Recent selling efforts of Yahoo brought an attention regarding its more than 6000 patents. Several patent professionals expect that the value of patents could be reach around $4 B. Yahoo patents for Artificial Intelligence (AI) include 58 US issued patents. Yahoo AI patents cover diverse applications such as Search Engine, Online Advertisement, Email System, Web Applications, E-commerce, Data Management, and Social Networking. Some of key Yahoo AI patents are as follows.
O documento discute a aplicação da inteligência artificial para sistemas colaborativos, mencionando técnicas como ontologias, mineração de dados e redes neurais que podem apoiar a comunicação e coordenação entre pessoas. A inteligência artificial também pode ajudar na resolução de conflitos em trabalhos colaborativos.
Solution to Help Companies Patent their Inventions, License Technologies, and...Dr. Haxel Consult
With more than one million patent applications filed every year, searching for prior-art has become a daunting task. This constitutes an important challenge for technology companies and their legal representatives, as the value of its assets depends on their ability to demonstrate the novelty of their inventive efforts. Failing to identify prior-art makes it difficult for companies to patent their inventions and exposes them to costly litigation. The breadth and complexity of the IP space makes it all but impossible to search for prior-art without the help of machine-based intelligence that identifies relationships between a new invention and those described in millions of patent documents. Existing solutions fail to take into account that companies often use (and are strongly motivated to) different words to describe similar inventions. This makes search efforts based on the similarities between words prone to miss relevant prior-art. What is more, existing techniques do not account for temporal changes in the terminology used to describe particular inventions. This is not a trivial omission as, by definition, the search for prior-art requires comparing an invention with other produced at different points in time. AIP developed an advance search engine that addresses these shortcomings. AIP uses thousands of examination reports to learn about textual relationship that describe scientific concepts and applies this learning to compare inventions. That is, instead of comparing document on the basis that these contain similar words, our algorithm compares document on the basis that these describe similar ideas. We present a number of cases were AIP’s solution helped companies patent their inventions, license technologies, and address litigation challenges.
The Innovation Index for Hadoop analyses market attractiveness, business model maturity and infrastructure impact on Big Data. It intends to provide strategic recommendations as collected, analyses and shared on We Are Innovation networks of 1,200+ innovators.
Artificial Intelligence in Financial Trading and Educationsrparfitt
Content Technologies develops proprietary artificial intelligence systems using deep learning techniques. It has two business units: CTI Education which uses AI to monitor 25,000 academic disciplines and publish over 40,000 educational titles; and CTI Financial Markets which uses AI for currency trading, achieving a 60% success rate over 167 currencies and returning over 7 times the initial capital in 151 consecutive trades during testing. CTI Financial Markets is seeking financial partners to help launch its AI trading system for foreign exchange markets.
The document discusses graph algorithms and ranking systems used by social networks to optimize their news feeds. It provides examples of how Facebook uses EdgeRank to determine what content appears in a user's feed based on the number and strength of edges between users. The document then gives a walkthrough example of how EdgeRank scoring would work for a status update and comment on Facebook. It also briefly mentions that the Vietnamese social network Zing serves over 7 million users and ranks feeds in real-time for each user.
LogRhythm Advanced Intelligence Engine Data Sheetjordagro
LogRhythm's AI Engine is an optional module that offers sophisticated correlation and analysis of enterprise log data. It delivers real-time visibility into risks, threats, and critical operations issues. The AI Engine uses over 100 preconfigured correlation rule sets and a drag-and-drop GUI to detect intrusions, insider threats, fraud, and compliance violations. It correlates all log data, not just a subset, and provides immediate access to related forensic data to identify security issues and optimize operations with precision.
Recent selling efforts of Yahoo brought an attention regarding its more than 6000 patents. Several patent professionals expect that the value of patents could be reach around $4 B. Yahoo patents for Artificial Intelligence (AI) include 58 US issued patents. Yahoo AI patents cover diverse applications such as Search Engine, Online Advertisement, Email System, Web Applications, E-commerce, Data Management, and Social Networking. Some of key Yahoo AI patents are as follows.
O documento discute a aplicação da inteligência artificial para sistemas colaborativos, mencionando técnicas como ontologias, mineração de dados e redes neurais que podem apoiar a comunicação e coordenação entre pessoas. A inteligência artificial também pode ajudar na resolução de conflitos em trabalhos colaborativos.
Solution to Help Companies Patent their Inventions, License Technologies, and...Dr. Haxel Consult
With more than one million patent applications filed every year, searching for prior-art has become a daunting task. This constitutes an important challenge for technology companies and their legal representatives, as the value of its assets depends on their ability to demonstrate the novelty of their inventive efforts. Failing to identify prior-art makes it difficult for companies to patent their inventions and exposes them to costly litigation. The breadth and complexity of the IP space makes it all but impossible to search for prior-art without the help of machine-based intelligence that identifies relationships between a new invention and those described in millions of patent documents. Existing solutions fail to take into account that companies often use (and are strongly motivated to) different words to describe similar inventions. This makes search efforts based on the similarities between words prone to miss relevant prior-art. What is more, existing techniques do not account for temporal changes in the terminology used to describe particular inventions. This is not a trivial omission as, by definition, the search for prior-art requires comparing an invention with other produced at different points in time. AIP developed an advance search engine that addresses these shortcomings. AIP uses thousands of examination reports to learn about textual relationship that describe scientific concepts and applies this learning to compare inventions. That is, instead of comparing document on the basis that these contain similar words, our algorithm compares document on the basis that these describe similar ideas. We present a number of cases were AIP’s solution helped companies patent their inventions, license technologies, and address litigation challenges.
The Innovation Index for Hadoop analyses market attractiveness, business model maturity and infrastructure impact on Big Data. It intends to provide strategic recommendations as collected, analyses and shared on We Are Innovation networks of 1,200+ innovators.
Artificial Intelligence in Financial Trading and Educationsrparfitt
Content Technologies develops proprietary artificial intelligence systems using deep learning techniques. It has two business units: CTI Education which uses AI to monitor 25,000 academic disciplines and publish over 40,000 educational titles; and CTI Financial Markets which uses AI for currency trading, achieving a 60% success rate over 167 currencies and returning over 7 times the initial capital in 151 consecutive trades during testing. CTI Financial Markets is seeking financial partners to help launch its AI trading system for foreign exchange markets.
The document discusses graph algorithms and ranking systems used by social networks to optimize their news feeds. It provides examples of how Facebook uses EdgeRank to determine what content appears in a user's feed based on the number and strength of edges between users. The document then gives a walkthrough example of how EdgeRank scoring would work for a status update and comment on Facebook. It also briefly mentions that the Vietnamese social network Zing serves over 7 million users and ranks feeds in real-time for each user.
LogRhythm Advanced Intelligence Engine Data Sheetjordagro
LogRhythm's AI Engine is an optional module that offers sophisticated correlation and analysis of enterprise log data. It delivers real-time visibility into risks, threats, and critical operations issues. The AI Engine uses over 100 preconfigured correlation rule sets and a drag-and-drop GUI to detect intrusions, insider threats, fraud, and compliance violations. It correlates all log data, not just a subset, and provides immediate access to related forensic data to identify security issues and optimize operations with precision.
Formal Concept Analysis is a method used for investigating and processing explicitly given information, in order to allow for meaningful and comprehensive interpretation.
The AI Initiative aims to engage stakeholders to help shape global policy around addressing the challenges of developing and controlling artificial intelligence. Rapid improvements in machine learning, neuroscience, and high-performance computing are leading to new capabilities in AI that could benefit society but also pose risks. Both immediate and future actions are needed to ensure AI's promise is realized while managing its socio-political consequences, as computing systems are already outperforming humans in important tasks.
The document describes VacationChamp, the world's first intelligent marketplace for travel. It uses an AI engine and data normalizer to match consumer demand with travel supply from flights, hotels, rentals, cars and activities. The AI evaluates stated and unstated user preferences to provide personalized results. It performs continuous multi-factor searches, notifying users or automatically purchasing better options. The platform aims to disrupt online travel by harnessing dynamic consumer demand for the benefit of suppliers.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."
This document provides an overview of an adaptive AI engine project for real-time strategy (RTS) games. It discusses what game AI is, why an AI engine is needed, and the common structures of AI engines. It also outlines elements that require AI in RTS games, areas needing improvement, and common techniques used in AI engines, including decision making, planning, and learning approaches. The document notes that AI development has been slow in RTS games due to challenges like imperfect information and fast-paced action. It identifies several areas needing more research, such as adversarial planning, learning, and spatial/temporal reasoning. Recent papers on the topic focus on planning, reinforcement learning, genetic algorithms, and hybrid approaches.
How One Billion Salesforce records Can Be Replicated with Minimal API UsageBaruch Oxman
This document discusses how to efficiently replicate over 1 billion Salesforce records while minimizing API usage to avoid reaching limits. It recommends using the Bulk API to fetch large amounts of data in fewer calls, paginating queries, and only fetching changed records. Methods described include initial full fetching followed by incremental fetching of just changed records since the last sync. Error handling and dealing with unavailable objects are also covered.
Good Old Fashioned Artificial IntelligenceRobert Short
The document discusses concepts related to Good Old Fashioned Artificial Intelligence (GOFAI) including its full system dynamics model based on axiology, asymptotic analysis, and string theory. It describes how GOFAI differs from traditional software architectures by using descriptive frameworks, executable engines, and databases. The GOFAI model is trained using techniques like educating it on language and preparing it to pass the Turing Test to assess its ability to appear human-like.
This document provides an AI maturity index report. It indicates that the assessed organization has reached level V or "Visionary" status, meaning it has fully deployed a wide range of AI technologies across many areas. It advises the organization to continue pushing boundaries through blue sky thinking about future needs in 8-10 years. The report also notes that the organization ranked #1 out of 54 respondents in the automotive and aerospace vertical for AI maturity.
Artificial intelligence (AI) is defined as the science and engineering of making intelligent machines. There are four main categories of AI systems: systems that think like humans, systems that act like humans, systems that think rationally, and systems that act rationally. AI faces two main problems - deduction, reasoning, and problem solving, and knowledge representation.
How do you uncover the secret information, buried in your Salesforce data, without being an expert report analyst? The new world of Natural Language Discovery and Machine Learning holds the key. Salesforce is at the cutting edge of artificial intelligence and has partnered with leading AI companies to deliver solutions. Learn how Salesforce APIs are used, and innovative solutions that will soon be available to you.
The document provides definitions and formulas for key project management terms related to scheduling, cost, earned value management, and forecasting. It defines acronyms like AC, BAC, CPI, CV, EAC, ETC, EV, FV, PERT, PV, SPI, SV, and formulas to calculate values for schedule performance, cost performance, variance, estimates, and more. Formulas include calculations for earned value, cost and schedule variances, estimate at completion, estimate to complete, present and future values, PERT estimates, return on invested capital, and standard deviation.
This document discusses advances in artificial intelligence, machine learning, and deep learning. It provides an overview of key topics including:
- The relationship between big data and machine learning/AI and how machine learning has evolved from programmed systems to deep learning approaches.
- How major tech companies like Google, Facebook, Microsoft, Amazon, and IBM are heavily investing in and applying AI/machine learning across many areas from search and recommendations to personal assistants.
- Examples of real-world applications of deep learning like computer vision systems, machine translation, and AlphaGo as well as challenges that still exist around training deep learning models at scale.
Enabling Artificial Intelligence - Alison B. LowndesWithTheBest
This document discusses NVIDIA's deep learning technologies and platforms. It highlights NVIDIA's GPUs and deep learning software that accelerate major deep learning frameworks and power applications like self-driving cars, medical robotics, and natural language processing. It also introduces NVIDIA's deep learning supercomputer DGX-1 and embedded module Jetson TX1 for edge devices. The document promotes NVIDIA's deep learning events and career opportunities.
Artificial Intelligence: what value for intelligent machines?WeAreInnovation
The document analyzes the market attractiveness, business model maturity, and infrastructure/support impact of artificial intelligence (AI) through analysis of facts, figures, and keywords from various networks. It finds that while AI presents opportunities, there are also threats if its impact on humanity is not properly controlled. Business models need to be rethought to avoid harming humans. Major companies are developing AI but technical limitations remain around data processing and infrastructure.
This document provides an introduction to machine learning, including:
- Machine learning allows computers to learn without being explicitly programmed by using data to find patterns and make predictions.
- There are two main phases: the training phase where a model is built using sample data, and the operational phase where the model is used to make predictions on new data.
- Common machine learning tasks include supervised learning techniques like regression and classification, as well as unsupervised learning techniques like clustering and dimensionality reduction.
- The document outlines different machine learning algorithms categorized by their representation, evaluation, and optimization methods, but does not cover specifics of individual algorithms.
This document outlines the architecture of a medical leadership trainer scenario authoring engine. It includes components like a scenario editor, simulation manager, graphics engine, assessment engine, and learning management system. The architecture allows instructional designers to create scenarios for learners to participate in virtual field exercises through a 3D game engine, with lessons stored in a scenario repository within the learning management system. Learners' paths take them from a tactics operation center for planning through to the 3D game simulation and back for after action review.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Deep Learning and Intelligent Applications
Dr Xuedong Huang from Microsoft discusses deep learning and intelligent applications. He explains that big data and GPUs enable deep learning to perform tasks like speech recognition and computer vision. CNTK is introduced as Microsoft's deep learning toolkit that balances efficiency, performance, and flexibility. It allows describing models with code, languages, or scripts and supports CPU/GPU training. Project Oxford APIs are summarized, including APIs for vision, speech, language, and spelling. These APIs make it easy for developers to incorporate intelligent services into applications.
Formal Concept Analysis is a method used for investigating and processing explicitly given information, in order to allow for meaningful and comprehensive interpretation.
The AI Initiative aims to engage stakeholders to help shape global policy around addressing the challenges of developing and controlling artificial intelligence. Rapid improvements in machine learning, neuroscience, and high-performance computing are leading to new capabilities in AI that could benefit society but also pose risks. Both immediate and future actions are needed to ensure AI's promise is realized while managing its socio-political consequences, as computing systems are already outperforming humans in important tasks.
The document describes VacationChamp, the world's first intelligent marketplace for travel. It uses an AI engine and data normalizer to match consumer demand with travel supply from flights, hotels, rentals, cars and activities. The AI evaluates stated and unstated user preferences to provide personalized results. It performs continuous multi-factor searches, notifying users or automatically purchasing better options. The platform aims to disrupt online travel by harnessing dynamic consumer demand for the benefit of suppliers.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."
This document provides an overview of an adaptive AI engine project for real-time strategy (RTS) games. It discusses what game AI is, why an AI engine is needed, and the common structures of AI engines. It also outlines elements that require AI in RTS games, areas needing improvement, and common techniques used in AI engines, including decision making, planning, and learning approaches. The document notes that AI development has been slow in RTS games due to challenges like imperfect information and fast-paced action. It identifies several areas needing more research, such as adversarial planning, learning, and spatial/temporal reasoning. Recent papers on the topic focus on planning, reinforcement learning, genetic algorithms, and hybrid approaches.
How One Billion Salesforce records Can Be Replicated with Minimal API UsageBaruch Oxman
This document discusses how to efficiently replicate over 1 billion Salesforce records while minimizing API usage to avoid reaching limits. It recommends using the Bulk API to fetch large amounts of data in fewer calls, paginating queries, and only fetching changed records. Methods described include initial full fetching followed by incremental fetching of just changed records since the last sync. Error handling and dealing with unavailable objects are also covered.
Good Old Fashioned Artificial IntelligenceRobert Short
The document discusses concepts related to Good Old Fashioned Artificial Intelligence (GOFAI) including its full system dynamics model based on axiology, asymptotic analysis, and string theory. It describes how GOFAI differs from traditional software architectures by using descriptive frameworks, executable engines, and databases. The GOFAI model is trained using techniques like educating it on language and preparing it to pass the Turing Test to assess its ability to appear human-like.
This document provides an AI maturity index report. It indicates that the assessed organization has reached level V or "Visionary" status, meaning it has fully deployed a wide range of AI technologies across many areas. It advises the organization to continue pushing boundaries through blue sky thinking about future needs in 8-10 years. The report also notes that the organization ranked #1 out of 54 respondents in the automotive and aerospace vertical for AI maturity.
Artificial intelligence (AI) is defined as the science and engineering of making intelligent machines. There are four main categories of AI systems: systems that think like humans, systems that act like humans, systems that think rationally, and systems that act rationally. AI faces two main problems - deduction, reasoning, and problem solving, and knowledge representation.
How do you uncover the secret information, buried in your Salesforce data, without being an expert report analyst? The new world of Natural Language Discovery and Machine Learning holds the key. Salesforce is at the cutting edge of artificial intelligence and has partnered with leading AI companies to deliver solutions. Learn how Salesforce APIs are used, and innovative solutions that will soon be available to you.
The document provides definitions and formulas for key project management terms related to scheduling, cost, earned value management, and forecasting. It defines acronyms like AC, BAC, CPI, CV, EAC, ETC, EV, FV, PERT, PV, SPI, SV, and formulas to calculate values for schedule performance, cost performance, variance, estimates, and more. Formulas include calculations for earned value, cost and schedule variances, estimate at completion, estimate to complete, present and future values, PERT estimates, return on invested capital, and standard deviation.
This document discusses advances in artificial intelligence, machine learning, and deep learning. It provides an overview of key topics including:
- The relationship between big data and machine learning/AI and how machine learning has evolved from programmed systems to deep learning approaches.
- How major tech companies like Google, Facebook, Microsoft, Amazon, and IBM are heavily investing in and applying AI/machine learning across many areas from search and recommendations to personal assistants.
- Examples of real-world applications of deep learning like computer vision systems, machine translation, and AlphaGo as well as challenges that still exist around training deep learning models at scale.
Enabling Artificial Intelligence - Alison B. LowndesWithTheBest
This document discusses NVIDIA's deep learning technologies and platforms. It highlights NVIDIA's GPUs and deep learning software that accelerate major deep learning frameworks and power applications like self-driving cars, medical robotics, and natural language processing. It also introduces NVIDIA's deep learning supercomputer DGX-1 and embedded module Jetson TX1 for edge devices. The document promotes NVIDIA's deep learning events and career opportunities.
Artificial Intelligence: what value for intelligent machines?WeAreInnovation
The document analyzes the market attractiveness, business model maturity, and infrastructure/support impact of artificial intelligence (AI) through analysis of facts, figures, and keywords from various networks. It finds that while AI presents opportunities, there are also threats if its impact on humanity is not properly controlled. Business models need to be rethought to avoid harming humans. Major companies are developing AI but technical limitations remain around data processing and infrastructure.
This document provides an introduction to machine learning, including:
- Machine learning allows computers to learn without being explicitly programmed by using data to find patterns and make predictions.
- There are two main phases: the training phase where a model is built using sample data, and the operational phase where the model is used to make predictions on new data.
- Common machine learning tasks include supervised learning techniques like regression and classification, as well as unsupervised learning techniques like clustering and dimensionality reduction.
- The document outlines different machine learning algorithms categorized by their representation, evaluation, and optimization methods, but does not cover specifics of individual algorithms.
This document outlines the architecture of a medical leadership trainer scenario authoring engine. It includes components like a scenario editor, simulation manager, graphics engine, assessment engine, and learning management system. The architecture allows instructional designers to create scenarios for learners to participate in virtual field exercises through a 3D game engine, with lessons stored in a scenario repository within the learning management system. Learners' paths take them from a tactics operation center for planning through to the 3D game simulation and back for after action review.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Deep Learning and Intelligent Applications
Dr Xuedong Huang from Microsoft discusses deep learning and intelligent applications. He explains that big data and GPUs enable deep learning to perform tasks like speech recognition and computer vision. CNTK is introduced as Microsoft's deep learning toolkit that balances efficiency, performance, and flexibility. It allows describing models with code, languages, or scripts and supports CPU/GPU training. Project Oxford APIs are summarized, including APIs for vision, speech, language, and spelling. These APIs make it easy for developers to incorporate intelligent services into applications.
Emily Jiang gave a presentation on the future of Java developers and AI. She discussed how AI tools like IBM's WatsonX can help with tasks like code generation and debugging to improve developer experience. While some jobs may be at risk of replacement by AI, such as data entry clerks, new jobs will be created like AI model trainers. Developers should embrace AI, stay up to date on new technologies, learn new skills focused on areas like architecture and innovation, and not worry about being replaced by AI. The talk concluded with Emily thanking the audience and providing her contact information.
Awareness of design smells - indicators of common design problems - helps developers or software engineers understand mistakes made while designing and apply design principles for creating high-quality designs. This tech talk provides insights gained from performing refactoring in real-world projects to improve refactoring and reduce the time and costs of managing software projects. The tech talk also presents insightful anecdotes and case studies drawn from the trenches of real-world projects. By attending this tech talk, you will know pragmatic techniques for refactoring design smells to manage technical debt and to create and maintain high-quality software in practice.
Contents overview:
* Why care about design principles, design quality, or design smells?
* Refactoring as the primary means for repaying technical debt
* Smells that violate abstraction, encapsulation, modularisation, or hierarchy
* Tools and techniques for refactoring
Generative AI in CSharp with Semantic Kernel.pptxAlon Fliess
Join Alon Fliess, Azure MVP, and Microsoft RD in an enlightening lecture where C# meets the forefront of AI. Discover how the Semantic Kernel project bridges traditional programming with advanced AI, empowering C# developers to integrate AI functionalities into their software seamlessly.
Experience a paradigm shift in diagnostics through a real-world example: a sophisticated system crafted with C#, Semantic Kernel, and Azure. Witness the synergy of C# and AI in action, optimizing system analysis and problem-solving in complex environments.
Embark on a journey where C# and AI meet.
Reverse Engineering - Protecting and Breaking the SoftwareSatria Ady Pradana
First upload.
Introduction to reverse engineering. The focus of this presentation is software or code, emphasizing on common practice in reverse engineering of software
The Best Way to Become an Android Developer Expert with Android JetpackAhmad Arif Faizin
This document discusses how to become an expert Android developer using Android Jetpack. It recommends using Android Jetpack, which is a collection of components and libraries that make it easier to build Android apps. It describes some key components of Jetpack like architecture components like ViewModel and LiveData for lifecycle-aware data management. It also discusses other topics like navigation, testing, and architecture patterns that are important for Android development. The document encourages learning through online courses and emphasizes the importance of continuous learning and skills development for IT careers and the digital industry.
Reverse Engineering: Protecting and Breaking the SoftwareSatria Ady Pradana
Presentation on Let's Secure Your Code
Universitas Muhammadiyah Surakarta
Surakarta, 2017-05-01
Introduction to Reverse Engineering.
This presentation is focusing on software or code, emphasizing on common practice in reverse engineering of software.
This document provides an overview and summary of the Android deep dive presentation given by Marko Gargenta at Sprint Dev Con 2010. It discusses the Android stack including the Linux kernel, native libraries like WebKit and SQLite, the Dalvik VM, and the application framework. It also covers building a basic "Hello World" Android app, common app components like activities, services, content providers and broadcasts receivers. The document summarizes the Android user interface approach using XML layouts and views, and operating system features such as security, files system, and cloud integration.
This document provides a summary of Amit Prabhudesai's work portfolio. It outlines his educational background and work experience in image processing and computer vision. It then describes several projects he has worked on, including human detection using Adaboost for surveillance video, optimizing a Lane Departure Warning system for a Texas Instruments DSP, developing video analytics software for retail store customer counting and queue detection, and an Automatic Fingerprint Identification System. It also lists some relevant trainings and mentorship activities.
Framework design involves balancing many considerations, such as:
- Managing dependencies between components to allow for flexibility and evolution over time. Techniques like dependency injection and layering help achieve this.
- Designing APIs by first writing code samples for key scenarios and defining object models to support these samples to ensure usability.
- Treating simplicity as a feature by removing unnecessary requirements and reusing existing concepts where possible.
The document describes an application with a pipe-and-filter architecture pattern where sensor data flows through multiple components that each transform the data before passing it to the next component and finally to a modeling and visualization unit. It then asks questions about software architecture patterns and styles like pipe-and-filter, lambda architecture, decorator pattern, Conway's law, architecture drift, REST, event sourcing, and recommends architecture refactoring when dependency analysis finds numerous cycles and tangles.
Thug is a new low-interaction honeyclient for analyzing malicious web content and browser exploitation. It uses the Google V8 JavaScript engine and emulates different browser personalities to detect exploits. Thug analyzes content using static and dynamic analysis and logs results using MAEC format. Future work includes improving DOM emulation and JavaScript analysis to better identify vulnerabilities and exploit kits. The source code for Thug will be publicly released after the presentation.
This document summarizes a gamebook app created for iPad users, including fans of Fighting Fantasy and role-playing games. It describes the app's design, content, functionality, users, and the contributions of the two creators. The app was programmed in LUA using Corona SDK and contains over 50 pages of original narrative and artwork. Key features include an interactive combat system, character progression, and a bookmarking system. User testing was conducted via surveys, analytics, and testflight to evaluate the app's usability and design.
Designing the Call of Cthulhu app with Google App EngineChris Bunch
These are slides from a talk I gave at UCSB to the Senior Capstone class on 02/10/10 on how I developed the Call of Cthulhu application using Google App Engine.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Build a Module in Odoo 17 Using the Scaffold Method
Presentation 20110918 split
1. Dynamic State Based AI Decision Framework Presenter: Kuanhung Chen, MS in Software Engineering Committee Members: Dr. Junhua Ding, Dr. Masao Kishore, Dr. Ronnie Smith East Carolina University Fall 2011 Master’s Presentation
27. Question and Answer Presenter: Kuanhung Chen, MS in Software Engineering Committee Members: Dr. Junhua Ding, Dr. Masao Kishore, Dr. Ronnie Smith East Carolina University Fall 2011 Master’s Presentation
The need for better AI: FPS enemy AI fail, enemies may not always choose best possible choice, a lot of time they choose worst possible outcome.
The need for better AI: MMORPG AI fail, escort mission, people you are protecting runs straight toward danger.
Using problem statement instead of abstract to make things short and simple.
System overall functionalities, separated into three user layers: Player, developer, and Admin Player: Download and runs application using AI stubs Developer: Download and use game engine to develop AI stubs to be uploaded Admin: Oversee AI stub submission to ensure safety
Algorithm 1: Dynamic source file Pro: Efficiency, dynamic name change Con: Recompile time, user requires compiler Algorithm 2: Dynamic DLL inclusion Pro: Speed and efficiency Con: No dynamic naming
Algorithm 3: IPC Pro: Dedicated process, location freedom Con: Dump and reconstruct time Algorithm 4: Dynamic DLL Reference Pro: Dynamic inclusion, customizable Con: Efficiency and performance
.NET layer, data holder, stores game state data. Stores who is where and what the character is about.
Unity layer, uses the data layer to manipulate game objects and update game state. Could use physics based action or simple action.
Game is character driven. Character choose what to do and its action affects the game state.
AI prompt cycle. How character object interact with global game state and uses AI adaptor as well as the action manager. AI adaptor sends a copy of game state and caller character to AI stub is the key of this project. Note, each of the three layers can expand without effecting the other.
Character implementation by layers. First, an enumerator can be used to reference to a new character so that data layer and presentation layer can use the same flag. Then a generic singleton character fetching method can be created in game state to fetch a new instance of the character. The abstract singleton character factor look into character implementation to fetch necessary data to describe each character, where new characters can be added. Character reference can then be associated in graphical presentation layer by reference.
Character implementation by layers. First, an enumerator can be used to reference to a new character so that data layer and presentation layer can use the same flag. Then a generic singleton character fetching method can be created in game state to fetch a new instance of the character. The abstract singleton character factor look into character implementation to fetch necessary data to describe each character, where new characters can be added. Character reference can then be associated in graphical presentation layer by reference.
Character implementation by layers. First, an enumerator can be used to reference to a new character so that data layer and presentation layer can use the same flag. Then a generic singleton character fetching method can be created in game state to fetch a new instance of the character. The abstract singleton character factor look into character implementation to fetch necessary data to describe each character, where new characters can be added. Character reference can then be associated in graphical presentation layer by reference.
Character implementation by layers. First, an enumerator can be used to reference to a new character so that data layer and presentation layer can use the same flag. Then a generic singleton character fetching method can be created in game state to fetch a new instance of the character. The abstract singleton character factor look into character implementation to fetch necessary data to describe each character, where new characters can be added. Character reference can then be associated in graphical presentation layer by reference.
Usage of a stock character to dynamically effect how the character looks to reuse available resource. Then the character’s look can be dynamically altered by script to match the needed character’s specification.
Due to layered architecture dependencies, layers cannot be tested until they are all present. Thus scaffolding system testing is needed to test each component. Before the tests can be done, the depended layer has to be assumed to be correct.
Log-in by creating account using existing account or ECU intra account
Upload new stubs
Search/download existing stubs
Update project download packages
Select the characters for both sides. Some algorithm work best with a specific set of characters where as generic algorithm can work with random characters.
Select which AI stub to use from the “/AI Stubs” folder, where all the download needs to go.
Select how many rounds by click on the number bar then press [Up] or [Down] to go up or down, hold [Up] or [Down] to incrementally increase selection range. Same can be done by using mouse scroll. Or [Left] or [Right] key to skip by 10.
Battle until at least one side loses. Repeat if necessary.
Dynamic pie chart display.
The end, time for Q&A.
If necessary, here is a list of indexes to be used to answer things in Q&A section.
Three categories of users using two components, a simplified version of the Project Functionality diagram.
Test for expected output and expected failure.
Simplified class diagram from presentation layer point of view.
The more elaborated character class diagram from design point of view.
Simple example on how the action is carried out.
AnimationEngine flow diagram. How to fetch and get the animation by using an AnimationManager object associated with each character.
An example on how to use AnimationState flag that’s associated with ActionDriver to trigger which animation to play for the character when performing the said action.
Simplified character state diagram, much more simplified than before.
Design the interface in PhotoShop then place the GUI contents. Using guide lines to locate (x, y) location as well as (width, height) dimension of the GUI elements.
Aim the game camera constantly at where the target is.
Since the camera is looking at the target, then as long as the target moves or camera moves the view can be controlled easily.
Mouse control, using click and drag to move the target, scroll up and down to zoom in and out, and right click drag to move the camera up/down rotate left/right.
Audio manager that plays either background music or sound effect clip.
This is how the AI stub file is being recognized. By using this scheme I don’t need a secondary database to associate file with its content.
How to dynamically reference to a class, instantiate an object from the class, and how to reference to the class’ member.
The difference between Visual Studio .NET’s Reflection class library and MonoDevelop’s C# definition. While similar but syntax is different.
Since each component acts independently, there is no reason why a secondary form (manual control) can’t pretend to be an AI stub and inject action selection to an AI adaptor. Scaffolding testing.
Action driver testing, creating a fake game scene with dummy as targets. Display all available actions to visually test the effect of the action drivers before integrating them into the actual scene.
Built-in methods to help algorithm design.
Generic methods on how to design AI stubs using this framework.
Like character reference, there is an action enumerator. Each action has a cost and delay lookup. Action is separated into three types Attack, Projectile, and Defense. Which can be fetch via singleton action factory. Then action object can be associated with action drivers on graphical presentation side.
Like character reference, there is an action enumerator. Each action has a cost and delay lookup. Action is separated into three types Attack, Projectile, and Defense. Which can be fetch via singleton action factory. Then action object can be associated with action drivers on graphical presentation side.
Like character reference, there is an action enumerator. Each action has a cost and delay lookup. Action is separated into three types Attack, Projectile, and Defense. Which can be fetch via singleton action factory. Then action object can be associated with action drivers on graphical presentation side.
Like character reference, there is an action enumerator. Each action has a cost and delay lookup. Action is separated into three types Attack, Projectile, and Defense. Which can be fetch via singleton action factory. Then action object can be associated with action drivers on graphical presentation side.
Like character reference, there is an action enumerator. Each action has a cost and delay lookup. Action is separated into three types Attack, Projectile, and Defense. Which can be fetch via singleton action factory. Then action object can be associated with action drivers on graphical presentation side.