. It was this company that developed Siri.

Siri began life as a US military project funded by DARPA (Defense Advanced Research Projects Agency). The first name the intelligent assistant was given was PAL: Personalized Assistant that Learns.

The project grew, and the assistant was renamed as CALO: Cognitive Assistant that Learns and Organizes.

SRI was actually one of 25 research groups involved in the project, before making the decision to build its own standalone voice assistant. That project was subsequently spun off into its own company – Siri, Inc. – and it was this company Apple bought in order to launch the assistant as Siri in the iPhone 4S. SRI International, meantime, continued its own work as a non-profit.

Xerox donates PARC to SRI International

Xerox has now decided that PARC is a distraction from the company’s core focus on devices geared to print and digital document production and management.

Accordingly, it has donated PARC to SRI International under a deal that will see SRI provide consultancy services to Xerox.

FTC: We use income earning auto affiliate links. More.

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How To Common Keys In List And Dictionary Using Python

In this article, we will learn how to find common keys in a list and dictionary in python.

Methods Used

The following are the various methods to accomplish this task −

Using the ‘in’ operator and List Comprehension

Using set(), intersection() functions

Using keys() function & in operator

Using the Counter() function

Example

Assume we have taken an input dictionary and list. We will find the common elements in the input list and keys of a dictionary using the above methods.

Input inputDict = {"hello": 2, "all": 4, "welcome": 6, "to": 8, "tutorialspoint": 10} inputList = ["hello", "tutorialspoint", "python"] Output Resultant list: ['hello', 'tutorialspoint']

In the above example, ‘hello‘ and ‘tutorialspoint‘ are the common elements in the input list and keys of a dictionary. Hence they are printed.

Method 1: Using the ‘in’ operator and List Comprehension List Comprehension

When you wish to build a new list based on the values of an existing list, list comprehension provides a shorter/concise syntax.

Python ‘in’ keyword

The in keyword works in two ways −

The in keyword is used to determine whether a value exists in a sequence (list, range, string, etc).

It is also used to iterate through a sequence in a for loop

Algorithm (Steps)

Following are the Algorithm/steps to be followed to perform the desired task –.

Create a variable to store the input dictionary.

Create another variable to store the input list.

Traverse through the input list and check whether any input list element matches the keys of a dictionary using list comprehension.

Print the resultant list.

Example

The following program returns common elements in the input list and dictionary keys using the ‘in’ operator and list comprehension –

On executing, the above program will generate the following output –

Input dictionary: {'hello': 2, 'all': 4, 'welcome': 6, 'to': 8, 'tutorialspoint': 10} Input list: ['hello', 'tutorialspoint', 'python'] Resultant list: ['hello', 'tutorialspoint'] Method 2: Using set(), intersection() functions

set() function − creates a set object. A set list will appear in random order because the items are not ordered. It removes all the duplicates.

intersection() function − A set containing the similarity between two or more sets is what the intersection() method returns.

It means Only items that are present in both sets, or in all sets if more than two sets are being compared, are included in the returned set.

Example

The following program returns common elements in the input list and dictionary keys using set() and intersection() –

On executing, the above program will generate the following output –

Input dictionary: {'hello': 2, 'all': 4, 'welcome': 6, 'to': 8, 'tutorialspoint': 10} Input list: ['hello', 'tutorialspoint', 'python'] Resultant list: {'hello', 'tutorialspoint'} Method 3: Using keys() function & in operator

keys() function − the dict. keys() method provides a view object that displays a list of all the keys in the dictionary in order of insertion.

Example

The following program returns common elements in the input list and dictionary keys using the keys() function and in operator–

# input dictionary inputDict = {"hello": 2, "all": 4, "welcome": 6, "to": 8, "tutorialspoint": 10} # printing input dictionary print("Input dictionary:", inputDict) # input list inputList = ["hello", "tutorialspoint", "python"] # printing input list print("Input list:", inputList) # empty list for storing the common elements in the list and dictionary keys outputList = [] # getting the list of keys of a dictionary keysList = list(inputDict.keys()) # traversing through the keys list for k in keysList: # checking whether the current key is present in the input list if k in inputList: # appending that key to the output list outputList.append(k) # printing the resultant list print("Resultant list:", outputList) Output Input dictionary: {'hello': 2, 'all': 4, 'welcome': 6, 'to': 8, 'tutorialspoint': 10} Input list: ['hello', 'tutorialspoint', 'python'] Resultant list: ['hello', 'tutorialspoint'] Method 4: Using the Counter() function

Counter() function − a sub-class that counts the hashable objects. It implicitly creates a hash table of an iterable when called/invoked.

Here the Counter() function is used to get the frequency of input list elements.

Example

The following program returns common elements in the input list and dictionary keys using the Counter() function –

# importing a Counter function from the collections module from collections import Counter # input dictionary inputDict = {"hello": 2, "all": 4, "welcome": 6, "to": 8, "tutorialspoint": 10} # printing input dictionary print("Input dictionary:", inputDict) # input list inputList = ["hello", "tutorialspoint", "python"] # printing input list print("Input list:", inputList) # getting the frequency of input list elements as a dictionary frequency = Counter(inputList) # empty list for storing the common elements of the list and dictionary keys outputList = [] # getting the list of keys of a dictionary keysList = list(inputDict.keys()) # traversing through the keys list for k in keysList: # checking whether the current key is present in the input list if k in frequency.keys(): # appending/adding that key to the output list outputList.append(k) # printing the resultant list print("Resultant list:", outputList) Output Input dictionary: {'hello': 2, 'all': 4, 'welcome': 6, 'to': 8, 'tutorialspoint': 10} Input list: ['hello', 'tutorialspoint', 'python'] Resultant list: ['hello', 'tutorialspoint'] Conclusion

We studied four different methods in this article for displaying the Common keys in the given list and dictionary. We also learned how to get a dictionary of the iterables’ frequencies using the Counter() function.

History, Location And Architecture Of Ambaji Temple

About Ambaji Temple

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Location of Ambaji Temple

Ambaji temple is a famous pilgrimage place in Gujarat, India. It is present on the border of the states of Gujarat and Rajasthan adjacent to Abu Road. It is related to the Danta Taluka of Banaskantha Precinct, relatively close to the beginnings of the famous Vedic River SARASWATI. The temple is seen on the mountaintop of Arasur Parvat in the Ambica forest. This forest is towards the southwest side of the old mountains of Aravalli. It is at an altitude of about 480 meters and about 1600 feet high from sea level. It is one among the Fifty historic Shakti Piths (the major Centre of Cosmic Power in India) with an area of 8.33 sq km (5 sq. miles).

Periodic History

It is the main shrine of a deity adored from the pre-Vedic period. The Goddess is also known as Arasuri Amba, from the temple’s position in the Arasur hills, at the origin of the River at the southern extremity of the Aravali mountainous region. Among the 51 Shakti Peethas is Ambaji Mata Temple. It is a crucial Shakti Peeth in India.

Maa Amba’s brilliance makes her a cherished shrine among devotees of the Shaktism sect of Devout Hindus. Because Devi Sati’s heart fell here, this is a potent Shakti Peeth. Daksha Yaga Mythology has an intriguing narrative behind the acquisition of Shakti Peeth Status. Every Shakti Peeth had its establishment when a portion of Goddess Sati Devi’s body dropped into that location. According to the bards, Lord Shiva danced with Sati Devi’s corpse in dreadful fury and sadness, and her body split into 51 pieces. The Shakti Peeth has a significant tantric connection and a link to Batuk Bhairav.

Architecture

The temple’s inner sanctuary boasts silver-plated doors. The primary center of devotion is a gokh, or alcove, in the wall with an ancient marble engraving of the Viso Yantra, a Vedic book on sacred geometry. There is no goddess idol, maybe because the temple predates idol adoration, but the priests adorn the upper half of the gokh in a way that appears to be a goddess idol from a distance.

Places to visit Around the Temple

A few miles from the Ambaji shrine lies Mansarovar, a massive rectangular kund with stairs on all four sides. Near Ambaji temple is also the Stone Artisan Park Training Institute (SAPTI). SAPTI is hosting a series of symposiums called Shilpotsav. Participants created stunning sculptures at prominent Ambaji locales such as Gabbar Hill, Shaktipith Circle, and the Light and Sound Show Arena.

The euphoric holiday of Navratri lights up throughout Gujarat in honor of Ambaji, with the Garba dance surrounding the Holy Mother. The Nayak and Bhojok clans also conduct bhavai theatre on these nine evenings. On Bhadarvi Poornima (full moon day), a significant mela occurs here, and people from all over the nation walk from their homes. The whole town of Ambaji gets lit up as the country celebrates Diwali.

Dplyr Tutorial: Merge And Join Data In R With Examples

Introduction to Data Analysis

Data analysis can be divided into three parts:

Extraction: First, we need to collect the data from many sources and combine them.

Transform: This step involves the data manipulation. Once we have consolidated all the sources of data, we can begin to clean the data.

Visualize: The last move is to visualize our data to check irregularity.

Data Analysis Process

One of the most significant challenges faced by data scientists is the data manipulation. Data is never available in the desired format. Data scientists need to spend at least half of their time, cleaning and manipulating the data. That is one of the most critical assignments in the job. If the data manipulation process is not complete, precise and rigorous, the model will not perform correctly.

In this tutorial, you will learn:

R Dplyr

R has a library called dplyr to help in data transformation. The dplyr library is fundamentally created around four functions to manipulate the data and five verbs to clean the data. After that, we can use the ggplot library to analyze and visualize the data.

We will learn how to use the dplyr library to manipulate a Data Frame.

Merge Data with R Dplyr

dplyr provides a nice and convenient way to combine datasets. We may have many sources of input data, and at some point, we need to combine them. A join with dplyr adds variables to the right of the original dataset.

Dplyr Joins

Following are four important types of joins used in dplyr to merge two datasets:

Function Objective Arguments Multiple keys

left_join() Merge two datasets. Keep all observations from the origin table data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

right_join() Merge two datasets. Keep all observations from the destination table data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

inner_join() Merge two datasets. Excludes all unmatched rows data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

full_join() Merge two datasets. Keeps all observations data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

We will study all the joins types via an easy example.

First of all, we build two datasets. Table 1 contains two variables, ID, and y, whereas Table 2 gathers ID and z. In each situation, we need to have a key-pair variable. In our case, ID is our key variable. The function will look for identical values in both tables and bind the returning values to the right of table 1.

library(dplyr) df_primary <- tribble( ~ID, ~y, "A", 5, "B", 5, "C", 8, "D", 0, "F", 9) df_secondary <- tribble( ~ID, ~z, "A", 30, "B", 21, "C", 22, "D", 25, "E", 29) Dplyr left_join()

The most common way to merge two datasets is to use the left_join() function. We can see from the picture below that the key-pair matches perfectly the rows A, B, C and D from both datasets. However, E and F are left over. How do we treat these two observations? With the left_join(), we will keep all the variables in the original table and don’t consider the variables that do not have a key-paired in the destination table. In our example, the variable E does not exist in table 1. Therefore, the row will be dropped. The variable F comes from the origin table; it will be kept after the left_join() and return NA in the column z. The figure below reproduces what will happen with a left_join().

Example of dplyr left_join()

left_join(df_primary, df_secondary, by ='ID')

Output:

## # A tibble: 5 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 ## 5 F 9 NA Dplyr right_join()

The right_join() function works exactly like left_join(). The only difference is the row dropped. The value E, available in the destination data frame, exists in the new table and takes the value NA for the column y.

Example of dplyr right_join()

right_join(df_primary, df_secondary, by = 'ID')

Output:

## # A tibble: 5 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 ## 5 E NA 29 Dplyr inner_join()

When we are 100% sure that the two datasets won’t match, we can consider to return only rows existing in both dataset. This is possible when we need a clean dataset or when we don’t want to impute missing values with the mean or median.

The inner_join()comes to help. This function excludes the unmatched rows.

Example of dplyr inner_join()

inner_join(df_primary, df_secondary, by ='ID')

Output:

## # A tibble: 4 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 Dplyr full_join()

Finally, the full_join() function keeps all observations and replace missing values with NA.

Example of dplyr full_join()

full_join(df_primary, df_secondary, by = 'ID')

Output:

## # A tibble: 6 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 ## 5 F 9 NA ## 6 E NA 29 Multiple Key pairs

Last but not least, we can have multiple keys in our dataset. Consider the following dataset where we have years or a list of products bought by the customer.

If we try to merge both tables, R throws an error. To remedy the situation, we can pass two key-pairs variables. That is, ID and year which appear in both datasets. We can use the following code to merge table1 and table 2

df_primary <- tribble( ~ID, ~year, ~items, "A", 2023,3, "A", 2023,7, "A", 2023,6, "B", 2023,4, "B", 2023,8, "B", 2023,7, "C", 2023,4, "C", 2023,6, "C", 2023,6) df_secondary <- tribble( ~ID, ~year, ~prices, "A", 2023,9, "A", 2023,8, "A", 2023,12, "B", 2023,13, "B", 2023,14, "B", 2023,6, "C", 2023,15, "C", 2023,15, "C", 2023,13) left_join(df_primary, df_secondary, by = c('ID', 'year'))

Output:

## # A tibble: 9 x 4 ## ID year items prices ## 1 A 2023 3 9 ## 2 A 2023 7 8 ## 3 A 2023 6 12 ## 4 B 2023 4 13 ## 5 B 2023 8 14 ## 6 B 2023 7 6 ## 7 C 2023 4 15 ## 8 C 2023 6 15 ## 9 C 2023 6 13 Data Cleaning Functions in R

Following are the four important functions to tidy (clean) the data:

Function Objective Arguments

gather() Transform the data from wide to long (data, key, value, chúng tôi = FALSE)

spread() Transform the data from long to wide (data, key, value)

separate() Split one variables into two (data, col, into, sep= “”, remove = TRUE)

unit() Unit two variables into one (data, col, conc ,sep= “”, remove = TRUE)

If not installed already, enter the following command to install tidyr:

install tidyr : install.packages("tidyr")

The objectives of the gather() function is to transform the data from wide to long.

Syntax

gather(data, key, value, chúng tôi = FALSE) Arguments: -data: The data frame used to reshape the dataset -key: Name of the new column created -value: Select the columns used to fill the key column -na.rm: Remove missing values. FALSE by default

Example

Below, we can visualize the concept of reshaping wide to long. We want to create a single column named growth, filled by the rows of the quarter variables.

library(tidyr) # Create a messy dataset messy <- data.frame( country = c("A", "B", "C"), q1_2023 = c(0.03, 0.05, 0.01), q2_2023 = c(0.05, 0.07, 0.02), q3_2023 = c(0.04, 0.05, 0.01), q4_2023 = c(0.03, 0.02, 0.04)) messy

Output:

## country q1_2023 q2_2023 q3_2023 q4_2023 ## 1 A 0.03 0.05 0.04 0.03 ## 2 B 0.05 0.07 0.05 0.02 ## 3 C 0.01 0.02 0.01 0.04 # Reshape the data gather(quarter, growth, q1_2023:q4_2023) tidier

Output:

## country quarter growth ## 1 A q1_2023 0.03 ## 2 B q1_2023 0.05 ## 3 C q1_2023 0.01 ## 4 A q2_2023 0.05 ## 5 B q2_2023 0.07 ## 6 C q2_2023 0.02 ## 7 A q3_2023 0.04 ## 8 B q3_2023 0.05 ## 9 C q3_2023 0.01 ## 10 A q4_2023 0.03 ## 11 B q4_2023 0.02 ## 12 C q4_2023 0.04

In the gather() function, we create two new variable quarter and growth because our original dataset has one group variable: i.e. country and the key-value pairs.

The spread() function does the opposite of gather.

Syntax

spread(data, key, value) arguments: data: The data frame used to reshape the dataset key: Column to reshape long to wide value: Rows used to fill the new column

Example

We can reshape the tidier dataset back to messy with spread()

# Reshape the data spread(quarter, growth) messy_1

Output:

## country q1_2023 q2_2023 q3_2023 q4_2023 ## 1 A 0.03 0.05 0.04 0.03 ## 2 B 0.05 0.07 0.05 0.02 ## 3 C 0.01 0.02 0.01 0.04

The separate() function splits a column into two according to a separator. This function is helpful in some situations where the variable is a date. Our analysis can require focussing on month and year and we want to separate the column into two new variables.

Syntax

separate(data, col, into, sep= "", remove = TRUE) arguments: -data: The data frame used to reshape the dataset -col: The column to split -into: The name of the new variables -sep: Indicates the symbol used that separates the variable, i.e.: "-", "_", "&" -remove: Remove the old column. By default sets to TRUE.

Example

We can split the quarter from the year in the tidier dataset by applying the separate() function.

separate(quarter, c(“Qrt”, “year”), sep =”_”) head(separate_tidier)

Output:

## country Qrt year growth ## 1 A q1 2023 0.03 ## 2 B q1 2023 0.05 ## 3 C q1 2023 0.01 ## 4 A q2 2023 0.05 ## 5 B q2 2023 0.07 ## 6 C q2 2023 0.02

The unite() function concanates two columns into one.

Syntax

unit(data, col, conc ,sep= "", remove = TRUE) arguments: -data: The data frame used to reshape the dataset -col: Name of the new column -conc: Name of the columns to concatenate -sep: Indicates the symbol used that unites the variable, i.e: "-", "_", "&" -remove: Remove the old columns. By default, sets to TRUE

Example

In the above example, we separated quarter from year. What if we want to merge them. We use the following code:

unite(Quarter, Qrt, year, sep =”_”) head(unit_tidier)

Output:

## country Quarter growth ## 1 A q1_2023 0.03 ## 2 B q1_2023 0.05 ## 3 C q1_2023 0.01 ## 4 A q2_2023 0.05 ## 5 B q2_2023 0.07 ## 6 C q2_2023 0.02 Summary

Data analysis can be divided into three parts: Extraction, Transform, and Visualize.

R has a library called dplyr to help in data transformation. The dplyr library is fundamentally created around four functions to manipulate the data and five verbs to clean the data.

dplyr provides a nice and convenient way to combine datasets. A join with dplyr adds variables to the right of the original dataset.

The beauty of dplyr is that it handles four types of joins similar to SQL:

left_join() – To merge two datasets and keep all observations from the origin table.

right_join() – To merge two datasets and keep all observations from the destination table.

inner_join() – To merge two datasets and exclude all unmatched rows.

full_join() – To merge two datasets and keep all observations.

Using the tidyr Library, you can transform a dataset using following functions:

gather(): Transform the data from wide to long.

spread(): Transform the data from long to wide.

separate(): Split one variable into two.

unit(): Unit two variables into one.

Cryptosat And Dfns Labs Join Forcesto Unveil World’s First Space Wallet

Trail Blazers in space-based blockchain and cryptography, Cryptosat, and Dfns, the research wing of web3 wallet infrastructure and security firm Dfns, are set to launch the world’s first space wallet.

The collaboration is anchored to strengthen and safeguard critical and valuable transactions using the “ingenious threshold signature scheme.” The satellite will act as a co-signer, foiling physical security breaches.

To demystify, threshold signature schemes distribute private key shares among multiple signers. Thus, collective approval is required for the transaction confirmation to complete the transaction. The whole system thereby mitigates the risk associated with single-point failure.

Adding to the security layer, the Cryptosat satellite will be the co-signers to approve the transaction.

“We’re glad to support Dfns in implementing its threshold signature scheme with Cryptosat’s satellites as co-signers. It’s a great example of how we can leverage space as a perfect, physically-isolated environment for the level of security institutional wallet solutions require,” said Dr. Yan Michalevsky, co-founder of Cryptosat.

Cryptosat implements comprehensive auditing for all transactions to enhance the security of transactions further. The feature ensures that any compromised transaction incident due to ground-based infrastructure vulnerabilities is recorded in the crypto-satellite’s transaction audit ledger.

Additionally, these auditable logs bolster the accountability and traceability of transactions.

“Space Wallet offers an unprecedented level of security for threshold signatures,” noted Dr. Jonathan Katz, Chief Scientist of Dfns. “It also demonstrates that threshold signature schemes can be efficient enough to deploy in resource-constrained environments.”

The collaboration comes packed to create a robust space wallet system.

About Cryptosat

Cryptosat builds and launches satellites that power blockchain and cryptographic protocols. Satellites are physically inaccessible and can serve as the most secure root of trust, guaranteeing sensitive computations and data confidentiality and integrity. Such tamper-proof satellites can serve numerous use cases, including transaction signing, trusted setups for cryptographic schemes, a randomness oracle, a timing oracle (VDF), and more.

Founded by Stanford Ph.D. alums and second-time founders Yonatan Winetraub and Yan Michalevsky, Cryptosat’s team has a background in aerospace engineering, applied security, and cryptography. In 2023, they published the SpaceTEE paper on using small satellites to protect sensitive cryptographic operations and protocols. In May 2023, its first satellite, Crypto1, was launched into orbit, followed by its second, Crypto2, in January 2023.

About Dfns

Dfns is a cybersecurity company providing wallet infrastructure for Web3. Dfns is the most secure wallet-as-a-service infra founded in 2023 in Paris. Dfns is Techstars-backed, SOC 2-certified and has raised over $20M since its creation. Dfns is an API-first KMS designed to provide app developers with secure, plug-and-play access to blockchains based on a decentralized, MPC-driven key management network with built-in recovery mechanisms operated by error-and-attack-resilient tier 3+/4 data centers. Dfns is designed with a focus on developer experience to maximize programmability, minimize high-touch implementations, and provide granular sets of permissions, controls, and policies via secure API credentials (encrypted WebAuthn 2FA). Dfns supports 40+ blockchains and 1,000+ tokens.

Our mission is to be one of the favorite building blocks in web3 and provide enablement technology for builders devoted to making the future of finance safe and delightful.

To learn more, visit Dfns’s website, dfns.co.

About Dfns Labs

The mission of Dfns Labs is to push the boundaries of decentralized and trustless key management systems by making multi-party computation and threshold-based protocols more scalable, robust, and accountable.

Arrow Keys Not Working In Microsoft Teams? 9 Fixes To Try

The left and right arrow keys on your keyboard might not work in Microsoft Teams if the app is buggy or outdated. Driver-related issues can also cause some keys to malfunction on Windows keyboards.

Try the troubleshooting fixes below if your keyboard arrow keys are unresponsive in Microsoft Teams.

Table of Contents

1. Force Quit and Restart Team

Some Teams users fixed the arrow keys malfunction by restarting the app. Force quit Microsoft Teams in the Task Manager, relaunch the app, and check if the arrow keys now work.

Press Ctrl + Shift + Escape to open the Windows Task Manager. Select Microsoft Teams and select End task on the top menu.

2. Turn Off Scroll Lock

Scroll Lock is a Windows keyboard feature that changes the behavior of the arrow keys in Windows. Disable Screen Lock if you can’t use the arrow keys in Excel, Teams, or any Microsoft app.

Look for a Scroll Lock (ScrLk) key or indicator light on your keyboard. If the light is on, press the Scroll Lock (ScrLk) key to restore the arrow keys to their default configuration.

You can disable Screen Lock from the on-screen keyboard if your keyboard doesn’t have a physical Scroll Lock key. Open the Windows On-Screen Keyboard and deselect the ScrLk key.

3. Update Microsoft Teams

Microsoft releases new versions of the Teams desktop app every month. These updates introduce new features, fix bugs, and improve the app’s performance. Although the desktop app automatically updates itself, you sometimes have to install the updates manually.

An Update button appears next to your profile picture when there’s a new version available for your computer.

Connect your PC to the internet, select the Update button, and select Update and restart Teams to install the pending update.

4. Clear Microsoft Teams Cache Files

Microsoft recommends clearing Teams cache files if you’re experiencing issues using the desktop app. Deleting Microsoft Teams cache won’t sign out your account or delete your data.

Press

Windows

to open the Run dialog box. Paste %appdata%MicrosoftTeams in the dialog box and select

Press

Windows key

to select all the files and folders in the directory. Select the

Delete

icon on the toolbar to clear Microsoft Teams’ cache files.

Close the File Explorer and reopen Teams. Reboot your computer if the arrow keys still don’t work.

5. Restart Your Computer

Rebooting Windows can fix problems with Team’s web client and desktop app. Close all open programs before rebooting your computer, so you don’t lose unsaved files and data.

Press the Windows key, select the Power icon, and select Restart.

6. Clean the Arrow Keys

The arrow keys may become unresponsive if dust or dirt builds up beneath them. Use a toothpick to dislodge dust, debris, or dirt in the corners and spaces between the arrow keys.

Tilt your laptop or desktop’s keyboard and use compressed air to spray the keyboard or affected arrow keys. Again, turn your keyboard upside down and gently tap the base to dislodge particles stuck underneath the keys.

If you spilled liquid on the affected keys, refer to our tutorial on fixing a water-damaged keyboard.

7. Repair or Reset the Microsoft Teams App

Windows has a built-in repair tool that fixes apps and programs that aren’t running correctly. Here’s how to use the tool to repair Microsoft Teams.

Open the

Settings

app, select Apps on the sidebar, and select

Installed apps

Select the

three-dot menu icon

next to Microsoft Teams and select

Advanced options

Scroll down the “Advanced options” page and select the

Repair

button.

Reopen Teams when a checkmark appears next to the Repair button. Reset the app if the arrow keys issue persists.

Select the

Reset

button.

Select

Reset

again on the pop-up to reset Teams.

Wait until you see a checkmark next to the Reset button before reopening Teams.

8. Reinstall Your Keyboard Driver

Deleting and reinstalling your keyboard’s driver can potentially fix the arrow keys issues and other keyboard-related malfunctions.

Select

Uninstall device

on the context menu.

Select

Uninstall

to proceed.

You’ll get a prompt to restart your computer. Select

Yes

to proceed.

Windows will automatically reinstall the driver when your PC reboots. Open the Microsoft Teams app and check if you can use the arrow keys. Reset your laptop’s keyboard if the arrow keys still don’t work in Microsoft Teams.

9. Perform a System Restore

Do you suspect that a recently installed app or driver is messing with your keyboard? Performing a System Restore might fix the problem, especially if the arrow keys also don’t work in other apps.

A system restore reverts your computer to a previous state (called “Restore Point”) before the arrow keys stopped working. You must have previously enabled System Restore in Windows to perform a system restore.

Open the Windows Start menu, type

restore point

in the search bar, and select

Create a restore point

Head to the “System Protection” tab and select the

System Restore

button.

Select

to proceed.

Choose a restore point before the arrow keys stopped working in Team (or other apps) and select

The System Restore operation deletes programs and drivers installed after the selected restore point. Conversely, programs deleted after the restore point are reinstalled when you perform a System Restore.

Select Scan for affected programs to see programs and drivers that might be deleted or restored.

Select

Finish

to confirm your restore point and take your computer back to an initial state.

System Restore will restart your computer to apply the changes. Reopen Microsoft Teams and check if the arrow keys now work.

Troubleshooting Checks for External Keyboards

If you have a wired keyboard, unplug it and connect it to a different USB port on your PC. Some keys on wireless keyboards may not work if the battery’s low. Charge your keyboard or change its battery and check if the arrow keys work in Microsoft Teams. Unpair and reconnect the keyboard to your computer if the issue persists. Contact Microsoft Teams Support if nothing changes.

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