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DSC Webinar Series: Mathematical Optimization Modeling: Learn the Basics - DataScienceCentral.com

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Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems. How to formulate an MO model. How to build an MO model using the Gurobi Python API.


Introducing The DataHour Series - Webinars with Industry Leaders

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The word community has become a buzzword across the globe. Businesses have realized the power of community-led growth and are heavily invested in building and continuously giving to the audience. At Analytics Vidhya, the community has been at the forefront since its inception with aim of building the best AI ML ecosystem any company can offer. With a Leading community knowledge portal, our ecosystem is magnifying at a 3x speed. Keeping the community in mind, we are happy to announce that we have launched a webinar series: The DataHour.


Artificial Intelligence in Web Design (2022 Special Edition)

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In the beginning website, design developers and designers designed websites using HTML. Soon, the internet was formless and empty, darkness was over the surface of the deep web, and the Spirit of Code was hovering over the pinnacle of utmost ignorance. We've come a long way from that time. The internet is still a dark, dreadful place, but it's much more stylish, sophisticated, and amazing now. Website Design has grown exponentially in scale and sophistication over the last few years, thanks to new Artificial Intelligence-based website creation tools that are dominating the digital marketing industry.


Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

arXiv.org Artificial Intelligence

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model updates are locally computed and shared for aggregation to produce a global model. While federated learning greatly alleviates the privacy concerns as opposed to learning with centralized data, sharing model updates still poses privacy risks. In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation. Our federated learning system first departs from prior works by supporting lightweight encryption and aggregation, and resilience against drop-out clients with no impact on their participation in future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency optimization in the ciphertext domain and the support of security against an actively adversarial cloud server, which we also fully explore in this paper and provide effective and efficient mechanisms. Extensive experiments over several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system achieves accuracy comparable to the plaintext baseline, with practical performance.


Low-resource Learning with Knowledge Graphs: A Comprehensive Survey

arXiv.org Artificial Intelligence

Machine learning methods especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for training. In real-world applications, we often need to address sample shortage due to e.g., dynamic contexts with emerging prediction targets and costly sample annotation. Therefore, low-resource learning, which aims to learn robust prediction models with no enough resources (especially training samples), is now being widely investigated. Among all the low-resource learning studies, many prefer to utilize some auxiliary information in the form of Knowledge Graph (KG), which is becoming more and more popular for knowledge representation, to reduce the reliance on labeled samples. In this survey, we very comprehensively reviewed over $90$ papers about KG-aware research for two major low-resource learning settings -- zero-shot learning (ZSL) where new classes for prediction have never appeared in training, and few-shot learning (FSL) where new classes for prediction have only a small number of labeled samples that are available. We first introduced the KGs used in ZSL and FSL studies as well as the existing and potential KG construction solutions, and then systematically categorized and summarized KG-aware ZSL and FSL methods, dividing them into different paradigms such as the mapping-based, the data augmentation, the propagation-based and the optimization-based. We next presented different applications, including not only KG augmented tasks in Computer Vision and Natural Language Processing (e.g., image classification, text classification and knowledge extraction), but also tasks for KG curation (e.g., inductive KG completion), and some typical evaluation resources for each task. We eventually discussed some challenges and future directions on aspects such as new learning and reasoning paradigms, and the construction of high quality KGs.


Narrative Cartography with Knowledge Graphs

arXiv.org Artificial Intelligence

Narrative cartography is a discipline which studies the interwoven nature of stories and maps. However, conventional geovisualization techniques of narratives often encounter several prominent challenges, including the data acquisition & integration challenge and the semantic challenge. To tackle these challenges, in this paper, we propose the idea of narrative cartography with knowledge graphs (KGs). Firstly, to tackle the data acquisition & integration challenge, we develop a set of KG-based GeoEnrichment toolboxes to allow users to search and retrieve relevant data from integrated cross-domain knowledge graphs for narrative mapping from within a GISystem. With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping. Two use cases - Magellan's expedition and World War II - are presented to show the effectiveness of this approach. In the meantime, several limitations are identified from this approach, such as data incompleteness, semantic incompatibility, and the semantic challenge in geovisualization. For the later two limitations, we propose a modular ontology for narrative cartography, which formalizes both the map content (Map Content Module) and the geovisualization process (Cartography Module). We demonstrate that, by representing both the map content and the geovisualization process in KGs (an ontology), we can realize both data reusability and map reproducibility for narrative cartography.


AI led Digital Transformation of Manufacturing: Time is NOW

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As we evolve from pandemic to next normal world, 36% of manufacturers are already engaged on AI projects and 23% more have plans to use AI in coming months to unlock the anticipated trillions of dollars in value in industrial sectors. Based on the lessons learned during the pandemic they are all seeking the best way to achieve new levels of productivity, safety and agility by unlocking the AI driven insights from petabytes of data they harvest from their connected factories. To answer these and other questions, many manufactures are turning to AI to speed time to value. Are you a manufacturer looking at how AI can benefit your business? If so, join this webinar with two leaders in AI as they discuss where and how manufacturers are seeing value from AI in areas such as inspection, predictive maintenance, quality control, video analytics and digital twins.


Artificial Intelligence in Web Design (Special Edition)

#artificialintelligence

In the beginning website, design developers and designers designed websites using HTML. Soon, the internet was formless and empty, darkness was over the surface of the deep web, and the Spirit of Code was hovering over the pinnacle of utmost ignorance. We've come a long way from that time. The internet is still a dark, dreadful place, but it's much more stylish, sophisticated, and amazing now. Website Design has grown exponentially in scale and sophistication over the last few years, thanks to new Artificial Intelligence-based website creation tools that are dominating the digital marketing industry.


Webinar - Statistical hypothesis testing with Python

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Clicking on "Register", you agree to our Privacy Policy In this webinar, some statistical hypothesis testing will be introduced both in theory and in practice using Python programming language. This webinar will be given remotely and streaming using LiveWebinar platform, which works on every updated internet browser. No installation is then required. The duration is about 60 minutes. The speaker will show some slides for the theoretical part of the content and will write code during the event using Google Colaboratory for the practical part.


The future of AI in the contact center: Webinar highlights

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Recently, Talkdesk hosted a webinar on The future of AI in contact centers that featured Paul Lasserre, global segment lead – applied AI Solutions at AWS, Margi Deinlein, customer insights manager at Talkdesk, and Jay Gupta, director of product marketing at Talkdesk. The panel discussed the present-day challenges holding organizations back on a progressive path to AI maturity and how these limitations can be overcome in an increasingly automated world. They also discussed the evolving perceptions of AI and its maturity in the field of customer experience (CX). In this webinar, Margi Deinlein from Talkdesk discussed the results from a recent global survey of over 900 CX professionals. These results indicated that 69% of CX professionals believed in the importance of AI in the contact center, endowing contact centers with a pivotal role in accepting the challenge and adopting AI to create value.