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9618 Computer Science Advanced Theory > Set 1 (random) > Flashcards

Set 1 (random) Flashcards

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1
Q

Define interrupt handling

A

Transferring control to another routine when a service is required

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2
Q

Define multi-tasking

A

Managing the execution of many programs that appear to run at the same time

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3
Q

Define paging

A

Reading/writing same-size blocks of data from/to secondary storage when required

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4
Q

Define scheduling

A

Managing the process running on the CPU

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5
Q

Define virtual memory

A
  • using secondary storage to simulate additional main memory
  • Secondary storage is used to extent the RAM available so the CPU appears to be able to access more memory space than is available in RAM
  • Only the data in use needs to be in main memory so data can be swapped between RAM and virtual memory as necessary

-Virtual memory is created temporarily

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6
Q

Describe the purpose of a user-defined data type

A
  • To create a new data type (from existing data types)
  • To allow data types not available in a programming language to be constructed // to extend the flexibility of the programming language
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7
Q

Describe what is meant by a declarative programming language

A

Instructs a program on what needs to be done instead of how to do it using facts and rules using queries to satisfy goals. It can be logical or functional.

Logical - states a program as a set of logical relations

Functional - constructed by applying functions to arguments / uses a mathematical style

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8
Q

Describe what is meant by an imperative (procedural) programming language

A
  • Imperative languages use variables
  • which are changed using (assignment) statements
  • they rely on a method of repetition/iteration
  • The statements provide a sequence of commands for the computer to perform
  • in the order written/given
  • each line of code changes something in the program run.
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9
Q

Explain how an interpreter executes a program without producing a complete translated version of it [4]

A
  • The interpreter examines source code one statement at a time and checks for errors
  • If no errors were found, the statement is executed
  • If an error is found, it’s reported and the interpreter halts
  • interpretation has to be repeated every time the program runs
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10
Q

Explain the use of graphs to aid Artificial Intelligence (AI)

A

-Artificial Neural Networks can be represented using graphs

  • Graphs provide structures for relationships // graphs provide relationships between nodes
  • AI problems can be defined/solved as finding a path in a graph
  • Graphs may be analysed/ingested by a range of algorithms
  • … eg A* or Dijkstra’s algorithm
  • … used in machine learning
  • Examples of methods eg Backpropagation of errors/regression methods
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11
Q

Explain why Reverse Polish Notation (RPN) is used to carry out the evaluation of expressions

A
  • RPN provides an unambiguous method of representing an expression
  • reading from left to right
  • without the need to use brackets
  • without the need for rules of precedence / BEDMAS
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12
Q

Give benefits and drawbacks of packet switching

A

Benefits
- Accuracy - ensures accurate delivery of the message

  • Completeness - missing packets can be easily detected and re-send request sent so the message arrives complete
  • Resilience - if a network changes the router can detect it and send the data another way to ensure it arrives
  • Path also available to other users // Doesn’t use the whole bandwidth // allows simultaneous use of the channel by multiple users
  • Better security as packets are hashed and sent by different routers

Drawbacks
- Time delays to correct errors // Network problems may introduce errors in packets

  • Requires complex protocols for delivery
  • Unsuitable for real-time transmission applications
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13
Q

How can we use a Binary Tree to implement a Reverse Polish Notation (RPN)

A

A tree allows both infix and postfix to be evaluated (tree traversal)

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14
Q

How can we use a Stack to implement a Reverse Polish Notation (RPN)

A

The operands are popped from the stack in the reverse order of how they were pushed since it’s a FILO data structure

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15
Q

What is a process control block (PCB) where can this be created

A

A data structure contains all the data needed for a process to run. This can be created in memory when data needs to be received during execution time

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16
Q

What is stored in the process control block (PCB)

A
  • current process state (ready, running, blocked)
  • Process privileges (such as which resources it is allowed to access)
  • Register values (PC, MAR, MDR, ACC)
  • Process priority and any scheduling information
  • The amount of PCU time the process will need to complete
  • A process ID which allows it to be uniquely identified
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17
Q

Outline the function of a router in packet switching

A
  • The router examines the packet headers
  • It can read the IP address of the destination
  • The router also has access to the routing table, containing information about available hops, netmask, gateway used and the status of routes along the route
  • The router decides on the next hop/best route and sends the packet to its next hop.
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18
Q

Describe the purpose of the Secure Sockets Layer (SSL) and the Transport Layer Security (TLS) protocols

A
  • The SSL and TLS protocols provide communication security over the internet as they provide encryption
  • They enable two parties to identify and authenticate each other and communicate with confidentiality and integrity
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19
Q

Explain how SSL/TSL protocols are used when a client-server communication is initiated

A
  • An SSL/TLS becomes initialised by an application which becomes the client
  • The application which receives the connection becomes the server
  • Every new session begins with a handshake (as defined by the SSL/TSL protocol)
  • The client requests the digital certificate from the server and the server sends it back
  • The client verifies the certificate and obtains the public key
  • The encryption is agreed and the symmetrical session key is generated
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20
Q

State the reason for having multiple hidden layers in an artificial neural network

A
  • Enables deep learning to take place
  • Where the problem you are trying to solve has a higher level of complexity it requires more layers to solve
  • To enable the neural network to learn and make decisions on its own
  • to improve the accuracy o the result
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21
Q

Explain how artificial neural networks enable machine learning

A
  • Artificial neural networks are intended to replicate the way human brains work
  • Weights/values are assigned for each connection between nodes
  • The data are input at the input layer and are passed into the system
  • They are analysed at each subsequent (hidden) layer where characteristics are extracted/outputs are calculated. This process of training/learning is repeated many times to achieve optimum outputs // reinforcement learning takes place
  • Decisions can be made without being specifically programmed
  • The deep learning net will have created complex feature detectors
  • The output layer provides the results
  • Backpropagation (of errors) will be used to correct any errors that have been made
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22
Q

What are the essential features of recursion?

A
  • Must have a base condition/exit condition
  • Must have a general case which calls itself recursively // defined in terms of itself
  • The state changes and moves toward the base case and unwinding can occur when the base case is reached
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23
Q

Explain the reasons why a stack is a suitable Abstract Data Type (ADT) to implement recursion

A
  • A stack is a FILO data structure so each recursive call/layer is pushed onto the stack and is popped when the function ends
  • It enables backtracking to maintain the required order
24
Q

Define machine learning

A

a system that learns without being programmed to learn

25
Q

Define deep learning

A
  • Simulates the data processing capabilities of the human brain to make decisions
  • A machine that thinks in a way similar to the human brain.
  • They handle huge amounts of data using artificial neural networks.
  • The main thing is their application of neural network
26
Q

Define Labelled data

A

Data where we know the target answer and the data object is fully recognised

27
Q

Define Unlabelled data

A
  • Data where objects are undefined and need to be manually recognised
28
Q

Define Supervised learning

A
  • Enables learning by mapping an input to an output based on examples input-output pairs
  • A system which is able to predict future outcomes based on past data.
  • It requires both input and output values to be used in the training process
29
Q

Define Unsupervised learning

A

Enables learning by allowing the process to discover patterns on its own that were previously undetected

  • The system is not trained on the “right” answer
30
Q

Define Reinforcement learning

A
  • Enables learning in an interactive environment by trial and error using its own experience
  • System which is given no training
  • Learns on the basis of “reward and punishment”
31
Q

Define Semi-supervised (active) learning

A
  • A system that interactively queries source data to reach the desired result.
  • It sues both labelled and unlabelled data, but mainly unlabelled data on cost grounds
32
Q

Define Reward and punishment

A
  • Improvements to a model based on whether the feedback is positive or negative
  • Actions are optimised to receive an increase in positive feedback
33
Q

Explain what the link is between artificial intelligence, machine learning and deep learning

A
  • In layers, AI is the outer layer, M/L is the second and D/L is the inner layer
  • AI is intelligent machines think and behaves like humans
  • M/L is a system that learns without being programmed
  • D/L is machines that think in a way similar to the human brain; they handle huge amounts of data using artificial neural networks
34
Q

Define narrow AI

A

When a machine has superior performance to a human when doing one specific task

35
Q

Define general AI

A

When a machine is similar to its performance to a human in any intellectual task

36
Q

Define strong AI

A

When a machine has superior performance to a human in many tasks

37
Q

Define and describe the functions and capabilities of machine learning

A
  • Its a subset of AI in which the algorithms are trained and learn from their past experiences and examples
  • It is possible for the system to make predictions or even take decisions based on previous scenarios
  • Due to their powerful processing capability, they can offer fast and accurate outcomes
  • One key factor is the ability to manage and analyse considerable volumes of complex data - some that would otherwise take humans years
38
Q

Define Lexical Analysis

A
  • The first stage in the process of compilation
  • Removes unnecessary characters and tokenises the program
39
Q

Define Syntax Analysis

A
  • The second stage in the process of compilation
  • Output from the lexical analysis is checked for grammatical (syntax) errors
40
Q

Define Code Generation

A
  • The third stage in the process of compilation
  • This stage produces an object program
41
Q

Define Optimisation (Complication)

A
  • The fourth stage in the process of compilation
  • This stage creates an efficient object program
42
Q

Define Backus-Naur Form (BNF)

A
  • a formal method of defining the grammatical rules of a programming language
  • Recursion can be used here too
  • Eg : ::=
    ::= |
43
Q

Define a guest operating system

A
  • This is the OS running in/on a VM
  • It contains the virtual hardware during the emulation
  • This OS is being emulated by another OS (the Host OS)
  • The guest OS is running under the control of the host OS software
44
Q

Define the host operating system

A
  • The OS that is controlling the actual, physical hardware
  • The normal OS for the host machine
  • The OS runs/monitors the virtual machine software (the hypervisor)
45
Q

Define Hypervisor

A
  • It is the emulation engine
  • It handles the virtual hardware (CPU, memory, HDD and other devices) and maps them to the physical hardware on the host computer
46
Q

Describe the tasks performed by a Virtual Machine software

A
  • Create/delete a VM
  • Relay request and data between the guest and host OS
  • Protect data/instruction from the other OS
47
Q

Describe what it’s meant by a massively parallel computer

A
  • A massively parallel computer is where a large number of processors work simultaneously on the same program communicating via a messaging interface
  • This is subtly different to cluster computers where each computer processor remains largely independent.
  • In massively parallel computers, each processor will carry out part of the processing and communication between computer is achieved via interconnected data pathways.
48
Q

Describe parallel processing system SISD

A
  • Single instruction, single data
  • Uses a single processor that can handle a single instruction set to one data source at a time
  • Since there is a single processor, this architecture does not allow for parallel processing.
  • This is most commonly found in applications such as early personal computers
49
Q

Describe parallel processing system SIMD

A
  • Single instruction, multiple data
  • Uses many processors
  • Each processor executes the same instructions but uses different data inputs: they are all doing the same calculation but on different data at the same time
  • Eg all doing a fetch but from different data
  • SMID is often referred to as an array processor. They are often used in graphic cards
50
Q

Describe parallel processing system MISD

A
  • Uses several processors, each using a different instruction but using the same shared data source
  • MISD is not a commonly used architecture (MIMD tends to be used instead)
51
Q

Describe parallel processing system MIMD

A
  • Uses multiple processors. Each takes its instructions independently and each can use data from a separate data source (the data may be a single memory unit which has been suitably partitioned)
  • The MIMD architecture is used in multicore systems (Eg supercomputers or in the architecture of a multi-core chip)
52
Q

Define Pipelining

A
  • Pipelining is an instruction-level parallelism
  • The instructions are split into different stages and when the first stage of an instruction is completed, the first stage of the next instruction can start executing
  • Another instruction can start expecting before the previous one is finished
  • Thus processing a number of instructions can be concurrent/simultaneous
53
Q

What are the 5 stages of the F-E cycle?

A
  • Instruction fetch cycle (IF)
  • Instruction decode cycle (ID)
  • Operand fetch cycle (OF)
  • Instruction execution cycle (IE)
  • Writeback result process (WB)
54
Q

Describe CISC Processors

A
  • Complex instruction set computers
  • THey want the emphasis to be on the hardware used
  • Makes use of more internal instruction formats than RISC
  • They strive to carry out a given task with as few lines of assembly code as possible
  • Thus the processor hardware must therefore be capable of handling more complex assembly code instructions
  • Essentially, the architecture is based on single complex instructions which need to be converted by the processor into a number of sub-instructions to carry out the required operation

Eg to add A & B, we use ADD A, B (this is a single instruction that requires several sub-instructions/multi-cycle to complete)

55
Q

Describe the RISC processor

A
  • Reduced instruction set computer
  • Has fewer built in instruction formats than the CISC, thus leading to higher processor performance
  • Emphasis on software / instruction set to be used; this is driven by ever-faster execution times
  • Less complex instructions, which is done by breaking up the assembly code instructions into a number of simpler single-cycle instructions
  • Ultimately, this means there is a smaller, but more optimised set of instructions than CISC.
56
Q

Outline the reasons for using deep learning

A
  • Deep learning makes good use of unstructured data.
  • Deep learning outperforms other methods if the data size is large.
  • Deep learning systems enable machines to process data with a nonlinear approach.
  • Deep learning is effective at identifying (hidden) patterns / patterns that humans might not be able to see / patterns that are too complex / time consuming for humans to carry out.
  • It can provide a more accurate outcome with higher numbers of hidden layers.

9618 Computer Science Advanced Theory (3 decks)

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