What is Computer Science?
Computer Science and the Liberal Arts
The Apollo Guidance Computer
Recursive Definitions and hippopotomonstrosesquipedaliophobia
This document provides an overview of programming with lists in Scheme. It begins with a recap of pairs using cons, car, and cdr. It then introduces implementing triples, quadruples, and higher-order multuples as pairs. The document defines lists as either a pair where the second element is a list, or the empty list (null). It provides examples of constructing and examining lists. Finally, it presents a problem of defining a procedure to count the number of true values in a list.
This document provides an introduction and overview of natural language processing (NLP). It discusses how NLP aims to allow computers to communicate with humans using everyday language. It also discusses related areas like artificial intelligence, linguistics, and cognitive science. The document outlines some key aspects of communication like intention, generation, perception, analysis, and incorporation. It discusses the roles of syntax, semantics, and pragmatics. It also covers challenges in NLP like ambiguity and how ambiguity is pervasive and can lead to many possible interpretations. The document contrasts natural languages with computer languages and provides examples of common NLP tasks.
The document provides an introduction to natural language processing (NLP), discussing key related areas and various NLP tasks involving syntactic, semantic, and pragmatic analysis of language. It notes that NLP systems aim to allow computers to communicate with humans using everyday language and that ambiguity is ubiquitous in natural language, requiring disambiguation. Both manual and automatic learning approaches to developing NLP systems are examined.
Adnan: Introduction to Natural Language Processing Mustafa Jarrar
This document provides an introduction to natural language processing (NLP). It discusses key topics in NLP including languages and intelligence, the goals of NLP, applications of NLP, and general themes in NLP like ambiguity in language and statistical vs rule-based methods. The document also previews specific NLP techniques that will be covered like part-of-speech tagging, parsing, grammar induction, and finite state analysis. Empirical approaches to NLP are discussed including analyzing word frequencies in corpora and addressing data sparseness issues.
NLP introduced and in 47 slides Lecture 1.pptOlusolaTop
This document provides an overview, syllabus, and introductory information for a computational linguistics course. It outlines the administrative details, topics to be covered including history and applications of computational linguistics, relationships to other fields, and challenges of natural language processing and machine translation. Key concepts like ambiguity and the NLP pipeline are also introduced.
The document discusses knowledge representation and reasoning in artificial intelligence. It provides examples of how humans acquire and combine knowledge with reasoning to make inferences. Logical agents represent complex knowledge about the world and use inference to derive new information. Key issues in knowledge-based agents include representing knowledge in a knowledge base and performing reasoning and inference. The document discusses using a formal knowledge representation language to represent knowledge with clear syntax and semantics. It provides the example of the Wumpus World, where an agent explores a world using perception and reasoning about the rules of the world.
What is Computer Science?
Computer Science and the Liberal Arts
The Apollo Guidance Computer
Recursive Definitions and hippopotomonstrosesquipedaliophobia
This document provides an overview of programming with lists in Scheme. It begins with a recap of pairs using cons, car, and cdr. It then introduces implementing triples, quadruples, and higher-order multuples as pairs. The document defines lists as either a pair where the second element is a list, or the empty list (null). It provides examples of constructing and examining lists. Finally, it presents a problem of defining a procedure to count the number of true values in a list.
This document provides an introduction and overview of natural language processing (NLP). It discusses how NLP aims to allow computers to communicate with humans using everyday language. It also discusses related areas like artificial intelligence, linguistics, and cognitive science. The document outlines some key aspects of communication like intention, generation, perception, analysis, and incorporation. It discusses the roles of syntax, semantics, and pragmatics. It also covers challenges in NLP like ambiguity and how ambiguity is pervasive and can lead to many possible interpretations. The document contrasts natural languages with computer languages and provides examples of common NLP tasks.
The document provides an introduction to natural language processing (NLP), discussing key related areas and various NLP tasks involving syntactic, semantic, and pragmatic analysis of language. It notes that NLP systems aim to allow computers to communicate with humans using everyday language and that ambiguity is ubiquitous in natural language, requiring disambiguation. Both manual and automatic learning approaches to developing NLP systems are examined.
Adnan: Introduction to Natural Language Processing Mustafa Jarrar
This document provides an introduction to natural language processing (NLP). It discusses key topics in NLP including languages and intelligence, the goals of NLP, applications of NLP, and general themes in NLP like ambiguity in language and statistical vs rule-based methods. The document also previews specific NLP techniques that will be covered like part-of-speech tagging, parsing, grammar induction, and finite state analysis. Empirical approaches to NLP are discussed including analyzing word frequencies in corpora and addressing data sparseness issues.
NLP introduced and in 47 slides Lecture 1.pptOlusolaTop
This document provides an overview, syllabus, and introductory information for a computational linguistics course. It outlines the administrative details, topics to be covered including history and applications of computational linguistics, relationships to other fields, and challenges of natural language processing and machine translation. Key concepts like ambiguity and the NLP pipeline are also introduced.
The document discusses knowledge representation and reasoning in artificial intelligence. It provides examples of how humans acquire and combine knowledge with reasoning to make inferences. Logical agents represent complex knowledge about the world and use inference to derive new information. Key issues in knowledge-based agents include representing knowledge in a knowledge base and performing reasoning and inference. The document discusses using a formal knowledge representation language to represent knowledge with clear syntax and semantics. It provides the example of the Wumpus World, where an agent explores a world using perception and reasoning about the rules of the world.
Towards and Enjoyable Career in Scientific ResearchSagar Sen
This document summarizes the key points from a talk on enjoying a career in scientific research. The talk focused on tips for scientific writing and socio-political aspects of being a researcher. For scientific writing, tips included reading a variety of authors to simplify complex topics, taking a creative writing course, motivating the reader, using an active voice and conversational style, and being honest in reporting results. For socio-political aspects, tips were to adapt to local customs, learn the local language, socialize with diverse people, express ideas rationally, and embrace art, travel, and teamwork. The overall message was to develop a broad and flexible approach to further an enjoyable career in research.
Konstantin Filtschew presents examples of how eLearning systems can provide educational assistance through interactive exercises in various subjects like vocabulary, history, math, and foreign languages. Natural language processing techniques like part-of-speech tagging and parsing can help generate automatic questions and analyze student answers, though human oversight is still needed. Mobile devices open opportunities for learners to make use of spare time for educational podcasts, tutorials, and customized information on the go.
Can programming be liberated from the von neumman styleshady_10
John Backus.
Can programming be liberated from the von neumman style.
Conventional programming languages are growing ever more enormous, but not stronger. Inherent defects at the most basic level cause them to be both fat and weak: their primitive word-at-a-time style of programming inherited from their common ancestor --the von Neumann computer, their close coupling of semantics to state transitions, their division of programming into a world of expressions and a world of statements, their inability to effectively use powerful combining forms for building new programs from existing ones, and their lack of useful mathematical properties for reasoning about programs.
The document provides an overview of the Pascal programming language. It discusses [1] the early history and development of Pascal by Niklaus Wirth from 1968-1970, [2] the key features and structure of Pascal programs including the basic parts of a program and main data types, and [3] how Pascal combined features from other languages like ALGOL, COBOL and FORTRAN to create a structured and teachable language.
- High-level overview
- Challenges in natural language processing
- What is intelligence?
- Sequence prediction
- A very short history of Solomonoff induction
- Meaning acquisition
- Logistic loss
- Gradient descent
- Applications
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
How to write scientific papers correctly, clearly, and concisely - Part II Wr...Sajid Iqbal
This document provides guidance on writing scientific papers clearly. It discusses avoiding repetition, using active voice, placing complex information at the end of sentences, using strong verbs, avoiding turning verbs into nouns, and not burying the main verb. It also covers developing an outline before writing, focusing on organization in the first draft, and revising for clarity. Key recommendations include using parallel structures, proper punctuation and grammar, and having empathy for readers.
This document discusses John McCarthy and the early history of artificial intelligence. It then provides reasons for learning the Python programming language.
The document discusses how John McCarthy organized the first conference on artificial intelligence in 1956 and created the term. It describes some of the early computers from the 1950s-1960s that McCarthy worked with, including the IBM 7090. The document then provides five reasons for learning Python: 1) It is a useful vocational skill, 2) It expands your programming skills, 3) It deepens your understanding of programming concepts, 4) It builds confidence for learning new languages, and 5) Programming in Python can be fun. It concludes by discussing learning the syntax, semantics, and style of a new programming
This document summarizes how neural networks can be used for named entity recognition (NER). It discusses how recurrent neural networks (RNNs), specifically long short-term memory networks (LSTMs), can be applied to NER by processing text sequentially and labeling each word. LSTMs can use character encodings in addition to word encodings to improve performance. Conditional random fields (CRFs) are commonly used for decoding to consider label dependencies. The document examines specific network architectures and combinations that have been effective for NER.
This document provides an introduction to natural language processing (NLP). It discusses the importance and challenges of language, provides a brief history of NLP, and introduces the Natural Language Toolkit (NLTK) for practical NLP. The key points covered are: (1) Language is complex and diverse, yet critical for human communication and civilization. (2) Early NLP drew from formal language theory, symbolic logic, and the principle of compositionality to computationally model language. (3) Current challenges include analyzing vast online text and developing more human-like dialogue systems.
This document summarizes the internship of Ho Xuan Vinh at Kyoto Institute of Technology aimed at creating a bilingual annotated corpus of Vietnamese-English for machine learning purposes. Vinh experimented with several semantic tagsets, including WordNet, LLOCE, and UCREL, but faced challenges due to the lack of Vietnamese language resources. His goal was to find an effective method for annotating a bilingual corpus to provide training data for natural language processing tasks, but he was unable to validate his annotation approaches due to limitations in the available data and tools.
This document provides an overview of an automata theory course. The course will cover regular languages and their descriptors like finite automata and regular expressions. It will also cover context-free languages and their descriptors including context-free grammars and pushdown automata. Finally, the course examines recursive and recursively enumerable languages as well as intractable problems and the limits of computation.
Ted Willke - The Brain’s Guide to Dealing with Context in Language UnderstandingMLconf
The Brain’s Guide to Dealing with Context in Language Understanding
Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
This document provides a summary of a 15-unit textbook on computing. It includes 3 main sections: introduction, textbook design, and organization. The introduction discusses the intended readership, objectives, and authors. The textbook is designed for both teachers and students, using a mixture of technical and non-technical texts. It is organized into 15 units, with each unit starting generally before focusing on a specific topic, and including 14 language focus sections.
Object oriented programming has its origins in the 1960s and 1970s, with early programming languages incorporating object-oriented principles. It grew out of the need for more structured programming methods to build very large programs. Key figures like Alan Kay developed early interactive graphics and object-oriented programming systems decades before their importance was widely recognized.
This document contains notes from a course on theory of computation taught by Professor Michael Sipser at MIT in Fall 2012. The notes were taken by Holden Lee and cover 25 lectures on topics including finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, decidability, and complexity theory. In particular, the notes summarize key definitions, theorems, and problems discussed in each lecture, with the overarching goal of understanding what types of problems can and cannot be solved by a computer.
A brief historical survey of how programming languages have evolved over the decades. We revisit several milestones along the way, reminding ourselves of a few of the missed opportunities. We examine the broad families into which programming languages fall -- an informal phylogenetic tree. We try to recognize the convergence of features among several mainstream languages. Finally, we discuss the current state of affairs in the world of programming languages.
This document contains the notes from a class about cryptocurrency. It discusses the final exam, which will involve explaining bitcoin to different audiences and answering substantive questions. It then lists the names of students in the class divided into teams based on their answers to a registration question. The rest of the document outlines a jeopardy game about cryptocurrency topics played between the student teams, including questions about Satoshi Nakamoto, hashing, scripts, cryptography, randomness, and altcoins.
Trick or Treat?: Bitcoin for Non-Believers, Cryptocurrencies for CypherpunksDavid Evans
David Evans
DC Area Crypto Day
Johns Hopkins University
30 October 2015
This (non-research) talk will start with a tutorial introduction to cryptocurrencies and how bitcoin works (and doesn’t work) today. We’ll touch on some of the legal, policy, and business aspects of bitcoin and discuss some potential research opportunities in cryptocurrencies.
Towards and Enjoyable Career in Scientific ResearchSagar Sen
This document summarizes the key points from a talk on enjoying a career in scientific research. The talk focused on tips for scientific writing and socio-political aspects of being a researcher. For scientific writing, tips included reading a variety of authors to simplify complex topics, taking a creative writing course, motivating the reader, using an active voice and conversational style, and being honest in reporting results. For socio-political aspects, tips were to adapt to local customs, learn the local language, socialize with diverse people, express ideas rationally, and embrace art, travel, and teamwork. The overall message was to develop a broad and flexible approach to further an enjoyable career in research.
Konstantin Filtschew presents examples of how eLearning systems can provide educational assistance through interactive exercises in various subjects like vocabulary, history, math, and foreign languages. Natural language processing techniques like part-of-speech tagging and parsing can help generate automatic questions and analyze student answers, though human oversight is still needed. Mobile devices open opportunities for learners to make use of spare time for educational podcasts, tutorials, and customized information on the go.
Can programming be liberated from the von neumman styleshady_10
John Backus.
Can programming be liberated from the von neumman style.
Conventional programming languages are growing ever more enormous, but not stronger. Inherent defects at the most basic level cause them to be both fat and weak: their primitive word-at-a-time style of programming inherited from their common ancestor --the von Neumann computer, their close coupling of semantics to state transitions, their division of programming into a world of expressions and a world of statements, their inability to effectively use powerful combining forms for building new programs from existing ones, and their lack of useful mathematical properties for reasoning about programs.
The document provides an overview of the Pascal programming language. It discusses [1] the early history and development of Pascal by Niklaus Wirth from 1968-1970, [2] the key features and structure of Pascal programs including the basic parts of a program and main data types, and [3] how Pascal combined features from other languages like ALGOL, COBOL and FORTRAN to create a structured and teachable language.
- High-level overview
- Challenges in natural language processing
- What is intelligence?
- Sequence prediction
- A very short history of Solomonoff induction
- Meaning acquisition
- Logistic loss
- Gradient descent
- Applications
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
How to write scientific papers correctly, clearly, and concisely - Part II Wr...Sajid Iqbal
This document provides guidance on writing scientific papers clearly. It discusses avoiding repetition, using active voice, placing complex information at the end of sentences, using strong verbs, avoiding turning verbs into nouns, and not burying the main verb. It also covers developing an outline before writing, focusing on organization in the first draft, and revising for clarity. Key recommendations include using parallel structures, proper punctuation and grammar, and having empathy for readers.
This document discusses John McCarthy and the early history of artificial intelligence. It then provides reasons for learning the Python programming language.
The document discusses how John McCarthy organized the first conference on artificial intelligence in 1956 and created the term. It describes some of the early computers from the 1950s-1960s that McCarthy worked with, including the IBM 7090. The document then provides five reasons for learning Python: 1) It is a useful vocational skill, 2) It expands your programming skills, 3) It deepens your understanding of programming concepts, 4) It builds confidence for learning new languages, and 5) Programming in Python can be fun. It concludes by discussing learning the syntax, semantics, and style of a new programming
This document summarizes how neural networks can be used for named entity recognition (NER). It discusses how recurrent neural networks (RNNs), specifically long short-term memory networks (LSTMs), can be applied to NER by processing text sequentially and labeling each word. LSTMs can use character encodings in addition to word encodings to improve performance. Conditional random fields (CRFs) are commonly used for decoding to consider label dependencies. The document examines specific network architectures and combinations that have been effective for NER.
This document provides an introduction to natural language processing (NLP). It discusses the importance and challenges of language, provides a brief history of NLP, and introduces the Natural Language Toolkit (NLTK) for practical NLP. The key points covered are: (1) Language is complex and diverse, yet critical for human communication and civilization. (2) Early NLP drew from formal language theory, symbolic logic, and the principle of compositionality to computationally model language. (3) Current challenges include analyzing vast online text and developing more human-like dialogue systems.
This document summarizes the internship of Ho Xuan Vinh at Kyoto Institute of Technology aimed at creating a bilingual annotated corpus of Vietnamese-English for machine learning purposes. Vinh experimented with several semantic tagsets, including WordNet, LLOCE, and UCREL, but faced challenges due to the lack of Vietnamese language resources. His goal was to find an effective method for annotating a bilingual corpus to provide training data for natural language processing tasks, but he was unable to validate his annotation approaches due to limitations in the available data and tools.
This document provides an overview of an automata theory course. The course will cover regular languages and their descriptors like finite automata and regular expressions. It will also cover context-free languages and their descriptors including context-free grammars and pushdown automata. Finally, the course examines recursive and recursively enumerable languages as well as intractable problems and the limits of computation.
Ted Willke - The Brain’s Guide to Dealing with Context in Language UnderstandingMLconf
The Brain’s Guide to Dealing with Context in Language Understanding
Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
This document provides a summary of a 15-unit textbook on computing. It includes 3 main sections: introduction, textbook design, and organization. The introduction discusses the intended readership, objectives, and authors. The textbook is designed for both teachers and students, using a mixture of technical and non-technical texts. It is organized into 15 units, with each unit starting generally before focusing on a specific topic, and including 14 language focus sections.
Object oriented programming has its origins in the 1960s and 1970s, with early programming languages incorporating object-oriented principles. It grew out of the need for more structured programming methods to build very large programs. Key figures like Alan Kay developed early interactive graphics and object-oriented programming systems decades before their importance was widely recognized.
This document contains notes from a course on theory of computation taught by Professor Michael Sipser at MIT in Fall 2012. The notes were taken by Holden Lee and cover 25 lectures on topics including finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, decidability, and complexity theory. In particular, the notes summarize key definitions, theorems, and problems discussed in each lecture, with the overarching goal of understanding what types of problems can and cannot be solved by a computer.
A brief historical survey of how programming languages have evolved over the decades. We revisit several milestones along the way, reminding ourselves of a few of the missed opportunities. We examine the broad families into which programming languages fall -- an informal phylogenetic tree. We try to recognize the convergence of features among several mainstream languages. Finally, we discuss the current state of affairs in the world of programming languages.
This document contains the notes from a class about cryptocurrency. It discusses the final exam, which will involve explaining bitcoin to different audiences and answering substantive questions. It then lists the names of students in the class divided into teams based on their answers to a registration question. The rest of the document outlines a jeopardy game about cryptocurrency topics played between the student teams, including questions about Satoshi Nakamoto, hashing, scripts, cryptography, randomness, and altcoins.
Trick or Treat?: Bitcoin for Non-Believers, Cryptocurrencies for CypherpunksDavid Evans
David Evans
DC Area Crypto Day
Johns Hopkins University
30 October 2015
This (non-research) talk will start with a tutorial introduction to cryptocurrencies and how bitcoin works (and doesn’t work) today. We’ll touch on some of the legal, policy, and business aspects of bitcoin and discuss some potential research opportunities in cryptocurrencies.
This document summarizes a class about hidden services using Tor and zero knowledge proofs. It discusses the rise of Bitcoin prices in August 2015, provides an overview of how Tor hidden services work through a network of nodes, and how the FBI was able to locate the Silk Road server. It also mentions that Problem Set 3 is due and lists upcoming office hours for students to attend.
This document summarizes anonymity and unlinkability in bitcoin transactions. It discusses how using different bitcoin addresses, or pseudonyms, makes it difficult to link transactions. Techniques like coinjoin and mixers are described that further confound tracing transactions by combining inputs from multiple users. The document mentions Silk Road, an illegal darknet market, and how its founder Ross Ulbricht now aims to create an economic simulation without coercion. It covers some threats to validity in analyzing anonymity and ends discussing communication privacy techniques like onion routing.
1) The midterm discussion covered confirmations in cryptocurrency transactions and the average wait time for the first confirmation.
2) It was noted that the threshold for being considered a "bitcoin expert" based on answering questions well on the midterm was around 85% of questions answered correctly.
3) Students were given updates on assignment due dates and opportunities to improve their midterm score by identifying and correcting incorrect statements in a referenced blockchain report.
The document summarizes a class on scripting and transactions in cryptocurrency. It discusses how Bitcoin core code has evolved over time to interpret scripts for locking and unlocking transactions. Examples are provided of common script patterns used prior to 2010, including pay-to-pubkey-hash and an important bug discovered that could allow stealing outputs. More advanced scripting options are also mentioned, such as checkmultisig.
The document summarizes a class on cryptocurrency and Bitcoin script. It discusses generating Bitcoin addresses through hashing public keys, describes the Bitcoin script language as a stack-based language similar to JVML used to write programs in transactions. It also notes that while Bitcoin script has limitations, altcoins are taking different approaches to scripting languages. Finally, it reminds students that project 2 is due Friday and the next class will feature a guest lecture from Tom Dukes on cyberlaw.
- Cryptocurrency mining requires a massive amount of energy. A single large bitcoin mining facility in China uses $60,000 worth of electricity per month.
- The total hashing power of the bitcoin network is estimated to be around 4.2 x 10^17 hashes per second, equivalent to around 212 megawatts of power continuously. This is around 9 times the power output of Dominion Power's Lake Anna Power Station.
- It is estimated that it takes around 35,395 kWh of electricity to mine a single bitcoin, costing $2,831 at a rate of $0.08 per kWh. However, the reward for mining a block is currently around 25 BTC, worth $5
This document summarizes a class lecture on cryptocurrency mining. It discusses the mining process, which involves finding a nonce value that satisfies the mining difficulty target for a block. Miners include transactions and solve cryptographic puzzles to validate blocks and earn rewards. The document explains Merkle trees, which improve transaction verification scalability. It also discusses the high computational costs and energy requirements of mining, noting specialized mining hardware can solve puzzles thousands of times faster than CPUs. The goal of mining is to process and validate transactions in a decentralized manner to maintain blockchain integrity.
- The document provides an overview of the schedule and topics for a cryptography class, including an introduction to cryptography today, Elliptic Curve Cryptography and signatures on Wednesday, and a checkup on the first three classes next Monday.
- It also lists the assigned readings for chapters 1-4 of the textbook and provides information about the backgrounds of students in the class.
- The remainder of the document discusses setting up a Bitcoin wallet, downloading the blockchain, hierarchical deterministic wallets, and provides a recap of the concepts from the previous class around what makes something a currency and how ownership of digital goods can be established.
This document provides an overview of a class on cryptocurrency and bitcoin. It discusses what makes a good currency, the history of currencies like salt and fiat currency, and challenges with decentralized digital currencies. It introduces bitcoin's approach of using a public ledger recorded through mining to record all transactions in a decentralized way without requiring trust in a central authority. The class will cover cryptography, computer science, economics and other topics through studying bitcoin as a concrete system. Students are assigned to set up a bitcoin wallet and complete readings before the next class.
This document contains the agenda for a cryptocurrency class. It lists several student presentations on topics related to cryptocurrency that will take place, including analyses of SHA hashing in Bitcoin, financial markets and game theory related to cryptocurrencies, and studying coinbase reserves to predict market price. It also references materials on the history of banking and reserve requirements. The document provides details on cryptocurrency student projects and presentations for an upcoming class.
This document summarizes a class on cryptocurrency and Silk Road. It discusses sidechains and how they allow bitcoin to evolve. It covers the legality of bitcoin in different jurisdictions, with some considering it legal, others contentious, and some viewing it as hostile. It then discusses Silk Road, the illegal online marketplace that was shut down, and how it used Tor and bitcoin. It summarizes how the FBI claims to have found the Silk Road server despite its use of Tor anonymity technology.
This document discusses Bloom filters and their use in Bitcoin simplified payment verification (SPV) nodes. It also covers merged mining, which allows mining of multiple cryptocurrencies like Bitcoin and Namecoin using the same hashing power. Sidechains are also mentioned. The document provides details on Bloom filter design and analysis, including the probability of false matches. It notes examples of merged mining blocks and addresses potential issues like those found in the Namecoin code. Project presentation dates are provided at the end.
This document summarizes a class about proofs-of-work for cryptocurrencies like Bitcoin. It discusses how Bitcoin and other cryptocurrencies use computationally intensive but useless proofs-of-work like SHA-256 to motivate investment in specialized hardware. It also explores the possibility of proofs-of-work that have useful outputs, like protein folding, and challenges in designing proofs-of-work that produce useful work while maintaining security properties. Finally, it announces an upcoming class about project proposals.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
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Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
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).
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.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
2. Question from Class 1
What other things have changed as much as
(or more that!) computing power in your
lifetime? (Post your guesses/answers as
comments on the course blog.)
Only one attempted guess (which I’m not sure I understand)!
2
4. Webster’s Dictionary Definition
A systematic means of
communicating ideas or feelings
by the use of conventionalized
signs, sounds, gestures, or marks
having understood meanings.
4
5. Linguist’s Definition
(Charles Yang)
A description of pairs (S, M), where S
stands for sound, or any kind of surface
forms, and M stands for meaning.
A theory of language must specify the
properties of S and M, and how they
are related.
5
6. A language is:
- a set of surface forms (usually
strings of characters), and
- a way to map any surface form
in the language to a meaning
Caveat: computer scientists often use language to
mean just a set of surface forms.
6
7. What are languages made of?
Primitives (all languages have these)
The simplest surface forms with meaning
Means of Combination (all languages have
these)
Ways to make new surface forms from ones
you already have
Means of Abstraction (all powerful languages
have these)
Ways to use simple surface forms to
represent complicated ones
7
8. Does English have these?
Primitives
Words (?)
“hippopotomonstrosesquipedaliophobia” is not a primitive
Morphemes – smallest units of meaning
e.g., anti- (“opposite”)
Means of combination
e.g., Sentence ::= Subject Verb Object
Precise rules, but not the ones you learned in grammar
school
Ending a sentence with a preposition is something
up with which we will not put.
Winston Churchill
8
9. Does English have these?
Means of abstraction
Pronouns: she, he, it, they, which, etc.
Confusing since they don’t always mean the same
thing, it depends on where they are used.
The “these” in the slide title is an abstraction for the
three elements of language introduced 2 slides ago.
The “they” in the confusing sentence is an
abstraction for pronouns.
9
11. Course Roadmap (SIS Name)
Computer Science from
(Intellectual)
Liberal Arts Illiberal Arts
Class 1
Euclid and Ada
to PS 1-7
Quantum Computing Lecture
and
($$$$)
the World Wide Web PS 8-9
12. Course Roadmap (New Name)
Introduction to
Computing:
Explorations in
Language, Chapters 2-5, 9-11; PS1-9
Logic, and Chapters 6, 7, 8, 12; PS1-9
Machines Chapter 6, 12; PS1-9
Also: XLLM is better acronym than FAEQCWWW
13. Why Learning
Computer Science is Hard
New way of thinking
Both abstract and concrete
Dynamic
Finite, but quadrillions are common
Everything is connected
Need to understand lots of new things at once
13
14. Like Drinking from a Firehose
flickr:jdawg
It may hurt a little bit, and a lot of water will go
by you, but you won’t go away thirsty!
Don’t be overwhelmed! You will do fine.
15. “Typical” cs1120 Grades
A
A Overall Class
A+ Students entering A-
with no
programming
C experience A-
C
B+
B-
B- B
B B+
15
16. Background Expected
Language
Reasonable reading and writing in English
Understanding of subject, verb and object
Math
Numbers, add, subtract, multiply, divide
Exponentiation, logarithms (we will review)
Logic: and, or, not
Computer Literacy: read email, browse web
If I ever appear to expect anything else, stop me!
17. What I Expect of You
You are a “Jeffersonian Student”
1. Believe knowledge is
powerful
2. Interested in lots of
things, ahead of your time
3. Want to use what you learn
to do good things
4. Care more about what you
learn than grades and
degree requirements http://soundstrings.wordpress.com
18. http://www.wm.edu/about/history/tjcollege/tjcollegelife/:
Thomas Jefferson enrolled in the College of William
and Mary on March 25, 1760, at the age of sixteen.
… By the time he came to Williamsburg, the young
scholar was proficient in the classics and able to
read Greek and Latin authors in the original… He
was instructed in natural philosophy
(physics, metaphysics, and mathematics) and
moral philosophy (rhetoric, logic, and ethics). A
keen and diligent student, he displayed an avid
curiosity in all fields and, according to family
tradition, he frequently studied fifteen hours a day.
18
23. How should we describe
precise languages
precisely?
23
24. Requirements
Describe infinitely many surface forms with a
short description
Listing them all doesn’t work: need ways to
generate the surface forms
Today: formally
Way to map each surface form to exactly one
precise meaning
Chapter 3, Monday: informally (using English)
Later (PS7): more formally (defining an interpreter)
24
25. ENIAC: Electronic Numerical Integrator and Computer
Early WWII computer
but not the first
(PS4)
Built to calculate
bombing tables
Memory size:
twenty 10 decimal digit accumulators = 664 bits
ENIAC (1946): ½ mm
Apollo Guidance Computer (1969): 1 inch
You: ~10 miles
26. Directions for Getting 6
1. Choose any regular accumulator (ie. Accumulator #9).
2. Direct the Initiating Pulse to terminal 5i.
3. The initiating pulse is produced by the initiating unit's Io terminal each time the
Eniac is started. This terminal is usually, by default, plugged into Program Line 1-
1 (described later). Simply connect a program cable from Program Line 1-1 to
terminal 5i on this Accumulator.
4. Set the Repeat Switch for Program Control 5 to 6.
5. Set the Operation Switch for Program Control 5 to ADD.
6. Set the Clear-Correct switch to C.
7. Turn on and clear the Eniac.
8. Normally, when the Eniac is first started, a clearing process is begun. If the Eniac
had been previously started, or if there are random neons illuminated in the
accumulators, the “Initial Clear” button of the Initiating device can be pressed.
9. Press the “Initiating Pulse Switch” that is located on the Initiating device.
10.Stand back.
27. Admiral Grace Hopper
(1906-1992)
• Mathematics PhD Yale, 1934
• Entered Navy, 1943
• First to program Mark I (first
“large” computer, 51 feet long)
• Wrote first compiler (1952) –
program for programming
“Nobody believed that I computers and designed FLOW-
had a running compiler
MATIC programming language
and nobody would touch
it. They told me • “Mother” of COBOL (most widely
computers could only do used programming language in
arithmetic.” 21st century)
28. USS Hopper
“Dare and Do”
Guest on David Letterman
29. Nanostick
How far does light travel in 1 nanosecond?
> (define nanosecond (/ 1 (* 1000 1000 1000))) ;; 1 billionth of a s
> (define lightspeed 299792458) ; m / s
> (* lightspeed nanosecond)
149896229/500000000
> (exact->inexact (* lightspeed nanosecond))
0.299792458
= just under 1 foot
Current machines have at least “2 GHz Pentium 4 CPU”
GHz = GigaHertz = 1 Billion times per second
They must finish a step before light travels 11.5 cm!
30. Code written by
humans
Compiler translates
from code in a high-
Compiler level language to
machine code
Code machine can run
Scheme uses an interpreter. An interpreter is like a
compiler, except it runs quickly and quietly on small
bits of code at a time.
31. Charge
Problem Set 0: due Sunday, 5:59pm
Help: 3-5pm in Thorton Stacks (Joeseph
and Kristina)
Readings:
by Monday: Chapter 3 of course book
by next Friday:
Chapter 4 of course book
Chapters 1-3 of The Information
31