AP Midterm Review - 2024 Students PDF

AP midterm review - 2024 Students 1. Collaboration and Crowdsourcing User Feedback and Collaboration: ○ Users can provide feedback that can help inform program design and development. Information gathered from potential users can be used to understand the purpose of a prog...

AP midterm review - 2024 Students 1. Collaboration and Crowdsourcing User Feedback and Collaboration: ○ Users can provide feedback that can help inform program design and development. Information gathered from potential users can be used to understand the purpose of a program from diverse perspectives and to develop a program that fully incorporates these perspectives. ○ Having group members write code in isolation is unlikely to facilitate collaboration among them. The quality of the application may suffer from the lack of communication between group members. ○ The need for software testing is not eliminated, even with effective collaborators. Testing ensures that an algorithm or program is producing the expected outcomes. Crowdsourcing: ○ Crowdsourcing is the practice of obtaining input or information from a large number of people via the Internet. This application would benefit from the use of crowdsourcing the most, as the application could allow users to contribute descriptions and photographs of landmarks. ○ Crowdsourcing is the practice of obtaining information from a large number of people. In this application, users can provide information about traffic and road condition updates in real time. This information can be used by the application to make recommendations to other users. Collaboration Example: Google Docs Group Project Scenario: Three students are working on a presentation for their history class. They use Google Docs to: ○ Write their research together in real-time. ○ Add comments to provide feedback on each other’s sections. ○ Track changes to see who contributed what. Why It Works: ○ Everyone can work on the same document, even if they’re in different locations. ○ Frequent communication and feedback ensure their work is aligned and avoids duplication or errors. Crowdsourcing Example: Waze Traffic App Scenario: Waze is a GPS navigation app that relies on users to share real-time traffic updates, like: ○ Reporting accidents or road closures. ○ Adding notes about speed traps or construction zones. ○ Highlighting changes in traffic flow. Why It Works: ○ Users contribute small pieces of information that help create a detailed, real-time map for everyone. ○ The more users participate, the better the app becomes for the community. Key Takeaway: Crowdsourcing uses contributions from many people to solve a big problem (in this case, traffic) 2. Ethics and Legal Issues Creative Commons and Licensing: ○ The use of material created by someone else without permission raises ethical concerns and can have legal consequences. Using a Creative Commons license to publish a derived work does not free the musician from having to first obtain permission. ○ A Creative Commons license allows an author to impose restrictions on the use of the licensed product, including restrictions on commercial use. ○ Information published in an open access journal is free of any and all restrictions on access and free of many restrictions on use, such as copyright or license restriction. This allows other researchers and members of the general public broad access to the results. Privacy Concerns: ○ A phishing attack is an attempt to trick individuals into providing personal information, often by getting them to fill out a form on a malicious Web site. ○ The data collected by the cameras could be misused by anyone with access to the database to track the movements of drivers, even when there is no legal justification. Examples: 1. Facial Recognition Technology and Privacy Scenario: Companies like Clearview AI have developed facial recognition systems that scrape billions of photos from social media without users’ consent. These systems are used by law enforcement to identify individuals. Ethical Concern: ○ Lack of consent for data collection violates privacy. ○ Potential misuse of technology for surveillance and targeting marginalized groups. Legal Aspect: ○ Laws like the General Data Protection Regulation (GDPR) in Europe regulate how companies collect and use personal data. Clearview AI has faced legal challenges for violating these laws. 5. Music and Streaming Platforms Scenario: Artists like Taylor Swift have spoken out about streaming platforms and music rights. Many artists earn very little from platforms like Spotify, even when their songs generate millions of streams. Ethical Concern: ○ Is it fair for platforms to take a significant portion of the revenue while artists struggle financially? ○ Raises questions about the power imbalance in the music industry. Legal Aspect: ○ Contracts and copyright laws dictate how royalties are split. Recent movements push for fairer compensation models for creators. 3. Bias and Inclusivity Avoiding Bias: Basing the recommendation algorithm on data from a random sample of users will help ensure that the data gathered are more representative of all users of the application. Gathering data from a representative sample can help the developers avoid bias. Testing the system with people of different ages, genders, and ethnicities will help reduce the chances that the facial recognition system recognizes only people who look like the developers of the system. Bias can exist based on where people live because the availability and accessibility of resources, like points of interest, can vary between different areas. For example, urban areas often have a higher concentration of landmarks and attractions, which may make it easier for people to access certain opportunities, such as special items in a game. In contrast, people in suburban or rural areas might have fewer points of interest nearby or face greater challenges traveling to them. Bias and Inclusivity Example: Video Game Characters and Accessibility Scenario: Many popular video games (like first-person shooters or role-playing games) have been criticized for: Lack of diverse character options, with most protagonists being male and white and Features that don’t consider accessibility for players with disabilities, such as limited subtitle options or no colorblind modes. Bias Concern: Representation Bias: ○ Players from underrepresented groups may feel excluded or misrepresented when they don’t see characters who look like them or reflect their experiences. Accessibility Bias: ○ Players with visual or physical impairments may struggle to enjoy the game because of poor design. Inclusivity Solutions: ○ Representation: Games like The Sims allow players to customize characters with a range of skin tones, hair textures, body types, and even cultural attire. Fortnite added inclusive features like sign language emotes, increasing accessibility for deaf players. ○ Accessibility: The Last of Us Part II introduced features like customizable subtitles, colorblind modes, and audio cues to make the game playable for those with disabilities. Key Takeaway Bias in technology impacts how people experience and interact with it. Building inclusive solutions requires both diversity in development teams and active efforts to design for all users. Encourage students to think critically about the tech they use and how it could improve inclusivity. 4. Variables and Programming Logic Data Types Numeric Variables: ○ A person’s age is typically represented as a number. ○ Example: age = 15 Boolean Variables: ○ The status of whether a store is open can be represented as true or false. ○ Example: isOpen = true String Variables: ○ A student’s name is best represented as a string (text). ○ Example: studentName = "John Doe" Meaningful Variable Names: ○ Using clear, descriptive variable names improves readability and makes the code easier to understand. ○ Example: isAbsent is better than x, and studentName is better than sn. Example Problem: x = 25 y = 50 z = 75 x=y y=z z=x What is the value of X? What is the value of Z? Answer - Highlight with your cursor: x and z both have the value 50. Example: p = 20 q = 30 r = 40 p=q q=r r=p What is the value of r? Answer - Highlight with your cursor: r has the value 20 after all assignments. 5. Compression Compression is like packing a suitcase—lossless is rolling clothes to save space, lossy is not packing some items to make more space. Lossless vs. Lossy Compression: Lossless compression allows for complete reconstruction of the original data and typically reduces the size of the data. Lossless compression is guaranteed to be able to reconstruct the original data, while lossy compression is not. Although fewer bits may be stored, information is not necessarily lost when lossy compression is applied to an image. Lossless Compression Definition: Lossless compression reduces the size of data but allows for the exact original data to be reconstructed after decompression. How It Works: Lossless algorithms identify patterns or redundancies in the data and represent them more efficiently. ○ Example: In a text file, the word "the" might appear multiple times. Instead of storing each occurrence, the algorithm stores "the" once and points to its locations. Real-Life Examples: ○ ZIP Files: When you compress files into a ZIP folder, the exact original files can be restored. ○ PNG Images: PNG uses lossless compression, which is why it preserves the quality of images. When to Use: ○ When precision is critical (e.g., text files, code files, medical imaging). Lossy Compression Definition: Lossy compression reduces data size by permanently removing some information, typically the least important or least noticeable to the user. How It Works: ○ Lossy algorithms eliminate data deemed unnecessary to reduce file size. For example, in an image, they might remove tiny color variations that aren’t easily noticeable. Real-Life Examples: ○ JPEG Images: Compress images by discarding subtle details. ○ MP3 Files: Reduce audio file size by removing frequencies that are harder for humans to hear. ○ YouTube Videos: Videos are compressed to stream efficiently, often sacrificing fine details. When to Use: When file size matters more than perfect accuracy ( streaming videos, sharing photos) Key Differences Feature Lossless Lossy Reconstruction Perfect reconstruction possible Some data is permanently lost File Size Moderate reduction Greater reduction Use Critical data (text, programs) Media (images, audio, video) 6. Data Representation Binary Numbers and Overflow What is Binary? Computers represent all data using binary (0s and 1s). Each bit corresponds to a power of 2. 4-Bit Representation: ○ The largest value in 4 bits is 15 (binary 1111). ○ If you try to add numbers that exceed this limit (e.g., 15 + 1 → 10000), an overflow error occurs because 4 bits cannot store the result. Real-Life Analogy: ○ Think of a 4-digit odometer in a car. If it hits 9999 and you drive one more mile, it resets to 0000 (overflow). Key Point: Overflow happens when a number is too large to fit into the available bits. Digital vs. Analog Data What is Analog Data? Analog data changes smoothly (e.g., a sound wave or a runner’s position). What is Digital Data? Digital data is represented by sampling analog signals at regular intervals. How It Works: ○ Sampling: Measure analog data at fixed intervals. ○ Quantization: Convert those measurements into binary values. ○ Example: A sound wave sampled 44,100 times per second for a CD. Why Sampling Matters: ○ More samples = Better approximation of the original analog data. ○ Too few samples = Loss of details (like a blurry picture). Examples: ○ Runner’s Position: Analog: Smooth, continuous tracking. Digital: GPS samples the position at intervals and stores it as binary coordinates. ○ Images: Colors are stored as binary RGB values (e.g., pure red = 255, 0, 0). ○ Sound: Audio is captured as samples of amplitudes over time and converted into binary. Key Concept: Everything in computers is represented as binary sequences, including text, colors, and sounds.