This document summarizes a study on improving Chinese handwritten character synthesis using generative networks. The study aimed to address the challenges of generating handwritten characters due to their large number, complex structures, and limited training samples. It proposed three new training methods: 1) using only hard-pen handwriting samples, 2) reducing common radicals within fonts, and 3) curriculum learning from less to more stylized fonts. Experiments showed the new methods generated less blurred images and improved quality over the baseline Zi2Zi model, demonstrating curriculum learning was effective for this task. The results indicated potential for generating TrueType fonts from only a few handwriting samples.
This document provides guidance and assignments for Project 2 of a course. It includes instructions on using different text fitting and formatting tools in CorelDraw. Students are given assignments to take pictures of fonts in the real world, review Project 1 work, and complete a 20-minute challenge to tell a story using a single word formatted with different fonts. Homework includes reviewing CorelDraw tools, typography history, font terms, and electricity/plasma cutting concepts.
Presentation of work that will be published at EMNLP 2016.
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel. emoji2vec: Learning Emoji Representations from their Description. SocialNLP at EMNLP 2016. https://arxiv.org/abs/1609.08359
Georgios Spithourakis, Isabelle Augenstein, Sebastian Riedel. Numerically Grounded Language Models for Semantic Error Correction. EMNLP 2016. https://arxiv.org/abs/1608.04147
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva. Stance Detection with Bidirectional Conditional Encoding. EMNLP 2016. https://arxiv.org/abs/1606.05464
This document provides an overview of user interface design principles and best practices. It covers topics such as the goals of UI design, layout types, typography guidelines, using color effectively, and common UI patterns. The key points emphasized are that the interface should be simple, clear and consistent; typography must have good readability; color should be chosen based on semantic meaning and context; and patterns help solve common design problems. Usability principles like the 7±2 rule for menus and 3-click rule for tasks are also reviewed.
This document provides graphic design tips for PowerPoint presentations and online courses. It discusses using meaningful titles, legible text through proper formatting and color contrast, relevant and labeled images and charts, limited special effects, and a consistent overall format. Key tips include limiting text per slide, aligning bulleted lists, avoiding awkward line breaks, proper font styles and sizes, high contrast background and text colors, and thoroughly proofreading the presentation.
The document discusses the key aspects of programming language grammar and compilers. It defines lexical and syntactic features, formal languages, grammars, terminals, non-terminals, productions, derivation, syntax trees, ambiguity in grammars, compilers, cross-compilers, p-code compilers, phases of compilation including analysis of source text and synthesis of target text, and code optimization techniques. The overall goal of a compiler is to translate a high-level language program into an equivalent machine language program.
DeepWriting: Making Digital Ink Editable via Deep Generative Modelingivaderivader
This paper proposes a conditional variational recurrent neural network (C-VRNN) model to make digital ink editable by disentangling handwriting style and text content. The C-VRNN model allows for conditional handwriting generation, content-preserving style transfer, and word-level editing such as spell checking. A new dataset of handwritten text with character-level annotations is also collected to train and evaluate the model. Preliminary user evaluations suggest the model can effectively beautify handwriting.
Game Design 2: UI in Games - Revision LectureDavid Farrell
The document provides an overview of the topics and content that will be covered in the Game Design 2 exam. It includes:
1) A reminder of the key topics and content from each lecture that could appear in the exam, such as menus, text, UI components, prototyping techniques, and usability principles.
2) Information about the format of the exam, including that it will contain 6 questions worth 10 marks each, and students must answer 4 out of the 6 questions.
3) Advice for students on how to focus their studying, including reviewing the content and examples from each lecture, as the exam will test understanding of both technical and conceptual aspects of the topics.
This document provides guidance and assignments for Project 2 of a course. It includes instructions on using different text fitting and formatting tools in CorelDraw. Students are given assignments to take pictures of fonts in the real world, review Project 1 work, and complete a 20-minute challenge to tell a story using a single word formatted with different fonts. Homework includes reviewing CorelDraw tools, typography history, font terms, and electricity/plasma cutting concepts.
Presentation of work that will be published at EMNLP 2016.
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel. emoji2vec: Learning Emoji Representations from their Description. SocialNLP at EMNLP 2016. https://arxiv.org/abs/1609.08359
Georgios Spithourakis, Isabelle Augenstein, Sebastian Riedel. Numerically Grounded Language Models for Semantic Error Correction. EMNLP 2016. https://arxiv.org/abs/1608.04147
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva. Stance Detection with Bidirectional Conditional Encoding. EMNLP 2016. https://arxiv.org/abs/1606.05464
This document provides an overview of user interface design principles and best practices. It covers topics such as the goals of UI design, layout types, typography guidelines, using color effectively, and common UI patterns. The key points emphasized are that the interface should be simple, clear and consistent; typography must have good readability; color should be chosen based on semantic meaning and context; and patterns help solve common design problems. Usability principles like the 7±2 rule for menus and 3-click rule for tasks are also reviewed.
This document provides graphic design tips for PowerPoint presentations and online courses. It discusses using meaningful titles, legible text through proper formatting and color contrast, relevant and labeled images and charts, limited special effects, and a consistent overall format. Key tips include limiting text per slide, aligning bulleted lists, avoiding awkward line breaks, proper font styles and sizes, high contrast background and text colors, and thoroughly proofreading the presentation.
The document discusses the key aspects of programming language grammar and compilers. It defines lexical and syntactic features, formal languages, grammars, terminals, non-terminals, productions, derivation, syntax trees, ambiguity in grammars, compilers, cross-compilers, p-code compilers, phases of compilation including analysis of source text and synthesis of target text, and code optimization techniques. The overall goal of a compiler is to translate a high-level language program into an equivalent machine language program.
DeepWriting: Making Digital Ink Editable via Deep Generative Modelingivaderivader
This paper proposes a conditional variational recurrent neural network (C-VRNN) model to make digital ink editable by disentangling handwriting style and text content. The C-VRNN model allows for conditional handwriting generation, content-preserving style transfer, and word-level editing such as spell checking. A new dataset of handwritten text with character-level annotations is also collected to train and evaluate the model. Preliminary user evaluations suggest the model can effectively beautify handwriting.
Game Design 2: UI in Games - Revision LectureDavid Farrell
The document provides an overview of the topics and content that will be covered in the Game Design 2 exam. It includes:
1) A reminder of the key topics and content from each lecture that could appear in the exam, such as menus, text, UI components, prototyping techniques, and usability principles.
2) Information about the format of the exam, including that it will contain 6 questions worth 10 marks each, and students must answer 4 out of the 6 questions.
3) Advice for students on how to focus their studying, including reviewing the content and examples from each lecture, as the exam will test understanding of both technical and conceptual aspects of the topics.
Game design 2 (2013): Lecture 14 - RevisionDavid Farrell
The document provides guidance to students on preparing for an exam for a game design course. It includes sample exam questions covering various topics from lectures such as menu design, use of text/fonts, UI components, prototyping, evaluation techniques, color theory, and data visualization. Students are advised to thoroughly review lecture materials and handouts and practice answering question styles. Doing so for this set of example questions and getting feedback would help guarantee passing the exam.
The document discusses different classifications and properties of typefaces, including serif, sans serif, script, monospaced, and display fonts. It provides examples for each classification and describes characteristics like readability, uses, and styles. The document also covers topics like type spacing, rasterization, anti-aliasing, sub pixel rendering, font embedding, web fonts, and encoding as they relate to digital typography.
Learn what goes into creating professional-looking books! Join India Amos, Managing Editor of Print and Digital Production at CN Times Books, and Allan Lieberman, Special Projects Manager, Data Conversion Laboratory, Inc., on Monday, June 30th, at 1:00pm EDT to discover what you need to know about production and design.
Whether you are publishing in print, digital, or both, this webinar will help you determine what choices you need to make for your book. We’ll cover:
• Fonts – what works?
• Paper stock, cost, and quality
• eBook conversion
• Print-on-Demand
• Cover design
• Proofing and galleys
By the end of this webinar, you should have the information you need to make informed choices about how your book will look on different ebook readers and on bookshelves.
This document provides tips for designing effective PowerPoint presentations. It recommends using serif fonts for body text and sans serif fonts for headlines, limiting fonts to 2-3 for consistency. Text size should be between 28-36 points and mixed case is easiest to read. Color schemes should provide good contrast between text and background. Presentations should have 7 words per line, 7 lines per slide, and 25 words total. Images and animation should be used sparingly to emphasize key points without distracting from the content. The focus should remain on the message, not flashy effects.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
This document discusses typography and type design principles. It covers what typography is, how type conveys meaning through attributes like size, weight, and color, and how first impressions are important. Different typefaces have different characters that contribute to the message. The anatomy of a letter and classifications of typefaces like serif, sans-serif, slab-serif, and more are explained. Terms like letterspacing, line height, line length, and x-height are defined. The document concludes with discussions on legibility of type on screens and an assignment involving analyzing logo design principles.
The document provides tips for improving layout and design techniques to make content more readable, including using compositional techniques like establishing a focal point and hierarchy, choosing fonts appropriately for different mediums, reducing visual density by breaking up content with subheadings and white space, and working collaboratively between designers and editors from an early stage. Key recommendations include establishing goals and discussing audience needs, incorporating infographics, and giving constructive feedback when reviewing designs.
1) Understanding your tools, the content, and media format is essential before designing for print. Know which Adobe programs to use for different tasks and understand the print process.
2) Optimizing images, fonts, colors, and links is important so the design looks as intended in print. Proofs should be checked closely before sending to the printer.
3) Different printing methods like offset, digital, and screen printing use various paper formats and ink types, so the design needs to be suitable for the chosen media and process.
The document provides instructions and examples for creating and formatting slides in PowerPoint. It includes examples of slides with different elements like bullets, tables, text boxes, graphs and pictures. Guidelines are provided for color schemes, fonts, styles and use of templates for presentations. Permission is given to use the templates for personal and business presentations with certain restrictions.
Jack Hickman has created plans for a gaming magazine and website. The document includes flat plans for the magazine's front cover, double page spread, and three website pages. It also includes a style sheet outlining six fonts to be used and their purposes. The website plan describes the background, navigation bar, and color scheme. A five week schedule is outlined with two weeks for planning, two weeks for production, and one week for processes and evaluation.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
We have chosen Statistical machine translation approach for our thesis. Statistical machine translation work on parallel data. We performed our thesis on Hindi-English language pair. SMT uses different models for performing translation.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
This document summarizes an experiment conducted on statistical machine translation models. The experiment compared phrase-based, hierarchical, and syntax-based statistical machine translation models. The document outlines the process of data preparation including tokenization, alignment, and training on the Moses platform. It then describes how each model - phrase-based, hierarchical, and syntax-based - works, including rule extraction for the hierarchical model. The document concludes by discussing the advantage of the hierarchical model and how it was able to automatically annotate Hindi data.
The document summarizes work done on experimenting with different models of statistical machine translation (SMT). It discusses various SMT models studied including phrase-based, hierarchical, syntax-based, and hybrid translation models. The document outlines the process of data preparation, training, tuning and evaluation of models on a Hindi-English language pair. Results showed that the hierarchical and syntax-based models performed better than phrase-based in terms of reordering words and producing grammatically correct sentences for the given language pair.
This document discusses font issues in e-books and recommendations for addressing them. It notes that problems arise from file conversions, font choices, and content complexity, and are sometimes introduced or affected by technology. Solutions include using CSS to control fonts, embedding fonts fully, and testing across devices. However, hardware, software, user behavior and ideal vs practical solutions limit outcomes. The document recommends best practices like thorough quality control, accessibility, licensing tracking, and standards compliance to help manage font problems in e-books.
English is not the language of habitual use for all technical authors writing in English. Similarly, users of English-language software products may not have first-language proficiency in the language. This presentation looks at strategies for ensuring the quality of technical documentation in a global IT environment.
This document summarizes an introductory computer science class that teaches Ruby programming. It introduces the course, resources, instructors, and covers data types, variables, math operations, string methods, printing to the screen, comments, and naming conventions. The class assumes no prior experience and aims to teach programming fundamentals in Ruby, an easy-to-learn and widely used language.
The document discusses sequence to sequence models for speech recognition. It describes how traditional automatic speech recognition (ASR) works using acoustic, pronunciation, and language models. The document then introduces sequence to sequence models like Listen, Attend and Spell (LAS) which uses an encoder, attender, and decoder. LAS improves upon traditional ASR by integrating all models into a single neural network with attention and other optimizations like minimum word error rate training and scheduled sampling. Sequence to sequence models provide around 11% relative improvement in word error rate over traditional ASR systems.
The document provides information about an upcoming bootcamp on natural language processing (NLP) being conducted by Anuj Gupta. It discusses Anuj Gupta's background and experience in machine learning and NLP. The objective of the bootcamp is to provide a deep dive into state-of-the-art text representation techniques in NLP and help participants apply these techniques to solve their own NLP problems. The bootcamp will be very hands-on and cover topics like word vectors, sentence/paragraph vectors, and character vectors over two days through interactive Jupyter notebooks.
This document summarizes an experiment comparing different character-level embedding approaches for Korean sentence classification tasks. Dense character-level embeddings using pre-trained fastText vectors outperformed sparse one-hot encodings. Character-level embeddings preserved local semantics around character boundaries better than Jamo-level encodings, which performed best with self-attention. While Jamo-level features may be useful for syntax-semantic tasks, character-level approaches had better performance and computation efficiency. These findings provide insights for character-rich languages beyond Korean.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Game design 2 (2013): Lecture 14 - RevisionDavid Farrell
The document provides guidance to students on preparing for an exam for a game design course. It includes sample exam questions covering various topics from lectures such as menu design, use of text/fonts, UI components, prototyping, evaluation techniques, color theory, and data visualization. Students are advised to thoroughly review lecture materials and handouts and practice answering question styles. Doing so for this set of example questions and getting feedback would help guarantee passing the exam.
The document discusses different classifications and properties of typefaces, including serif, sans serif, script, monospaced, and display fonts. It provides examples for each classification and describes characteristics like readability, uses, and styles. The document also covers topics like type spacing, rasterization, anti-aliasing, sub pixel rendering, font embedding, web fonts, and encoding as they relate to digital typography.
Learn what goes into creating professional-looking books! Join India Amos, Managing Editor of Print and Digital Production at CN Times Books, and Allan Lieberman, Special Projects Manager, Data Conversion Laboratory, Inc., on Monday, June 30th, at 1:00pm EDT to discover what you need to know about production and design.
Whether you are publishing in print, digital, or both, this webinar will help you determine what choices you need to make for your book. We’ll cover:
• Fonts – what works?
• Paper stock, cost, and quality
• eBook conversion
• Print-on-Demand
• Cover design
• Proofing and galleys
By the end of this webinar, you should have the information you need to make informed choices about how your book will look on different ebook readers and on bookshelves.
This document provides tips for designing effective PowerPoint presentations. It recommends using serif fonts for body text and sans serif fonts for headlines, limiting fonts to 2-3 for consistency. Text size should be between 28-36 points and mixed case is easiest to read. Color schemes should provide good contrast between text and background. Presentations should have 7 words per line, 7 lines per slide, and 25 words total. Images and animation should be used sparingly to emphasize key points without distracting from the content. The focus should remain on the message, not flashy effects.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
This document discusses typography and type design principles. It covers what typography is, how type conveys meaning through attributes like size, weight, and color, and how first impressions are important. Different typefaces have different characters that contribute to the message. The anatomy of a letter and classifications of typefaces like serif, sans-serif, slab-serif, and more are explained. Terms like letterspacing, line height, line length, and x-height are defined. The document concludes with discussions on legibility of type on screens and an assignment involving analyzing logo design principles.
The document provides tips for improving layout and design techniques to make content more readable, including using compositional techniques like establishing a focal point and hierarchy, choosing fonts appropriately for different mediums, reducing visual density by breaking up content with subheadings and white space, and working collaboratively between designers and editors from an early stage. Key recommendations include establishing goals and discussing audience needs, incorporating infographics, and giving constructive feedback when reviewing designs.
1) Understanding your tools, the content, and media format is essential before designing for print. Know which Adobe programs to use for different tasks and understand the print process.
2) Optimizing images, fonts, colors, and links is important so the design looks as intended in print. Proofs should be checked closely before sending to the printer.
3) Different printing methods like offset, digital, and screen printing use various paper formats and ink types, so the design needs to be suitable for the chosen media and process.
The document provides instructions and examples for creating and formatting slides in PowerPoint. It includes examples of slides with different elements like bullets, tables, text boxes, graphs and pictures. Guidelines are provided for color schemes, fonts, styles and use of templates for presentations. Permission is given to use the templates for personal and business presentations with certain restrictions.
Jack Hickman has created plans for a gaming magazine and website. The document includes flat plans for the magazine's front cover, double page spread, and three website pages. It also includes a style sheet outlining six fonts to be used and their purposes. The website plan describes the background, navigation bar, and color scheme. A five week schedule is outlined with two weeks for planning, two weeks for production, and one week for processes and evaluation.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
We have chosen Statistical machine translation approach for our thesis. Statistical machine translation work on parallel data. We performed our thesis on Hindi-English language pair. SMT uses different models for performing translation.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
This document summarizes an experiment conducted on statistical machine translation models. The experiment compared phrase-based, hierarchical, and syntax-based statistical machine translation models. The document outlines the process of data preparation including tokenization, alignment, and training on the Moses platform. It then describes how each model - phrase-based, hierarchical, and syntax-based - works, including rule extraction for the hierarchical model. The document concludes by discussing the advantage of the hierarchical model and how it was able to automatically annotate Hindi data.
The document summarizes work done on experimenting with different models of statistical machine translation (SMT). It discusses various SMT models studied including phrase-based, hierarchical, syntax-based, and hybrid translation models. The document outlines the process of data preparation, training, tuning and evaluation of models on a Hindi-English language pair. Results showed that the hierarchical and syntax-based models performed better than phrase-based in terms of reordering words and producing grammatically correct sentences for the given language pair.
This document discusses font issues in e-books and recommendations for addressing them. It notes that problems arise from file conversions, font choices, and content complexity, and are sometimes introduced or affected by technology. Solutions include using CSS to control fonts, embedding fonts fully, and testing across devices. However, hardware, software, user behavior and ideal vs practical solutions limit outcomes. The document recommends best practices like thorough quality control, accessibility, licensing tracking, and standards compliance to help manage font problems in e-books.
English is not the language of habitual use for all technical authors writing in English. Similarly, users of English-language software products may not have first-language proficiency in the language. This presentation looks at strategies for ensuring the quality of technical documentation in a global IT environment.
This document summarizes an introductory computer science class that teaches Ruby programming. It introduces the course, resources, instructors, and covers data types, variables, math operations, string methods, printing to the screen, comments, and naming conventions. The class assumes no prior experience and aims to teach programming fundamentals in Ruby, an easy-to-learn and widely used language.
The document discusses sequence to sequence models for speech recognition. It describes how traditional automatic speech recognition (ASR) works using acoustic, pronunciation, and language models. The document then introduces sequence to sequence models like Listen, Attend and Spell (LAS) which uses an encoder, attender, and decoder. LAS improves upon traditional ASR by integrating all models into a single neural network with attention and other optimizations like minimum word error rate training and scheduled sampling. Sequence to sequence models provide around 11% relative improvement in word error rate over traditional ASR systems.
The document provides information about an upcoming bootcamp on natural language processing (NLP) being conducted by Anuj Gupta. It discusses Anuj Gupta's background and experience in machine learning and NLP. The objective of the bootcamp is to provide a deep dive into state-of-the-art text representation techniques in NLP and help participants apply these techniques to solve their own NLP problems. The bootcamp will be very hands-on and cover topics like word vectors, sentence/paragraph vectors, and character vectors over two days through interactive Jupyter notebooks.
This document summarizes an experiment comparing different character-level embedding approaches for Korean sentence classification tasks. Dense character-level embeddings using pre-trained fastText vectors outperformed sparse one-hot encodings. Character-level embeddings preserved local semantics around character boundaries better than Jamo-level encodings, which performed best with self-attention. While Jamo-level features may be useful for syntax-semantic tasks, character-level approaches had better performance and computation efficiency. These findings provide insights for character-rich languages beyond Korean.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
The Microsoft 365 Migration Tutorial For Beginner.pptx
Presentation for dissertation liu
1. A Study on Handwritten Chinese Character Synthesis
using Generative Networks
Graduate School of System and Engineering
Advisor: Hidemoto Nakada
Author: Liangyu Liu
2. Overview
• The challenge of a large amount of characters, fonts
and complex structure for Chinese characters.
• Zi2Zi is a powerful model for printed type Chinese
character synthesis, while underwhelming for
handwritings generation.
Overview
Badly formed
handwritings
3. Overview
• We aim at improving zi2zi on Chinese handwriting
character synthesis using three new training
methods.
• Result shows that using our methods can get a
better image quality and learning from easy to hard
tasks using curriculum learning can improve
learning effect.
Overview
Badly formed
handwritings
6. Background
• Numerous characters.
• Complex structure.
• A few handwriting samples for a certain font.
• A sample shows the difference between Chinese
characters and alphabets.
Challenges of Chinese
character synthesis
7. Background
• Re-purpose a well-trained
model onto another related
task.
• Train faster and more effectively.
• Improve performance when
tackling another related task by
fine-tuning.
Transfer Learning
Taken from https://ruder.io/transfer-learning/index.html
8. Background
• Based on conditional GAN for image to image transition.
• Main loss functions: Adversarial loss and L1 loss.
• Discriminator to distinguish
whether the images are
real or fake.
• Generator to synthesize
more realistic images.
Pix2Pix
Taken from Image-to-Image Translation with Conditional Adversarial Networks
https://arxiv.org/abs/1611.07004
9. Background
• An encoder-decoder model
based on the fully convolutional
network.
• Skip-connections between the
encoding layers and the
corresponding decoding layers.
U-net
Taken from U-Net: Convolutional Networks for Biomedical Image Segmentation
https://arxiv.org/pdf/1505.04597
10. Background
• Based on pix2pix model for Chinese characters synthesis.
• Learn multiple fonts at the same time.
• A non-trainable Gaussian noise to map the corresponding style.
• A multi-class category loss to avoid confusion of multiple styles
by predicting the style of the character.
• A constant loss to make the generated character resemble the
source.
• Good result in printed font synthesis while underwhelming on
generating handwritings.
Zi2Zi
Badly formed
result
11. Background
Architecture of Zi2Zi
• Main loss functions:
• G loss, calculated by L1 loss,
category loss and constant
loss.
• D loss, calculated by true or
fake loss and category loss.
• L1 loss to measure the
difference between
generated and real images.
12. Background
• Training process:
• Prepare plenty of fonts, sampling
characters from each font and draw
with the source font in pair.
• Training all the paired images
together at the same time.
• Test process:
• Select the test font by designating
the embedding id.
Process of Zi2Zi
Paired images for
different fonts
13. Background
• Evaluate the quality and similarity of images.
• SSIM (Structural Similarity Index) :
• Perceived similarity between the two given images.
• From 0 to 1
• The larger the more similar.
• PSNR (Peak Signal to Noise Ratio) :
• The quality of generated image.
• The larger the clearer.
Image quality assessment
15. Method
• Different stroke weight between printed type,
hard-pen and brush calligraphy.
• Same component (radical) showed many times
in one font.
• More personal stylized fonts.
Hypothesis of causes for badly
formed handwritings by Zi2Zi
Badly formed
samples
16. Method
1. Using all hard-pen handwritings
for training.
2. Reducing the characters that have
common radical in the same font.
3. Training with less stylized fonts
and fine-tune with more stylized
fonts.
Training methods
Hard-
pen
Printed
type
Brush
calligraphy
17. Method
• Different stroke weights between each
font have a critical influence on training.
• Better to concentrate on learning a
similar type of handwritings.
• Hard-pen handwritings tend to have
clear structure because of their light
stroke weight.
1. Using all hard-pen handwritings
Hard-
pen
Printed
type
Brush
calligraph
y
18. Method
2.Reducing the characters
that have common radical
• Same radical in different characters
looks similar in same hard-pen
handwriting font, while differs from
other hard-pen handwriting font.
• Learning an excessively
repeated radical in the same font
are less effective than learning
other totally different characters.
Font I
Font II
Fonts
Samples
Same
radical
Same
radical
19. Method
• Big differences between handwritings
for personal writing habits.
• Curriculum learning: Learn from easy
to hard tasks.
• Train the more printed like
handwritings and then fine-tune with
more personal stylized handwritings.
3.Training from less stylized fonts
to more stylized fonts
easy
normal
hard
21. Experiment and Result
• Three types of handwriting fonts are prepared: Printed,
hard-pen and bush calligraphy.
• Format of font: TrueType.
• Source font: SIMSUN.
• A sample of SIMSUN in a TrueType file is shown.
Dataset
22. Experiment and Result
• Experiment I
Experiment design
Use a mixed dataset, including
printed, hard-pen and brush
calligraphy fonts for training.
Mixed
关键词
Use all hard-pen handwriting
fonts for training.
Hard-pen
关键词
Hard-
pen
Printed
type
Brush
calligraphy
Hard-
pen
Test: 5 hard-pen fonts, each font includes 3 characters.
23. Experiment and Result
• Experiment II
Experiment design
Use 30 fonts, 500 characters in
each font including 25 characters
with common radical 亻.
Original
关键词
Use 50 fonts, 300 characters in
each font including 15 characters
with common radical 亻.
Reduced
关键词
15 亻
285 B
15 亻
285 B
fonts
characters ……
1 50
关键词
25 亻
475 B
25 亻
475 B
fonts
characters ……
1 30
Test: 5 hard-pen fonts, each font includes 5 characters.
24. Experiment and Result
• Experiment III
Experiment design
Mix 25 printed like handwriting
fonts and 5 more stylized fonts
together for training.
Normal
关键词
Train
together
Test: 5 hard-pen fonts, each font includes 5 characters.
Train with 25 printed like
handwriting fonts and fine-tune
with 5 more stylized fonts.
Easy to hard(e2h)
easy
hard
Fine-tune
Train
25. Experiment and Result
Result-experiment I
Method SSIM PSNR
Mixed 0.401 8.791
Hard-
pen
0.387 8.861
Loss function Generated samples Evaluation
mixed hard-pen
d_loss
g_loss
L1_loss
truth mixed hard-pen
26. Experiment and Result
Result-experiment II
Loss function Generated samples Evaluation
original reduced
d_loss
g_loss
L1_loss
truth original reduced
Method SSIM PSNR
Original 0.434 9.643
Reduce
d
0.430 9.912
27. Experiment and Result
Result-experiment III
Loss function Generated samples Evaluation
train fine-tune
d_loss
g_loss
L1_loss
truth normal e2h
Method SSIM PSNR
Normal 0.541 10.489
E2h 0.553 10.638
28. Experiment and Result
• Our methods get a higher image quality, the generated samples are less blurred.
• Training from easy to hard tasks using curriculum learning makes sense for
Chinese handwriting character synthesis.
• The similarity are not improved for the first two methods, the reason might be:
• The strokes show more significant differences in handwriting than in mixed
type fonts.
• The selected radical has fewer strokes and simpler structure than other
components.
Discussion
30. Conclusion
• The initial results show that we can get less blurred generated images
and improve the image quality by using our training methods.
• Learning from easy to hard tasks using curriculum learning is effective
to improve learning effect for zi2zi on Chinese handwriting synthesis
tasks.
• We want to use the model to create TrueType fonts by given only a
few samples and generate all the others, as our future work.
Conclusion and future work
Editor's Notes
Hello everyone.
Today I want to introduce our study on Handwritten Chinese Character Synthesis using Generative Networks
Here is a brief introduction of our work
We focus on a Chinese handwriting characters synthesis task.
This task is thought to be challenging because of a large amount of characters and fonts in Chinese, and also the complex structure for most Chinese characters.
In our previous research, we found a generative model called zi2zi get a good result in generating printed type Chinese characters but underwhelming in handwriting synthesis.
We aim at improving this model on handwriting synthesis tasks, by proposing three new training methods.
The initial result shows that we can improve the image quality using our methods. And learning from easy to hard tasks using curriculum learning can also improve learning effect.
Here are the contents of this presentation.
Let’s begin with the first part, the background
We summarize the challenges of Chinese character synthesis as show.
Because of the numerous characters and complex structure of Chinese characters, it is impossible for a font designer to design all the characters. Generally, they only design a few characters, for a poster or their artwork. But sometimes, we are interested in what other characters would be like.
Dealing with such issue, we assumes that we can use a generative model to infer all the other characters by given a few samples of a certain font.
Meanwhile, there are a large amount of fonts for Chinese, so we also expect our model can learn multiple fonts at the same time.
Next, I want to talk about transfer learning.
The main method of transfer learning is as the figure shows, we transfer the knowledge accumulated from past training and apply it to a different problem.
Basically, we re-purpose a well-trained model onto another related tasks. In this way we can effectively reduce the training time and source cost. Moreover, we can improve the performance of the original model by fine-tuning
Pix2pix is a conditional GAN based model, the generation of the output image is conditional on the source image.
There are 2 main loss functions in pix2pix, an adversarial loss and a L1 loss,
adversarial loss is provided from the discriminator when it makes the determination. While L1 loss measures between the generated image and the expected output image. Both the loss functions encourage the generator to generate more realistic images.
During training, both the generated and source images are provided to discriminator, and discriminator is trained to distinguish whether the images are real or fake.
And Generator is trained to synthesize more realistic images.
The most distinctive part of pix2pix is generator, it is constructed as an encoder-decoder model with a novel architecture named U-Net.
In a U-net model, The skip-connections added between encoding layers and decoding layers are corresponding.
U-Net takes a source image, encodes the input image and down-sampling to a bottleneck layer, after that it decodes the bottleneck representation by up-sampling to generate the target image.
the encoder path and the decoder path are like the left and right side of capital U.
z2z is based on pix2pix model for Chinese character synthesis.
The greatest function of zi2zi is learning multiple fonts at the same time.
To achieve this, they apply a non-trainable Gaussian noise as style embedding to map the characters and their corresponding styles.
To avoid the model confusing and mixing the styles together, they use a multi-class category loss to predict the style of the character. ########supervise the discriminator to penalize such scenario
Considering that the generated character should resemble the source, they also add a constant loss. #########they must appear close to each other in the embedded space as well, to narrow down the possible search space
They get a good result in printed font synthesis. however, there are many badly formed samples when generating handwritings.
###the embedding is to shrink the network, by dimensionality reduction. for zi2zi, the binaries of characters are sparse, we need more layers if we don’t use embedding.
This is the architecture of zi2zi,we can see the embedding and many loss functions there.
The loss functions can be divided into three kinds. G loss is for generator, D loss is for discriminator and L1 loss is to measure the difference between generated and real images.
This is the process of zi2zi for training and test.
We prepare a lot of fonts, designate the quantity of characters for each fonts and draw them with the source font in pair. Then we can train all these paired images together.
For test. We Select the test font by designating the embedding id
For IQA
We use SSIM and PSNR to evaluate the similarity and quality of generated images.
Structural Similarity Index measures the perceived similarity between the two given images. The range of SSIM is from 0 to 1, where a larger value means the two images are more similar.
PSNR measures the image based on the quality of generated image. If there are lots of noises or blurs, the value will be small.
next
I want to introduce about the method
The badly formed samples using original training method for zi2zi are as the figure shows. After we analyzing the dataset and generated samples, we give our hypothesis of causes as follows
1st, we assume that the stroke weights have a critical effect on training. If the stroke weights differ a lot, the generated characters will become blurred (like the 2nd char)
we also consider that the same component (radical) showed too many times in one font. And this may reduce the learning effect on that font.
Besides, we find so many characters are with strong personal style in handwritings, which are hard to recognize.
To test our hypothesis, we proposed three new training methods.
The first is to
The first method I want to introduce is using all hard-pen handwritings
We found that the same stroke with different weight will show a big difference in structure. Basically, the characters written by brush tend to have heavier strokes than those by hard-pen. If we train them together, the model might learn the wrong formed structure
So we assume that it is better to concentrate on learning a similar type of handwritings at the same time.
We also think that if the strokes are light, the learning effect is considered to be better, because the characters written in light strokes tend to have clear structure, which make it easier for model to distinguish which the radical is shown and which part the stroke belongs to, even the character has a lot of strokes.
############a radical written in heavy stroke might belong to other part and thus the model would learn the wrong formed structure. This kind of misleading might be the main reason of blur in some generated characters.
The 2nd method I want to introduce is Reducing the characters that have common radical
We found the same radical in different characters looks similar in same hard-pen handwriting font, but differs from other hard-pen handwriting font
We assume that if we learn too many characters with the same radical in one font, it would be less effective than learning entirely different characters instead. We give an extreme case to explain it more concretely. For case one all the chosen characters are with the same radical In font A, while for case 2, each font only have one character with the same radical. We consider that the case II has a better learning effect than case 1
The 3nd method I want to introduce is Training from less stylized fonts to more stylized fonts
There are Big differences between handwritings for their personal writing habits, as the figure shows.
the characters upwards have a clearer structure and are easy to identify, more like printed font, while the downwards are more stylized and not so easy to recognize.
Inspired by the common sense that learning from easy to hard knowledge will get a better learning effect. we propose a training method, begin the training with more printed like handwriting at first and then fine-tune with the more stylized handwriting. This is also known as curriculum learning. We assume that this method would be also effective on improving the learning effect.
Then let's talk about the experiment and result
For the dataset, we mainly use three types of handwriting font, including printed, hard-pen, and bush calligraphy. for each type 40 fonts are prepared.
The original format of the dataset is TrueType (.ttf), an outline font standard. it gives a standard style for all the characters included in it. We can choose any characters in it and draw them down as image.
In our experiment, we choose SIMSUN as source font, the glyph is as the figure shows.
To test our hypothesis, we carry out 3 comparison experiments
For the first experiment, we prepared two types of training set, one includes only hard-pen handwriting fonts for training model, compared with another model trained with a mixed dataset, including printed, hard-pen and brush calligraphy fonts. We give a shorten name for the first method as "hard-pen", and the 2nd named as "mixed".
For the 2nd experiment
we similarly prepared two types of training set, as Figure shows. For the first training set, we use 50 fonts and choose 300 characters from each font, each font includes 15 characters that contain the common radical, while others are not. For another training set, we use 30 fonts and choose 500 characters from each font, where 25 characters with common radical. Then we carry out our experiment to compare these two training methods. We give a shorten name for the first method as “reduced", and the 2nd named as “original".
For the 3rd experiment
we choose 25 hard-pen handwriting fonts that are easy to be identified as the easier tasks. Also prepare another 5 hard-pen handwriting fonts which are difficult to recognize as the harder task, we first train with the easier task and then fine-tune with the harder one. We named this method as "e2h". To compared with this method, we combined all the 30 fonts together, training another model with the same epochs. We named the second method as “normal”.
To display our results the generated samples and the evaluation For each experiment.
##we will show the plots of loss function
For the 1st experiment,
(From the evaluation metric, )we find that hard-pen got a better image quality, while not got a higher similarity.
For the 2nd experiment,
(From the evaluation metric) we can find that that the reduced also got a better image quality, while not improved the similarity.
For the 3rd experiment,
(From the evaluation metric) we can find that e2h got a better image quality and similarity.
We give a brief discussion on the shown results.
We found that All the three methods get a higher image quality by PSNR value. The generated samples of ours are less blurred.
Training from easy to hard tasks using curriculum learning makes sense for Chinese handwriting character synthesis.
However the first 2 methods did not improve the similarity,
for the first method, we assume the reason might be that The strokes show more significant differences in handwriting than in mixed type fonts
for the second method, The selected radical has fewer strokes and simpler structure than other components.
According to the experiment results and discussion, main conclusions are listed as follow: