Class Presentation 2 18 2009
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The document discusses progress that has been made on a project including revising class and sequence diagrams, implementing initial code, designing a GUI, and revising package structures. It also lists Java interfaces and classes created for the project including search engines, recommenders, queries, and preprocessing tools. Finally, it shows sample recommendations returned from the system on the topic of natural language processing.
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Domain Model
The document describes a domain model with entities including a search engine, text page, URL, term, and query string. The search engine performs searches on text pages located at URLs using terms within a query string.
Class Diagram V3
This class diagram shows interfaces and classes for a web recommender system. The interfaces include WebRecommender, SearchEngine, QueryFormulator, and QueryTermGenerator. The classes include StructureFeatureRecommender, SemanticFeatureRecommender, StatisticFeatureRecommender, YahooEngine, GoogleEngine, YahooQueryFormulator, GoogleQueryFormulator, and TFIDFQueryTermGenerator. The classes implement methods like Recommend(), Search(), CreateQuery(), and generateQueryTerms().
Class Presentation 02 11
The document discusses different components involved in web recommendation systems including preprocessing steps, interfaces, classes, and relationships between components. It presents a class diagram showing interfaces like WebRecommender and SearchEngine, and classes that implement interfaces like YahooEngine and GoogleEngine. It also addresses challenges in two design approaches and the need to balance practicality and flexibility.
Class Presentation 02 04
The document outlines the key steps and class diagram for a web page recommender system. It describes preprocessing steps like stripping HTML tags and stemming words. It then shows the class diagram with interfaces like WebRecommender, SearchEngine and QueryFormulator, and classes that implement these interfaces like YahooEngine and YahooQueryFormulator. It also mentions next steps like implementing the interfaces and classes.
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Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
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20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Uni Systems Copilot event_05062024_C.Vlachos.pdf
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AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
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Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
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Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
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My slides at Nordic Testing Days 6.6.2024
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Class Presentation 2 18 2009
- 3. WebRecommender(SearchEngine se) WebRecommender(SearchEngine se) WebRecommender(SearchEngine se) Util StopwordRemover Remove(String s) : String <<interface>> SearchEngine Search(Query q) : List<Page> StructureFeatureRecommender Recommend(Page p): List<Page> SemanticFeatureRecommender Recommend(Page p): List<Page> YahooSearch Search(Query q) : List<Page> BasicRecommender Recommend(Page p): List<Page> <<interface>> WebRecommender Recommend(Page p): List<Page> GoogleSearch Search(Query q) : List<Page> <<interface>> QueryFormulator And(Query q1, Query q2) : Query Or(Query q1, Query q2) : Query QueryFormulator1 And(Query q1, Query q2) : Query Or(Query q1, Query q2) : Query Stemmer Stem(String s) : String HTMLStripper Strip(Page p) : String HTMLParser Parse(Page p) : ParseTree <<interface>> QueryTermFilter FilterQueryTerms(List<String> keyTerms) : List<String> TFIDFQueryTermFilter FilterQueryTerms(List<String> keyTerms) : List<String>
- 4. http://en.wikipedia.org/wiki/Natural_language_processing Recommend Natural language processing From Wikipedia, the free encyclopedia Natural language processing ( NLP ) is a field of computer science concerned with the interactions between computers and human (natural) languages. Natural language generation systems convert information from computer databases into readable human language. Natural language understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate. Many problems within NLP apply to both generation and understanding; for exa mple, a computer must be able to model morphology (the structure of words) in order to understand an English sentence, but a model of morphology is also needed for producing a grammatically correct English sentence. NLP has significant overlap with the field of computational linguistics , and is often considered a sub-field of artificial intelligence . The term natural language is used to distinguish human languages (such as Spanish, Swahili or Swedish) from formal or computer languages (such as C++, Java or LISP). Although NLP may encompass both text and speech, work on speech processing has evolved into a separate field. http://en.wikipedia.org/wiki/Natu .... | |__http://en.wikipedia.org/wiki.. |__http://www.yahoo.com/nlp.. |__http://en.wikipedia.org/wiki.. |__http://www.yahoo.com/nlp.. |__http://en.wikipedia.org/wiki.. |__http://www.yahoo.com/nlp.. | |__ http://www.msn.com.. |__http://www.cnn.com.. …… . |__http://www.yahoo.com/nlp.. |__http://en.wikipedia.org/wiki.. |__http://www.yahoo.com/nlp.. |__http://en.wikipedia.org/wiki.. |__http://www.yahoo.com/nlp.. Very useful Useful Can’t tell Not Useful Very useful Show