The document provides instructions for using an annotation tool to determine if products have been correctly or incorrectly classified by an automated system. It explains how to log in, review the product details, and select "Right" if the age/gender, category, and subcategory match the product or "Wrong" if they do not match. It also provides descriptions of different product categories and subcategories to aid in classification.
A presentation of Hermansky & Morgan's 1994 paper, RASTA Processing of Speech. Learn the dramatic effect of RASTA on critical band analysis when combined with PLP to do speech detection!
Hermansky, Hynek, and Nelson Morgan. "RASTA processing of speech." Speech and Audio Processing, IEEE Transactions on 2.4 (1994): 578-589.
Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Devel...Benjamin Bengfort
This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
An Interactive Visual Analytics Dashboard for the Employment Situation ReportBenjamin Bengfort
The Employment Situation Report is a monthly news release by the Bureau of Labor Statistics which describes the results of the Current Population Survey. Its release is widely anticipated by economists, journalists, and politicians as it is used to forecast the economic condition of the United States by describing ongoing trends and has a broad impact on public and corporate economic confidence leading directly to investment decisions. The report itself is in a PDF format that is comprised primarily of text and tabular information. Quickly and correctly interpreting the results of the jobs report is vital for quality reporting and decision making, but the report is more suited for longer study than deriving insights. In this project we explore the use of an interactive dashboard for visual analytics upon the released BLS data. Using an application demonstration and a usability study we will show that visually interacting with the most current employment data, users are able to rapidly achieve rich insights similar to those reported on in the text of the Employment Situation Report.
A presentation of Hermansky & Morgan's 1994 paper, RASTA Processing of Speech. Learn the dramatic effect of RASTA on critical band analysis when combined with PLP to do speech detection!
Hermansky, Hynek, and Nelson Morgan. "RASTA processing of speech." Speech and Audio Processing, IEEE Transactions on 2.4 (1994): 578-589.
Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Devel...Benjamin Bengfort
This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
An Interactive Visual Analytics Dashboard for the Employment Situation ReportBenjamin Bengfort
The Employment Situation Report is a monthly news release by the Bureau of Labor Statistics which describes the results of the Current Population Survey. Its release is widely anticipated by economists, journalists, and politicians as it is used to forecast the economic condition of the United States by describing ongoing trends and has a broad impact on public and corporate economic confidence leading directly to investment decisions. The report itself is in a PDF format that is comprised primarily of text and tabular information. Quickly and correctly interpreting the results of the jobs report is vital for quality reporting and decision making, but the report is more suited for longer study than deriving insights. In this project we explore the use of an interactive dashboard for visual analytics upon the released BLS data. Using an application demonstration and a usability study we will show that visually interacting with the most current employment data, users are able to rapidly achieve rich insights similar to those reported on in the text of the Employment Situation Report.
Slide deck from my presentation at NYC's #Pydata 2012 conference - http://nyc2012.pydata.org/abstracts/#gephi
Talk abstract:
Are you interested in working with social data to map out communities and connections between friends, fans and followers? In this session I'll show ways in which we use the python networkx library along with the open source gephi visualization tool to make sense of social network data. We'll take a few examples from Twitter, look at how a hashtag spreads through the network, and then analyze the connections between users posting to the hashtag. We'll be constructing graphs, running stats on them and then visualizing the output.
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
An Overview of Spanner: Google's Globally Distributed DatabaseBenjamin Bengfort
Spanner is a globally distributed database that provides external consistency between data centers and stores data in a schema based semi-relational data structure. Not only that, Spanner provides a versioned view of the data that allows for instantaneous snapshot isolation across any segment of the data. This versioned isolation allows Spanner to provide globally consistent reads of the database at a particular time allowing for lock-free read-only transactions (and therefore no communications overhead for consensus during these types of reads). Spanner also provides externally consistent reads and writes with a timestamp-based linear execution of transactions and two phase commits. Spanner is the first distributed database that provides global sharding and replication with strong consistency semantics.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
District Data Labs Workshop
Current Workshop: August 23, 2014
Previous Workshops:
- April 5, 2014
Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights - they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”
These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. Applied machine learning has come a long way from it's beginnings in academia, and with tools like Scikit-Learn, it's easier than ever to generate operational models for a wide variety of applications. Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. However, the search space is large, and grid search approaches to machine learning can easily lead to failure and frustration. Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. In this talk, we will discuss interactive visual methods for better understanding, steering, and tuning machine learning models.
Network analyses are powerful methods for both visual analytics and machine learning but can suffer as their complexity increases. By embedding time as a structural element rather than a property, we will explore how time series and interactive analysis can be improved on Graph structures. Primarily we will look at decomposition in NLP-extracted concept graphs using NetworkX and Graph Tool.
Slide deck from my presentation at NYC's #Pydata 2012 conference - http://nyc2012.pydata.org/abstracts/#gephi
Talk abstract:
Are you interested in working with social data to map out communities and connections between friends, fans and followers? In this session I'll show ways in which we use the python networkx library along with the open source gephi visualization tool to make sense of social network data. We'll take a few examples from Twitter, look at how a hashtag spreads through the network, and then analyze the connections between users posting to the hashtag. We'll be constructing graphs, running stats on them and then visualizing the output.
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
An Overview of Spanner: Google's Globally Distributed DatabaseBenjamin Bengfort
Spanner is a globally distributed database that provides external consistency between data centers and stores data in a schema based semi-relational data structure. Not only that, Spanner provides a versioned view of the data that allows for instantaneous snapshot isolation across any segment of the data. This versioned isolation allows Spanner to provide globally consistent reads of the database at a particular time allowing for lock-free read-only transactions (and therefore no communications overhead for consensus during these types of reads). Spanner also provides externally consistent reads and writes with a timestamp-based linear execution of transactions and two phase commits. Spanner is the first distributed database that provides global sharding and replication with strong consistency semantics.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
District Data Labs Workshop
Current Workshop: August 23, 2014
Previous Workshops:
- April 5, 2014
Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights - they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”
These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. Applied machine learning has come a long way from it's beginnings in academia, and with tools like Scikit-Learn, it's easier than ever to generate operational models for a wide variety of applications. Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. However, the search space is large, and grid search approaches to machine learning can easily lead to failure and frustration. Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. In this talk, we will discuss interactive visual methods for better understanding, steering, and tuning machine learning models.
Network analyses are powerful methods for both visual analytics and machine learning but can suffer as their complexity increases. By embedding time as a structural element rather than a property, we will explore how time series and interactive analysis can be improved on Graph structures. Primarily we will look at decomposition in NLP-extracted concept graphs using NetworkX and Graph Tool.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
How Does XfilesPro Ensure Security While Sharing Documents in Salesforce?XfilesPro
Worried about document security while sharing them in Salesforce? Fret no more! Here are the top-notch security standards XfilesPro upholds to ensure strong security for your Salesforce documents while sharing with internal or external people.
To learn more, read the blog: https://www.xfilespro.com/how-does-xfilespro-make-document-sharing-secure-and-seamless-in-salesforce/
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
First Steps with Globus Compute Multi-User Endpoints
Annotation with Redfox
1. Annotation Tutorial
Using the Redfox annotator, you will be determining whether
products have been tagged correctly or incorrectly by our automated
classifier.
4. Now review the Age & Gender, Category
and Sub-category data. If these items
match the displayed product, then select
‘Right’. If not, select ‘Wrong’. In the
example, below, the Metadata correctly
identifies the product as being for an
Adult Male, the category is correctly
marked as a ‘Top’ and the ‘Subcategory’
is correctly marked as ‘Shirt’. Therefore,
this item has been classified correctly and
so should be marked ‘Right’.
Note that our automated classifier only
considers the ‘Product Name’ when
determining the Metadata, Category and
Subcategory. If there is incongruity
between the Product Name, Description
and Image, consider only the Product
Name when deciding whether the
classification is Right or Wrong.
Age & Gender
Category &
Subcategory
This item is
classified correctly
5. In the example below, this shoe is
correctly marked as for an Adult Male but
the category and subcategory are
incorrectly marked as ‘Accessories’ and
‘Ties’ respectively. This item
classification would be marked ‘Wrong’.
Incorrectly
marked as
‘Accessory’ and
as ‘Ties’
This item is NOT
classified correctly
6. If any of the classification data items is
incorrect, then the product classification
should be marked ‘Wrong’. In this
example, the product is correctly
identified as for adult females and is
correctly categorized as a shoe. However,
the subcategory is incorrectly marked as
‘Boots’. Even though only one of the
classification items is wrong, this should
be marked ‘Wrong’.
Incorrectly marked
as a ‘boot’
This item is NOT
classified correctly
7. If you accidentally select the incorrect
option, then you can click on ‘Prev’ to go
to the previous item.
Note that your history is only maintained
per session and only contains the
previous 32 items. It will be erased if you
log out and log in again.
If you are not sure whether
the classification is right or
wrong, just click on ‘Unsure’
to skip to the next item.
8. You can view your progress in your
profile page, which can be accessed by
clicking on the drop down with your name
in it, then selecting “View Profile”.
Here you will see your overall annotation
statistics as well as your current
annotation rate.
9. Category Descriptions
The following pages include descriptions of the different categories
and subcategories used by the classifier. Do an image search of
the category/subcategory if you are not sure what it looks like
10. Tops Any piece of light clothing worn on the part of the body above the waist
Polos An informal style of cotton shirt, with short sleeves, a collar, and some buttons at the neck
Sweatshirts A piece of informal clothing with long sleeves, usually made of thick cotton, worn on the upper
part of the body
Sweaters A piece of clothing with long sleeves that is usually made from wool, worn on the upper part of
the body
Shirts A piece of clothing worn, especially by men, on the upper part of the body, made of light cloth
like cotton and usually having a collar and buttons at the front
Tees or Tanks A simple piece of clothing, usually with short or no sleeves and no collar, that covers the top
part of the body
Top level
Category
Subcategories
11. Bottoms Any piece of clothing worn on the part of the body below the waist
Capris Close-fitting calf-length tapered trousers, usually worn by women and girls
Jeans Hard-wearing trousers made of denim or other cotton fabric, for informal wear
Leggings Tight-fitting stretch pants worn by women and children
Pants An outer garment covering the body from the waist to the ankles, with a separate part for each
leg. (Trousers not under garments)
Shorts Short pants that reach only to the thighs or knees
Skirts A woman's outer garment fastened around the waist and hanging down around the legs
12. Dresses A one-piece garment for a woman or girl that covers the body and extends down over the
legs
Career (Dresses) A dress that is appropriate for business use
Casual (Dresses) A dress that emphasizes comfort and personal expression over presentation and
uniformity
Formal (Dresses) A dress suitable for formal social events, such as a wedding, formal garden party or
dinner
Evening (Dresses) A dress to wear on formal occasions in the evening
13. Suits Clothes that usually consists of a jacket and a skirt or pair of pants that are made out of the
same material
(Suit) Jackets An outer garment extending either to the waist or the hips, typically having sleeves and a
fastening down the front
Suits A complete set of clothes that usually consists of a jacket and a skirt or pair of pants that are
made out of the same material
Vests A close-fitting waist-length garment, typically having no sleeves or collar and buttoning down
the front
14. Activewear Clothing designed to be worn for sports, exercise, and outdoor activities
(Athletic) Equipment Equipment needed to participate in a particular sport
(Athletic) Jackets A thin outer coat designed to resist wind chill and light rain, usually of light
construction and made of some type of synthetic material
(Athletic) Pants or Tights Warm trousers with an elasticized or drawstring waist, worn when exercising or as
leisurewear
(Athletic) Shirts A shirt with short or long sleeves designed for comfort and to perform sports,
exercise, and outdoor activities
(Athletic) Shorts Knee-length short trousers made of a stretch fabric, designed to be worn by
athletes
(Athletic) Bras A bra that provides additional support to female breasts during physical exercise
(Athletic) Suits An item of clothing designed to be worn by people engaging in a water-based
activity or water sports, such as swimming
15. Outerwear Clothing worn over other clothes, esp. for the outdoors
Coats An outer garment worn outdoors, having sleeves and typically extending below the hips
Leather (Coat) An outer garment made of leather
Overcoats A long warm coat worn over other clothin
Rain (Coat) A long coat made from waterproofed or water-resistant fabric
Trench (Coat) A loose, belted, double-breasted raincoat in a military style
Winter (Coat) A heavy coat worn over clothes in the winter
16. Underwear Clothing worn under other clothes, typically next to the skin
Sleepwear or
Loungewear
Casual, comfortable clothing suitable for wearing at home
Boxers Men's loose underpants similar in shape to the shorts worn by boxers
Boxer-Briefs A type of men's undergarment which are long in the leg, like boxer shorts but tighter-fitting,
like briefs; a hybrid between the two main types of male underpants
Bras An undergarment worn by women to support the breasts
Briefs Close-fitting legless underpants that are cut so as to cover the body to the waist, in contrast to
a bikini
Hosiery Stockings, socks, and tights collectively
Lingerie Women's underwear and nightclothes
Panties Legless underpants worn by women and girls
Socks A garment for the foot and lower part of the leg, typically knitted from wool, cotton, or nylon
Underwear Clothing worn under other clothes, typically next to the skin
17. Shoes A covering for the foot, typically made of leather, with a sturdy sole and not reaching above
the ankle
Athletic (Shoes) A soft shoe with a rubber sole worn for sports or casual occasions
Boots A sturdy item of footwear covering the foot, the ankle, and sometimes the leg below the knee
Casual (Shoes) Footwear that emphasizes comfort and personal expression
Dress (Shoes) Shoe to be worn at smart casual or more formal events. A dress shoe is typically contrasted to
an athletic shoe
Heels High-heeled shoes typically worn by women
Sandals A light shoe with either an openwork upper or straps attaching the sole to the foot
Slippers A comfortable slip-on shoe that is worn indoors
18. Accessories A small article or item of clothing carried or worn to complement a garment or outfit
Bags A container made of flexible material with an opening at the top, used for carrying things
Belts A strip of leather or other material worn around the waist or across the chest, esp. in order to
support clothes
Eyewear Things worn on the eyes, such as spectacles and contact lenses
Gloves A covering for the hand worn for protection against cold or dirt and typically having separate
parts for each finger and the thumb
Hats A shaped covering for the head worn for warmth, as a fashion item, or as part of a uniform
Jewelry Personal ornaments, such as necklaces, rings, or bracelets, that are typically made from or
contain jewels and precious metal
Scarves A length or square of fabric worn around the neck or head
Watches A small timepiece worn typically on a strap on one's wrist
Wallets A pocket-sized, flat, folding holder for money and plastic cards
Ties A strip of material worn around the collar and tied in a knot at the front with the ends hanging
down, typically forming part of a man's business or formal outfit; a necktie
Other Miscellaneous accessories that do not fit into other categories