Safeguarding Abila: Discovering Evolving Activist Networks
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This document summarizes two interfaces presented as part of a VAST challenge solution: a News Analyzer and Email Analyzer. The News Analyzer allows exploration of news articles to identify events using search, entity recognition, and visualization tools. The Email Analyzer visualizes email networks through a co-occurrence matrix, radial graph, and word cloud to examine relationships. Both interfaces were designed specifically for the challenge and utilize techniques like search engines, named entity recognition, spectral co-clustering, and D3 visualization.
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PROCESO DE ELABORACION DE UNA INVESTIGACION DOCUMENTAL
Negotiating contracts as user experiences - WIAD Rome 2016
Negotiating contracts as user experiences - WIAD Rome 2016
New Internet Marketing YouTube Channel Titled IAMPAULJAMES Launches This Mont...
New Internet Marketing YouTube Channel Titled IAMPAULJAMES Launches This Mont...
Similar to Safeguarding Abila: Discovering Evolving Activist Networks
Efficient Filtering Algorithms for Location- Aware Publish/subscribe
Location-based services have been mostly used in many systems. preceding systems uses a pull model or user-initiated model, where a user arrival a query to a server which gives response with location-aware answers. To offer outcomes to users with fast responses, a push model or server-initiated model is flattering an important computing model in the next-generation location-based services. In the push model, subscribers arrive spatio-textual subscriptions to fastening their curiosities, and publishers send spatio-textual messages. It is used for a high-performance location-aware publish/subscribe system to send publishers’ messages to valid subscribers. In this paper, we find the exploration happenstances that start in manipulative a location-aware publish/subscribe system. We recommend an R-tree based index by merging textual descriptions into R-tree nodes. We design efficient filtering algorithms and effective pruning techniques to accomplish high performance. This method can support likewise conjunctive queries and ranking queries.
Linked Open Data about Springer Nature conferences. The story so far
Despite many efforts for making data about scholarly publications available on the Web of Data, lots of information about academic conferences is still contained in (at best) free-text format. When available in a structured format, these data would provide an essential input for the decisions researchers, libraries, publishers, funding and evaluation bodies take every day.
This talk will describe the project about having such data available as Linked Open Data (LOD) at lod.springer.com for around 10,000 computer science conferences. In addition, we will have a closer look at the lessons learnt from launching this portal and cover other Linked Data projects in Springer Nature. Finally, a novel semi-automated approach for classifying conference proceedings in Springer Nature will also be presented.
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
This document discusses the design of querying interfaces for big data analysis platforms used in crisis research. It describes the EPIC Analyze platform and evaluations conducted with research analysts. Key findings were that queries need to be visible, tangible, and expressive. Analysts wanted to filter data out rather than qualifying data in and see the "big picture" of the dataset. Future work includes adding more advanced analysis features and moving to a microservices architecture. Effective query design requires using human-centered techniques and understanding the application domain.
Personal Research Overview presented at the KU-NAIST Research Meeting
Chawanat Nakasan presented a personal research overview for a KU-NAIST research meeting. He summarized his academic history, including undergraduate studies in computer engineering at Kasetsart University and graduate studies at NAIST, where he focused on software-defined networking and multipath networking. At NAIST, his research optimized Multipath TCP performance using a software-defined network with an OpenFlow controller that identifies MPTCP subflow groups and assigns them to network paths. Evaluation showed the approach improved throughput. Nakasan discussed achievements including publications and awards. He is graduating from NAIST and the PRAGMA Students Steering Committee, and moving to an academic career in Japan.
Clustering of Big Data Using Different Data-Mining Techniques
This document discusses clustering techniques for big data using different data mining approaches. It begins with an introduction to big data and some of its key characteristics like volume, variety, velocity, etc. It then discusses two main data mining techniques - clustering and classification. For clustering, it describes algorithms like K-means and bisecting K-means. It proposes a methodology using these algorithms with MapReduce for big data clustering. Several modules are implemented and results are presented with figures. It concludes that big data frameworks need to consider complex relationships in data and high performance platforms are required for big data mining.
Beyond Collaborative Filtering: Learning to Rank Research Articles
This document provides an overview of Elsevier's work using machine learning techniques like collaborative filtering and learning to rank to improve article recommendations for researchers. It discusses using collaborative filtering on browsing logs to initially recommend related articles, then using learning to rank to re-rank those recommendations higher based on features like reputation, topics, citations and user engagement data. Evaluation shows the learning to rank approach improved user engagement by 9-10% over collaborative filtering alone. Future work may explore alternative approaches like graph-based methods or deep learning.
A machine learning and data science pipeline for real companies
Comcast is one of the largest cable and telecommunications providers in the country built on decades of mergers, acquisitions, and subscriber growth. The success of our company depends on keeping our customers happy and how quickly we can pivot with changing trends and new technologies. Data abounds within our internal data centers and edge networks as well as both the private and public cloud across multiple vendors.
Within such an environment and given such challenges, how do we get AI, machine learning, and data science platforms built so our company can respond to the market, predict our customers’ needs and create new revenue generating products that delight our customers? If you don’t happen to be our friends and colleagues at Google, Facebook, and Amazon, what are technologies, strategies, and toolkits you can employ to bring together disparate data sets and quickly get them into the hands of your data scientists and then into your own production systems for use by your customers and business partners?
We’ll explore our journey and evolution and look at specific technologies and decisions that have gotten us to where we are today and demo how our platform works.
Speaker
Ray Harrison, Comcast, Enterprise Architect
Prashant Khanolkar, Comcast, Principal Architect Big Data
Real time streaming analytics
Real time analytics with Spark Streaming by Padma at Bangalore I & D meetup (https://www.meetup.com/Bengaluru-Insights-and-Data-Meetup/events/238459154)
artrec.pptx
This document describes a recommender system project done by Lakshya Karwa and Tarun Kumar I.S. guided by Dr. J. Shana. It used user interaction data from an internal communications platform from 2016-2017 to build and compare three recommender models: Alternating Least Squares (ALS), Bayesian Personalized Ranking (BPR), and Logistic Matrix Factorization (LMF). ALS had the highest accuracy at 98.1% while BPR and LMF also performed well at 82.6% and 97.89% respectively. ALS was the fastest to train while BPR was the slowest. The models provided personalized article recommendations to users based on their interactions.
Cloudera Movies Data Science Project On Big Data
The document describes a data science project conducted on streaming log data from Cloudera Movies, an online streaming video service. The goals of the project were to understand which user accounts are used most by younger viewers, segment user sessions to improve site usability, and build a recommendation engine. Key steps included exploring and cleaning the data, classifying users as children or adults using a SimRank approach, clustering user sessions to identify behavior patterns, and predicting user ratings through user-user and item-item similarity models to build a recommendation system. Accuracy of 99.64% was achieved in classifying users.
Mohan C R CV
This document provides information about Mohan C R, including his education and qualifications, skills, projects, publications and awards. He has over 2 years of experience as a data scientist and machine learning engineer. He has a B.Tech in electronics and communication engineering as well as nanodegree certificates in data science and machine learning foundations from Udacity. His skills include Python, SQL, AWS, TensorFlow and he has worked on projects involving image classification, recommendation engines, disaster response pipelines and customer segmentation.
An adaptive clustering and classification algorithm for Twitter data streamin...
On-going big data from social networks sites alike Twitter or Facebook has been an entrancing
hotspot for investigation by researchers in current decades as a result of various aspects including
up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy.
Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which
can cluster data within a lesser computational time, especially for data streaming is needed. The presented
adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome
the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is
viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally
improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data
streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is
classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization.
At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to
discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs
dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of
presented approach comparing with the existing methods in terms of precision, recall, F-score,
convergence, ROC curve and accuracy.
Survey on Location Based Recommendation System Using POI
This document summarizes a survey on location-based recommendation systems using points of interest (POIs). It discusses how data mining techniques can be used to analyze user check-in data from location-based social networks to discover patterns and interests in order to provide personalized POI recommendations to users. The system architecture involves collecting check-in data, performing data mining, building user profiles, and generating recommendations. It also reviews several related works on topic modeling, matrix factorization methods, and exploiting sequential check-in data and geographical influences to provide successive POI recommendations. The goal is to develop improved recommendation methods that can recommend new locations for users to explore based on their interests and behaviors.
Service Level Comparison for Online Shopping using Data Mining
The term knowledge discovery in databases (KDD) is the analysis step of data mining. The data mining goal is to extract the knowledge and patterns from large data sets, not the data extraction itself. Big-Data Computing is a critical challenge for the ICT industry. Engineers and researchers are dealing with the cloud computing paradigm of petabyte data sets. Thus the demand for building a service stack to distribute, manage and process massive data sets has risen drastically. We investigate the problem for a single source node to broadcast the big chunk of data sets to a set of nodes to minimize the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed data centers. The Big-data broadcasting problem is modeled into a LockStep Broadcast Tree (LSBT) problem. And the main idea of the LSBT is defining a basic unit of upload bandwidth, r, a node with capacity c broadcasts data to a set of [c=r] children at the rate r. Note that r is a parameter to be optimized as part of the LSBT problem. The broadcast data are further divided into m chunks. In a pipeline manner, these m chunks can then be broadcast down the LSBT. In a homogeneous network environment in which each node has the same upload capacity c, the optimal uplink rate r, of LSBT is either c=2 or 3, whichever gives the smaller maximum completion time. For heterogeneous environments, an O(nlog2n) algorithm is presented to select an optimal uplink rate r, and to construct an optimal LSBT. With lower computational complexity and low maximum completion time, the numerical results shows better performance.The methodology includes Various Web applications Building and Broadcasting followed by the Gateway Application and Batch Processing over the TSV Data after which the Web Crawling for Resources and MapReduce process takes place and finally Picking Products from Recommendations and Purchasing it.
sigir16
This document presents research on predicting user engagement with direct displays like knowledge panels using mouse cursor data. The researchers conducted a crowdsourcing study tracking users' mouse cursors during search tasks. They developed predictive models to determine when users notice, find useful, and perceive faster task completion from direct displays. Their models outperformed baselines in accuracy and other metrics, showing mouse cursor data can predict user engagement without explicit feedback. The researchers conclude this approach offers an efficient way to analyze interactions and optimize direct display placement and content.
Splunk App for Stream
Wire data provides deep insights across IT, security and business use cases by capturing the communications transmitted over the wire between machines and applications in real-time. The Splunk App for Stream enables new operational intelligence by indexing this wire data without needing instrumentation. It provides enhanced visibility, efficient cloud-ready collection, and fast time to value through interface-driven deployment. Key features include protocol decoding, attribute filtering, aggregations, and custom content extraction for analysis in Splunk.
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
The document discusses a novel method called ProMiSH (Projection and Multi Scale Hashing) for keyword search in multi-dimensional datasets. ProMiSH uses random projection and hash-based index structures to achieve high scalability and speedup of more than four orders over state-of-the-art tree-based techniques. Empirical studies on real and synthetic datasets of sizes up to 10 million objects and 100 dimensions show ProMiSH scales linearly with dataset size, dimension, query size, and result size. The method groups objects embedded in a vector space that are tagged with keywords matching a given query.
Chi2011 Case Study: Interactive, Dynamic Sparklines
This document describes the development of interactive sparklines to help electronic engineers debug circuits. Sparklines condense hundreds of data points into a small visual space. Initially designed for static displays, the author's team created interactive sparklines to assist with debugging circuits in real-time. Through user research, they identified engineers' needs for fast, accurate data acquisition and visualizations that quickly detect problems. The team developed iterative designs based on user feedback to refine the sparklines for interactive debugging of high-speed serial data circuits.
Improving the search mechanism for unstructured peer to-peer networks using t...
In a traditional file search mechanism, such as flooding, a peer broadcasts a query to its neighbours through an unstructured Peer-to-Peer (P2P) network until the Time-To-Live (TTL) decreases to zero.
The proposed method called the Statistical Matrix Form (SMF), which improves the flooding mechanism by selecting neighbors according to their capabilities.
R at Microsoft
This document summarizes R at Microsoft, including its acquisition of Revolution Analytics. Key points include:
- R is a widely used open-source statistical programming language. Microsoft acquired Revolution Analytics to help customers use advanced analytics within its data platforms.
- Microsoft products like SQL Server will integrate Revolution R Open, an enhanced open-source R distribution, to allow running R scripts directly from SQL queries.
- Microsoft aims to make R and advanced analytics more accessible and scalable through products like the Machine Learning marketplace and by running R on servers to handle large datasets within SQL Server and Azure.
Similar to Safeguarding Abila: Discovering Evolving Activist Networks (20)
Efficient Filtering Algorithms for Location- Aware Publish/subscribe
Efficient Filtering Algorithms for Location- Aware Publish/subscribe
Linked Open Data about Springer Nature conferences. The story so far
Linked Open Data about Springer Nature conferences. The story so far
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
Personal Research Overview presented at the KU-NAIST Research Meeting
Personal Research Overview presented at the KU-NAIST Research Meeting
Clustering of Big Data Using Different Data-Mining Techniques
Clustering of Big Data Using Different Data-Mining Techniques
Beyond Collaborative Filtering: Learning to Rank Research Articles
Beyond Collaborative Filtering: Learning to Rank Research Articles
A machine learning and data science pipeline for real companies
A machine learning and data science pipeline for real companies
An adaptive clustering and classification algorithm for Twitter data streamin...
An adaptive clustering and classification algorithm for Twitter data streamin...
Survey on Location Based Recommendation System Using POI
Survey on Location Based Recommendation System Using POI
Service Level Comparison for Online Shopping using Data Mining
Service Level Comparison for Online Shopping using Data Mining
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
Chi2011 Case Study: Interactive, Dynamic Sparklines
Chi2011 Case Study: Interactive, Dynamic Sparklines
Improving the search mechanism for unstructured peer to-peer networks using t...
Improving the search mechanism for unstructured peer to-peer networks using t...
More from Parang Saraf
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
Abstract: We describe the EMBERS AutoGSR system that conducts automated coding of civil unrest events from news articles published in multiple languages. The nuts and bolts of the AutoGSR system constitute an ecosystem of filtering, ranking, and recommendation models to determine if an article reports a civil unrest event and, if so, proceed to identify and encode specific characteristics of the civil unrest event such as the when, where, who, and why of the protest. AutoGSR is a deployed system for the past 6 months continually processing data 24x7 in languages such as Spanish, Portuguese, English and encoding civil unrest events in 10 countries of Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. We demonstrate the superiority of AutoGSR over both manual approaches and other state-of-the-art en- coding systems for civil unrest.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
EMBERS at 4 years: Experiences operating an Open Source Indicators Forecastin...
Abstract: EMBERS is an anticipatory intelligence system forecasting population-level events in multiple countries of Latin America. A deployed system from 2012, EMBERS has been generating alerts 24x7 by ingesting a broad range of data sources including news, blogs, tweets, machine coded events, currency rates, and food prices. In this paper, we describe our experiences operating EMBERS continuously for nearly 4 years, with specific attention to the discoveries it has enabled, correct as well as missed forecasts, and lessons learnt from participating in a forecasting tournament including our perspectives on the limits of forecasting and ethical considerations.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Slides: Forex-Foreteller: Currency Trend Modeling using News Articles
Abstract: Financial markets are quite sensitive to unanticipated news and events. Identifying the effect of news on the market is a challenging task. In this demo, we present Forex-foreteller (FF) which mines news articles and makes forecasts about the movement of foreign currency markets. The system uses a combination of language models, topic clustering, and sentiment analysis to identify relevant news articles. These articles along with the historical stock index and currency exchange values are used in a linear regression model to make forecasts. The system has an interactive visualizer designed specifically for touch-sensitive devices which depicts forecasts along with the chronological news events and financial data used for making the forecasts.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Slides: Epidemiological Modeling of News and Rumors on Twitter
This document presents research on modeling the spread of news and rumors on Twitter using epidemiological models. It finds that a SEIZ (Susceptible-Exposed-Infected-Skeptics) model more accurately describes the spread of information on Twitter compared to a simpler SIS (Susceptible-Infected-Susceptible) model, especially at the initial stages. The researchers analyzed several real-world events and found the SEIZ model produced lower errors between the model and actual tweet volumes. Parameters extracted from fitting the SEIZ model to data could help identify rumors versus factual news on Twitter. Limitations include not incorporating information about followers or population characteristics.
Slides: Concurrent Inference of Topic Models and Distributed Vector Represent...
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
EMBERS AutoGSR is a novel, web based framework that generates a comprehensive database of validated civil unrest events using minimal human effort. AutoGSR is a deployed system for the past 6 months that is continually processing data 24X7 in an automated fashion. The system extracts civil unrest events of type "who protested where, when and why?" from news articles published in over 7 languages, and collected from 16 countries.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
DMAP: Data Aggregation and Presentation Framework
DMAP (Data Mining and Automation for Platforms) is an online framework that presents a wide variety of official data, news and information about companies. It was developed with an aim to act as a one-stop platform for displaying everything official related to a company and its competitors. It aggregates data from the following sources: Bing News, Companys' official blogs, RSS Sources, Facebook, Twitter, Google Trends, Crunchbase, Financial Data Sources, and Alexa Web Analytics.
The aggregated information is shown in an intuitive fashion that allows a user to perform exploratory analysis of a particular company. Specifically, the interface makes it easy to do comparative analysis of a company with respect to its competitors. The user can use filters to select a given set of companies, data sources and date ranges. All the information is presented within the context of the framework such that the user doesn't have to go to different domains. The fetched news articles are cleaned, enriched, and presented in a manner that allows an analyst to navigate through news articles using named entities. For each news article, named entities: people, location, organization, and name of companies are extracted. For a given search query, the interface returns matching news articles along with associated entities which are shown using word clouds. This allows for easy discovery of connections between entities.
The framework was developed as a part of the 2015 summer internship with The Center for Global Enterprise (CGE). Conceptualization, Design and Development of the framework was done by me during the three month period.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
EMBERS Posters
For more detail about the project, please visit: http://people.cs.vt.edu/parang/embers/ or contact parang at firstname at cs vt edu
Concurrent Inference of Topic Models and Distributed Vector Representations
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Bayesian Model Fusion for Forecasting Civil Unrest
Abstract: With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and
spurring them into action. We are interested in using Big Data approaches to generate forecasts of civil unrest from open source indicators. The heterogenous nature of data coupled with the rich and diverse origins of civil unrest call for a multi-model approach to such forecasting. We present a modular approach wherein a collection of models use overlapping sources of data to independently forecast protests. Fusion of alerts into one single alert stream becomes a key system informatics problem and we present a statistical framework to accomplish such fusion. Given an alert from one of the numerous models, the decision space
for fusion has two possibilities: (i) release the alert or (ii) suppress the alert. Using a Bayesian decision theoretical framework, we present a fusion approach for releasing or suppressing alerts. The resulting system enables real-time decisions and more importantly tuning of precision and recall. Supplementary materials for this article are available online.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
‘Beating the News’ with EMBERS: Forecasting Civil Unrest using Open Source In...
Abstract: We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific
predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Safeguarding Abila through Multiple Data Perspectives
This document describes a visual analytics system developed to analyze multiple datasets related to the disappearance of employees from an organization. The system allows analysis of unstructured news articles, email headers, GPS tracking data, financial transactions, and streaming microblog data through different interactive interfaces. It was created as a solution to the 2014 VAST Grand Challenge to help law enforcement investigate the case by combining insights from all the different data sources.
Safeguarding Abila: Real-time Streaming Analysis
Abstract: This paper introduces a system for visualization and analysis of geo-spatial and temporal data from call center and microblog sources. The system provides a streaming client for interacting with the data in real time. The system presents the data in a partitioned format using coordinated visualizations
that allows the analyst to view the data in multiple dimension
simultaneously. This allows the user to see patterns that occur in space and time. This project was developed in response to the VAST 2014 Mini-Challenge 3.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Safeguarding Abila: Spatio-Temporal Activity Modeling
Award: This system won the "Honorable Mention for Effective Presentation" award.
Abstract: This paper introduces a system for visual analysis of GPS tracking and financial data. The system was developed in response to VAST Mini-Challenge 2 and comprises of different interfaces for mining spatio-temporal and financial data.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Forex-Foreteller: Currency Trend Modeling using News Articles
Abstract: Financial markets are quite sensitive to unanticipated news and events. Identifying the effect of news on the market is a challenging task. In this demo, we present Forex-foreteller (FF) which mines news articles and makes forecasts about the movement of foreign currency markets. The system uses a combination of language models, topic clustering, and sentiment analysis to identify relevant news articles. These articles along with the historical stock index and currency exchange values are used in a linear regression model to make forecasts. The system has an interactive visualizer designed specifically for touch-sensitive devices which depicts forecasts along with the chronological news events and financial data used for making the forecasts.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Epidemiological Modeling of News and Rumors on Twitter
Abstract: Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Merseyside Crime Analysis
Abstract : Crime prediction is a topic of significant research across the fields of criminology, data mining, city planning, law enforcement, and political science. Crime patterns exist on a spatial level; these patterns can be grouped geographically by physical location, and analyzed contextually based on the region
in which crime occurs. This paper proposes a mechanism to parameterize street-level crime, localize crime hotspots, identify correlations between spatiotemporal crime patterns and social trends, and analyze the resulting data for the purposes of knowledge discovery and anomaly detection. The subject of this study is the county of Merseyside in the United Kingdom, over a span of 21 months beginning in December 2010 (monthly) through August 2012. Several types of crime are analyzed in this dataset, including Burglary and Antisocial Behavior. Through this analysis, several interesting findings are drawn about crime in Merseyside, including: hotspots with steadily increasing crime levels, hotspots with unstable crime levels, synchronous changes in crime trends throughout Merseyside as a whole, individual months in which certain hotspots behaved anomalously, and a strong correlation between crime hotspot locations and borough/postal code locations. We believe that this type of statistical and correlative analysis of crime patterns will help law enforcement agencies predict criminal activity, allocate resources, and promote community awareness to reduce overall crime rates.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Virtual time round-robin scheduler presented by Parang Saraf (CS4204 VT)
Presentation on the paper: "Virtual time round-robin O(1) proportional share scheduler"
If you need a copy of this presentation, feel free to msg me at parang[DOT]saraf[AT]gmail
A fast file system for unix presentation by parang saraf (cs5204 VT)
This is the presentation of the paper "A fast file system for unix"
In case if you need a copy of these slides feel free to contact me at parang[DOT]saraf[AT]gmail
More from Parang Saraf (19)
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
EMBERS at 4 years: Experiences operating an Open Source Indicators Forecastin...
EMBERS at 4 years: Experiences operating an Open Source Indicators Forecastin...
Slides: Forex-Foreteller: Currency Trend Modeling using News Articles
Slides: Forex-Foreteller: Currency Trend Modeling using News Articles
Slides: Epidemiological Modeling of News and Rumors on Twitter
Slides: Epidemiological Modeling of News and Rumors on Twitter
Slides: Concurrent Inference of Topic Models and Distributed Vector Represent...
Slides: Concurrent Inference of Topic Models and Distributed Vector Represent...
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
Concurrent Inference of Topic Models and Distributed Vector Representations
Concurrent Inference of Topic Models and Distributed Vector Representations
Bayesian Model Fusion for Forecasting Civil Unrest
Bayesian Model Fusion for Forecasting Civil Unrest
‘Beating the News’ with EMBERS: Forecasting Civil Unrest using Open Source In...
‘Beating the News’ with EMBERS: Forecasting Civil Unrest using Open Source In...
Safeguarding Abila through Multiple Data Perspectives
Safeguarding Abila through Multiple Data Perspectives
Safeguarding Abila: Spatio-Temporal Activity Modeling
Safeguarding Abila: Spatio-Temporal Activity Modeling
Forex-Foreteller: Currency Trend Modeling using News Articles
Forex-Foreteller: Currency Trend Modeling using News Articles
Epidemiological Modeling of News and Rumors on Twitter
Epidemiological Modeling of News and Rumors on Twitter
Virtual time round-robin scheduler presented by Parang Saraf (CS4204 VT)
Virtual time round-robin scheduler presented by Parang Saraf (CS4204 VT)
A fast file system for unix presentation by parang saraf (cs5204 VT)
A fast file system for unix presentation by parang saraf (cs5204 VT)
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原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版制作【微信:41543339】【多伦多大学毕业证(UofT毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(harvard毕业证书）哈佛大学毕业证如何办理
一模一样【微信：A575476】【(harvard毕业证书）哈佛大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
原版办理【微信号:BYZS866】【巴斯大学毕业证(Bath毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路、）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
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Safeguarding Abila: Discovering Evolving Activist Networks
- 1. Safeguarding Abila through Multiple Data Perspectives VAST 2014 Grand Challenge Award: Effective Analysis and Presentation VAST 2014 Mini Challenge 2 Award: Honorable Mention for Effective Presentation Parang Saraf; Patrick Butler; Naren Ramakrishnan Discovery Analytics Center, Department of Computer Science, Virginia Tech 1 Presented By: Parang Saraf
- 3. VAST Challenge Solution From DAC • The DAC solution offers three key advantages: 1. Provides an efficient front-end interface for user-centered exploration of data 2. Very little analysis or cleaning of data is performed in the backend, thereby helping an analyst to understand the data better ! Example: Faulty news sources or GPS coordinates are displayed 3. Offers an intuitive interface to present data in several different ways • Each Interface was designed from scratch specifically for the VAST Challenge 3
- 4. Mini Challenge - I 4
- 5. Mini Challenge - I • Two Interfaces: – News Analyzer • Helps an Analyst explore news articles and Identify Events – Email Analyzer • Helps an Analyst visualize and examine Email network 5
- 10. News Analyzer 10
- 11. News Analyzer 11
- 12. News Analyzer 12
- 13. News Analyzer 13
- 14. News Analyzer 14
- 15. News Analyzer Example: How POK Leadership has changed over Time 15 1998 – 1999
- 16. News Analyzer Example: How POK Leadership has changed over Time 16 1998 – 1999 2000 – 2009
- 17. News Analyzer Example: How POK Leadership has changed over Time 17 1998 – 1999 2000 – 2009 2000 – 2014
- 18. News Analyzer Packages Used • Search Engine – Whoosh (Python search engine library) • Provides Logical Query of Articles • Returns Similar articles • Named Entity Recognition – Stanford NER Parser • Provides Person, Organization and Locations in the input document set • Visualization – NVD3 • Line Chart – D3 • Word Clouds 18
- 26. Email Analyzer 26 Spectral Co-Clustering • Given an n x m matrix of n documents and m words, the algorithm performs co-clustering of documents and words. • The clustering problem is posed in terms of finding minimum cut vertex partitions in a bipartite graph between document and words • We provide an m x m matrix where rows and columns denote employees and a cell denotes the number of emails exchanged • Implemented using the scikit-learn package
- 30. Email Analyzer - Example 30
- 31. Email Analyzer - Example 31
- 32. Email Analyzer - Example 32
- 33. Email Analyzer Packages Used • Co-Occurrence Matrix – Spectral Co-Clustering Algorithm – Visualization • D3 – Email Radial, Co-Occurrence matrix and Word Cloud 33
- 34. Thank You 92