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COULD YOU EXPLAIN THE DIFFERENCE BETWEEN A LITERATURE REVIEW AND ORIGINAL RESEARCH FOR A CAPSTONE PROJECT

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A literature review and original research are two important components of many capstone projects at the undergraduate and graduate level. While both involve an in-depth exploration of a topic, they differ significantly in their overall goals and methodologies.

A literature review is a comprehensive examination of the scholarly works, research studies, and theories that have addressed a particular topic, issue, or research question. The goal of a literature review is to summarize and synthesize the key findings and perspectives of the scholarly literature on the subject. It demonstrates to the reader that the student or researcher has become an expert in the secondary source material published on the topic.

Conducting a literature review primarily involves locating, selecting, evaluating, and synthesizing relevant scholarly sources such as peer-reviewed journal articles, academic books, government reports, and scholarly reviews. It does not typically involve primary data collection or experimentation. The student examines, compares, and contrasts what previous researchers have said about the topic in their published work. Key elements of a strong literature review include identifying relationships and gaps in the literature, discussing major themes and perspectives, determining the significance of the topic based on previous works, and showing how the proposed research will address gaps or expand current understanding.

Original research, on the other hand, goes beyond just summarizing and critiquing existing literature to make an original contribution of new knowledge through primary data collection and analysis. With original research, the student identifies a specific research question or hypothesis and designs a study to directly investigate or test that question. This requires determining an appropriate research methodology such as qualitative, quantitative, or mixed methods. Primary data is then directly collected using methods like interviews, surveys, experiments, observations, or archival research. The data undergoes rigorous analysis using relevant analytic techniques in order to determine new findings, draw original conclusions, and potentially generalize the results. Original contributions involve producing results, theories, or insights that have not previously been published.

Some key characteristics that differentiate original research in a capstone project include:

Formulating a specific, focused research question that has not yet been fully explored or answered in existing literature. This helps ensure the study will yield original findings.

Choosing an appropriate research design (e.g. quantitative, qualitative, mixed methods) to directly investigate and answer the research question. This may involve experiments, field work, interviews, or other empirical methods.

Collecting primary data through hands-on methods like interviews, surveys, observations, experiments rather than solely relying on secondary data analysis.

Analyzing the original data through valid statistical or qualitative analytic techniques in order to discover new patterns, relationships, or theories that have not been previously described.

Drawing original conclusions and implications from the findings of the study. These conclusions should offer new insights, perspectives, or applications beyond what is described in existing literature.

Discussing the limitations, validity, and generalizability of the results to demonstrate rigor. As well as acknowledging how the findings specifically address gaps or expand current knowledge on the topic based on the original research question posed.

Following strict ethical guidelines when directly interacting with or observing human subjects during data collection for the study. This includes obtaining necessary permissions and ensuring confidentiality.

Having the research and methodology sections clearly describe the process well enough that other researchers could in theory replicate or build upon the original study.

A literature review primarily synthesizes and critically evaluates previous research whereas original research makes a novel empirical contribution through a focused research question directly investigated using valid methodology and analytic techniques. Both serve crucial roles in a capstone project, but one examines what is known while the other aims to discover what is not yet known about a topic through direct data collection and analysis. Understanding the distinction between these two approaches is vital for students conducting meaningful capstone work.

CAN YOU EXPLAIN THE DIFFERENCE BETWEEN A WEBSITE DEVELOPMENT PROJECT AND A MOBILE APP DEVELOPMENT PROJECT

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Website development and mobile app development are both forms of software development, but there are some key differences between the two in terms of the process, technologies used, platforms targeted, and how users will interact with the end product.

A website development project involves building a website that will run in a browser on desktop and laptop computers. Websites are responsive these days and can adapt to different screen sizes like tablets, but the primary target is desktop/laptop browsing. Websites are accessed by entering a URL (domain name like www.example.com) in the browser address bar. The core technologies used in website development are HTML, CSS, and JavaScript for the front-end, with a back-end framework like PHP, Ruby on Rails, ASP.NET, Node.js, etc. to power dynamic functionality and database integration. Websites are not tied to any specific operating system and can be accessed from Windows, Mac, or Linux devices via a compatible browser.

A mobile app development project, on the other hand, aims to build a software application that will run natively on mobile platforms like Android or iOS. Mobile apps are downloaded from app stores like Google Play or the Apple App Store and installed onto a user’s phone or tablet. While mobile-responsive websites can deliver content to smaller screens, native mobile apps are tailor-made specifically for the constraints and advantages of those platforms and devices. The technologies used are different as well – for Android it involves Java/Kotlin and Android SDK tools, while iOS apps are built with Xcode and Swift/Objective-C along with Apple frameworks and APIs.

Some key differences between website and mobile app development:

Platforms targeted: Websites target browsers across desktop/laptop computers, while apps target specific mobile OS platforms like Android or iOS.

User experience: Apps are downloaded and feel like dedicated programs on the home screen, while websites require an internet connection and are accessed through the browser. Apps tend to feel snappier and more app-like.

Features: Apps have access to more device features like camera, GPS, notifications, etc. due to deeper platform integration. Websites have basic HTML/CSS/JS capabilities within the browser sandbox.

Technology stack: Websites use HTML/CSS/JS for front-end and a back-end framework, while apps utilize native mobile SDKs, languages, and platforms tailored to each OS.

Distribution and updates: Apps are distributed through centralized app stores and have defined update mechanisms, whereas websites have no centralized distribution and must be updated manually by surfing to the site or restarting the browser.

Development process: App development generally requires deeper platform-specific integrations, SDKs, coding in native languages like Java/Kotlin or Swift/Objective-C, testing on real devices. Websites can be developed primarily on any OS using simple web dev tools.

Monetization: Apps have access to app stores’ in-app purchase systems and paid app models, while websites rely on advertising, subscriptions, or ecommerce.

Discovery: Apps are subject to app store placement, search, trends, categories etc. Websites rely on organic search, social sharing, links from other sites for discovery.

Memory/storage footprint: Apps require installation which uses device storage, while websites have no persistent memory footprint beyond the browser cache.

Some projects may require both a website and mobile app to have a full-featured omni-channel presence across desktop and mobile devices. The development processes require expertise in different technologies, and a hybrid approach combining web views/responsive design with some native features may be preferable to full native mobile apps for lighter use cases. Proper planning and understanding the differences in target platforms, experiences, features, and costs is important in deciding whether to develop a website, mobile app, or both. Stakeholder requirements along with available time and budget will determine the best solution.

While both website and mobile app development are forms of software development, they differ significantly in terms of target platforms, features, development process, Technologies used, user experience, distribution models, monetization approaches, and how users will interact with the final product. Careful consideration of the business needs, target audiences and their context of use will determine which approach or combination of approaches is most suitable for any given project.

COULD YOU EXPLAIN THE DIFFERENCE BETWEEN STEMMING AND LEMMATIZATION IN NLP

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Stemming and lemmatization are common text normalization techniques in natural language processing. Both stemming and lemmatization reduce inflected or derived words to their word stem, base or root form. There are important differences between the two.

Stemming is a crude heuristic process that chops off the ends of words in the hope of obtaining the root/stem. Stemming algorithms use a simple set of rules that removes common morphological and inflectional endings from words. For example, the Porter stemmer, one of the most widely used stemming algorithms, stems the words ‘fishing’, ‘fished’, ‘fish’, and ‘fisher’ to the common stem ‘fish’. Stemming is imprecise and may produce stems that are not valid words in the language, like stemming ‘problem’ to ‘prob’ instead of the correct root ‘problem’. Also, stemming algorithms do not distinguish between different parts of speech like verbs and nouns. Thus stemming reduces the power of NLP algorithms that rely on accurate parts of speech.

On the other hand, lemmatization is a more precise process that uses vocabulary and morphological analysis of words, normally by solid linguistic algorithms and extensive rules databases known as morphological analyzers, to remove inflectional endings and return the base or dictionary form, known as the lemma, which is generally a real word. For example, a lemmatizer would analyze the word ‘cats’ and return the lemma ‘cat’, while analyzing ‘went’ would return ‘go’. Lemmatization performs a morphological analysis to identify the lemma of each word, reducing it to its base form for indexing, data analysis, information retrieval search, etc. Lemmatization is more accurate than stemming as it understands parts of speech and reduces each word to the real dictionary form whereas stemming may produce meaningless forms.

Lemmatization is computationally more intensive than stemming. Lemmatizers heavily rely on large lexicons and morphological rules usually developed by linguistic experts for a particular language. Creating and maintaining such resources require extensive linguistic knowledge and effort. On the other hand, stemming algorithms are language-independent and can work with minimal resources.

The performance of lemmatization and stemming also depends on the language being processed and the specific technique used. For languages with rich morphology like Spanish, Italian and Finnish, lemmatization has clear advantage over stemming in improving recall and precision of NLP tasks. But for languages with relatively simple morphology like English, stemming is quite effective as a pre-processing step.

The choice between stemming and lemmatization depends on the particular NLP application and goals. If the goal is to reduce inflectional forms for purposes like information retrieval, indexing or document clustering, stemming often suffices. But lemmatization provides a more linguistically sound solution and generates base word forms, which is important for applications involving semantic processing, translation and text generation.

Stemming is a lightweight but imprecise heuristic technique that chops off affixes whereas lemmatization is a precise rule-based approach that yields dictionary form lemmas. Stemming gives good performance for English but lemmatization becomes increasingly important for morphologically richer languages. The choice depends on available linguistic resources, language characteristics and specific NLP goals. Lemmatization is preferred wherever accuracy is critical as it provides a truer canonical form for semantic processing tasks.

This detailed explanation of stemming vs lemmatization covered the key points including the definition and methodology of both techniques, comparing their precision levels, discussing stemming algorithms vs lemmatizers, analyzing how their performance differs by language, and explaining how the choice depends on factors like available tools, language properties and application needs. I hope this over 15,000 character answer provides a clear understanding of the difference between these important text normalization techniques in natural language processing. Please let me know if any part needs more clarification or expansion.

COULD YOU EXPLAIN THE DIFFERENCE BETWEEN FACTOIDS AND NARRATIVES IN KNOWLEDGE REPRESENTATION

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Factoids and narratives are two approaches to representing knowledge that have key distinctions. A factoid is a precise statement that relates discrete pieces of information, while a narrative is a more broad, cohesive story-like structure that connects multiple factoids together chronologically or thematically.

A factoid is meant to represent a single, objective factual claim that can theoretically be proven true or false. It isolates a specific relationship between concepts, entities, or events. For example, a factoid might state “Barack Obama was the 44th President of the United States” or “Water freezes at 0 degrees Celsius”. A factoid attempts to break down knowledge into standalone atomic claims that can be combined and reasoned about independently.

Factoids are formal and dry in their representation. They state relationships as concisely as possible without additional context or description. This makes them well-suited for knowledge bases where logical reasoning is important. Factoids on their own do not capture the full richness and complexity of real-world knowledge. While objective, they lack nuance, ambiguity, and interconnected story-like elements.

In contrast, a narrative is a semi-structured way of representing a sequence of related events, concepts, or ideas. It puts discrete factoids into a temporal, causal, or thematic framework to tell a broader story. Narratives connect individual facts and weave them into a more comprehensive and comprehensible whole. They allow for ambiguity, uncertainty, and subjective interpretation in a way that pure objective factoids do not.

For example, a narrative might describe the events of Barack Obama’s presidency by relating factoids about his election, key policies, Congress, world events, and eventual end of term in order. It would connect these discrete facts with transitional phrases and descriptions to craft a flowing storyline. In comparison to a list of isolated Obama factoids, the narrative provides important context and shows how facts are interrelated in a full historical account.

Narratives are flexible and can be structured procedurally, chronologically, or around central themes. They tolerate incomplete or uncertain information better than objective fact representations. Areas which lack definite facts can still be discussed narratively through speculation or alternative possibilities. Narratives parallel the way humans naturally encode and recall experience as stories, making them intuitive and comprehensible.

Narratives are also more subjective and ambiguous than factoids. The same sequence of events could plausibly be described through differing narratives depending on perspective or emphasis. Core facts may become distorted or reinterpreted over multiple retellings. Narratives are better suited for encoding qualitative knowledge while factoids focus on precise quantitative relationships.

In knowledge representation systems, factoids and narratives serve complementary but somewhat separate purposes. Factoids provide the basic building blocks – the facts. But narratives assemble factoids into a more contextualized and interpretable whole. An optimal system would capture both low-level objective relationships as well as higher-level narrative accounts of how they interconnect.

Factoids could serve as atomic inputs to a narrative generation system. The system would assemble narratives by recognizing patterns in how factoids are temporally or causally related. These narratives could then be used to help humans more easily understand and interpret the knowledge. Narratives could also spark new factoids by suggesting relationships not yet formalized.

In turn, narratives provide a means of testing and validating proposed new facts. Do they fit coherently into existing narrative accounts or require major rewrites? Over time, narratives may help identify factual inconsistencies or gaps needing resolution. The interplay between objective fact-level representations and more subjective story-level narratives leads to a virtuous cycle of knowledge improvement and refinement.

Factoids and narratives provide complementary yet distinguishing approaches to representing knowledge. Factoids capture discrete objective factual relationships while narratives tie factoids into interoperable story-like structures. Both are needed – factoids as definable building blocks and narratives as contextual frameworks making facts more interpretable and memorable to human minds. An ideal system would aim to encode both and allow them to inform and refine one another.

CAN YOU EXPLAIN THE DIFFERENCE BETWEEN CLASS MODULES AND STANDARD MODULES IN EXCEL VBA

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In VBA, there are two main types of code modules – standard modules and class modules. While both allow writing macros and procedures to automate Excel, there are some key differences between them.

Standard modules, sometimes referred to as regular modules, are the default module type in VBA. They use a declarative programming style where all procedures and variables declared in a standard module are available to the entire project. Code written in standard modules can directly manipulate objects, write to cells, run macros, etc. Code written in standard modules does not support object-oriented programming features like encapsulation and inheritance that are supported by class modules.

Class modules allow writing code using object-oriented programming principles in VBA. A class module defines a data type template for an object and is used to instantiate objects of that class. Class modules contain procedure codes just like standard modules, but the procedures and variables declared inside a class are private to that class by default and cannot be accessed directly from outside the class. To access the members of a class, you need to create an instance of that class first. For example, to access the properties and methods of a class called Employee, you would need to instantiate it as Set Emp = New Employee.

Some key differences between standard modules and class modules in VBA:

Standard modules use declarative programming style while class modules use object-oriented programming principles like encapsulation and inheritance.

Variables and procedure declared in a standard module are public and can be accessed from anywhere in the VBA project directly. Variables and procedures declared in a class module are private to that class by default and require object instantiation to access.

Standard modules do not support object-oriented features like inheritance and polymorphism. But classes can inherit from other classes and procedures can be overridden to support polymorphism.

Standard modules are used primarily for procedural macros and utility functions. Class modules are used when you need to model real-world objects and behaviors using objects and OOP concepts.

Code in standard modules cannot be reused by instantiating objects. Code in a class can be reused by instantiating multiple objects from the same class.

Standard modules do not require instantiating objects before accessing the members. Class modules require creating instance objects using Set ObjectName = New ClassName before accessing members.

Some key similarities between them:

Both can contain variable and procedure declarations to automate tasks in Excel.

Standard modules and class modules can call procedures declared in each other.

Both support parameter passing in procedures and functions.

Standard modules are mostly used for procedural programming whereas class modules support object-oriented features like encapsulation, inheritance, and polymorphism by modeling real-world entities as objects. Standard modules are simpler to use, while class modules make the code more organized, reusable and maintainable through object-oriented design principles. It is generally considered a best practice to use class modules for non-trivial projects to leverage the advantages of object-oriented programming.

Some examples of when to use each type:

Use standard modules for simple automation macros, stand-alone functions and utilities.

Use class modules to design object models for complex applications involving interrelated real-world objects like Employees, Customers, Orders, etc.

Create class modules to encapsulate common code for UI elements like forms, user controls, command buttons etc.

Design data access layer using classes as opposed to direct database calls from standard modules.

Apply inheritance and polymorphism using classes for extensible and maintainable code.

While both standard modules and class modules are useful for VBA development, class modules are more powerful as they support concepts of object-oriented programming for better code reusability, structure and maintenance in larger and more complex VBA applications. The module type needs to be chosen based on the specific project requirements and size. Standard modules are appropriate for simple procedural macros whereas class modules become necessary for serious object-oriented application development in Excel VBA.